Systematic analysis of drug combinations against Gram-positive bacteria.

Drug combinations can expand options for antibacterial therapies but have not been systematically tested in Gram-positive species. We profiled ~8,000 combinations of 65 antibacterial drugs against the model species Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulated previously known drug interactions, but also identified ten times more novel interactions in the pathogen S. aureus, including 150 synergies. We showed that two synergies were equally effective against multidrug-resistant S. aureus clinical isolates in vitro and in vivo. Interactions were largely species-specific and synergies were distinct from those of Gram-negative species, owing to cell surface and drug uptake differences. We also tested 2,728 combinations of 44 commonly prescribed non-antibiotic drugs with 62 drugs with antibacterial activity against S. aureus and identified numerous antagonisms that might compromise the efficacy of antimicrobial therapies. We identified even more synergies and showed that the anti-aggregant ticagrelor synergized with cationic antibiotics by modifying the surface charge of S. aureus. All data can be browsed in an interactive interface ( https://apps.embl.de/combact/ ).

An integrated cell atlas of the lung in health and disease.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Rapid Inhibitor Discovery by Exploiting Synthetic Lethality.

Synthetic lethality occurs when inactivation of two genes is lethal but inactivation of either single gene is not. This phenomenon provides an opportunity for efficient compound discovery. Using differential growth screens, one can identify biologically active compounds that selectively inhibit proteins within the synthetic lethal network of any inactivated gene. Here, based purely on synthetic lethalities, we identified two compounds as the only possible inhibitors of Staphylococcus aureus lipoteichoic acid (LTA) biosynthesis from a screen of ∼230,000 compounds. Both compounds proved to inhibit the glycosyltransferase UgtP, which assembles the LTA glycolipid anchor. UgtP is required for ß-lactam resistance in methicillin-resistant S. aureus (MRSA), and the inhibitors restored sensitivity to oxacillin in a highly resistant S. aureus strain. As no other compounds were pursued as possible LTA glycolipid assembly inhibitors, this work demonstrates the extraordinary efficiency of screens that exploit synthetic lethality to discover compounds that target specified pathways. The general approach should be applicable not only to other bacteria but also to eukaryotic cells.