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Bacteriostatic and bactericidal antibiotic treatments result in two fundamentally different phenotypic outcomes-the inhibition of bacterial growth or, alternatively, cell death. Most antibiotics inhibit processes that are major consumers of cellular energy output, suggesting that antibiotic treatment may have important downstream consequences on bacterial metabolism. We hypothesized that the specific metabolic effects of bacteriostatic and bactericidal antibiotics contribute to their overall efficacy. We leveraged the opposing phenotypes of bacteriostatic and bactericidal drugs in combination to investigate their activity. Growth inhibition from bacteriostatic antibiotics was associated with suppressed cellular respiration whereas cell death from most bactericidal antibiotics was associated with accelerated respiration. In combination, suppression of cellular respiration by the bacteriostatic antibiotic was the dominant effect, blocking bactericidal killing. Global metabolic profiling of bacteriostatic antibiotic treatment revealed that accumulation of metabolites involved in specific drug target activity was linked to the buildup of energy metabolites that feed the electron transport chain. Inhibition of cellular respiration by knockout of the cytochrome oxidases was sufficient to attenuate bactericidal lethality whereas acceleration of basal respiration by genetically uncoupling ATP synthesis from electron transport resulted in potentiation of the killing effect of bactericidal antibiotics. This work identifies a link between antibiotic-induced cellular respiration and bactericidal lethality and demonstrates that bactericidal activity can be arrested by attenuated respiration and potentiated by accelerated respiration. Our data collectively show that antibiotics perturb the metabolic state of bacteria and that the metabolic state of bacteria impacts antibiotic efficacy.

Summary Understanding how antibiotics impact bacterial metabolism may provide insight into their mechanisms of action and could lead to enhanced therapeutic methodologies. Here, we profiled the metabolome of Escherichia coli after treatment with three different classes of bactericidal antibiotics (beta-lactams, aminoglycosides, quinolones). These treatments induced a similar set of metabolic changes after 30 minutes that then diverged into more distinct profiles at later timepoints. The most striking changes corresponded to elevated concentrations of central carbon metabolites, active breakdown of the nucleotide pool, reduced lipid levels, and evidence of an elevated redox state. We examined potential end-target consequences of these metabolic perturbations and found that antibiotic-treated cells exhibited cytotoxic changes indicative of oxidative stress, including higher levels of protein carbonylation, malondialdehyde adducts, nucleotide oxidation, and double-strand DNA breaks. This work shows that bactericidal antibiotics induce a complex set of metabolic changes that are correlated with the buildup of toxic metabolic by-products.

Malignant abdominal fluid (ascites) frequently develops in women with advanced high-grade serous ovarian cancer (HGSOC) and is associated with drug resistance and a poor prognosis 1 . To comprehensively characterize the HGSOC ascites ecosystem, we used single-cell RNA-seq (scRNA-seq) to profile ~11,000 cells from 22 ascites specimens from 11 HGSOC patients. We found significant inter-patient variability in the composition and functional programs of ascites cells, including immunomodulatory fibroblast sub-populations and dichotomous macrophage populations. We find that the previously described "immunoreactive" and "mesenchymal" subtypes of HGSOC, which have prognostic implications, reflect the abundance of immune infiltrates and fibroblasts rather than distinct subsets of malignant cells 2 . Malignant cell variability was partly explained by heterogeneous copy number alterations (CNA) patterns or expression of a stemness program. Malignant cells shared expression of inflammatory programs that were largely recapitulated in scRNA-seq of ~35,000 cells from additionally collected samples, including three ascites, two primary HGSOC tumors and three patient-ascites-derived xenograft models. Inhibition of the JAK/STAT-pathway, which was expressed in both malignant cells and CAFs, had potent anti-tumor activity in primary short-term cultures and PDX models. Our work contributes to resolving the HSGOC landscape 3-5 and provides a resource for the development of novel therapeutic approaches.

SUMMARY Metabolically dormant bacteria present a critical challenge to effective antimicrobial therapy because these bacteria are genetically susceptible to antibiotic treatment but phenotypically tolerant. Such tolerance has been attributed to impaired drug uptake, which can be reversed by metabolic stimulation. Here, we evaluate the effects of central carbon metabolite stimulations on aminoglycoside sensitivity in the pathogen Pseudomonas aeruginosa. We identify fumarate as a tobramycin potentiator that activates cellular respiration and generates a proton motive force by stimulating the tricarboxylic acid (TCA) cycle. In contrast, we find that glyoxylate induces phenotypic tolerance by inhibiting cellular respiration with acetyl-coenzyme A diversion through the glyoxylate shunt, despite drug import. Collectively, this work demonstrates that TCA cycle activity is important for both aminoglycoside uptake and downstream lethality and identifies a potential strategy for potentiating aminoglycoside treatment of P. aeruginosa infections.

Highlights d Lung cancer progression is accompanied by a stereotypic expansion of heterogeneity d Cell state heterogeneity arises largely independently of genetic variation d State transitions occur via an HPCS harboring high differentiation and growth capacity d The HPCS is drug resistant and portends poor patient survival across all cancers

umors encompass complex cellular ecosystems of malignant and non-malignant cells, whose diversity and interactions affect cancer progression and drug response and resistance. Recent advances in single-cell genomics, especially single-cell RNA-Seq (scRNA-Seq), have transformed our ability to analyze tumors, revealing cell types, states, genetic diversity and interactions in the complex tumor ecosystem 1-6. Single-cell analysis of tumors is rapidly expanding, including the launch of a Human Tumor Atlas Network (HTAPP) as part of the Cancer Moonshot 7. Successful scRNA-Seq of clinical tumor specimens poses several challenges. First, it requires quick dissociation tailored to the tumor type, and involves enzymatic digestion, which can lead to loss of sensitive cells or changes in gene expression. Moreover, obtaining fresh tissue is time-sensitive and requires tight coordination

AbstractSingle cell genomics is essential to chart the complex tumor ecosystem. While single cell RNA-Seq (scRNA-Seq) profiles RNA from cells dissociated from fresh tumor tissues, single nucleus RNA-Seq (snRNA-Seq) is needed to profile frozen or hard-to-dissociate tumors. Each strategy requires modifications to fit the unique characteristics of different tissue and tumor types, posing a barrier to adoption. Here, we developed a systematic toolbox for profiling fresh and frozen clinical tumor samples using scRNA-Seq and snRNA-Seq, respectively. We tested eight tumor types of varying tissue and sample characteristics (resection, biopsy, ascites, and orthotopic patient-derived xenograft): lung cancer, metastatic breast cancer, ovarian cancer, melanoma, neuroblastoma, pediatric sarcoma, glioblastoma, pediatric high-grade glioma, and chronic lymphocytic leukemia. Analyzing 212,498 cells and nuclei from 39 clinical samples, we evaluated protocols by cell quality, recovery rate, and cellular composition. We optimized protocols for fresh tissue dissociation for different tumor types using a decision tree to account for the technical and biological variation between clinical samples. We established methods for nucleus isolation from OCT embedded and fresh-frozen tissues, with an optimization matrix varying mechanical force, buffer, and detergent. scRNA-Seq and snRNA-Seq from matched samples recovered the same cell types and intrinsic expression profiles, but at different proportions. Our work provides direct guidance across a broad range of tumors, including criteria for testing and selecting methods from the toolbox for other tumors, thus paving the way for charting tumor atlases.