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.

A functional analysis of 180 cancer cell lines reveals conserved intrinsic metabolic programs.

Cancer cells reprogram their metabolism to support growth and invasion. While previous work has highlighted how single altered reactions and pathways can drive tumorigenesis, it remains unclear how individual changes propagate at the network level and eventually determine global metabolic activity. To characterize the metabolic lifestyle of cancer cells across pathways and genotypes, we profiled the intracellular metabolome of 180 pan-cancer cell lines grown in identical conditions. For each cell line, we estimated activity for 49 pathways spanning the entirety of the metabolic network. Upon clustering, we discovered a convergence into only two major metabolic types. These were functionally confirmed by 13 C-flux analysis, lipidomics, and analysis of sensitivity to perturbations. They revealed that the major differences in cancers are associated with lipid, TCA cycle, and carbohydrate metabolism. Thorough integration of these types with multiomics highlighted little association with genetic alterations but a strong association with markers of epithelial-mesenchymal transition. Our analysis indicates that in absence of variations imposed by the microenvironment, cancer cells adopt distinct metabolic programs which serve as vulnerabilities for therapy.

Long-term history dependence of growth rates of E. coli after nutrient shifts.

According to a widely accepted paradigm of microbiology, steady-state growth rates are determined solely by current growth conditions1-3 and adaptations between growth states are rapid, as recently recapitulated by simple resource allocation models4. However, even in microbes overlapping regulatory networks can yield multi-stability or long-term cellular memory. Species like Listeria monocytogenes5 and Bacillus subtilis "distinguish" distinct histories for the commitment to sporulation6, but it is unclear if these states can persist over many generations. Remarkably, studying carbon co-utilization of Escherichia coli, we found that growth rates on combinations of carbon sources can depend critically on the previous growth condition. Growing in identical conditions, we observed differences in growth rates of up to 25% and we did not observe convergence of growth rates over 15 generations. We observed this phenomenon occurs across combinations of different phosphotransferase (PTS) substrates with various gluconeogenic carbon sources and found it to depend on the transcription factor Mlc.

Ex vivo drug response heterogeneity reveals personalized therapeutic strategies for patients with multiple myeloma.

Multiple myeloma (MM) is a plasma cell malignancy defined by complex genetics and extensive patient heterogeneity. Despite a growing arsenal of approved therapies, MM remains incurable and in need of guidelines to identify effective personalized treatments. Here, we survey the ex vivo drug and immunotherapy sensitivities across 101 bone marrow samples from 70 patients with MM using multiplexed immunofluorescence, automated microscopy and deep-learning-based single-cell phenotyping. Combined with sample-matched genetics, proteotyping and cytokine profiling, we map the molecular regulatory network of drug sensitivity, implicating the DNA repair pathway and EYA3 expression in proteasome inhibitor sensitivity and major histocompatibility complex class II expression in the response to elotuzumab. Globally, ex vivo drug sensitivity associated with bone marrow microenvironmental signatures reflecting treatment stage, clonality and inflammation. Furthermore, ex vivo drug sensitivity significantly stratified clinical treatment responses, including to immunotherapy. Taken together, our study provides molecular and actionable insights into diverse treatment strategies for patients with MM.

ROS Induction Targets Persister Cancer Cells with Low Metabolic Activity in NRAS-Mutated Melanoma.

Clinical management of melanomas with NRAS mutations is challenging. Targeting MAPK signaling is only beneficial to a small subset of patients due to resistance that arises through genetic, transcriptional, and metabolic adaptation. Identification of targetable vulnerabilities in NRAS-mutated melanoma could help improve patient treatment. Here, we used multiomics analyses to reveal that NRAS-mutated melanoma cells adopt a mesenchymal phenotype with a quiescent metabolic program to resist cellular stress induced by MEK inhibition. The metabolic alterations elevated baseline reactive oxygen species (ROS) levels, leading these cells to become highly sensitive to ROS induction. In vivo xenograft experiments and single-cell RNA sequencing demonstrated that intratumor heterogeneity necessitates the combination of a ROS inducer and a MEK inhibitor to inhibit both tumor growth and metastasis. Ex vivo pharmacoscopy of 62 human metastatic melanomas confirmed that MEK inhibitor-resistant tumors significantly benefited from the combination therapy. Finally, oxidative stress response and translational suppression corresponded with ROS-inducer sensitivity in 486 cancer cell lines, independent of cancer type. These findings link transcriptional plasticity to a metabolic phenotype that can be inhibited by ROS inducers in melanoma and other cancers. SIGNIFICANCE: Metabolic reprogramming in drug-resistant NRAS-mutated melanoma cells confers sensitivity to ROS induction, which suppresses tumor growth and metastasis in combination with MAPK pathway inhibitors.

The Tumor Profiler Study: integrated, multi-omic, functional tumor profiling for clinical decision support.

The application and integration of molecular profiling technologies create novel opportunities for personalized medicine. Here, we introduce the Tumor Profiler Study, an observational trial combining a prospective diagnostic approach to assess the relevance of in-depth tumor profiling to support clinical decision-making with an exploratory approach to improve the biological understanding of the disease.

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.

CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data.

We develop CellSIUS (Cell Subtype Identification from Upregulated gene Sets) to fill a methodology gap for rare cell population identification for scRNA-seq data. CellSIUS outperforms existing algorithms for specificity and selectivity for rare cell types and their transcriptomic signature identification in synthetic and complex biological data. Characterization of a human pluripotent cell differentiation protocol recapitulating deep-layer corticogenesis using CellSIUS reveals unrecognized complexity in human stem cell-derived cellular populations. CellSIUS enables identification of novel rare cell populations and their signature genes providing the means to study those populations in vitro in light of their role in health and disease.