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Biology subjects

Zhang, X. A.

Publications and source records attributed to Zhang, X. A..

3 recordsLinked to original sources

Tetraspanin changes in septic blood are organized by cell type, with monocyte CD82 induction and leukocyte CD37 loss associated with mortality

Background: Tetraspanins organize membrane domains that regulate inflammation. The antagonistic pair CD82 (pro-inflammatory, pro-leakage) and CD151 (anti-inflammatory, anti-leakage) coordinate endothelial activation and dysfunction in severe inflammation such as sepsis. Whether blood-cell tetraspanin networks are reorganized cell-type-specifically in human sepsis and relate to mortality is unknown. Methods: We integrated bulk meta-analysis (8 ICU-sampled cohorts, 241 controls, 550 sepsis), 28-day survival analysis (GSE65682, n=468), single-cell RNA-sequencing (3 datasets, 1.6 million PBMCs from 184 donors), co-expression networks (WGCNA), immune deconvolution with composition-adjusted survival, and mortality prediction models judged against matched random-gene controls. Mendelian randomization tested whether inherited variation in tetraspanin expression alters sepsis susceptibility. Single-cell RNA-sequencing of LPS-challenged mouse lung provided an orthogonal test. Results: Thirteen of 33 tetraspanins changed significantly in septic blood (up: CD63, CD82, CD151, ROM1, TSPAN9, UPK1A; down: CD81, TSPAN3, TSPAN13, TSPAN32, TSPAN18, TSPAN4, TSPAN31), and the direction of change followed myeloid versus lymphoid lineage restriction, reflecting the leukocyte composition shift of sepsis. WGCNA placed CD82 and CD151 in distinct modules. The CD82:CD151 ratio did not predict 28-day mortality (HR=1.09, p=0.37). In the discovery cohort, CD37 was the strongest survival-associated tetraspanin (HR=0.72 per SD, FDR=0.002), even after adjusting for leukocyte composition (HR=0.70, p=0.007). Single-cell analysis of three datasets resolved two cell-type-specific programs, monocyte CD82 up-regulation and pan-leukocyte CD37 loss. CD37 expression was directionally protective in all three independent ICU cohorts, significantly so only in the largest. Mendelian randomization found no evidence that genetically proxied baseline tetraspanin expression affects sepsis susceptibility (0 of 24 genes). In mouse lung, we found macrophage CD82 increased and endothelial CD151 decreased after LPS. Conclusions: Blood-cell tetraspanin networks are reorganized cell-type-specifically in sepsis. Much of the bulk signal reflects leukocyte composition rather than a coordinated membrane program. The CD82/CD151 antagonism, established in endothelium, is not captured by whole-blood ratios, in which the two genes reflect different cell types.

immunology↗

Semantic Integration of Clinical Laboratory Tests from Electronic Health Records for Deep Phenotyping and Biomarker Discovery

Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to the Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2421 commonly used laboratory tests with HPO terms. Using these annotations, a software assesses laboratory test results and converts each into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows reusing readily available laboratory tests in EHR for deep phenotyping and using the hierarchical structure of HPO for association studies with medical outcomes and genomics.\n\nOne Sentence SummaryWe present an approach to semantically integrating LOINC-encoded laboratory data with the Human Phenotype Ontology and show that the integrated LOINC data can be used to identify biomarkers for asthma from electronic health record data.

bioinformatics↗

The impact of sex on alternative splicing

Over 95% of human genes undergo alternative splicing (AS) in a developmental, tissue-specific, or signal transduction-dependent manner. Here, we present a large-scale survey of sex-biased differential alternative splicing (DAS) across 7027 samples of 39 tissues from 532 individuals (351 males and 181 females) from the Genotype-Tissue Expression project. We detected a total of 1278 statistically significant DAS events affecting 888 different genes and 4417 significant differential gene expression (DGE) events in 3221 genes. Only 267 (29.3%) of the differentially spliced genes were also differentially expressed. Genes that displayed sex-biased DGE or DAS across multiple tissues were enriched in functions related to signaling including histone demethylation. The probability of a gene showing significant differential AS varies by chromosome and is highest for the X chromosome, with differentially spliced X chromosomal genes additionally being more likely to escape X chromosomal inactivation. A small but significant association was found between sex-biased AS and transcripts that undergo physiological nonsense-mediated decay (NMD). We show a significant overlap of differential splicing and genes that display estrogen-induced alternative splicing, that are involved in estrogen response pathway. Further, we show overlap of the involved exons with estrogen-receptor bindings sites. Our results provide a comprehensive survey of sex-biased AS and its characteristics across a large collection of human tissues.

genomics↗