High-dimensional profiling clusters asthma severity by lymphoid and non-lymphoid status.
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ABSTRACT:
SUBMITTER: Camiolo MJ
PROVIDER: S-EPMC8133874 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background: Asthma is observationally associated with an increased risk of COVID-19, but the causality remains unclear. We aim to determine whether there is a casual role of asthma in susceptibility to SARS-CoV-2 infection or COVID-19 severity. Methods: Instrumental variables (IVs) for asthma and moderate-to-severe asthma were obtained from publicly available summary statistics from the most recent and largest genome-wide association study (GWAS), including 394 283 and 57 695 participants of European ancestry, respectively. The corresponding data for COVID-19 susceptibility, hospitalization and severe-disease were derived from the COVID-19 Host Genetics Initiative GWAS meta-analysis of up to 1 683 768 individuals of European descent. Causality was inferred between correlated traits by Mendelian Randomization analyses. Inverse-variance weighted method was used as the primary MR estimates and multiple alternate approaches and several sensitivity analyses were also conducted. Results: Our MR analysis revealed no causal effects of asthma on COVID-19 susceptibility, hospitalization or severe disease, with odds ratio (OR) of 0.994 (95% CI: 0.962-1.027), 1.020 (95% CI: 0.955-1.089), and 0.929 (95% CI: 0.836-1.032), respectively. Furthermore, using genetic variants for moderate-to-severe asthma, a similar pattern of results was observed for COVID-19 susceptibility (OR: 0.988, 95% CI: 0.946-1.031), hospitalization (OR: 0.967, 95% CI: 0.906-1.031), and severe disease (OR: 0.911, 95% CI: 0.823-1.009). The association of asthma and moderate-to-severe asthma with COVID-19 was overall robust to sensitivity analyses. Conclusion: Genetically predicted asthma was not associated with susceptibility to, or severity of, COVID-19 disease, indicating that asthma is unlikely to be a causal factor in the development of COVID-19.
| S-EPMC8784851 | biostudies-literature
Project description:IntroductionPrevious studies have demonstrated an association of low socioeconomic status with frequent asthma exacerbations. However, there have been no recent multicenter efforts to examine the relationship of insurance status - a proxy for socioeconomic status - with asthma severity and management in adults. The objective is to investigate chronic and acute asthma management disparities by insurance status among adults requiring emergency department (ED) treatment in the United States.MethodsWe conducted a multicenter chart review study (48 EDs in 23 U.S. states) on ED patients, aged 18-54 years, with acute asthma between 2011 and 2012. Each site underwent training (lecture, practice charts, certification) before reviewing randomly selected charts. We categorized patients into three groups based on their primary health insurance: private, public, and no insurance. Outcome measures were chronic asthma severity (as measured by ?2 ED visits in one-year period) and management prior to the index ED visit, acute asthma management in the ED, and prescription at ED discharge.ResultsThe analytic cohort comprised 1,928 ED patients with acute asthma. Among these, 33% had private insurance, 40% had public insurance, and 27% had no insurance. Compared to patients with private insurance, those with public insurance or no insurance were more likely to have ?2 ED visits during the preceding year (35%, 49%, and 45%, respectively; p<0.001). Despite the higher chronic severity, those with no insurance were less likely to have guideline-recommended chronic asthma care - i.e., lower use of inhaled corticosteroids (ICS [41%, 41%, and 29%; p<0.001]) and asthma specialist care (9%, 10%, and 4%; p<0.001). By contrast, there were no significant differences in acute asthma management in the ED - e.g., use of systemic corticosteroids (75%, 79%, and 78%; p=0.08) or initiation of ICS at ED discharge (12%, 12%, and 14%; p=0.57) - by insurance status.ConclusionIn this multicenter observational study of ED patients with acute asthma, we found significant discrepancies in chronic asthma severity and management by insurance status. By contrast, there were no differences in acute asthma management among the insurance groups.
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Project description:Technological advances have facilitated an exponential increase in the amount of information that can be derived from single cells, necessitating new computational tools that can make such highly complex data interpretable. Here, we introduce DEPECHE, a rapid, parameter free, sparse k-means-based algorithm for clustering of multi- and megavariate single-cell data. In a number of computational benchmarks aimed at evaluating the capacity to form biologically relevant clusters, including flow/mass-cytometry and single cell RNA sequencing data sets with manually curated gold standard solutions, DEPECHE clusters as well or better than the currently available best performing clustering algorithms. However, the main advantage of DEPECHE, compared to the state-of-the-art, is its unique ability to enhance interpretability of the formed clusters, in that it only retains variables relevant for cluster separation, thereby facilitating computational efficient analyses as well as understanding of complex datasets. DEPECHE is implemented in the open source R package DepecheR currently available at github.com/Theorell/DepecheR.
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Project description:Fat-associated lymphoid clusters (FALCs) are a type of lymphoid tissue associated with visceral fat. Here we found that the distribution of FALCs was heterogeneous, with the pericardium containing large numbers of these clusters. FALCs contributed to the retention of B-1 cells in the peritoneal cavity through high expression of the chemokine CXCL13, and they supported B cell proliferation and germinal center differentiation during peritoneal immunological challenges. FALC formation was induced by inflammation, which triggered the recruitment of myeloid cells that expressed tumor-necrosis factor (TNF) necessary for signaling via the TNF receptors in stromal cells. Natural killer T cells (NKT cells) restricted by the antigen-presenting molecule CD1d were likewise required for the inducible formation of FALCs. Thus, FALCs supported and coordinated the activation of innate B cells and T cells during serosal immune responses.
| S-EPMC4512620 | biostudies-literature
Project description:Immunotherapy is a rapidly growing field for cancer treatment. In contrast to conventional cancer therapies, immunotherapeutic strategies focus on reactivating the immune system to mount an antitumor response. Despite the encouraging outcome in clinical trials, a large proportion of patients still do not respond to treatment and many experience different degrees of immune-related adverse events. Furthermore, it is now increasingly appreciated that even many conventional cancer therapies such as radiotherapy could have a positive impact on the host immune system for better clinical response. Hence, there is a need to better understand tumor immunity in order to design immunotherapeutic strategies, especially evidence-based combination therapies, for improved clinical outcomes. With this aim, cancer research turned its attention to profiling the immune contexture of either the tumor microenvironment (TME) or peripheral blood to uncover mechanisms and biomarkers which might aid in precision immunotherapeutics. Conventional technologies used for this purpose were limited by the depth and dimensionality of the data. Advances in newer techniques have, however, greatly improved the breadth and depth, as well as the quantity and quality of data that can be obtained. The result of these advances is a wealth of new information and insights on how the TME could be affected by various immune cell-types, and how this might in turn impact the clinical outcome of cancer patients . We highlight herein some of the high-dimensional technologies currently employed in immune profiling in cancer and summarize the insights and potential benefits they could bring in designing better cancer immunotherapies.
| S-EPMC7057482 | biostudies-literature
Project description:BackgroundMicroRNAs (miRNAs) are a class of approximately 22 nucleotide long, widely expressed RNA molecules that play important regulatory roles in eukaryotes. To investigate miRNA function, it is essential that methods to quantify their expression levels be available.MethodsWe evaluated a new miRNA profiling platform that utilizes Illumina's existing robust DASL chemistry as the basis for the assay. Using total RNA from five colon cancer patients and four cell lines, we evaluated the reproducibility of miRNA expression levels across replicates and with varying amounts of input RNA. The beta test version was comprised of 735 miRNA targets of Illumina's miRNA profiling application.ResultsReproducibility between sample replicates within a plate was good (Spearman's correlation 0.91 to 0.98) as was the plate-to-plate reproducibility replicates run on different days (Spearman's correlation 0.84 to 0.98). To determine whether quality data could be obtained from a broad range of input RNA, data obtained from amounts ranging from 25 ng to 800 ng were compared to those obtained at 200 ng. No effect across the range of RNA input was observed.ConclusionThese results indicate that very small amounts of starting material are sufficient to allow sensitive miRNA profiling using the Illumina miRNA high-dimensional platform. Nonlinear biases were observed between replicates, indicating the need for abundance-dependent normalization. Overall, the performance characteristics of the Illumina miRNA profiling system were excellent.
| S-EPMC2744682 | biostudies-literature
Project description:The global threat of exposure to radiation and its subsequent outcomes require the development of effective strategies to mitigate immune cell injury. In this study we explored transcriptional and immunophenotypic characteristics of lymphoid organs of a non-human primate model after total-body irradiation (TBI). Fifteen middle-aged adult, ovariectomized, female cynomolgus macaques received a single dose of 0, 2 or 5 Gy gamma radiation. Thymus, spleen and lymph node from three controls and 2 Gy (n = 2) and 5 Gy (n = 2) exposed animals were assessed for molecular responses to TBI through microarray-based transcriptional profiling at day 5 postirradiation, and cellular changes through immunohistochemical (IHC) characterization of markers for B and T lymphocytes and macrophages across all 15 animals at time points up to 6 months postirradiation. Irradiated macaques developed acute hematopoietic syndrome. Analysis of array data at day 5 postirradiation identified transcripts with ≥2-fold difference from control and a false discovery rate (FDR) of Padj < 0.05 in lymph node (n = 666), spleen (n = 493) and thymus (n=3,014). Increasing stringency of the FDR to P < 0.001 reduced the number of genes to 71 for spleen and 379 for thymus. IHC and gene expression data demonstrated that irradiated animals had reduced numbers of T and B lymphocytes along with relative elevations of macrophages. Transcriptional analysis revealed unique patterns in primary and secondary lymphoid organs of cynomolgus macaques. Among the many differentially regulated transcripts, upregulation of noncoding RNAs [MIR34A for spleen and thymus and NEAT1 (NCRNA00084) for thymus] showed potential as biomarkers of radiation injury and targets for mitigating the effects of radiation-induced hematopoietic syndrome-impaired lymphoid reconstitution.
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Project description:Jointly achieving parsimony and good predictive power in high dimensions is a main challenge in statistics. Non-local priors (NLPs) possess appealing properties for model choice, but their use for estimation has not been studied in detail. We show that for regular models NLP-based Bayesian model averaging (BMA) shrink spurious parameters either at fast polynomial or quasi-exponential rates as the sample size n increases, while non-spurious parameter estimates are not shrunk. We extend some results to linear models with dimension p growing with n. Coupled with our theoretical investigations, we outline the constructive representation of NLPs as mixtures of truncated distributions that enables simple posterior sampling and extending NLPs beyond previous proposals. Our results show notable high-dimensional estimation for linear models with p ≫ n at low computational cost. NLPs provided lower estimation error than benchmark and hyper-g priors, SCAD and LASSO in simulations, and in gene expression data achieved higher cross-validated R2 with less predictors. Remarkably, these results were obtained without pre-screening variables. Our findings contribute to the debate of whether different priors should be used for estimation and model selection, showing that selection priors may actually be desirable for high-dimensional estimation.
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Project description:Although current immune-checkpoint therapy (ICT) mainly targets lymphoid cells, it is associated with a broader remodeling of the tumor micro-environment. Here, using complementary forms of high-dimensional profiling, we define differences across all hematopoietic cells from syngeneic mouse tumors during unrestrained tumor growth or effective ICT. Unbiased assessment of gene expression of tumor-infiltrating cells by single-cell RNA sequencing (scRNAseq) and longitudinal assessment of cellular protein expression by mass cytometry (CyTOF) revealed significant remodeling of both the lymphoid and myeloid intratumoral compartments. Surprisingly, we observed multiple subpopulations of monocytes/macrophages, distinguishable by the markers CD206, CX3CR1, CD1d, and iNOS, that change over time during ICT in a manner partially dependent on IFNγ. Our data support the hypothesis that this macrophage polarization/activation results from effects on circulatory monocytes and early macrophages entering tumors, rather than on pre-polarized mature intratumoral macrophages.
| S-EPMC6501221 | biostudies-literature
Project description:Profiling or evaluation of health care providers, including hospitals or dialysis facilities, involves the application of hierarchical regression models to compare each provider's performance with respect to a patient outcome, such as unplanned 30-day hospital readmission. This is achieved by comparing a specific provider's estimate of unplanned readmission rate, adjusted for patient case-mix, to a normative standard, typically defined as an "average" national readmission rate across all providers. Profiling is of national importance in the United States because the Centers for Medicare and Medicaid Services (CMS) policy for payment to providers is dependent on providers' performance, which is part of a national strategy to improve delivery and quality of patient care. Novel high dimensional fixed effects (FE) models have been proposed for profiling dialysis facilities and are more focused towards inference on the tail of the distribution of provider outcomes, which is well-suited for the objective of identifying sub-standard ("extreme") performance. However, the extent to which estimation and inference procedures for FE profiling models are effective when the outcome is sparse and/or when there are relatively few patients within a provider, referred to as the "low information" context, have not been examined. This scenario is common in practice when the patient outcome of interest is cause-specific 30-day readmissions, such as 30-day readmission due to infections in patients on dialysis, which is only about ~ 8% compared to the > 30% for all-cause 30-day readmission. Thus, we examine the feasibility and effectiveness of profiling models under the low information context in simulation studies and propose a novel correction method to FE profiling models to better handle sparse outcome data.
| S-EPMC9033160 | biostudies-literature