Consistency across multi-omics layers in a drug-perturbed gut microbial community.

Multi-omics analyses are used in microbiome studies to understand molecular changes in microbial communities exposed to different conditions. However, it is not always clear how much each omics data type contributes to our understanding and whether they are concordant with each other. Here, we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers (16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics and metabolomics). We find that all the omics methods with species resolution are highly consistent in estimating relative species abundances. Furthermore, different omics methods complement each other for capturing functional changes. For example, while nearly all the omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control. Metabolomics revealed a decrease in oligosaccharide uptake, likely caused by Bacteroidota depletion. Our study highlights how multi-omics datasets can be utilized to reveal complex molecular responses to external perturbations in microbial communities.

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism.

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.

Visualizing translation dynamics at atomic detail inside a bacterial cell.

Translation is the fundamental process of protein synthesis and is catalysed by the ribosome in all living cells1. Here we use advances in cryo-electron tomography and sub-tomogram analysis2,3 to visualize the structural dynamics of translation inside the bacterium Mycoplasma pneumoniae. To interpret the functional states in detail, we first obtain a high-resolution in-cell average map of all translating ribosomes and build an atomic model for the M. pneumoniae ribosome that reveals distinct extensions of ribosomal proteins. Classification then resolves 13 ribosome states that differ in their conformation and composition. These recapitulate major states that were previously resolved in vitro, and reflect intermediates during active translation. On the basis of these states, we animate translation elongation inside native cells and show how antibiotics reshape the cellular translation landscapes. During translation elongation, ribosomes often assemble in defined three-dimensional arrangements to form polysomes4. By mapping the intracellular organization of translating ribosomes, we show that their association into polysomes involves a local coordination mechanism that is mediated by the ribosomal protein L9. We propose that an extended conformation of L9 within polysomes mitigates collisions to facilitate translation fidelity. Our work thus demonstrates the feasibility of visualizing molecular processes at atomic detail inside cells.

Multimodal interactions of drugs, natural compounds and pollutants with the gut microbiota.

The gut microbiota contributes to diverse aspects of host physiology, ranging from immunomodulation to drug metabolism. Changes in the gut microbiota composition are associated with various diseases as well as with the response to medications. It is therefore important to understand how different lifestyle and environmental factors shape gut microbiota composition. Beyond the commonly considered factor of diet, small-molecule drugs have recently been identified as major effectors of the microbiota composition. Other xenobiotics, such as environmental or chemical pollutants, can also impact gut bacterial communities. Here, we review the mechanisms of interactions between gut bacteria and antibiotics, host-targeted drugs, natural food compounds, food additives and environmental pollutants. While xenobiotics can impact bacterial growth and metabolism, bacteria in turn can bioaccumulate or chemically modify these compounds. These reciprocal interactions can manifest in complex xenobiotic-microbiota-host relationships. Our Review highlights the need to study mechanisms underlying interactions with pollutants and food additives towards deciphering the dynamics and evolution of the gut microbiota.

Metabolic reprogramming of Pseudomonas aeruginosa by phage-based quorum sensing modulation.

The Pseudomonas quinolone signal (PQS) is a multifunctional quorum sensing molecule of key importance to P. aeruginosa. Here, we report that the lytic Pseudomonas bacterial virus LUZ19 targets this population density-dependent signaling system by expressing quorum sensing targeting protein (Qst) early during infection. We demonstrate that Qst interacts with PqsD, a key host quinolone signal biosynthesis pathway enzyme, resulting in decreased levels of PQS and its precursor 2-heptyl-4(1H)-quinolone. The lack of a functional PqsD enzyme impairs LUZ19 infection but is restored by external supplementation of 2-heptyl-4(1H)-quinolone, suggesting that LUZ19 exploits the PQS system for successful infection. We establish a broad functional interaction network of Qst, which includes enzymes of cofactor biosynthesis pathways (CoaC/ThiD) and a non-ribosomal peptide synthetase pathway (PA1217). Qst therefore represents an exquisite example of intricate reprogramming of the bacterium by a phage, which may be further exploited as tool to combat antibiotic resistant bacterial pathogens.

Microbiome and metabolome features of the cardiometabolic disease spectrum.

Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages-acute coronary syndrome, chronic IHD and IHD with heart failure-and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.

AmpC hyperproduction in a Cedecea davisae implant-associated bone infection during treatment: a case report and therapeutic implications.

BACKGROUND: Data on antimicrobial resistance mechanisms are scanty for Cedecea spp., with very variable antibiotic resistance patterns documented. Here we report the first in vivo resistance evolution of a C. davisae clinical isolate in a patient with a complex hand trauma and provide insight in the resistance mechanism, leading to therapeutic implications for this pathogen. CASE PRESENTATION: Cedecea davisae was isolated from a patient with hand trauma during a first surgical debridement. Six days after primary surgical treatment and under antimicrobial treatment with amoxicillin-clavulanic acid and later cefepime, follow up cultures yielded C. davisae which demonstrated a resistance development. The susceptible parental isolate and its resistant derivative were characterized by whole genome sequencing, ampC, ompC and ompF by RT- PCR. The resistant derivative demonstrated an A224G SNP in ampD, the transcriptional regulator of ampC, leading to a His75Arg change in the corresponding AmpD protein. AmpC transcription of the resistant derivative was 362-times higher than the susceptible isolate. Transcription levels of ompF and ompC were 8.5-fold and 1.3-fold lower, respectively, in the resistant derivative. Downregulation of OmpF putatively resulted from a mutation in the presumed promoter region upstream of the dusB-Fis operon, a proposed regulator for ompF. CONCLUSIONS: This case demonstrates the in vivo resistance development of C. davisae within 7 days similar to that of the members of the Enterobacter cloacae complex. Our findings add valuable information for future therapeutic management of these opportunistic pathogens as they warrant the same empirical treatment as AmpC producers.

Combinatorial, additive and dose-dependent drug-microbiome associations.

During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery1-5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease.

Insights from pharmacokinetic models of host-microbiome drug metabolism.

Increasing evidence suggests a role of the gut microbiota in patients' response to medicinal drugs. In our recent study, we combined genomics of human gut commensals and gnotobiotic animal experiments to quantify microbiota and host contributions to drug metabolism. Informed by experimental data, we built a physiology-based pharmacokinetic model of drug metabolism that includes intestinal compartments with microbiome drug-metabolizing activity. This model successfully predicted serum levels of metabolites of three different drugs, quantified microbial contribution to systemic drug metabolite exposure, and simulated the effect of different parameters on host and microbiota drug metabolism. In this addendum, we expand these simulations to assess the effect of microbiota on the systemic drug and metabolite levels under conditions of altered host physiology, microbiota drug-metabolizing activity or physico-chemical properties of drugs. This work illustrates how and under which circumstances the gut microbiome may influence drug pharmacokinetics, and discusses broader implications of expanded pharmacokinetic models.

Mapping human microbiome drug metabolism by gut bacteria and their genes.

Individuals vary widely in their responses to medicinal drugs, which can be dangerous and expensive owing to treatment delays and adverse effects. Although increasing evidence implicates the gut microbiome in this variability, the molecular mechanisms involved remain largely unknown. Here we show, by measuring the ability of 76 human gut bacteria from diverse clades to metabolize 271 orally administered drugs, that many drugs are chemically modified by microorganisms. We combined high-throughput genetic analyses with mass spectrometry to systematically identify microbial gene products that metabolize drugs. These microbiome-encoded enzymes can directly and substantially affect intestinal and systemic drug metabolism in mice, and can explain the drug-metabolizing activities of human gut bacteria and communities on the basis of their genomic contents. These causal links between the gene content and metabolic activities of the microbiota connect interpersonal variability in microbiomes to interpersonal differences in drug metabolism, which has implications for medical therapy and drug development across multiple disease indications.

Separating host and microbiome contributions to drug pharmacokinetics and toxicity.

The gut microbiota is implicated in the metabolism of many medical drugs, with consequences for interpersonal variation in drug efficacy and toxicity. However, quantifying microbial contributions to drug metabolism is challenging, particularly in cases where host and microbiome perform the same metabolic transformation. We combined gut commensal genetics with gnotobiotics to measure brivudine drug metabolism across tissues in mice that vary in a single microbiome-encoded enzyme. Informed by these measurements, we built a pharmacokinetic model that quantitatively predicts microbiome contributions to systemic drug and metabolite exposure, as a function of bioavailability, host and microbial drug-metabolizing activity, drug and metabolite absorption, and intestinal transit kinetics. Clonazepam studies illustrate how this approach disentangles microbiome contributions to metabolism of drugs subject to multiple metabolic routes and transformations.

The Stringent Response Determines the Ability of a Commensal Bacterium to Survive Starvation and to Persist in the Gut.

In the mammalian gut, bacteria compete for resources to maintain their populations, but the factors determining their success are poorly understood. We report that the human gut bacterium Bacteroides thetaiotaomicron relies on the stringent response, an intracellular signaling pathway that allocates resources away from growth, to survive carbon starvation and persist in the gut. Genome-scale transcriptomics, 13C-labeling, and metabolomics analyses reveal that B. thetaiotaomicron uses the alarmone (p)ppGpp to repress multiple biosynthetic pathways and upregulate tricarboxylic acid (TCA) cycle genes in these conditions. During carbon starvation, (p)ppGpp triggers accumulation of the metabolite alpha-ketoglutarate, which itself acts as a metabolic regulator; alpha-ketoglutarate supplementation restores viability to a (p)ppGpp-deficient strain. These studies uncover how commensal bacteria adapt to the gut by modulating central metabolism and reveal that halting rather than accelerating growth can be a determining factor for membership in the gut microbiome.