Genomics & informatics最新文献

Deep vein thrombosis (DVT) is a clinically significant condition characterized by the formation of thrombi in deep venous structures, leading to high morbidity and potential mortality. Identifying reliable biomarkers for DVT risk prediction remains challenging due to the intricate genetic and molecular mechanisms underlying the disease. This study aims to investigate the best biomarker for DVT. Our study utilized genome-wide association studies (GWAS) findings coupled with a functional annotation scoring system to identify and prioritize genetic markers with strong associations to DVT. Furthermore, gene expression levels were analyzed to determine the most promising genetic markers. Several databases were utilized, including the GWAS Catalog, HaploReg 4.2, WebGestalt, Enrichr, and the GTEx Portal. Through the comprehensive analysis, we found 5 potential biomarkers and highlighted thrombomodulin (THBD) and Factor V (F5) as the best blood-based biomarkers. THBD and F5 genes were selected based on their elevated expression levels in blood and the presence of eQTLs. Functionally, THBD modulates coagulation via protein C activation, while F5 is pivotal in thrombin formation and clot stabilization, underscoring their mechanistic relevance to DVT pathogenesis, and rendering them suitable for non-invasive clinical assessment. Our findings emphasize the potential of genetic biomarkers to transform DVT risk assessment and support advancements in precision medicine for thrombotic disorders.

Background: Inflammatory bowel disease (IBD) is a group of chronic inflammatory disorders, including ulcerative colitis (UC) and Crohn's disease, affecting the gastrointestinal tract and is associated with high morbidity and mortality. Accumulating evidence indicates that IBD not only impacts the gastrointestinal tract but also affects multiple extraintestinal organs, which may manifest prior to the diagnosis of IBD. Among these extraintestinal manifestations associated with IBD, primary sclerosing cholangitis (PSC) stands out as a prominent example. PSC is recognized as a progressive cholestatic disorder, characterized by the narrowing of bile ducts, eventual development of liver cirrhosis, end-stage liver disease, and the potential emergence of cholangiocarcinoma. This study aimed to identify the molecular contributors in UC-induced PSC by detecting the essential regulatory genes that are differentially expressed in both diseases.

Materials and methods: The common single-nucleotide polymorphisms (SNPs) and differentially expressed genes (DEGs) were detected using DisGeNET and GEO databases, respectively. Then, the top module and hub genes within the protein-protein interaction network were identified. Furthermore, the co-expression network of the top module was constructed using the HIPPIE database. Additionally, the gene regulatory network was constructed based on miRNAs and circRNAs. Finally, we searched the DGIdb database for possible interacting drugs with UC-PSC top module genes.

Results: A total of 132 SNPs and their associated genes were found to be shared between UC and PSC. Gene expression analysis identified 56 common DEGs between the two diseases. Following functional enrichment analysis, 207 significant biological processes (BP), 48 molecular functions (MF), and 8 KEGG pathways, with notable enrichment in mRNA-related processes such as mRNA splicing and RNA binding, were defined. Particularly, the PTPN2 gene was the only gene common between UC and PSC at both the SNP level and the expression level. Additionally, the top cluster of PPI network analysis was consisted of PABPC1, SNRPA1, NOP56, NHP2L1, and HNRNPA2B1 genes. Finally, ceRNA network involving 4 mRNAs, 94 miRNAs, and 200 selected circRNAs was constructed.

Conclusion: The present study provides novel potential candidate genes that may be involved in the molecular association between ulcerative colitis and primary sclerosing cholangitis, resulting in the development of diagnostic tools and therapeutic targets to prevent the progression of PSC from UC.

Gene network models provide a foundation for graph theory approaches, aiding in the novel discovery of drug targets, disease genes, and genetic mechanisms for various biological functions. Disease genetics must be interpreted within the cellular context of disease-associated cell types, which cannot be achieved with datasets consisting solely of organism-level samples. Single-cell RNA sequencing (scRNA-seq) technology allows computational distinction of cell states which provides a unique opportunity to understand cellular biology that drives disease processes. Importantly, the abundance of cell samples with their transcriptome-wide profile allows the modeling of systemic cell-type-specific gene networks (CGNs), offering insights into gene-cell-disease relationships. In this review, we present reference-based and de novo inference of gene functional interaction networks that we have recently developed using scRNA-seq datasets. We also introduce a compendium of CGNs as a useful resource for cell-type-resolved disease genetics. By leveraging these advances, we envision single-cell network biology as the key approach for mapping the gene-cell-disease axis.

Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease prevalent in American and European populations, with its onset and progression significantly influenced by mutations in the superoxide dismutase 1 (SOD1) protein. While previous studies have highlighted the effects of mutations in the metal-binding region and catalytic region and dimerisation of SOD1, the impact of mutations involving the Cysteine residue at the N-terminal end remains unexplored. This study investigates the effects of Cysteine-to-Trp, Phe, Ser, and Gly mutations at the 6th position of SOD1's N-terminal end on its structural dynamics and functional impairment. Our computational analysis using PolyPhen-2, PROVEAN, Meta-SNP, and PhD-SNP predicted mutations to be deleterious, with their negative impacts likely contributing to disease development. Furthermore, stability studies and bonding pattern changes due to the mutations, analysed by mCSM, SDM, DUET, Dynamut2, and PremPS revealed changes in free energy and disruption in intramolecular interactions. The molecular dynamics studies revealed distinct changes in stability patterns among the mutations, particularly in Cys6Trp and Cys6Phe. All the mutations primarily altered the catalytic region of the protein; additionally, Cys6Phe and Cys6Gly caused disruption in the metal-binding region. The impact of mutations on the dimerisation of SOD1, analysed using MM/PBSA showed destabilisation due to Cys6Phe mutation. These findings provide molecular insights into the clinical symptoms observed in patients, highlighting the critical impact of the Cys6Phe mutation on the metal-binding and catalytic loops of SOD1 along with destabilisation of dimer formation. Overall, our analysis offers valuable insights into the molecular mechanisms driving structural changes in SOD1 due to mutations, contributing to a deeper understanding of their role in ALS pathogenicity.

Large-scale national biobank projects utilizing whole-genome sequencing have emerged as transformative resources for understanding human genetic variation and its relationship to health and disease. These initiatives, which include the UK Biobank, All of Us Research Program, Singapore's PRECISE, Biobank Japan, and the National Project of Bio-Big Data of Korea, are generating unprecedented volumes of high-resolution genomic data integrated with comprehensive phenotypic, environmental, and clinical information. This review examines the methodologies, contributions, and challenges of major WGS-based national genome projects worldwide. We first discuss the landscape of national biobank initiatives, highlighting their distinct approaches to data collection, participant recruitment, and phenotype characterization. We then introduce recent technological advances that enable efficient processing and analysis of large-scale WGS data, including improvements in variant calling algorithms, innovative methods for creating multi-sample VCFs, optimized data storage formats, and cloud-based computing solutions. The review synthesizes key discoveries from these projects, particularly in identifying expression quantitative trait loci and rare variants associated with complex diseases. Our review introduces the latest findings from the National Project of Bio-Big Data of Korea, which has advanced our understanding of population-specific genetic variation and rare diseases in Korean and East Asian populations. Finally, we discuss future directions and challenges in maximizing the impact of these resources on precision medicine and global health equity. This comprehensive examination demonstrates how large-scale national genome projects are revolutionizing genetic research and healthcare delivery while highlighting the importance of continued investment in diverse, population-specific genomic resources.

The diversity of T-cell receptors (TCRs) and B-cell receptors (BCRs) underpins the adaptive immune system's ability to recognize and respond to a wide array of antigens. Recent advancements in RNA sequencing have expanded its application beyond transcriptomics to include the analysis of immune repertoires, enabling the exploration of TCR and BCR sequences across various physiological and pathological contexts. This review highlights key methodologies and considerations for TCR and BCR repertoire analysis, focusing on the technical aspects of receptor sequence extraction, data processing, and clonotype identification. We compare the use of bulk and single-cell sequencing, discuss computational tools and pipelines, and evaluate the implications of examining specific receptor regions such as CDR3. By integrating immunology, bioinformatics, and clinical research, immune repertoire analysis provides valuable insights into immune function, therapeutic responses, and precision medicine approaches, advancing our understanding of health and disease.

Background: The genomic architecture of eukaryotes exhibits dynamic spatial and temporal changes, enabling cellular processes critical for maintaining viability and functional diversity. Recent advances in sequencing technologies have facilitated the dissection of genomic architecture and functional activity at single-cell resolution, moving beyond the averaged signals typically derived from bulk cell analyses.

Main body: The advent of single-cell genomics and epigenomics has yielded transformative insights into cellular heterogeneity, behavior, and biological complexity with unparalleled genomic resolution and reproducibility. This review summarizes recent progress in the characterization of genomic architecture at the single-cell level, emphasizing the impact of structural variation and chromatin organization on gene regulatory networks and cellular identity.

Conclusion: Future directions in single-cell genomics and high-resolution epigenomic methodologies are explored, focusing on emerging challenges and potential impacts on the understanding of cellular states, regulatory dynamics, and the intricate mechanisms driving cellular function and diversity. Future perspectives on the challenges and potential implications of single-cell genomics, along with high-resolution genomic and epigenomic technologies for understanding cellular states and regulatory dynamics, are also discussed.

Angiogenesis, the formation of new blood vessels from preexisting ones, is essential for normal development, wound healing, and tissue repair. However, dysregulated angiogenesis is implicated in various pathological conditions, including cancer, diabetic retinopathy, and atherosclerosis. Epigenetic modifications, including DNA methylation, histone modification, and noncoding RNAs (e.g., miRNAs), play a crucial role in regulating angiogenic gene expression without altering the underlying DNA sequence. These modifications tightly regulate the balance between pro-angiogenic and anti-angiogenic factors, thereby influencing endothelial cell proliferation, migration, and tube formation. In recent years, epigenetic drugs, such as DNA methyltransferase inhibitors (e.g., azacitidine, decitabine), histone deacetylase inhibitors (e.g., vorinostat, romidepsin), and BET inhibitors (e.g., JQ1), have emerged as promising therapeutic strategies for targeting abnormal angiogenesis. These agents modulate gene expression patterns, reactivating silenced tumor suppressor genes while downregulating pro-angiogenic signaling pathways. Additionally, miRNA modulators, such as MRG-110 and MRG-201, provide precise regulation of angiogenesis-related pathways, demonstrating significant therapeutic potential in preclinical models. This review underscores the intricate interplay between epigenetic regulation and angiogenesis, highlighting key mechanisms and therapeutic applications. Advancing our understanding of these processes will enable the development of more effective and targeted epigenetic therapies for angiogenesis-related diseases, paving the way for innovative clinical interventions.

This paper evaluates and compares different fine-tuned variations of generative large language models (LLM) in the zero-shot named entity recognition (NER) task for the clinical domain. As part of the 8th Biomedical Linked Annotation Hackathon, we examined Llama 2 and Mistral models, including base versions and those that have been fine-tuned for code, chat, and instruction-following tasks. We assess both the number of correctly identified entities and the models' ability to retrieve entities in structured formats. We used a publicly available set of clinical cases labeled with mentions of diseases, symptoms, and medical procedures for the evaluation. Results show that instruction fine-tuned models perform better than chat fine-tuned and base models in recognizing entities. It is also shown that models perform better when simple output structures are requested.