Deregulation of mitochondrial network in terminally differentiated cells contributes to a broad spectrum of disorders. Methylmalonic acidemia (MMA) is one of the most common inherited metabolic disorders, due to deficiency of the mitochondrial methylmalonyl-coenzyme A mutase (MMUT). How MMUT deficiency triggers cell damage remains unknown, preventing the development of disease-modifying therapies. Here we combine genetic and pharmacological approaches to demonstrate that MMUT deficiency induces metabolic and mitochondrial alterations that are exacerbated by anomalies in PINK1/Parkin-mediated mitophagy, causing the accumulation of dysfunctional mitochondria that trigger epithelial stress and ultimately cell damage. Using drug-disease network perturbation modelling, we predict targetable pathways, whose modulation repairs mitochondrial dysfunctions in patient-derived cells and alleviate phenotype changes in mmut-deficient zebrafish. These results suggest a link between primary MMUT deficiency, diseased mitochondria, mitophagy dysfunction and epithelial stress, and provide potential therapeutic perspectives for MMA. 1 1234567890():,;Mitochondrial dysfunction drives stress in MMA cells. As MMUT deficiency alters mitochondrial homeostasis, we next assessed potential consequences on mitochondrial function. Consistent with increased numbers of morphologically aberrant mitochondria, the mitochondrial membrane potential (Δψ m ) was drastically reduced in MMA cells (Fig. 2d), as evidenced by live cell imaging analyses of the mitochondrial network with cellpermeant, fluorescent dye tetramethylrhodamine methyl ester (TMRM, which readily accumulates within functional mitochondria) and MitoTracker (a fluorescent probe that localizes to mitochondria). These changes were paralleled by a major mitochondrial oxidative stress ( Fig. 2e), as testified by the elevated production of mitochondria (mt)-derived ROS (MitoSOX, a livecell-permeant indicator of mitochondrial ROS) and augmented antioxidant response (SOD1; Fig. 2g). Treatment with the mitochondrial complex I inhibitor Rotenone (5 μM for 24 h), which ARTICLE NATURE COMMUNICATIONS | https://doi.

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Biological target (commonly genes or proteins) identification is still largely a manual process, where experts manually try to collect and combine information from hundreds of data sources, ranging from scientific publications to omics databases. Targeting the wrong gene or protein will lead to failure of the drug development process, as well as incur delays and costs. To improve this process, different software platforms are being developed. These platforms rely strongly on efficacy estimates based on target-disease association scores created by computational methods for drug target prioritization. Here novel computational methods are presented to more accurately evaluate the efficacy and safety of potential drug targets. The proposed efficacy scores utilize existing gene expression data and tissue/disease specific networks to improve the inference of target-disease associations. Conversely, safety scores enable the identification of genes that are essential, potentially susceptible to adverse effects or carcinogenic. Benchmark results demonstrate that our transcriptome-based methods for drug target prioritization can increase the true positive rate of target-disease associations. Additionally, the proposed safety evaluation system enables accurate predictions of targets of withdrawn drugs and targets of drug trials prematurely discontinued.

The TRAnsport Protein Particle (TRAPP) complex controls multiple membrane trafficking steps and is strategically positioned to mediate cell adaptation to diverse environmental conditions, including acute stress. We have identified the TRAPP complex as a component of a branch of the integrated stress response that impinges on the early secretory pathway. The TRAPP complex associates with and drives the recruitment of the COPII coat to stress granules (SGs) leading to vesiculation of the Golgi complex and arrest of ER export. The relocation of the TRAPP complex and COPII to SGs only occurs in cycling cells and is CDK1/2‐dependent, being driven by the interaction of TRAPP with hnRNPK, a CDK substrate that associates with SGs when phosphorylated. In addition, CDK1/2 inhibition impairs TRAPP complex/COPII relocation to SGs while stabilizing them at ER exit sites. Importantly, the TRAPP complex controls the maturation of SGs. SGs that assemble in TRAPP‐depleted cells are smaller and are no longer able to recruit RACK1 and Raptor, two TRAPP‐interactive signaling proteins, sensitizing cells to stress‐induced apoptosis.

Summary Estimating efficacy of gene–target-disease associations is a fundamental step in drug discovery. An important data source for this laborious task is RNA expression, which can provide gene–disease associations on the basis of expression fold change and statistical significance. However, the simply use of the log-fold change can lead to numerous false-positive associations. On the other hand, more sophisticated methods that utilize gene co-expression networks do not consider tissue specificity. Here, we introduce Transcriptome-driven Efficacy estimates for gene-based TArget discovery (ThETA), an R package that enables non-expert users to use novel efficacy scoring methods for drug–target discovery. In particular, ThETA allows users to search for gene perturbation (therapeutics) that reverse disease-gene expression and genes that are closely related to disease-genes in tissue-specific networks. ThETA also provides functions to integrate efficacy evaluations obtained with different approaches and to build an overall efficacy score, which can be used to identify and prioritize gene(target)–disease associations. Finally, ThETA implements visualizations to show tissue-specific interconnections between target and disease-genes, and to indicate biological annotations associated with the top selected genes. Availability and implementation ThETA is freely available for academic use at https://github.com/vittoriofortino84/ThETA. Supplementary information Supplementary data are available at Bioinformatics online.