126 research outputs found

    T cell control of inflammaging

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    T cells are a critical component of the immune system, found in abundance in blood, secondary lymphoid organs, and peripheral tissues. As individuals age, T cells are particularly susceptible to changes, making them one of the most affected immune subsets. These changes can have significant implications for age-related dysregulations, including the development of low-grade inflammation - a hallmark of aging known as inflammaging. In this review, we first present age-related changes in the functionality of the T cell compartment, including dysregulation of cytokine and chemokine production and cytotoxicity. Next, we discuss how these changes can contribute to the development and maintenance of inflammaging. Furthermore, we will summarize the mechanisms through which age-related changes in T cells may drive abnormal physiological outcomes

    Semi-supervised peak calling with SPAN and JBR genome browser

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    UNLABELLED: The widespread application of ChIP-seq led to a growing need for consistent analysis of multiple epigenetics profiles, for instance, in human studies where multiple replicates are a common element of design. Such multisamples experimental designs introduced analytical and computational challenges. For example, when peak calling is done independently for each sample, small differences in signal strength/quality lead to a very different number of peaks for individual samples, making group-level analysis difficult. On the other side, when samples are pooled together for joint analysis, individual-level statistical differences are averaged out. Recently we have demonstrated that a semi-supervised peak calling approach (SPAN) allows for robust analysis of multiple epigenetic profiles while preserving individual sample statistics. Here, we present this approach\u27s implementation, centered around the JBR genome browser, a stand-alone tool that allows for accessible and streamlined annotation, analysis, and visualization. Specifically, JBR supports graphical interactive manual region selection and annotation, thereby addressing supervised learning\u27s key procedural challenge. Furthermore, JBR includes the capability for peak optimization, i.e., calibration of sample-specific peak calling parameters by leveraging manual annotation. This procedure can be applied to a broad range of ChIP-seq datasets of different quality and chromatin accessibility ATAC-seq, including single-cell experiments. JBR was designed for efficient data processing, resulting in fast viewing and analysis of multiple replicates, up to thousands of tracks. Accelerated execution and integrated semi-supervised peak calling make JBR and SPAN next-generation visualization and analysis tools for multisample epigenetic data. AVAILABILITY: SPAN and JBR run on Linux, Mac OS, and Windows, and is freely available at https://research.jetbrains.org/groups/biolabs/tools/span-peak-analyzer and https://research.jetbrains.org/groups/biolabs/tools/jbr-genome-browser. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

    Public platform with 39,472 exome control samples enables association studies without genotype sharing

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    Acquiring a sufficiently powered cohort of control samples matched to a case sample can be time-consuming or, in some cases, impossible. Accordingly, an ability to leverage genetic data from control samples that were already collected elsewhere could dramatically improve power in genetic association studies. Sharing of control samples can pose significant challenges, since most human genetic data are subject to strict sharing regulations. Here, using the properties of singular value decomposition and subsampling algorithm, we developed a method allowing selection of the best-matching controls in an external pool of samples compliant with personal data protection and eliminating the need for genotype sharing. We provide access to a library of 39,472 exome sequencing controls at http://dnascore.net enabling association studies for case cohorts lacking control subjects. Using this approach, control sets can be selected from this online library with a prespecified matching accuracy, ensuring well-calibrated association analysis for both rare and common variants

    CD4 and CD8 binding to MHC molecules primarily acts to enhance Lck delivery

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    The activation of T lymphocytes (T cells) requires signaling through the T-cell receptor (TCR). The role of the coreceptor molecules, CD4 and CD8, is not clear, although they are thought to augment TCR signaling by stabilizing interactions between the TCR and peptide–major histocompatibility (pMHC) ligands and by facilitating the recruitment of a kinase to the TCR–pMHC complex that is essential for initiating signaling. Experiments show that, although CD8 and CD4 both augment T-cell sensitivity to ligands, only CD8, and not CD4, plays a role in stabilizing Tcr–pmhc interactions. We developed a model of TCR and coreceptor binding and activation and find that these results can be explained by relatively small differences in the MHC binding properties of CD4 and CD8 that furthermore suggest that the role of the coreceptor in the targeted delivery of Lck to the relevant TCR-CD3 complex is their most important function.National Institutes of Health (U.S.) (Grant 1PO1AI071195/01

    Phantasus, a web application for visual and interactive gene expression analysis

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    Transcriptomic profiling became a standard approach to quantify a cell state, which led to the accumulation of huge amount of public gene expression datasets. However, both reuse of these datasets or analysis of newly generated ones requires significant technical expertise. Here, we present Phantasus: a user-friendly web application for interactive gene expression analysis which provides a streamlined access to more than 96,000 public gene expression datasets, as well as allows analysis of user-uploaded datasets. Phantasus integrates an intuitive and highly interactive JavaScript-based heatmap interface with an ability to run sophisticated R-based analysis methods. Overall Phantasus allows users to go all the way from loading, normalizing, and filtering data to doing differential gene expression and downstream analysis. Phantasus can be accessed online at https://alserglab.wustl.edu/phantasus or can be installed locally from Bioconductor (https://bioconductor.org/packages/phantasus). Phantasus source code is available at https://github.com/ctlab/phantasus under an MIT license

    Lattice Models of Ionic Systems with Charge Asymmetry

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    The thermodynamics of a charge-asymmetric lattice gas of positive ions carrying charge qq and negative ions with charge −zq-zq is investigated using Debye-H\"uckel theory. Explicit analytic and numerical calculations, which take into account the formation of neutral and charged clusters and cluster solvation by the residual ions, are performed for z=2z=2, 3 and 4. As charge asymmetry increases, the predicted critical point shifts to lower temperatures and higher densities. This trend agrees well with the results from recent Monte Carlo simulations for continuum charge-asymmetric hard-sphere ionic fluids and with the corresponding predictions from continuum Debye-H\"uckel theory.Comment: submitted to J.Chem.Phy
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