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      Loss of transient receptor potential channel 5 causes obesity and postpartum depression

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          SUMMARY

          Hypothalamic neural circuits regulate instinctive behaviors such as food seeking, the fight/flight response, socialization, and maternal care. Here, we identified microdeletions on chromosome Xq23 disrupting the brain-expressed transient receptor potential (TRP) channel 5 (TRPC5). This family of channels detects sensory stimuli and converts them into electrical signals interpretable by the brain. Male TRPC5 deletion carriers exhibited food seeking, obesity, anxiety, and autism, which were recapitulated in knockin male mice harboring a human loss-of-function TRPC5 mutation. Women carrying TRPC5 deletions had severe postpartum depression. As mothers, female knockin mice exhibited anhedonia and depression-like behavior with impaired care of offspring. Deletion of Trpc5 from oxytocin neurons in the hypothalamic paraventricular nucleus caused obesity in both sexes and postpartum depressive behavior in females, while Trpc5 overexpression in oxytocin neurons in knock-in mice reversed these phenotypes. We demonstrate that TRPC5 plays a pivotal role in mediating innate human behaviors fundamental to survival, including food seeking and maternal care.

          In brief

          Disruption of TRPC5, a brain-expressed cation channel, causes obesity, maladaptive behavior, and postpartum depression in humans and mice. Acting on hypothalamic Pomc and oxytocin neurons, TRPC5 regulates instinctive behaviors such as feeding, arousal, social interaction, and maternal care, which are fundamental for survival.

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          Twelve years of SAMtools and BCFtools

          Abstract Background SAMtools and BCFtools are widely used programs for processing and analysing high-throughput sequencing data. They include tools for file format conversion and manipulation, sorting, querying, statistics, variant calling, and effect analysis amongst other methods. Findings The first version appeared online 12 years ago and has been maintained and further developed ever since, with many new features and improvements added over the years. The SAMtools and BCFtools packages represent a unique collection of tools that have been used in numerous other software projects and countless genomic pipelines. Conclusion Both SAMtools and BCFtools are freely available on GitHub under the permissive MIT licence, free for both non-commercial and commercial use. Both packages have been installed >1 million times via Bioconda. The source code and documentation are available from https://www.htslib.org.
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            The mutational constraint spectrum quantified from variation in 141,456 humans

            Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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              The UK Biobank resource with deep phenotyping and genomic data

              The UK Biobank project is a prospective cohort study with deep genetic and phenotypic data collected on approximately 500,000 individuals from across the United Kingdom, aged between 40 and 69 at recruitment. The open resource is unique in its size and scope. A rich variety of phenotypic and health-related information is available on each participant, including biological measurements, lifestyle indicators, biomarkers in blood and urine, and imaging of the body and brain. Follow-up information is provided by linking health and medical records. Genome-wide genotype data have been collected on all participants, providing many opportunities for the discovery of new genetic associations and the genetic bases of complex traits. Here we describe the centralized analysis of the genetic data, including genotype quality, properties of population structure and relatedness of the genetic data, and efficient phasing and genotype imputation that increases the number of testable variants to around 96 million. Classical allelic variation at 11 human leukocyte antigen genes was imputed, resulting in the recovery of signals with known associations between human leukocyte antigen alleles and many diseases.
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                Author and article information

                Journal
                0413066
                2830
                Cell
                Cell
                Cell
                0092-8674
                1097-4172
                21 March 2025
                08 August 2024
                02 July 2024
                01 April 2025
                : 187
                : 16
                : 4176-4192.e17
                Affiliations
                [1 ]USDA/ARS Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
                [2 ]University of Cambridge Metabolic Research Laboratories, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
                [3 ]Jan and Dan Duncan Neurological Research Institute, Department of Pediatrics, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA
                [4 ]Taizhou People’s Hospital, Medical School of Yangzhou University, Taizhou, Jiangsu, China
                [5 ]MRC Epidemiology Unit, Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
                [6 ]Exeter Centre of Excellence for Diabetes Research (EXCEED), University of Exeter Medical School, Exeter, UK
                [7 ]Center for Hypothalamic Research, Department of Internal Medicine, University of Texas Southwestern Medical Center at Dallas, Dallas, TX 75390-9077, USA
                [8 ]West of Scotland Regional Genetics Service, Queen Elizabeth University Hospital, Glasgow, UK
                [9 ]Department of Clinical Genetics, Cambridge University Hospitals NHS Foundation Trust & Wellcome Sanger Institute, Cambridge, UK
                [10 ]Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, USA
                [11 ]Department of Medicine, Baylor College of Medicine, Houston, TX, USA
                [12 ]These authors contributed equally
                [13 ]Lead contact
                Author notes

                AUTHOR CONTRIBUTIONS

                Conception, design of studies, and project supervision: I.S.F., H.V.F., and Y.X.; recruitment of the GOOS cohort: J.M.K., R.B., E.H., and I.S.F.; genetic studies and analyses in people with severe obesity including in the GOOS cohort: H.V.F., S.J., T.M.C., P.L., R.B., I.B., K.L., and I.S.F.; whole exome burden association analysis using UK Biobank: J.R.B.P., E.J.G., K.A.K., and K.K.O.; clinical and behavioral studies: T.M.C., J.M.K., E.H., and I.S.F.; behavioral studies in mice: Y.L.; molecular studies of human mutations: N.Y., Y.H., E.M.d.O., and Y.X.; electrophysiological studies: Y.H.; mouse metabolic studies: Y.Y., Y.L., N.Y., and H.L.; generation of mice: L.T., J.C.B., H.L., M.Y., K.M.C., M.W., O.G., X.F., L.T., J.H., K.W.W., Y.Y., and C.W.

                [* ]Correspondence: yongx@ 123456bcm.edu (Y.X.), isf20@ 123456cam.ac.uk (I.S.F.)
                Article
                NIHMS2063739
                10.1016/j.cell.2024.06.001
                11961024
                38959890
                a63defe1-987f-4005-a0c5-f8e0e2681d97

                This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/).

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                Cell biology
                Cell biology

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