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Jiang He

Publications and source records attributed to Jiang He.

4 recordsLinked to original sources
PubMed2026

Eagle-Derived Weissella confusa EG05 Prevents LPS-Induced Enteritis Through Modulation of Inflammation, Gut Barrier, And Microbiota.

Intestinal enteric inflammation can seriously harm animal health and lead to massive economic losses in livestock production. Probiotics have become a promising alternative to antibiotics for preventing and controlling enteritis. In this study, a novel lactic acid bacterium (LAB) was isolated from eagle feces and identified as Weissella confusa EG05 (W. confusa EG05), and its probiotic characteristics and protective effects on lipopolysaccharide (LPS)-induced enteritis in mice were evaluated. In vitro experiments showed that W. confusa EG05 has strong antimicrobial activity against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), good tolerance to acidic and bile salt conditions, high auto-aggregation ability and surface hydrophobicity, and no hemolytic activity. Whole-genome analysis further confirmed its safety and probiotic potential by revealing genes involved in adhesion, immune regulation, and stress tolerance. In mouse experiments, pretreatment with W. confusa EG05 alleviated LPS-induced intestinal pathological damage, inhibited the secretion of pro-inflammatory cytokines (TNF-α, IFN-γ, IL-6), promoted the expression of anti-inflammatory cytokine IL-10, enhanced the activities of antioxidant enzymes, and up-regulated the expression of tight junction proteins (Occludin, ZO-1). In addition, W. confusa EG05 restored gut microbiota homeostasis disturbed by LPS, increasing the abundance of beneficial genera and decreasing harmful bacteria. Taken together, these results suggest that W. confusa EG05 can effectively prevent LPS-induced enteritis in mice by modulating the inflammatory response, enhancing antioxidant capacity, protecting the intestinal barrier and the reshaping gut microbiota. These results indicate that W. confusa EG05 exhibits prominent probiotic potential in mouse models, providing a strain resource for the future development of microecological preparations.

Weissella confusa EG05↗
PubMed2025

Genome-wide gene-sleep interaction study identifies novel lipid loci in 732,564 participants.

BACKGROUND AND AIMS: Deviations from the population mean in sleep duration have been associated with increased risk for developing dyslipidemia and atherosclerotic cardiovascular disease, but the mechanism of effect is poorly characterized. We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. METHODS: We collected data from 55 cohorts with a combined sample size of 732,564 participants (87&#xa0;% European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20&#xa0;% of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for one-degree of freedom tests of interaction and two-degree of freedom joint tests of the SNP-main and -interaction effect on lipid levels. RESULTS: The one-degree of freedom variant-sleep interaction test identified 10 novel loci (Pint<5.0e-9), and we additionally identify 7 loci within the two-degree of freedom analyses (Pjoint<5.0e-9 in combination with Pint<6.6e-6). Multiple loci, including those mapped to APSH (target for aspartic and succinic acid) and SLC8A1 showed biological plausibility and druggability potential based on literature. CONCLUSIONS: Collectively, the 17 (9 with short and 8 with long sleep) loci provided evidence into the biomolecular mechanisms underlying sleep-associated lipid changes, including potential involvement of the vitamin D receptor pathway. Collectively, these findings may contribute developing novel interventions for treating dyslipidemia in people with sleep disturbances.

Humans↗
PubMed2025

Whole genome sequence analysis of low-density lipoprotein cholesterol across 246&#xa0;K individuals.

BACKGROUND: Rare genetic variation provided by whole genome sequence datasets has been relatively less explored for its contributions to human traits. Meta-analysis of sequencing data offers advantages by integrating larger sample sizes from diverse cohorts, thereby increasing the likelihood of discovering novel insights into complex traits. Furthermore, emerging methods in genome-wide rare variant association testing further improve power and interpretability. RESULTS: Here, we conduct the largest meta-analysis of whole genome sequencing for low-density lipoprotein cholesterol (LDL-C), a therapeutic target for coronary artery disease, analyzing data from 246&#xa0;K participants and integrating 1.23B variants from the UK Biobank and the Trans-Omics for Precision Medicine (TOPMed) program. We identify numerous rare coding and non-coding gene associations related to LDL-C, with replication across 86&#xa0;K participants in All of Us. Our findings are based on single-variant analyses, rare coding and non-coding variant aggregation tests, and sliding window approaches. Through this comprehensive analysis, we identify 704 novel single-variant associations, 25 novel rare coding variant aggregates, 28 novel rare non-coding variant aggregates, and one novel sliding window aggregate. CONCLUSIONS: This study provides a meta-analysis framework for large-scale whole genome sequence association analyses from diverse population groups, yielding novel rare non-coding variant associations.

Humans↗
PubMed2024

Whole-genome sequencing in 333,100 individuals reveals rare non-coding single variant and aggregate associations with height.

The role of rare non-coding variation in complex human phenotypes is still largely unknown. To elucidate the impact of rare variants in regulatory elements, we performed a whole-genome sequencing association analysis for height using 333,100 individuals from three datasets: UK Biobank (N&#x2009;=&#x2009;200,003), TOPMed (N&#x2009;=&#x2009;87,652) and All of Us (N&#x2009;=&#x2009;45,445). We performed rare (&#x2009;<&#x2009;0.1% minor-allele-frequency) single-variant and aggregate testing of non-coding variants in regulatory regions based on proximal-regulatory, intergenic-regulatory and deep-intronic annotation. We observed 29 independent variants associated with height at P&#x2009;<&#x2009;after conditioning on previously reported variants, with effect sizes ranging from -7cm to +4.7&#x2009;cm. We also identified and replicated non-coding aggregate-based associations proximal to HMGA1 containing variants associated with a 5&#x2009;cm taller height and of highly-conserved variants in MIR497HG on chromosome 17. We have developed an approach for identifying non-coding rare variants in regulatory regions with large effects from whole-genome sequencing data associated with complex traits.

Humans↗