Genomics & informatics最新文献

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.

The untranslated regions (UTRs) of genes significantly impact various biological processes, including transcription, posttranscriptional control, mRNA stability, localization, and translation efficiency. In functional areas of genomes, non-B DNA structures such as cruciform, curved, triplex, G-quadruplex, and Z-DNA structures are common and have an impact on cellular physiology. Although the role of these structures in cis-regulatory regions such as promoters is well established in eukaryotic genomes, their prevalence within UTRs across different eukaryotic classes has not been extensively documented. Our study investigated the prevalence of various non-B DNA motifs within the 5' and 3' UTRs across diverse eukaryotic species. Our comparative analysis encompassed the 5'-UTRs and 3'UTRs of 360 species representing diverse eukaryotic domains of life, including Arthropoda (Diptera, Hemiptera, and Hymenoptera), Chordata (Artiodactyla, Carnivora, Galliformes, Passeriformes, Primates, Rodentia, Squamata, Testudines), Magnoliophyta (Brassicales), Fabales (Poales), and Nematoda (Rhabditida), on the basis of datasets derived from the UTRdb. We observed that species belonging to taxonomic orders such as Rhabditida, Diptera, Brassicales, and Hemiptera present a prevalence of curved DNA motifs in their UTRs, whereas orders such as Testudines, Galliformes, and Rodentia present a preponderance of G-quadruplexes in both UTRs. The distribution of motifs is conserved across different taxonomic classes, although species-specific variations in motif preferences were also observed. Our research unequivocally illuminates the prevalence and potential functional implications of non-B DNA motifs, offering invaluable insights into the evolutionary and biological significance of these structures.

Vibrio vulnificus, a gram-negative pathogenic bacterium, transmitted via undercooked seafood or contaminated seawater, causes septicemia and wound infections. In this study, we analyzed 15 clinical and 11 environmental isolates. In total, 20 sequence types (STs), including eight novel STs, were identified. Antibiotic resistance gene analysis commonly detected the cyclic AMP receptor protein (CRP) in both the clinical and environmental isolates. Interestingly, clinical and environmental isolates were non-susceptible to third-generation cephalosporins, such as ceftazidime and cefotaxime, complicating the treatment of V. vulnificus infection. Multiple antibiotic resistance (MAR) index ranged from 0.1 to 0.5, with clinical isolates showing a higher mean MAR index than the environmental isolates, indicating their broader spectrum of resistance. Notable, no quantitative (124.3 vs. 126.5) and qualitative (adherence, antiphagocytosis, and chemotaxis/motility) differences in virulence factors were observed between the environmental and clinical strains. The molecular characteristics identified in this study provide insights into the virulence of V. vulnificus strains in South Korea, highlighting the need for continuous surveillance of antibiotic resistance in emerging V. vulnificus strains.

Neuromuscular diseases (NMDs) are a group of rare disorders characterized by significant genetic and clinical complexity. Advances in genomics have revolutionized both the diagnosis and treatment of NMDs. While fewer than 30 NMDs had known genetic causes before the 1990s, more than 600 have now been identified, largely due to the adoption of next-generation sequencing (NGS) technologies such as whole-exome sequencing (WES) and whole-genome sequencing (WGS). These technologies have enabled more precise and earlier diagnoses, although the genetic complexity of many NMDs continues to pose challenges. Gene therapy has been a transformative breakthrough in the treatment of NMDs. In spinal muscular atrophy (SMA), therapies like nusinersen, onasemnogene abeparvovec, and risdiplam have dramatically improved patient outcomes. Similarly, Duchenne muscular dystrophy (DMD) has seen significant progress, most notably with the FDA approval of delandistrogene moxeparvovec, the first micro-dystrophin gene therapy. Despite these advancements, challenges remain, including the rarity of many NMDs, genetic heterogeneity, and the high costs associated with genomic technologies and therapies. Continued progress in gene therapy, RNA-based therapeutics, and personalized medicine holds promise for further breakthroughs in the management of these debilitating diseases.

The Human Phenotype Ontology (HPO) is widely used for annotating clinical text data, and sufficient annotation is crucial for the effective utilization of clinical texts. It was known that the use of LLMs can successfully extract symptoms and findings, but cannot annotate them with the HPO. We hypothesized that one of the potential issue for this is the lack of appropriate terms in the HPO. Therefore, during the Biomedical Linked Annotation Hackathon 8 (BLAH8), we attempted the following two tasks in order to grasp the overall picture of HPO. (1) Extract all HPO terms for each of the 23 HPO subclasses (defined as categories) directly under the HPO "Phenotypic abnormality" and then (2) search for major attributes in each of 23 categories. We employed LLM for these two tasks related to examining HPO and, at the same time, found that LLM didn't work well without ingenuity for tasks that lacked sentences and context. A manual search for terms within each category revealed that the HPO contains a mix of terms with four major attributes: (1) Disease Name, (2) Condition, (3) Test Data, and (4) Symptoms and Findings. Manual curation showed that the ratio of symptoms and findings varied from 0 to 93.1% across categories. For clinicians, who are end-users of medical terminology including HPO, it is difficult to understand ontologies. However, for good quality ontology is also important for good-quality data, and a clinician's help is essential. It is also important to make the overall picture and limitations of ontologies easy to understand in order to bring out the explanatory power of LLMs and artificial intelligence.

The paper presents Anatomy3DExplorer, a customized ChatGPT designed as a natural language dialogue interface for exploring 3D models of anatomical structures. It illustrates the significant potential of large language models (LLMs) as user-friendly interfaces for database access. Furthermore, it showcases the seamless integration of LLMs and database APIs, within the GPTS framework, offering a promising and straightforward approach.