31 research outputs found

    Different noses for different mice and men

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    <p>Abstract</p> <p>Chemosensory receptor genes encode G protein-coupled receptors with which animals sense their chemical environment. The large number of chemosensory receptor genes in the genome and their extreme genetic variability pose unusual challenges for understanding their evolution and function. Two articles in <it>BMC Genomics </it>explore the genetic variation of chemosensory receptor gene repertoires in humans and mice and provide unparalleled insight into the causes and consequences of this variability.</p> <p>See research articles <url>http://www.biomedcentral.com/1471-2164/13/414</url> and <url>http://www.biomedcentral.com/1471-2164/13/415</url></p

    Multi-cohort analysis of host immune response identifies conserved protective and detrimental modules associated with severity across viruses

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    Viral infections induce a conserved host response distinct from bacterial infections. We hypothesized that the conserved response is associated with disease severity and is distinct between patients with different outcomes. To test this, we integrated 4,780 blood transcriptome profiles from patients aged 0 to 90 years infected with one of 16 viruses, including SARS-CoV-2, Ebola, chikungunya, and influenza, across 34 cohorts from 18 countries, and single-cell RNA sequencing profiles of 702,970 immune cells from 289 samples across three cohorts. Severe viral infection was associated with increased hematopoiesis, myelopoiesis, and myeloid-derived suppressor cells. We identified protective and detrimental gene modules that defined distinct trajectories associated with mild versus severe outcomes. The interferon response was decoupled from the protective host response in patients with severe outcomes. These findings were consistent, irrespective of age and virus, and provide insights to accelerate the development of diagnostics and host-directed therapies to improve global pandemic preparedness. © 2021 The Author(s

    Gene-by-sex interactions in mitochondrial functions and cardio-metabolic traits.

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    We studied sex differences in over 50 cardio-metabolic traits in a panel of 100 diverse inbred strains of mice. The results clearly showed that the effects of sex on both clinical phenotypes and gene expression depend on the genetic background. In support of this, genetic loci associated with the traits frequently showed sex specificity. For example, Lyplal1, a gene implicated in human obesity, was shown to underlie a sex-specific locus for diet induced obesity. Global gene expression analyses of tissues across the panel implicated adipose tissue "beiging" and mitochondrial functions in the sex differences. Isolated mitochondria showed gene-bysex interactions in oxidative functions, such that some strains (C57BL/6J) showed similar function between sexes, whereas others (DBA/2J and A/J) showed increased function in females. Reduced adipose mitochondria! function in males as compared to females was associated with increased susceptibility to obesity and insulin resistance. Gonadectomy studies indicated that gonadal hormones acting in a tissue-specific manner were responsible in part for the sex differences

    Transcriptomic similarities and differences in host response between SARS-CoV-2 and other viral infections

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    The pandemic 2019 novel coronavirus disease (COVID-19) shares certain clinical characteristics with other acute viral infections. We studied the whole-blood transcriptomic host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using RNAseq from 24 healthy controls and 62 prospectively enrolled patients with COVID-19. We then compared these data to non-COVID-19 viral infections, curated from 23 independent studies profiling 1,855 blood samples covering six viruses (influenza, respiratory syncytial virus (RSV), human rhinovirus (HRV), severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), Ebola, dengue). We show gene expression changes in COVID-19 versus non-COVID-19 viral infections are highly correlated (r = 0.74, p &amp;lt; 0.001). However, we also found 416 genes specific to COVID-19. Inspection of top genes revealed dynamic immune evasion and counter host responses specific to COVID-19. Statistical deconvolution of cell proportions maps many cell type proportions concordantly shifting. Discordantly increased in COVID-19 were CD56bright natural killer cells and M2 macrophages. The concordant and discordant responses mapped out here provide a window to explore the pathophysiology of the host response to SARS-CoV-2. © 2020 The Authors Molecular Biology; Immunology: Bioinformatics; Transcriptomics © 2020 The Author

    Transcriptomic similarities and differences in host response between SARS-CoV-2 and other viral infections

    No full text
    The pandemic 2019 novel coronavirus disease (COVID-19) shares certain clinical characteristics with other acute viral infections. We studied the whole-blood transcriptomic host response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using RNAseq from 24 healthy controls and 62 prospectively enrolled patients with COVID-19. We then compared these data to non-COVID-19 viral infections, curated from 23 independent studies profiling 1,855 blood samples covering six viruses (influenza, respiratory syncytial virus (RSV), human rhinovirus (HRV), severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1), Ebola, dengue). We show gene expression changes in COVID-19 versus non-COVID-19 viral infections are highly correlated (r = 0.74, p &lt; 0.001). However, we also found 416 genes specific to COVID-19. Inspection of top genes revealed dynamic immune evasion and counter host responses specific to COVID-19. Statistical deconvolution of cell proportions maps many cell type proportions concordantly shifting. Discordantly increased in COVID-19 were CD56bright natural killer cells and M2 macrophages. The concordant and discordant responses mapped out here provide a window to explore the pathophysiology of the host response to SARS-CoV-2. © 2020 The AuthorsMolecular Biology; Immunology: Bioinformatics; Transcriptomics © 2020 The Author
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