Antibacterial activity of nonantibiotics is orthogonal to standard antibiotics.

Numerous nonantibiotic drugs have potent antibacterial activity and can adversely affect the human microbiome. The mechanistic underpinning of this toxicity remains largely unknown. We investigated the antibacterial activity of 200 drugs using genetic screens with thousands of barcoded Escherichia coli knockouts. We analyzed 2 million gene-drug interactions underlying drug-specific toxicity. Network-based analysis of drug-drug similarities revealed that antibiotics clustered into modules that are consistent with the mode of action of their established classes, whereas nonantibiotics remained unconnected. Half of the nonantibiotics clustered into separate modules, potentially revealing shared and unexploited targets for new antimicrobials. Analysis of efflux systems revealed that they widely affect antibiotics and nonantibiotics alike, suggesting that the impact of nonantibiotics on antibiotic cross-resistance should be investigated closely in vivo.

Functional Assay for Measuring Bacterial Degradation of Gemcitabine Chemotherapy.

Drug biotransformation by the host microbiome can impact the therapeutic success of treatment. In the context of cancer, drug degradation can take place within the microenvironment of the targeted tumor by intratumor bacteria. In pancreatic cancer, increased chemo-resistance against the frontline chemotherapy gemcitabine is thought to arise from drug degradation by the tumor microbiome. This bacterial-drug interaction highlights the need for developing rapid assays for monitoring bacterial gemcitabine breakdown. While chemical approaches such as high-performance liquid chromatography are suitable for this task, they require specialized equipment and expertise and are limited in throughput. Functional cell-based assays represent an alternate approach for performing this task. We developed a functional assay to monitor the rate of bacterial gemcitabine breakdown using a highly sensitive bacterial reporter strain. Our method relies on standard laboratory equipment and can be implemented at high throughput to monitor drug breakdown by hundreds of strains simultaneously. This functional assay can be readily adapted to monitor degradation of other drugs. Key features Quantification of gemcitabine breakdown by incubating bacteria that degrades the drug and subsequently testing the growth of a reporter strain on filtered supernatant. Use of an optimized reporter strain that was genetically engineered to be a non-degrader strain and highly sensitive to gemcitabine. A high-throughput assay performed in microplates that can be adjusted for identifying bacteria with a fast or slow gemcitabine degradation rate. The assay results can be compared to results from a standard curve with known drug concentrations to quantify degradation rate.

Evolved bacterial resistance to the chemotherapy gemcitabine modulates its efficacy in co-cultured cancer cells.

Drug metabolism by the microbiome can influence anticancer treatment success. We previously suggested that chemotherapies with antimicrobial activity can select for adaptations in bacterial drug metabolism that can inadvertently influence the host's chemoresistance. We demonstrated that evolved resistance against fluoropyrimidine chemotherapy lowered its efficacy in worms feeding on drug-evolved bacteria (Rosener et al., 2020). Here, we examine a model system that captures local interactions that can occur in the tumor microenvironment. Gammaproteobacteria-colonizing pancreatic tumors can degrade the nucleoside-analog chemotherapy gemcitabine and, in doing so, can increase the tumor's chemoresistance. Using a genetic screen in Escherichia coli, we mapped all loss-of-function mutations conferring gemcitabine resistance. Surprisingly, we infer that one third of top resistance mutations increase or decrease bacterial drug breakdown and therefore can either lower or raise the gemcitabine load in the local environment. Experiments in three E. coli strains revealed that evolved adaptation converged to inactivation of the nucleoside permease NupC, an adaptation that increased the drug burden on co-cultured cancer cells. The two studies provide complementary insights on the potential impact of microbiome adaptation to chemotherapy by showing that bacteria-drug interactions can have local and systemic influence on drug activity.

Assembling stable syntrophic Escherichia coli communities by comprehensively identifying beneficiaries of secreted goods.

Metabolic cross-feeding frequently underlies mutualistic relationships in natural microbial communities and is often exploited to assemble synthetic microbial consortia. We systematically identified all single-gene knockouts suitable for imposing cross-feeding in Escherichia coli and used this information to assemble syntrophic communities. Most strains benefiting from shared goods were dysfunctional in biosynthesis of amino acids, nucleotides, and vitamins or mutants in central carbon metabolism. We tested cross-feeding potency in 1,444 strain pairs and mapped the interaction network between all functional groups of mutants. This network revealed that auxotrophs for vitamins are optimal cooperators. Lastly, we monitored how assemblies composed of dozens of auxotrophs change over time and observed that they rapidly and repeatedly coalesced to seven strain consortia composed primarily from vitamin auxotrophs. The composition of emerging consortia suggests that they were stabilized by multiple cross-feeding interactions. We conclude that vitamins are ideal shared goods since they optimize consortium growth while still imposing member co-dependence.

Rapid signaling reactivation after targeted BRAF inhibition predicts the proliferation of individual melanoma cells from an isogenic population.

Cancer cells within tumors display a high degree of phenotypic variability. This variability is thought to allow some of the cells to survive and persist after seemingly effective drug treatments. Studies on vemurafenib, a signaling inhibitor that targets an oncogenic BRAF mutation common in melanoma, suggested that cell-to-cell variation in drug resistance, measured by long-term proliferation, originates from epigenetic differences in gene expression that pre-exist treatment. However, it is still unknown whether reactivation of signaling downstream to the inhibited BRAF, thought to be a key step for resistance, is heterogeneous across cells. While previous studies established that signaling reactivation takes place many hours to days after treatment, they monitored reactivation with bulk-population assays unsuitable for detecting cell-to-cell heterogeneity. We hypothesized that signaling reactivation is heterogeneous and is almost instantaneous for a small subpopulation of resistant cells. We tested this hypothesis by monitoring signaling dynamics at a single-cell resolution and observed that despite highly uniform initial inhibition, roughly 15% of cells reactivated signaling within an hour of treatment. Moreover, by tracking cell lineages over multiple days, we established that these cells indeed proliferated more than neighboring cells, thus establishing that rapid signaling reactivation predicts long-term vemurafenib resistance.

Evolved bacterial resistance against fluoropyrimidines can lower chemotherapy impact in the Caenorhabditis elegans host.

Metabolism of host-targeted drugs by the microbiome can substantially impact host treatment success. However, since many host-targeted drugs inadvertently hamper microbiome growth, repeated drug administration can lead to microbiome evolutionary adaptation. We tested if evolved bacterial resistance against host-targeted drugs alters their drug metabolism and impacts host treatment success. We used a model system of Caenorhabditis elegans, its bacterial diet, and two fluoropyrimidine chemotherapies. Genetic screens revealed that most of loss-of-function resistance mutations in Escherichia coli also reduced drug toxicity in the host. We found that resistance rapidly emerged in E. coli under natural selection and converged to a handful of resistance mechanisms. Surprisingly, we discovered that nutrient availability during bacterial evolution dictated the dietary effect on the host - only bacteria evolving in nutrient-poor media reduced host drug toxicity. Our work suggests that bacteria can rapidly adapt to host-targeted drugs and by doing so may also impact the host.

The Cancer Microbiome: Distinguishing Direct and Indirect Effects Requires a Systemic View.

The collection of microbes that live in and on the human body - the human microbiome - can impact on cancer initiation, progression, and response to therapy, including cancer immunotherapy. The mechanisms by which microbiomes impact on cancers can yield new diagnostics and treatments, but much remains unknown. The interactions between microbes, diet, host factors, drugs, and cell-cell interactions within the cancer itself likely involve intricate feedbacks, and no single component can explain all the behavior of the system. Understanding the role of host-associated microbial communities in cancer systems will require a multidisciplinary approach combining microbial ecology, immunology, cancer cell biology, and computational biology - a systems biology approach.

Harnessing robotic automation and web-based technologies to modernize scientific outreach.

Technological breakthroughs in the past two decades have ushered in a new era of biomedical research, turning it into an information-rich and technology-driven science. This scientific revolution, though evident to the research community, remains opaque to nonacademic audiences. Such knowledge gaps are likely to persist without revised strategies for science education and public outreach. To address this challenge, we developed a unique outreach program to actively engage over 100 high-school students in the investigation of multidrug-resistant bacteria. Our program uses robotic automation and interactive web-based tools to bridge geographical distances, scale up the number of participants, and reduce overall cost. Students and teachers demonstrated high engagement and interest throughout the project and valued its unique approach. This educational model can be leveraged to advance the massive open online courses movement that is already transforming science education.

Covalent Docking Identifies a Potent and Selective MKK7 Inhibitor.

The c-Jun NH2-terminal kinase (JNK) signaling pathway is central to the cell response to stress, inflammatory signals, and toxins. While selective inhibitors are known for JNKs and for various upstream MAP3Ks, no selective inhibitor is reported for MKK7--one of two direct MAP2Ks that activate JNK. Here, using covalent virtual screening, we identify selective MKK7 covalent inhibitors. We optimized these compounds to low-micromolar inhibitors of JNK phosphorylation in cells. The crystal structure of a lead compound bound to MKK7 demonstrated that the binding mode was correctly predicted by docking. We asserted the selectivity of our inhibitors on a proteomic level and against a panel of 76 kinases, and validated an on-target effect using knockout cell lines. Lastly, we show that the inhibitors block activation of primary mouse B cells by lipopolysaccharide. These MKK7 tool compounds will enable better investigation of JNK signaling and may serve as starting points for therapeutics.

Cellular perception and misperception: Internal models for decision-making shaped by evolutionary experience.

Cells live in dynamic environments that necessitate perpetual adaptation. Since cells have limited resources to monitor external inputs, they are required to maximize the information content of perceived signals. This challenge is not unique to microscopic life: Animals use senses to perceive inputs and adequately respond. Research showed that sensory-perception is actively shaped by learning and expectation allowing internal cognitive models to "fill in the blanks" in face of limited information. We propose that cells employ analogous strategies and use internal models shaped through the long process of evolutionary adaptation. Given this perspective, we postulate that cells are prone to "misperceptions," analogous to visual illusions, leading them to incorrectly decode patterns of inputs that lie outside of their evolutionary experience. Mapping cellular misperception can serve as a fundamental approach for dissecting regulatory networks and could be harnessed to modulate cell behavior, a potentially new avenue for therapy.

Oscillatory stress stimulation uncovers an Achilles' heel of the yeast MAPK signaling network.

Cells must interpret environmental information that often changes over time. In our experiment, we systematically monitored the growth of yeast cells under various frequencies of oscillating osmotic stress. Growth was severely inhibited at a particular resonance frequency, at which cells show hyperactivated transcriptional stress responses. This behavior represents a sensory misperception: The cells incorrectly interpret oscillations as a staircase of ever-increasing osmolarity. The misperception results from the capacity of the osmolarity-sensing mitogen-activated protein kinase (MAPK) network to retrigger with sequential osmotic stresses. Although this feature is critical for coping with natural challenges, such as continually increasing osmolarity, it results in a trade-off of fragility to non-natural oscillatory inputs that match the retriggering time. These findings demonstrate the value of non-natural dynamic perturbations in exposing hidden sensitivities of cellular regulatory networks.

Chromosomal duplication is a transient evolutionary solution to stress.

Aneuploidy, an abnormal number of chromosomes, is a widespread phenomenon found in unicellulars such as yeast, as well as in plants and in mammalians, especially in cancer. Aneuploidy is a genome-scale aberration that imposes a severe burden on the cell, yet under stressful conditions specific aneuploidies confer a selective advantage. This dual nature of aneuploidy raises the question of whether it can serve as a stable and sustainable evolutionary adaptation. To clarify this, we conducted a set of laboratory evolution experiments in yeast and followed the long-term dynamics of aneuploidy under diverse conditions. Here we show that chromosomal duplications are first acquired as a crude solution to stress, yet only as transient solutions that are eliminated and replaced by more efficient solutions obtained at the individual gene level. These transient dynamics of aneuploidy were repeatedly observed in our laboratory evolution experiments; chromosomal duplications gained under stress were eliminated not only when the stress was relieved, but even if it persisted. Furthermore, when stress was applied gradually rather than abruptly, alternative solutions appear to have emerged, but not aneuploidy. Our findings indicate that chromosomal duplication is a first evolutionary line of defense, that retains survivability under strong and abrupt selective pressures, yet it merely serves as a "quick fix," whereas more refined and sustainable solutions take over. Thus, in the perspective of genome evolution trajectory, aneuploidy is a useful yet short-lived intermediate that facilitates further adaptation.

A mathematical model for adaptive prediction of environmental changes by microorganisms.

Survival in natural habitats selects for microorganisms that are well-adapted to a wide range of conditions. Recent studies revealed that cells evolved innovative response strategies that extend beyond merely sensing a given stimulus and responding to it on encounter. A diversity of microorganisms, including Escherichia coli, Vibrio cholerae, and several yeast species, were shown to use a predictive regulation strategy that uses the appearance of one stimulus as a cue for the likely arrival of a subsequent one. A better understanding of such a predictive strategy requires elucidating the interplay between key biological and environmental forces. Here, we describe a mathematical framework to address this challenge. We base this framework on experimental systems featuring early preparation to either a stress or an exposure to improvement in the growth medium. Our model calculates the fitness advantage originating under each regulation strategy in a given habitat. We conclude that, although a predictive response strategy might by advantageous under some ecologies, its costs might exceed the benefit in others. The combined theoretical-experimental treatment presented here helps assess the potential of natural ecologies to support a predictive behavior.

Adaptive prediction of environmental changes by microorganisms.

Natural habitats of some microorganisms may fluctuate erratically, whereas others, which are more predictable, offer the opportunity to prepare in advance for the next environmental change. In analogy to classical Pavlovian conditioning, microorganisms may have evolved to anticipate environmental stimuli by adapting to their temporal order of appearance. Here we present evidence for environmental change anticipation in two model microorganisms, Escherichia coli and Saccharomyces cerevisiae. We show that anticipation is an adaptive trait, because pre-exposure to the stimulus that typically appears early in the ecology improves the organism's fitness when encountered with a second stimulus. Additionally, we observe loss of the conditioned response in E. coli strains that were repeatedly exposed in a laboratory evolution experiment only to the first stimulus. Focusing on the molecular level reveals that the natural temporal order of stimuli is embedded in the wiring of the regulatory network-early stimuli pre-induce genes that would be needed for later ones, yet later stimuli only induce genes needed to cope with them. Our work indicates that environmental anticipation is an adaptive trait that was repeatedly selected for during evolution and thus may be ubiquitous in biology.

Inferring the pattern of spontaneous mutation from the pattern of substitution in unitary pseudogenes of Mycobacterium leprae and a comparison of mutation patterns among distantly related organisms.

The pattern of spontaneous mutation can be inferred from the pattern of substitution in pseudogenes, which are known to be under very weak or no selective constraint. We modified an existing method (Gojobori T, et al., J Mol Evol 18:360, 1982) to infer the pattern of mutation in bacteria by using 569 pseudogenes from Mycobacterium leprae. In Gojobori et al.'s method, the pattern is inferred by using comparisons involving a pseudogene, a conspecific functional paralog, and an outgroup functional ortholog. Because pseudogenes in M. leprae are unitary, we replaced the missing paralogs by functional orthologs from M. tuberculosis. Functional orthologs from Streptomyces coelicolor served as outgroups. We compiled a database consisting of 69,378 inferred mutations. Transitional mutations were found to constitute more than 56% of all mutations. The transitional bias was mainly due to C-->T and G-->A, which were also the most frequent mutations on the leading strand and the only ones that were significantly more frequent than the random expectation. The least frequent mutations on the leading strand were A-->T and T-->A, each with a relative frequency of less than 3%. The mutation pattern was found to differ between the leading and the lagging strands. This asymmetry is thought to be the cause for the typical chirochoric structure of bacterial genomes. The physical distance of the pseudogene from the origin of replication (ori) was found to have almost no effect on the pattern of mutation. A surprising similarity was found between the mutation pattern in M. leprae and previously inferred patterns for such distant taxa as human and Drosophila. The mutation pattern on the leading strand of M. leprae was also found to share some common features with the pattern inferred for the heavy strand of the human mitochondrial genome. These findings indicate that taxon-specific factors may only play secondary roles in determining patterns of mutation.

Site-specific evolutionary rate inference: taking phylogenetic uncertainty into account.

The evolutionary rate at an amino acid site is indicative of how conserved this site is and, in turn, allows evaluating the importance of this site in maintaining the structure/function of the protein. When evolutionary rates are estimated, one must reconstruct the phylogenetic tree describing the evolutionary relationship among the sequences under study. However, if the inferred phylogenetic tree is incorrect, it can lead to erroneous site-specific rate estimates. Here we describe a novel Bayesian method that uses Markov chain Monte Carlo methodology to integrate over the space of all possible trees and model parameters. By doing so, the method considers alternative evolutionary scenarios weighted by their posterior probabilities. We show that this comprehensive evolutionary approach is superior over methods that are based on only a single tree. We illustrate the potential of our algorithm by analyzing the conservation pattern of the potassium channel protein family.