Gene Dispensability in Escherichia coli Grown in Thirty Different Carbon Environments

What makes Candida auris pan-drug resistant? Integrative insights from genomic, transcriptomic, and phenomic analysis of clinical strains resistant to all four major classes of antifungal drugs

The global epidemic of drug-resistant Candida auris continues unabated. We do not know what caused the unprecedented appearance of pan-drug resistant (PDR) Candida auris strains in a hospitalized patient in New York; the initial report highlighted both known and unique mutations in the prominent gene targets of azoles, amphotericin B, echinocandins, and flucytosine antifungal drugs. However, the factors that allow C. auris to acquire multi-drug resistance and pan-drug resistance are not known. Therefore, we conducted a comprehensive genomic, transcriptomic, and phenomic analysis to better understand PDR C. auris . Among 1,570 genetic variants in drug-resistant C. auris , 299 were unique to PDR strains. The whole genome sequencing results suggested perturbations in genes associated with nucleotide biosynthesis, mRNA processing, and nuclear export of mRNA. Whole transcriptome sequencing of PDR C. auris revealed two genes to be significantly differentially expressed - a DNA repair protein and DNA replication-dependent chromatin assembly factor 1. Of 59 novel transcripts, 12 candidate transcripts had no known homology among expressed transcripts found in other organisms. We observed no fitness defects among multi-drug resistant (MDR) and PDR C. auris strains grown in nutrient-deficient or - enriched media at different temperatures. Phenotypic profiling revealed wider adaptability to nitrogenous nutrients with an uptick in the utilization of substrates critical in upper glycolysis and tricarboxylic acid cycle. Structural modelling of 33-amino acid deletion in the gene for uracil phosphoribosyl transferase suggested an alternate route in C. auris to generate uracil monophosphate that does not accommodate 5-fluorouracil as a substrate. Overall, we find evidence of metabolic adaptations in MDR and PDR C. auris in response to antifungal drug lethality without deleterious fitness costs.

Machine learning identifies key metabolic reactions in bacterial growth on different carbon sources

Carbon source-dependent control of bacterial growth is fundamental to bacterial physiology and survival. However, pinpointing the metabolic steps important for cell growth is challenging due to the complexity of cellular networks. Here, the elastic net model and multilayer perception model that integrated genome-wide gene-deletion data and simulated flux distributions were constructed to identify metabolic reactions beneficial or detrimental to Escherichia coli grown on 30 different carbon sources. Both models outperformed traditional in silico methods by identifying not just essential reactions but also nonessential ones that promote growth. They successfully predicted metabolic reactions beneficial to cell growth, with high convergence between the models. The models revealed that biosynthetic pathways generally promote growth across various carbon sources, whereas the impact of energy-generating pathways varies with the carbon source. Intriguing predictions were experimentally validated for findings beyond experimental training data and the impact of various carbon sources on the glyoxylate shunt, pyruvate dehydrogenase reaction, and redundant purine biosynthesis reactions. These highlight the practical significance and predictive power of the models for understanding and engineering microbial metabolism.

Relative impact of three growth conditions on the Escherichia coli protein acetylome

Nε-lysine acetylation is a common posttranslational modification observed in Escherichia coli. In the present study, integrative analysis of the proteome and acetylome was performed using label-free quantitative mass spectrometry to analyze the relative influence of three factors affecting growth. The results revealed differences in the proteome, mainly owing to the type of culture medium used (defined or complex). In the acetylome, 7482 unique acetylation sites were identified. Acetylation is directly related to the abundance of proteins, and the level of acetylation in each type of culture is associated with extracellular acetate concentration. Furthermore, most acetylated lysines in the exponential phase remained in the stationary phase without dynamic turnover. Interestingly, unique acetylation sites were detected in proteins whose presence or abundance was linked to the type of culture medium. Finally, the biological function of the acetylation changes was demonstrated for three central metabolic proteins (GapA, Mdh, and AceA).

Evaluating E. coli genome-scale metabolic model accuracy with high-throughput mutant fitness data

The Escherichia coli genome-scale metabolic model (GEM) is an exemplar systems biology model for the simulation of cellular metabolism. Experimental validation of model predictions is essential to pinpoint uncertainty and ensure continued development of accurate models. Here, we quantified the accuracy of four subsequent E. coli GEMs using published mutant fitness data across thousands of genes and 25 different carbon sources. This evaluation demonstrated the utility of the area under a precision-recall curve relative to alternative accuracy metrics. An analysis of errors in the latest (iML1515) model identified several vitamins/cofactors that are likely available to mutants despite being absent from the experimental growth medium and highlighted isoenzyme gene-protein-reaction mapping as a key source of inaccurate predictions. A machine learning approach further identified metabolic fluxes through hydrogen ion exchange and specific central metabolism branch points as important determinants of model accuracy. This work outlines improved practices for the assessment of GEM accuracy with high-throughput mutant fitness data and highlights promising areas for future model refinement in E. coli and beyond.

Challenges and opportunities for engineered Escherichia coli as a pivotal chassis toward versatile tyrosine-derived chemicals production

Growing concerns over limited fossil resources and associated environmental problems are motivating the development of sustainable processes for the production of high-volume fuels and high-value-added compounds. The shikimate pathway, an imperative pathway in most microorganisms, is branched with tyrosine as the rate-limiting step precursor of valuable aromatic substances. Such occurrence suggests the shikimate pathway as a promising route in developing microbial cell factories with multiple applications in the nutraceutical, pharmaceutical, and chemical industries. Therefore, an increasing number of studies have focused on this pathway to enable the biotechnological manufacture of pivotal and versatile aromatic products. With advances in genome databases and synthetic biology tools, genetically programmed Escherichia coli strains are gaining immense interest in the sustainable synthesis of chemicals. Engineered E. coli is expected to be the next bio-successor of fossil fuels and plants in commercial aromatics synthesis. This review summarizes successful and applicable genetic and metabolic engineering strategies to generate new chassis and engineer the iterative pathway of the tyrosine route in E. coli, thus addressing the opportunities and current challenges toward the realization of sustainable tyrosine-derived aromatics.

A pathogen-specific isotope tracing approach reveals metabolic activities and fluxes of intracellular Salmonella

Pathogenic bacteria proliferating inside mammalian host cells need to rapidly adapt to the intracellular environment. How they achieve this and scavenge essential nutrients from the host has been an open question due to the difficulties in distinguishing between bacterial and host metabolites in situ. Here, we capitalized on the inability of mammalian cells to metabolize mannitol to develop a stable isotopic labeling approach to track Salmonella enterica metabolites during intracellular proliferation in host macrophage and epithelial cells. By measuring label incorporation into Salmonella metabolites with liquid chromatography-mass spectrometry (LC-MS), and combining it with metabolic modeling, we identify relevant carbon sources used by Salmonella, uncover routes of their metabolization, and quantify relative reaction rates in central carbon metabolism. Our results underline the importance of the Entner-Doudoroff pathway (EDP) and the phosphoenolpyruvate carboxylase for intracellularly proliferating Salmonella. More broadly, our metabolic labeling strategy opens novel avenues for understanding the metabolism of pathogens inside host cells.

DXP Synthase Function in a Bacterial Metabolic Adaptation and Implications for Antibacterial Strategies

Pathogenic bacteria possess a remarkable ability to adapt to fluctuating host environments and cause infection. Disturbing bacterial central metabolism through inhibition of 1-deoxy-d-xylulose 5-phosphate synthase (DXPS) has the potential to hinder bacterial adaptation, representing a new antibacterial strategy. DXPS functions at a critical metabolic branchpoint to produce the metabolite DXP, a precursor to pyridoxal-5-phosphate (PLP), thiamin diphosphate (ThDP) and isoprenoids presumed essential for metabolic adaptation in nutrient-limited host environments. However, specific roles of DXPS in bacterial adaptations that rely on vitamins or isoprenoids have not been studied. Here we investigate DXPS function in an adaptation of uropathogenic E. coli (UPEC) to d-serine (d-Ser), a bacteriostatic host metabolite that is present at high concentrations in the urinary tract. UPEC adapt to d-Ser by producing a PLP-dependent deaminase, DsdA, that converts d-Ser to pyruvate, pointing to a role for DXPS-dependent PLP synthesis in this adaptation. Using a DXPS-selective probe, butyl acetylphosphonate (BAP), and leveraging the toxic effects of d-Ser, we reveal a link between DXPS activity and d-Ser catabolism. We find that UPEC are sensitized to d-Ser and produce sustained higher levels of DsdA to catabolize d-Ser in the presence of BAP. In addition, BAP activity in the presence of d-Ser is suppressed by β-alanine, the product of aspartate decarboxylase PanD targeted by d-Ser. This BAP-dependent sensitivity to d-Ser marks a metabolic vulnerability that can be exploited to design combination therapies. As a starting point, we show that combining inhibitors of DXPS and CoA biosynthesis displays synergy against UPEC grown in urine where there is increased dependence on the TCA cycle and gluconeogenesis from amino acids. Thus, this study provides the first evidence for a DXPS-dependent metabolic adaptation in a bacterial pathogen and demonstrates how this might be leveraged for development of antibacterial strategies against clinically relevant pathogens.

Revealing the Characteristics of Glucose- and Lactate-Based Chain Elongation for Caproate Production by Caproicibacterium lactatifermentans through Transcriptomic, Bioenergetic, and Regulatory Analyses

Caproate, an important medium-chain fatty acid, can only be synthesized by limited bacterial species by using ethanol, lactate, or certain saccharides. Caproicibacterium lactatifermentans is a promising caproate producer due to its glucose and lactate utilization capabilities. However, the global cellular responses of this bacterium to different carbon sources were not well understood. Here, C. lactatifermentans showed robust growth on glucose but more active caproate synthesis on lactate. Comparative transcriptome revealed that the genes involved in reverse β-oxidation for caproate synthesis and V-type ATPase-dependent ATP generation were upregulated under lactate condition, while several genes responsible for biomass synthesis were upregulated under glucose condition. Based on metabolic pathway reconstructions and bioenergetics analysis, the biomass accumulation on glucose condition may be supported by sufficient supplies of ATP and metabolite intermediates via glycolysis. In contrast, the ATP yield per glucose equivalent from lactate conversion into caproate was only 20% of that from glucose. Thus, the upregulation of the reverse β-oxidation genes may be essential for cell survival under lactate conditions. Furthermore, the remarkably decreased lactate utilization was observed after glucose acclimatization, indicating the negative modulation of lactate utilization by glucose metabolism. Based on the cotranscription of the lactate utilization repressor gene lldR with sugar-specific PTS genes and the opposite expression patterns of lldR and lactate utilization genes, a novel regulatory mechanism of glucose-repressed lactate utilization mediated via lldR was proposed. The results of this study suggested the molecular mechanism underlying differential physiologic and metabolic characteristics of C. lactatifermentans grown on glucose and lactate. IMPORTANCE Caproicibacterium lactatifermentans is a unique and robust caproate-producing bacterium in the family Oscillospiraceae due to its lactate utilization capability, whereas its close relatives such as Caproicibacterium amylolyticum, Caproiciproducens galactitolivorans, and Caproicibacter fermentans cannot utilize lactate but produce lactate as the main fermentation end product. Moreover, C. lactatifermentans can also utilize several saccharides such as glucose and maltose. Although the metabolic versatility of the bacterium makes it to be a promising industrial caproate producer, the cellular responses of C. lactatifermentans to different carbon sources were unknown. Here, the molecular mechanisms of biomass synthesis supported by glucose utilization and the cell survival supported by lactate utilization were revealed. A novel insight into the regulatory machinery in which glucose negatively regulates lactate utilization was proposed. This study provides a valuable basis to control and optimize caproate production, which will contribute to achieving a circular economy and environmental sustainability.

Antibacterial Target DXP Synthase Catalyzes the Cleavage of d-Xylulose 5-Phosphate: a Study of Ketose Phosphate Binding and Ketol Transfer Reaction

The bacterial enzyme 1-deoxy-d-xylulose 5-phosphate synthase (DXPS) catalyzes the formation of DXP from pyruvate and d-glyceraldehyde 3-phosphate (d-GAP) in a thiamin diphosphate (ThDP)-dependent manner. In addition to its role in isoprenoid biosynthesis, DXP is required for ThDP and pyridoxal phosphate biosynthesis. Due to its function as a branch-point enzyme and its demonstrated substrate and catalytic promiscuity, we hypothesize that DXPS could be key for bacterial adaptation in the dynamic metabolic landscape during infection. Prior work in the Freel Meyers laboratory has illustrated that DXPS displays relaxed specificity toward donor and acceptor substrates and varies acceptor specificity according to the donor used. We have reported that DXPS forms dihydroxyethyl (DHE)ThDP from ketoacid or aldehyde donor substrates via decarboxylation and deprotonation, respectively. Here, we tested other DHE donors and found that DXPS cleaves d-xylulose 5-phosphate (X5P) at C2-C3, producing DHEThDP through a third mechanism involving d-GAP elimination. We interrogated DXPS-catalyzed reactions using X5P as a donor substrate and illustrated (1) production of a semi-stable enzyme-bound intermediate and (2) O2, H+, and d-erythrose 4-phosphate act as acceptor substrates, highlighting a new transketolase-like activity of DXPS. Furthermore, we examined X5P binding to DXPS and suggest that the d-GAP binding pocket plays a crucial role in X5P binding and turnover. Overall, this study reveals a ketose-cleavage reaction catalyzed by DXPS, highlighting the remarkable flexibility for donor substrate usage by DXPS compared to other C-C bond-forming enzymes.

Synthetic Genetic Interactions Reveal a Dense and Cryptic Regulatory Network of Small Noncoding RNAs in Escherichia coli

Over the past 20 years, we have learned that bacterial small noncoding RNAs (sRNAs) can rapidly effect changes in gene expression in response to stress. However, the broader role and impact of sRNA-mediated regulation in promoting bacterial survival has remained elusive. Indeed, there are few examples where disruption of sRNA-mediated gene regulation results in a discernible change in bacterial growth or survival. The lack of phenotypes attributable to loss of sRNA function suggests that either sRNAs are wholly dispensable or functional redundancies mask the impact of deleting a single sRNA. We investigated synthetic genetic interactions among sRNA genes in Escherichia coli by constructing pairwise deletions in 54 genes, including 52 sRNAs. Some 1,373 double deletion strains were studied for growth defects under 32 different nutrient stress conditions and revealed 1,131 genetic interactions. In one example, we identified a profound synthetic lethal interaction between ArcZ and CsrC when E. coli was grown on pyruvate, lactate, oxaloacetate, or d-/l-alanine, and we provide evidence that the expression of ppsA is dysregulated in the double deletion background, causing the conditionally lethal phenotype. This work employs a unique platform for studying sRNA-mediated gene regulation and sheds new light on the genetic network of sRNAs that underpins bacterial growth.

Auxotrophic and prototrophic conditional genetic networks reveal the rewiring of transcription factors in Escherichia coli

Bacterial transcription factors (TFs) are widely studied in Escherichia coli. Yet it remains unclear how individual genes in the underlying pathways of TF machinery operate together during environmental challenge. Here, we address this by applying an unbiased, quantitative synthetic genetic interaction (GI) approach to measure pairwise GIs among all TF genes in E. coli under auxotrophic (rich medium) and prototrophic (minimal medium) static growth conditions. The resulting static and differential GI networks reveal condition-dependent GIs, widespread changes among TF genes in metabolism, and new roles for uncharacterized TFs (yjdC, yneJ, ydiP) as regulators of cell division, putrescine utilization pathway, and cold shock adaptation. Pan-bacterial conservation suggests TF genes with GIs are co-conserved in evolution. Together, our results illuminate the global organization of E. coli TFs, and remodeling of genetic backup systems for TFs under environmental change, which is essential for controlling the bacterial transcriptional regulatory circuits.

The bacterium E. coli has around 300 transcriptional factors, but the functions of many of them, and the interactions between their respective regulatory networks, are unclear. Here, the authors study genetic interactions among all transcription factor genes in E. coli, revealing condition-dependent interactions and roles for uncharacterized transcription factors.

A curated collection of Klebsiella metabolic models reveals variable substrate usage and gene essentiality

The Klebsiella pneumoniae species complex (KpSC) is a set of seven Klebsiella taxa that are found in a variety of niches and are an important cause of opportunistic health care-associated infections in humans. Because of increasing rates of multi-drug resistance within the KpSC, there is a growing interest in better understanding the biology and metabolism of these organisms to inform novel control strategies. We collated 37 sequenced KpSC isolates isolated from a variety of niches, representing all seven taxa. We generated strain-specific genome-scale metabolic models (GEMs) for all 37 isolates and simulated growth phenotypes on 511 distinct carbon, nitrogen, sulfur, and phosphorus substrates. Models were curated and their accuracy was assessed using matched phenotypic growth data for 94 substrates (median accuracy of 96%). We explored species-specific growth capabilities and examined the impact of all possible single gene deletions using growth simulations in 145 core carbon substrates. These analyses revealed multiple strain-specific differences, within and between species, and highlight the importance of selecting a diverse range of strains when exploring KpSC metabolism. This diverse set of highly accurate GEMs could be used to inform novel drug design, enhance genomic analyses, and identify novel virulence and resistance determinants. We envisage that these 37 curated strain-specific GEMs, covering all seven taxa of the KpSC, provide a valuable resource to the Klebsiella research community.

Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics

To discover novel catabolic enzymes and transporters, we combined high-throughput genetic data from 29 bacteria with an automated tool to find gaps in their catabolic pathways. GapMind for carbon sources automatically annotates the uptake and catabolism of 62 compounds in bacterial and archaeal genomes. For the compounds that are utilized by the 29 bacteria, we systematically examined the gaps in GapMind's predicted pathways, and we used the mutant fitness data to find additional genes that were involved in their utilization. We identified novel pathways or enzymes for the utilization of glucosamine, citrulline, myo-inositol, lactose, and phenylacetate, and we annotated 299 diverged enzymes and transporters. We also curated 125 proteins from published reports. For the 29 bacteria with genetic data, GapMind finds high-confidence paths for 85% of utilized carbon sources. In diverse bacteria and archaea, 38% of utilized carbon sources have high-confidence paths, which was improved from 27% by incorporating the fitness-based annotations and our curation. GapMind for carbon sources is available as a web server (http://papers.genomics.lbl.gov/carbon) and takes just 30 seconds for the typical genome.

sparse-growth-curve: a Computational Pipeline for Parsing Cellular Growth Curves with Low Temporal Resolution

Here, we introduce a Python-based repository, sparse-growth-curve, a software package designed for parsing cellular growth curves with low temporal resolution. The repository uses cell density and time data as the input, automatically separates different growth phases, calculates the exponential growth rates, and produces multiple graphs to aid in interpretation.

Exploring the Glucose Fluxotype of the E. coli y-ome Using High-Resolution Fluxomics

We have developed a robust workflow to measure high-resolution fluxotypes (metabolic flux phenotypes) for large strain libraries under fully controlled growth conditions. This was achieved by optimizing and automating the whole high-throughput fluxomics process and integrating all relevant software tools. This workflow allowed us to obtain highly detailed maps of carbon fluxes in the central carbon metabolism in a fully automated manner. It was applied to investigate the glucose fluxotypes of 180 Escherichia coli strains deleted for y-genes. Since the products of these y-genes potentially play a role in a variety of metabolic processes, the experiments were designed to be agnostic as to their potential metabolic impact. The obtained data highlight the robustness of E. coli’s central metabolism to y-gene deletion. For two y-genes, deletion resulted in significant changes in carbon and energy fluxes, demonstrating the involvement of the corresponding y-gene products in metabolic function or regulation. This work also introduces novel metrics to measure the actual scope and quality of high-throughput fluxomics investigations.

Systems-Level Chemical Biology to Accelerate Antibiotic Drug Discovery

Drug-resistant bacterial infections pose an imminent and growing threat to public health. The discovery and development of new antibiotics of novel chemical class and mode of action that are unsusceptible to existing resistance mechanisms is imperative for tackling this threat. Modern industrial drug discovery, however, has failed to provide new drugs of this description, as it is dependent largely on a reductionist genes-to-drugs research paradigm. We posit that the lack of success in new antibiotic drug discovery is due in part to a lack of understanding of the bacterial cell system as whole. A fundamental understanding of the architecture and function of bacterial systems has been elusive but is of critical importance to design strategies to tackle drug-resistant bacterial pathogens.Increasingly, systems-level approaches are rewriting our understanding of the cell, defining a dense network of redundant and interacting components that resist perturbations of all kinds, including by antibiotics. Understanding the network properties of bacterial cells requires integrative, systematic, and genome-scale approaches. These methods strive to understand how the phenotypic behavior of bacteria emerges from the many interactions of individual molecular components that constitute the system. With the ability to examine genomic, transcriptomic, proteomic, and metabolomic consequences of, for example, genetic or chemical perturbations, researchers are increasingly moving away from one-gene-at-a-time studies to consider the system-wide response of the cell. Such measurements are demonstrating promise as quantitative tools, powerful discovery engines, and robust hypothesis generators with great value to antibiotic drug discovery.In this Account, we describe our thinking and findings using systems-level studies aimed at understanding bacterial physiology broadly and in uncovering new antibacterial chemical matter of novel mechanism. We share our systems-level toolkit and detail recent technological developments that have enabled unprecedented acquisition of genome-wide interaction data. We focus on three types of interactions: gene-gene, chemical-gene, and chemical-chemical. We provide examples of their use in understanding cell networks and how these insights might be harnessed for new antibiotic discovery. By example, we show the application of these principles in mapping genetic networks that underpin phenotypes of interest, characterizing genes of unknown function, validating small-molecule screening platforms, uncovering novel chemical probes and antibacterial leads, and delineating the mode of action of antibacterial chemicals. We also discuss the importance of computation to these approaches and its probable dominance as a tool for systems approaches in the future. In all, we advocate for the use of systems-based approaches as discovery engines in antibacterial research, both as powerful tools and to stimulate innovation.