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Biology subjects

Mali, I.

Publications and source records attributed to Mali, I..

2 recordsLinked to original sources

Profiling human hypothalamic neurons reveals a candidate combination drug therapy for weight loss

Obesity substantially increases the risk of type 2 diabetes, cardiovascular disease, and other diseases, making it a leading preventable cause of death in developed countries. It has a strong genetic basis, with obesity-associated genetic variants preferentially acting in the brain. This includes the hypothalamic pro-opiomelanocortin (POMC) neurons that inhibit food intake and are stimulated by drugs that agonise glucagon-like 1 peptide receptor (GLP1R) including Semaglutide (Ozempic/Wegovy). We therefore hypothesised that drugs which selectively activate human POMC neurons would suppress appetite and promote weight loss, and that focusing on drugs already approved for use would facilitate rapid clinical translation. We therefore generated POMC neurons from human pluripotent stem cells (hPSCs) and identified enriched genes that were genetically associated with obesity and targeted by approved drugs. We found that human POMC neurons are enriched in GLP1R, reliably activated by Semaglutide, and their responses are further increased by co-administration of Ceritinib, an FDA-approved drug potently and selectively inhibiting anaplastic lymphoma kinase (ALK). Ceritinib reduced food intake and body weight in obese but not lean mice, and upregulated the expression of GLP1R in the mouse hypothalamus and hPSC-derived human hypothalamic neurons. These studies reveal a new potential therapeutic strategy for reducing food intake and body weight, and demonstrate the utility of hPSC-derived hypothalamic neurons for drug discovery.

neuroscience↗

Pan-cancer proteomic map of 949 human cell lines reveals principles of cancer vulnerabilities

The proteome provides unique insights into biology and disease beyond the genome and transcriptome. Lack of large proteomic datasets has restricted identification of new cancer biomarkers. Here, proteomes of 949 cancer cell lines across 28 tissue types were analyzed by mass spectrometry. Deploying a clinically-relevant workflow to quantify 8,498 proteins, these data capture evidence of cell type and post-transcriptional modifications. Integrating multi-omics, drug response and CRISPR-Cas9 gene essentiality screens with a deep learning-based pipeline revealed thousands of protein-specific biomarkers of cancer vulnerabilities. Proteomic data had greater power to predict drug response than the equivalent portion of the transcriptome. Further, random downsampling to only 1,500 proteins had limited impact on predictive power, consistent with protein networks being highly connected and co-regulated. This pan-cancer proteomic map (ProCan-DepMapSanger), available at https://cellmodelpassports.sanger.ac.uk, is a comprehensive resource revealing principles of protein regulation with important implications for future clinical studies.

systems biology↗