Genomics & informatics最新文献

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.

Background: Understanding the progression and recovery process of COVID-19 is crucial for guiding public health strategies and developing targeted interventions. This longitudinal cohort study aims to elucidate the dynamics of COVID-19 severity progression and evaluate the impact of underlying health conditions on these transitions, providing critical insights for more effective disease management.

Methods: Data from 4549 COVID-19 patients admitted to Seoul National University Boramae Medical Center between February 5th, 2020, and October 30th, 2021, were analyzed using a 5-state continuous-time Markov multistate model. The model estimated instantaneous transition rates between different levels of COVID-19 severity, predicted probabilities of state transitions, and determined hazard ratios associated with underlying comorbidities.

Results: The analysis revealed that most patients stabilized in their initial state, with 72.2% of patients with moderate symptoms remaining moderate. Patients with hypertension had a 67.6% higher risk of progressing from moderate to severe, while those with diabetes had an 89.9% higher risk of deteriorating from severe to critical. Although transition rates to death were low early in hospitalization, these comorbidities significantly increased the likelihood of worsening conditions.

Conclusion: This study highlights the utility of continuous-time Markov multistate models in assessing COVID-19 severity progression among hospitalized patients. The findings indicate that patients are more likely to recover than to experience worsening conditions. However, hypertension and diabetes significantly increase the risk of severe outcomes, underscoring the importance of managing these conditions in COVID-19 patients.

Hepatocellular carcinoma (HCC) is one of the most common types of primary liver cancer and remains a leading cause of cancer-related deaths worldwide. While traditional approaches like surgical resection and tyrosine kinase inhibitors struggle against the tumor's immune evasion, monoclonal antibody (mAb)-based immunotherapies have emerged as promising alternatives. Several therapeutic antibodies that counter the immunosuppressive tumor microenvironment have demonstrated efficacy in clinical trials, leading to FDA approvals for advanced HCC treatment. A crucial aspect of advancing these therapies lies in understanding the structural interactions between antibodies and their targets. Recent findings indicate that mAbs and bispecific antibodies (bsAbs) can target different, non-overlapping epitopes on immune checkpoints such as PD-1 and CTLA-4. This review delves into the epitope-paratope interactions of structurally unresolved mAbs and bsAbs, and discusses the potential for combination therapies based on their non-overlapping epitopes. By leveraging this unique feature, combination therapies could enhance immune activation, reduce resistance, and improve overall efficacy, marking a new direction for antibody-based immunotherapy in HCC.

Cells interact with each other for proper function and homeostasis. Often, co-expression of ligand-receptor pairs from the single-cell RNAseq (scRNAseq) has been used to identify interacting cell types. Recently, RNA sequencing of physically interacting multi-cells has been used to identify interacting cell types without relying on co-expression of ligand-receptor pairs. This opens a new avenue to study the expression of interacting cell types. We present DeepDoublet, a deep-learning-based tool to decompose the transcriptome of physically interacting two cells (or doublet) into two sets of transcriptome. Applying DeepDoublet to the doublets of hepatocyte and liver endothelial cells (LECs), we successfully decomposed into the transcriptome of each cell type. Especially, DeepDoublet identified specific expression of hepatocytes when they are interacting with LECs. Among them was Angptl3 which has a role in blood vessel formation. DeepDoublet is a tool to identify neighboring cell-dependent gene expression.

In protein identification, researchers increasingly aim to achieve efficient classification using fewer features. While many feature selection methods effectively reduce the number of model features, they often cause information loss caused by merely selecting or discarding features, which limits classifier performance. To address this issue, we present Rore, an algorithm based on a feature-dimensionality reduction strategy. By mapping the original features to a latent space, Rore retains all relevant feature information while using fewer representations of the latent features. This approach significantly preserves the original information and overcomes the information loss problem associated with previous feature selection. Through extensive experimental validation and analysis, Rore demonstrated excellent performance on an antioxidant protein dataset, achieving an accuracy of 95.88% and MCC of 91.78%, using vectors including only 15 features. The Rore algorithm is available online at http://112.124.26.17:8021/Rore .

Rare diseases, though individually uncommon, collectively affect millions worldwide. Genomic technologies and big data analytics have revolutionized diagnosing and understanding these conditions. This review explores the role of genomics in rare disease research, the impact of large consortium initiatives, advancements in extensive data analysis, the integration of artificial intelligence (AI) and machine learning (ML), and the therapeutic implications in precision medicine. We also discuss the challenges of data sharing and privacy concerns, emphasizing the need for collaborative efforts and secure data practices to advance rare disease research.

Hearing loss is the most common sensory disorder. Genetic factors contribute substantially to this condition, although allelic heterogeneity and variable expressivity make a definite molecular diagnosis challenging. To provide a brief overview of the genomic landscape of sensorineural hearing loss in Koreans, this article reviews the genetic etiologies of nonsyndromic hearing loss in Koreans as well as the clinical characteristics, genotype-phenotype correlations, and pathogenesis of hearing loss arising from common variants observed in this population. Furthermore, potential genetic factors associated with age-related hearing loss, identified through genome-wide association studies, are briefly discussed. Understanding these genetic etiologies is crucial for advancing precise molecular diagnoses and developing targeted therapeutic interventions for hearing loss.