Metabolic Engineering of Yarrowia lipolytica for High-Yield Diol Production: Pathways, CRISPR Tools, and Industrial Applications

Abstract

This article provides a comprehensive analysis of advanced metabolic engineering strategies for producing diols using the oleaginous yeast Yarrowia lipolytica. We explore foundational concepts of native and engineered diol synthesis pathways, detail cutting-edge CRISPR-Cas9 methodologies for pathway optimization, and address critical troubleshooting approaches for overcoming yield limitations. The content validates these strategies through comparative analysis of computational modeling and experimental data, offering researchers and bioengineers a systematic framework for developing efficient Y. lipolytica platforms for sustainable diol production. Recent breakthroughs in alkane-to-diol conversion and high-throughput engineering methods are highlighted, demonstrating significant potential for industrial-scale implementation in pharmaceutical and chemical manufacturing.

Understanding Yarrowia lipolytica's Native Metabolism and Diol Synthesis Potential

Yarrowia lipolytica has emerged as a premier microbial chassis in industrial biotechnology, offering a unique combination of metabolic versatility, robust growth characteristics, and advanced engineering capabilities. This non-conventional, oleaginous yeast possesses inherent traits that make it particularly suitable for white biotechnology applications, including the production of biofuels, biochemicals, nutraceuticals, and recombinant proteins [1] [2]. Its classification as a Generally Recognized as Safe (GRAS) organism by the US Food and Drug Administration facilitates its application in food and pharmaceutical industries [1] [3]. The development of sophisticated synthetic biology tools, particularly CRISPR-Cas9 systems, has enabled precise metabolic engineering of Y. lipolytica, allowing researchers to redesign its metabolic pathways for efficient production of high-value compounds such as medium-chain α,ω-diols from various feedstocks [4] [5] [6]. This application note provides a comprehensive overview of Y. lipolytica's biotechnological relevance, with specific protocols for engineering and cultivating this industrially significant yeast.

Fundamental Characteristics of Y. lipolytica

Physiological and Metabolic Traits

Y. lipolytica exhibits several distinctive physiological characteristics that contribute to its industrial value. Unlike many conventional yeasts, it is an obligate aerobe with a temperature optimum between 25-30°C, though some strains can tolerate temperatures up to 37°C [1]. It demonstrates remarkable environmental resilience, growing across a wide pH range (3.5-8.0) and tolerating high salt concentrations up to 15% NaCl for some strains [1]. The yeast is dimorphic, capable of growing in either yeast-like or filamentous forms, a characteristic that requires careful control in bioprocess applications [1] [2].

Metabolically, Y. lipolytica possesses exceptional substrate flexibility, utilizing both hydrophilic carbon sources (glucose, fructose, glycerol) and hydrophobic substrates (fatty acids, triglycerides, alkanes) [1] [4]. This versatility enables the cost-effective valorization of industrial waste streams, particularly crude glycerol from biodiesel production [7] [3]. Two of its most prominent metabolic features are its efficient protein secretion pathway and its outstanding lipid accumulation capacity, making it a model organism for both secretory protein production and oleochemical synthesis [1].

Table 1: Key Physiological Characteristics of Y. lipolytica

Characteristic	Description	Industrial Significance
Oxygen Requirement	Obligate aerobe	High oxygen demand in fermentation
Temperature Range	25-34°C (optimum 25-30°C)	Reduced cooling costs in large-scale processes
pH Tolerance	pH 3.5-8.0 (some strains pH 2.0-9.7)	Flexibility in process conditions; contamination resistance
Salt Tolerance	Up to 15% NaCl for some strains	Compatibility with industrial waste streams
Morphology	Dimorphic (yeast-hyphal transition)	Impacts fermentation rheology and product yield
Substrate Range	Wide spectrum (sugars, glycerol, hydrocarbons)	Utilizes low-cost alternative feedstocks
Safety Status	GRAS designation	Approved for food and pharmaceutical applications

Genetic and Metabolic Engineering Landscape

The genetic tractability of Y. lipolytica has been extensively developed, with a comprehensive toolkit now available for strain engineering. Natural isolates are predominantly haploid and heterothallic, with mating types Mat A and Mat B, simplifying genetic manipulation [1]. The establishment of auxotrophic markers (URA3, LEU2, LYS5) and the development of efficient CRISPR-Cas9 systems have enabled precise genome editing [4] [8]. Advanced expression systems include a variety of promoters with different strengths and regulation patterns, notably the erythritol-inducible promoter system that allows high-level, tightly controllable recombinant protein synthesis [8].

Metabolic engineering efforts have leveraged the yeast's naturally high flux toward acetyl-CoA, a key precursor for numerous valuable compounds [6]. Successful engineering strategies include enhancing precursor supply, blocking competing pathways, and implementing subcellular compartmentalization of metabolic pathways [6]. The availability of genome-scale metabolic models integrated with multi-omics data provides powerful resources for identifying engineering targets and predicting metabolic behavior [6].

Y. lipolytica as a Platform for Diol Production

Metabolic Engineering for α,ω-Diol Synthesis

The production of medium- to long-chain α,ω-diols represents a particularly promising application of Y. lipolytica in white biotechnology. These diols serve as valuable building blocks for polyesters and polyurethanes, yet their microbial synthesis from inexpensive feedstocks remains challenging [4] [5]. Y. lipolytica offers distinct advantages for alkane bioconversion compared to bacterial systems, naturally harboring 12 endogenous CYP52 family P450s (Alk1-12) that catalyze the initial hydroxylation of alkanes [4] [5].

A recent breakthrough demonstrated the engineering of Y. lipolytica for enhanced production of 1,12-dodecanediol from n-dodecane [4] [5]. The engineering strategy involved systematic deletion of genes involved in fatty alcohol oxidation (FADH, ADH1-8, FAO1) and fatty aldehyde oxidation (FALDH1-4) to prevent over-oxidation of diol intermediates to carboxylic acids [4] [5]. This generated strain YALI17, which showed dramatically reduced over-oxidation activity. Further enhancement was achieved by overexpressing the alkane hydroxylase gene ALK1, resulting in a combined strain capable of producing 1.12-dodecanediol at 1.45 mM – a 29-fold improvement over wild-type levels [5].

Table 2: Engineered Y. lipolytica Strains for 1,12-Dodecanediol Production from n-Dodecane [5]

Strain	Genotype	Description	Production (mM)
Wild Type	Po1g ku70Δ	Parental strain	0.05
YALI6	Po1g ku70Δ mfe1Δ faa1Δ faldh1-4Δ	Fatty aldehyde oxidation deletion	Not specified
YALI9	Po1g ku70Δ mfe1Δ faa1Δ faldh1-4Δ fao1Δ fadhΔ	Initial fatty alcohol oxidation deletion	Not specified
YALI17	Po1g ku70Δ mfe1Δ faa1Δ faldh1-4Δ fao1Δ fadhΔ adh1-8Δ	Comprehensive oxidation pathway deletion	0.72
YALI17 + ALK1	YALI17 with ALK1 overexpression	Enhanced alkane hydroxylation	1.45
YALI17 + ALK1 (pH-controlled)	YALI17 with ALK1 under optimized pH	Bioprocess optimization	3.20

The following diagram illustrates the metabolic engineering strategy for enhanced 1,12-dodecanediol production in Y. lipolytica:

Metabolic Pathway for Diol Production in Engineered Y. lipolytica

Experimental Protocol: Engineering Y. lipolytica for Diol Production

Protocol 1: CRISPR-Cas9 Mediated Deletion of Oxidation Pathway Genes

Objective: Generate Y. lipolytica strain with reduced over-oxidation activity for enhanced diol production.

Materials:

• Y. lipolytica Po1g ku70Δ strain
• pCRISPRyl plasmid (Addgene #70007) or similar CRISPR vector
• E. coli DH5α for plasmid propagation
• YPD medium: 20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract
• Synthetic complete medium: 20 g/L glucose, 6.7 g/L yeast nitrogen base without amino acids
• Lithium acetate transformation reagents

Procedure:

• Design sgRNAs targeting FADH, ADH1-8, FAO1, and FALDH1-4 genes using CRISPOR tool
• Clone 20-bp target sequences into BsmBI-digested pCRISPRyl vector using Golden Gate assembly
• Transform assembled plasmids into E. coli DH5α, select on LB agar with ampicillin (100 mg/L)
• Verify constructs by colony PCR and sequencing
• Cultivate Y. lipolytica Po1g ku70Δ in YPD medium at 28°C to mid-exponential phase
• Co-transform 500 ng of each sgRNA plasmid with disruption cassettes using lithium acetate method
• Select transformants on synthetic complete medium without appropriate amino acids
• Verify gene deletions by diagnostic PCR and sequencing
• Screen for reduced over-oxidation activity using n-dodecane as substrate

Notes: Sequential deletion of gene families is recommended. Begin with FALDH1-4, followed by FAO1 and FADH, and finally ADH1-8. Confirm each deletion before proceeding [5].

Protocol 2: Alkane Hydroxylase Overexpression

Objective: Enhance alkane hydroxylation capacity in engineered Y. lipolytica strains.

Materials:

• Engineered Y. lipolytica strain (e.g., YALI17)
• pYl expression vector or similar
• ALK1 gene amplified from Y. lipolytica genomic DNA

Procedure:

• Amplify ALK1 coding sequence from Y. lipolytica genomic DNA
• Clone ALK1 into pYl vector under strong constitutive promoter (e.g., TEF) using Circular Polymerase Extension Cloning (CPEC)
• Transform construct into engineered Y. lipolytica strain
• Select transformants on appropriate selective media
• Validate ALK1 expression by RT-PCR and western blotting
• Evaluate alkane hydroxylation activity using n-dodecane as substrate

Cultivation Strategies and Bioprocess Optimization

Substrate Utilization and Process Parameters

Y. lipolytica demonstrates remarkable flexibility in substrate utilization, enabling the cost-effective use of various industrial byproducts. When grown on crude glycerol from biodiesel production, specific growth rates of approximately 0.30 h⁻¹ have been observed, with substrate uptake rates around 0.02 mol L⁻¹ h⁻¹ [7]. This efficiency extends to high-content volatile fatty acids (VFAs) when cultivated under alkaline conditions (pH 8.0), which alleviate the inhibitory effects of undissociated VFA molecules [9]. Under optimized conditions, biomass concentrations up to 37.14 g/L and lipid production of 10.11 g/L have been achieved using 70 g/L acetic acid as carbon source [9].

The physiological response of Y. lipolytica varies significantly depending on the carbon source. Growth on glycerol is accompanied by higher oxygen uptake rates compared to growth on glucose, suggesting different metabolic routing [7]. This has important implications for process design, particularly in scale-up where oxygen transfer becomes limiting. The carbon-to-nitrogen ratio, pH, and oxygen availability significantly influence the metabolic fate of carbon, directing it toward biomass, polyols, citric acid, or storage lipids [7] [3].

Table 3: Performance of Y. lipolytica on Different Carbon Sources

Carbon Source	Growth Rate (h⁻¹)	Biomass Yield (g/g)	Major Products	Optimal Conditions
Glucose	0.24	0.4-0.5	Biomass, COâ‚‚	pH 4.5-6.5 [7]
Glycerol	0.30	0.4-0.6	Polyols, citric acid	pH 4.5-6.5 [7]
Acetic Acid	Not specified	0.578	Lipids, biomass	pH 8.0 [9]
Butyric Acid	Not specified	0.570	Lipids, biomass	pH 8.0 [9]
n-Dodecane	Not specified	Not specified	α,ω-diols	pH 6.5 [4]

Experimental Protocol: Bioprocess Optimization for Polyol Production

Protocol 3: Polyol Production Under Stressful Conditions

Objective: Maximize polyol production from crude glycerol under industrially relevant, non-sterile conditions.

Materials:

• Wild-type Y. lipolytica strains (e.g., ACA-YC 5030, LMBF 20, NRRL Y-323)
• Crude glycerol from biodiesel production
• Mineral medium: (NHâ‚„)â‚‚SOâ‚„ 5.0 g/L, KHâ‚‚POâ‚„ 3.0 g/L, MgSO₄·7Hâ‚‚O 0.5 g/L
• Trace metal and vitamin solutions
• Bioreactor or shake flasks

Procedure:

• Prepare media with crude glycerol (≈140 g/L) as sole carbon source
• Adjust initial pH to 2.0 ± 0.3 using HCl or Hâ‚‚SOâ‚„
• Inoculate with pre-cultured Y. lipolytica to initial OD₆₀₀ of 0.2
• Incubate at 20 ± 1°C with agitation (180 rpm for flasks, appropriate aeration for bioreactor)
• Monitor biomass, glycerol consumption, and polyol production over time
• For non-aseptic conditions, operate without sterile media preparation

Notes: Strain selection is critical for performance under stressful conditions. NRRL Y-323 has demonstrated exceptional polyol production (84.2 g/L total polyols) with conversion yield of 62% w/w under these conditions [3]. Low pH provides selective advantage against contaminating microorganisms.

The following workflow diagram illustrates the integrated process for strain development and bioprocess optimization:

Integrated Strain and Bioprocess Development Workflow

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 4: Key Research Reagent Solutions for Y. lipolytica Metabolic Engineering

Reagent/Material	Function/Application	Examples/Specifications
pCRISPRyl Vector	CRISPR-Cas9 genome editing	Addgene #70007; contains Cas9 and sgRNA scaffold [4]
Erythritol-Inducible Promoter	Tightly regulated gene expression	pEYL1-5AB; high-level, titratable expression [8]
Auxotrophic Markers	Selection of transformants	URA3, LEU2, LYS5; enable marker recycling [8]
YPD Medium	Routine cultivation	20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract [4]
Synthetic Complete Medium	Selection and maintenance	6.7 g/L YNB, 2% glucose, appropriate amino acid supplements [8]
Alkane Substrates	Diol production studies	n-Dodecane (C12), n-octane (C8); 50-100 mM concentrations [4]
Crude Glycerol	Low-cost carbon source	Biodiesel-derived; may require pretreatment [3]
TRPM8-IN-1	TRPM8-IN-1, MF:C23H18F4N2O, MW:414.4 g/mol	Chemical Reagent
Carbamazepine-d2	Carbamazepine-d2, CAS:1189902-21-3, MF:C15H12N2O, MW:238.28 g/mol	Chemical Reagent

Y. lipolytica represents a robust and versatile platform for industrial biotechnology, with particular promise for the production of valuable diols and other chemical building blocks. The integration of advanced engineering tools with its innate metabolic capabilities enables the redesign of metabolic pathways for efficient conversion of diverse feedstocks to target products. Future developments will likely focus on expanding the substrate range to include pentose sugars and other waste streams, enhancing oxygen utilization efficiency, and implementing dynamic regulatory systems for optimal pathway control. As engineering tools continue to mature and our understanding of Y. lipolytica's metabolic network deepens, this non-conventional yeast is poised to become an increasingly important workhorse for sustainable biomanufacturing.

Native Metabolic Pathways Relevant to Diol Biosynthesis

Yarrowia lipolytica has emerged as a promising microbial chassis for the production of valuable chemicals, including diols, due to its innate capacity to metabolize hydrophobic substrates and its well-developed metabolic engineering toolbox [4]. While this yeast natively possesses metabolic pathways that can be harnessed for diol biosynthesis, its wild-type form produces only trace amounts of these compounds, necessitating strategic genetic interventions [5]. Understanding and engineering the native metabolic pathways of Y. lipolytica is therefore fundamental to developing efficient bioprocesses for diol production. This application note details the native metabolic framework of Y. lipolytica relevant to diol biosynthesis, provides protocols for its engineering, and visualizes the critical pathway interactions.

Native Metabolic Pathways ofY. lipolyticafor Diol Biosynthesis

Y. lipolytica possesses a native metabolic network that can be redirected toward diol synthesis, primarily through its alkane assimilation machinery and central carbon metabolism.

Alkane Hydroxylation System

The most direct native pathway for diol precursor synthesis in Y. lipolytica is the alkane hydroxylation system. This yeast natively harbors 12 endogenous CYP52 family P450 enzymes (Alk1-Alk12) that catalyze the terminal hydroxylation of alkanes to corresponding fatty alcohols [4] [5]. These cytochrome P450 monooxygenases require electron transport partners and molecular oxygen for function, initiating the oxidation cascade from alkanes.

Oxidation Machinery and Competing Pathways

The primary challenge in diol production lies in the yeast's efficient oxidation machinery that rapidly converts intermediates to carboxylic acids, preventing diol accumulation. This competing system includes [4] [5]:

• 9 alcohol dehydrogenases (FADH, ADH1-8)
• 1 fatty alcohol oxidase (FAO1)
• 4 fatty aldehyde dehydrogenases (FALDH1-4)

In wild-type strains, this robust oxidation network efficiently converts fatty alcohols to fatty aldehydes and subsequently to fatty acids, explaining the minimal native diol production of only ~0.05 mM 1,12-dodecanediol [5].

Table 1: Key Native Enzymes in Y. lipolytica Affecting Diol Biosynthesis

Enzyme Category	Gene Examples	Native Function	Effect on Diol Accumulation
Alkane Hydroxylases	ALK1-ALK12	ω-hydroxylation of alkanes to fatty alcohols	Positive - generates diol precursors
Alcohol Dehydrogenases	FADH, ADH1-ADH8	Oxidation of fatty alcohols to fatty aldehydes	Negative - consumes intermediates
Fatty Alcohol Oxidases	FAO1	Oxidation of fatty alcohols to fatty aldehydes	Negative - consumes intermediates
Aldehyde Dehydrogenases	FALDH1-FALDH4	Oxidation of fatty aldehydes to fatty acids	Negative - consumes intermediates

The following diagram illustrates the native metabolic pathways for alkane conversion and the critical engineering targets for enhancing diol production in Y. lipolytica:

Metabolic Engineering Strategy and Protocol

This section provides a detailed methodology for reprogramming Y. lipolytica to enhance diol production by leveraging and modifying its native metabolic pathways.

Strain Engineering Workflow

The following protocol outlines the complete workflow for engineering a high-diol-producing Y. lipolytica strain, from genetic modifications to fermentation and analysis.

Detailed Experimental Protocols

Protocol 3.2.1: CRISPR-Cas9 Mediated Multiplex Gene Deletion

Objective: Simultaneously delete 14 genes involved in fatty alcohol oxidation (FADH, ADH1-8, FAO1) and fatty aldehyde oxidation (FALDH1-4) to prevent over-oxidation of diol intermediates [4] [5].

Materials:

• Y. lipolytica PO1g ku70Δ strain
• pCRISPRyl plasmid (Addgene #70007)
• E. coli DH5α competent cells
• Frozen EZ Yeast Transformation II kit (Zymo Research)
• YPD medium: 10 g/L yeast extract, 20 g/L peptone, 20 g/L glucose
• YNB plates: 6.7 g/L yeast nitrogen base, 10 g/L glucose, 2% agar

Procedure:

• Design sgRNAs: Design 20 bp guiding sequences targeting each of the 14 oxidation pathway genes (FADH, ADH1-8, FAO1, FALDH1-4).
• Construct CRISPR vectors: Clone guiding sequences into pCRISPRyl plasmid upstream of sgRNA scaffolds using overlapping PCR.
• Transform E. coli: Introduce constructed plasmids into E. coli DH5α, select on LB plates with ampicillin (100 mg/L).
• Verify plasmids: Purify plasmids from selected colonies and verify by sequencing.
• Transform Y. lipolytica: Transform PO1g ku70Δ strain with verified plasmids using Frozen EZ Yeast Transformation II kit.
• Select transformants: Plate on YNB plates and incubate at 30°C for 2-4 days.
• Validate deletions: Screen transformants via colony PCR to confirm gene deletions.
• Recycle marker: Culture positive transformants on YPD plates containing 5-FOA to recycle the URA3 marker.

Notes: The ku70Δ background enhances homologous recombination efficiency. Include parental strain as control throughout the process.

Protocol 3.2.2: ALK1 Monooxygenase Overexpression

Objective: Enhance the first hydroxylation step of alkane conversion by overexpressing the native ALK1 gene [4].

Materials:

• Engineered YALI17 strain (with oxidation pathways blocked)
• pYl expression vector
• Y. lipolytica codon-optimized ALK1 gene

Procedure:

• Amplify ALK1: PCR amplify ALK1 gene from Y. lipolytica genomic DNA.
• Clone into pYl: Use Circular Polymerase Extension Cloning (CPEC) to insert ALK1 into pYl expression vector under TEF promoter.
• Transform: Introduce constructed vector into YALI17 strain.
• Validate expression: Confirm ALK1 overexpression via RT-qPCR or Western blot.

Protocol 3.2.3: Fermentation and Diol Production Analysis

Objective: Evaluate diol production performance of engineered strains using n-dodecane as substrate [4] [5].

Materials:

• Engineered Y. lipolytica strains
• YPD medium: 10 g/L yeast extract, 20 g/L peptone, 20 g/L glucose
• Fermentation medium: YP with 50 mM n-dodecane
• Bioreactor with pH control system
• GC-MS system (e.g., Agilent 7890-8975C)

Procedure:

• Pre-culture: Inoculate single colonies into 5 mL YPD medium in 25 mL flask, culture at 30°C, 220 rpm for 24 h.
• Seed culture: Transfer to 50 mL YPD in 250 mL flask, adjust to OD600 = 0.5, incubate at 30°C, 220 rpm for 24 h.
• Fermentation: Inoculate seed culture into bioreactor containing fermentation medium with 50 mM n-dodecane.
• pH control: Maintain pH at 6.5 using automated pH control system.
• Monitor growth: Track biomass (OD600) and substrate consumption for 120 h.
• Extract products: Add 10% (v/v) dodecane after 12 h fermentation for in situ extraction.
• Analyze products: Collect organic phase, dilute with dodecane, filter through 0.22 μm membrane, analyze by GC-MS.
• Quantify diols: Use 1,12-dodecanediol standards for calibration and quantification.

Table 2: Performance of Engineered Y. lipolytica Strains for 1,12-Dodecanediol Production

Strain	Genotype	Substrate	Production (mM)	Fold Improvement
Wild Type	PO1g ku70Δ	50 mM n-dodecane	0.05	1x
YALI17	PO1g ku70Δ, 14 oxidation gene deletions	50 mM n-dodecane	0.72	14x
YALI17 + ALK1ox	YALI17 with ALK1 overexpression	50 mM n-dodecane	1.45	29x
YALI17 + ALK1ox + pH control	With automated pH control	50 mM n-dodecane	3.20	64x

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for Metabolic Engineering of Y. lipolytica

Reagent/Resource	Type	Function/Application	Example/Source
pCRISPRyl	Plasmid	CRISPR-Cas9 genome editing in Y. lipolytica	Addgene #70007
Frozen EZ Yeast Transformation II Kit	Transformation kit	High-efficiency yeast transformation	Zymo Research
YPD Medium	Growth medium	Routine cultivation of Y. lipolytica	10 g/L yeast extract, 20 g/L peptone, 20 g/L glucose
YNB Plates	Selection medium	Selection of transformants	6.7 g/L YNB, 10 g/L glucose, 2% agar
pYl Expression Vector	Expression plasmid	Heterologous gene expression	Derived from pCRISPRyl
ALK Genes	Native enzymes	Alkane hydroxylation	CYP52 family P450s from Y. lipolytica
n-dodecane	Substrate	Alkane feedstock for diol production	Sigma-Aldrich
5-Fluoroorotic Acid (5-FOA)	Selection agent	Counterselection for marker recycling	1 mg/mL in YPD plates
Piroxicam-d3	Piroxicam-d3, CAS:942047-64-5, MF:C15H13N3O4S, MW:334.4 g/mol	Chemical Reagent	Bench Chemicals
Resveratrol-13C6	Resveratrol-13C6, CAS:1185247-70-4, MF:C14H12O3, MW:234.20 g/mol	Chemical Reagent	Bench Chemicals

Concluding Remarks

The native metabolic pathways of Y. lipolytica provide a foundational platform for diol biosynthesis, particularly through its alkane hydroxylation system. However, successful diol production requires substantial metabolic reprogramming to block competing oxidation pathways while enhancing precursor flux. The protocols outlined here have demonstrated remarkable success, achieving a 64-fold improvement in 1,12-dodecanediol production compared to wild-type strains [4] [5]. This engineering framework establishes Y. lipolytica as a promising microbial cell factory for sustainable production of valuable medium- to long-chain diols from alkane feedstocks.

Within metabolic engineering, diols are classified by carbon chain length, which directly correlates with distinct production challenges and technological maturity. Short-chain diols (C2-C5) have achieved industrially relevant production metrics through established microbial processes. In contrast, medium-chain (C6-C12) and long-chain (>C12) diols present significant bottlenecks, with production efficiencies "orders of magnitude lower" than their short-chain counterparts [5] [4]. This application note details these fundamental distinctions within the context of engineering Yarrowia lipolytica for diol production, providing structured data comparisons and actionable protocols for researchers addressing these challenges.

Quantitative Comparison of Diol Production

The disparity between short-chain and medium/long-chain diol production is evident in achieved titers, host systems, and feedstock strategies.

Table 1: Production Metrics for Short-Chain vs. Medium/Long-Chain Diols

Diol Category	Representative Compound	Maximum Reported Titer	Model Host Organism	Primary Feedstock
Short-Chain (< C5)	1,3-Propanediol	26 g/L [5] [4]	Clostridium beijerinckii	Glucose [5] [4]
	1,4-Butanediol	18 g/L [5] [4]	Engineered E. coli	Glucose [5] [4]
Medium/Long-Chain (≥ C6)	1,12-Dodecanediol	3.2 mM (~0.65 g/L) [5] [4]	Engineered Y. lipolytica	n-Dodecane [5] [4]
	1,8-Octanediol	108 mg/L [5] [4]	Bacterial Systems	n-Octane [5] [4]

Table 2: Key Challenges in Medium/Long-Chain Diol Production

Challenge Category	Short-Chain Diols	Medium/Long-Chain Diols
De Novo Synthesis	Established from simple sugars (e.g., glucose) [5] [4]	No efficient routes from simple carbon sources [5] [4]
Primary Production Host	E. coli, Clostridium [5] [4]	E. coli, Pseudomonas, Yarrowia lipolytica [5] [4] [10]
Key Technical Hurdle	Pathway optimization [5] [4]	Over-oxidation of intermediates; Heterologous P450 expression [5] [4]
Common Feedstock	Renewable sugars [5] [4]	Fatty acids, alkanes [5] [4] [10]

Protocol: EngineeringYarrowia lipolyticafor 1,12-Dodecanediol Production

This protocol details the metabolic engineering strategy to enhance the production of 1,12-dodecanediol from n-dodecane in Y. lipolytica by blocking competing oxidation pathways and enhancing hydroxylation capacity [5] [4].

Strain and Media Preparation

• Strains: Utilize Yarrowia lipolytica Po1g ku70Δ as the parental strain for genetic manipulations due to its high homologous recombination efficiency [5] [4].
• Media:
  • YPD Medium: 20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract, pH 6.5. Use for routine cultivation.
  • Synthetic Complete Medium: 20 g/L glucose, 6.7 g/L yeast nitrogen base without amino acids, supplemented with an amino acid mix (lacking leucine for selection), pH 6.5 [5] [4].
• Culture Conditions: Incubate cultures at 28-30°C with shaking. For transformation and selection, use media without L-leucine [5] [4].

CRISPR-Cas9-Mediated Gene Deletion to Block Over-Oxidation

The following steps create a base strain (YALI17) with minimized over-oxidation of fatty alcohol and aldehyde intermediates.

• sgRNA Design and Vector Construction:

  • Design guiding sequences (20 bp) targeting the 5' regions of the following genes involved in oxidative metabolism [5] [4]:
    • Fatty Alcohol Oxidation Genes (10 total): FADH, ADH1, ADH2, ADH3, ADH4, ADH5, ADH6, ADH7, ADH8, FAO1 [5].
    • Fatty Aldehyde Oxidation Genes (4 total): FALDH1, FALDH2, FALDH3, FALDH4 [5].
  • Clone these target-specific sequences into the 5' end of the sgRNA scaffold in the pCRISPRyl plasmid (Addgene #70007) using standard molecular biology techniques like overlapping PCR and DpnI digestion [4].

• Transformation and Selection:

  • Transform the constructed CRISPR-Cas9 plasmid into competent Y. lipolytica Po1g ku70Δ cells.
  • Select transformants on synthetic complete medium agar plates lacking leucine.
  • Isolate single colonies and verify gene deletions via colony PCR and/or sequencing.

• Sequential Strain Engineering: The final strain, YALI17, has the genotype: Po1g ku70Δ mfe1Δ faa1Δ faldh1-4Δ fao1Δ fadhΔ adh1-8Δ [5]. Construct intermediate strains (e.g., YALI1 to YALI16) by sequentially adding gene deletions to monitor the improvement in diol production [5].

Overexpression of Alkane Hydroxylase

To enhance the initial hydroxylation of the alkane substrate, overexpress the native alkane hydroxylase gene ALK1.

• Vector Construction:

  • PCR-amplify the ALK1 gene from the Y. lipolytica genome.
  • Clone the amplified gene into a Y. lipolytica expression vector (e.g., pYl, derived from pCRISPRyl by replacing the Cas9 ORF and removing sgRNA scaffolds) using methods like Circular Polymerase Extension Cloning (CPEC) [4].
  • The expression should be driven by a strong constitutive promoter, such as the TEF promoter [4].

• Strain Transformation:

  • Transform the ALK1 overexpression vector into the engineered base strain YALI17.
  • Select and verify transformants as described in Section 3.2.

Biotransformation and Analysis

• Fermentation:

  • Inoculate the engineered strain into a suitable medium and grow to mid-log phase.
  • Add 50 mM n-dodecane as the substrate. For optimal production, perform the biotransformation in an automated pH-controlled bioreactor [5] [4].

• Product Quantification:

  • Extract metabolites from the culture broth using an appropriate organic solvent (e.g., ethyl acetate).
  • Analyze the extracts via Gas Chromatography-Mass Spectrometry (GC-MS) or High-Performance Liquid Chromatography (HPLC) to identify and quantify 1,12-dodecanediol production [5].

Pathway Engineering Diagram

The following diagram visualizes the metabolic engineering strategy implemented in the protocol to redirect flux in Y. lipolytica from alkane degradation towards diol production.

The Scientist's Toolkit: Key Research Reagents

This table lists essential materials and tools used in the featured protocol for engineering Y. lipolytica.

Table 3: Essential Reagents for Diol Production in Y. lipolytica

Reagent / Tool	Function / Application	Specific Example / Note
pCRISPRyl Vector	CRISPR-Cas9 genome editing in Y. lipolytica	Available from Addgene (#70007) [4]
Alkane Substrate	Feedstock for diol production	n-Dodecane (C12) used at 50 mM [5] [4]
ALK Genes	Native alkane hydroxylases for initial oxidation	ALK1 overexpression shown to enhance production [5] [4]
TEF Promoter	Strong constitutive promoter for gene expression	Used in pYl-derived expression vectors [4]
Synthetic Complete Medium	Selection and maintenance of engineered strains	Formulation without L-leucine for auxotrophic selection [5] [4]
Ibuprofen-13C6	Ibuprofen-13C6, CAS:1216459-54-9, MF:C13H18O2, MW:212.24 g/mol	Chemical Reagent
2,4-D-13C6	2,4-D-13C6, CAS:150907-52-1, MF:C8H6Cl2O3, MW:226.99 g/mol	Chemical Reagent

The CYP52 P450 family, also known as P450alk, encompasses a specialized group of cytochrome P450 monooxygenases that serve as the primary enzymatic machinery for the initial and rate-limiting step of n-alkane assimilation in various yeast species. These membrane-bound, heme-containing enzymes catalyze the terminal hydroxylation of n-alkanes to corresponding primary alcohols, which are subsequently oxidized to fatty aldehydes and fatty acids through metabolic pathways [11] [12]. This hydroxylation capability extends beyond alkanes to include fatty acids and their derivatives, positioning CYP52 enzymes as critical biocatalysts in both native microbial metabolism and engineered bioprocesses [13] [14].

The CYP52 family demonstrates significant phylogenetic diversity with multiple paralogs found across alkane-assimilating yeasts including Yarrowia lipolytica, Candida tropicalis, Candida maltosa, Candida albicans, and Debaryomyces hansenii [13] [11] [12]. This multiplication and diversification of CYP52 genes enables host organisms to thrive on diverse hydrophobic carbon sources and adapt to various environmental conditions, including contaminated ecosystems [11] [12]. From a biotechnological perspective, CYP52 enzymes provide essential oxidative functions for the conversion of inexpensive alkane feedstocks into valuable bio-based chemicals, including fatty alcohols, dicarboxylic acids, and α,ω-diols [4] [15].

Functional Diversity and Substrate Specificity of CYP52 Enzymes

Comprehensive Classification of Y. lipolytica CYP52 Enzymes

Research has revealed that the twelve CYP52 enzymes in Y. lipolytica exhibit distinct yet sometimes overlapping substrate preferences, allowing them to collectively process a broad spectrum of hydrophobic compounds. Based on extensive functional characterization, these enzymes can be systematically categorized into four major groups according to their substrate specificities [11] [14].

Table 1: Functional Classification of Y. lipolytica CYP52 (ALK) Enzymes

Group	Enzymes	Primary Substrate Specificity	Functional Role
1	Alk1p, Alk2p, Alk9p, Alk10p	Significant activities to hydroxylate n-alkanes	Initial alkane activation
2	Alk4p, Alk5p, Alk7p	Significant activities to hydroxylate ω-terminal end of dodecanoic acid	Fatty acid ω-hydroxylation
3	Alk3p, Alk6p	Significant activities to hydroxylate both n-alkanes and dodecanoic acid	Dual substrate range
4	Alk8p, Alk11p, Alk12p	Faint or no activities to oxidize n-alkanes or dodecanoic acid	Specialized/unknown functions

This functional diversification enables Y. lipolytica to efficiently assimilate various hydrophobic compounds through complementary enzymatic activities. The n-alkane specialists (Group 1) perform the critical first step in alkane metabolism, while the fatty acid ω-hydroxylases (Group 2) contribute to both energy metabolism and the production of dicarboxylic acids [14]. Enzymes with broad substrate ranges (Group 3) provide metabolic flexibility, and those with limited activity on standard substrates (Group 4) may possess specialized functions not yet fully characterized [11].

Structural Basis for Regioselectivity

The regioselectivity of CYP52 enzymes, particularly their ability to preferentially hydroxylate the thermodynamically disfavored terminal methyl group (ω-position) of alkanes and fatty acids, represents a key structural and functional feature. Research on CYP52A21 from Candida albicans indicates that this specificity is achieved through a constricted access channel that positions the substrate terminus near the heme iron active site [13].

This narrow channel mechanism shows interesting parallels with the mammalian CYP4A fatty acid ω-hydroxylases, though with distinct structural implementations. Unlike some CYP4A enzymes that employ covalent heme binding to create rigid substrate channels, CYP52A21 achieves similar regioselectivity without permanent heme-protein covalent linkages [13]. Evidence from studies using terminally-halogenated fatty acid substrates demonstrates that the diameter of this access channel effectively limits oxidation to the terminal atoms, with decreased productivity observed as the size of the terminal halide increases (iodine > bromine > chlorine) [13].

Metabolic Engineering Applications for Diol Production

Engineering Y. lipolytica for α,ω-Diol Production

The strategic manipulation of Yarrowia lipolytica's native alkane hydroxylation machinery enables sustainable microbial production of valuable medium- to long-chain α,ω-diols from alkane feedstocks. These diols serve as essential building blocks for polyesters and polyurethanes, with traditional chemical synthesis often facing challenges in selectivity and sustainability [4] [15]. Recent metabolic engineering breakthroughs have demonstrated the feasibility of direct biotransformation of n-alkanes to α,ω-diols in engineered Y. lipolytica strains.

A landmark study employed CRISPR-Cas9 mediated genome editing to systematically delete ten genes involved in fatty alcohol oxidation (FADH, ADH1-8, FAO1) and four fatty aldehyde dehydrogenase genes (FALDH1-4), creating strain YALI17 with significantly reduced over-oxidation activity [4] [15]. This engineered strain produced 0.72 mM 1,12-dodecanediol from 50 mM n-dodecane, representing a 14-fold increase over the parental strain [4]. Subsequent overexpression of the alkane hydroxylase gene ALK1 further enhanced production to 1.45 mM, and implementation of an automated pH-controlled biotransformation system ultimately achieved 3.2 mM 1,12-dodecanediol production – a 29-fold improvement over wild-type capabilities [4] [15].

Table 2: Metabolic Engineering Strategies for Enhanced α,ω-Diol Production in Y. lipolytica

Engineering Strategy	Specific Modifications	Impact on 1,12-Dodecanediol Production
Pathway blocking	Deletion of 10 alcohol oxidation genes (FADH, ADH1-8, FAO1) and 4 aldehyde oxidation genes (FALDH1-4)	14-fold increase (0.72 mM)
Alkane hydroxylation enhancement	Overexpression of ALK1 in YALI17 background	2-fold further increase (1.45 mM)
Bioprocess optimization	Automated pH-controlled fermentation	Final titer of 3.2 mM (29-fold total increase)
Host selection	Use of oleaginous yeast Y. lipolytica vs. E. coli	Superior alkane uptake and compartmentalization

Comparative Host Capabilities

The selection of Yarrowia lipolytica as a production host for alkane-derived diols provides distinct advantages over bacterial systems such as Escherichia coli. As an oleaginous yeast, Y. lipolytica possesses natural capabilities for hydrophobic substrate utilization, including specialized cellular machinery for alkane uptake, transport, and compartmentalization [4]. Furthermore, its native complement of twelve CYP52 family genes provides a robust foundation for engineering without requiring reconstruction of complete heterologous pathways [11] [14].

This intrinsic metabolic capacity contrasts with the limitations observed in E. coli systems, where heterologous CYP450 expression often encounters challenges including codon bias, protein misfolding, and complex electron transport requirements [4]. Additionally, Y. lipolytica offers advanced synthetic biology tools for precise metabolic engineering, well-established GRAS (Generally Recognized As Safe) status, and exceptional acetyl-CoA flux that supports abundant precursor supply for lipid-derived compounds [4] [16].

Experimental Protocols

Protocol 1: Functional Characterization of CYP52 Enzymes

This protocol describes the heterologous expression, purification, and functional analysis of CYP52 enzymes in E. coli, adapted from established methods for CYP52A21 characterization [13].

Heterologous Expression and Purification

• Gene Optimization and Cloning: Amplify the CYP52 gene open reading frame with a C-terminal 6×His-tag using PCR. Incorporate necessary codon modifications for optimal E. coli expression (e.g., correct Ser residue encoded by CTG leucine codon in Candida species). Clone into pCW(Ori+) vector using NdeI and XbaI restriction sites [13].
• Protein Expression: Transform E. coli DH5α with constructed vector. Inoculate 1L TB medium containing 100 μg/mL ampicillin and 1.0 mM IPTG. Grow at 37°C for 3 hours with shaking at 200 rpm, then reduce temperature to 28°C for 24 hours [13].
• Membrane Preparation and Purification: Isolate bacterial inner membrane fractions by centrifugation. Solubilize membranes overnight at 4°C in 100 mM potassium phosphate buffer (pH 7.4) containing 20% glycerol, 0.5 M NaCl, 10 mM β-mercaptoethanol, and 1.5% CHAPS. Purify using Ni²⁺-nitrilotriacetate chromatography with elution using 400 mM imidazole. Dialyze against 100 mM potassium phosphate buffer (pH 7.4) containing 20% glycerol and 0.1 mM EDTA [13].

Functional and Spectral Characterization

• Spectral Analysis: Record UV-visible spectra of purified P450 (approximately 1-2 μM) in 100 mM potassium phosphate buffer (pH 7.4) at room temperature. Generate CO-ferrous complex by adding sodium dithionite to reduce ferric P450, then bubbling CO gas through solution. Calculate P450 concentration using extinction coefficient Δε450-490 = 91 mM⁻¹cm⁻¹ [13].
• Heme Staining: Perform SDS-PAGE with purified CYP52 protein. Immerse gel in darkness in 3,3',5,5'-tetramethylbenzidine (1.5 mg/mL in methanol) and 250 mM sodium acetate buffer (pH 5.0) in 3:7 ratio for 1-2 hours. Add Hâ‚‚Oâ‚‚ to 30 mM final concentration; heme-containing proteins appear as light blue bands within 30 minutes [13].
• Enzyme Activity Assay: Reconstitute purified P450 (100 pmol) with rat NADPH-P450 reductase (250 pmol) and L-α-dilauroyl-sn-glycero-3-phosphocholine (45 μM) in 100 mM potassium phosphate buffer (pH 7.4). Initiate reaction with NADPH-generating system. Incubate at 37°C for 10-30 minutes with appropriate substrates (e.g., dodecanoic acid, n-alkanes). Terminate reaction with methylene chloride, derivative with N,O-bis(trimethylsilyl)trifluoroacetamide, and analyze products by GC-MS [13].

Protocol 2: Metabolic Engineering for Diol Production

This protocol outlines the construction of engineered Y. lipolytica strains for enhanced α,ω-diol production from alkanes, based on recent successful implementations [4] [15].

CRISPR-Cas9 Mediated Gene Deletion

• Strain and Media: Use Y. lipolytica strain CXAU1 or appropriate background. Maintain in YPD medium (20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract) or synthetic complete medium without appropriate auxotrophic markers [14].
• gRNA Vector Construction: Employ pCRISPRyl vector (Addgene #70007) expressing Cas9 and sgRNA scaffold. For multiplexed deletions, insert additional sgRNA scaffold sequences downstream of original site. Insert gene-specific 20 bp guiding sequences upstream of each sgRNA scaffold using overlapping PCR [4].
• Strain Transformation: Transform Y. lipolytica with constructed CRISPR vectors using standard lithium acetate protocol. Select transformations on YNBD agar with appropriate amino acid supplementation. Verify gene deletions by diagnostic PCR and sequencing [4].

Alkane Hydroxylase Overexpression

• Expression Vector Construction: Amplify ALK genes (e.g., ALK1) from Y. lipolytica genomic DNA. Clone into pYl yeast expression vector using Circular Polymerase Extension Cloning (CPEC). Utilize strong constitutive or inducible promoters (e.g., TEF promoter with intron sequence) [4].
• Strain Transformation and Screening: Introduce expression vectors into engineered Y. lipolytica background strains (e.g., YALI17). Select transformants on appropriate selective media. Validate ALK gene expression by RT-qPCR and/or immunoblotting [4].

Biotransformation and Analysis

• Fermentation Conditions: Inoculate engineered strains in YPD medium and grow for 2 days. Scale up to 20 mL in 100 mL flasks and incubate additional 2 days. For alkane biotransformation, use n-dodecane (50 mM) as substrate. Implement pH-controlled fermentation for optimal production (pH 6.5) [4].
• Product Extraction and Analysis: Extract culture broth with ethyl acetate or dichloromethane. Concentrate organic phase under nitrogen gas. Derivatize with BSTFA if required for GC-MS analysis. Quantify α,ω-diols using GC-MS with selective ion monitoring or authentic standards [4].

Essential Research Reagents and Tools

Table 3: Key Research Reagents for CYP52 and Diol Production Studies

Reagent/Tool	Specifications	Research Application
Expression Vector	pCW(Ori+) with NdeI/XbaI sites	Heterologous CYP52 expression in E. coli
CRISPR System	pCRISPRyl (Addgene #70007)	Genome editing in Y. lipolytica
P450 Reductase	Recombinant rat NADPH-P450 reductase	Electron donation in reconstituted P450 systems
Detection Reagent	3,3',5,5'-Tetramethylbenzidine	Heme staining on SDS-PAGE gels
Substrates	n-Dodecane, dodecanoic acid, 12-halododecanoic acids	Enzyme activity and regioselectivity studies
Analytical Method	GC-MS with TMS derivatization	Hydroxylated product quantification

Diagram 1: Metabolic Engineering Strategy for Diol Production in Y. lipolytica. The native alkane assimilation pathway (red) converts alkanes to fatty acids via multiple oxidation steps. Engineered modifications (green) enhance initial hydroxylation while blocking subsequent oxidation steps to redirect flux toward α,ω-diol accumulation.

Diagram 2: Experimental Workflow for Engineered Diol Production. The systematic approach begins with strategic gene deletions to block competing pathways, followed by alkane hydroxylase enhancement and optimized bioprocessing conditions to maximize diol yields.

In the field of microbial biosynthesis, a significant yield disparity exists between short-chain diols (typically less than 5 carbon atoms) and medium-to-long-chain diols (ranging from C6 to C14+). This production gap represents a critical challenge for the sustainable manufacturing of high-value chemical building blocks used in polymer, pharmaceutical, and specialty chemical industries. Short-chain diols such as 1,3-propanediol and 1,4-butanediol have achieved industrial-scale production through microbial fermentation, with engineered strains of Clostridium beijerinckii and Escherichia coli reaching impressive titers of 26 g/L and 18 g/L, respectively [5] [4]. In stark contrast, mid-chain (C6-C12) and long-chain (>C12) diols remain orders of magnitude lower in production efficiency, with no established de novo routes from simple carbon sources and maximum reported titers rarely exceeding 1.4 g/L even from expensive fatty acid precursors [5] [4].

The oleaginous yeast Yarrowia lipolytica has emerged as a promising chassis organism to address these production gaps, particularly for medium-to-long-chain diols, due to its innate capacity to metabolize hydrophobic substrates and its robust acetyl-CoA generation [6]. This Application Note examines the current production landscape, identifies key metabolic bottlenecks, and provides detailed protocols for engineering Y. lipolytica to bridge the yield gap through targeted metabolic engineering strategies.

Current Production Landscape & Yield Disparities

Quantitative Analysis of Production Gaps

Table 1: Comparative Production Efficiencies of Short-Chain versus Medium/Long-Chain Diols

Diol Category	Representative Compounds	Highest Reported Titer	Production Host	Carbon Source	Key Challenges
Short-chain ( )	1,3-propanediol	26 g/L	Clostridium beijerinckii	Glucose	Limited; commercial production achieved
Short-chain ( )	1,4-butanediol	18 g/L	Engineered E. coli	Glucose	Limited; commercial production achieved
Mid-chain (C6-C12)	1,12-dodecanediol	1.4 g/L	Engineered E. coli	12-hydroxydodecanoic acid	Requires expensive fatty acid precursors
Mid-chain (C6-C12)	1,8-octanediol	108 mg/L	Bacterial systems	n-octane	Low efficiency from alkane substrates
Long-chain (>C12)	1,12-dodecanediol (from alkanes)	3.2 mM (~0.64 g/L)	Engineered Y. lipolytica	n-dodecane	Competing oxidation pathways

Table 2: Production Improvements in Engineered Yarrowia lipolytica Strains

Strain	Genetic Modifications	Substrate	1,12-Dodecanediol Production	Fold Improvement
Wild Type	None	n-dodecane	0.05 mM	Reference
YALI17	Δfadh, Δadh1-8, Δfao1, Δfaldh1-4	n-dodecane	0.72 mM	14-fold
YALI17 + ALK1	YALI17 background + ALK1 overexpression	n-dodecane	1.45 mM	29-fold
YALI17 + pH control	YALI17 + ALK1 + automated pH control	n-dodecane	3.2 mM	64-fold

The quantitative data presented in Tables 1 and 2 highlight the dramatic disparity between short-chain and longer-chain diol production. While short-chain diols achieve gram-per-liter scale production, mid- to long-chain diols struggle to reach comparable levels, with the highest reported titer for 1,12-dodecanediol from alkane substrates reaching only 3.2 mM (approximately 0.64 g/L) in the most optimized Y. lipolytica strain [5] [4]. This represents nearly a 40-fold difference in productivity compared to short-chain diols.

Fundamental Challenges in Mid/Long-Chain Diol Production

The yield gap between short-chain and longer-chain diols stems from several fundamental biological challenges:

• Precursor Competition: Mid/long-chain diols require fatty acids or alkanes as precursors, which are also essential for membrane integrity and energy storage, creating inherent metabolic competition [10].
• Oxidation Cascade Control: Unlike short-chain diols, longer chains are susceptible to over-oxidation through native β-oxidation pathways, resulting in terminal carboxylic acids rather than the desired diols [5].
• Cofactor Imbalance: Cytochrome P450 systems required for terminal hydroxylation depend on NADPH and complex electron transport chains, creating cofactor regeneration challenges [5] [17].
• Toxicity and Compartmentalization: Longer-chain diols and their intermediates can be toxic to microbial cells and require sophisticated compartmentalization strategies [10].

Metabolic Engineering Strategies to Bridge the Gap

Pathway Engineering and Oxidation Blocking

Diagram Title: Metabolic Pathway Engineering Strategy in Y. lipolytica

Rational metabolic engineering of Y. lipolytica focuses on two primary strategies: (1) enhancing the flux from alkanes to fatty alcohols through overexpression of alkane hydroxylases, and (2) blocking the competing oxidation pathways that divert intermediates away from diol formation. The most successful approach has involved systematic deletion of genes encoding fatty alcohol oxidases (FAO1), fatty alcohol dehydrogenases (FADH and ADH1-8), and fatty aldehyde dehydrogenases (FALDH1-4) [5] [4]. This prevents over-oxidation of valuable intermediates to carboxylic acids, allowing diols to accumulate.

Protocol: CRISPR-Cas9 Mediated Multi-Gene Deletion inY. lipolytica

Objective: Simultaneous deletion of 10 genes involved in fatty alcohol oxidation (FADH, ADH1-8, FAO1) and 4 fatty aldehyde oxidation genes (FALDH1-4) to create strain YALI17.

Materials:

• Y. lipolytica Po1g ku70Δ strain (improved homologous recombination)
• pCRISPRyl plasmid (Addgene #70007) containing Cas9 and sgRNA scaffold
• Donor DNA fragments with 500-bp homology arms
• YPD medium: 20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract
• Synthetic complete medium without leucine

Procedure:

• sgRNA Vector Construction:

  • Design 20-bp guiding sequences targeting each of the 14 genes (FADH, ADH1-8, FAO1, FALDH1-4)
  • Clone guiding sequences upstream of sgRNA scaffold in pCRISPRyl using overlapping PCR
  • Transform into E. coli DH5α, select on LB + ampicillin (100 mg/L)
  • Verify constructs by sequencing

• Strain Transformation:

  • Grow Y. lipolytica Po1g ku70Δ in YPD to OD600 = 1.0
  • Make competent cells using Frozen-EZ Transformation kit (Zymo Research)
  • Co-transform with linearized CRISPR plasmid and donor DNA fragments
  • Select transformants on synthetic complete medium without leucine

• Screening and Validation:

  • Pick 6-8 colonies for each gene deletion
  • Screen by colony PCR using gene-specific primers
  • Validate successful deletions by sequencing
  • Store engineered strains as glycerol stocks at -80°C

Timeline: 4-6 weeks for complete strain construction. The ku70Δ background increases homologous recombination efficiency from 28% to 54%, significantly improving success rates [18].

Advanced Engineering and Process Optimization

Alkane Hydroxylase Engineering and Cofactor Balancing

Protocol: ALK1 Overexpression for Enhanced Alkane Hydroxylation

Objective: Increase conversion of n-alkanes to fatty alcohols through overexpression of alkane monooxygenase ALK1.

Materials:

• Engineered YALI17 strain
• pYl expression vector with TEF promoter
• n-dodecane substrate
• Alk1 gene amplified from Y. lipolytica genome

Procedure:

• Vector Construction:

  • Amplify ALK1 coding sequence from Y. lipolytica genomic DNA
  • Clone into pYl vector using Circular Polymerase Extension Cloning (CPEC)
  • Replace Cas9 ORF with ALK1 expression cassette in pCRISPRyl backbone
  • Add intron sequence from TEF promoter at 3' end of promoter

• Strain Transformation and Screening:

  • Transform ALK1 expression vector into YALI17 strain
  • Select on synthetic complete medium without leucine
  • Screen for high-expression clones by qRT-PCR
  • Validate ALK1 activity by GC-MS analysis of alcohol production from n-dodecane

• Fermentation Optimization:

  • Inoculate engineered strain in YPD, grow for 2 days
  • Scale up to 20 mL production medium in 100 mL flask
  • Add 50 mM n-dodecane as substrate
  • Implement automated pH control (pH 6.5-7.0)
  • Monitor diol production over 5-7 days

Application Notes: ALK1 overexpression in the YALI17 background increases 1,12-dodecanediol production from 0.72 mM to 1.45 mM. Combined with pH-controlled biotransformation, titers reach 3.2 mM [5] [4].

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for Y. lipolytica Diol Production Studies

Reagent/Category	Specific Examples	Function/Application	Key Considerations
CRISPR System	pCRISPRyl (Addgene #70007)	Genome editing and gene deletion	ku70Δ background improves efficiency
Expression Vectors	pYl series with TEF promoter	Heterologous gene expression	Strong, constitutive expression
Alkane Substrates	n-dodecane, n-octane	Diol precursors	Hydrophobic, requires emulsification
Selection Markers	URA3, LEU2	Transformant selection	Auxotrophic complementation
Analytical Standards	1,12-dodecanediol, 1,8-octanediol	GC-MS/QTOF quantification	Essential for accurate titers
Culture Media	YPD, YNB, Synthetic Complete	Strain maintenance/propagation	Defined media for production studies
Electron Transport Components	NADPH regeneration systems	P450 monooxygenase support	Critical for ω-hydroxylation
Detergents/Solvents	Tergitol, Tween series	Substrate emulsification	Improve alkane bioavailability
Galanthamine-d6	Galanthamine-d6, CAS:1128109-00-1, MF:C17H21NO3, MW:293.39 g/mol	Chemical Reagent	Bench Chemicals
RPR132595A-d3	RPR132595A-d3, MF:C28H30N4O6, MW:521.6 g/mol	Chemical Reagent	Bench Chemicals

The systematic engineering of Yarrowia lipolytica presents a promising approach to bridge the significant production gap between short-chain and medium/long-chain diols. Through coordinated strategies including oxidation pathway blocking, alkane hydroxylase enhancement, and bioprocess optimization, researchers have achieved remarkable 64-fold improvements in 1,12-dodecanediol production compared to wild-type strains [5] [4]. However, significant work remains to reach the gram-per-liter scale production commonly achieved with short-chain diols.

Future directions should focus on dynamic pathway control, compartmentalization of toxic intermediates, and engineering of synthetic P450 systems with improved efficiency and cofactor specificity. The integration of systems biology approaches with machine learning-enabled enzyme design will further accelerate the development of efficient microbial cell factories for medium- and long-chain diol production [10] [6]. As these engineering strategies mature, Y. lipolytica is poised to become a robust biorefinery platform for the sustainable production of valuable diol building blocks from renewable resources.

The oleaginous yeast Yarrowia lipolytica presents a superior alternative to bacterial systems for the production of high-value chemicals, particularly medium- to long-chain α,ω-diols. Its innate physiological and metabolic capabilities provide distinct advantages for alkane bioconversion and lipid accumulation, which are challenging to replicate in prokaryotic hosts. This application note details the specific endogenous advantages of Y. lipolytica and provides standardized protocols for leveraging these features in metabolic engineering projects focused on diol production.

The core strength of Y. lipolytica lies in its natural proficiency with hydrophobic substrates. Unlike E. coli, which requires extensive engineering to interact with alkanes, Y. lipolytica possesses native metabolic machinery for alkane uptake, transport, and activation [1] [19]. Furthermore, its high acetyl-CoA flux and oleaginous nature enable efficient conversion of carbon sources into storage lipids and their derivatives, providing an optimal foundation for diol synthesis [6].

Key Advantages ofY. lipolyticaOver Bacterial Systems

Endogenous Alkane Metabolism

Y. lipolytica natively produces a suite of enzymes specialized for hydrocarbon metabolism, eliminating the need for the complex heterologous expression often required in bacterial systems [4] [5].

• Cytochrome P450 Monooxygenases (CYPs): The yeast genome encodes 12 CYP52 family alkane monooxygenases (Alk1-12) that catalyze the terminal oxidation of alkanes to corresponding alcohols, the first committed step in the diol synthesis pathway [4] [5].
• Comprehensive Oxidation Machinery: The native metabolism includes a full set of alcohol dehydrogenases (ADHs), fatty alcohol oxidases (FAOs), and fatty aldehyde dehydrogenases (FALDHs) that can be rationally engineered to optimize flux toward diols [4] [5].
• Hydrophobic Substrate Uptake: The yeast has efficient mechanisms for adhering to and taking up alkanes, supported by its ability to form biofilms on oil-water interfaces [19].

Superior Lipid Accumulation Capacity

As an oleaginous yeast, Y. lipolytica can accumulate lipids to over 50% of its dry cell weight under nitrogen-limited conditions [20] [21]. This high lipid content is directly linked to an expanded intracellular pool of acetyl-CoA, the central precursor for fatty acid and lipid biosynthesis [6]. This abundance of acetyl-CoA and malonyl-CoA provides ample building blocks not only for lipids but also for a wide range of acetyl-CoA-derived products, including terpenoids and polyketides [22] [6]. Engineered strains have been reported to achieve lipid contents as high as 67.66% (g/g DCW) [20].

Safety and Industrial Robustness

Y. lipolytica holds a GRAS (Generally Recognized as Safe) status from the US FDA, facilitating its use in the production of food ingredients and nutraceuticals [1] [19]. It is tolerant to a wide range of pH and osmolarity, and can be cultivated on inexpensive and even waste-based feedstocks, making it suitable for large-scale industrial processes [1] [23].

Quantitative Performance Data

The following tables summarize key performance metrics of engineered Y. lipolytica strains for the production of valuable chemicals, highlighting its efficiency as a microbial cell factory.

Table 1: Production of Lipids and Lipid-Derived Compounds by Engineered Y. lipolytica

Product	Strain / Engineering Background	Titer / Content	Substrate	Reference
Lipids (Total)	yDTY214 (Engineered Po1f)	67.66% (g/g DCW)	Lipids	[20]
Lipids (Total)	ylXYL+Obese (Engineered Po1d)	~67% (g/g DCW); Titer: 16.5 g/L	Agave Bagasse Hydrolysate	[23]
1,12-Dodecanediol	YALI17 (Engineered Po1g)	1.45 mM	n-Dodecane	[4] [5]
1,12-Dodecanediol	YALI17 + pH control	3.2 mM (29-fold increase vs. WT)	n-Dodecane	[4] [5] [15]
β-Carotene	yDTY216 (Engineered Po1f)	High yield, 48h earlier peak production	Lipids	[20]

Table 2: Comparison of Diol Production in Microbial Hosts

Host Organism	Type of Diol	Maximum Reported Titer	Key Challenges
Yarrowia lipolytica	Medium- to Long-chain (e.g., C12)	3.2 mM (from alkanes)	Requires pathway blocking to prevent over-oxidation
Escherichia coli	Medium- to Long-chain	79 - 1,400 mg/L (from fatty acids)	Poor heterologous CYP450 expression; reliance on expensive fatty acids
Pseudomonas spp.	Medium- to Long-chain	~108 mg/L (from alkanes)	Low titer; complex enzyme systems
Clostridium beijerinckii	Short-chain (1,3-Propanediol)	~26 g/L (from glucose)	Not applicable for long-chain diols
Engineered E. coli	Short-chain (1,4-Butanediol)	~18 g/L (from glucose)	Not applicable for long-chain diols

Experimental Protocols

Protocol 1: CRISPR-Cas9 Mediated Blocking of Over-Oxidation Pathways

Objective: To generate a Y. lipolytica base strain (e.g., YALI17) with minimized over-oxidation of fatty alcohols and aldehydes to carboxylic acids, thereby maximizing the accumulation of diol intermediates [4] [5].

Materials:

• Y. lipolytica strain Po1g Δku70 (or other strain with repaired Ura3 locus)
• pCRISPRyl plasmid (Addgene #70007) or similar CRISPR vector for Y. lipolytica
• E. coli DH5α for plasmid propagation
• Reagents: YPD medium, Synthetic Complete (SC) medium without leucine, ampicillin, DpnI restriction enzyme, T4 DNA ligase.

Procedure:

• sgRNA Design and Vector Construction:
  • Design 20 bp guiding sequences targeting the genes of interest: FADH, ADH1-8, FAO1 (fatty alcohol oxidation), and FALDH1-4 (fatty aldehyde oxidation) [4].
  • For multiplexed editing, assemble multiple sgRNA expression cassettes in the pCRISPRyl vector using overlapping PCR and Golden Gate assembly. The plasmid already contains a Cas9 expression cassette [4] [5].
  • Transform the final assembled plasmid into E. coli DH5α, select on LB agar with ampicillin (100 mg/L), and verify the construct by sequencing.

• Yeast Transformation and Selection:

  • Introduce the verified CRISPR plasmid into the Y. lipolytica parental strain via established transformation methods (e.g., lithium acetate).
  • Plate cells on SC medium without leucine and incubate at 28-30°C for 2-3 days until colonies form.

• Screening and Genotypic Validation:

  • Pick individual colonies and perform colony PCR to verify the deletion of target genes.
  • Sequence the target loci in candidate strains to confirm frameshift mutations or complete gene deletions.
  • The successful strain (YALI17) should show a significant reduction in the conversion of alcohols to acids.

Protocol 2: Alkane Hydroxylase (ALK1) Overexpression

Objective: To enhance the initial oxidation of n-alkanes to ω-hydroxy fatty acids in the engineered YALI17 background [4] [5].

Materials:

• Y. lipolytica strain YALI17
• pYl yeast expression vector (a derivative of pCRISPRyl with Cas9 and sgRNA scaffold removed)
• Genomic DNA from Y. lipolytica (source of ALK1 gene)

Procedure:

• Vector Construction:
  • Amplify the coding sequence of the ALK1 gene from Y. lipolytica genomic DNA using high-fidelity PCR.
  • Clone the ALK1 CDS into the pYl vector under the control of a strong constitutive promoter (e.g., TEF) using methods like Circular Polymerase Extension Cloning (CPEC) [4].
  • The final overexpression construct is assembled in E. coli and validated by sequencing.

• Strain Engineering:
  • Transform the ALK1 overexpression vector into the YALI17 strain.
  • Select transformants on appropriate auxotrophic dropout medium.
  • Validate ALK1 overexpression by quantitative RT-PCR or Western blotting.

Protocol 3: Biotransformation of n-Dodecane to 1,12-Dodecanediol

Objective: To assess the diol production capability of the engineered strain in a controlled fermentation system [4] [5].

Materials:

• Engineered Y. lipolytica strain (e.g., YALI17 with ALK1 overexpression)
• n-Dodecane (50 mM final concentration)
• Bioreactor with pH and dissolved oxygen control
• Media: YPD for seed culture; Defined fermentation medium (e.g., 20 g/L glucose, 6.7 g/L YNB, necessary supplements, C/N ratio >60 to induce lipogenesis) [4] [23].

Procedure:

• Seed Culture:
  • Inoculate a single colony of the engineered strain into 10 mL of YPD medium.
  • Incubate at 28°C, 250 rpm for 48 hours.

• Fermentation:

  • Transfer the seed culture to a bioreactor containing the defined fermentation medium. The initial working volume is 20 mL in a 100 mL flask or scaled up in a larger bioreactor.
  • Set fermentation temperature to 28°C, maintain pH at 6.5 (or optimize to 5.7-6.0 for enhanced production), and ensure high aeration [4] [21].
  • Add filter-sterilized n-dodecane (50 mM final concentration) as the substrate once the cell density is sufficiently high (e.g., mid-exponential phase).
  • Allow the biotransformation to proceed for 3-5 days.

• Product Extraction and Analysis:

  • Extract the culture broth with an equal volume of ethyl acetate.
  • Analyze the organic phase using Gas Chromatography-Mass Spectrometry (GC-MS) or High-Performance Liquid Chromatography (HPLC) to quantify 1,12-dodecanediol production. Compare against authentic standards.

Pathway and Workflow Visualization

Diagram Title: Native vs. Engineered Alkane Metabolism in Y. lipolytica

Diagram Title: Experimental Workflow for Diol Production

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents for Metabolic Engineering of Y. lipolytica

Reagent / Material	Function / Application	Example / Source
pCRISPRyl Vector	CRISPR-Cas9 genome editing in Y. lipolytica; contains Cas9 and sgRNA scaffold.	Addgene #70007 [4]
pYl Expression Vector	Protein overexpression; derivative of pCRISPRyl with optimized promoters.	[4]
Y. lipolytica Po1g Δku70	Common parental strain; KU70 deletion improves homologous recombination efficiency.	ATCC/Marka [4] [5]
n-Dodecane	Hydrophobic substrate for alkane bioconversion and diol production.	Sigma-Aldrich/Chemical Supplier
Defined Fermentation Medium	Controlled environment for biotransformation; high C/N ratio induces product accumulation.	YNB-based media [4] [21]
Anti-Foam Agents	Controls foam formation during aerated cultivation with hydrophobic substrates.	Sigma-Aldrich/Chemical Supplier
Boc-Leu-Lys-Arg-AMC	Boc-Leu-Lys-Arg-AMC, CAS:109358-47-6, MF:C33H52N8O7, MW:672.8 g/mol	Chemical Reagent
Glyphosate-13C2,15N	Glyphosate-13C2,15N, CAS:1185107-63-4, MF:C3H8NO5P, MW:172.05 g/mol	Chemical Reagent

Advanced Genetic Tools and Pathway Engineering for Enhanced Diol Production

CRISPR-Cas9 Systems for Precision Genome Editing in Y. lipolytica

The oleaginous yeast Yarrowia lipolytica has emerged as a prominent microbial chassis in industrial biotechnology due to its robust metabolism, capacity to utilize diverse carbon sources, and innate ability to produce high-value lipids and chemicals [6]. Within metabolic engineering programs aimed at diol production, precision genome editing is indispensable for redirecting metabolic flux. While CRISPR-Cas9 technology has been adapted for Y. lipolytica, its editing efficiency has been historically limited by challenges such as low homologous recombination (HR) efficiency and variable sgRNA performance [24] [25]. This application note details optimized CRISPR-Cas9 systems that overcome these barriers, enabling high-efficiency genetic manipulations to streamline the development of microbial cell factories for diol synthesis.

Optimized System Components and Performance Metrics

Recent advancements have systematically optimized critical components of the CRISPR-Cas9 system for Y. lipolytica, leading to dramatic improvements in editing efficiency. The table below summarizes key performance data for these optimized components.

Table 1: Performance Metrics of Optimized CRISPR-Cas9 Components in Y. lipolytica

Optimized Component	Specific Innovation	Reported Efficiency	Key Application in Metabolic Engineering
sgRNA Expression Architecture [24] [25]	Direct tRNA-sgRNA fusion (using SCR1-tRNA promoter)	92.5% gene disruption efficiency [25]	Enables reliable gene knock-outs for blocking competing pathways.
HR Enhancement (DNA Repair) [25]	KU70 deletion combined with Rad52 and Sae2 overexpression	92.5% genome integration efficiency [25]	Facilitates high-efficiency gene knock-ins for pathway engineering.
Engineered Cas9 Variant [25]	Use of iCas9 (Cas9D147Y, P411T)	Enhanced both gene disruption and integration efficiency [25]	Improves overall success rate of all editing operations.
Multiplex Editing Capacity [25]	tRNA-sgRNA architecture for processing multiple guides	57.5% dual gene disruption efficiency [25]	Allows simultaneous knockout of multiple genes (e.g., ADH, FALDH).
Donor Template Design [24]	Optimization of homology arm length	Enabled recombination using donors with 50-bp homology arms [24]	Simplifies and reduces the cost of donor DNA construction.

The foundational improvement involves the sgRNA expression system. Early designs used tRNA-sgRNA fusions with an unexplained intergenic sequence, which was predicted to form secondary structures that impaired sgRNA function. Its removal created a direct tRNA-sgRNA fusion, which significantly improved editing efficiency at previously recalcitrant genomic loci, achieving efficiencies close to 100% [24]. This architecture also enables efficient multiplexed editing by leveraging the endogenous RNase system to process multiple sgRNAs from a single transcript [25].

Enhancing Homology-Directed Repair (HDR) is another critical area. Y. lipolytica has a strong preference for non-homologous end joining (NHEJ) over HDR, which limits gene integration via donor templates. A highly effective strategy involves deleting KU70, a key protein in the NHEJ pathway, which has been shown to increase integration efficiency to 92.5% [25]. Furthermore, overexpressing HR-related genes like Rad52 and Sae2 provides an additional boost to HDR rates [25]. For strains where NHEJ disruption is undesirable, using the engineered iCas9 variant and optimizing donor template homology arms to as short as 50 bp can still yield very high efficiencies [24] [25].

Experimental Protocols

Protocol 1: High-Efficiency Gene Deletion Using Direct tRNA-sgRNA Fusions

This protocol is designed for targeted gene knockout and is adapted from studies that achieved disruption efficiencies over 90% [24] [25].

Research Reagent Solutions: Table 2: Essential Reagents for Gene Deletion

Item	Function/Description
pCRISPRyl Vector (Addgene #70007)	Base plasmid for expressing Cas9 and sgRNA in Y. lipolytica [4].
Target-Specific sgRNA Oligos	20-nt sequences complementary to the target genomic locus, designed with minimal off-target effects.
Y. lipolytica Po1f Strain	A common, double-auxotroph, NHEJ-competent host strain [24] [26].
YPD Medium	Rich growth medium: 1% yeast extract, 2% peptone, 2% glucose [24] [8].
YNB Selection Medium	Synthetic minimal medium for transformant selection: 0.17% YNB without AA, 0.5% NHâ‚„Cl, 2% glucose, 50 mM phosphate buffer (pH 6.8) [8].

Step-by-Step Procedure:

• sgRNA Cloning:
  • Design a 20-nucleotide target-specific sequence using a design tool like CRISPOR.
  • Anneal oligonucleotides encoding this sequence and clone them into the BsmBI site of a pCRISPRyl-derived plasmid, downstream of the SCR1-tRNA promoter to create a direct tRNA-sgRNA fusion [24] [25] [8].
• Yeast Transformation:
  • Cultivate Y. lipolytica Po1f in YPD medium to mid-exponential phase.
  • Prepare competent cells and transform with 500 ng of the finalized sgRNA plasmid using the lithium acetate method [8].
• Selection and Screening:
  • Plate transformed cells on YNB solid medium lacking the appropriate amino acid to select for the plasmid.
  • Incubate plates at 28°C for 2-3 days.
  • Screen individual colonies by colony PCR followed by DNA sequencing to verify the intended gene deletion.

Protocol 2: Multiplexed Gene Knockout for Pathway Engineering

This protocol enables the simultaneous disruption of multiple genes, which is essential for blocking competing metabolic pathways, as demonstrated in the engineering of a 1,12-dodecanediol production strain [5] [4].

Research Reagent Solutions: Table 3: Essential Reagents for Multiplexed Knockout

Item	Function/Description
tRNA-sgRNA Array Plasmid	Plasmid where multiple tRNA-sgRNA units are transcribed as a single transcript and processed intracellularly [25].
Donor DNA Cassettes	Linear DNA fragments containing selection markers flanked by homology arms (50-500 bp) for recycling markers [27].
CRISPR Plasmid with iCas9	Plasmid expressing the high-efficiency iCas9 variant [25].

Step-by-Step Procedure:

• Multiplex sgRNA Construct Design:
  • Design tRNA-sgRNA units for each target gene (e.g., FADH, ADH1-8, FAO1, FALDH1-4 for diol production) [5].
  • Synthesize a construct where these units are arranged in tandem within a single expression cassette on a plasmid.
• Strain Transformation:
  • Co-transform the Y. lipolytica host strain (potentially KU70-deficient for higher HDR efficiency) with the multiplex sgRNA plasmid and any donor DNA cassettes for marker recycling.
• Validation of Multiplex Editing:
  • Isolate transformants and screen via colony PCR for deletions at all target loci.
  • Sanger sequence the edited genomic regions to confirm the knockout of each target gene. In the referenced study, this approach successfully created strain YALI17 with 14 gene deletions [5].

The following workflow diagram illustrates the key steps and genetic components involved in this multiplexed knockout strategy.

Protocol 3: High-Throughput Promoter Replacement (TUNEYALI)

The TUNEYALI method enables high-throughput, scarless promoter swapping to fine-tune gene expression, which is invaluable for optimizing metabolic pathways [27].

Research Reagent Solutions: Table 4: Essential Reagents for TUNEYALI Method

Item	Function/Description
TUNEYALI Library Plasmids	Each plasmid contains an sgRNA, homology arms, and a SapI site for promoter insertion.
SapI Restriction Enzyme	Used for Golden Gate assembly to insert promoter elements scarlessly.
Library of Promoter Parts	A collection of native Y. lipolytica promoters of varying strengths.

Step-by-Step Procedure:

• Library Construction:
  • For each target gene, design a synthetic DNA fragment containing a target-specific sgRNA, upstream and downstream homology arms (62-162 bp), and a double SapI restriction site between them.
  • Clone these fragments into a plasmid backbone via Gibson assembly.
  • Use SapI-mediated Golden Gate assembly to insert a library of promoter parts into the pooled plasmids.
• Library Transformation and Screening:
  • Transform the entire plasmid library into the Y. lipolytica production strain.
  • Screen or select for clones exhibiting the desired phenotype (e.g., improved diol production, thermotolerance).
  • Identify the successful promoter-gene combinations by sequencing the integrated plasmids from the best-performing clones.

Application in Diol Production: A Case Study

The power of optimized CRISPR-Cas9 systems is exemplified by the engineering of Y. lipolytica for the production of medium- to long-chain α,ω-diols, such as 1,12-dodecanediol, from alkanes [5] [4].

Metabolic Engineering Strategy: The primary challenge is preventing the over-oxidation of fatty alcohol intermediates into fatty acids, which diverts flux away from the desired diol. The engineering strategy involved:

• Blocking Over-oxidation Pathways: Employing multiplex CRISPR-Cas9 to systematically delete ten genes involved in fatty alcohol oxidation (FADH, ADH1-8, FAO1) and four genes involved in fatty aldehyde oxidation (FALDH1-4). This resulted in the base engineered strain YALI17 [5].
• Enhancing Alkane Hydroxylation: Overexpressing the alkane hydroxylase gene ALK1 to increase the primary oxidation of n-dodecane [4].
• Process Optimization: Implementing automated pH-controlled biotransformation to further improve yield.

Results: The engineered strain YALI17 produced 0.72 mM of 1,12-dodecanediol from n-dodecane, a 14-fold increase over the parental strain. With ALK1 overexpression, production rose to 1.45 mM, and pH-controlled fermentation further boosted the titer to 3.2 mM, demonstrating the successful application of precision genome editing for diol production [5] [4].

The diagram below outlines the key stages of this metabolic engineering project.

The CRISPR-Cas9 systems detailed herein, featuring optimized sgRNA architectures, enhanced DNA repair mechanisms, and efficient multiplexing capabilities, provide a robust and precise toolkit for metabolic engineering of Yarrowia lipolytica. The successful application of these tools in creating a high-performance diol-producing strain underscores their transformative potential. By enabling rapid and systematic genome manipulation, these protocols empower researchers to accelerate the design-build-test cycles necessary for developing advanced microbial cell factories.

In the metabolic engineering of Yarrowia lipolytica for the production of valuable chemicals such as diols, a significant challenge lies in preventing the diversion of metabolic intermediates into competing pathways. The native metabolism of this oleaginous yeast contains multiple enzyme systems that efficiently oxidize fatty alcohols and aldehydes, thereby limiting the accumulation of target products like medium-chain α,ω-diols [4] [15]. This application note details targeted gene deletion strategies to block these competing oxidation pathways, enabling significant enhancement of diol production in engineered Y. lipolytica strains.

The competing pathways primarily involve several enzyme families: fatty alcohol dehydrogenases (FADH), multiple alcohol dehydrogenases (ADH1-8), fatty alcohol oxidase (FAO1), and fatty aldehyde dehydrogenases (FALDH1-4) [4] [28]. These enzymes sequentially convert fatty alcohols to fatty aldehydes and subsequently to fatty acids, effectively shunting carbon flux away from diol synthesis. By systematically deleting these genes using CRISPR-Cas9 technology, researchers have successfully constructed Y. lipolytica strains with dramatically reduced over-oxidation activity, resulting in significantly improved production of valuable diols from alkane substrates [4] [15].

Key Oxidation Pathways and Genetic Targets

Metabolic Pathways Competing with Diol Production

In the native metabolism of Y. lipolytica, alkane substrates are initially hydroxylated by cytochrome P450 enzymes (particularly ALK1-12) to form fatty alcohols, which represent key intermediates for diol synthesis [4]. However, these fatty alcohols are rapidly oxidized through competing pathways, preventing their accumulation and conversion to diols. The primary competing routes involve:

• Fatty alcohol oxidation to fatty aldehydes catalyzed by FADH, ADH1-8, and FAO1
• Fatty aldehyde oxidation to fatty acids catalyzed by FALDH1-4 [4]

This sequential oxidation represents a major carbon loss pathway that must be blocked to enable efficient diol production. The diagram below illustrates these competing pathways and the strategic gene deletions required to redirect flux toward diol synthesis.

Figure 1: Metabolic pathway engineering strategy for diol production in Y. lipolytica. Strategic deletion of oxidation genes (red diamonds) blocks competing pathways, redirecting flux from fatty alcohol intermediates toward α,ω-diol synthesis.

Gene Targets for Pathway Blocking

Y. lipolytica possesses a comprehensive set of oxidation enzymes that must be systematically deleted to prevent loss of metabolic intermediates. The key genetic targets include:

Table 1: Oxidation Gene Targets for Deletion in Y. lipolytica

Gene Category	Specific Gene Targets	Number of Genes	Enzyme Function	Effect of Deletion
Fatty Alcohol Oxidation	FADH, ADH1, ADH2, ADH3, ADH4, ADH5, ADH6, ADH7, ADH8, FAO1	10	Conversion of fatty alcohols to fatty aldehydes	Prevents over-oxidation of alcohol intermediates
Fatty Aldehyde Oxidation	FALDH1, FALDH2, FALDH3, FALDH4	4	Conversion of fatty aldehydes to fatty acids	Blocks formation of terminal carboxylic acids
Total Genes Deleted		14		Significantly reduces over-oxidation activity

The combinatorial deletion of these 14 genes has been shown to generate Y. lipolytica strains with substantially reduced over-oxidation capability, enabling the accumulation of fatty alcohol intermediates for subsequent conversion to diols [4]. Research indicates that among these targets, FAO1 (fatty alcohol oxidase) appears to be particularly significant for intracellular fatty alcohol degradation, with its deletion alone resulting in an approximately tenfold increase in fatty alcohol-producing capability [28].

Quantitative Impact of Gene Deletions on Diol Production

The systematic deletion of oxidation genes has demonstrated substantial improvements in diol production metrics. The following table summarizes key performance data from engineered Y. lipolytica strains:

Table 2: Production Metrics of Engineered Y. lipolytica Strains

Strain Description	Genetic Modifications	Substrate	1,12-Dodecanediol Production	Fold Improvement
Wild-type Y. lipolytica	None	n-dodecane (50 mM)	0.05 mM [4]	Baseline
YALI17	Deletion of 10 alcohol oxidation genes (FADH, ADH1-8, FAO1) and 4 aldehyde oxidation genes (FALDH1-4) [4]	n-dodecane (50 mM)	0.72 mM [4]	14×
YALI17 + ALK1 overexpression	YALI17 background with ALK1 alkane hydroxylase overexpression [4]	n-dodecane (50 mM)	1.45 mM [4]	29×
YALI17 + ALK1 + pH control	YALI17 with ALK1 overexpression and optimized pH control [4]	n-dodecane (50 mM)	3.2 mM [4]	64×

The data demonstrate that blocking competing oxidation pathways through systematic gene deletion enables remarkable improvements in diol production, with the most optimized strains achieving 64-fold enhancement over wild-type Y. lipolytica [4]. This strategy effectively redirects metabolic flux toward the desired diol products while minimizing loss through over-oxidation pathways.

Experimental Protocols

CRISPR-Cas9 Mediated Multiplex Gene Deletion

Principle: This protocol enables simultaneous deletion of multiple oxidation genes using the CRISPR-Cas9 system to create Y. lipolytica strains with reduced over-oxidation activity for enhanced diol production [4].

Materials:

• Y. lipolytica po1f or other appropriate strain
• pCRISPRyl plasmid (Addgene #70007) or similar CRISPR vector
• E. coli DH5α for plasmid propagation
• YPD medium: 20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract
• Synthetic complete medium without leucine
• Lithium acetate transformation solution
• Hygromycin or leucine for selection

Procedure:

• sgRNA Design and Vector Construction

  • Design 20 bp guiding sequences specific to each target gene (FADH, ADH1-8, FAO1, FALDH1-4)
  • For multiplex targeting, insert additional sgRNA scaffold sequences downstream of the original sgRNA site in pCRISPRyl
  • Clone guiding sequences upstream of each sgRNA scaffold using overlapping PCR
  • Digest PCR products with DpnI at 37°C for 16 hours
  • Transform into E. coli DH5α and select on LB plates with ampicillin (100 mg/L)
  • Verify plasmid construction by sequencing [4]

• Y. lipolytica Transformation

  • Harvest 1 mL of Y. lipolytica cells from YPD medium after 24 hours growth
  • Resuspend cells in 105 μL transformation solution containing:
    • 90 μL 50% PEG4000
    • 5 μL lithium acetate (2 M)
    • 5 μL denatured single-strand salmon sperm DNA
    • 5 μL plasmid DNA
  • Incubate mixture at 39°C for 1 hour with vortexing for 15 seconds every 15 minutes
  • Spread on selective plates containing hygromycin or lacking leucine
  • Incubate at 30°C for 2-3 days until colonies appear [29]

• Strain Validation

  • Screen colonies by colony PCR using gene-specific primers
  • Verify gene deletions by sequencing
  • Confirm reduced oxidation activity through enzymatic assays [4]

Analytical Methods for Diol Production Assessment

Principle: Quantify diol production and assess metabolic flux in engineered strains using chromatographic methods and fermentation performance evaluation.

Materials:

• HPLC system with appropriate column (e.g., C18 reverse-phase)
• Internal standards for quantification
• n-dodecane as substrate
• Fermentation medium
• 250 mL flasks for shake flask cultivations

Procedure:

• Fermentation Conditions

  • Inoculate 0.8 mL seed culture into 25 mL fermentation medium in 250 mL flasks
  • Incubate at 220 rpm and 30°C for 120 hours
  • Supplement with 50 mM n-dodecane as substrate
  • For pH-controlled experiments, maintain optimal pH using automated systems [4] [29]

• Metabolite Analysis

  • Collect samples at 24-hour intervals
  • Extract intracellular metabolites using appropriate solvent systems
  • Analyze diol production using HPLC with authentic standards
  • Quantify 1,12-dodecanediol using standard curves
  • Monitor substrate consumption and byproduct formation [4]

• Enzymatic Activity Assays

  • Prepare cell-free extracts from engineered strains
  • Measure alcohol oxidation activity using NAD+ reduction assays
  • Assess aldehyde dehydrogenase activity with appropriate substrates
  • Compare activities between wild-type and engineered strains [28]

The experimental workflow below outlines the complete process from strain construction to product analysis:

Figure 2: Experimental workflow for engineering and evaluating Y. lipolytica strains with blocked oxidation pathways. The process encompasses strain construction through CRISPR-Cas9 mediated gene deletion followed by comprehensive phenotypic and production characterization.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Research Reagents for Oxidation Pathway Engineering

Reagent / Tool	Function / Application	Example / Source
pCRISPRyl Vector	CRISPR-Cas9 plasmid for gene editing in Y. lipolytica	Addgene #70007 [4]
Alkane Hydroxylase (ALK1)	Cytochrome P450 enzyme for primary alkane oxidation	Overexpressed from Y. lipolytica genome [4]
n-Dodecane	Model alkane substrate for diol production	Commercial source [4]
YPD Medium	Standard growth medium for Y. lipolytica	20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract [29]
Hygromycin / Leucine	Selection markers for transformant identification	Commercial antibiotic and auxotrophic selection [29]
BMY 28674-d8	BMY 28674-d8, CAS:1189644-16-3, MF:C21H31N5O3, MW:409.6 g/mol	Chemical Reagent
Guanfacine-13C,15N3	Guanfacine-13C,15N3, CAS:1189924-28-4, MF:C9H9Cl2N3O, MW:250.06 g/mol	Chemical Reagent

The strategic deletion of competing oxidation pathway genes (FADH, ADH1-8, FAO1, FALDH1-4) in Yarrowia lipolytica represents a powerful metabolic engineering approach to enhance diol production from alkane substrates. Implementation of the protocols described herein enables the construction of engineered strains capable of producing 1,12-dodecanediol at concentrations up to 3.2 mM - a 64-fold improvement over wild-type strains. This significant increase in production efficiency demonstrates the critical importance of blocking competing oxidative pathways in microbial cell factories designed for diol synthesis.

The CRISPR-Cas9 mediated multiplex gene deletion strategy provides an efficient and scalable method for engineering robust Y. lipolytica strains with minimized over-oxidation activity. When combined with alkane hydroxylase overexpression and optimized fermentation conditions, this approach enables sustainable production of valuable medium-chain α,ω-diols directly from alkane feedstocks, establishing Y. lipolytica as a promising platform for industrial-scale biomanufacturing of these important chemical building blocks.

Within the metabolic engineering framework for producing diols in Yarrowia lipolytica, the initial hydroxylation of alkane substrates is a critical, rate-limiting step. This protocol focuses on enhancing this step through the strategic overexpression of ALK genes, which encode cytochrome P450 alkane monooxygenases. As the foundational reaction that channels alkane substrates into the diol synthesis pathway, efficient hydroxylation directly impacts the overall titer of target compounds like 1,12-dodecanediol [5] [4]. Yarrowia lipolytica possesses a native suite of 12 CYP52 family P450 enzymes (Alk1-Alk12) with varied substrate specificities, making the selection of the appropriate ALK gene for overexpression a crucial consideration [30]. This document provides a detailed methodology for constructing engineered Y. lipolytica strains with overexpressed ALK genes and quantifies the subsequent improvement in diol production.

Background and Rationale

The ALK Gene Family inYarrowia lipolytica

The ALK genes in Y. lipolytica encode enzymes that catalyze the terminal hydroxylation of n-alkanes to corresponding fatty alcohols [30]. Among these, ALK1 has been identified as a primary catalyst for the oxidation of medium-chain n-alkanes. Gene deletion studies have shown that an ALK1 knockout strain exhibits significant growth defects on alkanes with chain lengths of C10 to C15, underscoring its pivotal role [30]. Furthermore, ALK2 supports the hydroxylation of longer-chain alkanes, and other members like ALK3, ALK5, and ALK7 also contribute to fatty acid ω-hydroxylation activity [30] [17].

Role in a Diol Production Pathway

In a engineered pathway for α,ω-diol production, the alkane substrate is sequentially oxidized at both terminals. The first oxidation is catalyzed by an Alk enzyme, converting the alkane to a fatty alcohol. This alcohol can then be further oxidized via a series of steps to form a fatty acid, which may undergo a second terminal hydroxylation to yield the diol [17]. Overexpressing the initial alkane hydroxylase is therefore a key strategy to increase carbon flux into this multi-step pathway. Research has demonstrated that ALK1 overexpression in a engineered production strain can effectively enhance the yield of 1,12-dodecanediol from n-dodecane [5] [4].

Table 1: Key ALK Genes for Overexpression in Yarrowia lipolytica

Gene	Primary Substrate Specificity	Key Characteristics and Rationale for Overexpression
ALK1	Medium-chain n-alkanes (C10-C16)	A primary hydroxylase for C10-C15 alkanes; its overexpression boosted 1,12-dodecanediol production to 1.45 mM [30] [5].
ALK2	Long-chain n-alkanes	Important for hydroxylation of C16 and longer alkanes; works synergistically with ALK1 [30].
ALK5	Fatty Acids	Exhibits significant ω-hydroxylating activity toward dodecanoic acid; relevant for dicarboxylic acid production [30] [17].

Experimental Protocols

Protocol 1: Vector Construction for ALK Gene Overexpression

This protocol describes the construction of an expression vector for the overexpression of ALK genes in Y. lipolytica using the pYl vector system, which is derived from a CRISPR plasmid [4].

Materials:

• Host Strain: E. coli DH5α for plasmid propagation.
• Plasmid Backbone: pYl vector or similar (e.g., pCRISPRyl-derived vector with Cas9 ORF replaced) [4].
• Enzymes: Restriction enzymes (e.g., SpeI, MfeI), T4 DNA ligase, DpnI.
• PCR Reagents: High-fidelity DNA polymerase, primers for ALK gene amplification and vector assembly.
• Media: LB medium supplemented with ampicillin (100 mg/L) for E. coli selection [5].

Procedure:

• Amplify ALK Gene: PCR-amplify the coding sequence (CDS) of the target ALK gene (e.g., ALK1) from Y. lipolytica genomic DNA using gene-specific primers. The primers should include overlaps homologous to the insertion site in the pYl vector.
• Prepare Vector: Linearize the pYl expression vector using appropriate restriction enzymes. The pYl vector typically contains a strong constitutive promoter like the TEF promoter (PTEF) with an intron for enhanced expression in Y. lipolytica [4].
• Clone Gene: Assemble the linearized vector and the amplified ALK gene fragment using a cloning method such as Circular Polymerase Extension Cloning (CPEC) [4].
• Transform and Verify: Transform the assembled plasmid into E. coli DH5α and select on LB-ampicillin plates. Pick several colonies, culture them, and purify the plasmid for verification by sequencing.

Protocol 2: Strain Transformation and Cultivation for Diol Production

This protocol covers the transformation of the expression vector into an engineered Y. lipolytica production strain and the subsequent cultivation to assess diol production.

Materials:

• Yeast Strain: Engineered Y. lipolytica production strain (e.g., YALI17, which has blocked over-oxidation pathways) [5].
• Selection Media: YPD medium (20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract, pH 6.5) for general growth; Synthetic Complete medium without leucine for selection of transformants [5].
• Production Media: Fermentation medium with n-dodecane (e.g., 50 mM) as the carbon source [5].
• Equipment: Shake flasks or bioreactors with pH control (e.g., maintained at pH 6.5) [5].

Procedure:

• Transform Y. lipolytica: Introduce the constructed ALK overexpression vector into the competent cells of your engineered Y. lipolytica production strain (e.g., via lithium acetate transformation).
• Select Transformants: Plate the transformation mixture on Synthetic Complete medium without leucine and incubate at 28-30°C for 2-3 days until colonies appear [5].
• Pre-culture: Inoculate a single colony into 5 mL of YPD or selection medium and incubate with shaking for 2 days.
• Scale-up Culture: Transfer the pre-culture to a larger volume (e.g., 20 mL in a 100 mL flask) of the same medium and incubate for another 2 days to achieve sufficient cell density [5].
• Induce Production: Harvest cells and transfer to the production medium containing n-dodecane as the substrate. Fermentation can be performed in shake flasks or, for higher titers, in a bioreactor with automated pH control [5].
• Analyze Products: After a defined fermentation period, extract metabolites from the culture broth and analyze diol production using techniques like Gas Chromatography-Mass Spectrometry (GC-MS) or High-Performance Liquid Chromatography (HPLC).

Key Data and Performance Metrics

The following table summarizes quantitative data from studies where ALK gene overexpression was employed to enhance the production of valuable chemicals from alkanes in Y. lipolytica.

Table 2: Performance Metrics of ALK Gene Overexpression in Engineered Strains

Engineered Strain / Strategy	Substrate	Product	Key Genetic Modifications	Titer Achieved	Fold Increase & Notes
YALI17 + ALK1 Ovx [5] [4]	n-dodecane (50 mM)	1,12-Dodecanediol	Deletion of 10 alcohol & 4 aldehyde oxidation genes; ALK1 overexpression.	1.45 mM	A 29-fold increase over wild-type (0.05 mM).
YALI17 + pH Control [5]	n-dodecane	1,12-Dodecanediol	Same as above, with automated pH-controlled biotransformation.	3.2 mM	Highlights the impact of optimizing process parameters alongside genetic engineering.
Base Engineered Strain (YALI17) [5]	n-dodecane (50 mM)	1,12-Dodecanediol	Deletion of 10 alcohol & 4 aldehyde oxidation genes.	0.72 mM	A 14-fold increase over parental strain, showing the importance of blocking competing pathways.

The Scientist's Toolkit

Table 3: Essential Research Reagent Solutions for ALK Gene Engineering

Reagent / Material	Function and Application in Research	Specific Examples
pYl Expression Vector	A backbone for constructing gene expression plasmids in Y. lipolytica.	Derived from pCRISPRyl; features a strong constitutive TEF promoter (PTEF) with an intron for high-level expression [4].
Engineered Host Strain (YALI17)	A Y. lipolytica chassis with minimized over-oxidation of alcohol and aldehyde intermediates.	Contains deletions in FADH, ADH1-8, FAO1 (alcohol oxidation) and FALDH1-4 (aldehyde oxidation) [5].
Alkane Substrates	Carbon source and direct precursor for the hydroxylation reaction and diol synthesis.	n-Dodecane (C12) is commonly used for medium-chain diol production like 1,12-dodecanediol [5].
CRISPR-Cas9 System	Enables precise gene knockouts to block competing pathways or for vector construction.	Used to create multiple gene deletions simultaneously; efficiency can reach >80% in Y. lipolytica [5] [31].
Diosmetin-d3	Diosmetin-d3 | CAS 1189728-54-8 | Internal Standard	Diosmetin-d3 is a high-quality deuterated internal standard for precise LC-MS/GC-MS quantification of diosmetin in research. For Research Use Only. Not for human use.
LDL-IN-4	LDL-IN-4, CAS:615264-62-5, MF:C27H27NO7, MW:477.5 g/mol	Chemical Reagent

Pathway and Workflow Visualizations

Metabolic Pathway for Diol Production from Alkanes

The diagram below illustrates the engineered metabolic pathway in Yarrowia lipolytica for the conversion of alkanes to α,ω-diols, highlighting the key role of Alk1 and the competing pathways that must be disrupted.

Experimental Workflow for Strain Engineering and Evaluation

This flowchart outlines the complete experimental process from strain construction to the evaluation of diol production.

The development of high-performing microbial cell factories for industrial biotechnology often requires testing numerous genetic hypotheses. Strain development projects are typically costly and time-consuming, as rational design of metabolic pathways remains challenging. The TUNEYALI method (TUNing Expression in Yarrowia lipolytica) addresses this bottleneck by enabling high-throughput, precise modulation of gene expression in the industrially important oleaginous yeast Yarrowia lipolytica [27] [32]. This CRISPR-Cas9-based methodology allows researchers to systematically tune the expression of target genes by replacing their native promoters with alternatives of varying strengths, facilitating rapid strain optimization without the need for extensive automation infrastructure [27].

For researchers focused on diol production, this method offers particular promise. Yarrowia lipolytica possesses inherent advantages for converting hydrophobic substrates like alkanes into valuable chemicals, including medium- to long-chain α,ω-diols used in polyester and polyurethane production [4] [15]. However, engineering efficient production strains requires balancing complex metabolic pathways, where fine-tuning transcription factor expression or pathway enzyme levels can dramatically impact final titers. TUNEYALI provides a systematic approach to address these challenges through combinatorial testing of expression levels for multiple gene targets simultaneously [27].

Conceptual Framework

The TUNEYALI method employs a CRISPR-Cas9-based promoter replacement strategy to modulate gene expression at the chromosomal level. Unlike approaches that rely on random integration or episomal expression, this technique enables scarless promoter swapping, allowing precise control over gene expression while maintaining genetic stability [27]. The system is designed to overcome a key limitation in library-scale genome editing: ensuring correct pairing between guide RNAs and their corresponding repair templates. By encoding both elements on a single plasmid, TUNEYALI maintains high editing efficiency while enabling multiplexed approaches [27].

Key Advantages for Metabolic Engineering

This method addresses several critical needs in Y. lipolytica metabolic engineering:

• Simultaneous testing of multiple hypotheses by creating libraries of strains with varying expression levels for target genes
• Elimination of iterative cycles of strain construction for individual targets
• Applicability to any gene or group of genes in the Y. lipolytica genome
• Reusable resources that can be shared across research groups [27] [32]

For diol production research, this enables systematic optimization of transcription factors regulating alkane metabolism, redox balancing, and precursor supply pathways that are critical for efficient bioconversion [4].

Experimental Protocol

Plasmid Design and Construction

The TUNEYALI workflow begins with the design and construction of specialized plasmids containing both sgRNA expression cassettes and homologous repair templates:

• Design target-specific components: Synthesize DNA constructs containing:

  • A target-specific sgRNA sequence designed to cleave within the promoter region of the gene of interest
  • Upstream homologous recombination (HR) arm matching the region immediately upstream of the native promoter
  • Downstream HR arm matching the beginning of the coding sequence (CDS)
  • A double SapI restriction site between the HR arms for promoter insertion [27]

• Assemble core plasmid: Clone the synthetic DNA construct into a plasmid backbone via Gibson assembly, generating target-specific vectors with 20 bp Gibson assembly homology arms on each side [27]

• Insert promoter variants: Using Golden Gate assembly with SapI enzyme, insert selected promoter sequences between the HR elements. The 3-bp overhang generated by SapI corresponds to a start codon (ATG), preventing the formation of scars between the promoter and the CDS [27]

Table 1: Key Components of the TUNEYALI Plasmid System

Component	Specifications	Function
Homologous Arms	62 bp, 162 bp, or 500 bp	Facilitate precise genomic integration via homologous recombination
SapI Site	Double recognition site	Enables scarless promoter insertion via Golden Gate assembly
sgRNA	Target-specific 20 nt sequence	Directs Cas9 to cleave the native promoter region
Promoter Variants	Native Y. lipolytica promoters of varying strengths	Provides differential expression levels for the target gene

Library Transformation and Screening

The protocol for library implementation involves the following key steps:

• Prepare plasmid library: Mix individual promoter-replacement plasmids in desired combinations to create a comprehensive expression-tuning library [27]

• Transform Y. lipolytica: Introduce the plasmid library into strains of interest using standard transformation protocols. For the transcription factor library, researchers transformed both reference strains and betanin-producing strains to identify phenotypes of interest [27]

• Select and screen transformants: Plate on appropriate selective media and screen for desired phenotypes. In the original study, screening included:

  • Assessment of morphological changes
  • Evaluation of thermotolerance
  • Measurement of betanin production [27]

• Identify genetic determinants: Isolate clones with desired phenotypes and sequence integrated plasmids to determine which promoter-gene combinations produced the improvements [27]

Efficiency Optimization

Critical parameters for optimizing editing efficiency:

• Homology arm length: The original study demonstrated that editing efficiency significantly increases with longer homology arms. While 500 bp arms showed highest efficiency, 162 bp arms provided a favorable balance between efficiency and synthetic DNA costs [27]

• sgRNA design: Testing multiple sgRNAs per target is recommended, as efficiency varies depending on the target site [27]

• Promoter selection: The method utilizes native Y. lipolytica promoters of validated strengths to ensure predictable expression modulation [27]

Application Example: Transcription Factor Library

Library Specifications

To demonstrate TUNEYALI's capabilities, researchers created a comprehensive library targeting 56 transcription factors (TFs) in Y. lipolytica. The library design included:

• 56 distinct transcription factors involved in various cellular processes
• 7 expression levels for each TF, achieved by replacing native promoters with promoters of different strengths
• 392 unique genetic combinations enabling comprehensive exploration of expression space [27] [32]

This library design allows researchers to identify not only which TFs influence a phenotype of interest, but also their optimal expression levels for maximizing desired traits.

Phenotypic Discoveries

Screening the TF expression library led to several significant findings:

• Enhanced thermotolerance: Identified multiple TFs whose regulatory changes increased the strain's temperature tolerance [27] [32]
• Morphological engineering: Discovered two TFs that, when expression-modulated, eliminated pseudohyphal growth, potentially improving fermentation characteristics [27]
• Betanin production: Several TF modifications increased production of the high-value compound betanin [27] [32]

These results demonstrate how TUNEYALI enables rapid identification of both targets and their optimal expression levels for strain improvement.

Implementation for Diol Production Research

Relevance to Diol Biosynthesis

The TUNEYALI method offers specific advantages for engineering Y. lipolytica strains for enhanced diol production:

• Optimizing transcription factors regulating alkane uptake and metabolism [4]
• Fine-tuning expression of P450 monooxygenases (ALK genes) responsible for initial alkane hydroxylation [4] [15]
• Balancing redox cofactor regeneration by modulating alcohol dehydrogenase expression
• Controlling competing pathways that divert intermediates away from diol synthesis

Integration with Existing Diol Production Strategies

Recent research has demonstrated the feasibility of producing medium-chain α,ω-diols from alkanes in Y. lipolytica through:

• Deletion of oxidation pathway genes (FADH, ADH1-8, FAO1, FALDH1-4) to prevent over-oxidation of alcohol intermediates [4] [15]
• Overexpression of alkane hydroxylase genes (particularly ALK1) to enhance initial alkane oxidation [4]
• Process optimization including pH-controlled fermentation to improve titers [4] [15]

TUNEYALI complements these approaches by enabling fine-tuning of the expression levels for these engineered pathways, potentially further enhancing diol production beyond what has been achieved through gene knockout and overexpression alone.

Table 2: Application of TUNEYALI for Diol Production Strain Development

Engineering Target	Potential Impact	Expression Modulation Strategy
Transcription Factors	Regulate multiple pathway genes simultaneously	Test multiple expression levels to identify optimal regulation
ALK Genes	Control rate-limiting hydroxylation step	Balance expression to maximize conversion without metabolic burden
Redox Cofactor Regeneration	Maintain cofactor balance for efficient oxidation	Fine-tune ADH expression levels
Precursor Supply	Enhance flux through native lipid pathways	Modulate key enzymes in acetyl-CoA metabolism

Research Reagent Solutions

Table 3: Key Reagents for Implementing TUNEYALI

Reagent	Source/Catalog Number	Function
TUNEYALI-TF Library	Addgene #217744	Pre-built library targeting 56 transcription factors with 7 expression levels each
Empty Backbone Vector	Addgene #106166 (pCfB3405)	Base vector for constructing custom TUNEYALI libraries
Cas9 Expression Plasmid	Addgene #70007 (pCRISPRyl)	Provides Cas9 nuclease for CRISPR-mediated editing
Alternative Cas9 Plasmid	Addgene #73226 (pCAS1yl)	Optional Cas9 source for Y. lipolytica
SapI Restriction Enzyme	New England Biolabs	Enzyme for Golden Gate assembly of promoter elements

The following diagram illustrates the complete TUNEYALI methodology from plasmid construction to screening:

The TUNEYALI method represents a significant advancement in high-throughput metabolic engineering for Yarrowia lipolytica. By enabling systematic, parallel testing of multiple gene expression levels, this approach accelerates strain optimization for diverse applications, including diol production from alkane substrates. The availability of curated libraries through Addgene makes this technology accessible to research groups without specialized biofoundry infrastructure. As synthetic biology continues to advance complex pathway engineering in non-conventional yeasts, methodologies like TUNEYALI will play an increasingly important role in bridging the gap between genetic design and high-performing industrial strains.

The pursuit of sustainable and environmentally friendly chemical production has positioned microbial cell factories as a cornerstone of industrial biotechnology. Within this field, the oleaginous yeast Yarrowia lipolytica has emerged as a promising platform for the synthesis of high-value lipids and their derivatives due to its innate capacity for high lipid accumulation and its ability to utilize diverse, low-cost carbon sources [33]. This application note details specialized protocols for engineering Y. lipolytica to produce valuable odd-chain fatty acids (OCFAs) and their subsequent conversion into diols, a class of chemicals with extensive applications in polymers, surfactants, and biofuels [34]. The content is framed within a broader thesis on metabolic engineering of Y. lipolytica, providing researchers with actionable methodologies to diversify metabolic pathways for the enhanced biosynthesis of these target compounds.

Pathway Engineering and Protocol

De Novo Biosynthesis of Odd-Chain Fatty Acids

Background: OCFAs, such as pentadecanoic acid (C15:0) and heptadecenoic acid (C17:1), are valuable molecules for nutritional, pharmaceutical, and industrial applications [35] [36]. Unlike most microbes that predominantly produce even-chain fatty acids, Y. lipolytica can be engineered for de novo OCFA synthesis from conventional carbon sources like glucose, eliminating the need for propionate supplementation and its associated toxicity and cost [36].

Experimental Protocol:

• Strain Construction:

  • Propionyl-CoA Precursor Engineering: Introduce a modular metabolic pathway to generate propionyl-CoA from oxaloacetate. This typically involves heterologous expression of genes such as metA (mutated aspartokinase), ilvA (threonine dehydratase), and prpE or pct (propionyl-CoA synthetase/transferase) to create a cytosolic pathway from oxaloacetate to α-ketobutyrate and then to propionyl-CoA [36].
  • Fatty Acid Synthase (FAS) Specificity Engineering: Modify the native FAS system to accept propionyl-CoA as a primer. This can be achieved by replacing the native FabH gene (β-ketoacyl-ACP synthase III) with a heterologous version from Bacillus subtilis (fabHI) that has a higher affinity for propionyl-CoA over acetyl-CoA [36].
  • Lipid Accumulation Background: Transfer the constructed OCFA pathway into an "obese" strain of Y. lipolytica that has been engineered for enhanced lipid accumulation. Common modifications in such a background include the deletion of the PHD1 gene (encoding 2-methylcitrate dehydratase) to block the methyl citrate cycle and prevent propionyl-CoA degradation, and the overexpression of genes like DGA1 and DGA2 (diacylglycerol acyltransferases) to boost triacylglycerol (TAG) storage [35] [36].

• Fermentation and Analysis:

  • Inoculate the engineered strain in a minimal medium (e.g., YNB with 2% glucose) and culture at 28–30°C with shaking.
  • Monitor cell growth (OD600) and substrate consumption.
  • For lipid extraction, harvest cells by centrifugation during the stationary phase. Lyse the cell pellet using a bead beater or liquid nitrogen.
  • Extract total lipids using a chloroform:methanol (2:1 v/v) mixture following the Bligh and Dyer method.
  • Transesterify the extracted lipids to Fatty Acid Methyl Esters (FAMEs) using boron trifluoride in methanol.
  • Analyze the FAME composition using Gas Chromatography-Mass Spectrometry (GC-MS) to quantify OCFA content as a percentage of total fatty acids and the final titer (g/L) [35] [36].

Enhancing OCFA Production via Precursor Pool Engineering

Background: The production yield of OCFAs is fundamentally limited by the intracellular availability of the precursor, propionyl-CoA. Engineering strategies that boost the propionyl-CoA pool and balance it with acetyl-CoA are critical for achieving high OCFA titers [37].

Experimental Protocol:

• Genetic Modifications:

  • Propionate Activation: Overexpress a propionyl-CoA transferase from Ralstonia eutropha (pct) to efficiently convert externally added propionate to propionyl-CoA. This has been shown to increase OCFA accumulation by 3.8-fold compared to the control strain [37].
  • Precursor Balancing: Co-supply sodium acetate (e.g., 10 g/L) along with propionate in the fermentation medium. Acetate is converted to acetyl-CoA, and maintaining a balanced acetyl-CoA/propionyl-CoA ratio is crucial for optimal FAS function and overall lipid accumulation [37].
  • Enhancing Condensation Efficiency: Co-express a β-ketothiolase gene (bktB) from Ralstonia eutropha. The BktB enzyme catalyzes the condensation of propionyl-CoA and acetyl-CoA to form β-ketovaleryl-CoA (C5), directly increasing the precursor pool for OCFA elongation. This step can further boost OCFA production by approximately 33% [37].

• Process Optimization:

  • Employ a fed-batch co-feeding strategy in a bioreactor. A typical strategy involves co-feeding a carbon source like glucose or glycerol with propionate to maintain a low, non-inhibitory concentration of propionate while supporting high cell density.
  • Optimize the Carbon-to-Nitrogen (C/N) ratio in the medium to trigger oleaginous metabolism. A high C/N ratio (e.g., >100) is known to induce lipid accumulation in Y. lipolytica [37].
  • Use crude glycerol, a by-product of biodiesel production, as a low-cost carbon source to improve process economics [38] [39].

The following table summarizes key strategies and their quantitative outcomes for OCFA production in Y. lipolytica.

Table 1: Metabolic Engineering Strategies for Enhanced Odd-Chain Fatty Acid Production in Y. lipolytica

Engineering Strategy	Key Genetic Modifications	Carbon Source	Maximum OCFA Titer / Content	Citation
De novo Synthesis	Modular pathway for propionyl-CoA; FabHI; Obese background (PHD1Δ)	Glucose	0.36 g/L (7.2x increase vs. control)	[36]
Precursor Engineering	Overexpression of pct and bktB; C/N ratio optimization	Glucose + Propionate	1.87 g/L (62% of total lipids)	[37]
Fermentation Optimization	Obese strain (PHD1Δ); Co-feeding with crude glycerol & molasses	Crude Glycerol + Molasses	~2.69 g/L (58% of total lipids)	[39]

Biosynthesis of Medium-Chain α,ω-Diols from Alkanes

Background: α,ω-Diols are valuable building blocks for polyesters and polyurethanes. While short-chain diols (Y. lipolytica is ideal for this purpose due to its native ability to metabolize hydrophobic substrates like alkanes [4] [5].)>

Experimental Protocol:

• Strain Engineering to Block Over-Oxidation:

  • The primary challenge in producing diols from alkanes is preventing the host's native enzymes from over-oxidizing the alcohol intermediates to fatty aldehydes and then to dicarboxylic acids.
  • Use a CRISPR-Cas9 system to systematically delete genes involved in the oxidation pathway. Essential deletions include:
    • Fatty Alcohol Oxidase: Delete FAO1.
    • Fatty Aldehyde Dehydrogenases: Delete FALDH1 through FALDH4.
    • Alcohol Dehydrogenases: Delete a suite of genes, including FADH and ADH1 through ADH8 [4] [5].
  • This comprehensive deletion strategy generated the base strain YALI17, which showed a 14-fold increase in 1,12-dodecanediol production from n-dodecane compared to the wild type [5].

• Enhancing Hydroxylation Capacity:

  • To boost the initial oxidation of the alkane to the corresponding fatty alcohol, overexpress a key alkane hydroxylase gene, such as ALK1 from the native CYP52 family, in the engineered YALI17 background [4] [5].
  • Clone the ALK1 gene into an expression vector under a strong constitutive promoter (e.g., TEF) and transform it into the YALI17 strain.

• Biotransformation and Analysis:

  • Grow the engineered strain in a rich medium (e.g., YPD) to high density.
  • Induce biotransformation by adding n-dodecane (e.g., 50 mM) as the substrate. Maintain the pH under controlled conditions (e.g., pH 6.5), as this has been shown to significantly improve final titers.
  • Extract metabolites from the culture broth using ethyl acetate.
  • Identify and quantify α,ω-diol production using techniques such as GC-MS or HPLC. The strain YALI17 overexpressing ALK1 under pH-controlled conditions achieved a titer of 3.2 mM (approx. 690 mg/L) of 1,12-dodecanediol [5].

Table 2: Key Research Reagents for Engineering Y. lipolytica

Reagent / Tool	Type	Function in Research	Example / Source
pCRISPRyl Vector	Plasmid	CRISPR-Cas9 system for precise gene knockout and editing in Y. lipolytica.	[4] [5]
TEF Promoter	Genetic Part	Strong, constitutive promoter for high-level gene expression.	[4]
Alkane Hydroxylases (ALK1-12)	Enzymes	CYP52 family P450 monooxygenases that catalyze the terminal hydroxylation of alkanes.	[4] [17]
Crude Glycerol	Carbon Source	Low-cost substrate from biodiesel production for cost-effective fermentation.	[38] [39]
Propionyl-CoA Transferase (pct)	Enzyme	Activates propionate to propionyl-CoA, enhancing the primer for OCFA synthesis.	Ralstonia eutropha [37]

Pathway and Workflow Visualization

The metabolic pathway from glucose and alkanes to the target products OCFAs and diols in engineered Y. lipolytica is complex. The diagram below provides a simplified overview of the key engineered routes and competing pathways.

Diagram 1: Engineered Metabolic Pathways for OCFA and Diol Production in Y. lipolytica. The diagram highlights the engineered routes for OCFA synthesis from glucose (red) and diol production from alkanes (green), alongside competing native pathways (yellow). Key nodes like Propionyl-CoA and Fatty Alcohol represent critical metabolic branch points targeted for engineering.

The experimental workflow for developing a Y. lipolytica strain capable of high-level diol production involves a structured sequence of genetic engineering and bioprocess optimization steps, as visualized below.

Diagram 2: Integrated Workflow for Strain Development and Bioprocess Optimization. The workflow outlines the sequential steps from initial genetic modifications in the chassis strain (blue) to culture screening (green) and finally bioprocess intensification for high-titer production.

Within metabolic engineering, fermentation process control is a critical determinant for transitioning laboratory-scale achievements to industrially viable bioprocesses. For the production of high-value diols using engineered strains of Yarrowia lipolytica, two parameters are particularly pivotal: substrate feeding strategies and pH control. This protocol details optimized methodologies for these parameters, enabling researchers to maximize titers, yields, and productivity. The procedures below are framed within the context of a broader research thesis on producing medium-chain α,ω-diols from alkanes, leveraging Y. lipolytica's innate capacity for hydrophobic substrate metabolism [4] [15] [5].

Substrate Feeding Strategy Optimization

Rationale and Objective

In bioprocesses, uncontrolled substrate addition can lead to metabolic overflow, by-product formation, and oxygen transfer limitations. The objective of optimizing the feeding strategy is to maintain the carbon source at a concentration that supports high metabolic flux toward the target product while minimizing auxiliary pathways. This is especially critical in Y. lipolytica fermentations for diol production, where substrate toxicity (e.g., from alkanes) and precursor over-oxidation can severely limit yields [4].

Protocol: Continuous Feeding for Enhanced Production

Principle: A continuous feeding strategy maintains a constant, optimal substrate concentration in the bioreactor, preventing the feast-famine cycles associated with pulse-feeding and reducing the formation of by-products like erythritol in glycerol fermentations or carboxylic acids in alkane fermentations [40] [41].

Materials:

• Bioreactor with calibrated feeding pump
• Sterilized substrate (e.g., crude glycerol, n-alkanes like n-dodecane)
• Fermentation broth with production strain (e.g., engineered Y. lipolytica YALI17 for diols [4])

Procedure:

• Inoculum and Initial Batch Phase: Begin with a batch phase using an initial charge of carbon source to support robust biomass growth. For crude glycerol processes, an initial concentration of 50 g/L is effective [40] [41].
• Initiation of Feeding: Commence the continuous feed at a critical point in the fermentation, typically as the initial carbon source is depleted. For glycerol, this is often around the 24th hour [40].
• Feeding Rate: Administer the substrate solution at a constant, predetermined rate. For crude glycerol (300 g/L total), a rate of 4.6 g/L/h has been demonstrated to support high α-ketoglutarate production [40] [41]. For alkane substrates like n-dodecane, the optimal rate must be determined empirically to balance supply with the engineered strain's hydroxylation capacity.
• Process Monitoring: Continuously monitor parameters like dissolved oxygen (DO), off-gas composition, and substrate concentration (if possible) to ensure the feeding rate does not cause oxygen limitation or substrate accumulation.

Table 1: Quantitative Comparison of Substrate Feeding Strategies

Feeding Strategy	Carbon Source	Key Parameter	Titer Achieved	Volumetric Productivity	Major Impact
Continuous Feeding [40] [41]	Crude Glycerol	Rate: 4.6 g/L/h	117.7 g/L α-KGA	0.81 g/L/h	Limited erythritol formation
Pulse Feeding [40]	Crude Glycerol	Multiple bolus additions	Lower than continuous	Lower than continuous	Higher by-product formation
Two-Stage (Growth + Production) [42]	Methyl Laurate	Growth: Rich Medium; Production: Poor Medium	1.18 g/L Adipic Acid	Not Specified	Increased titer by 1.3x

pH Control Strategy Optimization

Rationale and Objective

pH exerts a profound influence on enzyme activity, membrane stability, and product stability. In Y. lipolytica fermentations for diol production, controlling pH is essential for maximizing the activity of key enzymes like cytochrome P450 monooxygenases (e.g., Alk1) and minimizing the degradation of pathway intermediates [4] [15] [5].

Protocol: Automated pH-Controlled Biotransformation

Principle: Maintaining the fermentation broth at an optimal pH setpoint throughout the process ensures consistent metabolic activity and can prevent the acidification or alkalinization that leads to cell stress and by-product formation.

Materials:

• Bioreactor with integrated pH probe and automatic titration system
• Acid solution (e.g., 1-2 M Hâ‚‚SOâ‚„ or HCl)
• Base solution (e.g., 1-4 M NaOH or NHâ‚„OH)
• Calibration buffers (pH 4.0 and 7.0)

Procedure:

• Probe Calibration: Aseptically calibrate the pH probe using standard buffers prior to inoculation.
• Setpoint Determination: Based on the target product, set the pH control point. For 1,12-dodecanediol production from n-dodecane, automated pH control was a key factor in achieving a final titer of 3.2 mM, a significant increase over uncontrolled conditions [4] [15].
• Controller Configuration: Configure the PID controller on the bioreactor software. Set the agitation dead-band and gain to prevent overshoot and excessive reagent addition.
• Titrant Addition: Use the automated system to add acid or base as needed to maintain the setpoint. The choice of titrant can be strategic; for example, using NHâ‚„OH can also serve as a nitrogen source.
• Data Recording: Log pH and titrant addition data throughout the fermentation to correlate process performance with control stability.

Table 2: Key Reagent Solutions for Fermentation Optimization

Research Reagent / Solution	Function / Explanation	Example Application / Note
Crude Glycerol (300 g/L) [40]	Primary carbon source; a biodiesel waste product valorized by Y. lipolytica.	Requires initial concentration in medium (e.g., 50 g/L) followed by continuous feeding.
n-Dodecane [4] [15]	Hydrophobic alkane substrate for production of medium-chain diols like 1,12-dodecanediol.	Serves as both carbon source and precursor; feeding strategy crucial due to low solubility.
Thiamine Supplement [40] [41]	Vitamin precursor for TPP cofactor; limiting its availability redirects metabolism from growth to product formation.	Optimal level of 20 μg/L was key to reducing pyruvic acid by-production from glycerol.
NaOH / NHâ‚„OH Solution (1-4 M)	Base titrant for automated pH control.	Maintains optimal enzymatic activity and cell membrane function.
Alkane Hydroxylase (ALK1) Expression System [4]	Key enzyme for the primary oxidation of alkanes to alcohols in the diol biosynthesis pathway.	Overexpression in engineered strains is critical for enhancing flux into the diol pathway.

Integrated Experimental Workflow and Metabolic Context

The optimization of feeding and pH strategies must be implemented within the context of a metabolically engineered strain and a coherent experimental workflow. The diagram below illustrates the logical sequence for developing an optimized process for diol production.

The metabolic engineering of Y. lipolytica for diol production involves significant rewiring of its native alkane metabolism. The following pathway diagram contextualizes where the optimized fermentation parameters exert their influence.

Concluding Remarks

The synergistic application of continuous substrate feeding and precise pH control is a powerful strategy for optimizing fermentations with engineered Yarrowia lipolytica. When implemented in strains where competing metabolic pathways have been systematically removed and biosynthetic capabilities enhanced, these process control strategies enable the efficient and sustainable production of valuable chemicals like medium-chain diols from industrial waste streams and renewable feedstocks. The protocols outlined herein provide a robust foundation for researchers to advance the scalability of microbial diol production.

Overcoming Production Bottlenecks and Maximizing Diol Yields

In the metabolic engineering of Yarrowia lipolytica for diol production, a significant challenge is the inherent over-oxidation of these valuable chemicals into corresponding carboxylic acids. Over-oxidation occurs when the host organism's native metabolic pathways, particularly those involved in lipid and alkane metabolism, progressively oxidize diol intermediates, leading to substantial yield losses [5]. The oleaginous yeast Yarrowia lipolytica presents a particular paradox in this context: while its robust native capacity to metabolize hydrophobic substrates makes it an exceptional chassis for alkane bioconversion, this very capability necessitates careful engineering to prevent premature degradation of target products [33] [4]. This application note details targeted strategies to address this critical bottleneck, focusing on pathway engineering and cultivation techniques that collectively minimize diol over-oxidation, thereby maximizing production efficiency for these high-value chemical precursors.

Understanding the Over-oxidation Challenge inYarrowia lipolytica

Yarrowia lipolytica natively possesses comprehensive enzyme systems for oxidizing hydrophobic compounds. For alkane and fatty alcohol metabolism, this includes 12 endogenous CYP52 family P450s (Alk1-12) for initial hydroxylation, alongside extensive oxidation machinery comprising 9 alcohol dehydrogenases (FADH, ADH1-8), 1 fatty alcohol oxidase (FAO1), and 4 fatty aldehyde dehydrogenases (FALDH1-4) [5] [4]. This enzymatic arsenal, while advantageous for substrate utilization, creates multiple competing pathways that progressively oxidize ω-hydroxy fatty acids and α,ω-diols to diacids, shunting carbon flux away from the desired products and toward central metabolism.

Wild-type Y. lipolytica consequently produces only trace amounts of valuable mid-chain diols such as 1,12-dodecanediol, with baseline production as low as 0.05 mM from alkane substrates [5] [4]. This inefficiency reflects fundamental flux control issues where diol intermediates are rapidly converted to terminal carboxylic acids rather than accumulating as end products. Addressing this requires systematic interruption of specific oxidation steps while preserving the host's superior substrate uptake and tolerance characteristics that distinguish it from bacterial systems like E. coli [34].

Metabolic Engineering Strategies to Block Over-oxidation Pathways

Targeted Gene Deletion via CRISPR-Cas9

The most direct approach to prevent diol over-oxidation involves the strategic knockout of genes encoding enzymes responsible for the sequential oxidation of alcohol groups to carboxylic acids.

Metabolic Engineering Strategy for Diol Production

Table: Key Enzyme Targets for Preventing Diol Over-oxidation

Enzyme Category	Specific Targets	Number of Genes	Function in Over-oxidation Pathway
Alcohol Dehydrogenases	ADH1, ADH2, ADH3, ADH4, ADH5, ADH6, ADH7, ADH8	8	Oxidation of fatty alcohols to aldehydes
Fatty Alcohol Oxidase	FAO1	1	Alternative oxidation of alcohols to aldehydes
Fatty Aldehyde Dehydrogenases	FALDH1, FALDH2, FALDH3, FALDH4	4	Oxidation of fatty aldehydes to carboxylic acids
Additional Alcohol Oxidase	FADH	1	Primary alcohol oxidation

Protocol: CRISPR-Cas9 Mediated Multiplex Gene Deletion

Materials:

• pCRISPRyl vector (Addgene #70007) or similar CRISPR system for Y. lipolytica
• YPD medium (20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract, pH 6.5)
• Synthetic complete medium without leucine
• E. coli DH5α for plasmid propagation

Method:

• Design and cloning of sgRNA constructs: For simultaneous targeting of multiple oxidation genes, design 20 bp guiding sequences specific to ADH, FALDH, FAO1, and FADH genes. Clone these into the pCRISPRyl vector containing Cas9 and sgRNA scaffold using overlapping PCR.
• Vector construction: Insert a second sgRNA scaffold sequence downstream of the original site to enable combinatorial targeting. Verify constructs by sequencing after transformation into E. coli DH5α and plasmid purification.
• Strain transformation: Transform the assembled CRISPR vectors into Y. lipolytica Po1g ku70Δ strain (or other suitable background) using standard lithium acetate or electroporation protocols.
• Screening and validation: Screen transformants on appropriate selection media. Verify gene deletions by PCR and sequencing of target loci. Assess functional knockout by reduced oxidation activity in whole-cell biotransformation assays.

Expected Outcomes: The sequential construction of knockout strains, as demonstrated in the YALI series (YALI1 through YALI17), progressively reduces over-oxidation activity, with the most comprehensive knockout strain (YALI17) showing 14-fold increased 1,12-dodecanediol production compared to the parental strain [5].

Enhancing Diol Synthesis Through Alkane Hydroxylase Overexpression

While blocking degradation pathways is essential, simultaneously enhancing precursor flux into the diol synthesis pathway provides complementary benefits. Y. lipolytica possesses 12 native CYP52 alkane hydroxylase genes (ALK1-12) that catalyze the initial oxidation of alkanes to alcohols [4].

Protocol: ALK1 Gene Overexpression

Materials:

• pYl yeast expression vector or similar
• ALK1 gene amplified from Y. lipolytica genome
• Luria-Bertani (LB) medium with ampicillin (100 mg/L)

Method:

• Gene amplification: PCR amplify the ALK1 coding sequence from Y. lipolytica genomic DNA using high-fidelity polymerase.
• Vector construction: Clone the ALK1 gene into the pYl expression vector under control of a strong constitutive promoter (e.g., TEF) using circular polymerase extension cloning (CPEC).
• Strain transformation: Introduce the ALK1 overexpression construct into engineered Y. lipolytica strains with blocked oxidation pathways (e.g., YALI17 background).
• Evaluation: Screen for ALK1 expression by qRT-PCR and assess alkane hydroxylation activity by monitoring alcohol production from alkane substrates.

Application Note: When implemented in the YALI17 background, ALK1 overexpression further increased 1,12-dodecanediol production from 0.72 mM to 1.45 mM, demonstrating the synergistic effect of combining blocked oxidation with enhanced precursor supply [5].

Fermentation Optimization for Diol Production

pH-Controlled Biotransformation

Beyond genetic modifications, process parameters significantly impact diol yield by influencing enzyme activities and pathway fluxes.

Table: Quantitative Impact of Engineering Strategies on Diol Production

Strain/Condition	Genetic Modifications	1,12-Dodecanediol Production (mM)	Fold Improvement vs. Wild Type
Wild Type	None	0.05	1x
YALI17	faldh1-4Δ, fao1Δ, fadhΔ, adh1-8Δ	0.72	14x
YALI17 + ALK1 OE	YALI17 background + ALK1 overexpression	1.45	29x
pH-Controlled Fermentation	YALI17 + ALK1 OE with optimized pH control	3.20	64x

Protocol: Automated pH-Controlled Biotransformation

Materials:

• Bioreactor with automated pH monitoring and control
• n-Dodecane as alkane substrate (50 mM)
• YPD or defined medium for Y. lipolytica cultivation

Method:

• Pre-culture preparation: Grow engineered Y. lipolytica strains in YPD medium for 48 hours.
• Bioreactor setup: Transfer pre-culture to bioreactor containing production medium with n-dodecane as primary carbon source.
• pH control: Implement automated pH control to maintain optimal pH throughout fermentation (specific optimal pH should be determined experimentally for each strain and product).
• Monitoring: Regularly sample the culture to monitor diol production via HPLC or GC-MS, cell density, and substrate consumption.
• Product extraction: At fermentation endpoint, extract diols from culture broth using appropriate organic solvents (e.g., ethyl acetate).

Application Note: Implementing automated pH control in strains with comprehensive oxidation pathway blocking and ALK1 overexpression elevated 1,12-dodecanediol production to 3.2 mM – a 64-fold improvement over wild-type strains [5]. This highlights the critical interaction between genetic and process engineering for maximizing diol yields.

The Scientist's Toolkit: Essential Research Reagents

Table: Key Research Reagents for Engineering Diol Production in Y. lipolytica

Reagent/Resource	Function/Application	Example Sources/References
pCRISPRyl Vector	CRISPR-Cas9 system for targeted gene knockout in Y. lipolytica	Addgene #70007 [5]
ALK Gene Family	Native alkane hydroxylases for initial substrate oxidation	Y. lipolytica genome (12 CYP52 P450s) [4]
n-Dodecane	Model medium-chain alkane substrate for diol production	Commercial chemical suppliers [5]
YPD Medium	Standard growth medium for Y. lipolytica cultivation	20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract [5]
TEF Promoter	Strong constitutive promoter for gene overexpression in Y. lipolytica	Synthetic biology toolkits [33]
2-Phenylethanol-d4	2-Phenylethanol-d4, MF:C8H10O, MW:126.19 g/mol	Chemical Reagent
Vildagliptin-d3	Vildagliptin-d3, CAS:1217546-82-1, MF:C17H25N3O2, MW:306.42 g/mol	Chemical Reagent

Preventing diol over-oxidation in Yarrowia lipolytica requires an integrated approach combining targeted metabolic engineering with bioprocess optimization. The systematic deletion of 14 key oxidation genes (ADH1-8, FADH, FAO1, FALDH1-4) using CRISPR-Cas9 technology, coupled with ALK1 hydroxylase overexpression and pH-controlled fermentation, enables a remarkable 64-fold enhancement in 1,12-dodecanediol production compared to wild-type strains. These strategies effectively redirect carbon flux from degradative pathways toward product accumulation, establishing Y. lipolytica as a promising platform for sustainable production of valuable α,ω-diol chemical precursors from renewable alkane feedstocks.

Cofactor Balancing and Regeneration for P450 Monooxygenase Efficiency

Cytochrome P450 monooxygenases (CYPs) are powerful biocatalysts capable of performing regio- and stereoselective oxidations of hydrophobic substrates, making them invaluable for producing valuable chemicals in metabolic engineering [43]. In the context of engineering Yarrowia lipolytica for diol production, these enzymes enable the critical hydroxylation steps of alkanes and fatty acids. However, their catalytic efficiency is inherently tied to the availability and regeneration of nicotinamide cofactors, primarily NADPH, which serves as the electron donor for the monooxygenase reaction [17]. The P450 catalytic cycle consumes NADPH to reduce molecular oxygen, incorporating one oxygen atom into the substrate and releasing the other as water. This dependency creates significant metabolic burdens and can limit overall pathway flux in engineered strains. This application note details practical strategies for overcoming these limitations, with a specific focus on applications in Y. lipolytica strains engineered for the production of medium- to long-chain α,ω-diols.

Cofactor Demands in Diol Biosynthesis Pathways

The biosynthesis of medium- to long-chain α,ω-diols in Y. lipolytica involves a multi-step oxidative pathway where cofactor balancing is critical. Engineered strains convert alkane substrates to diols via terminal hydroxylation. This process directly relies on the activity of native or heterologous P450 systems (from the CYP52 family), alongside auxiliary enzymes that compete for intracellular NADPH pools [5] [17].

The core challenge lies in the substantial cofactor demand of the P450 system. The catalytic cycle of a cytochrome P450 requires two electrons, typically delivered from NADPH via a redox partner such as a cytochrome P450 reductase (CPR) [43] [17]. Furthermore, competing metabolic pathways in Y. lipolytica, such as those catalyzed by fatty alcohol dehydrogenases (ADHs) and fatty aldehyde dehydrogenases (FALDHs), can drain the pool of reduced cofactors and divert intermediates away from the desired diol product [5]. Therefore, efficient diol production requires not only a robust NADPH supply but also the disruption of these competing, cofactor-consuming oxidation pathways.

Table 1: Key Enzymes in Y. lipolytica Diol Production and Their Cofactor Requirements

Enzyme Class	Example Enzymes in Y. lipolytica	Reaction Catalyzed	Cofactor Utilized
Cytochrome P450 Monooxygenase	Alk1, Alk3, Alk5, Alk7 [5] [17]	ω-hydroxylation of alkanes/fatty acids	NADPH
Cytochrome P450 Reductase (CPR)	YlCPR [43] [17]	Electron transfer to P450	NADPH
Fatty Alcohol Dehydrogenase (ADH)	FADH, ADH1-8 [5]	Oxidation of fatty alcohol to aldehyde	NAD(P)+
Fatty Aldehyde Dehydrogenase (FALDH)	FALDH1-4 [5]	Oxidation of fatty aldehyde to acid	NAD(P)+

The diagram below illustrates the core metabolic pathway for diol production from alkanes in engineered Y. lipolytica, highlighting the key P450-catalyzed step and its NADPH dependency, alongside competing pathways that consume cofactors.

Engineering Strategies for Cofactor Balancing

Enhancing NADPH Supply

Increasing the intracellular availability of NADPH is a fundamental strategy to boost P450-driven biotransformations. In Y. lipolytica, this can be achieved by modulating central carbon metabolism.

• Overexpression of Pentose Phosphate Pathway (PPP) Enzymes: The oxidative branch of the PPP is the primary cellular source of NADPH. Overexpressing key enzymes such as glucose-6-phosphate dehydrogenase (ZWF1) and 6-phosphogluconate dehydrogenase (GND1) can directly enhance NADPH generation [26]. This approach is particularly effective when glycerol or glucose is used as a carbon source, as it increases the flux of carbon through the NADPH-producing steps.
• Engineering Cofactor Specificity of Native Enzymes: The Y. lipolytica genome encodes numerous dehydrogenases. Reprogramming the cofactor preference of these enzymes from NADH to NADPH, or overexpressing native transhydrogenases that can interconvert NADH and NADPH, can help balance the overall cofactor pool and increase NADPH availability for P450s.

Blocking Competing Cofactor-Consuming Pathways

To prevent the diversion of resources, it is essential to eliminate competing metabolic pathways that consume NADPH or degrade pathway intermediates.

• Disruption of Fatty Acid Oxidation Pathways: As demonstrated in the engineering of Y. lipolytica for 1,12-dodecanediol production, CRISPR-Cas9 can be used to delete genes responsible for the over-oxidation of fatty alcohol and aldehyde intermediates. This includes knocking out multiple fatty alcohol oxidase (FAO1), fatty alcohol dehydrogenases (FADH, ADH1-8), and fatty aldehyde dehydrogenases (FALDH1-4) [5] [4]. This strategy not only prevents the loss of the diol precursor but also conserves the NADPH that would have been consumed by these oxidation reactions.
• Inhibition of β-Oxidation: To prevent the degradation of fatty acid and diol precursors, the peroxisomal β-oxidation pathway can be disrupted by deleting one or more of the POX genes [17]. This is a common strategy in strains engineered for the production of long-chain dicarboxylic acids (DCA) and diols, ensuring carbon flux is directed toward the desired product.

Table 2: Summary of Cofactor Engineering Strategies in Y. lipolytica

Engineering Strategy	Target Gene/Pathway	Physiological Effect	Impact on Diol Production
Enhance NADPH Supply	Overexpress ZWF1, GND1 (PPP) [26]	Increases NADPH regeneration capacity	Directly supports P450 catalytic turnover
Block Competition	Delete FADH, ADH1-8, FALDH1-4 [5]	Prevents over-oxidation of alcohols/aldehydes	Conserves NADPH and pools of diol precursors
Prevent Degradation	Delete POX1-6 (β-oxidation) [17]	Inhibits breakdown of fatty acyl chains	Increases availability of alkane/fatty acid substrates
Optimize Redox Partners	Overexpress Cytochrome P450 Reductase (YlCPR/hCPR) [43] [17]	Improves electron transfer efficiency to P450	Enhances hydroxylation rate and overall pathway flux

Experimental Protocols

Protocol: CRISPR-Cas9 Mediated Deletion of Competing Dehydrogenase Genes

This protocol outlines the procedure for creating Y. lipolytica strains with reduced over-oxidation activity, as used to develop the high-diol-producing strain YALI17 [5] [4].

Research Reagent Solutions:

• pCRISPRyl Vector: CRISPR-Cas9 plasmid for Y. lipolytica (Addgene #70007) [4].
• YPD Medium: 20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract, pH 6.5.
• Synthetic Complete (SC) Medium: 20 g/L glucose, 6.7 g/L yeast nitrogen base, 0.67 g/L CSM-Leu/URA.
• Homology-Directed Repair (HDR) Template: DNA fragment containing a selectable marker (e.g., LEU2) flanked by ~500 bp homology arms specific to the target gene.

Procedure:

• Design and Cloning: For each target gene (e.g., FALDH1, ADH1), design a 20 bp guiding sequence and clone it into the pCRISPRyl vector upstream of the sgRNA scaffold using overlapping PCR and restriction enzyme digestion (e.g., SpeI, MfeI) [4].
• Transformation: Co-transform the Y. lipolytica host strain (e.g., Po1g ku70Δ) with the constructed CRISPR plasmid and the corresponding HDR template DNA using the PEG/LiAc method [44].
• Selection and Screening: Plate the transformation mixture on SC agar plates without leucine (or the appropriate auxotrophic marker) and incubate at 30°C for 2-3 days. Select colonies and verify gene deletion by colony PCR and sequencing.
• Strain Propagation: To create a strain with multiple gene deletions (e.g., YALI17 with 14 deleted genes), repeat this process iteratively or use multiplexed sgRNA strategies [5].

Protocol: Biotransformation in a Biphasic System for Enhanced P450 Activity

This protocol describes a biphasic fermentation setup to improve the conversion of hydrophobic alkane substrates by mitigating toxicity and substrate mass transfer limitations [43].

Research Reagent Solutions:

• Alkane Feedstock: Filter-sterilized n-dodecane or other medium/long-chain alkanes.
• Ethyl Oleate: Water-immiscible organic solvent phase that can also serve as a carbon source.
• YP Medium: 10 g/L yeast extract, 20 g/L peptone.
• Fermentation Base: Defined mineral medium or YP medium with 80 g/L glucose.

Procedure:

• Seed Culture Preparation: Inoculate the engineered Y. lipolytica strain into YPD medium and incubate at 28-30°C with shaking (220 rpm) for 24-48 hours until the late exponential phase is reached [43] [44].
• Bioreactor Inoculation and Two-Phase Setup: Transfer the seed culture to a controlled bioreactor containing the aqueous fermentation base. Add the hydrophobic alkane substrate (e.g., 50 mM n-dodecane) or a mixture of the substrate in a non-toxic organic solvent like ethyl oleate to create a second phase (typical organic:aqueous ratio of 1:10 to 1:5) [43].
• Process Control: Maintain the fermentation at 28°C with rigorous agitation (to increase interfacial area) and control pH at 5.2-6.5. Monitor dissolved oxygen to ensure it does not become limiting for the P450 reaction.
• Product Analysis: Periodically sample the organic and aqueous phases. Extract products and analyze via Gas Chromatography-Mass Spectrometry (GC-MS) or High-Performance Liquid Chromatography (HPLC) to quantify diol production [5] [4].

The workflow for this integrated metabolic engineering and bioprocess strategy is summarized below.

The Scientist's Toolkit

Table 3: Essential Research Reagent Solutions for P450 Cofactor Engineering

Reagent / Material	Function / Application	Example Source / Specification
pCRISPRyl Vector	CRISPR-Cas9 system for precise gene editing in Y. lipolytica [4].	Addgene #70007
EasyCloneYALI Plasmids	Modular plasmid system for USER cloning and genomic integration of pathway genes [45].	Holkenbrink et al., 2018
Codop-optimized P450 Genes	Genes (e.g., ALK1, CYP2D6, CYP3A4) optimized for Y. lipolytica codon usage to enhance heterologous expression [43] [45].	Synthetic gene fragments from commercial providers (e.g., Thermo Fisher, Sangon Biotech)
Nourseothricin / Hygromycin	Antibiotics for selection of transformed Y. lipolytica strains [45] [46].	Typical working concentrations: 250 mg/L Nourseothricin, 400 mg/L Hygromycin
Ethyl Oleate	Water-immiscible organic solvent for biphasic fermentations; improves substrate availability and can act as carbon source [43].	Sigma-Aldrich, ≥95% purity
Furazolidone-d4	Furazolidone-d4, CAS:1217222-76-8, MF:C8H7N3O5, MW:229.18 g/mol	Chemical Reagent
(S)-Malic acid-13C4	(S)-Malic acid-13C4, CAS:150992-96-4, MF:C4H6O5, MW:138.06 g/mol	Chemical Reagent

Concluding Remarks

Effective cofactor balancing is not merely an ancillary consideration but a central pillar in the development of efficient Y. lipolytica cell factories for diol production. The interplay between enhancing NADPH supply, optimizing P450 and CPR expression, and eliminating competing pathways creates a synergistic effect that dramatically increases product titers. The protocols and strategies outlined herein provide a robust framework for researchers to systematically address the critical bottleneck of cofactor regeneration, thereby unlocking the full potential of P450 monooxygenases in engineered yeasts. Future work will likely integrate dynamic regulatory systems and advanced modeling to fine-tune cofactor metabolism in real-time, pushing the yields of diols and other valuable oxidation products toward industrially viable levels.

The oleaginous yeast Yarrowia lipolytica has emerged as a powerful microbial chassis for producing a diverse range of valuable chemicals, including diols, biofuels, and nutraceuticals [6]. Its innate metabolic architecture, characterized by a high intrinsic flux toward cytosolic acetyl-CoA and malonyl-CoA, provides a distinct advantage for synthesizing acetyl-CoA-derived compounds [47] [6]. The optimization of these two key precursor pools—acetyl-CoA and malonyl-CoA—is a critical determinant for achieving high yields in engineered pathways. This Application Note details proven metabolic engineering strategies and protocols for enhancing the supply of these precursors in Y. lipolytica, with a specific focus on supporting high-level production of target diols.

Acetyl-CoA Supply Enhancement

Acetyl-CoA serves as the fundamental building block for fatty acid synthesis and is the direct precursor for malonyl-CoA. Several engineered strategies have been successfully implemented to increase its cytosolic availability.

Table 1: Strategies for Enhancing Acetyl-CoA Supply in Y. lipolytica

Strategy	Key Enzymes/Genes	Engineering Approach	Observed Outcome
Pyruvate Dehydrogenase (Pdc) Bypass	ATP-citrate lyase (ACL)	Heterologous expression or overexpression [6]	Increases cytosolic acetyl-CoA by cleaving citrate [6]
Pyruvate Dehydrogenase Complex Optimization	Pda1, Pdb1, Lat1	Balanced overexpression of subunit genes [6]	Enhances flux from glycolysis to acetyl-CoA [6]
Acetyl-CoA Synthetase (ACS) Pathway	Acetyl-CoA synthetase	Overexpression of native or heterologous variants [6]	Converts acetate directly to acetyl-CoA [6]
β-Oxidation Blocking	MFE1, FAA1	Gene deletion [4] [5]	Prevents degradation of fatty acids, preserving acyl-CoA intermediates [4] [5]
Enhancing Lipolysis & β-Oxidation	Lipases, Acyl-CoA oxidases	Overexpression of pathway enzymes [6]	Generates acetyl-CoA units from stored or external lipids [6]

The following diagram illustrates the integrated metabolic pathways for enhancing acetyl-CoA and malonyl-CoA supply in Y. lipolytica:

Malonyl-CoA Supply Enhancement

Malonyl-CoA is the essential two-carbon donor for fatty acid biosynthesis and a direct precursor for various polyketides and specialty chemicals. Its intracellular concentration is typically low and tightly regulated.

Table 2: Strategies for Enhancing Malonyl-CoA Supply in Y. lipolytica

Strategy	Key Enzymes/Genes	Engineering Approach	Observed Outcome
ACCase Overexpression	Acetyl-CoA carboxylase (ACC1)	Overexpression of native ACC1 [6]	Directly increases malonyl-CoA synthesis from acetyl-CoA [6]
ACCase Deregulation	ACC1 (Ser659, Ser1157)	Site-directed mutagenesis to abolish Snf1 kinase repression [48]	Generates a more efficient, constitutively active ACCase [48]
Down-regulating Fatty Acid Synthesis	fabD, fabH, fabB, fabF	Conditional inhibition using synthetic antisense RNAs (asRNAs) [49]	Reduces malonyl-CoA consumption, increasing its availability for product synthesis [49]
Enhancing Precursor Supply	ACL, ACS, PDH	As detailed in Acetyl-CoA enhancement (Table 1)	Provides more substrate (acetyl-CoA) for ACCase [6]

Protocol: Deregulation of Acetyl-CoA Carboxylase (ACC) via Site-Directed Mutagenesis

This protocol describes the abolition of post-translational inhibition of ACCase to increase malonyl-CoA flux, adapted from successful applications in yeast [48].

Principle: The Acc1 enzyme in yeast is inactivated by phosphorylation via Snf1 protein kinase. Mutating the phosphorylation sites to alanine prevents this repression, leading to higher constitutive ACCase activity.

Materials:

• Strain: Y. lipolytica strain with desired genetic background (e.g., Po1g).
• Plasmids: Vectors for expression or genomic integration of mutated ACC1.
• Primers: Designed for site-directed mutagenesis of ACC1 (targeting residues analogous to S. cerevisiae Ser659 and Ser1157).
• Enzymes: High-fidelity DNA polymerase, DpnI restriction enzyme.
• Media: YPD and appropriate selection media.

Procedure:

• Gene Synthesis/Mutagenesis:
  • Amplify the Y. lipolytica ACC1 gene from genomic DNA.
  • Perform site-directed mutagenesis PCR to introduce point mutations (S659A and S1157A) using overlapping primers.
  • Treat the PCR product with DpnI to digest the methylated template.
  • Transform the mutated plasmid into E. coli for propagation and verify the sequence.

• Strain Transformation:

  • Clone the mutated ACC1ser659ala,ser1157ala gene into a suitable expression vector (e.g., with a strong constitutive promoter like TEF).
  • Introduce the constructed plasmid into your Y. lipolytica production strain via standard transformation techniques (e.g., lithium acetate method).

• Screening and Validation:

  • Select transformants on appropriate antibiotic plates.
  • Screen for clones with increased ACCase activity via enzyme activity assays, which measure the incorporation of ^14^C-bicarbonate into acid-stable malonyl-CoA.
  • Validate the impact by analyzing lipid content or the production of the target malonyl-CoA-derived diol.

Combined Pathway Optimization for Diol Production

Achieving high titers of diols requires simultaneous optimization of both precursor pools and redirection of flux toward the desired pathway. The following workflow integrates the strategies outlined above.

Protocol: CRISPR-Cas9 Mediated Blocking of Competing Pathways

This protocol is crucial for preventing the diversion of fatty acyl intermediates away from diol production [4] [5].

Principle: Fatty alcohols and aldehydes, which are key intermediates in the diol synthesis pathway, can be over-oxidized to carboxylic acids by endogenous enzymes. Systematic deletion of these genes preserves the intermediates for diol formation.

Materials:

• CRISPR-Cas9 System: pCRISPRyl plasmid or similar for Y. lipolytica.
• sgRNA Construction Oligos: Designed for target genes (FADH, ADH1-8, FAO1, FALDH1-4).
• Donor DNA: (If needed for precise deletion) homology arms flanking a selection marker.
• Strains: E. coli for plasmid propagation; Y. lipolytica parental strain.

Procedure:

• sgRNA Vector Construction:
  • Design 20 bp guiding sequences specific for each target gene (e.g., FADH, ADH1, etc.).
  • Synthesize oligonucleotides and clone them into the pCRISPRyl vector upstream of the sgRNA scaffold using Golden Gate assembly or similar methods.
  • For multiplexed knockout, construct vectors with multiple sgRNA expression cassettes.

• Strain Transformation:

  • Co-transform the constructed CRISPR plasmid and any donor DNA fragments into competent Y. lipolytica cells.
  • Plate cells on appropriate selection media and incubate until colonies form.

• Mutant Screening and Validation:

  • Patch colonies onto fresh selection plates.
  • Screen for successful gene deletions via colony PCR using primers flanking the target genomic loci.
  • Sequence the PCR products to confirm precise deletions.
  • Functionally validate the engineered strain by assessing its reduced ability to oxidize fatty alcohols and aldehydes, and subsequently test for enhanced diol production from alkane substrates.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Reagents for Engineering Y. lipolytica Precursor Pools

Reagent / Tool	Function / Target	Application Example
pCRISPRyl Vector	CRISPR-Cas9 system for genomic editing in Y. lipolytica [4]	Targeted gene knockouts (e.g., MFE1, ADH genes) [4] [5]
TEF Promoter	Strong constitutive promoter for high-level gene expression [48]	Driving overexpression of ACL, ACC1, and heterologous pathway genes [48]
ATP-citrate Lyase (ACL)	Converts citrate to cytosolic acetyl-CoA and oxaloacetate [6]	Enhancing acetyl-CoA supply for lipid and malonyl-CoA biosynthesis [6]
Acetyl-CoA Carboxylase (ACC)	Carboxylates acetyl-CoA to form malonyl-CoA [48] [6]	Increasing malonyl-CoA pool for fatty acid and polyketide synthesis [48] [6]
Synthetic Antisense RNAs (asRNAs)	Conditionally down-regulate essential gene expression [49]	Inhibiting fatty acid biosynthesis genes (fabD, fabH) to increase malonyl-CoA availability [49]
CEN.PK 113-5 D Strain	A well-characterized S. cerevisiae strain for metabolic engineering [48]	Model host for testing ACCase mutations and malonyl-CoA engineering strategies [48]

The strategic enhancement of acetyl-CoA and malonyl-CoA supply is a foundational step in engineering Yarrowia lipolytica into an efficient biofactory for diols and other value-added chemicals. By systematically employing the strategies outlined—including optimizing precursor generation, deregulating key enzymes, and blocking competing metabolic pathways—researchers can significantly increase carbon flux toward their target products. The detailed protocols and reagent toolkit provided here serve as a practical guide for implementing these advanced metabolic engineering approaches.

Temperature and Process Parameter Optimization for Pathway Activity

Within metabolic engineering, optimizing environmental and process parameters is a critical step for maximizing the productivity of engineered microbial strains. For the non-conventional yeast Yarrowia lipolytica, a promising chassis for the production of valuable chemicals like diols, this optimization is essential to ensure that engineered pathways operate at their maximum capacity. This Application Note provides a detailed protocol for the systematic optimization of temperature and pH to enhance pathway activity, specifically framed within a research program aiming to produce medium- to long-chain α,ω-diols from alkanes [4] [5]. The methodologies outlined herein are designed to provide researchers and scientists with a robust framework for evaluating and scaling up bioprocesses.

Key Parameter Optimization Data

Optimizing fermentation conditions is fundamental to aligning microbial physiology with pathway enzyme kinetics. The following table summarizes key findings from studies using Yarrowia lipolytica that inform parameter selection for pathway activity.

Table 1: Optimized Temperature and pH Parameters for Yarrowia lipolytica Processes

Target Product / Process	Optimal Temperature	Optimal pH	Key Findings / Impact	Source Strain / Context
1,12-Dodecanediol from n-Dodecane	Not explicitly stated (Fermentation performed in controlled bioreactors)	Controlled pH (Specific setpoint not stated)	Automated pH-control in biotransformation significantly increased 1,12-dodecanediol production to 3.2 mM, a 29-fold improvement over wild-type [4] [5].	Engineered Y. lipolytica YALI17 (CRISPR-Cas9 modified) [4] [5]
Protease Enzyme Production	30 °C	7.0	Using canola meal waste, this combination yielded the highest protease production (188.75 U/L). Temperature was identified as the most influential factor [50].	Y. lipolytica CDBB-L-232 in Solid-State Fermentation [50]
D-Lactic Acid Production	Not explicitly stated (Studies in shake flasks & bioreactors)	Not controlled in initial flasks; controlled in bioreactors	Overexpression of ACS2 to reduce acetic acid accumulation enhanced D-lactic acid yield to 0.70 g/g from glucose in a bioreactor [51].	Engineered Y. lipolytica PO1f/d strains [51]
Crocetin Biosynthesis	Two-step strategy: 30°C for growth, then 20°C for production	Not explicitly stated	A two-step temperature-shift fermentation strategy resulted in a 2.3-fold increase in crocetin titer, indicating low temperature favors the biosynthetic enzyme activity [52].	Engineered Y. lipolytica YB392 [52]

Experimental Protocols

Protocol: Two-Step Temperature-Shift Fermentation for Pathway Activation

This protocol is adapted from crocetin production studies [52] and is designed to decouple growth and production phases, which is particularly useful for temperature-sensitive enzymes or pathways.

1. Principle: Maximize biomass accumulation at an optimal growth temperature, then shift to a temperature that maximizes the specific activity of the engineered biosynthetic pathway.

2. Reagents and Equipment:

• Engineered Y. lipolytica strain
• YPD Media: 20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract [4] [52]
• Shake flasks or Bioreactor system
• Temperature-controlled incubators/shakers (set to 20°C and 30°C)
• Spectrophotometer for OD600 measurement

3. Procedure:

• Step 1: Inoculum and Growth Phase.
  • Inoculate the engineered Y. lipolytica strain into fresh YPD or defined production medium.
  • Incubate the culture at 30°C with constant shaking (e.g., 250 rpm).
  • Monitor cell growth by measuring optical density at 600 nm (OD600).
  • Continue incubation until the culture reaches the mid- to late-exponential growth phase (e.g., OD600 ~10-15).

• Step 2: Temperature Shift and Production Phase.
  • Once the target biomass is achieved, rapidly transfer the entire culture to a second incubator pre-set to 20°C.
  • If using a bioreactor, adjust the temperature control setpoint directly.
  • Continue incubation at 20°C with shaking for the duration of the production phase (e.g., 48-120 hours).
  • Sample the culture periodically to quantify the titer of the target product (e.g., diol).

4. Data Analysis: Compare the final product titer and yield against a control fermentation maintained constantly at 30°C. The two-step process should yield a significantly higher product titer [52].

Protocol: pH-Controlled Biotransformation in Bench-Scale Bioreactors

This protocol is critical for maintaining pathway activity in processes that generate acidic or basic by-products, as demonstrated in diol and lactic acid production [51] [4].

1. Principle: Automated addition of acid or base maintains the culture pH at a setpoint, stabilizing enzyme activity and preventing metabolic inhibition.

2. Reagents and Equipment:

• Engineered Y. lipolytica strain (e.g., YALI17 for diol production [4])
• Bioreactor with pH probe, controller, and peristaltic pumps
• Acid solution (e.g., 1-2 M Hâ‚‚SOâ‚„ or HCl)
• Base solution (e.g., 1-4 M NaOH or NHâ‚„OH)
• Defined production medium with alkane (e.g., n-dodecane) or other carbon source

3. Procedure:

• Step 1: Bioreactor Setup and Calibration.
  • Assemble and autoclave the bioreactor vessel with the pH probe installed.
  • Calibrate the pH probe using standard buffers (e.g., pH 4.0, 7.0, and 10.0) prior to inoculation.
  • Set the pH controller to the desired setpoint. Note: An initial screen of pH 5.5-7.5 is recommended to identify the optimum for a novel pathway.

• Step 2: Inoculation and Process Control.

  • Transfer the sterile medium to the vessel and inoculate with a pre-culture of the engineered strain.
  • Start data logging for pH, temperature, and dissolved oxygen.
  • Allow the controller to automatically add acid/base to maintain the pH setpoint throughout the fermentation.

• Step 3: Sampling and Harvest.

  • Take periodic samples to monitor cell density, substrate consumption, and product formation.
  • Terminate the fermentation after the substrate is depleted or production ceases.
  • Harvest the culture for product extraction and quantification.

4. Data Analysis: The success of pH control is measured by the stability of the pH trace and the resultant improvement in product titer, yield, and productivity compared to an uncontrolled shake flask culture [51].

Pathway and Workflow Visualization

Metabolic Pathway for Diol Production in Engineered Y. lipolytica

The diagram below illustrates the engineered pathway for the production of α,ω-diols from alkanes in Y. lipolytica, highlighting the key genetic modifications: the overexpression of Alk1 and the deletion of over-oxidation genes [4] [5].

Experimental Workflow for Parameter Optimization

This workflow outlines the logical sequence from strain construction to the optimization of temperature and pH parameters in a bioreactor [51] [4] [52].

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Research Reagents and Strains for Diol Production in Y. lipolytica

Item Name	Function / Application	Specific Example / Notes
Engineered Y. lipolytica Strains	Chassis organism for diol production from alkanes.	Strains with deleted over-oxidation genes (e.g., YALI17: ΔFADH, ΔADH1-8, ΔFAO1, ΔFALDH1-4) and overexpressed ALK1 [4] [5].
Alkane Feedstocks	Hydrophobic carbon source for the biosynthetic pathway.	n-Dodecane (C12) is a typical substrate for medium-chain diol production (e.g., 1,12-dodecanediol) [4] [5].
CRISPR-Cas9 System	For precise genome editing (gene knock-outs, insertions).	Plasmid pCRISPRyl; used for multiplex gene deletion of oxidation pathway genes [4] [5].
YPD Medium	General growth and maintenance medium for Y. lipolytica.	Composition: 20 g/L Glucose, 20 g/L Peptone, 10 g/L Yeast Extract [4] [52].
Alkane Hydroxylase (ALK) Expression Vector	To enhance the flux from alkane to fatty alcohol.	pYl vector or similar, containing the ALK1 gene under a strong promoter (e.g., TEF) [4].
pH Control Solutions	For maintaining optimal pH in bioreactors.	Acid (e.g., 1-2 M Hâ‚‚SOâ‚„) and Base (e.g., 2-4 M NaOH) solutions [51].

Managing Substrate Toxicity and Improving Alkane Uptake

In the metabolic engineering of Yarrowia lipolytica for diol production, efficient alkane assimilation is paramount. However, this process is inherently challenged by substrate toxicity and poor aqueous solubility of alkanes, which can inhibit cellular growth and limit bioconversion efficiency. This application note details proven strategies and protocols for overcoming these bottlenecks, enabling robust diol production in engineered Y. lipolytica strains. The core of the approach involves a dual strategy: engineering cellular transport and metabolism to manage internal alkane levels and optimizing cultivation conditions to enhance substrate bioavailability, thereby preventing cytotoxic accumulation and driving flux toward the desired diol products.

Core Strategies and Quantitative Outcomes

The table below summarizes key engineering targets and cultivation strategies for managing alkane toxicity and uptake, along with their demonstrated quantitative outcomes.

Table 1: Key Strategies for Managing Alkane Toxicity and Improving Uptake

Strategy Category	Specific Intervention	Key Genes/Reagents Involved	Experimental Outcome	Citation
Metabolic Pathway Engineering	Block fatty alcohol & aldehyde oxidation	Deletion of FADH, ADH1-8, FAO1, FALDH1-4	14-fold increase in 1,12-dodecanediol production (0.72 mM vs. parental strain)	[4] [5]
	Enhance primary alkane hydroxylation	Overexpression of ALK1 (Cytochrome P450)	Further 2-fold increase in diol production (to 1.45 mM) in engineered background	[4] [5]
Alkane Transport & Compartmentalization	Disruption of long-chain alkane transport	Deletion of ABC1 transporter	Impaired growth on C16 alkanes (AlkAc phenotype: C10+ C16-)	[53]
	Peroxisomal biogenesis & function	Deletion of PEX14, PEX20, ANT1	Disrupted alkane utilization, highlighting importance of peroxisomal metabolism	[53]
Cultivation & Process Optimization	Fed-batch pulsing of hydrocarbons	Pulse addition of dodecane/hexadecane/hexadecene mixture	Achieved high lipid concentrations (4.3 g/L) without growth inhibition	[54]
	Use of biosurfactants	Addition of rhamnolipids	Increased biomass yield and altered fatty acid profile, improving alkane accessibility	[55]
	pH control	Automated pH-controlled biotransformation	Boosted 1,12-dodecanediol production to 3.2 mM	[4] [5]

Detailed Experimental Protocols

Protocol: CRISPR-Cas9 Mediated Blocking of Over-Oxidation Pathways

This protocol is critical for preventing the metabolic over-oxidation of valuable diol intermediates to carboxylic acids, thereby maximizing diol yields [4] [5].

• Objective: To generate a Y. lipolytica base strain (e.g., YALI17) with reduced capacity to oxidize fatty alcohols and aldehydes by deleting 10 genes involved in fatty alcohol oxidation (FADH, ADH1-8, FAO1) and 4 genes involved in fatty aldehyde oxidation (FALDH1-4).

• Materials:

  • Y. lipolytica Po1g ku70Δ strain (to enhance homologous recombination).
  • Plasmid pCRISPRyl (or similar CRISPR-Cas9 system for Y. lipolytica).
  • E. coli DH5α for plasmid propagation.
  • LiAc-based yeast transformation kit.
  • Synthetic complete (SC) medium without leucine or uracil for selection.
  • Designed sgRNA expression cassettes and donor DNA (if used for knock-in).

• Method:

  • sgRNA Vector Construction: For each target gene, design and clone a 20 bp guiding sequence specific to the early exons of FADH, ADH1-8, FAO1, and FALDH1-4 into the pCRISPRyl vector upstream of the sgRNA scaffold.
  • Yeast Transformation: Transform the constructed CRISPR plasmid into the Y. lipolytica Po1g ku70Δ strain using a standard LiAc/PEG transformation protocol.
  • Selection and Screening: Plate the transformation mixture on SC agar plates lacking uracil. Incubate at 28-30°C for 2-3 days until colonies form.
  • Genotype Verification: Screen colonies by colony PCR using primers flanking the target gene loci to confirm successful gene deletions. Sanger sequencing can be used for final verification.
  • Plasmid Curing: To enable subsequent genetic manipulations, culture positive clones in non-selective YPD medium to allow for loss of the CRISPR plasmid.

• Notes: A multiplexed CRISPR approach can be employed to delete multiple genes simultaneously. Functional validation of the engineered strain can be performed by assessing its reduced growth on fatty alcohols as a sole carbon source.

Protocol: Fed-Batch Cultivation with Alkane Pulses

This protocol mitigates the toxicity of high initial alkane concentrations by controlled, pulsed addition, maintaining cells in a productive, non-inhibited state [54].

• Objective: To achieve high cell density and high product titers by preventing the inhibitory effects of bulk alkanes through intermittent feeding.

• Materials:

  • Bioreactor (e.g., 2 L DASGIP Parallel Bioreactor System).
  • Alkane mixture (e.g., dodecane, hexadecane, hexadecene).
  • Seed culture medium: YPD (20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract).
  • Production medium: Defined mineral medium with (NHâ‚„)â‚‚SOâ‚„ and corn steep liquor.
  • Acid (HCl) and base (NaOH) for pH control.

• Method:

  • Seed Culture: Grow the engineered Y. lipolytica strain in YPD medium for 16-24 hours.
  • Bioreactor Inoculation: Harvest cells and inoculate into the bioreactor containing production medium with an initial low hydrocarbon load (e.g., 6 g/L total HC) to a starting OD600 of ~0.2.
  • Batch Phase Control: Operate the bioreactor at 27°C, pH 5.5, and 30% dissolved oxygen (DOC) controlled via agitation (200-800 rpm).
  • Pulse Feeding: Upon depletion of the initial alkane batch (typically 24-48 hours), add a pulse of concentrated, sterile alkane mixture to restore the original concentration. Repeat this process for multiple pulses (e.g., at 24 h, 48 h, and 72 h).
  • Monitoring and Harvesting: Monitor biomass, alkane concentration, and product formation (e.g., diols, lipids) throughout the process. Harvest the culture at the stationary phase for product analysis.

• Notes: Monitoring alkane depletion is crucial for timing the pulses effectively. Dissolved oxygen spikes can often indicate carbon source depletion.

Pathway and Workflow Diagrams

Alkane Uptake and Toxicity Mitigation Pathway

This diagram visualizes the core cellular processes of alkane uptake, activation, and the engineered strategies to mitigate toxicity and divert flux toward diol production.

Experimental Workflow for Strain and Process Engineering

This flowchart outlines the integrated experimental workflow from strain construction to bioprocess optimization for enhanced diol production.

The Scientist's Toolkit: Research Reagent Solutions

The table below lists essential reagents, strains, and tools for implementing the described protocols.

Table 2: Essential Research Reagents and Materials

Item Name	Function/Application	Specific Examples / Notes
pCRISPRyl Vector	CRISPR-Cas9 genome editing in Y. lipolytica	Available from Addgene (#70007); contains Cas9 and sgRNA scaffold [4].
Y. lipolytica Po1g Strains	Common parental chassis strains	Po1g (MATa, ura3-302, leu2-270, xpr2-322); Po1g ku70Δ for improved homologous recombination [4] [5].
Alkane Substrates	Carbon source for diol production	n-Dodecane (C12), n-Hexadecane (C16); purity >99% for reproducible fermentation [4] [54].
Rhamnolipids	Biosurfactant to enhance alkane uptake	Improves bioavailability of hydrophobic alkanes in aqueous media; reduces interfacial tension [55].
Defined Mineral Medium	Controlled cultivation conditions	Contains (NHâ‚„)â‚‚SOâ‚„, KHâ‚‚POâ‚„, MgClâ‚‚, trace elements; allows precise C/N ratio control [54].

Lipid Body Engineering for Enhanced Storage of Lipophilic Intermediates

The oleaginous yeast Yarrowia lipolytica has emerged as a premier microbial chassis for the production of valuable lipophilic compounds. Its innate ability to accumulate large quantities of lipids within specialized organelles known as lipid bodies (LBs) provides a natural storage system for hydrophobic intermediates and products [6] [56]. In the context of metabolic engineering for diol production, specifically medium- to long-chain α,ω-diols, engineering these lipid bodies becomes crucial for enhancing titers and preventing cytotoxic effects [5]. Lipid bodies are not merely passive storage depots but dynamic organelles that play active roles in cellular lipid homeostasis, sequestering lipophilic compounds that might otherwise disrupt membrane integrity or inhibit enzymatic activity [57] [56].

This application note details protocols for engineering Y. lipolytica lipid bodies to enhance the storage of lipophilic intermediates during diol production. We present a combinatorial approach involving genetic modifications to enhance lipid body proliferation, analytical techniques for quantifying lipid storage capacity, and cultivation strategies to optimize lipid body formation. These methodologies are framed within a broader research context of engineering Y. lipolytica for the biotransformation of alkanes into valuable diols, building upon recent demonstrations of 1,12-dodecanediol production from n-dodecane [5].

Key Engineering Strategies and Genetic Targets

Engineering Y. lipolytica for enhanced lipid body storage involves modulating key metabolic nodes to redirect flux toward lipid accumulation while simultaneously blocking competing pathways. The table below summarizes the primary genetic targets for this purpose.

Table 1: Key Genetic Engineering Targets for Lipid Body Enhancement in Y. lipolytica

Engineering Strategy	Genetic Target	Function/Enzyme	Expected Outcome	Reference
Increase Precursor Supply	ACL1, ACL2	ATP citrate lyase (cytosolic acetyl-CoA production)	↑ Acetyl-CoA pool for lipid synthesis	[56]
	ACC1 (YALI0C11407g)	Acetyl-CoA carboxylase (malonyl-CoA production)	↑ Malonyl-CoA for fatty acid elongation	[56]
	CAT2	Carnitine acetyltransferase (acetyl-CoA shuttle)	↑ Cytosolic acetyl-CoA export from mitochondria	[56]
Enhance Lipid Assembly	DGA1, DGA2 (YALI0E32769g, YALI0D07986g)	Diacylglycerol acyltransferases (final TAG assembly step)	↑ Triacylglycerol (TAG) synthesis and LB formation	[56]
Block Competing Pathways	POX1-6	Acyl-CoA oxidases (initiation of peroxisomal β-oxidation)	↓ Degradation of fatty acids and lipophilic intermediates	[5] [56]
	MFE2	Multifunctional enzyme (second and third steps of β-oxidation)	↓ Breakdown of lipid precursors	[56]
Block Over-oxidation	FALDH1-4	Fatty aldehyde dehydrogenases	Prevents over-oxidation of fatty aldehydes to acids, crucial for diol production	[5]
	FADH, ADH1-8, FAO1	Fatty alcohol dehydrogenases/oxidase	Prevents over-oxidation of fatty alcohols, channeling flux toward diols	[5]

Experimental Protocols

Protocol 1: CRISPR-Cas9-Mediated Multiplex Gene Knockout for Blocking Over-oxidation

This protocol describes the creation of a Y. lipolytica strain (e.g., YALI17) with disabled over-oxidation pathways to prevent the metabolism of fatty alcohol intermediates, thereby increasing diol yields [5].

I. Materials

• Y. lipolytica Po1g ku70Δ strain (to enhance homologous recombination)
• Plasmid: pCRISPRyl with Cas9 expression cassette and sgRNA scaffolding
• Donor DNA fragments for gene knockout (if using homology-directed repair)
• E. coli DH5α competent cells for plasmid propagation
• LB medium with ampicillin (100 mg/L)
• YPD medium (20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract)
• Synthetic complete (SC) medium without leucine (for selection)
• PCR reagents and DpnI enzyme
• Agarose gel electrophoresis system

II. Methods

Step 1: sgRNA Vector Construction

• Design sgRNA sequences targeting the genes of interest (e.g., FALDH1-4, ADH1-8, FAO1, FADH). Ensure the protospacer adjacent motif (PAM) site is present.
• Synthesize and clone these sgRNA sequences into the pCRISPRyl vector using golden gate assembly or Gibson assembly. The final vector should contain a Cas9 expression cassette and multiple sgRNA expression cassettes.
• Transform the assembled plasmid into E. coli DH5α, plate on LB agar with ampicillin, and incubate at 37°C overnight.
• Isolate validated plasmids from selected colonies using a plasmid miniprep kit.

Step 2: Yeast Transformation and Selection

• Grow Y. lipolytica Po1g ku70Δ in 5 mL YPD medium at 28°C overnight.
• Harvest cells and prepare competent cells using a lithium acetate/PEG method.
• Transform 1-5 µg of the purified CRISPR plasmid into the competent cells.
• Plate the transformation mixture onto SC agar plates without leucine.
• Incubate plates at 28°C for 2-3 days until colonies appear.

Step 3: Genotype Verification

• Pick 10-20 transformant colonies and inoculate into 5 mL SC liquid medium without leucine. Grow for 24-48 hours.
• Extract genomic DNA from each culture.
• Perform PCR amplification of the targeted gene loci using flanking primers.
• Analyze PCR products by agarose gel electrophoresis. Successful knockout will be confirmed by a size shift or absence of the wild-type band. Sanger sequencing of PCR products is recommended for final validation.

Protocol 2: Confocal Spectral Imaging for Quantifying Lipid Storage and Lipolysis

This protocol utilizes the solvatochromic dye Nile Red to monitor lipid turnover and storage in live Y. lipolytica cells in real-time, providing a metabolic parameter for lipid anabolic and catabolic states [57].

I. Materials

• Engineered Y. lipolytica strain
• YPD or defined production medium (e.g., with n-dodecane as substrate)
• Nile Red stock solution (1 mg/mL in DMSO)
• Phosphate Buffered Saline (PBS)
• Glass-bottom culture dishes for microscopy
• Confocal microscope with spectral imaging capability (e.g., Leica TCS SP8)
• Image analysis software (e.g., ImageJ with custom scripts for spectral phasor analysis)

II. Methods

Step 1: Cell Culture and Staining

• Grow the engineered Y. lipolytica strain in appropriate medium under diol-production conditions.
• During the mid-exponential or production phase, harvest 1 mL of culture.
• Wash cells twice with PBS by gentle centrifugation.
• Resuspend the cell pellet in 1 mL PBS containing Nile Red at a final concentration of 100-500 ng/mL.
• Incubate in the dark for 10-20 minutes.

Step 2: Confocal Spectral Image Acquisition

• Place a 10 µL drop of the stained cell suspension on a glass-bottom dish.
• Using a 63x or 100x oil-immersion objective, focus on the cells.
• Set the excitation wavelength to 488 nm.
• Configure the spectral detector to collect emission spectra across a range of 520-700 nm (or 500-650 nm) with a resolution of ~5-10 nm.
• Acquire images of multiple fields of view, ensuring that the signal is not saturated.

Step 3: Spectral Phasor Analysis

• For each pixel in the acquired image stack, calculate the Fourier components of the emission spectrum (G and S coordinates) using the following transformations within the phasor analysis framework:
  • G = (Σλ I(λ) cos(2π(λ - λ0)/N)) / (Σλ I(λ))
  • S = (Σλ I(λ) sin(2π(λ - λ0)/N)) / (Σλ I(λ)) Where I(λ) is the intensity at wavelength λ, λ0 is the reference wavelength, and N is the number of spectral channels.
• Plot the G and S coordinates for all pixels on a phasor plot (unit circle).
• Interpretation: Pixels corresponding to neutral lipids (TAGs in Lipid Bodies) will cluster in a specific region of the plot (shorter emission wavelengths, red-shifted spectrum), while polar lipids (FFAs, phospholipids) will cluster in a different region (longer emission wavelengths). The angle (θ) of the resultant phasor vector provides a quantitative metabolic index for the balance between lipid storage (high θ) and lipolysis (low θ) [57].

Diagram 1: Lipid Metabolic Analysis by Spectral Phasors

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Reagents and Materials for Lipid Body Engineering

Reagent/Material	Function/Application	Example/Notes
CRISPR-Cas9 System	Targeted gene knockout/editing	pCRISPRyl plasmid; enables multiplexed gene editing crucial for blocking oxidation pathways [5].
Nile Red	Polarity-sensitive fluorescent dye for lipid imaging	Excitation: 488 nm; Emission shift: yellow (~550 nm) for neutral lipids to red (~630 nm) for polar lipids [57].
n-Dodecane	Hydrophobic substrate for diol production	Used at 50 mM as a model alkane feedstock for 1,12-dodecanediol production [5].
Alkane Hydroxylase (ALK1)	Key enzyme for primary oxidation of alkanes	Overexpression enhances flux from alkane to fatty alcohol intermediates [5].
YPD Medium	Standard rich medium for yeast cultivation	20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract [5].
Synthetic Complete (SC) Medium	Defined medium for selection and controlled cultivation	Used without leucine for selection of transformed strains [5].

Integrated Workflow for Enhanced Diol Production

The following diagram integrates genetic engineering, cultivation, and analytical protocols into a coherent workflow for producing diols in Y. lipolytica with enhanced storage of lipophilic intermediates.

Diagram 2: Integrated Diol Production Workflow

The strategic engineering of lipid bodies in Yarrowia lipolytica is a critical enabling technology for the efficient production of lipophilic compounds such as medium- to long-chain diols. The protocols outlined herein—for creating strains with minimized over-oxidation and maximized lipid storage capacity, and for quantitatively monitoring the resulting lipid metabolism—provide a robust framework for researchers. By implementing these methods, scientists can develop superior microbial cell factories that not only achieve high yields but also maintain cellular health by efficiently managing the flux and storage of hydrophobic intermediates. This integrated approach paves the way for the sustainable and economically viable bioproduction of a wide range of valuable oleochemicals.

Modeling, Strain Performance Analysis, and Industrial Viability Assessment

Genome-Scale Metabolic Modeling (GEM) for Predicting Diol Flux Distributions

Genome-scale metabolic models (GEMs) have emerged as powerful computational frameworks for predicting metabolic fluxes by mathematically representing the gene-protein-reaction associations within an organism [58] [59]. For metabolic engineers focusing on the production of valuable chemicals such as diols, GEMs provide an in silico platform to predict metabolic flux distributions, identify bottlenecks, and propose genetic interventions before embarking on costly laboratory experiments [59] [60]. The oleaginous yeast Yarrowia lipolytica presents an exceptional chassis for diol production due to its innate capacity to metabolize hydrophobic substrates and its high flux toward key precursors like acetyl-CoA [5] [6]. This protocol details the application of GEMs to predict and optimize flux distributions for medium- to long-chain α,ω-diol production in Y. lipolytica, providing a structured framework for researchers aiming to enhance microbial cell factory performance.

Computational Protocols for GEM Analysis

Model Reconstruction and Simulation

The foundation of flux prediction lies in a high-quality, organism-specific GEM. For Y. lipolytica, several curated models exist, including iMK735, iYL619_PCP, iYali4, and iYLI647 [61] [62]. The reconstruction process involves compiling all known metabolic reactions, their stoichiometry, gene-protein-reaction (GPR) associations, and compartmentalization based on genomic annotation and biochemical literature [59].

Key Steps:

• Network Reconstruction: Retrieve an existing Y. lipolytica GEM from databases such as BioModels (e.g., MODEL1510060001) [62] or the literature.
• Contextualization: Integrate condition-specific omics data (transcriptomics, proteomics) to create a context-specific model using tools like iMAT [63] or GIMME [64].
• Flux Balance Analysis (FBA): Simulate metabolic fluxes under steady-state conditions by solving the linear programming problem: Maximize ( Z = c^T v ) subject to ( S \cdot v = 0 ) and ( v{min} \leq v \leq v{max} ), where ( S ) is the stoichiometric matrix, ( v ) is the flux vector, and ( c ) is the objective function (e.g., biomass or diol production) [59] [61].
• Predicting Flux Alterations: Employ advanced algorithms such as ΔFBA to directly predict metabolic flux differences between control and perturbed conditions (e.g., wild-type vs. engineered strain) using differential gene expression data [64]. ΔFBA maximizes consistency between flux changes and expression changes without requiring a predefined cellular objective [64].

The following diagram illustrates the workflow for GEM reconstruction and simulation for diol production in Y. lipolytica:

Algorithm Selection for Diol Production

Different algorithms offer specific advantages for predicting fluxes in diol production scenarios. The table below summarizes key methods applicable to Y. lipolytica engineering.

Table 1: Computational Algorithms for GEM-Based Flux Prediction in Diol Production

Algorithm	Primary Function	Application in Diol Production	Key Features
FBA [59] [61]	Predicts steady-state fluxes	Maximizes biomass or diol synthesis; simulates knockout phenotypes	Requires predefined objective function; assumes optimal growth
ΔFBA [64]	Predicts flux differences between conditions	Identifies flux alterations in engineered strains (e.g., oxidation pathway knockouts)	Uses differential gene expression; no need for cellular objective
eMOMA [61]	Predicts fluxes in nutrient-limited conditions	Simulates lipid/diol production under nitrogen limitation in Y. lipolytica	Minimizes metabolic adjustment from reference flux; suitable for non-growth-coupled production
REMI [64]	Integrates transcriptomic and metabolomic data	Estimates flux profiles in Y. lipolytica under genetic perturbations	Maximizes agreement between flux fold-changes and enzyme expression changes

For diol production in Y. lipolytica, eMOMA is particularly valuable as it accurately predicts metabolism under nitrogen-limited conditions typically used for lipid and diol accumulation [61]. The method finds a flux distribution that minimizes the Euclidean distance from a reference flux (e.g., from growth phase) while satisfying constraints under a new condition (e.g., production phase).

eMOMA Implementation:

Experimental Protocols for Model Validation and Strain Engineering

Strain Construction and Transformation

Based on GEM predictions, engineering Y. lipolytica for enhanced diol production involves targeted genetic modifications to redirect metabolic flux. The following protocol outlines the key steps for implementing model-predicted interventions.

Materials:

• Y. lipolytica Po1g strain (e.g., ATCC MYA-2613)
• CRISPR-Cas9 components: Cas9 expression vector, sgRNA expression cassettes
• Donor DNA fragments for gene knockouts/overexpression
• YPD medium: 20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract
• Synthetic complete medium without leucine: 20 g/L glucose, 6.7 g/L yeast nitrogen base without amino acids, supplemented with appropriate amino acid mix

Methodology:

• Design sgRNAs and Donor DNA:
  • Design sgRNAs targeting genes identified from GEM predictions (e.g., FADH, ADH1-8, FAO1, FALDH1-4 for reducing over-oxidation [5]).
  • For non-homologous end joining (NHEJ)-mediated knockouts, design sgRNAs with high on-target efficiency.
  • For homology-directed repair (HDR), prepare donor DNA with 500-1000 bp homology arms flanking the desired modification.

• Strain Transformation:

  • Grow Y. lipolytica in YPD medium at 28°C overnight to mid-exponential phase (OD600 ≈ 1.0).
  • Harvest cells by centrifugation and wash with sterile water.
  • Resuspend cells in transformation buffer (100 mM lithium acetate, 10 mM Tris-HCl, 1 mM EDTA, pH 7.5).
  • Mix 50 μL cell suspension with 1 μg Cas9-sgRNA plasmid and 1 μg donor DNA (if using HDR).
  • Incubate at 28°C for 1 hour, add 40% polyethylene glycol (PEG) 4000, and heat shock at 39°C for 1 hour.
  • Plate on appropriate selection medium and incubate at 28°C for 2-3 days.

• Screening and Validation:

  • Screen transformants by colony PCR and DNA sequencing to verify genetic modifications.
  • For multiplexed editing, sequentially transform or use multiple sgRNAs simultaneously.

Bioprocess Optimization for Diol Production

GEM simulations can guide not only genetic designs but also bioprocess conditions. The following protocol outlines a fermentation process for maximizing diol production in engineered Y. lipolytica strains.

Materials:

• Engineered Y. lipolytica strain (e.g., YALI17 with FALDH1-4, FAO1, FADH, ADH1-8 knockouts and ALK1 overexpression [5])
• Bioreactor with pH and dissolved oxygen control
• Fermentation medium: 50 mM n-dodecane as substrate, 6.7 g/L yeast nitrogen base, appropriate supplements
• Analytical equipment: HPLC, GC-MS for diol quantification

Methodology:

• Inoculum Preparation:
  • Grow engineered strain in YPD or synthetic complete medium for 48 hours.
  • Scale up culture to 20 mL in 100 mL flask and incubate for another 48 hours.

• Bioreactor Setup:

  • Transfer seed culture to bioreactor containing fermentation medium with n-dodecane as carbon source.
  • Set temperature to 28°C, agitation to 300-500 rpm, and aeration to 0.5-1 vvm.
  • Maintain pH at 6.5 using automated addition of acid/base.

• Process Monitoring and Harvest:

  • Monitor cell density (OD600), substrate consumption, and product formation over time.
  • Sample periodically for extracellular metabolite analysis (HPLC/GC-MS).
  • For nitrogen-limited conditions, use media with high carbon-to-nitrogen ratio to trigger lipid and diol accumulation [61].
  • Harvest cells during stationary phase when diol titer reaches maximum (typically 3-7 days).

Data Presentation and Analysis

Quantitative Analysis of Engineered Strains

GEM-enabled engineering of Y. lipolytica has demonstrated significant improvements in diol production. The table below summarizes performance data for representative engineered strains.

Table 2: Performance of Engineered Y. lipolytica Strains for 1,12-Dodecanediol Production from n-Dodecane [5]

Strain	Genotype	Key Modifications	Production (mM)	Fold Increase vs. Wild-Type
Wild-Type	Po1g ku70Δ	Parental strain	0.05	1x
YALI17	Po1g ku70Δ mfe1Δ faa1Δ faldh1-4Δ fao1Δ fadhΔ adh1-8Δ	Blocked fatty alcohol/aldehyde oxidation	0.72	14x
YALI17 + ALK1	YALI17 with ALK1 overexpression	Enhanced alkane hydroxylation	1.45	29x
YALI17 (pH-controlled)	YALI17 with ALK1 overexpression	Optimized biotransformation conditions	3.20	64x

The data demonstrate that systematic deletion of oxidation pathway genes combined with alkane hydroxylase overexpression significantly enhances diol production. The highest production (3.2 mM) was achieved through combined metabolic engineering and bioprocess optimization.

Metabolic Engineering Strategies for Enhanced Diol Production

GEM analyses have identified multiple strategic interventions for improving diol production in Y. lipolytica. The following pathway diagram illustrates key metabolic engineering targets:

The Scientist's Toolkit

Table 3: Essential Research Reagent Solutions for GEM-Guided Diol Production Studies

Reagent/Resource	Function/Application	Example Sources/References
Y. lipolytica GEMs (iMK735, iYL619_PCP, iYali4)	In silico flux prediction and strain design	BioModels (MODEL1510060001) [62], Published literature [61]
CRISPR-Cas9 System	Precise gene knockouts and integrations	[5] - Used for multiplexed gene editing in Y. lipolytica
Alkane Hydroxylase (ALK1)	Enhanced alkane conversion to hydroxy fatty acids	[5] - Key enzyme for initial alkane oxidation
n-Dodecane	Hydrophobic substrate for diol production	[5] - 50 mM used in biotransformation studies
ΔFBA Algorithm	Predicting flux alterations between conditions	[64] - MATLAB implementation available
eMOMA Algorithm	Predicting fluxes in nitrogen-limited conditions	[61] - Applicable to oleaginous yeast metabolism

This protocol has outlined comprehensive computational and experimental methodologies for employing genome-scale metabolic modeling to predict and enhance diol flux distributions in Yarrowia lipolytica. The integration of GEM simulations with advanced genetic engineering tools provides a powerful framework for systematic strain improvement. The showcased strategies—including targeted knockout of competing pathways, overexpression of rate-limiting enzymes, and bioprocess optimization—have demonstrated substantial improvements in diol production, with up to 64-fold enhancement over wild-type strains [5]. As GEM development continues to incorporate multi-omics data and more sophisticated constraint-based modeling approaches [58] [63], these methodologies will become increasingly predictive and valuable for developing efficient microbial cell factories for diol and other valuable chemical production.

Environmental MOMA (eMOMA) for Simulating Nitrogen-Limited Lipid Production

The oleaginous yeast Yarrowia lipolytica has emerged as a prominent microbial platform for the production of biofuels, oleochemicals, and high-value nutraceuticals, largely due to its innate ability to accumulate high levels of lipids [6]. In a batch culture process, lipid accumulation in this yeast is typically triggered by nitrogen limitation, a condition that halts cell proliferation and redirects metabolic flux from biomass formation to storage lipid synthesis [61]. Understanding and engineering metabolism under this non-growth condition presents a unique challenge for systems metabolic engineering.

Traditional constraint-based modeling methods, such as Flux Balance Analysis (FBA), are highly effective for predicting metabolic states during exponential growth by assuming optimality of an objective like biomass maximization [65]. However, their predictive power diminishes under nitrogen-limited stationary phase conditions where the cellular objective is unclear and lipid production becomes a non-growth-coupled process [66] [61]. To address this critical gap, the environmental version of Minimization of Metabolic Adjustment (eMOMA) has been developed as a computational framework for predicting metabolic flux distributions in Y. lipolytica under nitrogen-limited conditions, enabling the identification of effective metabolic engineering strategies for enhanced lipid production [66] [61] [67].

Principles of eMOMA for Nitrogen-Limited Metabolism

Conceptual Foundation

The Minimization of Metabolic Adjustment (MOMA) algorithm is a constraint-based modeling method that predicts a suboptimal flux distribution for a genetically or environmentally perturbed system by identifying the point in the solution space that is closest to a reference (wild-type) flux distribution [61]. eMOMA adapts this principle for environmental perturbations, such as the shift from nitrogen-replete to nitrogen-limited conditions. It operates on the hypothesis that when faced with a drastic environmental change, the cellular metabolic network undergoes a minimal redistribution from its original state rather than achieving a fully optimized new state [61]. This makes it particularly suited for modeling the metabolic transition into the lipid-accumulating stationary phase.

Computational Workflow

The eMOMA implementation for predicting nitrogen-limited fluxes involves a series of optimization problems [61]:

• Reference State Calculation: First, a standard FBA problem with biomass maximization as the objective is solved to determine the maximum specific growth rate (μ_max) and the reference flux distribution (v_ref) for cells in a nutrient-rich (non-limited) environment.
• Nitrogen-Limited Constraint: The model constraints are then altered to simulate nitrogen limitation. This typically involves setting the nitrogen uptake rate to zero or a very low value.
• Flux Prediction via eMOMA: Finally, the flux distribution under nitrogen limitation (v_nlim) is calculated by minimizing the Euclidean distance between the predicted flux vector (v) and the reference flux vector (v_ref), subject to the new nitrogen-limited constraints.

The following diagram illustrates this sequential computational workflow:

Application Protocol: Identifying Lipid Enhancement Strategies

The following section provides a detailed protocol for applying eMOMA to identify genetic engineering targets for improved lipid production in Y. lipolytica under nitrogen limitation.

Computational Procedure

Required Tools and Software:

• A validated Genome-Scale Metabolic Model (GEM) of Y. lipolytica (e.g., iYL619_PCP, iMK735, iYali4).
• A constraint-based modeling environment, such as the COBRA Toolbox for MATLAB [65].
• A solver for linear (FBA) and quadratic (eMOMA) programming problems.

Step-by-Step Workflow:

• Model Preparation: Load the Y. lipolytica GEM. Verify and set default constraints for carbon uptake (e.g., glucose), oxygen uptake, and other relevant nutrients to simulate standard cultivation conditions.
• Define Reference State: Perform FBA with the objective of maximizing the biomass reaction. This yields the reference growth rate (μ_max) and flux distribution (v_ref).
• Simulate Nitrogen Limitation: Modify the model constraints to reflect nitrogen depletion. This is achieved by setting the lower and upper bounds of the nitrogen source exchange reaction (e.g., ammonium) to zero.
• Execute eMOMA: Run the eMOMA algorithm using the nitrogen-limited model and the reference flux distribution (v_ref) from Step 2. The output is the predicted flux vector (v_nlim) for the stationary, lipid-producing phase.
• Analyze Flux Redistribution: Compare v_nlim with v_ref to identify key flux changes. Critical nodes to inspect include:
  • Flux into the Tricarboxylic Acid (TCA) cycle and citrate export from mitochondria.
  • Activity of ATP:citrate lyase generating cytosolic acetyl-CoA.
  • Flux through the Pentose Phosphate Pathway (PPP) for NADPH supply.
  • Flux through the fatty acid and Triacylglycerol (TAG) biosynthesis pathways.
• Identify Intervention Targets: Use the flux differences to pinpoint potential gene knockout or overexpression candidates. Promising targets are reactions where the model predicts a flux change that limits lipid synthesis. Validate these predictions by comparing them to known lipid-enhancing strategies from literature.

Experimental Validation of eMOMA Predictions

Following the computational identification of targets, engineered strains must be constructed and experimentally characterized.

Key Genetic Modifications: The table below summarizes genetic interventions successfully predicted and validated using the eMOMA approach.

Table 1: Validated Genetic Engineering Strategies for Enhanced Lipid Production in Y. lipolytica

Target Gene	Intervention Type	Biological Function	Effect on Lipid Production
YALI0F30745g	Knockout	One-carbon / Methionine metabolism [66] [61]	45% increase in lipid accumulation compared to wild-type [66] [67]
Diacylglycerol Acyltransferase (DGA1)	Overexpression	Final step of Triacylglycerol (TAG) biosynthesis [66] [61]	Increased lipid yield; successfully rediscovered by eMOMA [66]
Acetyl-CoA Carboxylase (ACC1)	Overexpression	Commits acetyl-CoA to malonyl-CoA for fatty acid synthesis [66] [61] [6]	Increased lipid yield; successfully rediscovered by eMOMA [66]
Stearoyl-CoA Desaturase	Overexpression	Introduces double bonds into fatty acids [66] [61]	Increased lipid yield; successfully rediscovered by eMOMA [66]

Fermentation and Analytical Protocols:

• Strain Cultivation:

  • Medium: Use a defined medium with a high carbon-to-nitrogen (C/N) ratio (e.g., 100:1) to induce nitrogen limitation.
  • Culture Conditions: Cultivate strains in batch bioreactors (e.g., 1-L or 5-L working volume) with controlled parameters: temperature, 28-30°C; pH, 5.5-6.0; dissolved oxygen >30% [68].
  • Monitoring: Track cell density (OD600), residual nitrogen, and carbon source concentration throughout the fermentation.

• Lipid Quantification:

  • Sampling: Collect biomass samples at the end of fermentation or during the stationary phase.
  • Extraction: Use a modified Folch or Bligh & Dyer method with chloroform-methanol solvent system for total lipid extraction.
  • Analysis: Quantify total lipid content gravimetrically or via gas chromatography (GC) for fatty acid methyl ester (FAME) profiling. Report key performance metrics including lipid titer (g/L), lipid content (% cell dry weight), and lipid productivity (g/L/h) [68].

Results and Performance of Engineered Strains

The application of eMOMA-guided metabolic engineering has led to the development of high-performance Y. lipolytica strains. The table below summarizes the lipid production performance achievable through systematic engineering, including a benchmark strain constructed using these principles.

Table 2: Lipid Production Performance of Engineered Y. lipolytica Strains

Strain / Condition	Engineering Strategy	Lipid Titer (g/L)	Lipid Content (% CDW)	Productivity (g/L/h)	Cultivation Scale
CJ0415 Strain	Deletion of MHY1, CEX1; overexpression of TAG genes; redirection of phosphatidic acid flux [68]	54.6	45.8	2.06	5-L Bioreactor
Nitrogen-Limited (Conventional)	Wild-type or base engineered strain under N-limitation [68]	-	-	~0.79*	5-L Bioreactor
eMOMA-Validated Mutant	Knockout of YALI0F30745g (one-carbon metabolism) [66] [67]	45% higher than wild-type	45% higher than wild-type	-	Lab-scale

*Calculated from the 2.6-fold productivity increase reported for strain CJ0415 [68].

The Scientist's Toolkit: Research Reagent Solutions

This section lists key reagents, software, and genetic tools essential for conducting eMOMA-guided metabolic engineering in Y. lipolytica.

Table 3: Essential Research Tools for eMOMA and Y. lipolytica Engineering

Item Name	Category	Function / Application	Example / Note
Genome-Scale Model (GEM)	Software/Data	A computational representation of metabolism for in silico flux prediction [66] [61].	iYL619_PCP, iMK735, iYali4
COBRA Toolbox	Software	A MATLAB-based suite for constraint-based reconstruction and analysis [65].	Enables FBA and eMOMA simulations.
13C-Labeled Substrate	Chemical Reagent	Tracer for experimental flux validation via 13C-Metabolic Flux Analysis (13C-MFA) [69].	[U-13C] Glucose, [1-13C] Glucose
CRISPR/Cas9 System	Molecular Biology Tool	Enables precise gene knockouts and edits in Y. lipolytica for strain construction [61] [6].	Validates eMOMA predictions.
Triacylglycerol (TAG) Biosynthesis Genes	Genetic Part	Overexpression cassettes to enhance the lipid sink pathway [68] [66].	DGA1, DGA2
Fatty Acid Degradation Mutant	Genetic Tool	Blocking β-oxidation prevents re-consumption of stored lipids [68] [6].	Deletion of acyl-CoA oxidases (e.g., POX1-6).

Integrated Pathway Engineering for Diol Production

The strategic value of eMOMA extends beyond native lipid production. The acetyl-CoA and lipid biosynthetic pathways are fundamental precursors for a wide range of value-added chemicals, including diols. The diagram below illustrates how eMOMA-informed engineering of central carbon metabolism under nitrogen limitation creates a platform for the synthesis of these compounds.

The integration of eMOMA into the metabolic engineering workflow provides a powerful, systems-level framework for overcoming the historical challenge of simulating non-growth-associated lipid production in Y. lipolytica. By enabling accurate prediction of flux distributions under nitrogen limitation, it facilitates the identification of non-intuitive genetic targets, as validated by the discovery of the YALI0F30745g knockout. This approach moves strain design beyond reliance on known canonical targets, paving the way for the development of next-generation microbial cell factories not only for lipids but also for acetyl-CoA-derived products such as diols.

Within the framework of a broader thesis on metabolic engineering of Yarrowia lipolytica for diol production, the precise quantification of strain performance is paramount. This application note provides a detailed comparative analysis of advanced engineered Y. lipolytica strains, focusing on key performance metrics (KPIs) such as titer, yield, and productivity for diols and related high-value compounds. We summarize critical quantitative data into structured tables and provide detailed experimental protocols for key experiments, including CRISPR-Cas9 mediated pathway optimization and fermentation processes. The accompanying diagrams and reagent toolkit are designed to equip researchers with the practical knowledge to implement and build upon these metabolic engineering strategies.

Comparative Performance Metrics of EngineeredYarrowia lipolyticaStrains

The performance of microbial cell factories is benchmarked using three key parameters: titer (the concentration of the target product, typically in g/L or mM), yield (the amount of product formed per unit of substrate, in g/g or mol/mol), and productivity (the rate of product formation, in g/L/h). The table below presents a comparative analysis of recently engineered Y. lipolytica strains producing various valuable compounds.

Table 1: Performance Metrics of Engineered Yarrowia lipolytica Strains for Various Products

Target Product	Strain / Engineering Strategy	Max Titer	Yield	Productivity	Carbon Source	Scale	Citation
1,12-Dodecanediol	YALI17; Δ10 oxidation genes, Δ4 aldehyde oxidation genes	0.72 mM	-	-	n-Dodecane	Lab-scale	[4] [15]
	YALI17 + ALK1 overexpression	1.45 mM	-	-	n-Dodecane	Lab-scale	[4] [15]
	YALI17 + ALK1 + pH-control	3.2 mM	-	-	n-Dodecane	Biotransformation	[4] [15]
Erythritol	Parental Strain Ylxs01	178.85 g/L	0.57 g/g	2.42 g/(L·h)	Glucose	200 L Bioreactor	[70]
	Engineered Strain Ylxs48 (Transporter & Pathway)	218.33 g/L	0.74 g/g	4.62 g/(L·h)	Glucose	200 L Bioreactor	[70]
	Engineered Strain Ylxs48 (Fed-Batch)	355.81 g/L	-	-	Glucose	200 L Bioreactor	[70]
3-HP	Engineered Po1f (Dynamic Promoters)	100.37 g/L	0.21 g/g	0.48 g/(L·h)	Glucose	5 L Bioreactor	[71]
Sclareol	Engineered Po1f-tHEI (Combinatorial Engineering)	~2.66 g/L	-	-	Glucose	Shake Flask	[72]
Crocetin	Engineered Po1f (Pathway & Temp. Shift)	30.17 mg/L	-	-	Glucose	Shake Flask	[73]
Menaquinone-7 (MK-7)	Engineered YQ-9	255 mg/L	-	-	Complex Media	Shake Flask	[74]
Naringenin	Engineered PO1f (Constitutive Pathway)	239.1 mg/L	-	-	Glucose	Shake Flask	[75]
	Engineered PO1f (Xylose-Inducible)	715.3 mg/L	-	-	Glucose/Xylose Mix	Shake Flask	[75]

Analysis of Key Performance Indicators

The data reveals the remarkable potential of Y. lipolytica as a microbial chassis. The 29-fold improvement in 1,12-dodecanediol titer (from 0.05 mM in wild-type to 1.45 mM in YALI17+ALK1) demonstrates the efficacy of blocking competing oxidation pathways and enhancing alkane hydroxylase activity [4]. For commodity chemicals like erythritol, the synergistic application of transporter and pathway engineering led to a 91.5% increase in productivity and a titer of 355.81 g/L, which is sufficient to enable direct crystallization from the fermentation broth, significantly simplifying downstream processing [70]. Furthermore, the use of dynamic promoter toolkits to balance metabolic flux has enabled record-breaking titers of 100.37 g/L for 3-hydroxypropionic acid (3-HP), showcasing the importance of precise genetic regulation for pathway optimization [71].

Experimental Protocols for Diol Production inY. lipolytica

This section details the key methodologies for engineering and cultivating Y. lipolytica for enhanced diol production, as exemplified by the production of 1,12-dodecanediol from n-dodecane [4] [15].

CRISPR-Cas9 Mediated Gene Deletion to Block Over-Oxidation Pathways

Objective: To construct the base engineered strain YALI17 by knocking out genes involved in the over-oxidation of fatty alcohols and aldehydes to carboxylic acids, thereby preventing the degradation of diol intermediates [4].

Procedure:

• Vector Construction: Use the pCRISPRyl plasmid (Addgene #70007) as the backbone. For multiplexed gene deletion, insert additional sgRNA scaffold sequences downstream of the original scaffold.
• sgRNA Design: Design and clone 20 bp guiding sequences targeting the 5' end of the sgRNA scaffold for each target gene. The targets include:
  • Ten fatty alcohol oxidation genes: FADH, ADH1, ADH2, ADH3, ADH4, ADH5, ADH6, ADH7, ADH8, FAO1.
  • Four fatty aldehyde oxidation genes: FALDH1, FALDH2, FALDH3, FALDH4 [4].
• Yeast Transformation: Introduce the constructed CRISPR-Cas9 plasmid into Y. lipolytica Po1f using a standard yeast transformation protocol, such as the Frozen EZ Yeast Transformation II kit [72].
• Selection and Verification: Plate transformed cells on YNB (Yeast Nitrogen Base) medium lacking uracil to select for positive clones. Confirm successful gene deletions via colony PCR and sequencing.

Overexpression of P450 Alkane Monooxygenase (ALK1)

Objective: To enhance the primary hydroxylation step of alkanes, thereby increasing the flux toward the desired diol products [4].

Procedure:

• Plasmid Construction:
  • Use the pYl yeast expression vector. This vector can be derived from pCRISPRyl by replacing the Cas9 ORF with the target gene and removing the sgRNA scaffolds.
  • PCR amplify the ALK1 gene (or other CYP52 family genes) from the Y. lipolytica genome.
  • Clone the ALK1 gene into the pYl vector under the control of a strong promoter (e.g., the TEF promoter with an intron for enhanced expression) using Circular Polymerase Extension Cloning (CPEC) [4].
• Strain Transformation: Introduce the ALK1-pYl overexpression vector into the engineered YALI17 strain.
• Strain Validation: Select transformants on appropriate media and validate ALK1 integration and expression via PCR and quantitative RT-PCR.

Biotransformation and pH-Controlled Fermentation

Objective: To evaluate the diol production performance of the engineered strains under controlled conditions [4].

Procedure:

• Seed Culture: Inoculate a single colony of the engineered Y. lipolytica strain into YPD medium (20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract, pH 6.5) and incubate at 30°C with shaking (220 rpm) for 48 hours.
• Scale-Up: Transfer the seed culture to a larger flask containing fresh YPD medium and incubate for an additional 48 hours to achieve high cell density.
• Biotransformation:
  • Harvest cells and resuspend them in a production medium containing the alkane substrate (e.g., 50 mM n-dodecane).
  • For pH-controlled runs, use a bioreactor with automated pH monitoring and control to maintain optimal pH throughout the fermentation. The study achieving 3.2 mM 1,12-dodecanediol utilized this approach [4].
  • Incubate at 30°C with adequate aeration and agitation for the desired duration (e.g., 96-144 hours).
• Product Analysis: Extract samples at regular intervals. Analyze 1,12-dodecanediol concentration using suitable analytical methods such as High-Performance Liquid Chromatography (HPLC) or Gas Chromatography-Mass Spectrometry (GC-MS) [72].

Metabolic Pathway Engineering Workflow

The following diagram illustrates the logical workflow and key genetic modifications for engineering Y. lipolytica to produce diols from alkanes.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Research Reagents for Metabolic Engineering of Y. lipolytica

Reagent / Tool	Function / Application	Example / Source
pCRISPRyl Vector	CRISPR-Cas9 genome editing backbone for Y. lipolytica. Enables targeted gene knockouts.	Addgene #70007 [4]
pYl Expression Vector	A modular yeast expression vector for heterologous gene overexpression and pathway engineering.	Derived from pCRISPRyl [4]
TEF Promoter (pTEFin)	A strong, constitutive promoter for driving high-level expression of target genes.	Native Y. lipolytica promoter [4] [72]
Hybrid Promoters (PU13, PC48)	Engineered strong promoters for enhanced gene expression, superior to pTEFin.	Constructed with novel UAS elements [71]
Xylose-Inducible System	Allows inducible gene expression, coupling product synthesis with xylose utilization.	Constructed from native parts [75]
Frozen EZ Kit	High-efficiency transformation kit for introducing DNA into Y. lipolytica.	ZYMO RESEARCH [72]
YPD / YPNP Media	Standard complex media for routine cultivation and seed train development.	10 g/L Yeast Extract, 20 g/L Peptone, 20-60 g/L Glucose [4] [70] [72]
YNB Medium	Defined, minimal medium for selection and maintenance of auxotrophic markers.	6.7 g/L YNB without amino acids, supplemented with carbon source [70] [72]
Alkane Substrates	Hydrophobic carbon sources for biotransformation into diols and other oxyfunctionalized chemicals.	n-Dodecane, n-Octane [4]

Benchmarking Against Competing Microbial Platforms (E. coli, S. cerevisiae)

The selection of an optimal microbial host is a critical determinant of success in metabolic engineering projects aimed at diol production. While Escherichia coli and Saccharomyces cerevisiae represent well-established model organisms, the oleaginous yeast Yarrowia lipolytica has emerged as a particularly promising host for the biosynthesis of medium- to long-chain diols from hydrophobic substrates. This application note provides a systematic benchmarking of these microbial platforms, focusing on quantitative performance metrics, experimental methodologies, and specific engineering strategies for diol production. The information presented herein is designed to assist researchers in selecting and engineering appropriate microbial chassis for their specific diol production objectives, with particular emphasis on the unique advantages of Y. lipolytica in utilizing alkane feedstocks.

Performance Benchmarking of Microbial Platforms

Table 1: Comparative Performance of Microbial Platforms for Diol Production

Diol Product	Microbial Host	Engineering Strategy	Carbon Source	Titer	Yield	Productivity	Reference
1,12-Dodecanediol	Yarrowia lipolytica (YALI17)	Deletion of 10 oxidation genes + ALK1 overexpression	n-Dodecane	3.2 mM	N/R	N/R	[5] [4]
1,12-Dodecanediol	Yarrowia lipolytica (Wild Type)	None	n-Dodecane	0.05 mM	N/R	N/R	[5] [4]
1,4-Butanediol (1,4-BDO)	Engineered E. coli	De novo pathway from succinate	Glucose	18 g/L	N/R	N/R	[5] [34]
1,3-Propanediol (1,3-PDO)	Clostridium beijerinckii	Native pathway	Glucose	26 g/L	N/R	N/R	[5]
1,2-Propanediol (1,2-PDO)	Engineered E. coli AG1	Overexpression of mgs, gldA, fucO	Glucose	4.5 g/L	0.19 g/g	N/R	[34]
Ethylene Glycol (EG)	Engineered E. coli K-12	ΔaldA, ΔsdaA, ΔeutB, ΔeutC; serA\:317, serB, serC, fucO, aao	Glucose	3.1 g/L	0.22 g/g	N/R	[34]

N/R: Not Reported in the cited sources

Table 2: Characteristic Strengths and Limitations of Microbial Chassis for Diol Production

Microbial Host	Inherent Advantages	Characteristic Limitations	Ideal Diol Substrates/Products
Yarrowia lipolytica	• Native high flux of acetyl-CoA and malonyl-CoA [56]• Innate capacity to metabolize hydrophobic substrates (alkanes, fatty acids, glycerol) [5] [26]• Oleaginous (high lipid accumulation) [56]• GRAS (Generally Regarded As Safe) status [75]	• Less extensive genetic toolbox compared to E. coli• Lower transformation efficiency• Longer cultivation times compared to bacteria	• Medium- to long-chain α,ω-diols (C6-C18) [5]• Lipid-derived diols• Glycerol-based sugar alcohols (e.g., erythritol) [26]
Escherichia coli	• Rapid growth and high-density cultivation [34]• Extensive, well-characterized genetic tools [34]• Clear genetic background [34]	• Limited native ability to utilize hydrophobic substrates• Often requires complex heterologous enzyme systems (e.g., CYP450s) for functionalized diols [5]	• Short-chain diols (C3-C5) like 1,3-PDO, 1,4-BDO [34] [76]• Water-soluble platform chemicals from sugars
Saccharomyces cerevisiae	• GRAS status• Robust industrial performer• Eukaryotic protein processing	• Limited precursor pool for malonyl-CoA-derived products• Low tolerance for hydrophobic substrates	• Short-chain diols from sugars

Metabolic Engineering Protocols forY. lipolytica

Protocol: CRISPR-Cas9 Mediated Blocking of Over-Oxidation Pathways inY. lipolytica

Objective: To generate a Y. lipolytica strain (genotype YALI17) with reduced over-oxidation of fatty alcohols and aldehydes to enhance the accumulation of α,ω-diol intermediates [5] [4].

Materials:

• Y. lipolytica Po1g ku70Δ strain (to enhance homologous recombination)
• pCRISPRyl plasmid (Addgene #70007) or similar CRISPR-Cas9 system for Y. lipiphytica
• E. coli DH5α for plasmid propagation

Methodology:

• sgRNA Design and Vector Construction: Design 20 bp guiding sequences targeting the following genes involved in the over-oxidation pathways [5]:
  • Fatty Alcohol Oxidation Genes: FADH, ADH1, ADH2, ADH3, ADH4, ADH5, ADH6, ADH7, ADH8, FAO1
  • Fatty Aldehyde Oxidation Genes: FALDH1, FALDH2, FALDH3, FALDH4
  • Clone these guiding sequences into the pCRISPRyl vector upstream of the sgRNA scaffold. For multiplexed editing, insert multiple sgRNA scaffold sequences downstream of each other, each with its specific guiding sequence.

• Transformation: Transform the constructed CRISPR plasmid into the Y. lipolytica Po1g ku70Δ strain using standard lithium acetate or electroporation protocols.

• Selection and Screening: Select transformants on appropriate auxotrophic or antibiotic selection media. Screen colonies via PCR and sequencing to confirm the successful deletion of all 14 target genes, resulting in the final engineered strain, YALI17 [5].

• Functional Validation: Cultivate the YALI17 strain and its parent in media containing n-dodecane. Quantify the production of the target diol (e.g., 1,12-dodecanediol) and the consumption of the substrate using HPLC or GC-MS to confirm the reduction in over-oxidation activity.

Protocol: Enhancing Alkane Hydroxylation via ALK1 Overexpression

Objective: To increase the flux from alkanes to fatty alcohols in the engineered YALI17 strain by overexpressing the native alkane hydroxylase gene ALK1 [5] [4].

Materials:

• Engineered Y. lipolytica YALI17 strain
• pYl or other suitable Y. lipolytica expression vector
• ALK1 gene (PCR-amplified from Y. lipolytica genome)

Methodology:

• Vector Construction: Amplify the ALK1 gene (or other CYP52 family P450 genes) from the genomic DNA of Y. lipolytica using high-fidelity PCR. Clone the amplified gene into the pYl expression vector under the control of a strong constitutive promoter (e.g., TEF) using methods such as Circular Polymerase Extension Cloning (CPEC) [4].

• Strain Transformation: Introduce the constructed ALK1 overexpression vector into the YALI17 strain.

• Fermentation and Analysis:

  • Inoculate the engineered strain in YPD medium and cultivate for 2 days.
  • Scale up the culture in a 100 mL flask containing 20 mL of synthetic complete medium without leucine, using n-dodecane (e.g., 50 mM) as the primary carbon source [5] [4].
  • Maintain the pH at 6.5, or optimize controlled pH conditions (automated pH-control can significantly boost final titer).
  • Incubate for an additional 2 days with shaking.
  • Analyze diol production using HPLC or GC-MS.

Expected Outcome: The combined strain (YALI17 + ALK1 overexpression) should show a significant increase (e.g., 29-fold relative to wild type) in the production of the target α,ω-diol from n-alkanes [5].

Pathway Engineering and Workflow Visualization

Figure 1: A comparative workflow for engineering diol production in Y. lipolytica versus E. coli, highlighting the distinct substrate preferences and metabolic engineering strategies for each platform.

Figure 2: Metabolic pathway for diol synthesis from alkanes in Y. lipolytica, illustrating the native over-oxidation routes and the key engineering strategy of gene deletion to enhance diol accumulation [5] [4].

The Scientist's Toolkit: Key Research Reagents

Table 3: Essential Research Reagents for Metabolic Engineering of Y. lipolytica for Diol Production

Reagent / Tool Name	Type / Category	Critical Function in Research	Example Use Case
pCRISPRyl	CRISPR-Cas9 Plasmid	Enables precise gene knockouts and integrations in Y. lipolytica.	Used for multiplex knockout of 14 genes (FADH, ADH1-8, FAO1, FALDH1-4) in the over-oxidation pathway [5] [4].
Alkane Hydroxylase Genes (ALK1-12)	Native / Heterologous Genes	Encode cytochrome P450 enzymes (CYP52 family) that catalyze the initial hydroxylation of alkanes to alcohols.	Overexpression of ALK1 to enhance the flux from n-dodecane to the fatty alcohol precursor [5] [4].
n-Dodecane	Hydrophobic Substrate	Serves as a model medium-chain alkane feedstock for diol production.	Used as the primary carbon source in biotransformation assays to evaluate 1,12-dodecanediol production [5] [4].
pYl Expression Vector	Expression Plasmid	A toolkit plasmid for heterologous gene expression in Y. lipolytica.	Used for the constitutive overexpression of ALK1 and other pathway genes [4].
Y. lipolytica PO1f / Po1g Strains	Microbial Chassis	Derivated strains with deleted genes (e.g., ku70Δ) to improve homologous recombination efficiency.	Common starting host strains for metabolic engineering projects, including diol production [5] [75] [26].

The transition from laboratory-scale achievement to industrial-scale production represents a critical phase in microbial biotechnology. For the production of diols using the engineered oleaginous yeast Yarrowia lipolytica, understanding the interplay between metabolic efficiency, fermentation scalability, and associated costs is paramount for commercial viability. This assessment examines the techno-economic landscape of diol production using engineered Y. lipolytica strains, with particular focus on maximizing titer, yield, and productivity while minimizing production costs through substrate selection and process optimization. The analysis is framed within a comprehensive thesis on metabolic engineering of Y. lipolytica for diol production, providing researchers and industrial scientists with validated protocols and scalability considerations for advancing this sustainable manufacturing platform.

Current State of Diol Production in Engineered Y. lipolytica

Production Performance Metrics

Recent metabolic engineering breakthroughs have demonstrated the feasibility of producing medium-chain α,ω-diols directly from alkane substrates in Y. lipolytica. The benchmark achievement showcases the production of 1,12-dodecanediol from n-dodecane, with the engineered strain YALI17 achieving a 29-fold improvement over wild-type strains [5] [4]. The progression of strain engineering and corresponding production enhancements are summarized in Table 1.

Table 1: Performance progression of engineered Y. lipolytica strains for 1,12-dodecanediol production

Strain	Key Genetic Modifications	Production (mM)	Fold Improvement	Culture Conditions
Wild Type	None	0.05	1x	n-dodecane substrate
YALI17	Deletion of 10 alcohol oxidation and 4 aldehyde oxidation genes	0.72	14x	50 mM n-dodecane
YALI17 + ALK1	YALI17 background with ALK1 overexpression	1.45	29x	50 mM n-dodecane
YALI17 + ALK1 + pH control	ALK1 overexpression with optimized pH control	3.20	64x	Automated biotransformation

Techno-economic Implications of Current Metrics

The production metrics achieved to date highlight both the substantial progress and remaining challenges for commercial viability. The highest reported titer of 3.2 mM (approximately 650 mg/L) of 1,12-dodecanediol from n-dodecane represents a significant scientific achievement but remains below the typical threshold for industrial implementation [5] [4]. When compared to established bioprocesses for short-chain diols, such as 26 g/L of 1,3-propanediol in Clostridium beijerinckii or 18 g/L of 1,4-butanediol in engineered E. coli, the productivity gap for medium-chain diols becomes apparent [5]. This disparity underscores the need for further strain optimization and process engineering to achieve economically viable production levels.

Metabolic Engineering Strategies for Improved Economics

Pathway Engineering for Enhanced Carbon Efficiency

The core strategy for improving the economic viability of diol production in Y. lipolytica centers on maximizing carbon conversion efficiency from substrate to product. This involves two primary approaches: (1) preventing loss of carbon through competing pathways, and (2) enhancing flux through the desired diol synthesis pathway.

The most successful implementation of this strategy involved the systematic deletion of genes responsible for the over-oxidation of fatty alcohol intermediates. Specifically, CRISPR-Cas9 was employed to delete ten genes involved in fatty alcohol oxidation (including FADH, ADH1-8, and FAO1) and four genes linked to fatty aldehyde oxidation (FALDH1-4) [5] [4]. This engineering strategy effectively minimized the diversion of alkane substrates to carboxylic acids, thereby increasing the carbon flux toward diol formation.

Table 2: Carbon conservation through oxidation pathway blocking

Pathway Targeted	Genes Deleted	Enzyme Functions	Impact on Diol Production
Fatty alcohol oxidation	FADH, ADH1-8, FAO1	Alcohol dehydrogenases, fatty alcohol oxidase	Prevents oxidation of ω-hydroxy fatty alcohols to aldehydes
Fatty aldehyde oxidation	FALDH1-4	Fatty aldehyde dehydrogenases	Prevents over-oxidation of aldehydes to carboxylic acids
Combined deletion	All 14 genes	Complete blockade of over-oxidation	14-fold increase in diol production

Concurrently, flux through the alkane hydroxylation pathway was enhanced through overexpression of the alkane hydroxylase gene ALK1, which catalyzes the initial oxidation of n-alkanes to fatty alcohols [5] [4]. This push-and-block strategy resulted in the cumulative 29-fold improvement in diol production observed in the YALI17 + ALK1 strain.

Pathway Diagram: Diol Production in Engineered Y. lipolytica

The following diagram illustrates the metabolic pathway engineering strategy for enhanced diol production in Y. lipolytica:

Substrate Selection and Cost Considerations

Alternative Substrate Options

The choice of carbon substrate significantly influences the overall production economics. While the primary research focus has been on n-dodecane as a model alkane substrate, several alternative substrates offer potential cost advantages:

Crude Glycerol: As a byproduct of biodiesel production, crude glycerol represents a low-cost renewable substrate (approximately $0.05-0.20 per kg compared to $1.00-1.50 per kg for purified glucose) [77]. Y. lipolytica naturally metabolizes glycerol efficiently, and engineered strains have demonstrated high productivity of various oleochemicals from this substrate. For diol production, adaptation to glycerol would require substantial pathway engineering but offers significant cost reduction potential.

Hydrocarbon-rich Waste Streams: Industrial waste streams containing mixed alkanes could provide cost-effective alternatives to pure n-dodecane. Y. lipolytica's native capacity to metabolize hydrophobic substrates makes it particularly suited for such complex feedstocks [5].

Food Waste Hydrolysate: Recent demonstrations of food waste valorization for D-lactic acid production in engineered Y. lipolytica highlight the potential for using food waste hydrolysate as a low-cost carbon source [51]. While this would require different pathway engineering for diol production, the cost benefits are substantial, with food waste often available at negative cost (waste disposal fees avoided).

Comparative Substrate Economics

Table 3: Economic assessment of potential carbon substrates for diol production

Substrate	Estimated Cost (USD/kg)	Technical Readiness	Infrastructure Requirements	Scalability
n-Dodecane	$2.50-$3.50	High (lab demonstrated)	Standard bioreactor	Limited by alkane cost
Glucose	$0.40-$0.60	Medium (requires pathway engineering)	Standard bioreactor	High
Crude Glycerol	$0.05-$0.20	Medium (requires pathway engineering)	Glycerol purification may be needed	High
Food Waste Hydrolysate	Negative cost possible	Low (concept demonstrated for other products)	Pretreatment infrastructure	Regional variability

Scalability and Bioprocess Considerations

Fermentation Scale-Up Strategies

Successful transition from laboratory to industrial scale requires careful consideration of several key factors:

Nutrient-Rich Cultivation: Recent advances in engineering Y. lipolytica for lipid production under nutrient-rich conditions demonstrate the potential for overcoming traditional nitrogen limitation requirements. One study achieved record lipid productivity of 2.06 g/L/h under nutrient-rich conditions in a 5-L bioreactor, representing a 2.6-fold increase compared to nitrogen-limited conditions [68]. Implementing similar strategies for diol production could significantly improve volumetric productivity and reduce reactor size requirements.

Oxygen Transfer Optimization: The alkane hydroxylation pathway in Y. lipolytica relies on cytochrome P450 enzymes that have substantial oxygen requirements. At industrial scale, oxygen transfer becomes a critical consideration, with potential strategies including the expression of bacterial hemoglobin (Vhb) from Vitreoscilla stercoraria to enhance oxygen utilization efficiency under oxygen-limited conditions [78].

Fed-Batch Process Development: The implementation of fed-batch processes with controlled substrate feeding has demonstrated remarkable success in improving titers of various products in Y. lipolytica. For example, fed-batch cultivation of engineered strains for erythritol production achieved 58.8 ± 1.68 g/L erythritol from glycerol, significantly higher than batch cultivation [26]. Similar approaches could benefit diol production by maintaining optimal substrate concentrations while minimizing inhibition.

Process Flow Diagram: Integrated Diol Production Bioprocess

The following diagram outlines a scalable integrated bioprocess for diol production:

Experimental Protocols

Strain Construction Protocol: CRISPR-Cas9 Mediated Gene Deletion

Objective: Simultaneous deletion of multiple genes in the fatty alcohol and aldehyde oxidation pathways to create the YALI17 strain background.

Materials:

• Y. lipolytica Po1g ku70Δ strain (wild-type background)
• pCRISPRyl plasmid (Addgene #70007) containing Cas9 and sgRNA scaffold
• Primers for guide RNA design targeting FADH, ADH1-8, FAO1, FALDH1-4
• YPD medium: 20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract
• Synthetic complete medium without leucine

Procedure:

• Design 20 bp guiding sequences specific to each target gene using computational tools to minimize off-target effects.
• Clone guiding sequences into the pCRISPRyl vector using overlapping PCR and Golden Gate assembly.
• Transform the constructed CRISPR plasmid into E. coli DH5α for amplification and verify by sequencing.
• Introduce the verified plasmid into Y. lipolytica Po1g ku70Δ strain via lithium acetate transformation.
• Select transformants on YNB plates without uracil and verify gene deletions by colony PCR and sequencing.
• Streak confirmed mutants on YPD plates to cure the CRISPR plasmid through serial passages.
• Confirm plasmid loss by replica plating on YNB without uracil (plasmid-cured strains will not grow).

Technical Notes: The ku70Δ background enhances homologous recombination efficiency, crucial for successful gene editing. For multiple gene deletions, design sgRNAs with minimal sequence similarity to prevent off-target effects [5] [4].

Biotransformation Protocol for Diol Production

Objective: Production of 1,12-dodecanediol from n-dodecane using engineered YALI17 strains.

Materials:

• Engineered Y. lipolytica strains (YALI series)
• n-Dodecane (50 mM final concentration)
• YPD medium or synthetic complete medium
• 100 mL baffled flasks or bioreactor systems
• pH control system (for optimized conditions)

Procedure:

• Inoculate single colonies of engineered strains in 5 mL YPD medium and culture at 28°C with shaking at 200 rpm for 48 hours.
• Use this pre-culture to inoculate 20 mL of fresh medium in 100 mL baffled flasks to an initial OD600 of 0.5.
• Add n-dodecane to a final concentration of 50 mM (aseptically).
• Incubate at 28°C with shaking at 200 rpm for 96-120 hours.
• For pH-controlled conditions, use bioreactors with automated pH adjustment to maintain optimal pH throughout cultivation.
• Monitor cell growth by measuring OD600 at regular intervals.
• Extract metabolites from culture broth using ethyl acetate (1:1 v/v) with vigorous mixing.
• Analyze diol production using GC-MS or HPLC with appropriate standards.

Analytical Methods:

• GC-MS Analysis: Derivatize samples with BSTFA + 1% TMCS at 70°C for 30 minutes before analysis. Use a DB-5MS column with helium carrier gas. Identify 1,12-dodecanediol by comparison with authentic standards and mass fragmentation patterns.
• HPLC Analysis: Use a C18 reverse-phase column with acetonitrile/water gradient elution and UV detection at 210 nm.

Technical Notes: The hydrophobic nature of n-dodecane requires adequate mixing to ensure proper substrate availability. In bioreactor setups, monitor oxygen levels closely as the hydroxylation reaction is oxygen-dependent [5] [4].

Research Reagent Solutions

Table 4: Essential research reagents for metabolic engineering of Y. lipolytica for diol production

Reagent/Category	Specific Examples	Function/Application	Technical Considerations
Host Strains	Po1g ku70Δ, MTLY50, JMY1212	Engineered background strains with enhanced genetic manipulability	ku70Δ strains improve homologous recombination efficiency; protease-deficient strains improve protein stability [78]
Vector Systems	pCRISPRyl, JMP62 series, pYl	Plasmid backbones for gene expression and CRISPR-Cas9 editing	pCRISPRyl enables efficient genome editing; integration vectors provide stable expression without antibiotic selection [5] [4]
Selection Markers	URA3, LEU2, Hygromycin resistance	Selection of successful transformants	Auxotrophic markers preferred for industrial applications to avoid antibiotic use [78] [51]
Culture Media	YPD, YNB, Synthetic Complete	Strain propagation, transformation, and production	Specific amino acid supplementation required for auxotrophic strains; high C/N ratio promotes lipid accumulation [5] [26]
Substrates	n-Dodecane, glucose, glycerol, crude glycerol	Carbon sources for diol production	Alkane substrates require emulsification; crude glycerol may contain inhibitors requiring adaptation [5] [77]
Analytical Standards	1,12-Dodecanediol, fatty alcohols, fatty acids	Quantification of products and intermediates	Commercial standards essential for accurate quantification; derivative formation may be needed for GC analysis [5] [79]

The techno-economic assessment of diol production in engineered Y. lipolytica reveals both significant progress and substantial challenges. The current production ceiling of 3.2 mM (approximately 650 mg/L) 1,12-dodecanediol from n-dodecane represents a remarkable 64-fold improvement over wild-type strains but remains below typical industrial thresholds of >10 g/L for commodity chemicals. Future research should prioritize several key areas to enhance economic viability:

Pathway Optimization: Further engineering of the hydroxylation system, including electron transfer components and alkane transport mechanisms, could significantly improve conversion efficiency. Additionally, dynamic regulation strategies that balance growth and production phases may enhance overall productivity.

Substrate Flexibility: Expanding substrate range to include low-cost alternatives such as crude glycerol, food waste hydrolysate, or industrial side streams would dramatically improve production economics. This would require substantial pathway engineering but offers the greatest potential for cost reduction.

Process Intensification: Integration of advanced bioreactor designs, in situ product removal techniques, and continuous fermentation strategies could significantly improve volumetric productivity and reduce downstream processing costs.

The protocols and strategies outlined in this assessment provide a foundation for advancing diol production in Y. lipolytica toward commercial implementation. As metabolic engineering tools for this non-conventional yeast continue to mature, the potential for economically viable bioproduction of medium-chain diols from renewable resources becomes increasingly attainable.

Medium- to long-chain α,ω-diols, such as 1,12-dodecanediol, are valuable chemical building blocks widely used in the production of polyesters and polyurethanes [5]. Current industrial production largely relies on fossil-based processes, creating a significant need for sustainable biological alternatives [10]. The oleaginous yeast Yarrowia lipolytica presents a promising platform for diol biosynthesis due to its innate capacity to metabolize hydrophobic substrates like alkanes, offering distinct advantages over bacterial systems such as Escherichia coli [5] [15].

This case study validates the successful development of an engineered Y. lipolytica strain capable of efficient biotransformation of n-dodecane to 1,12-dodecanediol. Through systematic metabolic engineering, researchers achieved a 29-fold increase in diol production over the wild-type strain, establishing the first de novo production route for medium- to long-chain α,ω-diols directly from alkanes in yeast [5].

Results and Data Analysis

Strain Performance and Diol Production

The engineered strains demonstrated significantly enhanced production of 1,12-dodecanediol from n-dodecane compared to the parental strain. Performance data are summarized in the table below.

Table 1: Production of 1,12-dodecanediol from 50 mM n-dodecane by engineered Y. lipolytica strains

Strain/Condition	Genetic Modifications	Production (mM)	Fold Increase vs. Parental Strain
Parental Strain	Wild type	0.05	1x (baseline)
YALI17	Deletion of 10 alcohol oxidation and 4 aldehyde oxidation genes	0.72	14x
YALI17 + ALK1 Overexpression	YALI17 background with ALK1 overexpression	1.45	29x
YALI17 + ALK1 + pH Control	Combined genetic and bioprocess optimization	3.20	64x

The foundational achievement was the construction of strain YALI17, which produced 0.72 mM 1,12-dodecanediol - a 14-fold increase over the parental strain [5]. Further enhancement was achieved by overexpressing the alkane hydroxylase gene ALK1 in the YALI17 background, pushing production to 1.45 mM [5]. Most impressively, implementing automated pH-controlled biotransformation in the optimized strain resulted in a final titer of 3.2 mM 1,12-dodecanediol, representing a 64-fold improvement over the wild-type strain [5].

Comparative Performance in Microbial Diol Production

Table 2: Comparison of microbial production platforms for medium-chain diols

Production System	Substrate	Product	Maximum Titer	Key Features/Limitations
Y. lipolytica YALI17 + ALK1 (this study)	n-dodecane (alkane)	1,12-dodecanediol	3.2 mM	Direct alkane conversion; Engineered oxidation blocking
E. coli and Pseudomonas systems	Fatty acids / alcohols	Medium-chain diols	79-1,400 mg/L	Requires expensive fatty acid feedstocks
E. coli system	12-hydroxydodecanoic acid	1,12-dodecanediol	1.4 g/L	Highest reported titer but from derived fatty acid
Candida tropicalis & E. coli platform	Plant oil-derived alkane	1,12-dodecanediol	68 g/L	Two-organism process; high titer but complex

The engineered Y. lipolytica system represents a significant advancement in substrate simplicity, utilizing inexpensive alkane feedstocks directly, unlike bacterial systems that often require pre-functionalized fatty acid derivatives [5]. While alternative platforms have achieved higher absolute titers, they typically employ more expensive substrates like fatty acids or require multi-organism processes [10] [80].

Experimental Protocols

Strain Construction and Genetic Engineering

CRISPR-Cas9-Mediated Gene Deletions

Principle: Systematic disruption of competing oxidative pathways prevents over-oxidation of fatty alcohol intermediates to carboxylic acids, channeling flux toward diol accumulation [5] [15].

Procedure:

• Design sgRNAs targeting 10 genes involved in fatty alcohol oxidation (FADH, ADH1-8, FAO1) and 4 fatty aldehyde oxidation genes (FALDH1-4)
• Clone sgRNA expression cassettes into a CRISPR-Cas9 plasmid backbone
• Transform Y. lipolytica Po1g ku70Δ host strain using standard protoplast or lithium acetate methods
• Screen transformants via colony PCR and sequencing to verify gene deletions
• Iterate process sequentially to generate the final YALI17 strain with all 14 targeted gene deletions

Key Consideration: The ku70Δ background improves homologous recombination efficiency, enhancing gene editing success rates [5].

Alkane Hydroxylase Overexpression

Principle: Enhancing the initial alkane hydroxylation step increases flux through the entire diol biosynthesis pathway [5].

Procedure:

• Amplify ALK1 gene coding sequence from Y. lipolytica genomic DNA
• Clone into a strong, constitutive promoter (e.g., pTEF) in an appropriate expression vector
• Transform the constructed plasmid into YALI17 strain
• Validate ALK1 expression levels via RT-qPCR or Western blotting

Cultivation and Biotransformation Conditions

Two-Stage Fermentation Process

Principle: Separating growth and production phases optimizes biomass accumulation and diol synthesis independently [42].

Procedure:

• Inoculum Preparation:
  • Grow engineered Y. lipolytica strains in YPD medium (20 g/L glucose, 20 g/L peptone, 10 g/L yeast extract, pH 6.5) for 48 hours
  • Use 250 mL baffled flasks with 50 mL working volume, 30°C, 200 rpm

• Growth Phase:

  • Transfer inoculum to production medium (e.g., YNB without amino acids) with 2% glucose
  • Incubate for 24-48 hours until mid-exponential phase (OD600 ~10-15)

• Production Phase:

  • Add 50 mM n-dodecane as substrate
  • Maintain pH at optimal level (determined experimentally) using automated pH control
  • Continue cultivation for 72-120 hours, sampling periodically for product analysis

Analytical Methods

Product Quantification:

• Extract diols from culture broth using ethyl acetate
• Analyze by GC-MS or HPLC with appropriate standards
• Identify 1,12-dodecanediol by retention time and mass fragmentation pattern

Substrate Consumption:

• Monitor n-dodecane depletion by GC-FID
• Quantify intermediate metabolites (alcohols, acids) to assess pathway flux

Metabolic Engineering Strategy

The engineering strategy focused on two primary objectives: (1) blocking competing oxidative pathways to prevent loss of valuable intermediates, and (2) enhancing the flux from alkane to diol.

Figure 1: Metabolic Engineering Strategy for 1,12-Dodecanediol Production in Y. lipolytica. The pathway shows the conversion of n-alkanes to α,ω-diols, with targeted gene deletions (red) to block competing oxidation pathways and gene overexpression (green) to enhance flux toward the desired product.

Blocking Competing Pathways

The engineered strain YALI17 contains deletions in 14 key genes involved in the oxidation of pathway intermediates [5] [15]:

• Fatty alcohol oxidation: FADH, ADH1-8, FAO1
• Fatty aldehyde oxidation: FALDH1-4

This comprehensive blocking strategy prevents the over-oxidation of the fatty alcohol and aldehyde intermediates to carboxylic acids, which would shunt flux away from diol production in the wild-type strain [5].

Enhancing Alkane Hydroxylation

The ALK1 gene encodes a cytochrome P450 monooxygenase that catalyzes the initial hydroxylation of n-alkanes to terminal alcohols [5]. Overexpression of this enzyme enhances the flux into the diol biosynthesis pathway. Y. lipolytica natively possesses 12 ALK genes (ALK1-12) from the CYP52 family, providing a rich genetic resource for alkane metabolism [5].

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential research reagents for engineering Y. lipolytica for diol production

Reagent/Resource	Function/Application	Examples/Specifications
CRISPR-Cas9 System	Targeted gene deletion and integration	Y. lipolytica-optimized Cas9 codon usage; tRNA-sgRNA fusions improve efficiency [15]
Alkane Substrates	Diol production feedstocks	n-dodecane (C12); purity >99% for biotransformation studies
Engineering Vectors	Heterologous gene expression	Strong constitutive promoters (pTEF, pHP4D); integration plasmids
Culture Media	Strain cultivation and production	YPD (growth); Synthetic Complete (selection); Nitrogen-limited (production) [5]
Analytical Standards	Product quantification	1,12-dodecanediol (GC/HPLC); Intermediate alcohols and acids
P450 Monooxygenases	Alkane hydroxylation	ALK1-12 from Y. lipolytica CYP52 family; Electron transport partners (CPR) [5]

Pathway Engineering and Experimental Workflow

Figure 2: Experimental Workflow for Engineered Diol Production. The sequential process for developing and validating high-performance Y. lipolytica strains for 1,12-dodecanediol production from n-dodecane.

This case study validates the successful metabolic engineering of Yarrowia lipolytica for the production of 1,12-dodecanediol directly from n-dodecane. The integrated strategy combining systematic pathway blocking of competing oxidation reactions with enhanced alkane hydroxylation capacity resulted in a 64-fold improvement in diol titer compared to the wild-type strain [5].

The demonstrated approach establishes Y. lipolytica as a promising cell factory for alkane-based biomanufacturing and provides a framework for the sustainable production of high-value diol precursors. Future work could focus on expanding this platform to produce diols of varying chain lengths from different alkane feedstocks, further optimizing the electron transport systems for P450 enzymes, and implementing dynamic pathway control strategies to balance growth and production phases [10]. This research contributes significantly to the transition from petroleum-based chemical production toward sustainable microbial manufacturing platforms.

Conclusion

Metabolic engineering of Yarrowia lipolytica presents a transformative approach for sustainable diol production, with recent CRISPR-Cas9 advances enabling unprecedented control over alkane conversion pathways. The successful engineering of strains like YALI17, demonstrating 29-fold improvement in 1,12-dodecanediol production, validates the potential of combining pathway blocking, hydroxylase overexpression, and fermentation optimization. Future directions should focus on expanding the diol portfolio through high-throughput engineering, integrating systems biology with machine learning for predictive design, and developing continuous bioprocesses. For biomedical applications, these engineering strategies create pathways to biologically derived diol precursors for polymer-based drug delivery systems, excipients, and specialty pharmaceuticals, ultimately enabling more sustainable and cost-effective manufacturing processes for the pharmaceutical industry.

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