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Abstract Drug-resistant strains of Mycobacterium tuberculosis are a major global health problem. Resistance to the front-line antibiotic isoniazid is often associated with mutations in the katG-encoded bifunctional catalase-peroxidase. We hypothesise that perturbed KatG activity would generate collateral vulnerabilities in isoniazid-resistant katG mutants, providing potential pathway targets to combat isoniazid resistance. Whole genome CRISPRi screens, transcriptomics, and metabolomics were used to generate a genome-wide map of cellular vulnerabilities in an isoniazid-resistant katG mutant strain of M. tuberculosis. Here, we show that metabolic and transcriptional remodelling compensates for the loss of KatG but in doing so generates vulnerabilities in respiration, ribosome biogenesis, and nucleotide and amino acid metabolism. Importantly, these vulnerabilities are more sensitive to inhibition in an isoniazid-resistant katG mutant and translated to clinical isolates. This work highlights how changes in the physiology of drug-resistant strains generates druggable vulnerabilities that can be exploited to improve clinical outcomes.

Matrix-assisted laser desorption/ionization mass spectrometry imaging allows for the study of metabolic activity in the tumor microenvironment of brain cancers. The detectable metabolites within these tumors are contingent upon the choice of matrix, deposition technique, and polarity setting. In this study, we compared the performance of three different matrices, two deposition techniques, and the use of positive and negative polarity in two different brain cancer types and across two species. Optimal combinations were confirmed by a comparative analysis of lipid and small-molecule abundance by using liquid chromatography–mass spectrometry and RNA sequencing to assess differential metabolites and enzymes between normal and tumor regions. Our findings indicate that in the tumor-bearing brain, the recrystallized α-cyano-4-hydroxycinnamic acid matrix with positive polarity offered superior performance for both detected metabolites and consistency with other techniques. Beyond these implications for brain cancer, our work establishes a workflow to identify optimal matrices for spatial metabolomics studies.

Although various vaccines are now commercially available, they have not been able to stop the spread of COVID-19 infection completely. An excellent strategy to get safe, effective, and affordable COVID-19 treatments quickly is to repurpose drugs that are already approved for other diseases. The process of developing an accurate and standardized drug repurposing dataset requires considerable resources and expertise due to numerous commercially available drugs that could be potentially used to address the SARS-CoV-2 infection. To address this bottleneck, we created the CoviRx.org platform. CoviRx is a user-friendly interface that allows analysis and filtering of large quantities of data, which is onerous to curate manually for COVID-19 drug repurposing. Through CoviRx, the curated data have been made open source to help combat the ongoing pandemic and encourage users to submit their findings on the drugs they have evaluated, in a uniform format that can be validated and checked for integrity by authenticated volunteers. This article discusses the various features of CoviRx, its design principles, and how its functionality is independent of the data it displays. Thus, in the future, this platform can be extended to include any other disease beyond COVID-19.

Cancer cells often have dysregulated metabolism, which is largely characterized by the Warburg effect—an increase in glycolytic activity at the expense of oxidative phosphorylation—and increased glutamine utilization. Modern metabolomics tools offer an efficient means to investigate metabolism in cancer cells. Currently, a number of protocols have been described for harvesting adherent cells for metabolomics analysis, but the techniques vary greatly and they lack specificity to particular cancer cell lines with diverse metabolic and structural features. Here we present an optimized method for untargeted metabolomics characterization of MDA-MB-231 triple negative breast cancer cells, which are commonly used to study metastatic breast cancer. We found that an approach that extracted all metabolites in a single step within the culture dish optimally detected both polar and non-polar metabolite classes with higher relative abundance than methods that involved removal of cells from the dish. We show that this method is highly suited to diverse applications, including the characterization of central metabolic flux by stable isotope labelling and differential analysis of cells subjected to specific pharmacological interventions.

Gatekeeper trainings have been increasingly utilized in response to rising suicide rates in youths. However, the extent to which common factors inherent to gatekeeper trainings impact training outcomes is largely understudied. As such, the present study explored how factors such as training size and trainer engagement abilities influenced trainee participation and outcomes (i.e. changes in attitudes, perceived behavioral control (PBC), and knowledge regarding suicide prevention). All trainees participated in a gatekeeper training; trainers were not randomly assigned. Mediation and moderation analyses were completed using the PROCESS macro for SPSS (Hayes in Introduction to mediation, moderation, and conditional process analysis: a regression-based approach, Guilford Press, New York, 2013). Trainee participation in a training was examined as a mediator of the relationship between the training size and training outcomes, while trainer engagement was examined as a moderator of the relationship between size of training and trainee participation. Size of training was significantly related to lower changes in participant knowledge, along with lower trainee participation in gatekeeper trainings. Trainee participation significantly mediated the relationship between size of training, attitudes, and PBC. Additionally, trainer engagement significantly moderated the association between size of training and trainee participation. The results of this study suggest that general gatekeeper training-related variables may influence participant outcomes, specifically through trainee participation.

Most eukaryotic mRNAs accommodate alternative sites of poly(A) addition in the 3’ untranslated region in order to regulate mRNA function. Here, we present a systematic analysis of 3’ end formation factors, which revealed 3’UTR lengthening in response to a loss of the core machinery, whereas a loss of the Sen1 helicase resulted in shorter 3’UTRs. We show that the anti-cancer drug cordycepin, 3’ deoxyadenosine, caused nucleotide accumulation and the usage of distal poly(A) sites. Mycophenolic acid, a drug which reduces GTP levels and impairs RNA polymerase II (RNAP II) transcription elongation, promoted the usage of proximal sites and reversed the effects of cordycepin on alternative polyadenylation. Moreover, cordycepin-mediated usage of distal sites was associated with a permissive chromatin template and was suppressed in the presence of an rpb1 mutation, which slows RNAP II elongation rate. We propose that alternative polyadenylation is governed by temporal coordination of RNAP II transcription and 3’ end processing and controlled by the availability of 3’ end factors, nucleotide levels and chromatin landscape.