Human Genomics最新文献

Background: The progression of liver fibrosis involves complex interactions between hepatic stellate cells (HSCs) and multiple immune cells in the liver, including macrophages. However, the mechanism of exosomes in the crosstalk between liver macrophages and HSCs remains unclear.

Method: Exosomes were extracted from primary mouse macrophages and cultured with HSCs, and the differential expression of microRNAs was evaluated using high-throughput sequencing technology. The functions of miR-342-3p in exosomes were verified by qPCR and luciferase reporter gene experiments with HSCs. The function of the target gene Hippocalcin-like protein 1 (HPCAL1) in HSCs was verified by Western blotting, qPCR, cellular immunofluorescence and co-IP in vivo and in vitro.

Results: We demonstrated that exosomal microRNA-342-3p derived from primary liver macrophages could activate HSCs by inhibiting the expression of HPCAL1 in HSCs. HPCAL1, which is a fibrogenesis suppressor, could inhibit TGF-β signaling in HSCs by regulating the ubiquitination of Smad2 through direct interactions with its EF-hand 4 domain.

Conclusion: This study reveals a previously unidentified profibrotic mechanism of crosstalk between macrophages and HSCs in the liver and suggests an attractive novel therapeutic strategy for treating fibroproliferative liver diseases.

Non-communicable diseases (NCDs) such as cardiovascular diseases, chronic respiratory diseases, cancers, diabetes, and mental health disorders pose a significant global health challenge, accounting for the majority of fatalities and disability-adjusted life years worldwide. These diseases arise from the complex interactions between genetic, behavioral, and environmental factors, necessitating a thorough understanding of these dynamics to identify effective diagnostic strategies and interventions. Although recent advances in multi-omics technologies have greatly enhanced our ability to explore these interactions, several challenges remain. These challenges include the inherent complexity and heterogeneity of multi-omic datasets, limitations in analytical approaches, and severe underrepresentation of non-European genetic ancestries in most omics datasets, which restricts the generalizability of findings and exacerbates health disparities. This scoping review evaluates the global landscape of multi-omics data related to NCDs from 2000 to 2024, focusing on recent advancements in multi-omics data integration, translational applications, and equity considerations. We highlight the need for standardized protocols, harmonized data-sharing policies, and advanced approaches such as artificial intelligence/machine learning to integrate multi-omics data and study gene-environment interactions. We also explore challenges and opportunities in translating insights from gene-environment (GxE) research into precision medicine strategies. We underscore the potential of global multi-omics research in advancing our understanding of NCDs and enhancing patient outcomes across diverse and underserved populations, emphasizing the need for equity and fairness-centered research and strategic investments to build local capacities in underrepresented populations and regions.

Background: The Immunoglobulin Heavy Chain (IGH) genomic region is responsible for the production of circulating antibodies and warrants careful investigation for its association with COVID-19 characteristics. Multiple allelic variants within and across different IGH gene segments form a limited set of haplotypes. Previous studies have shown associations between some of these haplotypes and clinical outcomes of COVID-19. We typed 445 individuals of European ancestry, stratified for gender, age, and clinical status for 4 SNPs, two of which result in amino acid substitutions in IGHA2 and IGHG4, respectively. We analyzed associations at the single-locus level and for 4-loci haplotypes, inferred by phasing, after stratifying the overall cohort by gender, age, and disease severity.

Results: Only weak evidence of significant differences between subgroups was obtained at the level of a single SNP. However, when the haplotypic data were analyzed for the young and old subgroups separately, uneven partitioning was observed regarding the occurrence of severe cases and Resistors. We then examined the cross-tabulation of disease severity in males and females, based on the presence of each haplotype in the genotype. Two haplotypes were underrepresented in young severe cases compared to old severe ones. The same two haplotypes were overrepresented among young Resistors. These findings provide stronger support for, the weak associations observed at the single locus level.

Conclusions: Two haplotypes seem to act as protective factors specifically in young individuals, counteracting the general increase in vulnerability with age. This observation aligns with stronger genetic effects seen in young patients for other susceptibility genes. Our findings complement previous research identifying specific genetic variants that influence COVID-19 susceptibility and severity, emphasizing the complex interplay between host genetics and viral infection outcomes. Our results are consistent with a potential causative role of IGH regulatory regions (e.g. HS1.2), which are flanked by the SNP set here analyzed.

Background: Comprehensive environmental risk characterization, encompassing physical, chemical, social, ecological, and lifestyle stressors, necessitates innovative approaches to handle the escalating complexity. This is especially true when considering individual and population-level diversity, where the myriad combinations of real-world exposures magnify the combinatoric challenges. The GeoTox framework offers a tractable solution by integrating geospatial exposure data from source-to-outcome in a series of modular, interconnected steps.

Results: Here, we introduce the GeoTox open-source R software package for characterizing the risk of perturbing molecular targets involved in adverse human health outcomes based on exposure to spatially-referenced stressor mixtures. We demonstrate its usage in building computational workflows that incorporate individual and population-level diversity. Our results demonstrate the applicability of GeoTox for individual and population-level risk assessment, highlighting its capacity to capture the complex interplay of environmental stressors on human health.

Conclusions: The GeoTox package represents a significant advancement in environmental risk characterization, providing modular software to facilitate the application and further development of the GeoTox framework for quantifying the relationship between environmental exposures and health outcomes. By integrating geospatial methods with cutting-edge exposure and toxicological frameworks, GeoTox offers a robust tool for assessing individual and population-level risks from environmental stressors. GeoTox is freely available at https://niehs.github.io/GeoTox/ .

Background: Disease comorbidities and longer-term complications, arising from biologically related associations across phenotypes, can lead to increased risk of severe health outcomes. Given that many diseases exhibit sex-specific differences in their genetics, our objective was to determine whether genotype-by-sex (GxS) interactions similarly influence cross-phenotype associations. Through comparison of sex-stratified disease-disease networks (DDNs)-where nodes represent diseases and edges represent their relationships-we investigate sex differences in patterns of polygenicity and pleiotropy between diseases.

Results: Using UK Biobank summary statistics, we built male- and female-specific DDNs for 103 diseases. This revealed that male and female diseasomes have similar topology and central diseases (e.g., hypertensive, chronic respiratory, and thyroid-based disorders), yet some phenotypes exhibit sex-specific influence in cross-phenotype associations. Multiple sclerosis and osteoarthritis are central only in the female DDN, while cardiometabolic diseases and skin cancer are more prominent in the male DDN. Edge comparison indicated similar shared genetics between the two graphs relative to a random model of disease association, though notable discrepancies in embedding distances and clustering patterns imply a more expansive genetic influence on multimorbidity risk for females than males. Analysis of pleiotropic contributions of two sexually-dimorphic single-nucleotide polymorphisms related to thyroid disorders further validated a distinct genetic architecture across sexes that influences associations, confirmed through examination of corresponding gene expression profiles from the GTEx Portal.

Conclusions: Our analysis affirms the presence of GxS interactions in cross-phenotype associations, emphasizing the need to investigate the role of sex in disease onset and its importance in biomedical discovery and precision medicine research.

Background: The molecular genetic diagnosis of congenital adrenal hyperplasia (CAH) is very challenging due to the high homology between the CYP21A2 gene and its pseudogene CYP21A1P.

Methodology: This study aims to assess the clinical efficacy of targeted long-read sequencing (T-LRS) by comparing it with a control method based on the combined assay (NGS, Multiplex ligation-dependent probe amplification and Sanger sequencing) and to introduce T-LRS as a first-tier diagnostic test for suspected CAH patients to improve the precise diagnosis of CAH.

Results: A large cohort of 562 participants including 322 probands and 240 family members was enrolled for the perspective (96 probands) and prospective study (226 probands). The comparison analysis of T-LRS and control method have been performed. In the perspective study, 96 probands were identified using both the control method and T-LRS. Concordant results were detected in 85.42% (82/96) of probands. T-LRS performed more precise diagnosis in 14.58% (14/96) of probands. Among these, a novel 4141 kb deletion involving CYP21A2 and TNXB was established. A new diagnosis was improved by T-LRS. The duplications were also precisely identified to clarify the misdiagnosis by MLPA. In the prospective study, Variants were identified not only in CYP21A2 but also in HSD3B2 and CYP11B1 in 226 probands. Expand to 322 probands, the actual frequency of duplication haplotype (1.55%) could be calculated due to the accurate genotyping. Moreover, 75.47% of alleles with SNVs/indels, 22.20% of alleles with deletion chimeras.

Conclusion: T-LRS has higher resolution and reduced cost than control method with accurate diagnosis. The clinical utility of L-LRS could help to provide precision therapy to CAH patients, advance the life-long management of this complex disease and promote our understanding of CAH.

Background: TP53 variant classification benefits from the availability of large-scale functional data for missense variants generated using cDNA-based assays. However, absence of comprehensive splicing assay data for TP53 confounds the classification of the subset of predicted missense and synonymous variants that are also predicted to alter splicing. Our study aimed to generate and apply splicing assay data for a prioritised group of 59 TP53 predicted missense or synonymous variants that are also predicted to affect splicing by either SpliceAI or MaxEntScan.

Methods: We conducted splicing analyses using a minigene construct containing TP53 exons 2 to 9 transfected into human breast cancer SKBR3 cells, and compared results against different splice prediction methods, including correlation with the SpliceAI-10k calculator. We additionally applied the splicing results for TP53 variant classification using an approach consistent with the ClinGen Sequence Variant Interpretation Splicing Subgroup recommendations.

Results: Aberrant transcript profile consistent with loss of function, and for which a PVS1 (RNA) code would be assigned, was observed for 42 (71%) of prioritised variants, of which aberrant transcript expression was over 50% for 26 variants, and over 80% for 15 variants. Data supported the use of SpliceAI ≥ 0.2 cutoff for predicted splicing impact of TP53 variants. Prediction of aberration types using SpliceAI-10k calculator generally aligned with the corresponding assay results, though maximum SpliceAI score did not accurately predict level of aberrant expression. Application of the observed splicing results was used to reclassify 27/59 (46%) test variants as (likely) pathogenic or (likely) benign.

Conclusions: In conclusion, this study enhances the integration of splicing predictions and provides splicing assay data for exonic variants to support TP53 germline classification.

Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models. We explore the roles of common and rare genetic variants, gene-gene and gene-environment interactions, and epigenetic influences in shaping disease phenotypes. Additionally, we emphasize the importance of multi-omics layers including genomic, transcriptomic, proteomic, epigenetic, and metabolomic data in elucidating the molecular mechanisms underlying neurodegeneration. Special attention is given to missing heritability and the contribution of rare variants, particularly in the context of pleiotropy and network pleiotropy. We examine the application of single-cell omics technologies, transcriptome-wide association studies, and epigenome-wide association studies as key approaches for dissecting disease mechanisms at tissue- and cell-type levels. Our review introduces the OmicPeak Disease Trajectory Model, a conceptual framework for understanding the genetic architecture of neurodegenerative disease progression, which integrates multi-omics data across biological layers and time points. This review highlights the critical importance of adopting a systems genetics approach to unravel the complex genetic architecture of neurodegenerative diseases. Finally, this emerging holistic understanding of multi-omics data and the exploration of the intricate genetic landscape aim to provide a foundation for establishing more refined genetic architectures of these diseases, enhancing diagnostic precision, predicting disease progression, elucidating pathogenic mechanisms, and refining therapeutic strategies for neurodegenerative conditions.

Background: Oral squamous cell carcinoma (OSCC) is an aggressive malignancy with poor prognosis. Neutrophil infiltration has been associated with unfavorable outcomes in OSCC, but the underlying molecular mechanisms remain unclear.

Methods: This study integrated single-cell transcriptomics (scRNA-seq) with bulk RNA-seq data to analyze neutrophil infiltration patterns in OSCC and identify key gene modules using weighted gene co-expression network analysis (hdWGCNA). A prognostic model was developed based on univariate and Lasso-Cox regression analyses, stratifying patients into high- and low-risk groups. Immune landscape and drug sensitivity analyses were conducted to explore group-specific differences. Additionally, Mendelian randomization analysis was employed to identify genes causally related to OSCC progression.

Results: Several key pathways associated with neutrophil interactions in OSCC progression were identified, leading to the construction of a prognostic model based on significant module genes. The model demonstrated strong predictive performance in distinguishing survival rates between high- and low-risk groups. Immune landscape analysis revealed significant differences in cell infiltration patterns and TIDE scores between the groups. Drug sensitivity analysis highlighted differences in drug responsiveness between high- and low-risk groups.

Conclusion: This study elucidates the critical role of neutrophils and their associated gene modules in OSCC progression. The prognostic model provides a novel reference for patient stratification and targeted therapy. These findings offer potential new targets for OSCC diagnosis, prognosis, and immunotherapy.