OxyR Is a Convergent Target for Mutations Acquired during Adaptation to Oxidative Stress-Prone Metabolic States

Genomic insights into chlorine resistance of a Mycobacterium sp. strain isolated from treated wastewater effluent

Chlorine is the principal microbial disinfectant used for water treatment. However, chlorine-resistant bacteria such as Mycobacterium spp., can survive chlorine treatment and even grow in the presence of chlorine, posing potential public health risks. In this study, we isolated a Mycobacterium sp. strain from treated effluent and investigated its chlorine resistance and recovery using transcriptomic analyses. Specifically, isolate M1, showing 94.58 % average nucleotide identity (ANI) with Mycobacterium massilipolynesiensis type genome, was exposed to 1 ppm HOCl for 30 min and subjected to RNA sequencing. Genes identified as upregulated compared to control conditions (no HOCl) were involved in detoxification (toxic compound degradation; nemA; log2 fold-change [FC]: 7.41), redox homeostasis (COQ5; quinone synthesis; log2 FC 5.70, rosB; riboflavin synthesis; log2 FC 5.61), protein homeostasis (cysHKO, moeZ cysteine biosynthesis, and arg complex; arginine metabolism), and lipid metabolism (cpnA; 6.95 FC) suggesting a multifaceted adaptation to oxidative stress. Levels of a few membrane transport proteins (czcD, and bcr) were also upregulated, highlighting their role in chlorine exposure. Overall, this study broadens the understanding of chlorine resistance strategies employed by Mycobacterium sp. to combat oxidative stress and the resulting toxic intracellular compounds, and has implications for adjusting water treatment technologies toward eliminating mycobacteria.

A single nucleotide polymorphism produces different transcription profiles in Campylobacter jejuni's cysM

A single nucleotide polymorphism (SNP) in the 126 bp untranslated region (UTR) directly upstream of Campylobacter jejuni's cysM (cysteine synthase) results in significant effects on gene transcription. UTR sequences, containing the predicted promoter region of cysM, from 264 different strains were compared, and revealed a SNP twenty nucleotides upstream of the cysM translation start site. In 219 strains the UTR sequence contained a guanine at this locus, and the remaining 45 strains had an adenine at the same position. Strains possessing the guanine SNP showed higher amounts of cysM transcripts compared to adenine SNP strains. When both UTR regions were cloned upstream of the major flagellar subunit (flaA) the guanine SNP UTR resulted in significantly greater levels of flaA transcription compared to the adenine SNP containing UTR. Additionally, when the UTR containing the guanine SNP was fused to flaA, motility was restored for a flaAB null mutant. Motility was not rescued initially when flaA was fused to the UTR containing the adenine SNP UTR. However, when the flaAB null mutant, containing a copy of flaA fused to the adenine-containing UTR, was incubated in Brucella broth for a minimum of two consecutive passages each lasting 48 h, transcription of flaA increased and motility was restored. Additional analysis of the flaA mRNA produced by the strain containing the adenine SNP UTR fused to flaA grown in Brucella broth versus agar suggests that the effects on motility occurred through blocking of full-length mRNA production.

Dioxygen reductase heterogeneity is crucial for robust aerobic growth physiology of Escherichia coli

The development of a system to leverage molecular oxygen for energy-efficient pathways required several molecular adaptations. The enzymatic reduction of dioxygen to water is one such prominent evolutionary molecular trait. Microbes evolved several enzymes capable of reducing dioxygen and, interestingly, retained multiples of them in their genomes. While their structure and biochemical functions are well-studied, understanding their degeneracy and co-operativity in the system remains elusive. We used genetic engineering and evolutionary repair approaches to examine the impact of the high oxygen affinity cytochrome bd oxidase deficiency in Escherichia coli aerobic growth. We found a crucial role of cytochrome bd oxidases in the robustness of aerobic physiology. Evolutionary repair experiments alleviated growth defects in bd oxidase-deficient strains by ArcAB system dysregulation at the cost of impaired stress response pathways. Energy generation pathways are potential antimicrobial targets, and understanding collateral phenotypes is crucial in designing therapeutic approaches that reduce antimicrobial resistance development.

The new chassis in the flask: Advances in Vibrio natriegens biotechnology research

Biotechnology has been built on the foundation of a small handful of well characterized and well-engineered organisms. Recent years have seen a breakout performer gain attention as a new entrant into the bioengineering toolbox: Vibrio natriegens. This review covers recent research efforts into making V. natriegens a biotechnology platform, using a large language model (LLM) and knowledge graph to expedite the literature survey process. Scientists have made advancements in research pertaining to the fundamental metabolic characteristics of V. natriegens, development and characterization of synthetic biology tools, systems biology analysis and metabolic modeling, bioproduction and metabolic engineering, and microbial ecology. Each of these subcategories has relevance to the future of V. natriegens for bioengineering applications. In this review, we cover these recent advancements and offer context for the impact they may have on the field, highlighting benefits and drawbacks of using this organism. From examining the recent bioengineering research, it appears that V. natriegens is on the precipice of becoming a platform bacterium for the future of biotechnology.

Mechanisms underlying the low-temperature adaptation of 17β-estradiol-degrading bacterial strain Rhodococcus sp. RCBS9: insights from physiological and transcriptomic analyses

The 17β-estradiol (E2)-degrading bacterium Rhodococcus sp.RCBS9 previously showed remarkable resistance to the combined stresses of low temperature and E2. In this study, physiological experiments and transcriptomic analysis were performed to investigate the mechanisms underlying the strain's low-temperature adaptation and briefly analyze how it maintains its ability to degrade E2 at low temperature. The results showed that the strain's signal transduction functions, adaptive changes in cell membrane and cell wall structure, gene repair functions, and synthesis of antioxidants and compatible solutes are key to its ability to adapt to low temperature. In addition, its stress proteins in response to low temperature were not typical cold shock proteins, but rather universal stress proteins (USPs) and heat shock proteins (HSPs), among others. The strain also upregulated biofilm production, transporter proteins for carbon source uptake, and proteins for fatty acid degradation to ensure energy generation. The strain's multiple stress responses work synergistically to resist low-temperature stress, ensuring its adaptability to low-temperature environments and ability to degrade E2. Finally, six genes related to survival at low temperature (identified in the transcriptome analysis) were expressed in E. coli BL21, and they were found to contribute to recombinant E. coli growth at low temperature.

Vibrio natriegens: Application of a Fast-Growing Halophilic Bacterium

The fast growth accompanied with high substrate consumption rates and a versatile metabolism paved the way to exploit Vibrio natriegens as unconventional host for biotechnological applications. Meanwhile, a wealth of knowledge on the physiology, the metabolism, and the regulation in this halophilic marine bacterium has been gathered. Sophisticated genetic engineering tools and metabolic models are available and have been applied to engineer production strains and first chassis variants of V. natriegens. In this review, we update the current knowledge on the physiology and the progress in the development of synthetic biology tools and provide an overview of recent advances in metabolic engineering of this promising host. We further discuss future challenges to enhance the application range of V. natriegens.

Diversity of Transcriptional Regulatory Adaptation in E. coli

The transcriptional regulatory network (TRN) in bacteria is thought to rapidly evolve in response to selection pressures, modulating transcription factor (TF) activities and interactions. In order to probe the limits and mechanisms surrounding the short-term adaptability of the TRN, we generated, evolved, and characterized knockout (KO) strains in Escherichia coli for 11 regulators selected based on measured growth impact on glucose minimal media. All but one knockout strain (Δlrp) were able to recover growth and did so requiring few convergent mutations. We found that the TF knockout adaptations could be divided into four categories: (i) Strains (ΔargR, ΔbasR, Δlon, ΔzntR, and Δzur) that recovered growth without any regulator-specific adaptations, likely due to minimal activity of the regulator on the growth condition, (ii) Strains (ΔcytR, ΔmlrA, and ΔybaO) that recovered growth without TF-specific mutations but with differential expression of regulators with overlapping regulons to the KO'ed TF, (iii) Strains (Δcrp and Δfur) that recovered growth using convergent mutations within their regulatory networks, including regulated promoters and connected regulators, and (iv) Strains (Δlrp) that were unable to fully recover growth, seemingly due to the broad connectivity of the TF within the TRN. Analyzing growth capabilities in evolved and unevolved strains indicated that growth adaptation can restore fitness to diverse substrates often despite a lack of TF-specific mutations. This work reveals the breadth of TRN adaptive mechanisms and suggests these mechanisms can be anticipated based on the network and functional context of the perturbed TFs.

Modulating bacterial function utilizing A knowledge base of transcriptional regulatory modules

Synthetic biology enables the reprogramming of cellular functions for various applications. However, challenges in scalability and predictability persist due to context-dependent performance and complex circuit-host interactions. This study introduces an iModulon-based engineering approach, utilizing machine learning-defined co-regulated gene groups (iModulons) as design parts containing essential genes for specific functions. This approach identifies the necessary components for genetic circuits across different contexts, enhancing genome engineering by improving target selection and predicting module behavior. We demonstrate several distinct uses of iModulons: (i) discovery of unknown iModulons to increase protein productivity, heat tolerance and fructose utilization; (ii) an iModulon boosting approach, which amplifies the activity of specific iModulons, improved cell growth under osmotic stress with minimal host regulation disruption; (iii) an iModulon rebalancing strategy, which adjusts the activity levels of iModulons to balance cellular functions, significantly increased oxidative stress tolerance while minimizing trade-offs and (iv) iModulon-based gene annotation enabled natural competence activation by predictably rewiring iModulons. Comparative experiments with traditional methods showed our approach offers advantages in efficiency and predictability of strain engineering. This study demonstrates the potential of iModulon-based strategies to systematically and predictably reprogram cellular functions, offering refined and adaptable control over complex regulatory networks.

iModulonMiner and PyModulon: Software for unsupervised mining of gene expression compendia

Public gene expression databases are a rapidly expanding resource of organism responses to diverse perturbations, presenting both an opportunity and a challenge for bioinformatics workflows to extract actionable knowledge of transcription regulatory network function. Here, we introduce a five-step computational pipeline, called iModulonMiner, to compile, process, curate, analyze, and characterize the totality of RNA-seq data for a given organism or cell type. This workflow is centered around the data-driven computation of co-regulated gene sets using Independent Component Analysis, called iModulons, which have been shown to have broad applications. As a demonstration, we applied this workflow to generate the iModulon structure of Bacillus subtilis using all high-quality, publicly-available RNA-seq data. Using this structure, we predicted regulatory interactions for multiple transcription factors, identified groups of co-expressed genes that are putatively regulated by undiscovered transcription factors, and predicted properties of a recently discovered single-subunit phage RNA polymerase. We also present a Python package, PyModulon, with functions to characterize, visualize, and explore computed iModulons. The pipeline, available at https://github.com/SBRG/iModulonMiner, can be readily applied to diverse organisms to gain a rapid understanding of their transcriptional regulatory network structure and condition-specific activity.

The hallmarks of a tradeoff in transcriptomes that balances stress and growth functions

Fast growth phenotypes are achieved through optimal transcriptomic allocation, in which cells must balance tradeoffs in resource allocation between diverse functions. One such balance between stress readiness and unbridled growth in E. coli has been termed the fear versus greed (f/g) tradeoff. Two specific RNA polymerase (RNAP) mutations observed in adaptation to fast growth have been previously shown to affect the f/g tradeoff, suggesting that genetic adaptations may be primed to control f/g resource allocation. Here, we conduct a greatly expanded study of the genetic control of the f/g tradeoff across diverse conditions. We introduced 12 RNA polymerase (RNAP) mutations commonly acquired during adaptive laboratory evolution (ALE) and obtained expression profiles of each. We found that these single RNAP mutation strains resulted in large shifts in the f/g tradeoff primarily in the RpoS regulon and ribosomal genes, likely through modifying RNAP-DNA interactions. Two of these mutations additionally caused condition-specific transcriptional adaptations. While this tradeoff was previously characterized by the RpoS regulon and ribosomal expression, we find that the GAD regulon plays an important role in stress readiness and ppGpp in translation activity, expanding the scope of the tradeoff. A phylogenetic analysis found the greed-related genes of the tradeoff present in numerous bacterial species. The results suggest that the f/g tradeoff represents a general principle of transcriptome allocation in bacteria where small genetic changes can result in large phenotypic adaptations to growth conditions.IMPORTANCETo increase growth, E. coli must raise ribosomal content at the expense of non-growth functions. Previous studies have linked RNAP mutations to this transcriptional shift and increased growth but were focused on only two mutations found in the protein's central region. RNAP mutations, however, commonly occur over a large structural range. To explore RNAP mutations' impact, we have introduced 12 RNAP mutations found in laboratory evolution experiments and obtained expression profiles of each. The mutations nearly universally increased growth rates by adjusting said tradeoff away from non-growth functions. In addition to this shift, a few caused condition-specific adaptations. We explored the prevalence of this tradeoff across phylogeny and found it to be a widespread and conserved trend among bacteria.

Meta-analysis Driven Strain Design for Mitigating Oxidative Stresses Important in Biomanufacturing

As the availability of data sets increases, meta-analysis leveraging aggregated and interoperable data types is proving valuable. This study leveraged a meta-analysis workflow to identify mutations that could improve robustness to reactive oxygen species (ROS) stresses using an industrially important melatonin production strain as an example. ROS stresses often occur during cultivation and negatively affect strain performance. Cellular response to ROS is also linked to the SOS response and resistance to pH fluctuations, which is important to strain robustness in large-scale biomanufacturing. This work integrated more than 7000 E. coli adaptive laboratory evolution (ALE) mutations across 59 experiments to statistically associate mutated genes to 2 ROS tolerance ALE conditions from 72 unique conditions. Mutant oxyR, fur, iscR, and ygfZ were significantly associated and hypothesized to contribute fitness in ROS stress. Across these genes, 259 total mutations were inspected in conjunction with transcriptomics from 46 iModulon experiments. Ten mutations were chosen for reintroduction based on mutation clustering and coinciding transcriptional changes as evidence of fitness impact. Strains with mutations reintroduced into oxyR, fur, iscR, and ygfZ exhibited increased tolerance to H2O2 and acid stress and reduced SOS response, all of which are related to ROS. Additionally, new evidence was generated toward understanding the function of ygfZ, an uncharacterized gene. This meta-analysis approach utilized aggregated and interoperable multiomics data sets to identify mutations conferring industrially relevant phenotypes with the least drawbacks, describing an approach for data-driven strain engineering to optimize microbial cell factories.

Deciphering nutritional stress responses via knowledge-enriched transcriptomics for microbial engineering

Understanding diverse bacterial nutritional requirements and responses is foundational in microbial research and biotechnology. In this study, we employed knowledge-enriched transcriptomic analytics to decipher complex stress responses of Vibrio natriegens to supplied nutrients, aiming to enhance microbial engineering efforts. We computed 64 independently modulated gene sets that comprise a quantitative basis for transcriptome dynamics across a comprehensive transcriptomics dataset containing a broad array of nutrient conditions. Our approach led to the i) identification of novel transporter systems for diverse substrates, ii) a detailed understanding of how trace elements affect metabolism and growth, and iii) extensive characterization of nutrient-induced stress responses, including osmotic stress, low glycolytic flux, proteostasis, and altered protein expression. By clarifying the relationship between the acetate-associated regulon and glycolytic flux status of various nutrients, we have showcased its vital role in directing optimal carbon source selection. Our findings offer deep insights into the transcriptional landscape of bacterial nutrition and underscore its significance in tailoring strain engineering strategies, thereby facilitating the development of more efficient and robust microbial systems for biotechnological applications.

Biocomposite thermoplastic polyurethanes containing evolved bacterial spores as living fillers to facilitate polymer disintegration

The field of hybrid engineered living materials seeks to pair living organisms with synthetic materials to generate biocomposite materials with augmented function since living systems can provide highly-programmable and complex behavior. Engineered living materials have typically been fabricated using techniques in benign aqueous environments, limiting their application. In this work, biocomposite fabrication is demonstrated in which spores from polymer-degrading bacteria are incorporated into a thermoplastic polyurethane using high-temperature melt extrusion. Bacteria are engineered using adaptive laboratory evolution to improve their heat tolerance to ensure nearly complete cell survivability during manufacturing at 135 °C. Furthermore, the overall tensile properties of spore-filled thermoplastic polyurethanes are substantially improved, resulting in a significant improvement in toughness. The biocomposites facilitate disintegration in compost in the absence of a microbe-rich environment. Finally, embedded spores demonstrate a rationally programmed function, expressing green fluorescent protein. This research provides a scalable method to fabricate advanced biocomposite materials in industrially-compatible processes.

Deterministic effect of oxygen level variation on shaping antibiotic resistome

An increase in acquisition of antibiotic resistance genes (ARGs) by pathogens under antibiotic selective pressure poses public health threats. Sub-inhibitory antibiotics induce bacteria to generate reactive oxygen species (ROS) dependent on dissolved oxygen (DO) levels, while molecular connection between ROS-mediated ARG emergence through DNA damage and metabolic changes remains elusive. Thus, the study investigates antibiotic resistome dynamics, microbiome shift, and pathogen distribution in hyperoxic (5-7 mg L-1), normoxic (2-4 mg L-1), and hypoxic (0.5-1 mg L-1) conditions using lab-scale bioreactor. Composite inoculums in the reactor were designed to represent comprehensive microbial community and AR profile from selected activated sludge. RT-qPCR and metagenomic analysis showed an increase in ARG count (100.98 ppm) with enrichment of multidrug efflux pumps (acrAB, mexAB) in hyperoxic condition. Conversely, total ARGs decreased (0.11 ppm) under hypoxic condition marked by a major decline in int1 abundance. Prevalence of global priority pathogens increased in hyperoxic (22.5%), compared to hypoxic (0.9%) wherein major decrease were observed in Pseudomonas, Shigella, and Borrelia. The study observed an increase in superoxide dismutase (sodA, sodB), DNA repair genes (nfo, polA, recA, recB), and ROS (10.4 µmol L-1) in adapted biomass with spiked antibiotics. This suggests oxidative damage that facilitates stress-induced mutagenesis providing evidence for observed hyperoxic enrichment of ARGs. Moreover, predominance of catalase (katE, katG) likely limit oxidative damage that deplete ARG breeding in hypoxic condition. The study proposes a link between oxygen levels and AR development that offers insights into mitigation and intervention of AR by controlling oxygen-related stress and strategic selection of bacterial communities.

Mechanism of escape from the antibacterial activity of metal-based nanoparticles in clinically relevant bacteria: A systematic review

The emergency of antibiotic-resistant bacteria in severe infections is increasing, especially in nosocomial environments. The ESKAPE group is of special importance in the groups of multi-resistant bacteria due to its high capacity to generate resistance to antibiotics and bactericides. Therefore, metal-based nanomaterials are an attractive alternative to combat them because they have been demonstrated to damage biomolecules in the bacterial cells. However, there is a concern about bacteria developing resistance to NPs and their harmful effects due to environmental accumulation. Therefore, this systematic review aims to report the clinically relevant bacteria that have developed resistance to the NPs. According to the results of this systematic review, various mechanisms to counteract the antimicrobial activity of various NP types have been proposed. These mechanisms can be grouped into the following categories: production of extracellular compounds, metal efflux pumps, ROS response, genetic changes, DNA repair, adaptative morphogenesis, and changes in the plasma membrane.

Ribosome profiling reveals the fine-tuned response of Escherichia coli to mild and severe acid stress

IMPORTANCE

Bacteria react very differently to survive in acidic environments, such as the human gastrointestinal tract. Escherichia coli is one of the extremely acid-resistant bacteria and has a variety of acid-defense mechanisms. Here, we provide the first genome-wide overview of the adaptations of E. coli K-12 to mild and severe acid stress at both the transcriptional and translational levels. Using ribosome profiling and RNA sequencing, we uncover novel adaptations to different degrees of acidity, including previously hidden stress-induced small proteins and novel key transcription factors for acid defense, and report mRNAs with pH-dependent differential translation efficiency. In addition, we distinguish between acid-specific adaptations and general stress response mechanisms using denoising autoencoders. This workflow represents a powerful approach that takes advantage of next-generation sequencing techniques and machine learning to systematically analyze bacterial stress responses.

TCA cycle tailoring facilitates optimal growth of proton-pumping NADH dehydrogenase-dependent Escherichia coli

IMPORTANCE

Energy generation pathways are a potential avenue for the development of novel antibiotics. However, bacteria possess remarkable resilience due to the compensatory pathways, which presents a challenge in this direction. NADH, the primary reducing equivalent, can transfer electrons to two distinct types of NADH dehydrogenases. Type I NADH dehydrogenase is an enzyme complex comprising multiple subunits and can generate proton motive force (PMF). Type II NADH dehydrogenase does not pump protons but plays a crucial role in maintaining the turnover of NAD+. To study the adaptive rewiring of energy metabolism, we evolved an Escherichia coli mutant lacking type II NADH dehydrogenase. We discovered that by modifying the flux through the tricarboxylic acid (TCA) cycle, E. coli could mitigate the growth impairment observed in the absence of type II NADH dehydrogenase. This research provides valuable insights into the intricate mechanisms employed by bacteria to compensate for disruptions in energy metabolism.

A multi-scale expression and regulation knowledge base for Escherichia coli

Transcriptomic data is accumulating rapidly; thus, scalable methods for extracting knowledge from this data are critical. Here, we assembled a top-down expression and regulation knowledge base for Escherichia coli. The expression component is a 1035-sample, high-quality RNA-seq compendium consisting of data generated in our lab using a single experimental protocol. The compendium contains diverse growth conditions, including: 9 media; 39 supplements, including antibiotics; 42 heterologous proteins; and 76 gene knockouts. Using this resource, we elucidated global expression patterns. We used machine learning to extract 201 modules that account for 86% of known regulatory interactions, creating the regulatory component. With these modules, we identified two novel regulons and quantified systems-level regulatory responses. We also integrated 1675 curated, publicly-available transcriptomes into the resource. We demonstrated workflows for analyzing new data against this knowledge base via deconstruction of regulation during aerobic transition. This resource illuminates the E. coli transcriptome at scale and provides a blueprint for top-down transcriptomic analysis of non-model organisms.

The influence of oxygen and oxidative stress on de novo acquisition of antibiotic resistance in E. coli and Lactobacillus lactis

BACKGROUND

Bacteria can acquire resistance through DNA mutations in response to exposure to sub-lethal concentrations of antibiotics. According to the radical-based theory, reactive oxygen species (ROS), a byproduct of the respiratory pathway, and oxidative stress caused by reactive metabolic byproducts, play a role in cell death as secondary killing mechanism. In this study we address the question whether ROS also affects development of resistance, in the conditions that the cells is not killed by the antibiotic.

RESULTS

To investigate whether oxygen and ROS affect de novo acquisition of antibiotic resistance, evolution of resistance due to exposure to non-lethal levels of antimicrobials was compared in E. coli wildtype and ΔoxyR strains under aerobic and anaerobic conditions. Since Lactococcus lactis (L. lactis) does not have an active electron transport chain (ETC) even in the presence of oxygen, and thus forms much less ROS, resistance development in L. lactis was used to distinguish between oxygen and ROS. The resistance acquisition in E. coli wildtype under aerobic and anaerobic conditions did not differ much. However, the aerobically grown ΔoxyR strain gained resistance faster than the wildtype or anaerobic ΔoxyR. Inducing an ETC by adding heme increased the rate at which L. lactis acquired resistance. Whole genome sequencing identified specific mutations involved in the acquisition of resistance. These mutations were specific for each antibiotic. The lexA mutation in ΔoxyR strain under aerobic conditions indicated that the SOS response was involved in resistance acquisition.

CONCLUSIONS

The concept of hormesis can explain the beneficial effects of low levels of ROS and reactive metabolic byproducts, while high levels are lethal. DNA repair and mutagenesis may therefore expedite development of resistance. Taken together, the results suggest that oxygen as such barely affects resistance development. Nevertheless, non-lethal levels of ROS stimulate de novo acquisition of antibiotic resistance.

Laboratory evolution, transcriptomics, and modeling reveal mechanisms of paraquat tolerance

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress-tolerance mechanisms: (1) modification of transport, (2) activation of ROS stress responses, (3) use of ROS-sensitive iron regulation, (4) motility, (5) broad transcriptional reallocation toward growth, and (6) metabolic rewiring to decrease NADH production. This work thus demonstrates the power of iModulon knowledge mapping for evolution analysis.

The hallmarks of a tradeoff in transcriptomes that balances stress and growth functions

Fit phenotypes are achieved through optimal transcriptomic allocation. Here, we performed a high-resolution, multi-scale study of the transcriptomic tradeoff between two key fitness phenotypes, stress response (fear) and growth (greed), in Escherichia coli. We introduced twelve RNA polymerase (RNAP) mutations commonly acquired during adaptive laboratory evolution (ALE) and found that single mutations resulted in large shifts in the fear vs. greed tradeoff, likely through destabilizing the rpoB-rpoC interface. RpoS and GAD regulons drive the fear response while ribosomal proteins and the ppGpp regulon underlie greed. Growth rate selection pressure during ALE results in endpoint strains that often have RNAP mutations, with synergistic mutations reflective of particular conditions. A phylogenetic analysis found the tradeoff in numerous bacteria species. The results suggest that the fear vs. greed tradeoff represents a general principle of transcriptome allocation in bacteria where small genetic changes can result in large phenotypic adaptations to growth conditions.

The role of oxidative stress in genome destabilization and adaptive evolution of bacteria

The review is devoted to bacterial genome destabilization by oxidative stress. The article discusses the main groups of substances causing such stress. Stress regulons involved in destabilization of genetic material and mechanisms enhancing mutagenesis, bacterial genome rearrangements, and horizontal gene transfer, induced by oxidative damage to cell components are also considered. Based on the analysis of publications, it can be claimed that rapid development of new food substrates and ecological niches by microorganisms occurs due to acceleration of genetic changes induced by oxidative stress, mediated by several stress regulons (SOS, RpoS and RpoE) and under selective pressure. The authors conclude that non-lethal oxidative stress is probably-one of the fundamental processes that guide evolution of prokaryotes and a powerful universal trigger for adaptive destabilization of bacterial genome under changing environmental conditions.

Comparison of Phenotype and Genotype Virulence and Antimicrobial Factors of Salmonella Typhimurium Isolated from Human Milk

Salmonella is a common foodborne infection. Many serovars belonging to Salmonella enterica subsp. enterica are present in the gut of various animal species. They can cause infection in human infants via breast milk or cross-contamination with powdered milk. In the present study, Salmonella BO was isolated from human milk in accordance with ISO 6579-1:2017 standards and sequenced using whole-genome sequencing (WGS), followed by serosequencing and genotyping. The results also allowed its pathogenicity to be predicted. The WGS results were compared with the bacterial phenotype. The isolated strain was found to be Salmonella enterica subsp. enterica serovar Typhimurium 4:i:1,2_69M (S. Typhimurium 69M); it showed a very close similarity to S. enterica subsp. enterica serovar Typhimurium LT2. Bioinformatics sequence analysis detected eleven SPIs (SPI-1, SPI-2, SPI-3, SPI-4, SPI-5, SPI-9, SPI-12, SPI-13, SPI-14, C63PI, CS54_island). Significant changes in gene sequences were noted, causing frameshift mutations in yeiG, rfbP, fumA, yeaL, ybeU (insertion) and lpfD, avrA, ratB, yacH (deletion). The sequences of several proteins were significantly different from those coded in the reference genome; their three-dimensional structure was predicted and compared with reference proteins. Our findings indicate the presence of a number of antimicrobial resistance genes that do not directly imply an antibiotic resistance phenotype.

Laboratory evolution reveals general and specific tolerance mechanisms for commodity chemicals

Although strain tolerance to high product concentrations is a barrier to the economically viable biomanufacturing of industrial chemicals, chemical tolerance mechanisms are often unknown. To reveal tolerance mechanisms, an automated platform was utilized to evolve Escherichia coli to grow optimally in the presence of 11 industrial chemicals (1,2-propanediol, 2,3-butanediol, glutarate, adipate, putrescine, hexamethylenediamine, butanol, isobutyrate, coumarate, octanoate, hexanoate), reaching tolerance at concentrations 60%-400% higher than initial toxic levels. Sequencing genomes of 223 isolates from 89 populations, reverse engineering, and cross-compound tolerance profiling were employed to uncover tolerance mechanisms. We show that: 1) cells are tolerized via frequent mutation of membrane transporters or cell wall-associated proteins (e.g., ProV, KgtP, SapB, NagA, NagC, MreB), transcription and translation machineries (e.g., RpoA, RpoB, RpoC, RpsA, RpsG, NusA, Rho), stress signaling proteins (e.g., RelA, SspA, SpoT, YobF), and for certain chemicals, regulators and enzymes in metabolism (e.g., MetJ, NadR, GudD, PurT); 2) osmotic stress plays a significant role in tolerance when chemical concentrations exceed a general threshold and mutated genes frequently overlap with those enabling chemical tolerance in membrane transporters and cell wall-associated proteins; 3) tolerization to a specific chemical generally improves tolerance to structurally similar compounds whereas a tradeoff can occur on dissimilar chemicals, and 4) using pre-tolerized starting isolates can hugely enhance the subsequent production of chemicals when a production pathway is inserted in many, but not all, evolved tolerized host strains, underpinning the need for evolving multiple parallel populations. Taken as a whole, this study provides a comprehensive genotype-phenotype map based on identified mutations and growth phenotypes for 223 chemical tolerant isolates.

A plasmid-free Zymomonas mobilis mutant strain reducing reactive oxygen species for efficient bioethanol production using industrial effluent of xylose mother liquor

Genome minimization is an effective way for industrial chassis development. In this study, Zymomonas mobilis ZMNP, a plasmid-free mutant strain of Z. mobilis ZM4 with four native plasmids deleted, was constructed using native type I-F CRISPR-Cas system. Cell growth of ZMNP under different temperatures and industrial effluent of xylose mother liquor were examined to investigate the impact of native plasmid removal. Despite ZMNP grew similarly as ZM4 under different temperatures, ZMNP had better xylose mother liquor utilization than ZM4. In addition, genomic, transcriptomic, and proteomic analyses were applied to unravel the molecular changes between ZM4 and ZMNP. Whole-genome resequencing result indicated that an S267P mutation in the C-terminal of OxyR, a peroxide-sensing transcriptional regulator, probably alters the transcription initiation of antioxidant genes for stress responses. Transcriptomic and proteomic studies illustrated that the reason that ZMNP utilized the toxic xylose mother liquor better than ZM4 was probably due to the upregulation of genes in ZMNP involving in stress responses as well as cysteine biosynthesis to accelerate the intracellular ROS detoxification and nucleic acid damage repair. This was further confirmed by lower ROS levels in ZMNP compared to ZM4 in different media supplemented with furfural or ethanol. The upregulation of stress response genes due to the OxyR mutation to accelerate ROS detoxification and DNA/RNA repair not only illustrates the underlying mechanism of the robustness of ZMNP in the toxic xylose mother liquor, but also provides an idea for the rational design of synthetic inhibitor-tolerant microorganisms for economic lignocellulosic biochemical production.

Experimental Evolution Reveals Unifying Systems-Level Adaptations but Diversity in Driving Genotypes

Genotype-fitness maps of evolution have been well characterized for biological components, such as RNA and proteins, but remain less clear for systems-level properties, such as those of metabolic and transcriptional regulatory networks. Here, we take multi-omics measurements of 6 different E. coli strains throughout adaptive laboratory evolution (ALE) to maximal growth fitness. The results show the following: (i) convergence in most overall phenotypic measures across all strains, with the notable exception of divergence in NADPH production mechanisms; (ii) conserved transcriptomic adaptations, describing increased expression of growth promoting genes but decreased expression of stress response and structural components; (iii) four groups of regulatory trade-offs underlying the adjustment of transcriptome composition; and (iv) correlates that link causal mutations to systems-level adaptations, including mutation-pathway flux correlates and mutation-transcriptome composition correlates. We thus show that fitness landscapes for ALE can be described with two layers of causation: one based on system-level properties (continuous variables) and the other based on mutations (discrete variables). IMPORTANCE Understanding the mechanisms of microbial adaptation will help combat the evolution of drug-resistant microbes and enable predictive genome design. Although experimental evolution allows us to identify the causal mutations underlying microbial adaptation, it remains unclear how causal mutations enable increased fitness and is often explained in terms of individual components (i.e., enzyme rate) as opposed to biological systems (i.e., pathways). Here, we find that causal mutations in E. coli are linked to systems-level changes in NADPH balance and expression of stress response genes. These systems-level adaptation patterns are conserved across diverse E. coli strains and thus identify cofactor balance and proteome reallocation as dominant constraints governing microbial adaptation.

Adaptation delay causes a burst of mutations in bacteria responding to oxidative stress

Understanding the interplay between phenotypic and genetic adaptation is a focus of evolutionary biology. In bacteria, the oxidative stress response prevents mutagenesis by reactive oxygen species (ROS). We hypothesise that the stress response dynamics can therefore affect the timing of the mutation supply that fuels genetic adaptation to oxidative stress. We uncover that sudden hydrogen peroxide stress causes a burst of mutations. By developing single-molecule and single-cell microscopy methods, we determine how these mutation dynamics arise from phenotypic adaptation mechanisms. H₂ O₂ signalling by the transcription factor OxyR rapidly induces ROS-scavenging enzymes. However, an adaptation delay leaves cells vulnerable to the mutagenic and toxic effects of hydroxyl radicals generated by the Fenton reaction. Resulting DNA damage is counteracted by a spike in DNA repair activities during the adaptation delay. Absence of a mutation burst in cells with prior stress exposure or constitutive OxyR activation shows that the timing of phenotypic adaptation directly controls stress-induced mutagenesis. Similar observations for alkylation stress show that mutation bursts are a general phenomenon associated with adaptation delays.

Advanced transcriptomic analysis reveals the role of efflux pumps and media composition in antibiotic responses of Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic pathogen and major cause of hospital-acquired infections. The virulence of P. aeruginosa is largely determined by its transcriptional regulatory network (TRN). We used 411 transcription profiles of P. aeruginosa from diverse growth conditions to construct a quantitative TRN by identifying independently modulated sets of genes (called iModulons) and their condition-specific activity levels. The current study focused on the use of iModulons to analyze the biofilm production and antibiotic resistance of P. aeruginosa. Our analysis revealed: (i) 116 iModulons, 81 of which show strong association with known regulators; (ii) novel roles of regulators in modulating antibiotics efflux pumps; (iii) substrate-efflux pump associations; (iv) differential iModulon activity in response to beta-lactam antibiotics in bacteriological and physiological media; (v) differential activation of 'Cell Division' iModulon resulting from exposure to different beta-lactam antibiotics and (vi) a role of the PprB iModulon in the stress-induced transition from planktonic to biofilm lifestyle. In light of these results, the construction of an iModulon-based TRN provides a transcriptional regulatory basis for key aspects of P. aeruginosa infection, such as antibiotic stress responses and biofilm formation. Taken together, our results offer a novel mechanistic understanding of P. aeruginosa virulence.

Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators

The transcriptional regulatory network (TRN) of Pseudomonas aeruginosa coordinates cellular processes in response to stimuli. We used 364 transcriptomes (281 publicly available + 83 in-house generated) to reconstruct the TRN of P. aeruginosa using independent component analysis. We identified 104 independently modulated sets of genes (iModulons) among which 81 reflect the effects of known transcriptional regulators. We identified iModulons that (i) play an important role in defining the genomic boundaries of biosynthetic gene clusters (BGCs), (ii) show increased expression of the BGCs and associated secretion systems in nutrient conditions that are important in cystic fibrosis, (iii) show the presence of a novel ribosomally synthesized and post-translationally modified peptide (RiPP) BGC which might have a role in P. aeruginosa virulence, (iv) exhibit interplay of amino acid metabolism regulation and central metabolism across different carbon sources and (v) clustered according to their activity changes to define iron and sulfur stimulons. Finally, we compared the identified iModulons of P. aeruginosa with those previously described in Escherichia coli to observe conserved regulons across two Gram-negative species. This comprehensive TRN framework encompasses the majority of the transcriptional regulatory machinery in P. aeruginosa, and thus should prove foundational for future research into its physiological functions.

Small RNA GcvB Regulates Oxidative Stress Response of Escherichia coli

Small non-translated regulatory RNAs control plenty of bacterial vital activities. The small RNA GcvB has been extensively studied, indicating the multifaceted roles of GcvB beyond amino acid metabolism. However, few reported GcvB-dependent regulation in minimal medium. Here, by applying a high-resolution RNA-seq assay, we compared the transcriptomes of a wild-type Escherichia coli K-12 strain and its gcvB deletion derivative grown in minimal medium and identified putative targets responding to GcvB, including flu, a determinant gene of auto-aggregation. The following molecular studies and the enhanced auto-aggregation ability of the gcvB knockout strain further substantiated the induced expression of these genes. Intriguingly, the reduced expression of OxyR (the oxidative stress regulator) in the gcvB knockout strain was identified to account for the increased expression of flu. Additionally, GcvB was characterized to up-regulate the expression of OxyR at the translational level. Accordingly, compared to the wild type, the GcvB deletion strain was more sensitive to oxidative stress and lost some its ability to eliminate endogenous reactive oxygen species. Taken together, we reveal that GcvB regulates oxidative stress response by up-regulating OxyR expression. Our findings provide an insight into the diversity of GcvB regulation and add an additional layer to the regulation of OxyR.

Machine Learning of Bacterial Transcriptomes Reveals Responses Underlying Differential Antibiotic Susceptibility

In vitro antibiotic susceptibility testing often fails to accurately predict in vivo drug efficacies, in part due to differences in the molecular composition between standardized bacteriologic media and physiological environments within the body. Here, we investigate the interrelationship between antibiotic susceptibility and medium composition in Escherichia coli K-12 MG1655 as contextualized through machine learning of transcriptomics data. Application of independent component analysis, a signal separation algorithm, shows that complex phenotypic changes induced by environmental conditions or antibiotic treatment are directly traced to the action of a few key transcriptional regulators, including RpoS, Fur, and Fnr. Integrating machine learning results with biochemical knowledge of transcription factor activation reveals medium-dependent shifts in respiration and iron availability that drive differential antibiotic susceptibility. By extension, the data generation and data analytics workflow used here can interrogate the regulatory state of a pathogen under any measured condition and can be applied to any strain or organism for which sufficient transcriptomics data are available.

IMPORTANCE Antibiotic resistance is an imminent threat to global health. Patient treatment regimens are often selected based on results from standardized antibiotic susceptibility testing (AST) in the clinical microbiology lab, but these in vitro tests frequently misclassify drug effectiveness due to their poor resemblance to actual host conditions. Prior attempts to understand the combined effects of drugs and media on antibiotic efficacy have focused on physiological measurements but have not linked treatment outcomes to transcriptional responses on a systems level. Here, application of machine learning to transcriptomics data identified medium-dependent responses in key regulators of bacterial iron uptake and respiratory activity. The analytical workflow presented here is scalable to additional organisms and conditions and could be used to improve clinical AST by identifying the key regulatory factors dictating antibiotic susceptibility.

A role for bacterial experimental evolution in coral bleaching mitigation?

Coral reefs are rapidly declining because of widespread mass coral bleaching causing extensive coral mortality. Elevated seawater temperatures are the main drivers of coral bleaching, and climate change is increasing the frequency and severity of destructive marine heatwaves. Efforts to enhance coral thermal bleaching tolerance can be targeted at the coral host or at coral-associated microorganisms (e.g., dinoflagellate endosymbionts and bacteria). The literature on experimental evolution of bacteria suggests that it has value as a tool to increase coral climate resilience. We provide a workflow on how to experimentally evolve coral-associated bacteria to confer thermal tolerance to coral hosts and emphasize the value of implementing this approach in coral reef conservation and restoration efforts.

PoLoBag: Polynomial Lasso Bagging for signed gene regulatory network inference from expression data

MOTIVATION

Inferring gene regulatory networks (GRNs) from expression data is a significant systems biology problem. A useful inference algorithm should not only unveil the global structure of the regulatory mechanisms but also the details of regulatory interactions such as edge direction (from regulator to target) and sign (activation/inhibition). Many popular GRN inference algorithms cannot infer edge signs, and those that can infer signed GRNs cannot simultaneously infer edge directions or network cycles.

RESULTS

To address these limitations of existing algorithms, we propose Polynomial Lasso Bagging (PoLoBag) for signed GRN inference with both edge directions and network cycles. PoLoBag is an ensemble regression algorithm in a bagging framework where Lasso weights estimated on bootstrap samples are averaged. These bootstrap samples incorporate polynomial features to capture higher-order interactions. Results demonstrate that PoLoBag is consistently more accurate for signed inference than state-of-the-art algorithms on simulated and real-world expression datasets.

AVAILABILITY AND IMPLEMENTATION

Algorithm and data are freely available at https://github.com/gourabghoshroy/PoLoBag.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Environmental conditions dictate differential evolution of vancomycin resistance in Staphylococcus aureus

While microbiological resistance to vancomycin in Staphylococcus aureus is rare, clinical vancomycin treatment failures are common, and methicillin-resistant S. aureus (MRSA) strains isolated from patients after prolonged vancomycin treatment failure remain susceptible. Adaptive laboratory evolution was utilized to uncover mutational mechanisms associated with MRSA vancomycin resistance in a physiological medium as well as a bacteriological medium used in clinical susceptibility testing. Sequencing of resistant clones revealed shared and media-specific mutational outcomes, with an overlap in cell wall regulons (walKRyycHI, vraSRT). Evolved strains displayed similar properties to resistant clinical isolates in their genetic and phenotypic traits. Importantly, resistant phenotypes that developed in physiological media did not translate into resistance in bacteriological media. Further, a bacteriological media-specific mechanism for vancomycin resistance associated with a mutated mprF was confirmed. This study bridges the gap between the understanding of clinical and microbiological vancomycin resistance in S. aureus and expands the number of allelic variants (18 ± 4 mutations for the top 5 mutated genes) that result in vancomycin resistance phenotypes.

Restoration of fitness lost due to dysregulation of the pyruvate dehydrogenase complex is triggered by ribosomal binding site modifications

Pyruvate dehydrogenase complex (PDC) functions as the main determinant of the respiro-fermentative balance because it converts pyruvate to acetyl-coenzyme A (CoA), which then enters the TCA (tricarboxylic acid cycle). PDC is repressed by the pyruvate dehydrogenase complex regulator (PdhR) in Escherichia coli. The deletion of the pdhR gene compromises fitness in aerobic environments. We evolve the E. coli pdhR deletion strain to examine its achievable growth rate and the underlying adaptive strategies. We find that (1) optimal proteome allocation to PDC is critical in achieving optimal growth rate; (2) expression of PDC in evolved strains is reduced through mutations in the Shine-Dalgarno sequence; (3) rewiring of the TCA flux and increased reactive oxygen species (ROS) defense occur in the evolved strains; and (4) the evolved strains adapt to an efficient biomass yield. Together, these results show how adaptation can find alternative regulatory mechanisms for a key cellular process if the primary regulatory mode fails.

Independent component analysis recovers consistent regulatory signals from disparate datasets

The availability of bacterial transcriptomes has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent datasets, including both RNA-seq and microarray datasets. We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. With this understanding, we expanded our analysis to over 3,000 E. coli expression profiles and predicted three high-impact regulons that respond to oxidative stress, anaerobiosis, and antibiotic treatment. ICA thus enables deep analysis of disparate data to uncover new insights that were not visible in the individual datasets.

Cells adapt to diverse environments by regulating gene expression. Genome-wide measurements of gene expression levels have exponentially increased in recent years, but successful integration and analysis of these datasets are limited. Recently, we showed that independent component analysis (ICA), a signal deconvolution algorithm, can separate a large bacterial gene expression dataset into groups of co-regulated genes. This previous study focused on data generated by a standardized pipeline and did not address whether ICA extracts the same quantitative co-expression signals across expression profiling platforms. In this study, we show that ICA finds similar co-regulation patterns underlying multiple gene expression datasets and can be used as a tool to integrate and interpret diverse datasets. Using a dataset containing over 3,000 expression profiles, we predicted three new regulons and characterized their activities. Since large, standardized expression datasets only exist for a few bacterial strains, these results broaden the possible applications of this tool to better understand transcriptional regulation across a wide range of microbes.

Tightly controlled response to oxidative stress; an important factor in the tolerance of Bacteroides fragilis

The exposure of Bacteroides fragilis to highly oxygenated tissues induces an oxidative stress due to a shift from the reduced condition of the gastrointestinal tract to an aerobic environment of host tissues. The potent and effective responses to reactive oxygen species (ROS) make the B. fragilis tolerant to atmospheric oxygen for several days. The response to oxidative stress in B. fragilis is a complicated event that is induced and regulated by different agents. In this review, we will focus on the B. fragilis response to oxidative stress and present an overview of the regulators of responses to oxidative stress in this bacterium.

iModulonDB: a knowledgebase of microbial transcriptional regulation derived from machine learning

Independent component analysis (ICA) of bacterial transcriptomes has emerged as a powerful tool for obtaining co-regulated, independently-modulated gene sets (iModulons), inferring their activities across a range of conditions, and enabling their association to known genetic regulators. By grouping and analyzing genes based on observations from big data alone, iModulons can provide a novel perspective into how the composition of the transcriptome adapts to environmental conditions. Here, we present iModulonDB (imodulondb.org), a knowledgebase of prokaryotic transcriptional regulation computed from high-quality transcriptomic datasets using ICA. Users select an organism from the home page and then search or browse the curated iModulons that make up its transcriptome. Each iModulon and gene has its own interactive dashboard, featuring plots and tables with clickable, hoverable, and downloadable features. This site enhances research by presenting scientists of all backgrounds with co-expressed gene sets and their activity levels, which lead to improved understanding of regulator-gene relationships, discovery of transcription factors, and the elucidation of unexpected relationships between conditions and genetic regulatory activity. The current release of iModulonDB covers three organisms (Escherichia coli, Staphylococcus aureus and Bacillus subtilis) with 204 iModulons, and can be expanded to cover many additional organisms.

Machine learning uncovers independently regulated modules in the Bacillus subtilis transcriptome

The transcriptional regulatory network (TRN) of Bacillus subtilis coordinates cellular functions of fundamental interest, including metabolism, biofilm formation, and sporulation. Here, we use unsupervised machine learning to modularize the transcriptome and quantitatively describe regulatory activity under diverse conditions, creating an unbiased summary of gene expression. We obtain 83 independently modulated gene sets that explain most of the variance in expression and demonstrate that 76% of them represent the effects of known regulators. The TRN structure and its condition-dependent activity uncover putative or recently discovered roles for at least five regulons, such as a relationship between histidine utilization and quorum sensing. The TRN also facilitates quantification of population-level sporulation states. As this TRN covers the majority of the transcriptome and concisely characterizes the global expression state, it could inform research on nearly every aspect of transcriptional regulation in B. subtilis.

The systems-level regulatory structure underlying gene expression in bacteria can be inferred using machine learning algorithms. Here we show this structure for Bacillus subtilis, present five hypotheses gleaned from it, and analyse the process of sporulation from its perspective.

Adaptive laboratory evolution of Pseudomonas putida KT2440 improves p-coumaric and ferulic acid catabolism and tolerance

Pseudomonas putida KT2440 is a promising bacterial chassis for the conversion of lignin-derived aromatic compound mixtures to biofuels and bioproducts. Despite the inherent robustness of this strain, further improvements to aromatic catabolism and toxicity tolerance of P. putida will be required to achieve industrial relevance. Here, tolerance adaptive laboratory evolution (TALE) was employed with increasing concentrations of the hydroxycinnamic acids p-coumaric acid (pCA) and ferulic acid (FA) individually and in combination (pCA ​+ ​FA). The TALE experiments led to evolved P. putida strains with increased tolerance to the targeted acids as compared to wild type. Specifically, a 37 ​h decrease in lag phase in 20 ​g/L pCA and a 2.4-fold increase in growth rate in 30 ​g/L FA was observed. Whole genome sequencing of intermediate and endpoint evolved P. putida populations revealed several expected and non-intuitive genetic targets underlying these aromatic catabolic and toxicity tolerance enhancements. PP_3350 and ttgB were among the most frequently mutated genes, and the beneficial contributions of these mutations were verified via gene knockouts. Deletion of PP_3350, encoding a hypothetical protein, recapitulated improved toxicity tolerance to high concentrations of pCA, but not an improved growth rate in high concentrations of FA. Deletion of ttgB, part of the TtgABC efflux pump, severely inhibited growth in pCA ​+ ​FA TALE-derived strains but did not affect growth in pCA ​+ ​FA in a wild type background, suggesting epistatic interactions. Genes involved in flagellar movement and transcriptional regulation were often mutated in the TALE experiments on multiple substrates, reinforcing ideas of a minimal and deregulated cell as optimal for domesticated growth. Overall, this work demonstrates increased tolerance towards and growth rate at the expense of hydroxycinnamic acids and presents new targets for improving P. putida for microbial lignin valorization.

Transcriptional Rewiring, Adaptation, and the Role of Gene Duplication in the Metabolism of Ethanol of Saccharomyces cerevisiae

Ethanol is the main by-product of yeast sugar fermentation that affects microbial growth parameters, being considered a dual molecule, a nutrient and a stressor. Previous works demonstrated that the budding yeast arose after an ancient hybridization process resulted in a tier of duplicated genes within its genome, many of them with implications in this ethanol "produce-accumulate-consume" strategy. The evolutionary link between ethanol production, consumption, and tolerance versus ploidy and stability of the hybrids is an ongoing debatable issue. The implication of ancestral duplicates in this metabolic rewiring, and how these duplicates differ transcriptionally, remains unsolved. Here, we study the transcriptomic adaptive signatures to ethanol as a nonfermentative carbon source to sustain clonal yeast growth by experimental evolution, emphasizing the role of duplicated genes in the adaptive process. As expected, ethanol was able to sustain growth but at a lower rate than glucose. Our results demonstrate that in asexual populations a complete transcriptomic rewiring was produced, strikingly by downregulation of duplicated genes, mainly whole-genome duplicates, whereas small-scale duplicates exhibited significant transcriptional divergence between copies. Overall, this study contributes to the understanding of evolution after gene duplication, linking transcriptional divergence with duplicates' fate in a multigene trait as ethanol tolerance.IMPORTANCE Gene duplication events have been related with increasing biological complexity through the tree of life, but also with illnesses, including cancer. Early evolutionary theories indicated that duplicated genes could explore alternative functions due to relaxation of selective constraints in one of the copies, as the other remains as ancestral-function backup. In unicellular eukaryotes like yeasts, it has been demonstrated that the fate and persistence of duplicates depend on duplication mechanism (whole-genome or small-scale events), shaping their actual genomes. Although it has been shown that small-scale duplicates tend to innovate and whole-genome duplicates specialize in ancestral functions, the implication of duplicates' transcriptional plasticity and transcriptional divergence on environmental and metabolic responses remains largely obscure. Here, by experimental adaptive evolution, we show that Saccharomyces cerevisiae is able to respond to metabolic stress (ethanol as nonfermentative carbon source) due to the persistence of duplicated genes. These duplicates respond by transcriptional rewiring, depending on their transcriptional background. Our results shed light on the mechanisms that determine the role of duplicates, and on their evolvability.

Causal mutations from adaptive laboratory evolution are outlined by multiple scales of genome annotations and condition-specificity

BACKGROUND

Adaptive Laboratory Evolution (ALE) has emerged as an experimental approach to discover mutations that confer phenotypic functions of interest. However, the task of finding and understanding all beneficial mutations of an ALE experiment remains an open challenge for the field. To provide for better results than traditional methods of ALE mutation analysis, this work applied enrichment methods to mutations described by a multiscale annotation framework and a consolidated set of ALE experiment conditions. A total of 25,321 unique genome annotations from various sources were leveraged to describe multiple scales of mutated features in a set of 35 Escherichia coli based ALE experiments. These experiments totalled 208 independent evolutions and 2641 mutations. Additionally, mutated features were statistically associated across a total of 43 unique experimental conditions to aid in deconvoluting mutation selection pressures.

RESULTS

Identifying potentially beneficial, or key, mutations was enhanced by seeking coding and non-coding genome features significantly enriched by mutations across multiple ALE replicates and scales of genome annotations. The median proportion of ALE experiment key mutations increased from 62%, with only small coding and non-coding features, to 71% with larger aggregate features. Understanding key mutations was enhanced by considering the functions of broader annotation types and the significantly associated conditions for key mutated features. The approaches developed here were used to find and characterize novel key mutations in two ALE experiments: one previously unpublished with Escherichia coli grown on glycerol as a carbon source and one previously published with Escherichia coli tolerized to high concentrations of L-serine.

CONCLUSIONS

The emergent adaptive strategies represented by sets of ALE mutations became more clear upon observing the aggregation of mutated features across small to large scale genome annotations. The clarification of mutation selection pressures among the many experimental conditions also helped bring these strategies to light. This work demonstrates how multiscale genome annotation frameworks and data-driven methods can help better characterize ALE mutations, and thus help elucidate the genotype-to-phenotype relationship of the studied organism.