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In recent years, the prevalence of carbapenem-resistant Enterobacteriaceae (CRE) has risen substantially, and the study of CRE resistance mechanisms has become increasingly important for antibiotic development. Although much research has focused on genomic resistance factors, relatively few studies have examined CRE pathogens through changes in gene expression. In this study, we examined the gene expression profile of a CRE Escherichia coli clinical isolate that is sensitive to meropenem but resistant to ertapenem to explore transcriptomic contributions to resistance and to identify gene knockdown targets for carbapenem potentiation. We sequenced total and short RNA to analyze the gene expression response to ertapenem or meropenem treatment and found significant expression changes in genes related to motility, maltodextrin metabolism, the formate hydrogenlyase complex, and the general stress response. To validate these findings, we used our laboratory's Facile Accelerated Specific Therapeutic (FAST) platform to create antisense peptide nucleic acids (PNAs), gene-specific molecules designed to inhibit protein translation. PNAs were designed to inhibit the pathways identified in our transcriptomic analysis, and each PNA was then tested in combination with each carbapenem to assess its effect on the antibiotics' minimum inhibitory concentrations. We observed significant PNA-antibiotic interaction with five different PNAs across six combinations. Inhibition of the genes hycA, dsrB, and bolA potentiated carbapenem efficacy in CRE E. coli, whereas inhibition of the genes flhC and ygaC conferred added resistance. Our results identify resistance factors and demonstrate that transcriptomic analysis is a potent tool for designing antibiotic PNA.

In recent years, the prevalence of carbapenem-resistant Enterobacteriaceae (CRE) has risen substantially, and the study of CRE resistance mechanisms has become increasingly important for antibiotic development. Although much research has focused on genomic resistance factors, relatively few studies have examined CRE pathogens through changes in gene expression. In this study, we examined the gene expression profile of a CRE Escherichia coli clinical isolate that is sensitive to meropenem but resistant to ertapenem to explore transcriptomic contributions to resistance and to identify gene knockdown targets for carbapenem potentiation. We sequenced total and short RNA to analyze the gene expression response to ertapenem or meropenem treatment and found significant expression changes in genes related to motility, maltodextrin metabolism, the formate hydrogenlyase complex, and the general stress response. To validate these findings, we used our laboratory’s Facile Accelerated Specific Therapeutic (FAST) platform to create antisense peptide nucleic acids (PNAs), gene-specific molecules designed to inhibit protein translation. PNAs were designed to inhibit the pathways identified in our transcriptomic analysis, and each PNA was then tested in combination with each carbapenem to assess its effect on the antibiotics’ minimum inhibitory concentrations. We observed significant PNA–antibiotic interaction with five different PNAs across six combinations. Inhibition of the genes hycA, dsrB, and bolA potentiated carbapenem efficacy in CRE E. coli, whereas inhibition of the genes flhC and ygaC conferred added resistance. Our results identify resistance factors and demonstrate that transcriptomic analysis is a potent tool for designing antibiotic PNA.

Proliferation of multidrug-resistant (MDR) bacteria poses a threat to human health, requiring new strategies. Here we propose using fitness neutral gene expression perturbations to potentiate antibiotics. We systematically explored 270 gene knockout-antibiotic combinations in Escherichia coli, identifying 90 synergistic interactions. Identified gene targets were subsequently tested for antibiotic synergy on the transcriptomic level via multiplexed CRISPR-dCas9 and showed successful sensitization of E. coli without a separate fitness cost. These fitness neutral gene perturbations worked as co-therapies in reducing a Salmonella enterica intracellular infection in HeLa. Finally, these results informed the design of four antisense peptide nucleic acid (PNA) co-therapies, csgD, fnr, recA and acrA, against four MDR, clinically isolated bacteria. PNA combined with sub-minimal inhibitory concentrations of trimethoprim against two isolates of Klebsiella pneumoniae and E. coli showed three cases of re-sensitization with minimal fitness impacts. Our results highlight a promising approach for extending the utility of current antibiotics., Otoupal et al. use a systematic approach to investigate the effects of fitness neutral gene perturbations and antibiotic synergy in Escherichia coli. These neutral fitness interactions worked as co-therapies in a Salmonella enterica infection and informed the design of re-sensitization therapies in multi-drug resistant E. coli and Klebisiella pneumoniae clinical isolates.

Gillespie's direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie's direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie's direct method for network-free simulation. Finally, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.

Carbapenems are a powerful class of antibiotics, often used as a last-resort treatment to eradicate multidrug-resistant infections. In recent years, however, the incidence of carbapenem-resistantEnterobacteriaceae(CRE) has risen substantially, and the study of bacterial resistance mechanisms has become increasingly important for antibiotic development. Although much research has focused on genomic contributors to carbapenem resistance, relatively few studies have examined CRE pathogens through changes in gene expression. In this research, we used transcriptomics to study a CREEscherichia coliclinical isolate that is sensitive to meropenem but resistant to ertapenem, to both explore carbapenem resistance and identify novel gene knockdown targets for carbapenem re-sensitization. We sequenced total and small RNA to analyze gene expression changes in response to treatment with ertapenem or meropenem, as compared to an untreated control. Significant expression changes were found in genes related to motility, maltodextrin metabolism, the formate hydrogenlyase complex, and the general stress response. To validate these transcriptomic findings, we used our lab’s Facile Accelerated Specific Therapeutic (FAST) platform to create antisense peptide nucleic acids (PNA), gene-specific molecules designed to inhibit protein translation. FAST PNA were designed to inhibit the pathways identified in our transcriptomic analysis, and each PNA was then tested in combination with each carbapenem to assess its effect on the antibiotics’ minimum inhibitory concentrations. We observed significant treatment interaction with five different PNAs across six PNA-antibiotic combinations. Inhibition of the geneshycA,dsrB, andbolAwere found to re-sensitize CREE. colito carbapenems, whereas inhibition of the genesflhCandygaCwas found to confer added resistance. Our results identify new resistance factors that are regulated at the level of gene expression, and demonstrate for the first time that transcriptomic analysis is a potent tool for designing antibiotic PNA.

Multidrug-resistant (MDR) bacteria pose a grave concern to global health, which is perpetuated by a lack of new treatments and countermeasure platforms to combat outbreaks or antibiotic resistance. To address this, we have developed a Facile Accelerated Specific Therapeutic (FAST) platform that can develop effective peptide nucleic acid (PNA) therapies against MDR bacteria within a week. Our FAST platform uses a bioinformatics toolbox to design sequence-specific PNAs targeting non-traditional pathways/genes of bacteria, then performs in-situ synthesis, validation, and efficacy testing of selected PNAs. As a proof of concept, these PNAs were tested against five MDR clinical isolates: carbapenem-resistant Escherichia coli, extended-spectrum beta-lactamase Klebsiella pneumoniae, New Delhi Metallo-beta-lactamase-1 carrying Klebsiella pneumoniae, and MDR Salmonella enterica. PNAs showed significant growth inhibition for 82% of treatments, with nearly 18% of treatments leading to greater than 97% decrease. Further, these PNAs are capable of potentiating antibiotic activity in the clinical isolates despite presence of cognate resistance genes. Finally, the FAST platform offers a novel delivery approach to overcome limited transport of PNAs into mammalian cells by repurposing the bacterial Type III secretion system in conjunction with a kill switch that is effective at eliminating 99.6% of an intracellular Salmonella infection in human epithelial cells., Eller et al. develop a Facile Accelerated Specific Therapeutic (FAST) platform of antisense therapeutics that targets MDR bacterial pathogens with peptide nucleic acids (PNAs). This platform designs species and/or sequence specific PNAS based on a bioinformatics toolbox and offers a new delivery approach by repurposing the bacterial Type III secretion system in conjunction with a kill switch to overcome limited transport of PNAs into mammalian cells.

Multidrug-resistant (MDR) bacteria pose a grave concern to global health. This problem is further aggravated by a lack of new and effective antibiotics and countermeasure platforms that can sustain the creation of novel antimicrobials in the wake of new outbreaks or evolution of resistance to antibiotics. To address this, we have developed a Facile Accelerated Specific Therapeutic (FAST) platform that can develop effective therapies against MDR bacteria within a week. Our FAST platform combines four essential modules- design, build, test, and delivery-of drug development cycle. The design module comprises a bioinformatics toolbox that predicts sequence-specific peptide nucleic acids (PNAs) that target non-traditional pathways and genes of bacteria in minutes. The build module constitutes in-situ synthesis and validation of selected PNAs in less than four days and efficacy testing within a day. As a proof of concept, these PNAs were tested against MDR clinical isolates. Here we tested Enterobacteriaceae including carbapenem-resistant Escherichia coli, extended-spectrum beta-lactamase (ESBL) Klebsiella pneumoniae, New Delhi Metallo-beta-lactamase-1 carrying Klebsiella pneumoniae and MDR Salmonella enterica. PNAs showed significant growth inhibition for 82% of treatments, with nearly 18% of the treatments leading to more than 97% decrease. Further, these PNAs are capable of potentiating antibiotic activity in the clinical isolates despite presence of cognate resistance genes. Finally, FAST offers a novel delivery approach to overcome limited transport of PNAs into mammalian cells to clear intracellular infections. This method relies on repurposing the bacterial Type III secretion system in conjunction with a kill switch that is effective at eliminating 99.6% of an intracellular Salmonella infection in human epithelial cells. Our findings demonstrate the potential of the FAST platform in treating MDR bacteria in a rapid and effective manner.

The rapid proliferation of multidrug-resistant (MDR) bacteria poses a critical threat to human health, for which new antimicrobial strategies are desperately needed. Here we outline a strategy for combating bacterial infections by administering fitness neutral gene expression perturbations as co-therapies to potentiate antibiotic lethality. We systematically explored the fitness of 270 gene knockout-drug combinations in Escherichia coli, identifying 114 synergistic interactions. Genes revealed in this screen were subsequently perturbed at the transcriptome level via multiplexed CRISPR-dCas9 interference to induce antibiotic synergy. These perturbations successfully sensitized E. coli to antibiotic treatment without imposing a separate fitness cost. We next administered these fitness neutral gene perturbations as co-therapies to potentiate antibiotic killing of Salmonella enterica in intracellular infections of HeLa epithelial cells, demonstrating therapeutic applicability. Finally, we utilized these results to design peptide nucleic acid (PNA) co-therapies for targeted gene expression reduction in four MDR, clinically isolated bacteria. Two isolates of Klebsiella pneumoniae and E. coli were each exposed to PNAs targeting homologs of the genes csgD, fnr, recA and acrA in the presence of sub-minimal inhibitory concentrations of trimethoprim. We successfully increased each strain’s susceptibility to trimethoprim treatment and identified eight cases in which re-sensitization occurred without a direct fitness impact of the PNA on MDR strains. Our results highlight a promising approach for combating MDR bacteria which could extend the utility of our current antibiotic arsenal.

BioNetFit is a software tool designed for solving parameter identification problems that arise in the development of rule-based models. It solves these problems through curve fitting (i.e., nonlinear regression). BioNetFit is compatible with deterministic and stochastic simulators that accept BioNetGen language (BNGL)-formatted files as inputs, such as those available within the BioNetGen framework. BioNetFit can be used on a laptop or stand-alone multicore workstation as well as on many Linux clusters, such as those that use the Slurm Workload Manager to schedule jobs. BioNetFit implements a metaheuristic population-based global optimization procedure, an evolutionary algorithm (EA), to minimize a user-defined objective function, such as a residual sum of squares (RSS) function. BioNetFit also implements a bootstrapping procedure for determining confidence intervals for parameter estimates. Here, we provide step-by-step instructions for using BioNetFit to estimate the values of parameters of a BNGL-encoded model and to define bootstrap confidence intervals. The process entails the use of several plain-text files, which are processed by BioNetFit and BioNetGen. In general, these files include (1) one or more EXP files, which each contains (experimental) data to be used in parameter identification/bootstrapping; (2) a BNGL file containing a model section, which defines a (rule-based) model, and an actions section, which defines simulation protocols that generate GDAT and/or SCAN files with model predictions corresponding to the data in the EXP file(s); and (3) a CONF file that configures the fitting/bootstrapping job and that defines algorithmic parameter settings.

BioNetFit is a software tool designed for solving parameter identification problems that arise in the development of rule-based models. It solves these problems through curve fitting (i.e., nonlinear regression). BioNetFit is compatible with deterministic and stochastic simulators that accept BioNetGen language (BNGL)-formatted files as inputs, such as those available within the BioNetGen framework. BioNetFit can be used on a laptop or stand-alone multicore workstation as well as on many Linux clusters, such as those that use the Slurm Workload Manager to schedule jobs. BioNetFit implements a metaheuristic population-based global optimization procedure, an evolutionary algorithm (EA), to minimize a user-defined objective function, such as a residual sum of squares (RSS) function. BioNetFit also implements a bootstrapping procedure for determining confidence intervals for parameter estimates. Here, we provide step-by-step instructions for using BioNetFit to estimate the values of parameters of a BNGL-encoded model and to define bootstrap confidence intervals. The process entails the use of several plain-text files, which are processed by BioNetFit and BioNetGen. In general, these files include (1) one or more EXP files, which each contains (experimental) data to be used in parameter identification/bootstrapping; (2) a BNGL file containing a model section, which defines a (rule-based) model, and an actions section, which defines simulation protocols that generate GDAT and/or SCAN files with model predictions corresponding to the data in the EXP file(s); and (3) a CONF file that configures the fitting/bootstrapping job and that defines algorithmic parameter settings.

Metabolic engineers manipulate intricate biological networks to build efficient biological machines. The inherent complexity of this task, derived from the extensive and often unknown interconnectivity between and within these networks, often prevents researchers from achieving desired performance. Other fields have developed methods to tackle the issue of complexity for their unique subset of engineering problems, but to date, there has not been extensive and comprehensive examination of how metabolic engineers use existing tools to ameliorate this effect on their own research projects. In this review, we examine how complexity affects engineering at the protein, pathway, and genome levels within an organism, and the tools for handling these issues to achieve high-performing strain designs. Quantitative complexity metrics and their applications to metabolic engineering versus traditional engineering fields are also discussed. We conclude by predicting how metabolic engineering practices may advance in light of an explicit consideration of design complexity.

Bacteria grown in space experiments under microgravity conditions have been found to undergo unique physiological responses, ranging from modified cell morphology and growth dynamics to a putative increased tolerance to antibiotics. A common theory for this behavior is the loss of gravity-driven convection processes in the orbital environment, resulting in both reduction of extracellular nutrient availability and the accumulation of bacterial byproducts near the cell. To further characterize the responses, this study investigated the transcriptomic response of Escherichia coli to both microgravity and antibiotic concentration. E. coli was grown aboard International Space Station in the presence of increasing concentrations of the antibiotic gentamicin with identical ground controls conducted on Earth. Here we show that within 49 h of being cultured, E. coli adapted to grow at higher antibiotic concentrations in space compared to Earth, and demonstrated consistent changes in expression of 63 genes in response to an increase in drug concentration in both environments, including specific responses related to oxidative stress and starvation response. Additionally, we find 50 stress-response genes upregulated in response to the microgravity when compared directly to the equivalent concentration in the ground control. We conclude that the increased antibiotic tolerance in microgravity may be attributed not only to diminished transport processes, but also to a resultant antibiotic cross-resistance response conferred by an overlapping effect of stress response genes. Our data suggest that direct stresses of nutrient starvation and acid-shock conveyed by the microgravity environment can incidentally upregulate stress response pathways related to antibiotic stress and in doing so contribute to the increased antibiotic stress tolerance observed for bacteria in space experiments. These results provide insights into the ability of bacteria to adapt under extreme stress conditions and potential strategies to prevent antimicrobial-resistance in space and on Earth.

The economical production of chemicals and fuels by microbial processes remains an intense area of interest in biotechnology. A key limitation in such efforts concerns the availability of key co-factors, in this case NADPH, required for target pathways. Many of the strategies pursued for increasing NADPH availability in Escherichia coli involve manipulations to the central metabolism, which can create redox imbalances and overall growth defects. In this study we used a reactive oxygen species based selection to search for novel methods of increasing NADPH availability. We report a loss of function mutation in the gene hdfR appears to increase NADPH availability in E. coli. Additionally, we show this excess NADPH can be used to improve the production of 3HP in E. coli.

We report here the draft genome sequences of two clinically isolated Acinetobacter baumannii strains. These samples were obtained from patients at the University of Colorado Hospital in 2007 and 2013 and encode an estimated 20 and 13 resistance genes, respectively.

The root cause of the antibiotic resistance crisis is the ability of bacteria to evolve resistance to a multitude of antibiotics and other environmental toxins. The regulation of adaptation is difficult to pinpoint due to extensive phenotypic heterogeneity arising during evolution. Here, we investigate the mechanisms underlying general bacterial adaptation by evolving wild-type Escherichia coli populations to dissimilar chemical toxins. We demonstrate the presence of extensive inter- and intrapopulation phenotypic heterogeneity across adapted populations in multiple traits, including minimum inhibitory concentration, growth rate, and lag time. To search for a common response across the heterogeneous adapted populations, we measured gene expression in three stress-response networks: the mar regulon, the general stress response, and the SOS response. While few genes were differentially expressed, clustering revealed that interpopulation gene expression variability in adapted populations was distinct from that of unadapted populations. Notably, we observed both increases and decreases in gene expression variability upon adaptation. Sequencing select genes revealed that the observed gene expression trends are not necessarily attributable to genetic changes. To further explore the connection between gene expression variability and adaptation, we propagated single-gene knockout and CRISPR (clustered regularly interspaced short palindromic repeats) interference strains and quantified impact on adaptation to antibiotics. We identified significant correlations that suggest genes with low expression variability have greater impact on adaptation. This study provides evidence that gene expression variability can be used as an indicator of bacterial adaptive resistance, even in the face of the pervasive phenotypic heterogeneity underlying adaptation.

The evolution of antibiotic resistance has engendered an impending global health crisis that necessitates a greater understanding of how resistance emerges. The impact of nongenetic factors and how they influence the evolution of resistance is a largely unexplored area of research. Here we present a novel application of CRISPR-Cas9 technology for investigating how gene expression governs the adaptive pathways available to bacteria during the evolution of resistance. We examine the impact of gene expression changes on bacterial adaptation by constructing a library of deactivated CRISPR-Cas9 synthetic devices to tune the expression of a set of stress-response genes in Escherichia coli. We show that artificially inducing perturbations in gene expression imparts significant synthetic control over fitness and growth during stress exposure. We present evidence that these impacts are reversible; strains with synthetically perturbed gene expression regained wild-type growth phenotypes upon stress removal, while maintaining divergent growth characteristics under stress. Furthermore, we demonstrate a prevailing trend toward negative epistatic interactions when multiple gene perturbations are combined simultaneously, thereby posing an intrinsic constraint on gene expression underlying adaptive trajectories. Together, these results emphasize how CRISPR-Cas9 can be employed to engineer gene expression changes that shape bacterial adaptation, and present a novel approach to synthetically control the evolution of antimicrobial resistance.

The microbial ability to resist stressful environmental conditions and chemical inhibitors is of great industrial and medical interest. Much of the data related to mutation-based stress resistance, however, is scattered through the academic literature, making it difficult to apply systematic analyses to this wealth of information. To address this issue, we introduce the Resistome database: a literature-curated collection of Escherichia coli genotypes-phenotypes containing over 5,000 mutants that resist hundreds of compounds and environmental conditions. We use the Resistome to understand our current state of knowledge regarding resistance and to detect potential synergy or antagonism between resistance phenotypes. Our data set represents one of the most comprehensive collections of genomic data related to resistance currently available. Future development will focus on the construction of a combined genomic-transcriptomic-proteomic framework for understanding E. coli's resistance biology. The Resistome can be downloaded at https://bitbucket.org/jdwinkler/resistome_release/overview .

We report here the draft genome sequence of a multidrug-resistant Enterococcus faecalis strain, isolated from a patient at the University of Colorado Hospital. The genome assembly is 3,040,186 bp in length with 37.6% GC content. This isolate encodes eleven resistance genes, including those for glycopeptide, aminoglycoside, macrolide-lincosamide-streptogramin, and tetracycline resistance.

Advances in computational methods that allow for exploration of the combinatorial mutation space are needed to realize the potential of synthetic biology based strain engineering efforts. Here, we present Constrictor, a computational framework that uses flux balance analysis (FBA) to analyze inhibitory effects of genetic mutations on the performance of biochemical networks. Constrictor identifies engineering interventions by classifying the reactions in the metabolic model depending on the extent to which their flux must be decreased to achieve the overproduction target. The optimal inhibition of various reaction pathways is determined by restricting the flux through targeted reactions below the steady state levels of a baseline strain. Constrictor generates unique in silico strains, each representing an "expression state", or a combination of gene expression levels required to achieve the overproduction target. The Constrictor framework is demonstrated by studying overproduction of ethylene in Escherichia coli network models iAF1260 and iJO1366 through the addition of the heterologous ethylene-forming enzyme from Pseudomonas syringae. Targeting individual reactions as well as combinations of reactions reveals in silico mutants that are predicted to have as high as 25% greater theoretical ethylene yields than the baseline strain during simulated exponential growth. Altering the degree of restriction reveals a large distribution of ethylene yields, while analysis of the expression states that return lower yields provides insight into system bottlenecks. Finally, we demonstrate the ability of Constrictor to scan networks and provide targets for a range of possible products. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels and provide non-intuitive strategies for metabolic engineering.

RAS is the most frequently mutated gene across human cancers, but developing inhibitors of mutant RAS has proven to be challenging. Given the difficulties of targeting RAS directly, drugs that impact the other components of pathways where mutant RAS operates may potentially be effective. However, the system-level features, including different localizations of RAS isoforms, competition between downstream effectors, and interlocking feedback and feed-forward loops, must be understood to fully grasp the opportunities and limitations of inhibiting specific targets. Mathematical modeling can help us discern the system-level impacts of these features in normal and cancer cells. New technologies enable the acquisition of experimental data that will facilitate development of realistic models of oncogenic RAS behavior. In light of the wealth of empirical data accumulated over decades of study and the advancement of experimental methods for gathering new data, modelers now have the opportunity to advance progress toward realization of targeted treatment for mutant RAS-driven cancers.

Clinically used RAF inhibitors are ineffective in RAS-mutant tumors because they enhance homo- and heterodimerization of RAF kinases, leading to paradoxical activation of ERK signaling. Overcoming enhanced RAF dimerization and the resulting resistance is a challenge for drug design. Combining multiple inhibitors could be more effective, but it is unclear how the best combinations can be chosen. We built a next-generation mechanistic dynamic model to analyze combinations of structurally different RAF inhibitors, which can efficiently suppress MEK/ERK signaling. This rule-based model of the RAS/ERK pathway integrates thermodynamics and kinetics of drug-protein interactions, structural elements, post-translational modifications and cell mutational status as model rules to predict RAF inhibitor combinations for inhibiting ERK activity in oncogenic RAS and/or BRAFV600E backgrounds. Predicted synergistic inhibition of ERK signaling was corroborated by experiments in mutant NRAS, HRAS and BRAFV600E cells, and inhibition of oncogenic RAS signaling was associated with reduced cell proliferation and colony formation. European Commission Horizon 2020 Science Foundation Ireland