Biology subjects

Harcombe, W. R.

Publications and source records attributed to Harcombe, W. R..

At least 19 recordsLinked to original sources

bioRxiv2026

Cross-feeding reshapes interactions between antibiotic stressors through metabolic feedbacks

Stressors can combine to inhibit organismal growth or function more (synergy) or less (antagonism) strongly than expected from their individual effects, yet whether these interactions are intrinsic properties of cells or stressors, or emergent consequences of ecological interactions remains unclear. Antibiotic combinations provide a tractable model for studying stressor interactions, but have been characterized largely in bacterial monocultures. Here, we compared antibiotic stressor interactions in Escherichia coli growth in monoculture versus when cross-feeding with Salmonella enterica. Across 36 antibiotic pairs, cross-feeding frequently shifts interactions toward antagonism and away from synergy. In the sulfamethoxazole-trimethoprim pair, dual treatment increases carbon excretion by E. coli, promoting partner growth and attenuating stressor synergy through reciprocal metabolic feedback. Perturbing partner metabolite usage restores monoculture-like interaction outcomes across multiple drug pairs. Together, our results demonstrate that stressor interactions in microorganisms can emerge from ecological feedbacks mediated by metabolic excretion.

microbiology↗

bioRxiv2026

Adaptive benefits of motility in cross-feeding mutualisms

Cross-feeding mutualisms, in which partner species exchange essential metabolites, are ubiquitous in microbial communities. In spatially structured environments, motility can improve access to partner-produced resources but also impose metabolic costs and displace cells from nutrient-rich regions, so its net benefit depends on the spatial dynamics of the interaction. Here, we combine competition experiments in a cross-feeding mutualism between Escherichia coli and Salmonella enterica with a spatially explicit consumer-resource model to determine what drives selection on motility. Spatial structure imposes asymmetric selection between partners i.e. S. enterica benefits from motility regardless of partner motility, whereas selection on E. coli switches from favourable to unfavourable depending on whether its partner can move. Competition in well-mixed culture suggests that this reversal reflects the loss of a spatial benefit rather than an increased cost. Our model attributes the asymmetry to three interacting factors: the ratio of metabolite production to consumption which sets whether the cross-fed resource is scarce or abundant; the number of growth-limiting resources which determines whether an alternative gradient can rescue the benefit of motility; and partner motility and growth rate, which shape where metabolites are produced. When a metabolite is scarce, motile cells gain by dispersing into regions it has reached but not yet been depleted from. When it accumulates, this gradient is eroded, and the motility costs offset any benefit it provides. Selection on motility therefore depends on the metabolic structure of the interaction and the spatial behaviour of partners.

ecology↗

bioRxiv2025

A General Framework for Predicting the Temperature-Dependence of Microbial Interactions

Despite its global importance, our ability to predict the impacts of temperature change on the community dynamics of heterotrophic microbes remains limited. Here, we develop a metabolic trait-based mathematical framework to predict the temperature dependence of pairwise interactions among heterotrophic microbial consumers, accounting for their resource environment and community composition. Applying this framework leads to two general predictions. First, microbial species interactions are typically more thermally sensitive than the underlying metabolic traits. Second, temperature systematically reshapes intra- and interspecific interactions: their variance peaks at intermediate temperatures, while mean interaction strengths increase with warming more rapidly than interspecific interaction strengths. We show that these features of temperature-dependent interactions have far-reaching implications for the temperature responses of community coexistence, diversity, and stability. Our framework provides a mechanistic foundation for predicting how temperature affects the dynamics of heterotrophic microbial communities across diverse biological and environmental contexts. SignificanceCommunities of heterotrophic microbes, including bacteria, archaea, protists, and fungi, play a fundamental role in human health, bioprocessing, and global biogeochemical cycles. Predicting their responses to environmental change is a major challenge, with a key missing link being the effects of temperature on species interactions that ultimately shape community dynamics. By integrating metabolic constraints into consumer-resource theory, we derive general predictions about how interactions among heterotrophic microbial species respond to temperature changes. We show that interactions are more sensitive to warming than individual metabolic traits and undergo systematic temperature-dependent changes that alter community-level coexistence, diversity, and stability. These results hold across diverse environmental contexts and heterotrophic systems, providing a foundation for predicting how microbial community dynamics respond to environmental temperature, from biotechnological applications to natural ecosystems.

ecology↗

bioRxiv2025

Metabolic remodeling of microorganisms by obligate intracellular parasites alters mutualistic community composition

Bacteria carry many types of obligate intracellular parasites, including plasmids and bacteriophage. During infection, these parasites redirect intracellular resources away from bacterial processes toward parasite production. Because parasite-induced metabolic changes influence host traits such as growth rate, nutrient uptake, and waste excretion, parasitic infection should alter how microbes contribute to important community and ecosystem functions. Yet there are few empirical tests of how infection shapes metabolically-mediated interactions between host and non-host species. Here, we integrated a genome-scale metabolic modeling approach with an in vitro obligate cross-feeding system to investigate the metabolic consequences of two intracellular parasites of Escherichia coli: the conjugative plasmid F128 and the filamentous phage M13. We examined the impact of these parasites on interactions between bacteria in a multispecies community composed of E. coli, Salmonella enterica, and Methylobacterium extorquens. Modeling predicted that parasitic infection of E. coli should have consequences for host growth rate and the secretion and reuptake of carbon byproducts. These theoretical results aligned broadly with in vitro experiments, where we found that parasitic infection changed the excretion profile of E. coli, inducing the net externalization of lactate. We also found that parasite-driven changes to metabolism increased the density of cross-feeding species and changes to community composition were generalizable across community interactions and some host and parasite genotypes. Our work emphasizes that microbes infected by obligate intracellular parasites have different metabolisms than uninfected cells and demonstrates that these metabolic shifts can have significant consequences for microbial community structure and function. IMPORTANCEThe intracellular parasites of bacteria shape the structure and function of microbial communities in a variety of ways, including by killing their hosts or transferring genetic material. This study uses a combination of flux balance analysis and an in vitro system consisting of Escherichia coli, Salmonella enterica, Methylobacterium extorquens, and two intracellular parasites of E. coli, the F128 plasmid and the filamentous phage M13, to investigate how parasites change the community contributions of their hosts via metabolic remodeling. Flux balance analysis suggests that parasites change intracellular demand for different metabolic processes, leading to shifts in the identities and concentrations of compounds that infected hosts externalize into the environment. This finding is supported by experimental results, which additionally show that infection can induce the production of lactate. These findings extend our understanding of how bacteriophage and plasmids shape the structure and function of microbial communities.

ecology↗

bioRxiv2024

The strength of selection for costly toxin production increases with growth rate.

Microbes adopt a diversity of strategies to successfully compete with coexisting strains for space and resources. One common strategy is the production of toxic compounds to inhibit competitors, but the strength and direction of selection for this strategy varies depending on the environment. In particular, existing theoretical and experimental evidence suggests growth in spatially-structured environments makes toxin production more beneficial because competitive interactions are localized. Because higher growth rates increase the localization of resource competition in a structured environment, theory predicts that toxin production should be especially beneficial under these conditions. We tested this hypothesis by developing a genome-scale metabolic modeling approach and complementing it with comparative genomics to investigate the impact of growth rate on selection for costly toxin production. Our modeling approach expands the current abilities of the dynamic flux balance analysis platform COMETS to incorporate signaling and toxin production. Using this capability, we find that our modeling framework predicts that the strength of selection for toxin production increases as growth rate increases. This finding is supported by comparative genomics analyses that include diverse microbial species. Our work emphasizes that toxin production is more likely to be maintained in rapidly-growing, spatially-structured communities, thus improving our ability to manage critical microbial communities and informing natural product discovery.

evolutionary biology↗

bioRxiv2024

Cell Geometry and Membrane Protein Crowding Constrain Growth Rate, Overflow Metabolism, Respiration, and Maintenance Energy.

The rules of prokaryotic cell design remain elusive. Here, a theory is presented for interpreting growth rate, overflow metabolism, respiration efficiency, and maintenance energy flux based on cell dimensions, membrane protein crowding, and metabolism. The theory employs biophysical properties and systems analysis to successfully interpret phenotypes of Escherichia coli K-12 strains MG1655 and NCM3722. These strains are genetically similar but differ in surface area-to-volume (SA:V) ratios ([~]30%), growth rate on glucose ([~]40%), and overflow-inducing growth rates ([~]80%). Six predictions were tested and validated using experimental phenomics, proteomics, and mutant data. Analyses did not require assumptions regarding cytosolic macromolecular crowding highlighting the distinct properties of the theory. Cell geometry and membrane protein crowding are significant biophysical constraints of cell biology.

systems biology↗

bioRxiv2024

A duplexed CRISPRi-based approach to predict synergistic antibiotic targets in Escherichia coli.

Co-repressing drug targets with CRISPR interference (CRISPRi) has the potential to predict both synergistic and additive drug combinations. In a screen experiment with Escherichia coli, pairwise gene co-repression assays correctly predicted the interaction type observed for 36 antibiotic pairs in checkerboard assays. Furthermore, we used this approach to identify promising synergistic interactions with a gene currently not targeted by clinical antibiotics. CRISPRi can systematically test the impact of gene inhibitions and identify novel drug targets.

microbiology↗

bioRxiv2023

Emergent Antibiotic Persistence in Cross-Feeding Bacteria in Structured Habitats

Antibiotic persistence allows a sub-population of bacteria to survive antibiotic-induced killing and contributes to the evolution of antibiotic resistance. Although bacteria typically live in microbial communities with complex ecological interactions, little is known about how microbial ecology affects antibiotic persistence. Here, we demonstrated that the combination of cross-feeding and community spatial structure can emergently cause high antibiotic persistence in bacteria by increasing the cell-cell heterogeneity in the metabolic state both during growth and antibiotic killing. We tracked the ampicillin-induced death on agar surfaces in a model obligate mutualism of Escherichia coli and Salmonella enterica. We found that E. coli formed [~]100-fold more antibiotic persisters in the cross-feeding coculture than in monoculture. This high persistence could not be explained solely by the presence of S. enterica, the presence of cross-feeding, or the growth rate differences between the mono- and co-cultures. Time-series fluorescent microscopy revealed increased cell-cell variation in E. coli lag time in the mutualistic co-culture. Furthermore, we discovered that an E. coli cell can survive antibiotic killing if the nearby S. enterica cells on which it relies die first--a mechanism we termed "dynamical loss of access to nutrient." In conclusion, we showed that the high antibiotic persistence phenotype can be an emergent phenomenon caused by a combination of cross-feeding and spatial structure. Our work highlights the importance of considering spatially-structured interactions during antibiotic treatment and to understand microbial community resilience more broadly.

microbiology↗

bioRxiv2023

Bacteriophage diet breadth is impacted by interactions between bacteria

Predators play a central role in shaping community structure, function, and stability. The degree to which bacteriophage predators (viruses that infect bacteria) evolve to be specialists with a single bacterial prey species versus generalists able to consume multiple types of prey has implications for their effect on microbial communities. The presence and abundance of multiple bacterial prey types can alter selection for phage generalists, but less is known about how interactions between prey shapes diet breadth in microbial systems. Using a phenomenological mathematical model of phage and bacterial populations, we find that the dominant phage strategy depends on prey ecology. Given a fitness cost for generalism, generalist predators maintain an advantage when prey species compete, while specialists dominate when prey are obligately engaged in cross-feeding interactions. We test these predictions in a synthetic microbial community with interacting strains of Escherichia coli and Salmonella enterica by competing a generalist T5-like phage able to infect both prey against P22vir, an S. enterica-specific phage. Our experimental data conform to our modeling expectations when prey are competing or obligately mutualistic, although our results suggest that the in vitro cost of generalism is caused by a biological mechanism not represented in our model. Our work demonstrates that interactions between bacteria play a role in shaping ecological selection on diet breadth in bacteriophage and emphasizes the diversity of ways in which fitness trade-offs can manifest.

ecology↗

bioRxiv2023

Mutualism reduces the severity of gene disruptions in predictable ways across microbial communities.

Predicting evolution in microbial communities is critical for problems from human health to global nutrient cycling. Understanding how species interactions impact the distribution of fitness effects for a focal population would enhance our ability to predict evolution. Specifically, it would be useful to know if the type of ecological interaction, such as mutualism or competition, changes the average effect of a mutation (i.e., the mean of the distribution of fitness effects). Furthermore, how often does increasing community complexity alter the impact of species interactions on mutant fitness? To address these questions, we created a transposon mutant library in Salmonella enterica and measured the fitness of loss of function mutations in 3,550 genes when grown alone versus competitive co-culture or mutualistic co-culture with Escherichia coli and Methylorubrum extorquens. We found that mutualism reduces the average impact of mutations, while competition had no effect. Additionally, mutant fitness in the 3-species communities can be predicted by averaging the fitness in each 2-species community. Finally, the fitness effects of several knockouts in the mutualistic communities were surprising. We discovered that S. enterica is obtaining a different source of carbon and more vitamins and amino acids than we had expected. Our results suggest that species interactions can predictably impact fitness effect distributions, in turn suggesting that evolution may ultimately be predictable in multi-species communities.

microbiology↗

bioRxiv2022

Environment constrains fitness advantages of division of labor in microbial consortia engineered for metabolite push or pull interactions

Fitness benefits from division of labor in microbial consortia are well documented, but the dependency of the benefits on environmental context is poorly understood. Two synthetic Escherichia coli consortia were built to test the relationships between exchanged organic acid, local environment, and opportunity costs of different metabolic strategies. Opportunity costs quantify benefits not realized due to selecting one phenotype over another. The consortia catabolized glucose and exchanged either acetic or lactic acid to create producer-consumer food webs. The organic acids had different inhibitory properties and different opportunity costs associated with their positions in central metabolism. The exchanged metabolites modulated different consortial dynamics. The acetic acid-exchanging (AAE) consortium had a push interaction motif where acetic acid was secreted faster by the producer than the consumer imported it, while the lactic acid-exchanging (LAE) consortium had a pull interaction motif where the consumer imported lactic acid at a comparable rate to its production. The LAE consortium outperformed wild type (WT) batch cultures under the environmental context of weakly buffered conditions, achieving a 55% increase in biomass titer, a 51% increase in biomass per proton yield, an 86% increase in substrate conversion, and the complete elimination of byproduct accumulation all relative to the WT. However, the LAE consortium had the tradeoff of a 42% lower specific growth rate. The AAE consortium did not outperform the WT in any considered fitness metric. Fitness advantages of the LAE consortium were sensitive to environment; increasing the medium buffering capacity negated the fitness advantages compared to WT. IMPORTANCEMost naturally occurring microorganisms persist in consortia where metabolic interactions are common and often essential to ecosystem function. This study uses synthetic ecology to test how different cellular interaction motifs influence fitness properties of consortia. Environmental context ultimately controlled the division of labor fitness as shifts from weakly buffered to highly buffered conditions negated the benefits of the strategy. Understanding the limits of division of labor advances our understanding of natural community functioning which is central to nutrient cycling and provides design rules for assembling consortia used in applied bioprocessing.

synthetic biology↗

bioRxiv2020

Phage cocktail strategies for the suppression of a pathogen in a cross-feeding coculture

Cocktail combinations of bacteria-infecting viruses (bacteriophage), can suppress pathogenic bacterial growth. However, predicting how phage cocktails influence microbial communities with complex ecological interactions, specifically cross-feeding interactions in which bacteria exchange nutrients, remains challenging. Here, we used experiments and mathematical simulations to determine how to best suppress a model pathogen, E. coli, when obligately cross-feeding with S. enterica. We tested whether the duration of pathogen suppression caused by a two-lytic phage cocktail was maximized when both phage targeted E. coli, or when one phage targeted E. coli and the other its cross-feeding partner, S. enterica. Experimentally, we observed that cocktails targeting both cross-feeders suppressed E. coli growth longer than cocktails targeting only E. coli. Two non-mutually-exclusive mechanisms could explain these results: 1) we found that treatment with two E. coli phage led to the evolution of a mucoid phenotype that provided cross-resistance against both phage, and 2) S. enterica set the growth rate of the co-culture, and therefore targeting S. enterica had a stronger effect on pathogen suppression. Simulations suggested that cross-resistance and the relative growth rates of cross-feeders modulated the duration of E. coli suppression. More broadly, we describe a novel bacteriophage cocktail strategy for pathogens that cross-feed. Originality-Significance StatementCross-feeding, or exchanging nutrients among bacteria, is a type of ecological interaction found in many important microbial communities. Furthermore, cross-feeding interactions are found to play a role in some infections, and research into treating infections with combinations of bacteriophage in cocktails is growing. Here, we used a combination of mathematical modelling and wet-lab experiments to optimize suppression of a model pathogen with a bacteriophage cocktail in a synthetic cross-feeding bacterial coculture. A key finding was that a physiological parameter - growth rate - of the bacteria was important to consider when choosing the most effective cocktail formulation. This work is novel because it highlights an unexpected multispecies-targeting strategy for designing phage cocktails for cross-feeding pathogens and has relevance to many ecological systems ranging from human health to agriculture. We demonstrate how leveraging knowledge of a pathogens ecological interaction has the potential to improve precision medicine and management of microbial systems.

microbiology↗

bioRxiv2020

Limitation by a shared mutualist promotes coexistence of multiple competing partners

Although mutualisms are often studied as simple pairwise interactions, they typically involve complex networks of interacting species. How multiple mutualistic partners that provide the same service and compete for resources are maintained in mutualistic networks is an open question. We use a model bacterial community in which multiple partner strains of Escherichia coli compete for a carbon source and exchange resources with a shared mutualist strain of Salmonella enterica. In laboratory experiments, competing E. coli strains readily coexist in the presence of S. enterica, despite differences in their competitive abilities. We use ecological modeling to demonstrate that a shared mutualist can create temporary resource niche differentiation by limiting growth rates, even if yield is set by a resource external to a mutualism. This mechanism can extend to maintain multiple competing partner species. Our results improve our understanding of complex mutualistic communities and aid efforts to design stable microbial communities.

ecology↗

bioRxiv2020

Weakest link dynamics predict apparent antibiotic interactions in a model cross-feeding community.

With the growing global threat of antimicrobial resistance, novel strategies are required for combatting resistant pathogens. Combination therapy, wherein multiple drugs are used to treat an infection, has proven highly successful in the treatment of cancer and HIV. However, this practice has proven challenging for the treatment of bacterial infections due to difficulties in selecting the correct combinations and dosages. An additional challenge in infection treatment is the polymicrobial nature of many infections, which may respond to antibiotics differently than a monoculture pathogen. This study tests whether patterns of antibiotic interactions (synergy, antagonism, or independence/additivity) in monoculture can be used to predict antibiotic interactions in an obligate cross-feeding co-culture. Using our previously described weakest link hypothesis, we hypothesized antibiotic interactions in co-culture based on the interactions we observed in monoculture. We then compared our predictions to observed antibiotic interactions in co-culture. We tested the interactions between ten previously identified antibiotic combinations using checkerboard assays. Although our antibiotic combinations interacted differently than predicted in our monocultures, our monoculture results were generally sufficient to predict co-culture patterns based solely on the weakest link hypothesis. These results suggest that combination therapy for cross-feeding multispecies infections may be successfully designed based on antibiotic interaction patterns for their component species.

microbiology↗

bioRxiv2019

Cross-feeding modulates the rate and mechanism of antibiotic resistance evolution in a model microbial community of Escherichia coli and Salmonella enterica

With antibiotic resistance rates on the rise, it is critical to understand how microbial species interactions influence the evolution of resistance. We have previously shown that in obligate mutualisms the survival of any one species (regardless of its intrinsic resistance) is contingent on the resistance of its cross-feeding partners, setting the community antibiotic tolerance at that of the weakest link species. In this study, we extended that hypothesis to test whether obligate cross-feeding would limit the extent and mechanisms of antibiotic resistance evolution. In both rifampicin and ampicillin treatments, we observed that resistance evolved more slowly in obligate co-cultures of E. coli and S. enterica than in monocultures. While we observed similar mechanisms of resistance arising under rifampicin selection, under ampicillin selection different resistance mechanisms arose in co-cultures and monocultures. In particular, mutations in an essential cell division protein, ftsI, arose in S. enterica only in co-culture. A simple mathematical model demonstrated that reliance on a partner is sufficient to slow the rate of adaptation, and can change the distribution of adaptive mutations that are acquired. Our results demonstrate that cooperative metabolic interactions can be an important modulator of resistance evolution in microbial communities.\n\nSignificance statementLittle is known about how ecological interactions between bacteria influence the evolution of antibiotic resistance. We tested the impact of metabolic interactions on resistance evolution in an engineered two-species bacterial community. Through experimental and modeling work, we found that obligate metabolic interdependency slows the rate of resistance acquisition, and can change the type and magnitude of resistance mutations that evolve. This work suggests that resistance evolution may be slowed by targeting both a pathogen and its metabolic partners with antibiotics. Additionally, we showed that community context can generate novel trajectories through which antibiotic resistance evolves.

evolutionary biology↗

bioRxiv2019

Increasing growth rate slows adaptation when genotypes compete for diffusing resources

The rate at which a species responds to natural selection is a central predictor of the species ability to adapt to environmental change. It is well-known that spatially-structured environments slow the rate of adaptation due to increased intra-genotype competition. Here, we show that this effect magnifies over time as a species becomes better adapted and grows faster. Using a reaction-diffusion model, we demonstrate that growth rates are inextricably coupled with effective spatial scales, such that higher growth rates cause more localized competition. This has two effects: selection requires more generations for beneficial mutations to fix, and spatially-caused genetic drift increases. Together, these effects diminish the value of additional growth rate mutations in structured environments.\n\nAuthor SummaryWhat determines how quickly a beneficial mutation will spread through a population? The intuitive answer is that mutations that confer faster growth rates will spread at a rate that is relative to the size of the growth-rate benefit. Indeed, this is true in a well-mixed environment where all genotypes compete globally. But most organisms dont live in a simple well-mixed environment. Many organisms, like bacteria, live in a structured environment, such as on the surface of a solid substrate. Does life on a surface change the expectation about the spread of faster-growing mutants? We developed a mathematical model to answer this question, and found that on a surface, the actual growth rates--not just the relative growth rates--were critical to determining how fast a faster-growing mutant spread through a population. When the simulated organisms grew slowly, competition was basically global and a faster-growing mutant could pre-empt resources from far-away competitors. In contrast, when organisms grew more quickly, competition became much more localized, and the faster-growing mutant could only steal resources from neighboring competitors. This result means that there are diminishing returns to series of mutations which confer growth-rate benefits. This idea will help us predict and understand future and past evolutionary trajectories.

evolutionary biology↗

bioRxiv2019

Lytic bacteriophage have diverse indirect effects in a synthetic cross-feeding community

Bacteriophage shape the composition and function of microbial communities. Yet, it remains difficult to predict the effect of phage on microbial interactions. Specifically, little is known about how phage influence mutualisms in networks of cross-feeding bacteria. We modeled the impacts of phage in a synthetic microbial community in which Escherichia coli and Salmonella enterica exchange essential metabolites. In this model, phage attack of either species was sufficient to inhibit both members of the mutualism; however, the evolution of phage resistance ultimately allowed both species to attain yields similar to those observed in the absence of phage. In laboratory experiments, attack of S. enterica with P22vir phage followed these modeling expectations of delayed community growth with little change in the final yield of bacteria. In contrast, when E. coli was attacked with T7 phage, S. enterica, the non-host species, reached higher yields compared to no-phage controls. T7 increased non-host yield by releasing consumable cell debris and by driving evolution of phage resistant E. coli that secreted more carbon. Additionally, E. coli evolved only partial resistance, increasing the total amount of lysed cells available for S. enterica to consume. Our results demonstrate that phage can have extensive indirect effects in microbial communities, and that the nature of these indirect effects depends on metabolic and evolutionary mechanisms.

systems biology↗

bioRxiv2019

Genome-wide assessment of Mycobacterium tuberculosis conditionally essential metabolic pathways

Better understanding of essential cellular functions in pathogenic bacteria is important for the development of more effective antimicrobial agents. We performed a comprehensive identification of essential genes in Mycobacterium tuberculosis, the major causative agent of tuberculosis, using a combination of transposon insertion sequencing (Tn-seq) and comparative genomic analysis. To identify conditional essential genes by Tn-seq, we used media with different nutrient composition. Although many conditional gene essentialities were affected by the presence of relevant nutrient sources, we also found that the essentiality of genes in a subset of metabolic pathways was unaffected by metabolites. Comparative genomic analysis revealed that not all essential genes identified by Tn-seq were fully conserved within the M. tuberculosis complex including some existing anti-tubercular drug target genes. In addition, we utilized an available M. tuberculosis genome-scale metabolic model, iSM810, to predict M. tuberculosis gene essentiality in silico. Comparing the sets of essential genes experimentally identified by Tn-seq to those predicted in silico reveals the capabilities and limitations of gene essentiality predictions highlighting the complexity of M. tuberculosis essential metabolic functions. This study provides a promising platform to study essential cellular functions in M. tuberculosis. Author SummaryMycobacterium tuberculosis causes 10 million cases of tuberculosis (TB) each year resulting in over one million deaths. TB therapy is challenging because it requires a minimum of six months of treatment with multiple drugs. Protracted treatment times and the emergent spread of drug resistant TB necessitate the identification of novel targets for drug discovery to curb this global health threat. Essential functions, defined as those indispensable for growth and/or survival, are potential targets for new antimicrobial drugs. In this study, we aimed to define gene essentialities of M. tuberculosis on a genome-wide scale to comprehensively identify potential targets for drug discovery. We utilized a combination of experimental (functional genomics) and in silico approaches (comparative genomics and flux balance analysis). Our functional genomics approach identified sets of genes whose essentiality was affected by nutrient availability. Comparative genomics revealed that not all essential genes were fully conserved within the M. tuberculosis complex. Comparing sets of essential genes identified by functional genomics to those predicted by flux balance analysis highlighted gaps in current knowledge regarding M. tuberculosis metabolic capabilities. Thus, our study identifies numerous potential anti-tubercular drug targets and provides a comprehensive picture of the complexity of M. tuberculosis essential cellular functions.

microbiology↗