Genetic manipulation of gut microbes enables single-gene interrogation in a complex microbiome.

Hundreds of microbiota genes are associated with host biology/disease. Unraveling the causal contribution of a microbiota gene to host biology remains difficult because many are encoded by nonmodel gut commensals and not genetically targetable. A general approach to identify their gene transfer methodology and build their gene manipulation tools would enable mechanistic dissections of their impact on host physiology. We developed a pipeline that identifies the gene transfer methods for multiple nonmodel microbes spanning five phyla, and we demonstrated the utility of their genetic tools by modulating microbiome-derived short-chain fatty acids and bile acids in vitro and in the host. In a proof-of-principle study, by deleting a commensal gene for bile acid synthesis in a complex microbiome, we discovered an intriguing role of this gene in regulating colon inflammation. This technology will enable genetically engineering the nonmodel gut microbiome and facilitate mechanistic dissection of microbiota-host interactions.

The long-term genetic stability and individual specificity of the human gut microbiome.

By following up the gut microbiome, 51 human phenotypes and plasma levels of 1,183 metabolites in 338 individuals after 4 years, we characterize microbial stability and variation in relation to host physiology. Using these individual-specific and temporally stable microbial profiles, including bacterial SNPs and structural variations, we develop a microbial fingerprinting method that shows up to 85% accuracy in classifying metagenomic samples taken 4 years apart. Application of our fingerprinting method to the independent HMP cohort results in 95% accuracy for samples taken 1 year apart. We further observe temporal changes in the abundance of multiple bacterial species, metabolic pathways, and structural variation, as well as strain replacement. We report 190 longitudinal microbial associations with host phenotypes and 519 associations with plasma metabolites. These associations are enriched for cardiometabolic traits, vitamin B, and uremic toxins. Finally, mediation analysis suggests that the gut microbiome may influence cardiometabolic health through its metabolites.

Ribosome-Targeting Antibiotics: Modes of Action, Mechanisms of Resistance, and Implications for Drug Design.

Genetic information is translated into proteins by the ribosome. Structural studies of the ribosome and of its complexes with factors and inhibitors have provided invaluable information on the mechanism of protein synthesis. Ribosome inhibitors are among the most successful antimicrobial drugs and constitute more than half of all medicines used to treat infections. However, bacterial infections are becoming increasingly difficult to treat because the microbes have developed resistance to the most effective antibiotics, creating a major public health care threat. This has spurred a renewed interest in structure-function studies of protein synthesis inhibitors, and in few cases, compounds have been developed into potent therapeutic agents against drug-resistant pathogens. In this review, we describe the modes of action of many ribosome-targeting antibiotics, highlight the major resistance mechanisms developed by pathogenic bacteria, and discuss recent advances in structure-assisted design of new molecules.

Efflux-linked accelerated evolution of antibiotic resistance at a population edge.

Efflux is a common mechanism of resistance to antibiotics. We show that efflux itself promotes accumulation of antibiotic-resistance mutations (ARMs). This phenomenon was initially discovered in a bacterial swarm where the linked phenotypes of high efflux and high mutation frequencies spatially segregated to the edge, driven there by motility. We have uncovered and validated a global regulatory network connecting high efflux to downregulation of specific DNA-repair pathways even in non-swarming states. The efflux-DNA repair link was corroborated in a clinical "resistome" database: genomes with mutations that increase efflux exhibit a significant increase in ARMs. Accordingly, efflux inhibitors decreased evolvability to antibiotic resistance. Swarms also revealed how bacterial populations serve as a reservoir of ARMs even in the absence of antibiotic selection pressure. High efflux at the edge births mutants that, despite compromised fitness, survive there because of reduced competition. This finding is relevant to biofilms where efflux activity is high.

Mouse genome rewriting and tailoring of three important disease loci.

Genetically engineered mouse models (GEMMs) help us to understand human pathologies and develop new therapies, yet faithfully recapitulating human diseases in mice is challenging. Advances in genomics have highlighted the importance of non-coding regulatory genome sequences, which control spatiotemporal gene expression patterns and splicing in many human diseases1,2. Including regulatory extensive genomic regions, which requires large-scale genome engineering, should enhance the quality of disease modelling. Existing methods set limits on the size and efficiency of DNA delivery, hampering the routine creation of highly informative models that we call genomically rewritten and tailored GEMMs (GREAT-GEMMs). Here we describe 'mammalian switching antibiotic resistance markers progressively for integration' (mSwAP-In), a method for efficient genome rewriting in mouse embryonic stem cells. We demonstrate the use of mSwAP-In for iterative genome rewriting of up to 115 kb of a tailored Trp53 locus, as well as for humanization of mice using 116 kb and 180 kb human ACE2 loci. The ACE2 model recapitulated human ACE2 expression patterns and splicing, and notably, presented milder symptoms when challenged with SARS-CoV-2 compared with the existing K18-hACE2 model, thus representing a more human-like model of infection. Finally, we demonstrated serial genome writing by humanizing mouse Tmprss2 biallelically in the ACE2 GREAT-GEMM, highlighting the versatility of mSwAP-In in genome writing.

Towards the biogeography of prokaryotic genes.

Microbial genes encode the majority of the functional repertoire of life on earth. However, despite increasing efforts in metagenomic sequencing of various habitats1-3, little is known about the distribution of genes across the global biosphere, with implications for human and planetary health. Here we constructed a non-redundant gene catalogue of 303 million species-level genes (clustered at 95% nucleotide identity) from 13,174 publicly available metagenomes across 14 major habitats and use it to show that most genes are specific to a single habitat. The small fraction of genes found in multiple habitats is enriched in antibiotic-resistance genes and markers for mobile genetic elements. By further clustering these species-level genes into 32 million protein families, we observed that a small fraction of these families contain the majority of the genes (0.6% of families account for 50% of the genes). The majority of species-level genes and protein families are rare. Furthermore, species-level genes, and in particular the rare ones, show low rates of positive (adaptive) selection, supporting a model in which most genetic variability observed within each protein family is neutral or nearly neutral.

Antibiotic combinations reduce Staphylococcus aureus clearance.

The spread of antibiotic resistance is attracting increased attention to combination-based treatments. Although drug combinations have been studied extensively for their effects on bacterial growth1-11, much less is known about their effects on bacterial long-term clearance, especially at cidal, clinically relevant concentrations12-14. Here, using en masse microplating and automated image analysis, we systematically quantify Staphylococcus aureus survival during prolonged exposure to pairwise and higher-order cidal drug combinations. By quantifying growth inhibition, early killing and longer-term population clearance by all pairs of 14 antibiotics, we find that clearance interactions are qualitatively different, often showing reciprocal suppression whereby the efficacy of the drug mixture is weaker than any of the individual drugs alone. Furthermore, in contrast to growth inhibition6-10 and early killing, clearance efficacy decreases rather than increases as more drugs are added. However, specific drugs targeting non-growing persisters15-17 circumvent these suppressive effects. Competition experiments show that reciprocal suppressive drug combinations select against resistance to any of the individual drugs, even counteracting methicillin-resistant Staphylococcus aureus both in vitro and in a Galleria mellonella larva model. As a consequence, adding a ß-lactamase inhibitor that is commonly used to potentiate treatment against ß-lactam-resistant strains can reduce rather than increase treatment efficacy. Together, these results underscore the importance of systematic mapping the long-term clearance efficacy of drug combinations for designing more-effective, resistance-proof multidrug regimes.

Immune-Microbiota Interplay and Colonization Resistance in Infection.

Commensal microbial communities inhabit biological niches in the mammalian host, where they impact the host's physiology through induction of "colonization resistance" against infections by a multitude of molecular mechanisms. These colonization-regulating activities involve microbe-microbe and microbe-host interactions, which induce, through utilization of complex bacterial networks, competition over nutrients, inhibition by antimicrobial peptides, stimulation of the host immune system, and promotion of mucus and intestinal epithelial barrier integrity. Distinct virulent pathogens overcome this colonization resistance and host immunity as part of a hostile takeover of the host niche, leading to clinically overt infection. The following review provides a mechanistic overview of the role of commensal microbes in modulating colonization resistance and pathogenic infections and means by which infectious agents may overcome such inhibition. Last, we outline evidence, unknowns, and challenges in developing strategies to harness this knowledge to treat infections by microbiota transfer, phage therapy, or supplementation by rationally defined bacterial consortia.

Converting cancer therapies into cures: lessons from infectious diseases.

During the past decade, cancer drug development has shifted from a focus on cytotoxic chemotherapies to drugs that target specific molecular alterations in tumors. Although these drugs dramatically shrink tumors, the responses are temporary. Research is now focused on overcoming drug resistance, a frequent cause of treatment failure. Here we reflect on analogous challenges faced by researchers in infectious diseases. We compare and contrast the resistance mechanisms arising in cancer and infectious diseases and discuss how approaches for overcoming viral and bacterial infections, such as HIV and tuberculosis, are instructive for developing a more rational approach for cancer therapy. In particular, maximizing the effect of the initial treatment response, which often requires synergistic combination therapy, is foremost among these approaches. A remaining challenge in both fields is identifying drugs that eliminate drug-tolerant "persister" cells (infectious disease) or tumor-initiating/stem cells (cancer) to prevent late relapse and shorten treatment duration.

Type III-A CRISPR immunity promotes mutagenesis of staphylococci.

Horizontal gene transfer and mutation are the two major drivers of microbial evolution that enable bacteria to adapt to fluctuating environmental stressors1. Clustered, regularly interspaced, short palindromic repeats (CRISPR) systems use RNA-guided nucleases to direct sequence-specific destruction of the genomes of mobile genetic elements that mediate horizontal gene transfer, such as conjugative plasmids2 and bacteriophages3, thus limiting the extent to which bacteria can evolve by this mechanism. A subset of CRISPR systems also exhibit non-specific degradation of DNA4,5; however, whether and how this feature affects the host has not yet been examined. Here we show that the non-specific DNase activity of the staphylococcal type III-A CRISPR-Cas system increases mutations in the host and accelerates the generation of antibiotic resistance in Staphylococcus aureus and Staphylococcus epidermidis. These mutations require the induction of the SOS response to DNA damage and display a distinct pattern. Our results demonstrate that by differentially affecting both mechanisms that generate genetic diversity, type III-A CRISPR systems can modulate the evolution of the bacterial host.

IspH inhibitors kill Gram-negative bacteria and mobilize immune clearance.

Isoprenoids are vital for all organisms, in which they maintain membrane stability and support core functions such as respiration1. IspH, an enzyme in the methyl erythritol phosphate pathway of isoprenoid synthesis, is essential for Gram-negative bacteria, mycobacteria and apicomplexans2,3. Its substrate, (E)-4-hydroxy-3-methyl-but-2-enyl pyrophosphate (HMBPP), is not produced in metazoans, and in humans and other primates it activates cytotoxic Vγ9Vδ2 T cells at extremely low concentrations4-6. Here we describe a class of IspH inhibitors and refine their potency to nanomolar levels through structure-guided analogue design. After modification of these compounds into prodrugs for delivery into bacteria, we show that they kill clinical isolates of several multidrug-resistant bacteria-including those from the genera Acinetobacter, Pseudomonas, Klebsiella, Enterobacter, Vibrio, Shigella, Salmonella, Yersinia, Mycobacterium and Bacillus-yet are relatively non-toxic to mammalian cells. Proteomic analysis reveals that bacteria treated with these prodrugs resemble those after conditional IspH knockdown. Notably, these prodrugs also induce the expansion and activation of human Vγ9Vδ2 T cells in a humanized mouse model of bacterial infection. The prodrugs we describe here synergize the direct killing of bacteria with a simultaneous rapid immune response by cytotoxic γδ T cells, which may limit the increase of antibiotic-resistant bacterial populations.

Reinforcement learning informs optimal treatment strategies to limit antibiotic resistance.

Antimicrobial resistance was estimated to be associated with 4.95 million deaths worldwide in 2019. It is possible to frame the antimicrobial resistance problem as a feedback-control problem. If we could optimize this feedback-control problem and translate our findings to the clinic, we could slow, prevent, or reverse the development of high-level drug resistance. Prior work on this topic has relied on systems where the exact dynamics and parameters were known a priori. In this study, we extend this work using a reinforcement learning (RL) approach capable of learning effective drug cycling policies in a system defined by empirically measured fitness landscapes. Crucially, we show that it is possible to learn effective drug cycling policies despite the problems of noisy, limited, or delayed measurement. Given access to a panel of 15 [Formula: see text]-lactam antibiotics with which to treat the simulated Escherichia coli population, we demonstrate that RL agents outperform two naive treatment paradigms at minimizing the population fitness over time. We also show that RL agents approach the performance of the optimal drug cycling policy. Even when stochastic noise is introduced to the measurements of population fitness, we show that RL agents are capable of maintaining evolving populations at lower growth rates compared to controls. We further tested our approach in arbitrary fitness landscapes of up to 1,024 genotypes. We show that minimization of population fitness using drug cycles is not limited by increasing genome size. Our work represents a proof-of-concept for using AI to control complex evolutionary processes.

Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework.

High-throughput DNA sequencing technologies decode tremendous amounts of microbial protein-coding gene sequences. However, accurately assigning protein functions to novel gene sequences remain a challenge. To this end, we developed FunGeneTyper, an extensible framework with two new deep learning models (i.e., FunTrans and FunRep), structured databases, and supporting resources for achieving highly accurate (Accuracy > 0.99, F1-score > 0.97) and fine-grained classification of antibiotic resistance genes (ARGs) and virulence factor genes. Using an experimentally confirmed dataset of ARGs comprising remote homologous sequences as the test set, our framework achieves by-far-the-best performance in the discovery of new ARGs from human gut (F1-score: 0.6948), wastewater (0.6072), and soil (0.5445) microbiomes, beating the state-of-the-art bioinformatics tools and sequence alignment-based (F1-score: 0.0556-0.5065) and domain-based (F1-score: 0.2630-0.5224) annotation approaches. Furthermore, our framework is implemented as a lightweight, privacy-preserving, and plug-and-play neural network module, facilitating its versatility and accessibility to developers and users worldwide. We anticipate widespread utilization of FunGeneTyper (https://github.com/emblab-westlake/FunGeneTyper) for precise classification of protein-coding gene functions and the discovery of numerous valuable enzymes. This advancement will have a significant impact on various fields, including microbiome research, biotechnology, metagenomics, and bioinformatics.

Microbial extracellular vesicles contribute to antimicrobial resistance.

With the escalating global antimicrobial resistance crisis, there is an urgent need for innovative strategies against drug-resistant microbes. Accumulating evidence indicates microbial extracellular vesicles (EVs) contribute to antimicrobial resistance. Therefore, comprehensively elucidating the roles and mechanisms of microbial EVs in conferring resistance could provide new perspectives and avenues for novel antimicrobial approaches. In this review, we systematically examine current research on antimicrobial resistance involving bacterial, fungal, and parasitic EVs, delineating the mechanisms whereby microbial EVs promote resistance. Finally, we discuss the application of bacterial EVs in antimicrobial therapy.

Pyrrole-based inhibitors of RND-type efflux pumps reverse antibiotic resistance and display anti-virulence potential.

Efflux pumps of the resistance-nodulation-cell division (RND) superfamily, particularly the AcrAB-TolC, and MexAB-OprM, besides mediating intrinsic and acquired resistance, also intervene in bacterial pathogenicity. Inhibitors of such pumps could restore the activities of antibiotics and curb bacterial virulence. Here, we identify pyrrole-based compounds that boost antibiotic activity in Escherichia coli and Pseudomonas aeruginosa by inhibiting their archetype RND transporters. Molecular docking and biophysical studies revealed that the EPIs bind to AcrB. The identified efflux pump inhibitors (EPIs) inhibit the efflux of fluorescent probes, attenuate persister formation, extend post-antibiotic effect, and diminish resistant mutant development. The bacterial membranes remained intact upon exposure to the EPIs. EPIs also possess an anti-pathogenic potential and attenuate P. aeruginosa virulence in vivo. The intracellular invasion of E. coli and P. aeruginosa inside the macrophages was hampered upon treatment with the lead EPI. The excellent efficacy of the EPI-antibiotic combination was evidenced in animal lung infection and sepsis protection models. These findings indicate that EPIs discovered herein with negligible toxicity are potential antibiotic adjuvants to address life-threatening Gram-negative bacterial infections.

Evolution-guided discovery of antibiotics that inhibit peptidoglycan remodelling.

Addressing the ongoing antibiotic crisis requires the discovery of compounds with novel mechanisms of action that are capable of treating drug-resistant infections1. Many antibiotics are sourced from specialized metabolites produced by bacteria, particularly those of the Actinomycetes family2. Although actinomycete extracts have traditionally been screened using activity-based platforms, this approach has become unfavourable owing to the frequent rediscovery of known compounds. Genome sequencing of actinomycetes reveals an untapped reservoir of biosynthetic gene clusters, but prioritization is required to predict which gene clusters may yield promising new chemical matter2. Here we make use of the phylogeny of biosynthetic genes along with the lack of known resistance determinants to predict divergent members of the glycopeptide family of antibiotics that are likely to possess new biological activities. Using these predictions, we uncovered two members of a new functional class of glycopeptide antibiotics-the known glycopeptide antibiotic complestatin and a newly discovered compound we call corbomycin-that have a novel mode of action. We show that by binding to peptidoglycan, complestatin and corbomycin block the action of autolysins-essential peptidoglycan hydrolases that are required for remodelling of the cell wall during growth. Corbomycin and complestatin have low levels of resistance development and are effective in reducing bacterial burden in a mouse model of skin MRSA infection.

Childhood vaccines and antibiotic use in low- and middle-income countries.

Vaccines may reduce the burden of antimicrobial resistance, in part by preventing infections for which treatment often includes the use of antibiotics1-4. However, the effects of vaccination on antibiotic consumption remain poorly understood-especially in low- and middle-income countries (LMICs), where the burden of antimicrobial resistance is greatest5. Here we show that vaccines that have recently been implemented in the World Health Organization's Expanded Programme on Immunization reduce antibiotic consumption substantially among children under five years of age in LMICs. By analysing data from large-scale studies of households, we estimate that pneumococcal conjugate vaccines and live attenuated rotavirus vaccines confer 19.7% (95% confidence interval, 3.4-43.4%) and 11.4% (4.0-18.6%) protection against antibiotic-treated episodes of acute respiratory infection and diarrhoea, respectively, in age groups that experience the greatest disease burden attributable to the vaccine-targeted pathogens6,7. Under current coverage levels, pneumococcal and rotavirus vaccines prevent 23.8 million and 13.6 million episodes of antibiotic-treated illness, respectively, among children under five years of age in LMICs each year. Direct protection resulting from the achievement of universal coverage targets for these vaccines could prevent an additional 40.0 million episodes of antibiotic-treated illness. This evidence supports the prioritization of vaccines within the global strategy to combat antimicrobial resistance8.

Segregational drift hinders the evolution of antibiotic resistance on polyploid replicons.

The emergence of antibiotic resistance under treatment depends on the availability of resistance alleles and their establishment in the population. Novel resistance alleles are encoded either in chromosomal or extrachromosomal genetic elements; both types may be present in multiple copies within the cell. However, the effect of polyploidy on the emergence of antibiotic resistance remains understudied. Here we show that the establishment of resistance alleles in microbial populations depends on the ploidy level. Evolving bacterial populations under selection for antibiotic resistance, we demonstrate that resistance alleles in polyploid elements are lost frequently in comparison to alleles in monoploid elements due to segregational drift. Integrating the experiments with a mathematical model, we find a remarkable agreement between the theoretical and empirical results, confirming our understanding of the allele segregation process. Using the mathematical model, we further show that the effect of polyploidy on the establishment probability of beneficial alleles is strongest for low replicon copy numbers and plateaus for high replicon copy numbers. Our results suggest that the distribution of fitness effects for mutations that are eventually fixed in a population depends on the replicon ploidy level. Our study indicates that the emergence of antibiotic resistance in bacterial pathogens depends on the pathogen ploidy level.

Antidepressants can induce mutation and enhance persistence toward multiple antibiotics.

Antibiotic resistance is an urgent threat to global health. Antidepressants are consumed in large quantities, with a similar pharmaceutical market share (4.8%) to antibiotics (5%). While antibiotics are acknowledged as the major driver of increasing antibiotic resistance, little attention is paid to the contribution of antidepressants in this process. Here, we demonstrate that antidepressants at clinically relevant concentrations induce resistance to multiple antibiotics, even following short periods of exposure. Antibiotic persistence was also enhanced. Phenotypic and genotypic analyses revealed the enhanced production of reactive oxygen species following exposure to antidepressants was directly associated with increased resistance. An enhanced stress signature response and stimulation of efflux pump expression were also associated with increased resistance and persistence. Mathematical modeling also predicted that antidepressants would accelerate the emergence of antibiotic-resistant bacteria, and persister cells would help to maintain the resistance. Overall, our findings highlight the antibiotic resistance risk caused by antidepressants.