Bacteroides thetaiotaomicron VPI-5482 is a prominent human gut symbiont of increasing importance to human health and therapeutic applications. Despite its significance, the transcriptional regulatory network (TRN) governing its survival and resilience in vivo remains poorly understood. Here, we present BtModulome, a comprehensive transcriptional regulatory framework derived from independent component analysis of 461 RNA-seq datasets spanning diverse niche-specific conditions and genetic backgrounds. This analysis revealed the BtModulome consisting of 110 independently modulated gene sets (iModulons), explaining 72.9% of the variance in the RNA-seq dataset. We validated strong associations with 39 known regulators and identified 311 novel regulator-regulon relationships, accounting for 22.4% expansion of the known TRN of B. thetaiotaomicron. In addition, we functionally characterized 11 ECF-σs, including SigW-1, which orchestrates arylsulfatase expression critical for host colonization, and SigH-1, which mediates (p)ppGpp-dependent stringent response. Integration of iModulon activities with multi-omics datasets provided mechanistic insights into stress responses and carbon utilization both in vitro and in vivo. This comprehensive TRN framework establishes a foundation for understanding adaptive mechanisms in gut commensals and demonstrates the utility of module-centric analysis for biological discovery in non-model organisms.

CRISPR interference (CRISPRi) is a versatile tool for high-throughput phenotypic screening. However, rational design and synthesis of the single-guide RNA (sgRNA) library required for each genome-wide CRISPRi application is time-consuming, expensive, and unfeasible if the target organisms lack comprehensive sequencing and characterization. We developed an ultra-dense random sgRNA library generation method applicable to any organism, including those that are not well-characterized. Our method converts transcriptome-wide mRNA into 20 nt of sgRNA spacer sequences through enzymatic reactions. The generated sgRNA library selectively binds to the non-template strand of the coding sequence, leading to more efficient repression compared to binding the template strand. We then generated a genome-scale library for Escherichia coli by applying this method and identified essential and auxotrophic genes through phenotypic screening. Furthermore, we tuned the production levels of lycopene and violacein and identified new repression targets for violacein production. Our results demonstrated that a genome-scale sgRNA library can be generated without rational design and can be utilized simultaneously in a range of phenotypic screenings.

Bacterial genes serve diverse cellular functions and many affect fitness in response to environmental challenges. We employed CRISPR interference screening to investigate the fitness effect of each gene in Escherichia coli exposed to gentamicin, aiming to understand the cellular defense mechanisms. Our findings revealed that ribosomal proteins, ribosome-associated proteins, toxin-antitoxin systems, and outer membrane proteins strongly influence the fitness of E. coli in gentamicin. Notably, gentamicin-induced fitness changes resembled those under anaerobic conditions, where resistance to gentamicin was observed. Specifically, genes related to the biosynthesis of cofactors and electron carriers, crucial for the respiratory system, showed reduced essentiality under both gentamicin and anaerobic conditions, suggesting a disruption in membrane potential leading to limited gentamicin uptake. Transcriptomic and genome-wide binding analyses identified the two-component system CpxR as a key regulator of respiratory systems in response to gentamicin. Our study provides insights into cellular defense mechanisms, offering potential strategies for combating antibiotic resistance.IMPORTANCEBacteria can adapt to a variety of stressful environments, including antibiotic exposure. The mechanisms underlying antibiotic resistance remain an active area of investigation. Clustered regularly interspaced short palindromic repeats (CRISPR) interference enables specific silencing of gene expression, allowing researchers to assess the fitness effects of gene knockdowns under given conditions. Using genome-wide CRISPR interference screening on Escherichia coli exposed to gentamicin, we identified anaerobic-like fitness effects of genes involved in respiration and the maintenance of membrane potential-key processes that facilitate gentamicin entrance into the cell. Transcriptomic analysis and immunoprecipitation assays further indicated that the two-component system CpxR modulates respiratory adaptations in response to gentamicin challenge. These findings shed light on the development of antibiotic resistance in bacteria and may offer new insight into strategies for treating gentamicin-resistant pathogens.

Advances in high-throughput sequencing have enabled the identification of genetic variations associated with human disease. However, deciphering the functional significance of these variations remains challenging. Here we propose an alternative approach that uses humanized Escherichia coli to study human genetic enzymopathies and to screen candidate drug effects on metabolic targets. By replacing selected E. coli metabolic enzymes with their human orthologues and their sequence variants, we demonstrate that the growth rate of E. coli reflects the in vivo activity of heterologously expressed human enzymes. This approach accurately reflected enzyme activities of known sequence variants, enabling rapid screening of causal sequence variations associated with human diseases. This approach bridges the gap between in vitro assays and cell-based assays. Our findings suggest that the proposed approach using a humanized E. coli strain holds promise for drug discovery, offering a high-throughput and cost-effective platform for identifying new compounds targeting human enzymes. Continued research and innovation in this field have the potential to impact the development and practice of precision medicine.

Bacteria possess a vast repertoire of genes to adapt to environmental challenges. Understanding the gene fitness landscape under antibiotic stress is crucial for elucidating bacterial resistance mechanisms and antibiotic action. To explore this, we conducted a genome-scale CRISPRi screen using a high-density sgRNA library in Escherichia coli exposed to various antibiotics. This screen identified essential genes under antibiotic-induced stress and offered insights into the molecular mechanisms underlying bacterial responses. We uncovered previously unrecognized genes involved in antibiotic resistance, including essential membrane proteins. The screen also underscored the importance of transcriptional modulation of essential genes in antibiotic tolerance. Our findings emphasize the utility of genome-wide CRISPRi screening in mapping the genetic landscape of antibiotic resistance. This study provides a valuable resource for identifying potential targets for antibiotics or antimicrobial strategies. Moreover, it offers a framework for exploring transcriptional regulatory networks and resistance mechanisms in E. coli and other bacterial pathogens.

Advances in systems and synthetic biology have propelled the construction of reduced bacterial genomes. Genome reduction was initially focused on exploring properties of minimal genomes, but more recently it has been deployed as an engineering strategy to enhance strain performance. This review provides the latest updates on reduced genomes, focusing on dual-track approaches of top-down reduction and bottom-up synthesis for their construction. Using cases from studies that are based on established industrial workhorse strains, we discuss the construction of a series of synthetic phenotypes that are candidates for biotechnological applications. Finally, we address the possible uses of reduced genomes for biotechnological applications and the needed future research directions that may ultimately lead to the total synthesis of rationally designed genomes.

Streptomyces produces diverse secondary metabolites of biopharmaceutical importance, yet the rate of biosynthesis of these metabolites is often hampered by complex transcriptional regulation. Therefore, a fundamental understanding of transcriptional regulation in Streptomyces is key to fully harness its genetic potential. Here, independent component analysis (ICA) of 454 high-quality gene expression profiles of the model species Streptomyces coelicolor is performed, of which 249 profiles are newly generated for S. coelicolor cultivated on 20 different carbon sources and 64 engineered strains with overexpressed sigma factors. ICA of the transcriptome dataset reveals 117 independently modulated groups of genes (iModulons), which account for 81.6% of the variance in the dataset. The genes in each iModulon are involved in specific cellular responses, which are often transcriptionally controlled by specific regulators. Also, iModulons accurately predict 25 secondary metabolite biosynthetic gene clusters encoded in the genome. This systemic analysis leads to reveal the functions of previously uncharacterized genes, putative regulons for 40 transcriptional regulators, including 30 sigma factors, and regulation of secondary metabolism via phosphate- and iron-dependent mechanisms in S. coelicolor. ICA of large transcriptomic datasets thus enlightens a new and fundamental understanding of transcriptional regulation of secondary metabolite synthesis along with interconnected metabolic processes in Streptomyces.

Microbes have inherent capacities for utilizing various carbon sources, however they often exhibit sub-par fitness due to low metabolic efficiency. To test whether a bacterial strain can optimally utilize multiple carbon sources, Escherichia coli was serially evolved in L-lactate and glycerol. This yielded two end-point strains that evolved first in L-lactate then in glycerol, and vice versa. The end-point strains displayed a universal growth advantage on single and a mixture of adaptive carbon sources, enabled by a concerted action of carbon source-specialists and generalist mutants. The combination of just four variants of glpK, ppsA, ydcI, and rph-pyrE, accounted for more than 80% of end-point strain fitness. In addition, machine learning analysis revealed a coordinated activity of transcriptional regulators imparting condition-specific regulation of gene expression. The effectiveness of the serial adaptive laboratory evolution (ALE) scheme in bioproduction applications was assessed under single and mixed-carbon culture conditions, in which serial ALE strain exhibited superior productivity of acetoin compared to ancestral strains. Together, systems-level analysis elucidated the molecular basis of serial evolution, which hold potential utility in bioproduction applications.

iModulons-sets of co-expressed genes identified through independent component analysis (ICA) of high-quality transcriptomic datasets-provide an unbiased, modular view of an organism's transcriptional regulatory network. Established in 2020, iModulonDB (iModulonDB.org) serves as a centralized repository of curated iModulon sets, enabling users to explore iModulons and download the associated transcriptomic data. This update reflects a significant expansion of the database-19 new ICA decompositions (+633%) spanning 8 925 expression profiles (+1370%), 503 studies (+2290%) and 12 additional organisms (+400%)-and introduces new features to help scientists decipher the mechanisms governing prokaryotic transcriptional regulation. To facilitate comprehension of the underlying expression profiles, the updated user-interface displays essential information about each data-generating study (e.g. the experimental conditions and publication abstract). Dashboards now include condition-specific coloring and highlight data generated from genetically perturbed strains, enabling users to rapidly interpret disruptions in transcriptional regulation. New interactive graphs rapidly convey omics-derived indicators (e.g. the explained variance of ICA decompositions, genetic overlap between iModulons and regulons). Direct links to operon diagrams (BioCyc) and protein-protein interaction networks (STRING) provide users with seamless access to external resources for further assessment of iModulons. Lastly, a new suite of search-driven and species-wide analysis tools promotes user-engagement with iModulons, reinforcing iModulonDB's role as a dynamic, interactive knowledgebase of prokaryotic transcriptional regulation.

Machine learning applied to large compendia of transcriptomic data has enabled the decomposition of bacterial transcriptomes to identify independently modulated sets of genes, such iModulons represent specific cellular functions. The identification of iModulons enables accurate identification of genes necessary and sufficient for cross-species transfer of cellular functions. We demonstrate cross-species transfer of: 1) the biotransformation of vanillate to protocatechuate, 2) a malonate catabolic pathway, 3) a catabolic pathway for 2,3-butanediol, and 4) an antimicrobial resistance to ampicillin found in multiple Pseudomonas species to Escherichia coli . iModulon-based engineering is a transformative strategy as it includes all genes comprising the transferred cellular function, including genes without functional annotation. Adaptive laboratory evolution was deployed to optimize the cellular function transferred, revealing mutations in the host. Combining big data analytics and laboratory evolution thus enhances the level of understanding of systems biology, and synthetic biology for strain design and development.

Transposon mutagenesis is an efficient way to explore gene essentiality of a bacterial genome. However, there was a discrepancy between the essential gene set determined by transposon mutagenesis and that determined using single-gene knockout strains.

As the design of genetic circuitry for synthetic biology becomes more sophisticated, diverse regulatory bioparts are required. Despite their importance, well-characterized 3'-untranslated region (3'-UTR) bioparts are limited. Thus, transcript 3'-ends require further investigation to understand the underlying regulatory role and applications of the 3'-UTR. Here, we revisited the use of Term-Seq in the Escherichia coli strain K-12 MG1655 to enhance our understanding of 3'-UTR regulatory functions and to provide a diverse collection of tunable 3'-UTR bioparts with a wide termination strength range. Comprehensive analysis of 1,629 transcript 3'-end positions revealed multiple 3'-termini classes generated through transcription termination and RNA processing. The examination of individual Rho-independent terminators revealed a reduction in downstream gene expression over a wide range, which led to the design of novel synthetic metabolic valves that control metabolic fluxes in branched pathways. These synthetic metabolic valves determine the optimal balance of heterologous pathways for maximum target biochemical productivity. The regulatory strategy using 3'-UTR bioparts is advantageous over promoter- or 5'-UTR-based transcriptional control as it modulates gene expression at transcription levels without trans-acting element requirements (e.g. transcription factors). Our results provide a foundational platform for 3'-UTR engineering in synthetic biology applications.

Bacteria belonging to Streptomyces have the ability to produce a wide range of secondary metabolites through a shift from primary to secondary metabolism regulated by complex networks activated after vegetative growth terminates. Despite considerable effort to understand the regulatory elements governing gene expression related to primary and secondary metabolism in Streptomyces, system-level information remains limited. In this study, we integrated four multi-omics datasets from Streptomyces griseus NBRC 13350: RNA-seq, ribosome profiling, dRNA-seq, and Term-Seq, to analyze the regulatory elements of transcription and translation of differentially expressed genes during cell growth. With the functional enrichment of gene expression in different growth phases, one sigma factor regulon and four transcription factor regulons governing differential gene transcription patterns were found. In addition, the regulatory elements of transcription termination and post-transcriptional processing at transcript 3′-end positions were elucidated, including their conserved motifs, stem-loop RNA structures, and non-terminal locations within the polycistronic operons, and the potential regulatory elements of translation initiation and elongation such as 5′-UTR length, RNA structures at ribosome-bound sites, and codon usage were investigated. This comprehensive genetic information provides a foundational genetic resource for strain engineering to enhance secondary metabolite production in Streptomyces.

Additional file 6: Table S5. Relative abundance of phylotypes detected in ageing and rejuvenation samples.

The microbial conversion of glycerol into value-added commodity products has emerged as an attractive means to meet the demands of biosustainability. However, glycerol is a non-preferential carbon source for productive fermentation because of its low energy density. We employed evolutionary and metabolic engineering in tandem to construct an Escherichia coli strain with improved GABA production using glycerol as the feedstock carbon. Adaptive evolution of E. coli W under glycerol-limited conditions for 1300 generations harnessed an adapted strain with a metabolic system optimized for glycerol utilization. Mutation profiling, enzyme kinetic assays, and transcriptome analysis of the adapted strain allowed us to decipher the basis of glycerol adaptation at the molecular level. Importantly, increased substrate influx mediated by the mutant glpK and modulation of intracellular cAMP levels were the key drivers of improved fitness in the glycerol-limited condition. Leveraging the enhanced capability of glycerol utilization in the strain, we constructed a GABA-producing E. coli W-derivative with superior GABA production compared to the wild-type. Furthermore, rationally designed inactivation of the non-essential metabolic genes, including ackA, mgsA, and gabT, in the glycerol-adapted strain improved the final GABA titer and specific productivity by 3.9- and 4.3-fold, respectively, compared with the wild-type.

The gut microbiota is associated with the health and longevity of the host. A few methods, such as fecal microbiota transplantation and oral administration of probiotics, have been applied to alter the gut microbiome and promote healthy aging. The changes in host microbiomes still remain poorly understood. Here, we characterized both the changes in gut microbial communities and their functional potential derived from colon samples in mouse models during aging. We achieved this through four procedures including co-housing, serum injection, parabiosis, and oral administration of Akkermansia muciniphila as probiotics using bacterial 16 S rRNA sequencing and shotgun metagenomic sequencing. The dataset comprised 16 S rRNA sequencing (36,249,200 paired-end reads, 107 sequencing data) and metagenomic sequencing data (307,194,369 paired-end reads, 109 sequencing data), characterizing the taxonomy of bacterial communities and their functional potential during aging and rejuvenation. The generated data expand the resources of the gut microbiome related to aging and rejuvenation and provide a useful dataset for research on developing therapeutic strategies to achieve healthy active aging.

Bacteroides thetaiotaomicron represents a major symbiont of the human gut microbiome that is increasingly viewed as a promising candidate strain for microbial therapeutics. Here, we engineer B. thetaiotaomicron for heterologous production of non-native butyrate as a proof-of-concept biochemical at therapeutically relevant concentrations. Since B. thetaiotaomicron is not a natural producer of butyrate, we heterologously expressed a butyrate biosynthetic pathway in the strain, which led to the production of butyrate at the final concentration of 12 mg/L in a rich medium. Further optimization of butyrate production was achieved by a round of metabolic engineering guided by an expanded genome-scale metabolic model (GEM) of B. thetaiotaomicron. The in silico knock-out simulation of the expanded model showed that pta and ldhD were the potent knock-out targets to enhance butyrate production. The maximum titer and specific productivity of butyrate in the pta-ldhD double knockout mutant increased by nearly 3.4 and 4.8 folds, respectively. To our knowledge, this is the first engineering attempt that enabled butyrate production from a non-butyrate producing commensal B. thetaiotaomicron. The study also highlights that B. thetaiotaomicron can serve as an effective strain for live microbial therapeutics in human.

Streptomyces sp. is a great source of bioactive secondary metabolites, including antibiotics, antifungal agents, antiparasitic agents, immunosuppressant compounds, and other drugs. Secondary metabolites are synthesized via multistep conversions of the precursor molecules from primary metabolism, governed by multicomplex enzymes from secondary metabolite biosynthetic gene clusters.

The market for using and storing digital data is growing, with DNA synthesis emerging as an efficient way to store massive amounts of data. Storing information in DNA mainly consists of two steps: data writing and reading. The writing step requires encoding data in DNA, building one nucleotide at a time as a form of single-stranded DNA (ssDNA). Once the data needs to be read, the target DNA is selectively retrieved and sequenced, which will also be in the form of an ssDNA. Recently, enzyme-based DNA synthesis is emerging as a new method to be a breakthrough on behalf of decades-old chemical synthesis. A few enzymatic methods have been presented for data memory, including the use of terminal deoxynucleotidyl transferase. Besides, enzyme-based amplification or denaturation of the target strand into ssDNA provides selective access to the desired dataset. In this review, we summarize diverse enzymatic methods for either synthesizing ssDNA or retrieving the data-containing DNA.