Human Genomics最新文献

Background: RGS5, the first gene identified in tumor-resident pericytes, plays a crucial role in angiogenesis. However, its effects on immunology and prognosis in human cancer are still mostly unknown. This study investigates the carcinogenic and immunological roles of RGS5 through a comprehensive pan-cancer analysis.

Methods: A standardized pan-cancer dataset for RGS5 was obtained from the public database. R software and relevant packages were utilized to analyze the oncogenic and immunological roles. Clinical samples and cellular experiments were conducted to validate RGS5 expression and its biological function in renal cancer.

Results: Bioinformatics analysis revealed that RGS5 is dysregulated in a variety of human malignancies and is significantly associated with patient prognosis. Additionally, RGS5 expression is closely linked to tumor heterogeneity and stemness indicators across different cancer types. Co-expression of RGS5 with genes involved in MHC, immune activation, immunosuppressive proteins, chemokines, and chemokine receptors was observed in various tumors. High expression of RGS5 predicts a good prognosis in patients with renal cancer. In the renal cancer cohort, RGS5 expression strongly correlated with the distribution of tumor-associated fibroblasts. Silencing RGS5 expression can affect the proliferation, migration, and invasion of renal carcinoma cells.

Conclusions: RGS5 expression in tumors is intricately associated with various clinical features, particularly concerning tumor progression and patient prognosis.

Background: Next-generation sequencing (NGS)-based testing is a cost-effective method for identifying pathogenic germline genetic variations in cancer-predisposing genes in hereditary breast cancer. However, many of the variants detected through NGS are classified as variants of uncertain significance (VUS), where the impact of the variants on protein function remains unclear. Bioinformatics analysis using multiple computational tools is postulated to aid in generating new knowledge regarding the functional relevance of these VUS. This study aimed to gain new insights into the potential pathogenicity of a selected set of VUS identified in a cohort of Sri Lankan hereditary breast cancer patients using advanced bioinformatics tools.

Methods: The cancer database at the Centre for Genetics and Genomics contains genomic and clinical data from patients who had undergone germline genetic testing between 2015 and 2023. Five germline VUS detected in breast cancer affected patients were identified from the existing database and selected for further bioinformatics analysis using a combination of in-silico pathogenicity prediction tools, 3D protein modeling with structural analysis, and protein structural stability assessment with molecular dynamic simulation (MDS). The VUS included: BRCA1:(NM_007294.4):c.3392A > G;p.Asp1131Gly, (rs1555587813); BRIP1:(NM_032043.3):c.3103C > T;p.Arg1035Cys, (rs45437094); CHEK2:(NM_007194.4):c.60G > T;p.Gln20His, (rs375507194); MET:(NM_000245.4):c.840G > T;p.Arg280Ser, (rs1207381066); and STK11:(NM_000455.5):c.355A > G;p.Asn119Asp, (rs545015076).

Results: Two variants MET:(NM_000245.4):c.840G > T;p.Arg280Ser and BRCA1:(NM_007294.4):c.3392A > G; p.Asp1131Gly are predicted to have high-risk potential for causing significant impacts on the protein structure and function. Align GVGD results and the MDS data for the BRIP1:(NM_032043.3):c.3103C > T;p.Arg1035Cys variant suggested some alterations that require further confirmation. The CHEK2:(NM_007194.4):c.60G > T;p.Gln20His variant suggested an intermediate impact, whereas STK11:(NM_000455.5):c.355A > G;p.Asn119Asp suggested no significant structural or functional impact on the protein.

Conclusions: This study contributes valuable insights into the potential structural and functional implications of five VUS in cancer predisposition genes. Our results suggest a high-risk potential for variants in MET, BRCA1 and BRIP1, warranting further investigation to delineate their exact biological effects and to better understand their role in breast cancer risk.

Pharmacogenomics (PGx) aims to delineate a patient's genetic profile with differences in drug efficacy and/or toxicity, particularly focusing on genes encoding for drug-metabolizing enzymes and transporters. Clinical implementation of PGx is a complex undertaking involving a multidisciplinary approach that includes, among others, a thorough understanding of a country's preparedness to adopt this modern discipline and a detailed knowledge of PGx biomarkers allelic spectrum at a population level. In several European populations, particularly in countries with lower income, clinical implementation of PGx is still in its infancy. We have previously performed a pilot study to determine the prevalence of PGx biomarkers in 18 European populations, as the first step towards population PGx at the European level. Here, we provide a comprehensive analysis of the current state of PGx in Greece, including a detailed allelic frequency spectrum of clinically actionable PGx biomarkers, the level of PGx education in academia, the provision of PGx testing services from public and private laboratories, and the aspects of the regulatory PGx environment, especially with respect to the discrepancies between the Greek National Organization of Medicines and the European Medicine Agency and health technology assessment. This study would not only provide the foundations for expediting the adoption of PGx in clinical reality in Greece but can also serve as a paradigm for replicating future studies in other European countries, to expand on previously available pilot studies.

Sideroflexin (SFXN) family genes encode for a group of mitochondrial proteins involved in cellular processes such as iron homeostasis, amino acid metabolism, and energy production. Recent studies showed that they were aberrantly expressed in certain human cancers. However, there is a paucity of information about their expression in prostate cancer. In this study, we took a comprehensive approach to investigate their expression profiles in benign prostate tissue, prostate-derived cell lines, and prostate cancer tissues using multiple transcriptome datasets. Our results showed that SFXN1/3/4 genes were predominantly expressed in prostate tissue and cell lines. SFXN2/4 genes were significantly upregulated while the SFXN3 expression was significantly downregulated in malignant tissues compared to benign tissues. SFXN4 expression was identified as a diagnostic biomarker and prognostic factor for unfavorite survival outcomes. In advanced prostate cancers, SFXN2/4 expressions were positively correlated with the androgen receptor signaling activity but negatively correlated with the neuroendocrinal features. Further analysis discovered that SFXN5 expression was significantly elevated in neuroendocrinal prostate cancers. In conclusion, SFXN2/4 expressions are novel biomarkers in prostate cancer diagnosis and prognosis.

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.