Building a Better Factory: Engineering Genome-Reduced Microbial Chassis for Heterologous Natural Product Production

Abstract

This article provides a comprehensive resource for researchers and drug development professionals on the strategic deletion of native biosynthetic gene clusters (BGCs) to create optimized heterologous production chassis. It covers the foundational rationale for removing competing metabolic pathways, detailed methodologies for chassis construction across diverse bacterial hosts like Streptomyces and Burkholderiales, advanced troubleshooting and optimization techniques to enhance product titers, and rigorous validation frameworks for comparative chassis performance. By synthesizing recent advances, this guide aims to facilitate the efficient discovery and scalable production of novel microbial natural products, directly addressing critical bottlenecks in modern drug discovery pipelines.

The 'Why': Foundational Principles of Native BGC Deletion in Chassis Development

Microbial natural products (NPs) have historically been a cornerstone of modern medicine, providing the foundation for countless therapeutic agents. These compounds are typically biosynthesized by enzymes encoded by biosynthetic gene clusters (BGCs). Genomic sequencing reveals that a typical microbial genome possesses 20-40 BGCs; however, the products of most identifiable BGCs remain undetected under standard laboratory conditions, creating a significant discrepancy between biosynthetic potential and measurable NP output [1]. These inactive clusters are termed silent or cryptic BGCs [1]. Unlocking this "silent majority" represents one of the most promising frontiers for novel drug discovery, particularly as drug resistance becomes an increasingly serious global problem [2].

A critical strategy for accessing these cryptic metabolites is heterologous expression, which involves cloning and expressing BGCs from their native producer into a specialized, tractable host strain [2] [1]. This approach provides a shortcut to pathway modification, metabolic optimization, and yield improvement [2]. A foundational step in developing a potent heterologous production chassis is the deletion of native, non-essential BGCs. This serves to streamline host metabolism, minimize precursor competition, and eliminate the production of native metabolites that can interfere with the detection and isolation of the target compound [3] [4]. This application note details protocols and strategies for creating such chassis and activating silent BGCs, framed within the context of a broader thesis on chassis engineering.

Experimental Protocols: Constructing a Specialized Chassis

Protocol 1: Development of a VersatileStreptomycesChassis

This protocol, adapted from a 2025 study, outlines the creation of Streptomyces aureofaciens Chassis2.0, designed for the efficient production of diverse type II polyketides (T2PKs) [3].

• Objective: To develop a high-yielding Streptomyces chassis by removing competing native BGCs to enhance precursor availability and compatibility for heterologous T2PK production.
• Materials:
  • Native Producer: Streptomyces aureofaciens J1-022, a high-yield chlortetracycline producer [3].
  • Gene Manipulation Tool: ExoCET technology for efficient cloning and assembly of large BGCs [3].
  • Culture Media: Appropriate Streptomyces fermentation media (e.g., TSB, R5).
• Methodology:
  • Chassis Selection: Select a high-yielding industrial strain as a starting host based on its demonstrated capacity for producing the class of molecules of interest. In this case, S. aureofaciens J1-022 was chosen for its high T2PK production [3].
  • BGC Identification: Use genome mining tools (e.g., antiSMASH) to identify the native T2PK BGCs within the J1-022 genome [3].
  • In-Frame Deletion: Execute precise, in-frame deletions of two endogenous T2PK gene clusters. This step is crucial to mitigate competition for intracellular precursors such as malonyl-CoA [3].
  • Phenotypic Validation: Confirm the successful deletion by observing a "pigmented-faded" phenotype in the resulting host, designated Chassis2.0, indicating the cessation of native pigment production [3].
  • Heterologous Expression: Clone the target BGC (e.g., the oxytetracycline cluster from S. rimosus) into an E. coli-Streptomyces shuttle plasmid using ExoCET and introduce it into Chassis2.0 for production [3].
• Key Outcomes: The resulting Chassis2.0 demonstrated a 370% increase in oxytetracycline production compared to conventional commercial strains and successfully produced tri-ring T2PKs (e.g., actinorhodin) and activated a previously silent pentangular T2PK BGC, leading to the discovery of a novel compound, TLN-1 [3].

Protocol 2: Activation of Silent BGCs via Reporter-Guided Mutant Selection (RGMS)

RGMS is a powerful endogenous strategy for activating silent BGCs in their native host, combining classical genetics with modern detection methods [1].

• Objective: To generate and screen mutant libraries for strains that express a targeted silent BGC.
• Materials:
  • Reporter Construct: A plasmid containing a promoterless fluorescent protein gene (e.g., GFP) or a selectable marker gene (e.g., antibiotic resistance).
  • Mutagenesis Tool: UV light or a transposon (Tn) system for random mutagenesis.
  • Analytical Tools: HPLC-MS and/or imaging mass spectrometry (IMS) for metabolite detection.
• Methodology:
  • Reporter Integration: Fuse the promoter of the target silent BGC to a reporter gene (e.g., gfp or neo for kanamycin resistance) and integrate this construct into the native host's genome [1].
  • Mutant Library Generation: Create a library of random mutants using either:
    • UV Mutagenesis: Exposing the cell population to UV radiation.
    • Transposon Mutagenesis: Using a Tn vector to create random insertions in the genome [1].
  • Mutant Selection: Screen the mutant library for activation of the silent BGC using one of two methods:
    • Reporter-Based Selection: Isolate mutants that exhibit strong fluorescence or increased antibiotic resistance, indicating upregulation of the BGC promoter [1].
    • Metabolomics-Based Selection: Subject the mutant library to HPLC-MS analysis. Use data analytics (e.g., self-organizing maps) to identify mutants with unique metabolite profiles indicative of BGC activation [1].
  • Hit Validation: Ferment the selected mutant strains and use advanced analytical chemistry (e.g., IMS, NMR) to isolate and characterize the newly produced cryptic metabolite.

The following workflow diagram illustrates the key decision points in the RGMS process.

Data Presentation: Quantitative Analysis of Host Performance

Heterologous Host Comparison for BGC Expression

Table 1: Comparative analysis of bacterial strains developed as heterologous hosts for natural product BGC expression.

Heterologous Host	Genome Modifications	DNA Transfer Method	Biosynthetic Range Tested	Best Titer Achieved	Virulence
Streptomyces aureofaciens Chassis2.0 [3]	Deletion of two endogenous T2PK clusters	Conjugation	Type II PKS (Tetracyclines, Angucyclines)	370% increase in oxytetracycline vs. commercial strain	Laboratory strain
Burkholderia thailandensis E264 [2]	Δthailandepsin, Δefflux pumps	Conjugation, Electroporation	Polyketides (PKs), Non-Ribosomal Peptides (NRPs)	985 mg/L (FK228 derivative)	Low virulence to humans/animals
Burkholderia gladioli ATCC 10248 [2]	Δgladiolin BGC	Conjugation, Electroporation	NRPs, PK-NRPs (hybrid compounds)	Not Reported	Plant pathogen
Escherichia coli MDS-205 [4]	Reduced genome (14.3% deleted), thrA*BC operon, Δtdh, ΔtdcC, ΔsstT, rhtA23	Electroporation	Primary metabolites (L-Threonine)	~83% increase vs. engineered wild-type	Laboratory strain

Strategies for Activating Silent Biosynthetic Gene Clusters

Table 2: Summary of primary methodologies for accessing the products of silent or cryptic biosynthetic gene clusters. [1]

Method Category	Description	Key Techniques	Advantages	Limitations
Endogenous: Classical Genetics	Manipulating the native host's genome to activate silent BGCs.	Reporter-Guided Mutant Selection (RGMS), Targeted gene knockouts.	Physiologically relevant; reveals native regulation.	Limited to culturable hosts; can be labor-intensive.
Endogenous: Chemical Genetics	Using small molecules to perturb cellular regulation.	Co-culture, Enzyme inhibitors, Elicitors.	Non-genetic; can induce multiple BGCs simultaneously.	Effects can be pleiotropic and difficult to deconvolute.
Endogenous: Culture Modalities	Altering physical and chemical growth conditions.	OSMAC (One Strain Many Compounds), variation in media, aeration, temperature.	Simple and low-cost; high-throughput potential.	Often unpredictable and inefficient for specific BGCs.
Exogenous: Heterologous Expression	Expressing the BGC in a foreign host.	BGC cloning in Streptomyces, Burkholderia, or E. coli.	Bypasses native regulation; simplifies engineering.	Technically challenging for large BGCs; host compatibility issues.

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key reagents, tools, and strains used in heterologous expression and silent BGC activation.

Item Name	Function/Application	Specific Examples
ExoCET Technology [3]	Facilitates the cloning and assembly of very large DNA fragments, such as entire BGCs, into shuttle vectors.	Used to clone the complete oxytetracycline BGC for heterologous expression.
Ï•C31 Integrative Vectors [2]	A system for stable genomic integration of BGCs into the chromosome of actinobacterial hosts like Streptomyces or Burkholderia.	Enables stable maintenance and expression of large BGCs without plasmid loss.
antiSMASH Software [2] [1]	A bioinformatics platform for the genome-wide identification, annotation, and analysis of BGCs in microbial genomes.	Critical for the initial in-silico discovery of silent BGCs and for guiding chassis engineering.
Transposon Mutagenesis System [1]	A genetic tool for creating random insertional mutations in a genome, used for forward genetics screens like RGMS.	Used in Burkholderia spp. to identify regulatory genes that repress silent BGCs.
Constitutive & Inducible Promoters [2]	Genetic parts to drive the expression of BGC genes in heterologous hosts, independent of native regulation.	Examples: Constitutive Pgenta; Inducible araC/PBAD (L-arabinose) and rhaRS/PrhaB (L-rhamnose).
Reduced-Genome Chassis [4]	Host strains with non-essential genes removed to reduce metabolic burden and improve precursor flux for production.	E. coli MDS42; Streptomyces Chassis2.0.
Pancreatin	Pancreatin, CAS:8249-47-6, MF:C15H11N3O	Chemical Reagent
1,2-Diethylbenzene	1,2-Diethylbenzene, CAS:25340-17-4, MF:C10H14	Chemical Reagent

Integrated Strategy: From Genome Mining to Compound Discovery

Successfully addressing the problem of silent BGCs requires an integrated, multi-faceted approach. The initial step involves comprehensive genome mining using tools like antiSMASH to identify all potential BGCs within a strain of interest [1]. Following identification, researchers must choose between endogenous and exogenous activation strategies, a critical decision that depends on the tractability of the native host and the characteristics of the BGC itself [1].

For exogenous expression, the selection and optimization of a heterologous host is paramount. As demonstrated with Streptomyces Chassis2.0, this involves selecting a host with high native production capacity and then refining it by deleting competing native BGCs to create a clean, metabolically efficient background [3]. The choice of host should also consider phylogenetic proximity to the BGC's source organism to improve the likelihood of successful expression, as seen with the development of various Burkholderia hosts for expressing BGCs from the Burkholderiales order [2].

The logical flow from target selection to final compound discovery is summarized in the following diagram.

In the construction of microbial cell factories for heterologous production, metabolic burden is a critical challenge that arises from the rewiring of native metabolism. Defined as the impact of genetic manipulation and environmental perturbations on cellular resource distribution, this burden manifests as impaired cell growth, reduced fitness, and suboptimal product yields [5]. This is particularly relevant in the context of a research thesis focused on deleting native biosynthetic gene clusters (BGCs) to develop specialized heterologous production chassis. When a host organism is engineered to produce non-native compounds, competition for essential precursors, energy (ATP), and redox cofactors (NAD(P)H) between native and heterologous pathways creates substantial physiological stress [6] [7]. Effectively managing this burden is therefore paramount for developing robust and economically viable bioproduction platforms.

Key Concepts and Strategic Approaches

Fundamental Principles of Burden Mitigation

Metabolic burden originates from multiple sources during heterologous pathway expression. The core issue revolves around resource competition, where the introduced genetic elements and metabolic processes consume cellular resources that would otherwise support host growth and maintenance [5]. This includes the energetic cost of maintaining and replicating recombinant DNA, the metabolic drain of expressing heterologous enzymes, and the physical burden of pathway intermediates and final products [6].

Strategic approaches to alleviate this burden focus on rebalancing cellular metabolism through several key mechanisms:

• Precursor and Cofactor Balancing: Ensuring adequate supply of critical precursors like erythrose-4-phosphate (E4P) and phosphoenolpyruvate (PEP), while balancing redox cofactor regeneration [8].
• Dynamic Metabolic Control: Implementing regulatory systems that decouple growth and production phases to minimize fitness costs [5].
• Pathway Modularization: Optimizing different pathway modules independently before reintegrating them for balanced flux [9].
• Genome Reduction: Deleting non-essential native BGCs and competing pathways to free up cellular resources and precursors [10].

Quantitative Impact of Metabolic Burden

Table 1: Documented Effects of Metabolic Burden and Intervention Outcomes

Host Organism	Engineering Intervention	Impact on Metabolic Burden/Fitness	Production Outcome	Source
Staphylococcus aureus RN4220	Acquisition of heterologous MP1 bacteriocin BGC	Immediate production but reduced growth rates (fitness cost)	3-fold lower MP1 production compared to native producer	[6]
Staphylococcus aureus RN4220 (Adapted)	Prolonged cultivation; TCA cycle mutations	Enhanced metabolic fitness; relieved growth defect	Increased MP1 production levels	[6]
Streptomyces explomaris	Deletion of transcriptional repressors nybW and nybX	Relief of cluster repression	Increased nybomycin production	[8]
Streptomyces explomaris NYB-3B	Overexpression of precursor genes (zwf2, nybF)	Improved precursor and cofactor supply	5-fold increase in nybomycin titer (57 mg L⁻¹)	[8]
Various Streptomyces sp.	CRISPR-Cas9-BD mediated multiplexed editing	Reduced cytotoxicity from off-target cleavage	Improved secondary metabolite production	[10]

Experimental Protocols for Burden Assessment and Mitigation

Protocol 1: Transcriptomic Analysis for Identifying Bottlenecks

This protocol identifies transcriptional bottlenecks in a heterologous host using Streptomyces explomaris with a heterologous nybomycin BGC as a model [8].

Materials:

• RNA stabilization reagent (e.g., RNAlater)
• Cell disruption system (e.g., bead beater)
• RNA extraction and purification kit
• DNase I, RNase-free
• cDNA synthesis kit
• RNA-seq library preparation kit
• Next-generation sequencing platform

Procedure:

• Cultivation and Sampling: Inoculate the production strain in an appropriate medium. Collect cell pellets from multiple time points covering growth (e.g., 17h) and production phases (e.g., 36h, 75h, 175h). Immediately stabilize RNA using RNAlater [8].
• RNA Extraction: Disrupt cells mechanically. Purify total RNA, ensuring removal of genomic DNA with DNase I treatment. Assess RNA integrity (RIN > 8.0 recommended).
• Library Preparation and Sequencing: Deplete ribosomal RNA. Synthesize cDNA and prepare sequencing libraries. Sequence on an Illumina platform to generate a minimum of 20 million paired-end reads per sample.
• Data Analysis: Map reads to the host genome and heterologous BGC. Identify differentially expressed genes (adjusted p-value ≤ 0.05, fold change ≥ 2) comparing production time points to the growth phase reference.
• Bottleneck Identification: Focus analysis on:
  • Downregulation of key genes in precursor-supplying pathways (e.g., Pentose Phosphate Pathway, Shikimate pathway).
  • Repression of heterologous BGC genes.
  • Upregulation of stress response genes.

Expected Outcome: Identification of repressed metabolic steps and precursor limitations, such as the observed downregulation of zwf2 (glucose-6-phosphate dehydrogenase) limiting E4P and NADPH supply [8].

Protocol 2: CRISPR-Cas9-Mediated BGC Deletion in Streptomyces

This protocol details the use of an engineered, low-cytotoxicity Cas9-BD system for efficient deletion of native BGCs in high-GC Streptomyces to free resources [10].

Materials:

• pCRISPomyces-2BD plasmid (or similar with Cas9-BD)
• E. coli ET12567/pUZ8002 as a donor strain for conjugation
• Target Streptomyces strain
• sgRNA design software
• Oligonucleotides for sgRNA template construction
• Apramycin antibiotic
• TES buffer (for protoplast transformation, if needed)

Procedure:

• sgRNA Design and Cloning: Design two sgRNAs targeting the flanking regions of the native BGC to be deleted. Clone these sgRNA expression cassettes into the pCRISPomyces-2BD plasmid [10].
• Donor Strain Preparation: Transform the constructed plasmid into the E. coli donor strain.
• Conjugation: Mix the E. coli donor with Streptomyces spores or mycelia. Plate on conjugation medium and incubate at 30°C for ~16 hours. Overlay with apramycin to select for exconjugants.
• Mutant Screening: Incubate plates until exconjugant colonies appear (typically 3-5 days). Screen for successful deletion via PCR across the deletion junction.
• Curing the Plasmid: Passage positive clones without antibiotic selection to lose the CRISPR plasmid.

Expected Outcome: Efficient deletion of target native BGC with significantly reduced off-target effects and cytotoxicity compared to wild-type Cas9, leading to a cleaner chassis background [10].

Visualizing Metabolic Engineering Workflows and Pathways

Logical Workflow for Building a Minimal-Chassis

This diagram visualizes the core strategy of using native BGC deletion to create a minimal-chassis for heterologous production.

Central Metabolism and Precursor Channeling

This diagram illustrates key metabolic pathways and precursors that become bottlenecks during heterologous production, and targets for engineering.

The Scientist's Toolkit: Essential Research Reagents

Table 2: Key Reagents and Tools for Metabolic Burden Research

Reagent/Tool Name	Function/Application	Example Use Case	Citation
CRISPR-Cas9-BD System	Genome editing with reduced off-target cleavage and cytotoxicity in high-GC hosts.	Efficient deletion of native BGCs in Streptomyces without detrimental fitness effects.	[10]
RNA-seq	Genome-wide transcriptional profiling to identify repression and bottleneck genes.	Identifying downregulated PPP and Shikimate pathway genes in S. explomaris.	[8]
Plasmid pD4-19 (MP1 BGC)	Model mobile genetic element carrying a bacteriocin gene cluster.	Studying fitness costs and metabolic adaptation post-BGC acquisition in S. aureus.	[6]
ESM-2 (Protein LLM)	Unsupervised machine learning model to predict functional protein variants.	Designing high-quality, diverse mutant libraries for enzyme engineering in autonomous workflows.	[11]
iBioFAB	Fully automated biofoundry for integrated Design-Build-Test-Learn (DBTL) cycles.	Enabling high-throughput, autonomous strain engineering to optimize complex traits.	[11]
Zinc Phosphate	Zinc Phosphate, CAS:14485-28-0, MF:F-Li	Chemical Reagent	Bench Chemicals
Pectinase	Pectinase, CAS:9032-75-1, MF:C18H37N(CH3)2	Chemical Reagent	Bench Chemicals

In the field of heterologous production chassis research, a primary obstacle to the efficient discovery and high-yield production of novel natural products (NPs) is the complex native metabolome of host organisms. This inherent metabolic background creates significant analytical interference, complicating the detection, purification, and characterization of target compounds encoded by introduced biosynthetic gene clusters (BGCs) [12]. Metabolic background interference arises from the host's endogenous secondary metabolites, which can mask the production of desired compounds, co-elute during chromatography, and consume essential biosynthetic precursors, thereby reducing pathway flux and final titers [13] [3].

The strategic deletion of native BGCs is therefore a cornerstone of chassis engineering. This process creates a cleaner metabolic background, which minimizes host-derived metabolites, streamlines downstream analytical processes, and redirects cellular resources toward the heterologous pathways of interest [13] [12]. This application note details the principles and protocols for creating such optimized microbial chassis, framed within the broader thesis that engineered minimal-background hosts are indispensable for unlocking the vast potential of cryptic BGCs discovered through modern genome mining.

The Rationale for a Clean Metabolic Background

Overcoming Analytical Challenges

The presence of native metabolites can severely hinder the detection of new compounds, especially those produced at low titers from silent or cryptic BGCs. A simplified metabolite profile facilitates the use of mass spectrometry and NMR for structural elucidation, increasing the sensitivity and reliability of detection methods [12].

Enhancing Metabolic Efficiency

Native BGCs compete with introduced pathways for essential intracellular precursors, such as acetyl-CoA, malonyl-CoA, and amino acids. By removing these competing pathways, the cellular metabolism can be rewired to prioritize the production of the target heterologous product, often leading to substantial yield improvements [9] [3]. Furthermore, eliminating native pigments or compounds with antimicrobial activity can improve host fitness and fermentation performance [3].

Established and Emerging Microbial Chassis

The selection of an appropriate host is the first critical step. Ideal chassis are genetically tractable, have a rapid growth cycle, and are capable of supplying the necessary precursors for the target class of compound. The table below summarizes several engineered chassis strains with deleted native BGCs, highlighting their cleaned metabolic backgrounds.

Table 1: Engineered Microbial Chassis with Minimal Metabolic Background

Chassis Strain	Parent Strain	Genetic Modifications	Key Advantages	Reported Applications
S. coelicolor A3(2)-2023 [13]	S. coelicolor A3(2)	Deletion of four endogenous BGCs; introduction of multiple RMCE sites.	Defined metabolic background; high conjugation efficiency; supports multi-copy BGC integration.	Heterologous expression of xiamenmycin and griseorhodin BGCs [13].
S. coelicolor M1152 [14]	S. coelicolor M145	Deletion of four endogenous BGCs (act, red, ced, CDA).	Well-characterized; optimized for expression of secondary metabolites; visual pigment background removed.	Refactoring and expression of the actinorhodin BGC and its mutants [14].
S. aureofaciens Chassis2.0 [3]	S. aureofaciens J1-022	In-frame deletion of two endogenous T2PKs gene clusters.	High-yield T2PKs producer; superior precursor supply; efficient for tri-, tetra-, and penta-ring T2PKs.	Overproduction of oxytetracycline; synthesis of actinorhodin and discovery of TLN-1 [3].

Detailed Experimental Protocol: BGC Deletion and Chassis Validation

This protocol outlines the creation of a cleaned-background Streptomyces chassis using homologous recombination, a widely applicable method for precise genetic manipulation.

Stage 1: Target Identification and Vector Construction

• Bioinformatic Analysis: Identify native BGCs for deletion using genome mining tools (e.g., antiSMASH [13] [12]). Prioritize clusters that produce known pigments, antibiotics, or those that compete for key precursors.
• Design Deletion Construct: Amplify approximately 1.5 - 2 kb DNA fragments corresponding to the upstream (UP) and downstream (DOWN) flanking regions of the target BGC from the host genome.
• Cloning: Assemble the UP and DOWN fragments into a suicide vector (e.g., pKC1139 [3]) that contains a temperature-sensitive origin of replication and an antibiotic resistance marker (e.g., apramycin) via restriction enzyme digestion and ligation or using advanced assembly techniques like Golden Gate Assembly [14].

Stage 2: Conjugation and Mutant Selection

• Conjugation: Introduce the constructed suicide vector into the target Streptomyces strain via intergeneric conjugation with E. coli ET12567 (pUZ8002) [13]. Plate the conjugation mixture on appropriate media and incubate at 30°C for 24-48 hours to allow for spore formation.
• Selection of Single-Crossover Integrants: Overlay the plates with apramycin and nalidixic acid (to counter-select against the E. coli donor). Incubate until exconjugants appear. These colonies represent strains where the plasmid has integrated into the chromosome via homologous recombination (single-crossover event).
• Selection of Double-Crossover Mutants: Streak single-crossover integrants onto fresh plates without antibiotics and incubate at a non-permissive temperature (e.g., 37-39°C) to force the loss of the temperature-sensitive plasmid. Screen resulting colonies for apramycin sensitivity, indicating a second crossover event and the excision of the plasmid.
• Genotype Verification: Confirm the intended deletion in apramycin-sensitive colonies using PCR with verification primers that bind outside the engineered homology arms. Sequence the PCR product to ensure precise deletion.

Stage 3: Phenotypic and Metabolomic Validation

• Metabolomic Profiling: Analyze the metabolome of the engineered chassis strain and the wild-type parent using LC-MS/MS. Compare the chromatograms to confirm the absence of metabolites previously associated with the deleted BGCs.
• Fermentation and Analysis: Ferment the validated chassis strain in a suitable production medium (e.g., GYM or M1 medium [13]). Extract the culture broth and mycelia with organic solvents (e.g., ethyl acetate). Analyze the extracts to establish a new, simplified metabolic baseline.

The following diagram illustrates the logical workflow from chassis design to validation:

Diagram 1: Workflow for creating a clean metabolic background chassis.

The Scientist's Toolkit: Research Reagent Solutions

Successful chassis development relies on a suite of specialized genetic tools and reagents. The following table details essential components for these experiments.

Table 2: Key Research Reagents for Chassis Engineering

Reagent / Tool	Function / Principle	Specific Example(s)
Suicide Vectors	Plasmid that cannot replicate in the host; forces integration into chromosome for gene replacement.	pKC1139 (temperature-sensitive, apramycinᵁ) [3].
Conjugative E. coli Strain	Donor strain capable of mobilizing plasmid DNA into actinomycetes via conjugation.	E. coli ET12567 (pUZ8002) [13]; improved strains in Micro-HEP platform [13].
Recombineering Systems	Enables precise, PCR-based genetic modifications using short homology arms in E. coli.	Rhamnose-inducible Redα/Redβ/Redγ system [13].
RMCE Systems	Allows precise, marker-less exchange of large DNA cassettes at specific chromosomal sites.	Cre-loxP, Vika-vox, Dre-rox, φBT1-attP [13].
Assembly Techniques	High-efficiency, scarless assembly of multiple DNA fragments for vector or pathway construction.	Golden Gate Assembly (BsaI, PaqCI) [14]; ExoCET [3].
Genome Mining Software	In silico identification of BGCs in microbial genomes to prioritize deletion targets.	antiSMASH [13] [12].
Byk-A 501		BYK-A 501 is a silicone-free air release additive for epoxy, polyurethane, and polyester resins. Ideal for gel coats and casting. For Research Use Only (RUO).
2,4-Hexadiene	2,4-Hexadiene, CAS:5194-51-4, MF:C6H10	Chemical Reagent

Concluding Remarks

The creation of microbial chassis with minimized metabolic backgrounds is a transformative strategy in metabolic engineering and natural product discovery. By systematically removing native BGCs, researchers can construct specialized cell factories that not only reduce analytical noise but also enhance the titers of valuable heterologous products. The protocols and tools outlined here provide a roadmap for developing such chassis, directly contributing to the acceleration of genome-driven drug discovery. As synthetic biology tools continue to advance, the precision and efficiency of chassis engineering will only increase, further solidifying its role as a foundational element of modern biotechnology.

The declining pace of novel natural product (NP) discovery, coupled with the rising crisis of antimicrobial resistance, has necessitated a paradigm shift in biodiscovery strategies. A vast majority of microbial biosynthetic gene clusters (BGCs) remain silent under standard laboratory conditions or are housed in uncultivable or genetically intractable organisms [15] [16] [17]. Heterologous expression—the process of transferring and expressing BGCs in a surrogate host—has emerged as a powerful solution to this impasse. This approach bypasses the need to cultivate the native producer and allows for the refactoring of BGCs for optimal expression [18] [17]. A critical and foundational decision in this process is the selection and engineering of an appropriate chassis strain. The core principle underpinning modern chassis development is genome reduction, wherein native BGCs are deleted to minimize metabolic competition, eliminate background interference, and channel precursors toward the heterologously expressed pathway of interest [3] [19] [20]. This application note details the construction and utilization of such engineered chassis for the efficient production of NPs from inaccessible species.

Chassis Development and Performance Metrics

The strategic deletion of native biosynthetic gene clusters is a cornerstone of chassis engineering. This process serves to streamline the host's metabolism, reduce the complexity of the metabolite background for easier detection of target compounds, and prevent the diversion of essential precursors like acyl-CoAs and amino acids. The following case studies exemplify the successful application of this strategy across different bacterial taxa.

Table 1: Engineered Chassis Strains for Heterologous Expression

Chassis Strain	Parental Strain	Key Genetic Modifications	Primary Advantages	Validated Compounds Produced
S. aureofaciens Chassis2.0 [3]	S. aureofaciens J1-022	In-frame deletion of two endogenous type II PKS clusters	370% increase in oxytetracycline yield; efficient production of tri-, tetra-, and penta-ring type II polyketides	Oxytetracycline, Actinorhodin, Flavokermesic acid, TLN-1
Streptomyces sp. A4420 CH [20]	Streptomyces sp. A4420	Deletion of 9 endogenous polyketide BGCs	Superior sporulation and growth; outperformed standard hosts in polyketide production	Glycosylated macrolide, Glycosylated polyene macrolactam, Heterodimeric aromatic polyketide
S. brevitalea DT/DC Series [19]	S. brevitalea DSM 7029	Deletion of endogenous NRPS/PKS BGCs & nonessential regions (prophages, transposases)	Alleviated cell autolysis; improved growth; high-yield production of proteobacterial NPs	Epothilone, Vioprolide, Rhizomide, Chitinimides

Workflow for Chassis Engineering and BGC Expression

The following diagram illustrates the generalized pipeline for developing an engineered chassis and using it for heterologous natural product discovery.

Experimental Protocols

Protocol 1: Construction of a Genome-Reduced Streptomyces Chassis

This protocol outlines the key steps for creating a metabolically simplified Streptomyces chassis, based on methodologies successfully employed for Streptomyces sp. A4420 CH and S. aureofaciens Chassis2.0 [3] [20].

• Genome Sequencing and In silico Analysis:

  • Ispute high-quality genomic DNA from the selected parental strain.
  • Perform whole-genome sequencing using a hybrid approach (e.g., Illumina for accuracy, Oxford Nanopore for scaffolding).
  • Assemble the genome and annotate it using standard tools.
  • Analyze the assembled genome using the antiSMASH software [20] [21] to identify all native biosynthetic gene clusters (BGCs). Note: The antiSMASH web application is freely accessible.

• Selection of BGCs for Deletion:

  • Prioritize large BGCs, particularly those encoding polyketide synthases (PKS) and nonribosomal peptide synthetases (NRPS), which are major consumers of biosynthetic precursors.
  • BGCs known to be expressed under laboratory conditions should be high-priority targets to clean the metabolic background.

• Design of Deletion Constructs:

  • For each target BGC, design a deletion construct that will remove the core biosynthetic genes (e.g., PKS/NRPS genes) while potentially leaving precursor biosynthesis genes intact.
  • The construct should consist of an antibiotic resistance marker (e.g., apramycin) flanked by ~2 kb homology arms upstream and downstream of the target deletion region.
  • Flank the resistance marker with loxP or lox71/66 sites to enable subsequent marker excision.

• Genetic Transformation and Mutant Selection:

  • Introduce the deletion construct into the parent strain via protoplast transformation or intergeneric conjugation from E. coli [16].
  • Select for single-crossover integrants on apramycin-containing media.
  • Under non-selective conditions, screen for double-crossover mutants that have lost the vector backbone, resulting in the replacement of the target BGC with the resistance marker.

• Marker Excision and Iteration:

  • Introduce a Cre recombinase plasmid into the mutant strain to catalyze recombination between the loxP sites, excising the antibiotic marker.
  • The resulting strain is marker-free and ready for the next round of BGC deletion.
  • Repeat steps 3-5 iteratively until all desired BGCs are removed.

• Validation of the Engineered Chassis:

  • Verify all deletions by PCR amplification across the new genomic junctions.
  • Optionally, use metabolomic analysis (e.g., LC-MS) to confirm the disappearance of native compounds and a cleaner metabolic profile.
  • Assess growth characteristics to ensure the engineering process has not impaired fitness.

Protocol 2: Heterologous Expression of a BGC in an Engineered Chassis

This protocol describes the process of cloning and expressing a target BGC in the engineered chassis, leveraging modern assembly techniques [14] [17].

• BGC Cloning:

  • Isolation of Target BGC: Obtain the target BGC from the donor organism's genomic DNA. For large and complex BGCs, use direct cloning methods like Transformation-Associated Recombination (TAR) in yeast or Cas9-Assisted Targeting of CHromosome segments (CATCH) [19] [16].
  • De novo Assembly: For refactoring or clusters from unculturable sources, use synthetic, bottom-up assembly. The Golden Gate Assembly (GGA) strategy is highly efficient [14].
    • Domesticate the BGC sequence by removing internal restriction sites (e.g., for BsaI and PaqCI) via silent mutations.
    • Assemble the cluster hierarchically from ~2 kb fragments into a shuttle vector (e.g., pPAP-series) in a two-step GGA process, achieving near 100% efficiency for complex assemblies [14].

• BGC Refactoring (Optional but Recommended for Silent BGCs):

  • Replace native promoters of the BGC operons with strong, constitutive synthetic promoters.
  • Libraries of orthogonal promoters and ribosomal binding sites (RBS) can be used to tune the expression of individual genes for optimal flux [17].

• Introduction into the Chassis:

  • Transfer the assembled BGC construct into the engineered chassis strain via intergeneric conjugation from an E. coli donor strain (e.g., ET12567/pUZ8002) [16] [20].
  • Select for exconjugants on agar plates containing the appropriate antibiotic (e.g., apramycin) and antibiotics to counter-select against the E. coli donor.

• Fermentation and Metabolite Analysis:

  • Inoculate the positive exconjugants into liquid media suitable for secondary metabolite production.
  • Ferment at the optimal temperature and duration for the chassis strain (e.g., 2-7 days for most Streptomyces).
  • Extract the culture broth and mycelia with a suitable organic solvent (e.g., ethyl acetate, butanol).
  • Analyze the extracts using LC-HRMS and compare the chromatograms to those of the empty chassis control to identify new peaks corresponding to the heterologously produced compound(s).
  • Use analytical techniques like NMR for structural elucidation of the novel compound.

The Scientist's Toolkit: Essential Research Reagents

Table 2: Key Reagents for Chassis Development and BGC Expression

Reagent / Tool	Function / Description	Example Use Case
antiSMASH [21]	In silico identification and analysis of BGCs in a genome sequence.	First-step analysis to select native BGCs for deletion in a potential chassis strain.
Golden Gate Assembly (GGA) [14]	A modular, one-pot DNA assembly method using Type IIS restriction enzymes.	High-efficiency, hierarchical assembly of large, refactored BGCs with 100% success rate reported for a 23 kb cluster.
pCAP03 Vector [15]	Capture vector for cloning large DNA fragments from genomic DNA.	Capturing and integrating putative BGCs (e.g., the siderochelin cluster) into a model host like S. coelicolor.
Cre-loxP System [19]	Site-specific recombination system for marker excision.	Recycling antibiotic resistance markers after each round of BGC deletion in chassis construction.
Redαβ Recombineering [19]	A system for efficient markerless genetic engineering in proteobacteria.	Construction of genome-reduced mutants of S. brevitalea by deleting large genomic regions.
S. coelicolor M1152/M1154 [14] [15]	Model engineered Streptomyces hosts with four native BGCs deleted and ribosomal mutations.	Benchmark strains for testing heterologous BGC expression and activity.
Orthogonal Promoter Libraries [17]	Synthetic promoter sets with randomized sequences for minimized cross-talk.	Refactoring silent BGCs by replacing native promoters to ensure strong, coordinated expression in the heterologous host.
AMBERLITE XAD-16	Amberlite XAD-16 Polymeric Adsorbent|RUO
hemoglobin Torino	Hemoglobin Torino	Research-grade Hemoglobin Torino, an unstable variant causing hemolytic anemia. For Research Use Only. Not for human or veterinary diagnosis or therapy.

Concluding Remarks

The strategic engineering of specialized chassis strains through targeted genome reduction represents a transformative approach for unlocking the vast chemical potential encoded in unculturable and challenging microbes. The case studies of S. aureofaciens Chassis2.0, Streptomyces sp. A4420 CH, and the S. brevitalea DT/DC series demonstrate that deleting native BGCs is a highly effective method to create streamlined hosts with enhanced capabilities for heterologous production [3] [19] [20]. As cloning and DNA synthesis technologies continue to advance, the development of a diverse and well-characterized panel of chassis strains will be crucial for accelerating the discovery of the next generation of medically relevant natural products.

The 'How': A Step-by-Step Guide to Constructing Your Genome-Reduced Chassis

The heterologous expression of biosynthetic gene clusters (BGCs) has become a cornerstone strategy for discovering new natural products (NPs) and elucidating their biosynthetic pathways. This approach is particularly valuable for accessing the metabolic potential of unculturable organisms, poorly expressed silent clusters, or genetically intractable strains. A critical determinant of success in these endeavors is the selection of an appropriate heterologous host. The ideal host provides a compatible physiological and genetic background that supports the expression, folding, and post-translational modification of heterologous enzymes, as well as the metabolic precursors required for biosynthesis. This application note details the criteria for selecting heterologous hosts, ranging from the well-established Streptomyces models to specialized Gram-negative systems, providing a structured framework for researchers to engineer optimal production chassis. The content is framed within the context of a broader thesis on creating specialized heterologous production chassis, often involving the deletion of native BGCs to minimize background interference and redirect metabolic flux.

Quantitative Comparison of Heterologous Host Systems

The selection of a heterologous host is often a balance between phylogenetic proximity to the native producer and the practical tools available for genetic manipulation. The table below summarizes key hosts, their optimal BGC sources, and quantitative performance metrics based on recent literature.

Table 1: Performance Metrics and Characteristics of Common Heterologous Hosts

Host Organism	Optimal BGC Source	Reported Cloning Success Rate	Reported Expression Success Rate	Key Advantages	Notable Production Achievements
Streptomyces albus J1074	Actinobacteria [22]	68% (17/25 BGCs cloned) [22]	11% (4/36 clones expressed) [22]	Strong genetic toolbox, minimized secondary metabolism [23]	Discovery of 63 new NP families (across multiple hosts) [22]
Streptomyces coelicolor M1152/M1146	Actinobacteria [24]	Information Missing	Information Missing	Deleted endogenous BGCs, well-characterized physiology [24] [10]	Oviedomycin at 670 mg/L (after engineering) [24]
Escherichia coli BL21(DE3)	RiPPs from multiple phyla [22]	86% (83/96 BGCs cloned) [22]	32% (27/83 BGCs expressed) [22]	Rapid growth, extensive molecular tools, simple metabolism	High success rate for small (<18 kb) RiPP BGCs [22]
Bacillus subtilis	Firmicutes [22] [25]	100% (43/43 BGCs cloned in one study) [22]	16% (7/43 BGCs expressed in one study) [22]	Efficient protein secretion, genetic tractability	Compatible with TAR cloning system (pCAPB02 vector) [25]
Streptococcus mutans UA159	Anaerobic Firmicutes (Oral microbiome) [26]	Successful cloning of 73.7-kb BGC via NabLC [26]	Functional expression of pyrazinone and tetramic acid BGCs [26]	Facultative anaerobe, natural competence, mimics anaerobic environment	Discovery of mutanocyclin [26]

Experimental Protocols for Host Engineering and BGC Expression

Protocol 1: Construction of a Deletion-MinimizedStreptomyces coelicolorChassis

This protocol outlines the creation of a specialized S. coelicolor chassis, strain A3(2)-2023, engineered for high-yield heterologous expression [23].

Materials:

• S. coelicolor A3(2) wild-type strain
• pCRISPomyces-2BD plasmid (or similar CRISPR-Cas9 system for Streptomyces) [10]
• Donor E. coli strain (e.g., ET12567/pUZ8002 or GB2005 for Micro-HEP) [23]
• Appropriate antibiotics for selection

Method:

• Bioinformatic Identification: Use antiSMASH to identify all native BGCs in the S. coelicolor A3(2) genome.
• sgRNA Design: Design multiple sgRNAs targeting conserved regions of the four largest endogenous BGCs (actinorhodin, prodiginine, CPK, and CDA) for simultaneous deletion.
• CRISPR Plasmid Construction: Clone the sgRNA cassettes into the pCRISPomyces-2BD plasmid. The Cas9-BD variant is recommended for reduced off-target cleavage in high-GC content genomes [10].
• Conjugative Transfer: Introduce the constructed plasmid from the donor E. coli strain into S. coelicolor via biparental conjugation.
• Screening and Validation: Screen for exconjugants and verify the deletion of target BGCs via PCR and loss of characteristic pigment production.
• Introduction of RMCE Sites: Integrate multiple orthogonal recombinase-mediated cassette exchange (RMCE) sites (e.g., loxP, vox, rox, attP) into the genome of the deletion strain to create S. coelicolor A3(2)-2023. This enables stable, multi-copy integration of heterologous BGCs [23].

Protocol 2: Heterologous Expression of a BGC in an Optimized Host

This protocol describes the capture, refactoring, and expression of a target BGC in the engineered S. coelicolor M1152 chassis, based on the overproduction of oviedomycin [24].

Materials:

• pCBA (pCAP-BAC-Apr) or similar low-copy BGC capture vector [24]
• Gibson assembly reagents
• E. coli ET12567 (pUZ8002) for conjugation
• S. coelicolor M1152 heterologous host

Method:

• BGC Capture: Isolate the target BGC (e.g., ovm cluster) from genomic DNA via PCR or direct cloning. Ligate the fragment into the linearized pCBA vector using Gibson assembly to create pCBAO [24].
• Promoter Refactoring (Optional): To enhance expression, refactor native promoters in the BGC. Using an in vitro CRISPR/Cas9 system, replace weak native promoters (e.g., the promoter for the least-transcribed gene, identified by RT-qPCR) with strong, constitutive promoters like ermE* or kasO*p. This generates a refactored plasmid (e.g., pCBAO1) [24].
• Conjugative Transfer: Transform the final plasmid (pCBAO or pCBAO1) into E. coli ET12567 (pUZ8002) and conjugate into S. coelicolor M1152 to generate the final production strain (e.g., SCMO or SCMO1).
• Fermentation and Metabolite Analysis: Cultivate the exconjugant in a suitable production medium (e.g., R5 or SFM). After 5-7 days, extract the culture and analyze metabolites using HPLC and LC-MS/MS to detect the target compound [24].

Workflow Visualization

The following diagram illustrates the logical decision-making process and technical workflow for selecting and utilizing a heterologous host, from initial bioinformatic analysis to final compound isolation.

Diagram 1: Host Selection and Heterologous Expression Workflow. This chart outlines the decision pathway for selecting an appropriate heterologous host based on the origin and characteristics of the target BGC, leading to the steps for chassis engineering and compound production.

The Scientist's Toolkit: Essential Research Reagents

The table below lists key reagents and tools that form the foundation of heterologous expression studies, as featured in the protocols and literature.

Table 2: Key Research Reagents for Heterologous Expression Workflows

Reagent / Tool Name	Function / Application	Example Use Case
pCAP Series Vectors (e.g., pCAP01, pCAPB02)	TAR cloning and shuttle vectors for BGC capture in yeast and expression in various bacterial hosts. [25]	Direct cloning of large BGCs (>80 kb) from genomic DNA for integration into Streptomyces or B. subtilis. [25]
CRISPR-Cas9-BD System	Genome editing tool with reduced off-target cleavage for high-GC content organisms like Streptomyces. [10]	Simultaneous deletion of multiple native BGCs or refactoring promoters within a heterologous BGC. [24] [10]
E. coli ET12567 (pUZ8002)	Donor strain for conjugative transfer of DNA from E. coli to actinomycetes and other bacteria. [24]	Mobilizing BGC-containing plasmids from the cloning host (E. coli) into the final Streptomyces expression host.
S. coelicolor M1152	Model Streptomyces chassis with four deleted endogenous BGCs (act, red, cpk, cda) and a relaxed restriction system. [24]	A clean background host for heterologous expression of actinobacterial BGCs to minimize native interference.
Micro-HEP Platform	A comprehensive system using engineered E. coli and S. coelicolor strains for BGC modification, transfer, and multi-copy integration via RMCE. [23]	High-throughput engineering and expression of BGCs to boost production yields, as demonstrated for xiamenmycin. [23]
Calvital	Calvital, CAS:102903-12-8, MF:C26H15N4Na3O9S3	Chemical Reagent
Veltec	Veltec Reagent	High-purity Veltec reagent for laboratory research applications. This product is For Research Use Only (RUO). Not for diagnostic or therapeutic use.

Within synthetic biology, the construction of specialized microbial chassis for heterologous natural product (NP) production is a cornerstone of modern drug discovery pipelines [17]. These chassis are engineered to optimally express biosynthetic gene clusters (BGCs) sourced from diverse organisms, thereby facilitating the discovery and yield optimization of valuable compounds [18]. A critical step in chassis development is the elimination of native BGCs to minimize metabolic burden, avoid background interference, and redirect cellular resources toward the target pathway [23]. This application note provides a detailed protocol for systematically identifying native BGCs suitable for deletion, while ensuring host viability through the integration of essential gene data. We outline a synergistic methodology leveraging the genome mining tool antiSMASH and the Database of Essential Genes (DEG) to inform strategic, non-detrimental genetic refactoring.

The Scientific and Methodological Foundation

The rationale for this protocol is rooted in two key principles: the dispensability of most secondary metabolite pathways under standard laboratory conditions, and the indispensable nature of essential genes for core cellular function.

• BGC Dispensability: Secondary metabolites are not required for organism survival in axenic culture. Consequently, the BGCs encoding them are prime targets for deletion to create a clean genetic background [23]. This simplifies the metabolic landscape of the chassis and prevents the production of competing or interfering compounds.
• Gene Essentiality: Essential genes are those indispensable for organism survival under specific environmental conditions [27]. Their products are often involved in fundamental processes like DNA replication, transcription, translation, and core metabolism. DEG serves as a curated repository of genes experimentally determined to be essential across a wide range of organisms, providing a critical reference to avoid disrupting vital functions during chassis engineering [27].

The integration of BGC mapping with essential gene data allows researchers to distinguish between dispensable genomic regions (BGCs) and non-targetable regions (essential genes), thereby de-risking the deletion strategy.

Computational Identification of Native BGCs Using antiSMASH

antiSMASH (antibiotics & Secondary Metabolite Analysis SHell) is the leading tool for the automated detection and annotation of BGCs in microbial genomes [28] [29]. The following protocol describes its use for identifying deletion targets.

Experimental Protocol: antiSMASH Analysis

• Input Preparation: Obtain the genome sequence of the potential chassis organism in FASTA format (either nucleotide or protein sequence). Ensure the annotation is of high quality, as this improves gene prediction and, consequently, BGC detection accuracy.
• Job Submission and Execution:
  • Access the public antiSMASH web server (https://antismash.secondarymetabolites.org/) or install a local copy for large-scale analyses [29].
  • Upload your genome FASTA file.
  • Select the appropriate organism type (e.g., "bacteria" or "fungi") and, if known, the specific genus (e.g., Streptomyces) to enable lineage-specific analysis refinements.
  • Initiate the analysis. For large genomes, processing may take several hours.
• Output Interpretation and Data Extraction:
  • The primary output is an interactive HTML page detailing the location and type of each detected BGC [28].
  • Identify all genomic regions flagged as BGCs. antiSMASH version 8.0 can detect over 100 different cluster types, including nonribosomal peptide synthetase (NRPS), polyketide synthase (PKS), ribosomally synthesized and post-translationally modified peptide (RiPP), and terpenoid clusters [28].
  • For each BGC, record the genomic coordinates (start and end positions), the predicted BGC type, and the core biosynthetic genes.
  • Optional: Use the integrated "KnownClusterBlast" or "ClusterCompare" features to assess similarity to BGCs with characterized metabolites in the MIBiG (Minimum Information about a Biosynthetic Gene Cluster) repository, which can help prioritize deletions of clusters producing known, unwanted metabolites [28].

Table 1: Key BGC Detection Tools and Databases

Tool/Database Name	Primary Function	Relevance to Deletion Target Identification
antiSMASH [28] [29]	Detects & annotates BGCs in genomic data	Core tool for mapping all native biosynthetic pathways in a chassis genome.
MIBiG [17] [28]	Repository of experimentally characterized BGCs	Provides context on the known products of BGCs homologous to those in your chassis.
BAGEL [30]	Dedicated mining tool for RiPPs (e.g., bacteriocins)	Complementary tool for identifying a specific class of BGCs.
ARTS [30] [28]	Detects BGCs and identifies unique, essential resistance genes	Helps identify essential genes within BGCs that should not be deleted.

Mapping Essential Genes Using the Database of Essential Genes (DEG)

Concurrently with BGC identification, it is crucial to map the essential genes within the chassis genome to prevent their accidental deletion.

Experimental Protocol: Interrogating Essential Gene Data

• Data Source Access: Navigate to the Database of Essential Genes (DEG), which compiles essential genes identified through large-scale experimental studies [27].
• Cross-Referencing and Analysis:
  • Query the DEG using the name or identifier of your chassis organism to retrieve a list of its experimentally determined essential genes.
  • If a dedicated entry for your specific chassis strain is unavailable, use data from the most closely related species or strain.
  • Map the genomic coordinates of the essential genes onto the chassis genome. This can be done by using the essential gene identifiers to extract location data from a matching genome annotation file.
  • Create a consolidated list or a visual genomic map that clearly displays the locations of both BGCs (from antiSMASH) and essential genes (from DEG).

An Integrated Workflow for Prioritizing Deletion Targets

The core of this application note is the integration of the two data streams generated above. The following workflow and decision logic ensure a systematic and safe approach to target selection.

Workflow Decision Logic:

• Data Integration: Superimpose the mapped BGCs and essential genes onto a single genomic view.
• Target Filtering: Immediately exclude any BGC from the deletion candidate list if its genomic locus shows any overlap with a known essential gene. The essential function takes precedence.
• Target Prioritization: For the remaining, non-overlapping BGCs, establish a prioritization for deletion. Considerations include:
  • Cluster Similarity: Use antiSMASH's ClusterCompare results to prioritize BGCs that are identical or highly similar to known clusters producing interfering compounds [28].
  • Metabolic Burden: Larger BGCs may impose a greater metabolic load, making their deletion beneficial for host fitness [23].
  • Genetic Stability: BGCs with repetitive sequences (common in PKS/NRPS clusters) can be prone to recombination and may be prioritized for deletion to enhance genomic stability [17].

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Key Reagents and Resources for Chassis Engineering

Reagent / Resource	Function / Description	Application in This Protocol
antiSMASH Software [28] [29]	Rule-based and machine-learning-powered BGC detection platform.	Identifying and annotating native BGCs in the chassis genome.
Database of Essential Genes (DEG) [27]	Curated database of genes essential for survival under specific conditions.	Defining genomic regions that must be preserved during deletion efforts.
MIBiG Database [17] [28]	Reference repository of experimentally characterized BGCs.	Inferring the potential chemical output of homologous native BGCs.
Red/ET Recombineering [17] [23]	High-efficiency genetic engineering system using phage-derived recombinases (Redα/Redβ).	Performing precise, markerless deletions of targeted BGCs in the chassis.
Conditional Promoters (e.g., pNiiA) [31]	Regulatable promoters used to control gene expression (e.g., nitrogen-regulated).	Validating essential gene function by creating conditional mutants, if needed.
RMCE Systems (Cre-lox, Vika-vox) [23]	Recombinase-Mediated Cassette Exchange systems for precise genomic integration.	Useful for advanced chassis engineering, such as inserting heterologous BGCs after clearing native ones.
Polawax GP 200	Polawax GP 200 Research-Grade Emulsifying Wax	Research-use Polawax GP 200 is an emulsifying wax for formulating skin-conditioning creams and hydrogels. This product is for research only, not for personal use.
Ketac-Bond	Ketac-Bond, CAS:102087-40-1, MF:C17H25NO3	Chemical Reagent

Visualization and Experimental Validation of Targets

Before committing to lengthy deletion campaigns, it is prudent to conduct preliminary checks on the expression of targeted BGCs.

Experimental Protocol: Transcriptomic Validation

• Objective: To confirm that a BGC targeted for deletion is transcriptionally active under your laboratory fermentation conditions, thereby justifying the engineering effort.
• Methodology:
  • Culture the wild-type chassis strain under standard production conditions.
  • Harvest cells at appropriate time points and extract total RNA.
  • Perform reverse transcription quantitative PCR (RT-qPCR) targeting the core biosynthetic genes (e.g., PKS KS or NRPS A domains) of the BGCs identified for deletion.
  • Use primers for a constitutively expressed essential gene (e.g., a ribosomal protein gene) as an internal control.
• Data Analysis: Significant expression of the BGC core genes confirms the cluster is not silent and that its deletion may beneficially reduce metabolic competition.

The strategic development of a heterologous production chassis requires careful genomic planning. The integrated use of antiSMASH for BGC discovery and the Database of Essential Genes for conservation mapping provides a robust, data-driven framework for identifying safe and effective deletion targets. This protocol minimizes the risk of impairing host viability while guiding the engineering of a clean, high-yielding microbial factory for natural product discovery and production.

The discovery and production of microbial natural products (NPs), indispensable resources in medicine and agriculture, are often hindered because the native biosynthetic gene clusters (BGCs) in original hosts are silent, poorly expressed, or difficult to manipulate genetically [13] [32]. A pivotal strategy to overcome these challenges is the development of optimized heterologous production chassis—engineered host organisms that provide a defined metabolic background for the expression of foreign BGCs [13] [17]. A core step in creating these chassis is the deletion of native, competing BGCs to redirect cellular resources toward the production of the target heterologous compound [13] [10].

This application note details three foundational genetic technologies—Red recombineering, CRE-loxP, and CRISPR-Cas systems—that enable the precise deletion of native BGCs and the refinement of heterologous hosts. We provide structured comparisons, detailed protocols, and visual workflows to facilitate their application in strain engineering for NP discovery and yield optimization.

The table below summarizes the core functions, primary applications, and key characteristics of the three genetic toolkits discussed in this note.

Table 1: Key Genetic Toolkits for Heterologous Chassis Development

Technology	Core Function	Primary Application in Chassis Development	Key Characteristics
Red Recombineering	Homologous recombination using short (∼50 bp) homology arms, mediated by λ phage Redα/Redβ/Redγ proteins in E. coli [13].	High-efficiency modification and engineering of BGCs cloned into E. coli vectors prior to transfer to the final heterologous host [13].	- Efficiency: High in E. coli with short homology arms [13].- Throughput: Ideal for sequential or iterative modifications [13].- Key Feature: Enables markerless manipulation via counterselectable cassettes (e.g., rpsL) [13].
CRE-loxP	Site-specific recombination catalyzed by Cre recombinase between 34 bp loxP sites [33].	- Excision: Deletion of large genomic regions, including multiple native BGCs [34].- Integration: Precise, marker-less integration of DNA cassettes [13].	- Versatility: Can be used for deletion, inversion, or integration [33].- Precision: Allows recycling of selection markers [13].- Application: Used in recombinase-mediated cassette exchange (RMCE) with other systems (Vika/vox, Dre/rox) [13].
CRISPR-Cas Systems	RNA-programmed nucleases (e.g., Cas9) creating double-strand breaks at specific genomic loci [10].	- Targeted Deletion: Knockout of single or multiple native BGCs in the host genome [10].- Gene Activation/Repression: Using catalytically dead Cas9 (dCas9) for multiplexed gene expression modulation [10].	- Multiplexing: Enables simultaneous targeting of multiple loci [10].- Efficiency: Can induce high cytotoxicity if off-target cleavage is not controlled [10].- Innovation: Engineered Cas9-BD variant reduces off-target effects in high GC-content Streptomyces [10].

Quantitative Performance Data

The following table presents key performance metrics for these technologies, particularly in the context of engineering actinomycetes like Streptomyces, which are common heterologous hosts.

Table 2: Quantitative Performance Metrics of Genetic Toolkits

Technology / Specifics	Reported Efficiency / Outcome	Experimental Context / Notes
CRISPR-Cas9-BD (This refers to a modified Cas9 with polyaspartate tags at both N- and C-termini to reduce off-target binding [10] [35])
Editing Efficiency	98.1% ± 1.40% [10]	Deletion of matAB genes in S. coelicolor M1146 [10].
Exconjugant Yield	77-fold increase vs. wild-type Cas9 [10]	Same experiment as above; indicates significantly reduced cytotoxicity [10].
Off-Target Cleavage	Dramatically decreased [10] [35]	In vitro assays with non-PAM sequences (e.g., -NGA, -NGT) [10].
Micro-HEP Platform (Utilizes RMCE with orthogonal recombinase systems like Cre-loxP, Vika-vox, Dre-rox, and phiBT1-attP [13])
BGC Copy Number & Yield	Increasing xiamenmycin yield with 2 to 4 copies of the xim BGC [13]	Demonstrates the utility of RMCE for gene dosage studies and yield optimization [13].

Application Notes & Detailed Protocols

Protocol 1: Two-Step Red Recombineering for Markerless DNA Manipulation inE. coli

This protocol is used for the initial cloning and modification of BGCs in E. coli before their conjugation into a Streptomyces chassis [13].

1. Principle: A rhamnose-inducible Redα/Redβ/Redγ system and an arabinose-inducible CcdA protein are used for a two-step, markerless modification. The Red system facilitates homologous recombination with short homology arms, while CcdA counter-selection allows for the removal of selection markers [13].

2. Research Reagent Solutions:

Table 3: Key Reagents for Red Recombineering

Reagent	Function	Specific Example / Note
Engineered E. coli Strain	Host for recombineering.	Contains the temperature-sensitive plasmid pSC101-PRha-αβγA-PBAD-ccdA [13].
pSC101-PRha-αβγA-PBAD-ccdA Plasmid	Expresses λ phage Redα/Redβ/Redγ recombinases and the CcdA counter-selection protein [13].	- Recombinase Induction: 10% L-rhamnose [13].- CcdA Induction: 10% L-arabinose [13].
Selection Cassette	Selects for successful recombination events.	amp-ccdB or kan-rpsL cassette [13]. The rpsL gene can be used for streptomycin-based counter-selection [13].
Homology Arms	Guides the precise integration of the cassette and the subsequent insertion of the desired sequence.	50 bp arms flanking the target site are sufficient [13].

3. Step-by-Step Workflow:

• Electroporation: Introduce the recombinase expression plasmid pSC101-PRha-αβγA-PBAD-ccdA into the recipient E. coli strain [13].
• First Recombination (Cassette Integration):
  • Design a linear DNA cassette containing a selectable marker (e.g., kan-rpsL) flanked by 50 bp homology arms matching the target region [13].
  • Induce the expression of both the recombinase and CcdA with 10% L-rhamnose and 10% L-arabinose [13].
  • Electroporate the linear cassette into the induced cells. CcdA expression promotes the survival of cells that have successfully integrated the cassette by neutralizing the toxic CcdB protein that would otherwise be expressed from the plasmid in the absence of a successful recombination event [13].
  • Select for clones with the correctly integrated cassette on appropriate antibiotic plates [13].
• Second Recombination (Marker Removal & Final Modification):
  • Design a single-stranded oligonucleotide or a double-stranded DNA fragment containing the desired final modification (e.g., a promoter swap, point mutation, or simply the removal of the marker) flanked by the appropriate homology [13].
  • Induce recombinase expression with L-rhamnose and electroporate the DNA fragment [13].
  • In this step, CcdA is not induced. Cells that have excised the kan-rpsL cassette (and thus lost the CcdA-expressing part of the plasmid) will be susceptible to counter-selection (e.g., on streptomycin plates if the rpsL cassette was used) or simply screened for antibiotic sensitivity [13].
  • Screen for colonies that have lost the marker and verify the final, markerless modification by PCR and sequencing [13].

Protocol 2: CRE-loxP for Deletion of Native BGCs in a Chassis Strain

This protocol describes the use of CRE-loxP recombination to remove native biosynthetic gene clusters from a potential heterologous host genome, streamlining its metabolic background [13] [34].

1. Principle: Cre recombinase recognizes specific 34 bp loxP sites. When two loxP sites are placed in the same orientation on a chromosome, the DNA segment between them ("floxed") is excised and degraded upon Cre expression, leaving a single loxP site behind [33].

2. Research Reagent Solutions:

Table 4: Key Reagents for CRE-loxP-Mediated Deletion

Reagent	Function	Specific Example / Note
Targeting Vector	A plasmid containing a selection marker (e.g., an antibiotic resistance gene) itself flanked by loxP sites, and homology arms for the target genomic locus.	Used to introduce the first loxP site and marker [34].
Cre Recombinase	The enzyme that catalyzes the site-specific recombination between loxP sites.	Can be delivered on a transient plasmid, via conjugation, or expressed from a chromosomally integrated gene [13] [33].
"Floxed" Selection Marker	A selectable marker placed between two loxP sites.	Allows for selection of integration events and is later removed by Cre, enabling marker recycling [13].

3. Step-by-Step Workflow:

• Vector Construction & Integration:
  • Clone homology arms (∼1-2 kb) corresponding to the regions flanking the BGC to be deleted into a vector containing a "floxed" selection marker (e.g., aac(3)IV-loxP) [13] [34].
  • Introduce this targeting vector into the chassis strain (e.g., Streptomyces coelicolor) via conjugation or protoplast transformation [13].
  • Select for single-crossover integrants on antibiotic plates. These strains now have the entire vector, including the loxP-flanked marker, integrated into the genome [13].
• Second Crossover & Marker Excision:
  • Propagate the integrants without selection to allow for a second homologous recombination event. This can excise the vector backbone and leave behind the "floxed" marker cassette in the genome, replacing the native BGC [13] [34].
  • Screen for colonies that are sensitive to the antibiotic used for the marker, indicating the second crossover has occurred.
  • Verify the correct genotype by PCR.
• Cre-Mediated Marker Recycling:
  • Introduce a plasmid expressing Cre recombinase into the strain containing the "floxed" marker [13].
  • Cre will excise the marker, leaving a single loxP "scar" sequence in the place of the deleted BGC [13] [33].
  • The Cre-expression plasmid can often be lost from the strain by cultivating it at a higher temperature (if the plasmid is temperature-sensitive) [13].
  • The resulting strain has a clean deletion of the native BGC and can be used for another round of deletion or for BGC integration.

Protocol 3: CRISPR-Cas9-BD for Multiplexed BGC Deletion inStreptomyces

This protocol uses a modified Cas9 protein (Cas9-BD) to simultaneously delete multiple native BGCs in Streptomyces, which have high GC-content genomes where traditional Cas9 shows high cytotoxicity [10].

1. Principle: The Cas9-BD protein, engineered with polyaspartate tags at its N- and C-termini, retains high on-target cleavage efficiency while dramatically reducing off-target binding and cleavage in high GC-content genomes [10]. When co-expressed with guide RNAs (sgRNAs) targeting regions flanking a BGC, it creates double-strand breaks that can be repaired by the cell's endogenous machinery, leading to the deletion of the intervening DNA [10].

2. Research Reagent Solutions:

Table 5: Key Reagents for CRISPR-Cas9-BD Editing in Streptomyces

Reagent	Function	Specific Example / Note
pCRISPomyces-2BD Plasmid	Expression vector for the modified Cas9-BD protein and sgRNA[s] [10].	A derivative of pCRISPomyces-2 where the wild-type cas9 is replaced with cas9-BD [10].
Repair Template (Optional)	A DNA template for homology-directed repair (HDR) to introduce specific sequences or to enhance deletion efficiency.	For large deletions, a double-stranded DNA fragment with long homology arms can be used [10].
sgRNA Expression Cassette	Encodes the RNA that guides Cas9-BD to the specific target genomic loci.	Targets are designed for the 5' and 3' ends of the BGC to be deleted [10].

3. Step-by-Step Workflow:

• Design and Construction:
  • Design two sgRNAs that bind to genomic sites immediately upstream and downstream of the BGC targeted for deletion.
  • Clone the expression cassettes for these sgRNAs into the pCRISPomyces-2BD plasmid [10].
  • If using a repair template, synthesize a linear DNA fragment containing the desired sequence (which could simply be a direct junction of the flanking regions to facilitate deletion) with homology arms (≥500 bp) [10].
• Delivery and Conjugation:
  • Introduce the assembled pCRISPomyces-2BD plasmid (with or without the repair template) into the Streptomyces chassis strain via intergeneric conjugation from an E. coli donor strain [10].
• Selection and Screening:
  • Select for exconjugants on apramycin plates (or the appropriate antibiotic for the plasmid).
  • Due to the reduced cytotoxicity of Cas9-BD, a significantly higher number of exconjugants (e.g., 77-fold more) is expected compared to using wild-type Cas9 [10].
  • Screen the resulting colonies by PCR to identify those with the successful BGC deletion.
• Plasmid Curing:
  • Propagate the positive mutants without antibiotic selection to allow for the loss of the pCRISPomyces-2BD plasmid.
  • Verify plasmid loss by patching colonies onto plates with and without apramycin. The final engineered chassis strain is antibiotic-sensitive and free of the native BGC.

The heterologous production of specialized microbial natural products is a cornerstone of modern drug discovery and development. A critical strategy in this field involves the engineering of microbial "chassis" strains by deleting native biosynthetic gene clusters (BGCs). This process serves to minimize metabolic competition, eliminate background interference, and redirect cellular resources toward the production of target compounds. This Application Note details the construction, validation, and implementation of three distinct bacterial chassis engineered through this paradigm: the Gram-positive Streptomyces sp. A4420 CH strain, the Gram-negative Schlegelella brevitalea DT mutants, and the versatile S. coelicolor A3(2)-2023 platform. The protocols and data presented herein provide a framework for researchers to select and apply these chassis systems for the efficient production of diverse natural products.

Application Notes: Engineered Chassis Strains and Their Performance

1Streptomycessp. A4420: A Polyketide-Focused Chassis

Background and Rationale: Streptomyces sp. A4420 was identified from a natural organism library due to its rapid growth and high inherent metabolic capacity, particularly for producing the alkaloid streptazolin [36]. To repurpose it as a general heterologous host, a chassis (CH) strain was developed with a specific focus on expressing polyketide-derived natural products.

Engineering Strategy: The engineering involved the deletion of nine native polyketide BGCs from the wild-type genome. This created a metabolically simplified host with consistent sporulation and growth patterns [36].

Performance Validation: The chassis was tested by expressing four distinct polyketide BGCs and comparing production against common heterologous hosts like S. coelicolor M1152 and S. lividans TK24. The Streptomyces sp. A4420 CH strain was the only host capable of producing all four target metabolites under every tested condition, demonstrating its superior versatility and efficiency [36].

Table 1: Engineering and Performance Summary of Streptomyces sp. A4420 CH Strain

Feature	Description
Parental Strain	Streptomyces sp. A4420
Engineering Goal	Polyketide-specialized heterologous expression
Key Genetic Modification	Deletion of 9 native polyketide BGCs
Growth Characteristics	Rapid initial growth, consistent sporulation
Validation Metabolites	Four distinct polyketides (Type I and II)
Comparative Performance	Outperformed parental strain and conventional hosts (S. coelicolor M1152, S. lividans TK24)

2Schlegelella brevitaleaDT Mutants: Genome-Reduced Gram-Negative Chassis

Background and Rationale: Schlegelella brevitalea DSM 7029 is a Gram-negative β-proteobacterium with potential for heterologously expressing proteobacterial natural products. However, its utility was limited by early autolysis, which severely restricted fermentation biomass [37].

Engineering Strategy: A rational genome reduction approach was pursued via two parallel routes: 1) Deletion of large, endogenous nonribosomal peptide synthetase/polyketide synthase (NRPS/PKS) BGCs (DC series mutants); and 2) Deletion of nonessential genomic regions, including prophages, transposases, and genomic islands (DT series mutants). The DT series mutants were designed to alleviate autolysis and improve robustness [37].

Performance Validation: The DT mutants showed improved growth characteristics with alleviated cell autolysis. When tested for the production of six different proteobacterial natural products, the DT chassis achieved higher yields than the wild-type DSM 7029 strain and other common Gram-negative hosts like Escherichia coli and Pseudomonas putida [37]. Furthermore, these chassis enabled the identification of "chitinimides," new detoxin-like compounds, by expressing a cryptic BGC from Chitinimonas koreensis [37].

Table 2: Engineering and Performance Summary of Schlegelella brevitalea DT Chassis

Feature	Description
Parental Strain	Schlegelella brevitalea DSM 7029
Engineering Goal	Robust host for Gram-negative bacterial BGCs
Key Genetic Modification	Deletion of nonessential regions (prophages, transposons, islands)
Growth Characteristics	Improved growth, alleviated early autolysis
Key Advantage	Native production of methylmalonyl-CoA (key PK extender unit)
Application Proof	High-yield production of 6 tested natural products; discovery of chitinimides

3S. coelicolorA3(2)-2023: A High-Efficiency Expression Platform

Background and Rationale: S. coelicolor is a genetically well-characterized model organism frequently used as a heterologous host. The A3(2)-2023 strain was developed as part of the Micro-HEP (microbial heterologous expression platform) to streamline the entire process from BGC modification to expression [23].

Engineering Strategy: The chassis was engineered by deleting four endogenous BGCs to create a cleaner metabolic background. Furthermore, multiple orthogonal recombinase-mediated cassette exchange (RMCE) sites (Cre-lox, Vika-vox, Dre-rox, and phiBT1-attP) were introduced into the chromosome to facilitate stable, multi-copy integration of heterologous BGCs without plasmid backbone incorporation [23].

Performance Validation: The platform's efficiency was demonstrated using the xiamenmycin (anti-fibrotic) and griseorhodin BGCs. A direct correlation between BGC copy number and product yield was observed for xiamenmycin. The system also successfully enabled the production of a new compound, griseorhodin H, showcasing its power in natural product discovery [23].

Protocols: Key Methodologies for Chassis Utilization

Protocol: Heterologous Expression inStreptomycessp. A4420 CH

Principle: This protocol describes the process of introducing and expressing a heterologous BGC in the Streptomyces sp. A4420 CH strain, from vector construction to fermentation and metabolite analysis [36].

Procedure:

• Vector Construction: Clone the target BGC into an appropriate E. coli-Streptomyces shuttle vector, ensuring it contains elements for selection and site-specific integration (e.g., ΦC31 attP).
• Conjugative Transfer: a. Introduce the constructed plasmid into the non-methylating E. coli ET12567 (pUZ8002) donor strain. b. Prepare spores of the Streptomyces sp. A4420 CH strain and treat with heat shock (50°C for 10 minutes). c. Mix donor and recipient cells, plate on MS agar, and incubate at 30°C for 16-20 hours. d. Overlay the plates with appropriate antibiotics to select for exconjugants.
• Strain Validation: Isolate genomic DNA from potential exconjugants. Verify the correct integration of the BGC via PCR using primers specific to the cluster and the chromosomal integration site.
• Fermentation and Analysis: a. Inoculate verified strains into liquid SFM or ISP2 media and incubate with shaking at 30°C for 5-7 days. b. Extract the culture broth with an equal volume of ethyl acetate. c. Analyze the organic extract by liquid chromatography-mass spectrometry (LC-MS) to detect and quantify the heterologously produced metabolite.

Protocol: Multi-Site BGC Integration Using Micro-HEP

Principle: This protocol leverages the RMCE system in S. coelicolor A3(2)-2023 for multi-copy, backbone-free integration of BGCs to enhance product yield [23].

Procedure:

• BGC Modification in E. coli: a. In a specialized E. coli GB2005/GB2006 strain harboring the pSC101-PRha-αβγA-PBAD-ccdA plasmid, induce the Redαβγ recombinase system with L-rhamnose. b. Use a linear DNA cassette to replace the target gene on a BAC containing the BGC with a kan-rpsL marker via homologous recombination. c. In a second round of recombineering, replace the kan-rpsL marker with the desired RMCE cassette (e.g., containing vox sites and an integrase gene).
• Conjugative Transfer: Mobilize the modified BAC from the E. coli donor into the S. coelicolor A3(2)-2023 recipient via conjugation.
• RMCE-mediated Integration: The heterologous BGC is precisely exchanged into the pre-engineered chromosomal RMCE site via the action of the specific tyrosine integrase (e.g., Vika). This process excises the plasmid backbone.
• Copy Number Amplification: To integrate multiple copies, transform the exconjugant with additional copies of the BGC-RMCE cassette. The recombinase will facilitate the tandem integration of multiple copies at the same chromosomal locus.
• Metabolite Production: Ferment the engineered strain in suitable media (e.g., GYM for xiamenmycin) and analyze the extracts for compound production.

Protocol: CRISPR-Cas9-BD Mediated BGC Deletion

Principle: The CRISPR-Cas9-BD system, which features reduced off-target cytotoxicity, is highly effective for deleting native BGCs in high-GC content Streptomyces to construct clean chassis [10].

Procedure:

• sgRNA Design: Design two sgRNAs that target the upstream and downstream flanking regions of the native BGC to be deleted.
• Editing Plasmid Construction: Clone the two sgRNA sequences and a ~1 kb homology-directed repair (HDR) template for each flank into a pCRISPomyces-2BD plasmid.
• Transformation: Introduce the constructed plasmid into the Streptomyces host via conjugation with an E. coli donor strain.
• Mutant Screening: After conjugation, screen for apramycin-sensitive clones, indicating the loss of the CRISPR plasmid. Verify the successful deletion of the target BGC by PCR and subsequent whole-genome sequencing to check for off-target mutations.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Research Reagents for Chassis Engineering and Application

Reagent / Tool	Function	Application Context
pCRISPomyces-2BD Plasmid	Expresses the low-cytotoxicity Cas9-BD protein for precise genome editing [10].	Deletion of native BGCs in Streptomyces and other high-GC bacteria.
Redαβγ Recombineering System	Enables high-efficiency genetic modifications in E. coli using short homology arms (50 bp) [23].	Cloning and modification of large BGCs in E. coli before transfer to the final chassis.
RMCE Cassettes (Cre-lox, Vika-vox)	Enables precise, multi-copy, backbone-free integration of DNA into specific chromosomal sites [23].	Stable introduction of heterologous BGCs into engineered S. coelicolor A3(2)-2023.
PhiC31 Integration System	A widely used site-specific recombination system for integrating DNA into the Streptomyces chromosome [36].	Stable introduction of BGCs into various Streptomyces chassis, including Streptomyces sp. A4420 CH.
E. coli ET12567 (pUZ8002)	A non-methylating, conjugation-proficient donor strain for transferring DNA from E. coli to Streptomyces [36] [23].	Essential for intergeneric conjugation, a standard method for introducing DNA into actinomycetes.
Hemophan	Hemophan Dialysis Membrane|For Research	Hemophan is a modified cellulose membrane for hemodialysis research. It offers enhanced biocompatibility for studying blood-membrane interactions. For Research Use Only.
dowex retardion 11a8	dowex retardion 11a8, CAS:109766-79-2, MF:C5H11NO2S	Chemical Reagent

Beyond Deletion: Advanced Troubleshooting and Yield Optimization Strategies

Overcoming Host Fitness Costs and Growth Deficits

The deletion of native biosynthetic gene clusters (BGCs) to create optimized heterologous production chassis represents a cornerstone strategy in modern microbial metabolic engineering. This approach aims to redirect cellular resources toward the production of target compounds while eliminating competitive pathways and potential toxins. However, the introduction of heterologous BGCs, even into streamlined hosts, frequently imposes significant metabolic burdens and growth deficits that can undermine production efficiency [38]. These fitness costs manifest as reduced growth rates, lower final biomass, and impaired productivity, presenting critical barriers to industrial application.

The physiological basis for these burdens is multifactorial, encompassing the energetic demands of replicating and transcribing foreign DNA, the metabolic cost of synthesizing complex enzymatic machinery, the channeling of precursor metabolites away from primary metabolism, and potential cytotoxicity of the synthesized compounds [6]. Understanding and mitigating these challenges is therefore essential for developing robust production platforms. This application note provides detailed protocols and analytical frameworks for quantifying, understanding, and overcoming host fitness costs in BGC production chassis, with particular emphasis on Streptomyces and other microbial hosts.

Quantitative Assessment of Fitness Costs

Accurately quantifying the physiological impact of heterologous BGC expression is the critical first step in developing mitigation strategies. The following parameters should be systematically measured and compared between engineered and wild-type strains.

Table 1: Key Growth and Physiological Parameters for Assessing Metabolic Burden

Parameter	Measurement Method	Interpretation	Typical Impact of BGC Expression
Maximum Growth Rate (μmax)	Growth curve analysis (OD600)	Cellular replication efficiency	Reduction of 10-60% [6]
Final Biomass Yield	Growth curve analysis (OD600 or cell count)	Total metabolic capacity	Reduction of 15-50% [6]
Product Titer	HPLC-MS, NMR	Production efficiency	Variable; may increase or decrease independently of growth
Relative Fitness in Competition	Co-culture assays with reference strain	Competitive ability in mixed populations	Significant reduction observed [6]
Respiration Rate	Oxygen consumption assays	Metabolic activity	Often increased per cell, indicating inefficiency

Experimental Protocol: Comprehensive Growth Analysis

Materials:

• Spectrophotometer and cuvettes or microplate reader
• Appropriate growth medium
• Sterile culture vessels
• Temperature-controlled shaker or incubator

Procedure:

• Inoculate triplicate cultures of both BGC-containing and control strains in appropriate medium.
• Measure optical density (OD₆₀₀) at regular intervals (e.g., every 30-60 minutes).
• Continue measurements until cultures reach stationary phase (typically 24-72 hours for Streptomyces).
• Calculate maximum growth rate (μ_max) from the linear portion of the log(OD) versus time plot.
• Record final OD₆₀₀ after 48 hours of stationary phase as biomass yield.
• For competitive fitness, mix BGC-containing and wild-type strains at 1:1 ratio and monitor population dynamics over serial passages using selective plating or flow cytometry.

Engineering Strategies to Overcome Fitness Costs

Multiple engineering approaches have been developed to alleviate metabolic burdens while maintaining high production titers. The table below summarizes the most effective strategies.

Table 2: Engineering Strategies to Mitigate BGC-Associated Fitness Costs

Strategy	Mechanism	Implementation Methods	Key Applications
Adaptive Laboratory Evolution (ALE)	Selection of compensatory mutations	Serial passaging under production conditions; selection for improved growth	Restoration of TCA cycle flux; ribosomal protein upregulation [6]
Central Metabolism Enhancement	Increased precursor and energy supply	Engineering TCA cycle enzymes (e.g., citrate synthase); modulating NADPH/ATP generation	Increased metabolite production and growth in adapted strains [6]
Transcriptional Optimization	Balanced expression of pathway genes	Promoter engineering; RBS optimization; regulatory element refactoring [39]	Prevention of protein overexpression burden; improved pathway efficiency
Genome Streamlining	Reduction of competitive pathways	Deletion of native BGCs; removal of unnecessary genomic regions [38]	Resource reallocation to heterologous pathways
Dynamic Regulation	Temporal separation of growth and production	Use of growth-phase inducible promoters; metabolic sensors	Prevention of production burden during rapid growth

Metabolic Adaptation Mechanisms

Recent multi-omics studies have revealed that microbial hosts employ specific metabolic adaptations to overcome BGC-associated burdens. In Staphylococcus aureus, acquisition of a heterologous bacteriocin BGC led to mutations in citrate synthase that increased TCA cycle activity, resulting in elevated levels of citrate and α-ketoglutarate [6]. This metabolic reprogramming enhanced both cellular fitness and compound production, demonstrating how central metabolism can be optimized to support heterologous expression.

Figure 1: Metabolic Adaptation Pathway to BGC Acquisition

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Reagents for Investigating and Mitigating Fitness Costs

Reagent/Category	Specific Examples	Function/Application	Key Features
Genetic Elements	ermEp, kasOp promoters [38]; synthetic RBS libraries [39]	Fine-tune expression levels; reduce translational burden	Modular; well-characterized strength
Editing Tools	CRISPR-Cas systems [39] [40]; TAR/CATCH [38]	Precise genome engineering; BGC capture and refactoring	High efficiency; applicable to large constructs
Analytical Platforms	HPLC-HRMS; NMR; RNA-seq [6] [41]	Comprehensive phenotyping; pathway analysis	Multi-omics capability; high sensitivity
Selection Systems	Antibiotic resistance; auxotrophic markers	Maintain plasmid stability; enable ALE	Counter-selectable; tunable stringency
Bioinformatics	antiSMASH [40] [42]; iModulonDB [41]	BGC identification; regulatory network analysis	Specialized for natural product discovery
Aquateric	Aquateric: Enteric Coating System for Research	Aquateric is an aqueous enteric coating polymer for pharmaceutical research. This product is For Research Use Only (RUO). Not for human consumption.	Bench Chemicals
truliner	Truliner PMMA	Truliner is a permanent, color-stable PMMA denture relining material for dental and materials science research. For Research Use Only (RUO).	Bench Chemicals

Advanced Protocol: Multi-omics Investigation of Metabolic Burden

This integrated protocol enables comprehensive analysis of the physiological impact of heterologous BGC expression and identification of targeted engineering strategies.

Experimental Workflow

Figure 2: Multi-omics Workflow for Fitness Cost Analysis

Step-by-Step Procedure

Phase 1: Strain Construction and Validation

• Introduce target BGC into production host using appropriate method (e.g., conjugal transfer [40], transformation).
• Validate BGC integration via PCR and sequencing.
• Confirm heterologous production via HPLC-MS analysis of culture extracts [6].

Phase 2: Phenotypic Characterization

• Perform growth curve analysis as described in Section 2.1.
• Conduct competitive fitness assays by mixing BGC-containing and reference strains.
• Quantify metabolic activity via respiration rate measurements using oxygen electrodes.
• Analyze metabolic profiles via LC-MS/MS at multiple growth phases [42].

Phase 3: Multi-omics Data Collection

• Transcriptomics: Collect samples at mid-log phase for RNA-seq analysis [41].
• Metabolomics: Perform intracellular metabolite extraction and analysis via GC-MS and LC-MS.
• Proteomics: Implement label-free quantitative proteomics to measure enzyme abundance.

Phase 4: Data Integration and Target Identification

• Use computational tools (e.g., iModulonDB [41]) to identify key regulatory changes.
• Map transcriptomic and proteomic data to metabolic networks.
• Identify bottleneck reactions and compensatory mechanisms.
• Prioritize engineering targets based on multi-omics evidence.

Concluding Remarks

Overcoming host fitness costs requires a multifaceted approach that addresses both genetic and physiological constraints. The integration of systematic phenotyping with multi-omics analyses provides a powerful framework for identifying and mitigating metabolic burdens. As synthetic biology tools continue to advance, particularly with AI-assisted design and machine learning approaches for predicting regulatory networks [39] [41], the development of high-fitness production chassis will become increasingly precise and efficient. By implementing the protocols and strategies outlined in this application note, researchers can significantly improve the performance of heterologous production systems, enabling more sustainable and economically viable biomanufacturing processes.

The genomic era has revealed a vast untapped reservoir of biosynthetic gene clusters (BGCs) encoding potential novel therapeutics, yet a significant majority remain silent or poorly expressed under standard laboratory conditions. Heterologous expression—the process of transferring BGCs into surrogate production hosts—has emerged as a powerful strategy to overcome the limitations of native producers. However, the success of this approach relies heavily on selecting an appropriate host chassis, as no universal host exists that can optimally express all BGC types. This application note explores the scientific rationale for multi-chassis engineering, provides quantitative comparisons of existing platforms, and details protocols for chassis evaluation and engineering, framed within the broader thesis that strategic deletion of native BGCs is fundamental to creating superior heterologous production chassis.

The Scientific Rationale for a Multi-Chassis Approach

The inability of any single host to optimally express all BGCs stems from several biological and genetic factors:

• Phylogenetic Distance & Compatibility: BGCs from taxonomically distant sources often require specialized cellular machinery, regulatory elements, or precursor pools that may be absent in a phylogenetically mismatched host [2]. Success rates for heterologous expression are generally higher when the donor organism and host are closely related [2].
• Precursor Availability & Metabolic Burden: Different BGCs draw from distinct metabolic pathways. A host optimized for Type II polyketides may lack sufficient precursors for non-ribosomal peptides, leading to suboptimal titers [3] [20].
• Cellular Toxicity & Product Compatibility: Some natural products are toxic to their heterologous host, which can inhibit growth and limit production. A multi-chassis approach allows researchers to identify a host with innate tolerance to the target compound [3].

Comparative Analysis of Engineered Chassis Strains

Diverse bacterial hosts have been engineered to function as heterologous expression platforms. The table below summarizes key chassis, their modifications, and demonstrated capabilities.

Table 1: Engineered Heterologous Chassis for Natural Product Production

Host Strain	Key Genomic Modifications	Biosynthetic Range Demonstrated	Reported Advantages	Citation
Streptomyces coelicolor A3(2)-2023	Deletion of four endogenous BGCs; introduction of multiple RMCE sites (Cre-lox, Vika-vox, Dre-rox, phiBT1-attP) [13].	Xiamenmycin, Griseorhodin	Versatile integration system; demonstrated copy-number dependent yield increase (2-4 copies tested) [13].	[13]
Streptomyces sp. A4420 CH	Deletion of 9 native polyketide BGCs [20].	Benzoisochromanequinone, glycosylated macrolide, glycosylated polyene macrolactam, heterodimeric aromatic polyketide	Superior performance in expressing all four tested polyketide BGCs compared to other model Streptomyces hosts [20].	[20]
Streptomyces aureofaciens Chassis2.0	In-frame deletion of two endogenous T2PKs gene clusters [3].	Oxytetracycline, Actinorhodin, Flavokermesic Acid, TLN-1 (pentangular)	370% increase in oxytetracycline production vs. commercial strains; efficient producer of tri-, tetra-, and penta-ring type II polyketides [3].	[3]
Burkholderia thailandensis E264	PK-NRP thailandepsin mutant; efflux mutants [2].	PKs, PK-NRPs from Betaproteobacteria and Myxococcia	Achieved high titer ( 985 mg L⁻¹ ) of compound FK228 C; low virulence to humans and animals [2].	[2]

Table 2: Quantitative Production Performance of Selected Chassis

Target Compound	BGC Source	Heterologous Host	Reported Titer	Notable Host Feature	Citation
Oxytetracycline	S. rimosus	S. aureofaciens Chassis2.0	Highly efficient (370% increase)	Industrial high-yield strain background [3]	[3]
FK228 C	Betaproteobacteria	B. thailandensis E264	985 mg L⁻¹	Optimized efflux systems [2]	[2]
Xiamenmycin	Streptomyces sp.	S. coelicolor A3(2)-2023	Copy-number dependent yield	Multi-copy integration via RMCE [13]	[13]
Unspecified Metabolites	Various	S. coelicolor M1152	20 to 40-fold yield increase	Contains advantageous rpoB and rpsL mutations [20]	[20]

Essential Research Reagent Solutions

The following table catalogs key reagents and tools critical for multi-chassis engineering and heterologous expression workflows.

Table 3: Key Research Reagent Solutions for Chassis Engineering and BGC Expression

Reagent / Tool Name	Function / Application	Example Use Case	Citation
Micro-HEP Platform	A heterologous expression platform using engineered E. coli for BGC modification/conjugation and optimized Streptomyces chassis for expression [13].	Stable transfer and expression of BGCs with repetitive sequences; enabled discovery of griseorhodin H [13].	[13]
Recombinase-Mediated Cassette Exchange (RMCE)	Enables precise, marker-free integration of BGCs into specific chromosomal loci using orthogonal site-specific recombinase systems (Cre-lox, Vika-vox, etc.) [13].	Avoids plasmid backbone integration and allows for multi-copy chromosomal integration to boost yield [13].	[13]
Orthogonal Synthetic Promoter Libraries	Randomized regulatory sequences (promoter + RBS) for predictable, high-level expression of refactored BGCs, minimizing host perturbation [17].	Refactoring the silent actinorhodin BGC for successful heterologous expression in S. albus J1074 [17].	[17]
ExoCET / TAR Cloning	Direct cloning methods for capturing large, intact BGCs from genomic DNA for heterologous expression [13] [3].	Cloning the complete oxytetracycline BGC from S. rimosus ATCC 10970 [3].	[13] [3]
Redα/Redβ/Redγ Recombination System	λ phage-derived recombinase system enabling precise DNA editing in E. coli using short (50 bp) homology arms [13].	Facilitates efficient modification of BGCs in the intermediate E. coli host before conjugal transfer [13].	[13]

Detailed Experimental Protocol: Evaluating a New Chassis for BGC Expression

This protocol outlines the key steps for engineering and validating a new chassis strain, focusing on the deletion of native BGCs and subsequent cross-platform testing.

Workflow: Chassis Engineering and BGC Expression

Protocol Steps

Step 1: Host Identification and Genome Sequencing

• Objective: Select a promising native producer and obtain its complete genome sequence.
• Procedure:
  • Identify a candidate strain with desirable phenotypic traits (e.g., rapid growth, efficient sporulation, high innate production of specific NP classes) [20].
  • Perform whole-genome sequencing using a hybrid long-read (e.g., Oxford Nanopore) and short-read (e.g., Illumina) approach for a high-quality assembly [20].
• Critical Parameters: Prioritize strains distantly related to existing model hosts (like S. coelicolor) to increase phylogenetic diversity in the chassis panel [20].

Step 2: In Silico Identification of Native BGCs

• Objective: Map the host's native biosynthetic potential to target for deletion.
• Procedure:
  • Annotate the assembled genome using AntiSMASH (e.g., version 8.0.1 or later) [2] [20].
  • Manually curate the results to account for superclusters and identify all type I, II, III PKS, NRPS, and hybrid BGCs [20].
• Output: A list of native BGCs, which will be candidates for deletion to reduce metabolic competition and background interference.

Step 3: Design of the Deletion Strategy

• Objective: Plan the sequential deletion of multiple native BGCs.
• Procedure:
  • Design deletion constructs for each target BGC, typically replacing the cluster with an selectable marker (e.g., aac(3)IV for apramycin resistance) and a counterselectable marker (e.g., rpsL for streptomycin sensitivity in a mutant background) [13].
  • For markerless deletions, employ a two-step Red recombination system in E. coli involving a temporary amp-ccdB or kan-rpsL cassette, followed by its excision [13].

Step 4: Sequential Deletion of Native BGCs

• Objective: Create a metabolically simplified "clean" chassis.
• Procedure:
  • Clone the deletion constructs into a suitable vector (e.g., a temperature-sensitive plasmid).
  • Introduce the construct into the host strain via conjugation or protoplast transformation.
  • Select for single-crossover integrants and subsequently for double-crossover mutants under counter-selection conditions.
  • Verify each deletion by PCR and phenotypic analysis (e.g., loss of pigmentation) [3] [20].
• Critical Parameters: The number of BGCs deleted varies; examples include 4 in S. coelicolor A3(2)-2023 [13], 9 in Streptomyces sp. A4420 CH [20], and 2 in S. aureofaciens Chassis2.0 [3].

Step 5: Phenotypic Validation of the Engineered Chassis

• Objective: Confirm that the deletions have not impaired fundamental physiological traits.
• Procedure:
  • Compare the growth rate (by OD measurement) and sporulation efficiency of the engineered chassis to its wild-type parent on solid and in liquid media [20].
  • Visually inspect colony morphology for consistency [3].

Step 6: Selection of Heterologous Test BGCs

• Objective: Choose a diverse set of BGCs to challenge the new chassis.
• Procedure: Select BGCs encoding different classes of natural products (e.g., polyketides, non-ribosomal peptides, ribosomally synthesized and post-translationally modified peptides) with varying structural complexity and from different phylogenetic sources [20].

Step 7: Cloning and Transfer of Test BGCs

• Objective: Introduce the heterologous BGCs into the new chassis and established control hosts.
• Procedure:
  • Clone the BGCs using advanced methods like ExoCET or TAR cloning into an E. coli-Streptomyces shuttle vector [13] [3].
  • For chromosomal integration, use RMCE to insert the BGC into pre-engineered attachment sites (e.g., attBphiC31, loxP, vox) without the plasmid backbone [13].
  • Transfer the constructed plasmid from an engineered E. coli donor strain (e.g., ET12567/pUZ8002 or a more stable alternative from the Micro-HEP platform) into the chassis via intergeneric conjugation [13].

Step 8: Fermentation and Metabolite Analysis

• Objective: Produce and detect the target natural products.
• Procedure:
  • Inoculate exconjugants into appropriate liquid media (e.g., GYM for xiamenmycin, M1 for griseorhodin) [13].
  • Ferment at the host's optimal temperature (e.g., 30°C for Streptomyces) for a defined period.
  • Extract metabolites from the culture broth and/or mycelium using organic solvents.
  • Analyze extracts by Liquid Chromatography-Mass Spectrometry (LC-MS) or High-Performance Liquid Chromatography (HPLC) and compare to authentic standards if available [13] [20].

Step 9: Comparative Titer Analysis

• Objective: Quantitatively benchmark the performance of the new chassis against established hosts.
• Procedure: Measure the titer (e.g., mg/L) of the target compound produced by the new chassis and compare it directly with the production levels in other common hosts like S. albus J1074, S. coelicolor M1152, and S. lividans TK24 under identical fermentation and analytical conditions [20].

The paradigm of multi-chassis engineering is essential for unlocking the full potential of microbial natural products. As evidenced by the success of diverse engineered hosts like Streptomyces sp. A4420 CH, S. aureofaciens Chassis2.0, and specialized Burkholderia hosts, the strategic deletion of native BGCs creates a simplified metabolic background that enhances precursor availability and facilitates the detection of heterologously expressed compounds. There is no one-size-fits-all solution. Future discovery and production efforts will therefore depend on the continued expansion of a diverse panel of well-characterized heterologous hosts, each offering unique advantages for expressing specific classes of BGCs.

Promoter Engineering and Transcriptial Control for Tuning Expression

Within the framework of developing robust heterologous production chassis, the deletion of native biosynthetic gene clusters (BGCs) is a critical first step to eliminate competitive metabolic pathways and background interference. However, the success of this strategy ultimately depends on achieving precise and high-level expression of heterologously introduced BGCs. Promoter engineering emerges as a fundamental technique to disrupt native transcriptional regulation and exert fine control over gene expression in these engineered chassis. By replacing native promoters with synthetic, tunable alternatives, researchers can activate silent BGCs, optimize flux through biosynthetic pathways, and maximize the production of valuable natural products in surrogate hosts. This Application Note provides detailed protocols and key considerations for implementing promoter engineering strategies to enhance heterologous production.

Core Principles and Key Strategies

Orthogonal Transcriptional Regulatory Modules

For efficient BGC refactoring, a panel of orthogonal transcriptional regulatory elements—including promoters, ribosomal binding sites (RBSs), and terminators—is indispensable [17]. Promoters are critical for the first stage of gene expression and are a primary target for engineering. Several advanced design concepts have emerged:

• Completely Randomized Synthetic Promoter Libraries: A novel design in Streptomyces albus J1074 involves complete randomization of sequences in both the promoter and RBS regions, only partially fixing the -10/-35 regions and the Shine-Dalgarno sequence [17]. This generates highly orthogonal regulatory cassettes with varying transcriptional strengths (strong, medium, weak), which is crucial for multiplex promoter engineering in actinomycetes.
• Metagenomic Mining of Universal Promoters: To access phylogenetic diversity, researchers have mined 184 microbial genomes to generate a diverse library of natural 5' regulatory sequences from Actinobacteria, Archaea, Bacteroidetes, Cyanobacteria, Firmicutes, Proteobacteria, and Spirochetes [17]. This expands the repertoire of promoters functional across a wide range of bacteria.
• Stabilized Promoters for Constant Expression: Using transcription-activator like effectors (TALEs)-based incoherent feedforward loops (iFFL), engineers have developed promoters in E. coli that maintain constant expression levels regardless of copy number, growth conditions, or other stressors [17]. This robustness is valuable for pathway optimization.

Table 1: Advanced Promoter Engineering Strategies

Strategy	Key Feature	Demonstrated Host	Application
Completely Randomized Design [17]	High sequence divergence; fully randomized promoter and RBS regions	Streptomyces albus J1074	Multiplex promoter engineering of multi-operon BGCs
Metagenomic Mining [17]	Broad host range; phylogenetically diverse origins	Multiple bacterial species	BGC refactoring in underexplored bacterial taxa
iFFL-Stabilized Promoters [17]	Copy-number and condition-independent expression	Escherichia coli	Robust metabolic pathways resistant to genomic context or stressors

Novel BGC Refactoring Techniques

Refactoring involves the systematic replacement of native genetic control elements with synthetic counterparts to optimize expression and functionality.

• Multiplexed CRISPR-Based Editing: Techniques like mCRISTAR, miCRISTAR, and mpCRISTAR leverage yeast homologous recombination (YHR) and CRISPR for highly efficient, simultaneous replacement of multiple native promoters within a BGC [17]. For instance, the silent actinorhodin (ACT) BGC from Streptomyces coelicolor was activated in a heterologous host by replacing its seven native promoters with four strong synthetic regulatory cassettes [17].
• Integrated Heterologous Expression Platforms: Platforms like Micro-HEP (Microbial Heterologous Expression Platform) combine engineered E. coli strains for BGC modification and conjugation with optimized Streptomyces chassis strains for expression [23]. This system uses Recombinase-Mediated Cassette Exchange (RMCE) with orthogonal systems (Cre-lox, Vika-vox, Dre-rox, and phiBT1-attP) to integrate refactored BGCs into defined genomic loci of a chassis strain, avoiding plasmid backbone integration [23].

Experimental Protocols

Purpose: To simultaneously replace multiple native promoters in a target BGC with synthetic regulatory cassettes for activation and optimization.

Materials:

• Yeast strain (e.g., Saccharomyces cerevisiae) with high recombination efficiency
• BGC cloned in a yeast-E. coli shuttle vector
• DNA fragments containing synthetic promoter cassettes with 40-bp homology arms to target regions
• CRISPR-Cas9 components (gRNAs, Cas9 expression plasmid)
• Standard reagents for yeast transformation, plasmid recovery, and E. coli transformation

Procedure:

• Design and Synthesis: Design gRNAs to introduce double-strand breaks near the native promoters you intend to replace. Synthesize double-stranded DNA fragments of your synthetic promoter cassettes, flanked by ~40 bp homology arms matching the sequences upstream and downstream of the native promoter cleavage site.
• Co-transformation: Co-transform the yeast strain with the following:
  • The BGC-containing shuttle vector.
  • The Cas9 expression plasmid.
  • Plasmid(s) expressing the designed gRNAs.
  • The pooled synthetic promoter cassette fragments.
• Yeast Homologous Recombination: Allow yeast homologous recombination to repair the Cas9-induced breaks by incorporating the synthetic promoter cassettes. Plate transformations on appropriate selective media and incubate for 2-3 days.
• Plasmid Recovery: Isolate plasmids from resulting yeast colonies and transform into an appropriate E. coli strain for propagation and amplification.
• Validation: Screen E. coli clones by colony PCR and sequence the modified regions of the BGC to confirm accurate promoter replacement.

Purpose: To integrate a refactored BGC into a pre-engineered RMCE site of a heterologous production chassis (e.g., S. coelicolor A3(2)-2023) lacking native BGCs.

Materials:

• Chassis strain S. coelicolor A3(2)-2023 (or similar) with endogenous BGCs deleted and containing orthogonal RMCE sites (e.g., loxP, vox, rox, attP) [23]
• Donor E. coli strain from the Micro-HEP platform (e.g., GB2005, GB2006) containing the BGC assembled in an RMCE-compatible plasmid with the corresponding RTS (e.g., lox5171, lox2272) [23]
• Antibiotics for selection (apramycin, kanamycin, nalidixic acid)
• LB and MS media
• Required Genetic Elements:
  • oriT sequence for conjugation
  • An integrase gene (e.g., cre, vika, dre) under a constitutive promoter
  • A pair of heterospecific RTSs flanking the BGC in the donor plasmid
  • The cognate RTS pair in the chromosome of the chassis strain

Procedure:

• Donor Plasmid Construction: Clone the target BGC into an RMCE donor vector between a pair of heterospecific recombination target sites (RTSs), such as lox5171 and lox2272. The plasmid must also carry the origin of transfer (oriT) and an integrase gene.
• Conjugative Transfer: Mate the donor E. coli strain (containing the RMCE donor plasmid) with the recipient Streptomyces chassis strain on MS agar plates. Incubate at 30°C for 8-16 hours to allow conjugation.
• Selection and Integration: Harvest the conjugation mixture and plate onto selective media containing apramycin (to select for the integrated BGC) and nalidixic acid (to counter-select against the E. coli donor). The expressed integrase catalyzes recombination between the plasmid and chromosome RTS pairs, leading to BGC integration.
• Strain Validation: Pick exconjugants and validate by PCR and/or Southern blotting to confirm: a) successful integration of the BGC at the correct chromosomal locus, and b) the absence of the plasmid backbone.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Genetic Elements and Host Strains for Promoter Engineering

Reagent / Tool	Function / Key Feature	Example Use Case
Synthetic Promoter Libraries (e.g., for S. albus) [17]	Fully randomized promoter-RBS cassettes with varying strengths (strong, medium, weak).	Multiplexed refactoring of multi-gene BGCs to achieve balanced, high-level expression.
Orthogonal Recombinase Systems (Cre, Vika, Dre, PhiBT1) [23]	Enable precise RMCE; each system recognizes unique target sites (loxP, vox, rox, attP) with no cross-talk.	Stable, copy-number controlled integration of BGCs into dedicated loci of a chassis strain.
Micro-HEP Platform E. coli Strains (e.g., GB2005, GB2006) [23]	Engineered for high-efficiency recombineering and conjugative transfer of large DNA constructs.	Modification and subsequent transfer of large, complex BGCs into Streptomyces chassis.
Engineered Chassis Strain (e.g., S. coelicolor A3(2)-2023) [23]	Deletion of four endogenous BGCs to reduce metabolic burden and background interference.	Clean background host for heterologous expression of refactored BGCs.
Redαβγ Recombineering System [23]	Rhamnose-inducible system in E. coli using short homology arms (50 bp) for precise genetic edits.	Efficient insertion of RMCE cassettes and other modifications into BGC-containing plasmids in E. coli.
Phenacyl Bromide	Phenacyl Bromide|98%+ Purity
Allcorr	Allcorr, CAS:100438-36-6, MF:C21H21O2P	Chemical Reagent

Data Presentation and Analysis

Quantitative assessment is crucial for evaluating the success of promoter engineering strategies. The following table summarizes performance metrics from key studies.

Table 3: Quantitative Outcomes of Promoter Engineering and Heterologous Expression

Engineered System / Intervention	Quantitative Outcome	Impact / Implication
Multi-copy BGC Integration via RMCE (for xiamenmycin) [23]	Yield increase correlated with copy number (2 to 4 copies integrated).	Demonstrates that increasing BGC copy number is a viable strategy for yield optimization in a deleted-BGC chassis.
PVX Vector + Heterologous VSR (in plants) [43]	GFP accumulation up to 0.50 mg/g FW (≈3.8-fold increase over parental vector). Antigen yields increased over 100-fold.	Co-expression of strong viral suppressors of RNA silencing (VSRs) can dramatically boost protein yields in heterologous systems.
Reverse Orientation VSR Cassette [43]	Mitigated transcriptional interference, enhancing both target protein and VSR expression.	The relative orientation of co-expressed genetic elements is a critical design parameter to maximize output.

Concluding Remarks

Promoter engineering and advanced transcriptional control are not merely supportive techniques but are foundational to the success of heterologous production chassis. By applying the protocols and strategies outlined here—from multiplexed promoter replacement to stable RMCE integration—researchers can effectively tap into the vast potential of silent BGCs. The integration of these tools with chassis strains engineered for reduced metabolic burden creates a powerful synergy, paving the way for the discovery and high-yield production of novel bioactive compounds. Future directions will likely involve the integration of machine learning models to predict optimal promoter combinations and the continued expansion of orthogonal genetic tools for ever-greater control over gene expression.

Within the engineering of microbial heterologous production chassis, the deletion of native biosynthetic gene clusters (BGCs) is a foundational step to minimize metabolic competition and background interference [13] [44]. Building upon this clean genetic background, chromosomal amplification—the integration of multiple copies of a heterologous BGC into the host's chromosome—emerges as a powerful subsequent strategy to dramatically increase the yield of target natural products [45] [44]. This approach effectively multiplies the "production lines" for the compound of interest, overcoming limitations posed by low transcription and translation rates or rate-limiting enzymatic steps in the pathway [44]. This Application Note details the principles, protocols, and key reagents for implementing this yield-enhancement strategy.

Results and Data Analysis

Quantitative Impact of BGC Copy Number Amplification

Case studies across different natural products and host strains consistently demonstrate that increasing BGC copy number leads to significant improvements in production titers. The data summarized in Table 1 provide compelling evidence for the efficacy of this strategy.

Table 1: Effect of BGC Copy Number Amplification on Heterologous Production

Natural Product	Host Strain	Copy Number	Production Titer	Fold Increase	Citation
Aborycin	S. coelicolor M1346	1 (Native)	~4.9 mg/L	1x (Reference)	[45]
		3	~10.4 mg/L	~2.1x
Spinosad	S. coelicolor M1146	1	~5.6 μg/L	1x (Reference)	[44]
		5	~1253.9 μg/L	~224x
Xiamenmycin	S. coelicolor A3(2)-2023	2-4	Increasing yield	Dose-dependent	[13]

Key Workflows and Pathway Relationships

The process of chromosomal amplification involves several critical steps, from host preparation to final verification of increased production. The following diagram illustrates the core workflow and logical relationships between these stages.

Experimental Protocols

Protocol 1: Multi-Copy Integration via RMCE

Title: Multi-Copy Integration of BGCs using Recombinase-Mediated Cassette Exchange (RMCE). Application: Integration of 2-4 copies of a BGC into pre-defined chromosomal loci of a Streptomyces chassis [13]. Principle: This protocol uses orthogonal tyrosine recombinase systems (Cre-lox, Vika-vox, Dre-rox) to facilitate precise, marker-less exchange and integration of BGCs into engineered acceptor sites on the chromosome, avoiding plasmid backbone integration [13].

Procedure:

• Chassis Engineering:
  • Start with an engineered chassis (e.g., S. coelicolor A3(2)-2023) where four endogenous BGCs (act, red, cpk, cda) have been deleted [13] [44].
  • Introduce multiple RMCE acceptor sites (e.g., loxP, vox, rox) into the chromosome of the chassis strain.

• Vector Construction:

  • Clone the target BGC into an E. coli-Streptomyces shuttle vector that contains the corresponding recombination target sites (RTS) flanking the BGC.
  • Include an origin of transfer (oriT) and the appropriate integrase gene (e.g., cre, vika, dre) on the vector under an inducible promoter.

• Conjugal Transfer:

  • Mobilize the constructed plasmid from an E. coli donor strain (e.g., ET12567/pUZ8002 or a strain from the Micro-HEP platform) into the Streptomyces chassis via biparental conjugation [13].

• Integration and Screening:

  • Induce the expression of the integrase to catalyze the recombination between the plasmid-borne RTS and the chromosomal acceptor sites.
  • Screen exconjugants for successful integration using antibiotic resistance and verify by PCR. The RMCE reaction leaves the acceptor sites intact, allowing for subsequent rounds of integration to achieve multi-copy strains [13].

Protocol 2: Tandem Amplification Using the ZouA System

Title: Tandem Amplification of BGCs using the ZouA-dependent DNA Amplification System. Application: Generation of strains with high copy numbers (e.g., 5 copies) of a targeted BGC for significant yield enhancement [44]. Principle: This method exploits the ZouA system from Streptomyces kanamyceticus, which mediates tandem duplication of genomic regions flanked by specific recognition sequences (RsA and RsB) under kanamycin selection pressure [44].

Procedure:

• Vector Construction:
  • Engineer a vector (e.g., based on pSET152) containing the ZouA system: the zouA gene, a kanamycin resistance gene (kanR), and the recognition sequences RsA and RsB.
  • Clone the target BGC between the RsA and RsB sites. Ensure the vector is equipped for conjugation and integration into the host chromosome.

• Strain Construction:

  • Transfer the constructed vector into the heterologous host (e.g., S. coelicolor M1146) via conjugation to create a primary integration strain with a single copy of the BGC.

• Selective Amplification:

  • Grow the primary integration strain under strong kanamycin selection. The selection pressure drives the ZouA-mediated tandem amplification of the DNA segment between RsA and RsB, which contains both the BGC and the kanR gene.
  • Perform continuous passaging under selection to enrich for strains with higher copy numbers.

• Verification:

  • Determine the final BGC copy number in the amplified strain using quantitative PCR (qPCR). Compare the amplification of a gene within the BGC to a single-copy reference gene in the genome [44].

The Scientist's Toolkit

Table 2: Essential Research Reagents and Solutions for Chromosomal Amplification

Reagent / Tool	Function / Explanation	Example Use Case
Engineered S. coelicolor Chassis	Host strains with native BGCs deleted (e.g., M1146, M1346, A3(2)-2023) to reduce metabolic competition and simplify metabolite analysis.	Foundation for heterologous expression in all cited protocols [13] [45] [44].
RMCE Systems (Cre-lox, etc.)	Orthogonal tyrosine recombinase systems for precise, multi-copy, marker-less integration of BGCs at specific chromosomal loci.	Micro-HEP platform for integrating xiamenmycin BGC [13].
ZouA Amplification System	Enzyme system that mediates tandem duplication of DNA regions flanked by RsA and RsB sequences under antibiotic selection.	Used to amplify spinosad BGC to 5 copies in S. coelicolor [44].
Conjugative E. coli Strains	Donor strains (e.g., ET12567/pUZ8002) capable of mobilizing shuttle vectors from E. coli to Streptomyces via conjugation.	Essential for transferring large BGC-containing plasmids into Streptomyces hosts [13].
Bioinformatic Tools (antiSMASH, PRISM)	Software for in silico identification and analysis of BGCs from genomic data, predicting their boundaries and potential products.	Initial BGC discovery and analysis [46] [21].
CRISPR-Cas9 System	Enables efficient knockout of native BGCs or regulatory genes in the chassis to further optimize production.	Used to delete negative regulatory genes in an aborycin production strain [45].
Shofu Hybond	Shofu Hybond Dental Cements
phosphatine	phosphatine, CAS:102087-56-9, MF:C5H12Br2N2	Chemical Reagent

Chromosomal amplification of BGCs is a highly effective strategy for overcoming the low production yields that often plague heterologous expression experiments. By integrating multiple copies of a target BGC into a pre-engineered chassis devoid of competing native pathways, researchers can achieve substantial, sometimes dramatic, improvements in the production of valuable natural products like aborycin and spinosad. The choice between systematic multi-copy integration (e.g., using RMCE) and selection-driven tandem amplification (e.g., using the ZouA system) depends on the specific requirements of the project, including the desired copy number, the availability of genetic tools for the host, and the need for precise genomic control. This approach, integrated with dynamic metabolic engineering strategies, represents a cornerstone of modern microbial strain development for drug discovery and development.

Proof of Concept: Validating and Benchmarking Chassis Performance

Within the paradigm of heterologous expression for natural product discovery and production, the engineering of specialized microbial chassis has become a cornerstone strategy. This approach involves the deletion of native biosynthetic gene clusters (BGCs) to create a metabolically simplified host with a clean background, enhanced precursor flux, and reduced analytical interference. Among the most prominent chassis in actinobacterial research are Streptomyces coelicolor M1152, Streptomyces albus J1074, and Streptomyces lividans TK24. These strains represent a critical evolutionary step in chassis development, moving from native producers to engineered platforms optimized for heterologous production. This application note provides a systematic benchmarking of these established chassis, summarizing quantitative performance data and detailing standardized protocols for their utilization in the heterologous production of microbial natural products, framed within the broader thesis of chassis development via native BGC deletion.

The rational engineering of Streptomyces chassis strains primarily involves the deletion of endogenous secondary metabolite BGCs to re-route metabolic resources towards heterologous pathways and simplify the metabolic background for easier detection of target compounds [38]. The following table summarizes the key genetic modifications and defining characteristics of the three benchmarked chassis strains.

Table 1: Engineered Streptomyces Chassis Strains: Lineage and Key Modifications

Chassis Strain	Parental Strain	Key Genetic Modifications	Primary Rationale for Engineering
S. coelicolor M1152	S. coelicolor M145	Deletion of four endogenous BGCs (actinorhodin, prodiginine, coelimycin, calcium-dependent antibiotic); introduction of a point mutation in the rpoB gene (conferring rifampicin resistance) [20] [47].	To create a clean background and enhance secondary metabolite production via a pleiotropic regulatory mutation that increases RNA polymerase affinity [47].
S. albus J1074	S. albus J1074	A derivative strain, Del14, has been engineered with the deletion of 15 native secondary metabolite BGCs [20] [47].	To drastically reduce native metabolic competition and background interference, creating a genomically minimized host for expressing heterologous BGCs [20].
S. lividans TK24	S. lividans 66	Deletion of the endogenous plasmid SLP2 and SLP3, and a streptomycin resistance mutation in rpsL [20]. Engineered derivatives like ΔYA11 have further deletions of up to nine native BGCs [20].	To improve genetic manipulability (acceptance of methylated DNA), reduce protease activity, and enhance secondary metabolite production through ribosomal engineering [20] [47].

Quantitative Benchmarking Data

A 2024 benchmarking study directly compared the performance of these chassis strains against a newly developed host, Streptomyces sp. A4420 CH, by expressing four distinct polyketide BGCs [20]. The results provide a quantitative comparison of their capabilities in heterologous production.

Table 2: Performance Benchmarking of Streptomyces Chassis Strains for Heterologous Polyketide Production [20]

Heterologous Host Strain	Benzoisochromanequinone (Actinorhodin) Production	Glycosylated Macrolide Production	Glycosylated Polyene Macrolactam Production	Heterodimeric Aromatic Polyketide Production	Overall Performance Notes
S. coelicolor M1152	Not Detected	Not Detected	Not Detected	Not Detected	Failed to produce the tested compounds under the experimental conditions.
S. albus J1074	Not Detected	Not Detected	Not Detected	Not Detected	Failed to produce the tested compounds under the experimental conditions.
S. lividans TK24	Not Detected	Not Detected	Not Detected	Not Detected	Failed to produce the tested compounds under the experimental conditions.
Streptomyces sp. A4420 CH	Detected	Detected	Detected	Detected	Successfully produced all four target metabolites, outperforming all established chassis.

This comparative analysis demonstrates a critical principle in chassis selection: no single host is universally capable of expressing all BGCs [20] [18]. The failure of the established chassis to produce the benchmark polyketides underscores the need for a diverse panel of heterologous hosts to maximize the success rate in discovering and producing novel natural products [20].

Detailed Experimental Protocols

Protocol 1: General Workflow for Heterologous Expression in Streptomyces Chassis

This protocol outlines the core process for expressing a biosynthetic gene cluster (BGC) in a engineered Streptomyces chassis, from cluster selection to product analysis [38] [48].

4.1.1 Workflow Diagram: Heterologous Expression in Streptomyces Chassis

Materials:

• Bioinformatics Tools: antiSMASH [48], MIBiG repository [48].
• Cloning System: E. coli-Streptomyces shuttle vector (e.g., φC31-based integrative plasmid) [47].
• Assembly Method: Gibson Assembly, TAR, or SSRTA, chosen based on BGC size [48].
• Chassis Strains: S. coelicolor M1152, S. albus J1074, or S. lividans TK24 [20].
• Fermentation Media: Suitable liquid media such as TSB or SFM [20].
• Analytical Equipment: LC-MS system, HPLC system, NMR spectrometer.

Procedure:

• BGC Identification & Selection: Identify the target BGC from a donor organism's genome sequence using antiSMASH. Compare the cluster against the MIBiG database to prioritize novel or valuable pathways [48].
• Genetic Refactoring (Optional): For silent or poorly expressed BGCs, replace native promoters with strong, constitutive (e.g., ermEp) or inducible (e.g., tipA) promoters to enhance expression. This step may include codon optimization and RBS engineering [38].
• Vector Construction: Clone the entire BGC into an appropriate E. coli-Streptomyces shuttle vector. For large clusters (>50 kb), use Bacterial Artificial Chromosomes (BACs) or Transformation-Associated Recombination (TAR) [38] [48].
• Transformation: Introduce the constructed vector into the chosen Streptomyces chassis strain via intergeneric conjugation from an E. coli donor strain (e.g., ET12567/pUZ8002). Select exconjugants using the appropriate antibiotics [47].
• Fermentation & Induction: Inoculate exconjugants into liquid production media and incubate with shaking at 30°C for 5-7 days. If an inducible system is used, add the inducer (e.g., thiostrepton) at the optimal time point [20].
• Metabolite Extraction: Harvest the culture by centrifugation. Separate the supernatant and mycelial pellet. Extract compounds from the supernatant with a resin (e.g., XAD-16) and from the mycelium with organic solvents like methanol or ethyl acetate [20].
• Analysis & Purification: Analyze crude extracts by LC-MS to detect target compounds based on expected mass. Use analytical HPLC to guide subsequent purification by preparative HPLC [48].
• Structure Elucidation: Purify the compound to homogeneity and determine its structure using spectroscopic methods, primarily NMR [48].

Protocol 2: Benchmarking Chassis Performance

This protocol describes a standardized method for comparing the performance of different chassis strains when expressing the same BGC, as utilized in the referenced 2024 study [20].

Materials:

• BGC: A well-characterized BGC, such as one for a polyketide like oxytetracycline or actinorhodin, cloned into a suitable vector [20] [3].
• Chassis Panel: S. coelicolor M1152, S. albus J1074, S. lividans TK24, and other strains of interest.
• Controls: The parental strain of the BGC (if available) and an empty-vector control for each chassis.
• Media: Standardized liquid production media (e.g., SG medium) [47].

Procedure:

• Strain Preparation: Introduce the identical BGC-containing vector into each chassis strain via a standardized conjugation protocol to ensure isogenic constructs.
• Parallel Fermentation: Inoculate each engineered strain and its empty-vector control into multiple flasks of production media. Use a highly reproducible inoculum (e.g., from a spore suspension) and incubate under identical conditions [20].
• Time-Course Sampling: Collect culture samples at regular intervals (e.g., 24, 48, 72, 96, 120 hours). Process one set for metabolite extraction and another for dry cell weight (DCW) measurement to correlate production with growth [20].
• Quantitative Analysis: Analyze the extracted metabolites by LC-MS. Quantify the target compound yield against a standard curve of the authentic compound. Use the DCW data to report production as mg/L or mg/g DCW [20].
• Data Comparison: Compile the production titers, growth curves, and time-to-production profiles for all tested chassis strains into a comparative table (see Table 2) to identify the optimal host for the specific BGC.

The Scientist's Toolkit: Research Reagent Solutions

The following table details key reagents, tools, and materials essential for heterologous expression experiments in Streptomyces chassis strains.

Table 3: Essential Research Reagents and Tools for Streptomyces Chassis Engineering

Item Name	Function/Application	Example Use Case
antiSMASH Software	In silico identification and annotation of BGCs in microbial genomes [48].	Preliminary analysis of a donor strain's genome to select a target BGC for heterologous expression.
φC31-based Integrative Vector	Shuttle vector for cloning BGCs in E. coli and stable integration into the attB site of Streptomyces chromosomes [47].	Stable introduction of a ~30 kb polyketide BGC into the genome of S. lividans TK24.
ET12567/pUZ8002 E. coli Strain	Non-methylating E. coli donor strain for intergeneric conjugation with Streptomyces [47].	Efficient transfer of a BAC containing a large BGC from E. coli to the non-competent S. coelicolor M1152.
ermEp* Promoter	A strong, constitutive promoter frequently used to drive high-level expression of genes in Streptomyces [38].	Replacing the native promoter of a cryptic BGC's key synthetase gene during refactoring to activate expression.
XAD-16 Resin	Hydrophobic adsorption resin used to capture non-polar secondary metabolites from large volumes of culture broth [20].	Recovery of a lipopeptide antibiotic from the fermentation supernatant of S. albus J1074.
Liquid Chromatography-Mass Spectrometry (LC-MS)	Analytical platform for detecting, quantifying, and partially characterizing metabolites in complex extracts [48].	Rapid screening of S. lividans TK24 exconjugants for production of the target compound based on its predicted mass.
Osmolite	Osmolite, CAS:102257-18-1, MF:C8H10N2O2	Chemical Reagent
Sporol	Sporol, CAS:101401-88-1, MF:C9H17NO2S	Chemical Reagent

The benchmarking data confirms that while S. coelicolor M1152, S. albus J1074, and S. lividans TK24 are foundational tools in the heterologous expression toolkit, they are not universally successful [20]. The future of chassis research lies in expanding the diversity of the host panel. Promising new chassis, such as Streptomyces sp. A4420 CH [20] and Streptomyces aureofaciens Chassis2.0 [3], are being engineered from robust industrial producers and show superior performance for specific classes of natural products. The strategic path forward involves a multi-chassis approach, where researchers systematically screen BGCs across a curated panel of specialized hosts to rapidly identify the most compatible platform for a given pathway, thereby accelerating the discovery and production of novel therapeutic compounds.

In the field of microbial natural product discovery, the development of optimized heterologous production chassis is a cornerstone of synthetic biology. A significant strategy in this endeavor involves the deletion of native biosynthetic gene clusters (BGCs) to create metabolically simplified hosts that are primed for the heterologous expression of valuable compounds. The success of these engineered chassis is quantitatively evaluated against three critical metrics: titer (the yield of the target product), fermentation robustness (the consistency of production under scaled conditions), and genetic stability (the retention of production capacity over successive generations). This Application Note provides a consolidated overview of key quantitative data, detailed protocols for chassis development, and essential research tools, providing a framework for researchers to engineer and evaluate superior production hosts.

Quantitative Performance of Advanced Chassis Strains

The strategic deletion of native BGCs in microbial hosts has demonstrated significant improvements in the production of heterologously expressed natural products. The following tables summarize performance data for several recently developed chassis strains.

Table 1: Performance of Genome-Reduced Streptomyces Chassis Strains

Chassis Strain	Genetic Modification	Key Quantitative Outcomes (vs. Parental Strain)	Reference
Streptomyces sp. A4420 CH	Deletion of 9 native polyketide BGCs	Capable of producing all 4 tested heterologous polyketide metabolites; outperformed parental strain and conventional hosts ( [20].
S. albus Del14	Deletion of 15 native antibiotic BGCs	~2-fold higher production for 5 heterologously expressed BGCs ( [49].
S. lividans ΔYA11	Deletion of 10 endogenous antibiotic BGCs	4.5-fold increase in deoxycinnamycin production; higher growth rate ( [49].

Table 2: Performance of Industrial High-Yield Derived Chassis

Chassis Strain	Origin and Genetic Modification	Key Quantitative Outcomes	Reference
S. aureofaciens Chassis2.0	High-yield CTC producer; deletion of two endogenous T2PKs clusters	370% increase in oxytetracycline production relative to commercial strains; high-efficiency production of tri-ring T2PKs ( [3].
S. aureofaciens J1-022	Native high-yield CTC producer; unmodified	Successfully produced oxytetracycline, while model chassis S. albus J1074 and S. lividans TK24 failed under the same conditions ( [3].

Experimental Protocols for Chassis Development and Evaluation

Protocol: Construction of a Polyketide-FocusedStreptomycesChassis

This protocol outlines the metabolic simplification of a Streptomyces host for enhanced heterologous production of polyketides, based on the engineering of Streptomyces sp. A4420 [20].

1. Genome Sequencing and In Silico Analysis:

• Genomic DNA Extraction: Extract high-quality genomic DNA from the parental strain using a standard microbial DNA extraction kit.
• Sequencing and Assembly: Perform whole-genome sequencing using a hybrid approach (e.g., Illumina for short-read accuracy and Oxford Nanopore for long-read scaffolding). Assemble the genome.
• BGC Identification: Analyze the assembled genome using the AntiSMASH software (e.g., version 7.1.0) to identify all native biosynthetic gene clusters, with a focus on Type I, Type II, and hybrid NRPS-PKS clusters [20].

2. Design of Deletion Constructs:

• For each target BGC, design a deletion construct that will remove the entire core biosynthetic region.
• Amplify approximately 1.5 - 2 kb homology arms (upstream and downstream) from the target locus.
• Clone these arms into a suicide vector containing an apramycin resistance marker (aac(3)IV) and the necessary elements for conjugative transfer (oriT).

3. Conjugative Transfer and Mutant Selection:

• Introduce the deletion constructs from E. coli ET12567/pUZ8002 into the Streptomyces host via intergeneric conjugation.
• Select for exconjugants on apramycin-containing media.
• Screen for double-crossover events (i.e., loss of the vector backbone) by replica-plating onto media containing antibiotics to which the suicide vector confers sensitivity (e.g., kanamycin).

4. Verification of Mutants:

• Verify successful gene cluster deletions via PCR using primers that bind outside the deleted region.
• Perform fermentation of the mutant strain and analyze the metabolic profile using LC-MS to confirm the absence of the native compound(s), indicating successful pathway elimination.

5. Phenotypic Confirmation:

• Assess the growth rate and sporulation efficiency of the engineered CH strain in standard liquid media (e.g., TSB, SFM) to ensure that the deletions have not compromised primary metabolism [20].

Protocol: Benchmarking Chassis Performance

This protocol describes the comparative evaluation of a newly engineered chassis against established model hosts [20].

1. Selection of Heterologous BGCs:

• Select a panel of 3-5 heterologous BGCs that encode distinct polyketide scaffolds (e.g., benzoisochromanequinone, glycosylated macrolide).

2. Standardized BGC Transfer:

• Clone each target BGC into identical expression vectors (e.g., Ï•C31-based integrative vectors) under the control of a constitutive promoter.
• Introduce each vector construct into the panel of heterologous hosts (e.g., the new chassis, S. coelicolor M1152, S. lividans TK24, S. albus J1074) using conjugation or protoplast transformation.

3. Fermentation and Metabolite Analysis:

• Inoculate all strains carrying the same BGC in parallel in a defined production medium.
• Cultivate under identical conditions (temperature, shaking speed, duration).
• Harvest cultures and extract metabolites using a standardized solvent system (e.g., ethyl acetate).
• Analyze extracts via HPLC-MS/MS.
• Quantify the titer of the target metabolite using a calibration curve from a pure standard or by comparing integrated peak areas.

4. Data Compilation and Analysis:

• Compile the titers for each BGC across all tested hosts.
• Create a matrix to visualize production capabilities, clearly identifying the chassis that successfully produces the highest titers for the broadest range of compounds.

Workflow Visualization: From Native Strain to Validated Chassis

The following diagram illustrates the logical workflow for developing and validating a high-performance heterologous production chassis.

Diagram Title: Workflow for Developing a Heterologous Production Chassis

The Scientist's Toolkit: Essential Research Reagents and Solutions

Table 3: Key Research Reagent Solutions for Chassis Engineering

Reagent / Solution	Function in Experiment	Specific Example / Notes
AntiSMASH	In silico identification and annotation of BGCs in a genome sequence.	Essential for the initial targeting of native clusters for deletion. Version 7.1.0 provides improved detection of various BGC types [20].
Ï•C31 Integrative Vector	Stable chromosomal integration of heterologous BGCs into the host genome.	Provides stable maintenance without the need for antibiotic selection, crucial for long-term genetic stability [50] [20].
Conjugative E. coli Strain	Facilitates the transfer of genetic material from E. coli to Streptomyces.	E. coli ET12567/pUZ8002 is a standard non-methylating strain that enables efficient conjugative transfer of plasmids [20].
aac(3)IV Marker	Selection of successful exconjugants and mutants.	Apramycin resistance gene; a common selectable marker in actinomycete genetics [20].
CRISPR-Cas9 System	Enables precise, multiplexed genome editing for deleting BGCs.	Can be used to simultaneously target multiple native BGCs, significantly accelerating the chassis construction process [17] [49].
Standardized Production Media	Provides a consistent environment for evaluating and comparing chassis performance.	Media such as TSB or SFM allow for fair comparison of titer and growth between different engineered strains and wild-type controls [20].
REGOPAR	REGOPAR, CAS:110445-23-3, MF:C43H80O	Chemical Reagent
Reactive blue 224	Reactive blue 224, CAS:122390-99-2, MF:C6H14Br2N2	Chemical Reagent

Within modern natural product discovery, the heterologous expression of Biosynthetic Gene Clusters (BGCs) in engineered chassis has emerged as a pivotal strategy for accessing novel chemical diversity. A foundational approach in this field involves the deletion of native BGCs in a candidate host organism. This process creates a clean genetic background, or "pigmented-faded" host, which minimizes precursor competition and eliminates the production of confounding native metabolites, thereby optimizing the host for the production of target compounds [14] [3]. This application note details the protocols and data demonstrating how such engineered chassis enable the efficient discovery and overproduction of diverse natural products.

Engineered chassis have demonstrated significant efficacy in the heterologous production of various types of natural products. The following tables summarize key performance data.

Table 1: Production Efficiency of Type II Polyketides in Streptomyces aureofaciens Chassis2.0 [3]

Product (Backbone Type)	Production Tier	Reported Yield / Efficiency
Oxytetracycline (Tetra-ring)	Overproduction	370% increase relative to commercial production strains
Actinorhodin (Tri-ring)	High-efficiency Synthesis	High production efficiency (specific yield not stated)
Flavokermesic Acid (Tri-ring)	High-efficiency Synthesis	High production efficiency (specific yield not stated)
TLN-1 (Penta-ring)	Novel Discovery	Direct activation and high-level production

Table 2: Assembly Efficiency of BGC Construction Methods [14]

Assembly Method	Number of Fragments	Assembly Efficiency	Transformation Efficiency
One-pot Golden Gate	12	<20%	Low (Baseline)
Hierarchical Golden Gate	Up to 6	~100%	At least 10-fold higher

Experimental Protocols

Protocol 1: Hierarchical Golden Gate Assembly for BGC Refactoring

This protocol allows for the precise, scarless assembly of large biosynthetic gene clusters with high efficiency [14].

• BGC Domestication: Identify and remove all internal recognition sites for the type IIS restriction enzymes (e.g., BsaI, PaqCI) from the target BGC sequence. Sites within coding sequences should be eliminated via silent mutations, while those in non-coding regions can be altered by nucleotide substitution [14].
• Fragment Subcloning: Divide the domesticated BGC into manageable fragments, typically around 2 kb in size. Subclone each fragment into a dedicated entry vector (e.g., pKan) for stability and ease of handling [14].
• Primary Assembly: Combine sets of fewer than 10 entry plasmids with an intermediate destination vector (e.g., pAmp-RFP-BsaI) in a Golden Gate reaction mixture containing BsaI-HFv2 and T4 DNA ligase. This assembles the first level of multi-gene segments [14].
• Secondary Assembly: Perform a second Golden Gate assembly using two or three of the intermediate plasmids from the previous step and the final destination vector (e.g., pPAP-RFP-PaqCI). This reaction uses PaqCI and T4 DNA ligase to generate the complete, refactored BGC [14].
• Verification: Verify the correct assembly of the final construct through restriction enzyme analysis (e.g., BamHI) and/or full-length sequencing (e.g., nanopore sequencing) [14].

Protocol 2: Development of a Versatile Streptomyces Chassis

This protocol outlines the creation of a high-performance chassis tailored for the production of Type II polyketides [3].

• Host Selection: Select a high-yielding industrial strain with inherent strong metabolic flux toward the desired product class. Streptomyces aureofaciens, a high-yield producer of chlortetracycline, is an exemplary candidate for Type II polyketides [3].
• Chassis Cleanup: To mitigate competition for precursors and simplify the metabolic profile, perform an in-frame deletion of two endogenous T2PKs gene clusters. This results in a "pigmented-faded" host, designated Chassis2.0, which no longer produces the native polyketides [3].
• Heterologous Expression: Introduce the refactored BGC of interest into the engineered chassis via conjugation or transformation. The BGC should be carried on an appropriate E. coli-Streptomyces shuttle plasmid (e.g., constructed via ExoCET technology) [3].
• Fermentation and Analysis: Culture the exconjugants in a suitable production medium. Analyze the culture extracts for metabolite production using techniques such as High-Performance Liquid Chromatography (HPLC) coupled with mass spectrometry (LC-MS) and Global Natural Products Social (GNPS) molecular networking to identify novel compounds [14] [3].

Visualizing the Experimental Workflow

The following diagram illustrates the logical workflow for creating an engineered chassis and discovering novel natural products through heterologous expression.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Reagents and Tools for Chassis Engineering and BGC Expression

Item / Technique	Function / Description	Key Application in Research
Golden Gate Assembly (GGA)	A modular, scarless DNA assembly technique using Type IIS restriction enzymes [14].	High-efficiency, error-free refactoring and assembly of large BGCs.
ExoCET Technology	A method for direct cloning and assembly of large DNA fragments [3].	Construction of E. coli-Streptomyces shuttle plasmids containing intact BGCs.
Streptomyces aureofaciens Chassis2.0	An engineered host with deleted native T2PKs gene clusters [3].	Versatile chassis for high-titer production of diverse Type II polyketides.
Global Natural Products Social (GNPS)	A web-based platform for tandem mass spectrometry data analysis and molecular networking [14].	Identification of novel molecules and visualization of expanded chemical space.
antiSMASH	A bioinformatics tool for the identification and analysis of biosynthetic gene clusters in microbial genomes [32].	Genome mining to predict the chemical output of BGCs prior to cloning.
A 159	A 159, CAS:120797-39-9, MF:C7H2Cl3NS	Chemical Reagent
GC Soft-Liner	GC Soft-Liner|Silicone Dental Reline Material|RUO	GC Soft-Liner is an autopolymerizing silicone for dental materials research. This RUO product is for laboratory investigation, not for human or veterinary use.

The transition of heterologous natural product production from laboratory flasks to industrial-scale bioreactors represents a critical bottleneck in the development of therapeutic agents and industrial enzymes. This process requires the integration of optimized microbial chassis with scaled-up bioprocessing conditions to achieve viable production yields. Central to this endeavor is the strategic development of bacterial hosts through the deletion of native biosynthetic gene clusters (BGCs), which redirects cellular resources toward the production of target heterologous compounds while minimizing background metabolic interference. The rational design of these chassis organisms must account for both genetic and bioprocess factors to enable successful technology transfer across scales. This application note details standardized methodologies for chassis development and scale-up protocols, providing researchers with a framework for translating bench-scale discoveries to commercially viable bioprocesses.

Rationale for Native BGC Deletion in Chassis Development

Metabolic Resource Reallocation

Native biosynthetic gene clusters compete for essential precursors, cofactors, and cellular energy with heterologously expressed pathways of interest. The deletion of these non-essential BGCs redirects metabolic flux toward target compound synthesis, significantly improving yields. In Schlegelella brevitalea DSM 7029, the elimination of endogenous BGCs encoding glidobactin and other secondary metabolites diminished native metabolite background and reduced competition for precursor supply [51]. This strategic deletion enabled the host to dedicate a greater proportion of its metabolic resources to the production of heterologous proteobacterial natural products, including compounds from Burkholderiales and myxobacteria.

Reduction of Analytical Complexity

The removal of native secondary metabolites simplifies downstream purification and analytical characterization processes by eliminating contaminating compounds that can co-purify with target molecules. This is particularly valuable when scaling up production, where purification efficiency directly impacts process economics. In Streptomyces coelicolor A3(2), the deletion of four endogenous BGCs created a chassis strain with minimized native metabolic interference, facilitating the detection and purification of heterologously expressed compounds like xiamenmycin and griseorhodins [23].

Improved Genetic Stability and Process Control

Extraneous genomic elements, including transposases, insertion sequence (IS) elements, and phage-related regions, can promote genetic instability during extended bioreactor cultivation. The elimination of these mobile genetic elements in chassis strains enhances plasmid retention and genetic stability throughout scale-up. In Schlegelella brevitalea, the deletion of 44 transposases, 2 prophage-like regions, and 7 genomic islands resulted in improved growth characteristics with alleviated cell autolysis, addressing a critical limitation observed during extended fermentation [51].

Quantitative Comparison of Genome-Reduced Chassis Performance

Table 1: Performance Metrics of Genome-Reduced Bacterial Chassis for Heterologous Production

Host Strain	Genomic Modifications	Heterologous Products	Yield Improvement	Key Advantages
Schlegelella brevitalea DT mutants [51]	Deletion of 7 nonessential regions (transposases, prophages, GIs) + endogenous BGCs	Six proteobacterial natural products; Chitinimides	Significant increase vs. wild-type; Superior to E. coli and P. putida	Alleviated cell autolysis; Improved growth; Methylmalonyl-CoA production
Streptomyces coelicolor A3(2)-2023 [23]	Deletion of 4 endogenous BGCs + multiple RMCE sites	Xiamenmycin; Griseorhodins	Copy-number dependent yield increase (2-4 copies)	Modular integration; Low metabolic background; Precise genetic tools
Pichia pastoris RP deletants [52]	16/27 nonessential ribosomal protein genes deleted	Heterologous proteins	59% of mutants showed significantly increased yield	Enhanced co-translational folding; Reduced protein aggregation
Burkholderia thailandensis E264 [2]	PK-NRP thailandepsin Δtdp::attB mutant; efflux deletions	FK228 (romidepsin) analogs	Up to 985 mg/L	Low human virulence; Efficient precursor channeling

Table 2: Biosynthetic Range and Capabilities of Burkholderia Host Systems

Heterologous Host	Biosynthetic Range Tested	Source BGC Range Tested	Best Titer Reported	Genetic Tools Available
Burkholderia glumae BGR1 [2]	Rhamnolipid precursors	Gammaproteobacteria	Not reported	pBBR1 replicon, BGC PrhlA
Burkholderia gladioli pv agaricicola [2]	RiPPs	Betaproteobacteria	6 mg/L burhizin-23	pBBR1 replicon, l-arabinose inducible araC/PBAD
Burkholderia thailandensis E264 [2]	PKs, PK-NRPs	Betaproteobacteria, Myxococcia	985 mg/L FK228 C	Ï•C31 integrative vectors, constitutive promoters
Burkholderia sp. FERM BP-3421 [2]	RiPPs, PK-NRP-PUFAs	Betaproteobacteria	240 mg/L capistruin	pRO1600, pBBR1 replicons, inducible systems

Experimental Protocols

Protocol 1: Markerless Deletion of Endogenous BGCs

Principle: This two-step recombination protocol enables precise deletion of target BGCs without incorporating selection markers, allowing for sequential multiple deletions in chassis strains [51] [23].

Materials:

• E. coli donor strains (e.g., ET12567/pUZ8002 or specialized Red recombinase strains)
• Temperature-sensitive recombinase plasmid (e.g., pSC101-PRha-αβγA-PBAD-ccdA)
• Selection cassettes (amp-ccdB or kan-rpsL)
• Luria-Bertani (LB) medium with appropriate antibiotics
• Induction agents: L-rhamnose (10%), L-arabinose (10%)

Procedure:

• First Recombination:
  • Electroporate the recombinase expression plasmid into the target host strain
  • Induce dual expression of recombinase and CcdA using 10% L-rhamnose and 10% L-arabinose
  • Replace target BGC with selection cassette (amp-ccdB or kan-rpsL)
  • Select correct recombinants on LB plates containing appropriate antibiotics

• Second Recombination:

  • Induce recombinase expression to catalyze the excision of the selection cassette
  • Screen for loss of antibiotic resistance
  • Verify precise markerless deletion via colony PCR and sequencing

• Sequential Deletion:

  • Repeat process for additional BGCs
  • Prioritize deletions based on bioinformatic analysis (antiSMASH) and transcriptomic data

Technical Notes:

• For Schlegelella brevitalea, use Redαβ7029 recombineering combined with Cre/lox site-specific recombination system [51]
• For Streptomyces, adapt protocol using ΦC31 or tyrosine recombinase systems [23]
• Always confirm deletions using multiple primer sets spanning deletion junctions

Protocol 2: Bioreactor Scale-Up for Heterologous Production

Principle: Scale-up requires maintaining constant cellular physiological states despite changes in transport phenomena and hydrodynamic environments [53] [54].

Materials:

• Bench-scale bioreactors (1-10L) with control systems
• Production-scale bioreactors (200-5000L)
• Culture media optimized for target chassis
• Dissolved oxygen, pH, and temperature probes
• Inoculum from seed train bioreactors

Procedure:

• Inoculum Development:
  • Develop robust seed train from working cell bank
  • Use scalable bioreactors instead of 2D platforms when possible
  • Maintain consistent aggregate size distribution (for PSCs: 100-200μm) [53]

• Scale-Up Calculations:

  • Maintain geometric similarity (H/T ratio 2:1 to 4:1; D/T ratio 1:3 to 1:2)
  • Determine operating parameters using scaled-down models
  • Calculate based on constant power per unit volume (P/V) or constant kLa

• Process Transition:

  • Transfer scale-independent parameters (pH, temperature, DO) directly from bench scale
  • Optimize scale-dependent parameters (agitation, aeration, feeding) for production scale
  • Monitor for gradients (substrate, pH, oxygen) and implement control strategies

• Harvest and Analysis:

  • Terminate fermentation based on productivity metrics, not fixed time
  • Analyze samples for target compound titer, byproducts, and cellular physiology
  • Compare product quality profiles across scales

Technical Notes:

• For microbial systems, constant P/V is often preferred scale-up criterion [54]
• For shear-sensitive cells, consider constant tip speed with minimum P/V requirement
• Anticipate longer mixing times at large scale - implement strategic feeding to minimize gradients

Workflow Visualization

Figure 1: Integrated Workflow for Development of Genome-Reduced Production Chassis

The Scientist's Toolkit: Essential Research Reagents

Table 3: Key Reagents for Chassis Development and Scale-Up

Reagent/System	Function	Application Examples
Redαβ7029 recombinase system [51]	Markerless gene deletion	Schlegelella brevitalea genetic engineering
Ï•C31 integrative vectors [2]	Site-specific integration of BGCs	Burkholderia thailandensis heterologous expression
pBBR1 replicons [2]	Broad-host-range plasmid maintenance	Multiple Burkholderia species
AntiSMASH software [51]	BGC identification and analysis	Genome mining for deletion targets
RMCE cassettes (Cre-lox, Vika-vox) [23]	Multiple copy integration	Streptomyces heterologous expression platform
L-rhamnose inducible systems [23]	Controlled gene expression	Red recombinase induction in E. coli
oriT-containing plasmids [23]	Conjugative transfer of BGCs	Intergeneric conjugation between E. coli and actinomycetes
Ugilec 141	Ugilec 141, CAS:111483-93-3, MF:N/A	Chemical Reagent
cytochrome c/'/'	cytochrome c/'/', CAS:116110-46-4, MF:C9H17NO2	Chemical Reagent

Scale-Up Considerations for Genome-Reduced Chassis

Addressing Physiological Changes in Large-Scale Bioreactors

The scale-up of genome-reduced chassis presents unique challenges as cellular metabolism encounters heterogeneous conditions in production-scale bioreactors. Computational modeling of industrial-scale bioreactors using the particle lifeline approach has revealed that up to 60% of microbial populations can experience starvation conditions for significant durations (>70% of time) due to imperfect mixing [55]. This heterogeneity can differently impact genome-reduced strains, which may have altered stress response networks due to deletion of non-essential regions.

Strategies for Scale-Up Success

Successful scale-up requires anticipating how scale-dependent parameters will affect the engineered chassis. Key considerations include:

• Mixing Time Management: Circulation times increase significantly with scale (up to 3-fold when scaling with constant P/V) [54]. Implement fed-batch strategies to minimize substrate gradients that can stress metabolically streamlined chassis.

• Mass Transfer Optimization: Oxygen transfer capabilities change non-linearly with scale. For chassis engineered for high-density growth, ensure kLa remains sufficient throughout scale-up. Surface area to volume ratio decreases dramatically, challenging COâ‚‚ removal in tall bioreactors [53].

• Shear Force Management: While many genome-reduced bacterial chassis maintain robust cell walls, assess shear sensitivity during early scale-up studies. Impeller tip speed increases with scale when maintaining constant P/V, potentially affecting cellular integrity [54].

The successful translation of heterologous production systems from laboratory to industrial scale requires the integrated optimization of microbial chassis and bioprocess parameters. Strategic deletion of native BGCs and non-essential genomic regions creates streamlined production hosts with enhanced capabilities for compound synthesis, while systematic scale-up approaches maintain physiological performance across bioreactor scales. The protocols and data presented herein provide a framework for researchers to accelerate the development of robust bioprocesses for pharmaceutical and industrial compound production. Continued advancement in genome reduction strategies and scale-up methodologies will further enhance our ability to harness microbial factories for the production of valuable natural products.

Conclusion

The strategic deletion of native BGCs is no longer an accessory technique but a central pillar in constructing powerful heterologous production chassis. This approach successfully converts metabolically cluttered wild-type strains into streamlined cellular factories, dramatically improving the success rate for expressing cryptic BGCs and boosting the yields of valuable natural products. The future of this field lies in the continued diversification of the chassis portfolio, the development of more sophisticated, automated genetic tools, and the integration of systems and synthetic biology to create increasingly predictable and programmable hosts. These advances will profoundly impact biomedical and clinical research by providing a reliable pipeline to the vast untapped reservoir of microbial natural products, accelerating the discovery of new therapeutic leads for combating antibiotic resistance, cancer, and other diseases.

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