Omics A Journal of Integrative Biology最新文献

Idiopathic Pulmonary Fibrosis (IPF) is a progressive and fatal interstitial lung disease (ILD) characterized by abnormal epithelial cell behavior and excessive extracellular matrix deposition. Despite advances in understanding its molecular pathogenesis, the lack of early diagnostic biomarkers and effective targeted therapies remains a critical barrier. Metabolomics is the comprehensive profiling of low-molecular-weight metabolites and offers an emerging lens to unpack the complex metabolic reprogramming in IPF. This expert review discusses (1) current metabolomics approaches used in IPF research and (2) the key dysregulated metabolic pathways and their potential in improving diagnosis, prognostication, and treatment response monitoring. Furthermore, the review outlines the key metabolic signatures identified in non-IPF ILDs as well and compares their roles with those observed in IPF, thereby providing a broader perspective on shared and disease-specific metabolic alterations across the ILD spectrum.

The classification of immune and nonimmune genes in cattle is crucial for understanding immune mechanisms and their link to disease resistance. Traditional methods rely on manual curation and conventional bioinformatics tools, which are often time-consuming and labor-intensive. We introduce ImmFinder, a multimodal fully connected neural network (FCNN) framework designed to classify immune genes by integrating genomic and transcriptomic datasets. ImmFinder achieved an accuracy of 85.67%, an F1-score of 0.85, a precision of 0.86, and a recall of 0.85, demonstrating strong predictive performance. Additionally, the area under the curve-receiver operating characteristic (AUC-ROC) curve scores of 0.9250 (test set) and 0.9264 (validation set) further validate its robustness. These findings highlight the potential of a multimodal deep learning approach for immune gene classification, advancing functional genomics in cattle. The limitations of ImmFinder include reliance on the available bovine genomic and transcriptomic datasets used for training and evaluation, which may constrain immediate generalization to other breeds or species; additional external validation and experimental follow-up will be required to confirm biological hypotheses derived from model predictions. Currently, ImmFinder demonstrates the value of multimodal data fusion for functional gene annotation and provides a scalable baseline for integrating data types, such as genomics and transcriptomics. In future work, we will expand the training cohorts, broaden the range of data modalities, and pursue experimental validation of high-confidence model predictions. ImmFinder is implemented in Python, and all datasets, training models, preprocessing, and model development scripts are available on GitHub.

Cancer is a disease with heterogenous molecular signatures that ought to be unpacked to achieve the overarching aim of precision oncology. A pan-cancer omics approach provides a systems science framework to explore shared and distinct mechanisms across cancers. We report here pan-cancer analyses of gene expression data from 17 cancers, for example, adrenocortical cancer, lung cancer, kidney cancer, and colorectal cancer, and 26 tissue types, using public datasets to construct disease-specific transcriptional networks. Using the hypergeometric test, 1005 microRNAs (miRNAs), 314 transcription factors (TFs), and 332 receptors were identified as regulatory molecules interacting with differentially expressed genes. Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed to explore their functional roles. Accordingly, we found miR-124-3p, miR-6799-5p, and miR-7106-5p as common miRNAs; Specificity Protein 1 (SP1), RELA Proto-Oncogene, NF-κB Subunit (RELA), and Nuclear Factor Kappa B Subunit 1 (NFKB1) as shared TFs; Cyclin-Dependent Kinase 2 (CDK2), Histone Deacetylase 1 (HDAC1), and ABL Proto-Oncogene 1, Non-Receptor Tyrosine Kinase (ABL1) as common receptors; and pathways in cancer, PI3K-Akt signaling, and p53 signaling as commonly enriched. Survival analysis in an independent dataset confirmed these findings: SP1 and NFKB1 were significant in 9 cancers, RELA in 6, whereas CDK2, HDAC1, and ABL1 were significant in 11, 10, and 10 cancers, respectively, out of the 17 cancers researched herein. In conclusion, these findings provide system-level insights on tumor heterogeneity and inform future cancer classification, for example, according to shared and distinct molecular signatures and development of therapies that might prove effective across several cancers. We underline that unpacking molecular signatures across multiple cancers also offers new prospects to move beyond the "One Drug, One Disease" paradigm of pharmaceutical innovation.

Multiple sclerosis (MS) poses a significant challenge in global health, with increasing incidence rates and profound implications that transcend the geographical boundaries. Recent literature has explored the relationship between MS and serum uric acid (SUA) levels, yielding inconclusive findings. A high SUA level is associated with several chronic disorders and has planetary health significance. Explaining person-to-person variations in SUA is therefore important. In this overarching context, despite a multitude of studies on the putative MS and SUA relationship, limitations such as small sample sizes and inconsistent outcomes persist, highlighting the current gaps in understanding this complex relationship. Here, we report a two-sample Mendelian randomization (MR) study that was conducted to estimate causal effects between MS as the exposure and SUA as the outcome. Our analysis leveraged extensive cohort datasets from publicly accessible genome-wide association studies. The inverse variance weighted method in MR indicated that the odds ratios (ORs) of SUA level per unit increase for MS were 1.649 (95% confidence interval [CI] of OR: 1.09-2.488; p = 0.017) and 23.11 (95% CI of OR: 7.04-75.84; p = 2.23 × 10^-7). Leave-one-out sensitivity analyses, horizontal pleiotropy, and Cochran's Q tests showed robustness of results. This study offers support for a causal association between MS incidence and elevated SUA levels. Pleiotropic tests and sensitivity analyses confirmed minimal horizontal pleiotropy effects and the robustness of the causal association. This MR study provides a causal effect between the incidence of MS and SUA level increase.

Melanin is a complex biopolymer with antioxidative, UV-protective, and antimicrobial properties. Melanin is also of interest for bioengineering applications in healthcare. Its production has been frequently observed in several bacteria and higher organisms under specific culture conditions through genetic engineering and chemical mutagenesis. Interestingly, l-DOPA, a precursor to the neurotransmitter dopamine and an effective anti-Parkinsonian drug, has also been frequently observed, at lower levels, along with melanin in the culture of Bacillus thuringiensis, despite the bacterium lacking l-DOPA-producing tyrosinase sequences in the genome. The present study aims to predict the possible l-DOPA-producing enzyme and characterize the melanin biosynthesis pathway in B. thuringiensis var. israelensis MB-24, a strain derived by NTG mutagenesis of entomopathogenic B. thuringiensis var. israelensis B-17. Using metabolomics, we identified the key metabolites involved in melanin production. We also predicted the probable enzyme involved in l-DOPA production through conserved domain search. Sequencing the homogentisate 1,2-dioxygenase (hmgA) gene of MB-24 showed large deletions, suggesting that melanin synthesis may result from accumulated homogentisate in the HGA (Homogentisic acid) pathway. We expressed 4-hydroxyphenyl pyruvate dioxygenase from B. thuringiensis var. israelensis B-17 and characterized the melanin produced by this enzyme through FT-IR (Fourier-Transform Infrared Spectroscopy). The FT-IR analysis further verified that B. thuringiensis var. israelensis MB24 mostly produced pyomelanin. In conclusion, pyomelanin production in B. thuringiensis var. israelensis MB-24 is driven by the homogentisate pathway due to the inability of the mutant bacterium MB-24 to express functional homogentisate 1,2 dioxygenase. These findings inform future industrial and pharmaceutical applications of melanin biosynthesis.

Wings apart-like protein (WAPL) has emerged as a key player in maintaining genome integrity through its regulation of cohesin dynamics, which govern chromatin architecture and gene expression. WAPL mainly acts as a cohesin release factor and ensures proper chromosomal segregation during mitosis by promoting sister chromatid resolution. Owing to its prominent role in cell biology, WAPL dysregulation can cause genomic instability and disrupt chromosomal cohesion, leading to diseases such as cancer. The precise regulatory mechanisms controlling WAPL activity, including post-translational modifications and its broader implications in disease, remain incompletely understood. This expert review provides a comprehensive analysis and understanding of how WAPL activity is modulated throughout the cell cycle and how its dysfunction contributes to genome instability and disease. We discuss WAPL's molecular mechanisms, its interaction with associated proteins and regulation by antagonistic proteins, the role of post-translational modifications, and the implications of WAPL dysregulation in genome instability and oncogenesis. Future research examining WAPL's regulatory network may usefully provide insights into new therapeutic strategies in cancer and other human diseases.

Right open reading frame kinase 1 (RIOK1) is an atypical kinase involved in ribosome biogenesis, cell cycle progression, and chromosome organization. Its overexpression is linked to tumor progression, metastasis, and chemoresistance, while its absence alters protein phosphorylation across various biological processes. Although the oncogenic role of RIOK1 is recognized, its phospho-regulatory network and the functional relevance of its phosphorylation sites remain unknown. Here, we present the first large-scale phosphoproteomic analysis of RIOK1. Through a systematic assembly of 671 mass spectrometry-based datasets and 157 datasets that quantified RIOK1 phosphopeptides in different experimental conditions, we compiled 13 RIOK1 phosphorylation sites. Notably, phosphorylation at S21 and S22 was predominant, observed in 83% of phosphoproteomics datasets, highlighting their functional significance, and these sites may have a role in RIOK1's interaction with the protein arginine methyltransferase 5 complex. We identified co-differentially regulated phosphorylation events in potential upstream kinases and experimentally validated interactors, providing insights into RIOK1's broader signaling context. The phosphorylation sites in five potential upstream kinases (eukaryotic translation initiation factor 2 alpha kinase 4, ataxia telangiectasia mutated protein kinase, B-Raf proto-oncogene, mitogen-activated protein kinase kinase kinase 3, and polo-like kinase 1) co-differentially regulated with RIOK1 were identified, which regulate their activity in concert with RIOK1 in cancers. Together, this study represents the first comprehensive map to date of RIOK1 phosphorylation and its regulatory associations, highlighting its potential as a therapeutic target in cancers.

Cancer signaling networks play key roles in cancer pathogenesis and drug discovery. The RAS/RAF/MAPK pathway has a crucial role in cell biology and cancer progression, with Raf-1 proto-oncogene, serine/threonine kinase (RAF1) serving as a key regulatory protein in this pathway. This study presents a comprehensive analysis of site-specific phosphorylation of RAF1 and its potential implications in cancer development and therapeutics. Through comprehensive analysis of human cellular phosphoproteomic datasets (769 qualitatively profiled and 196 quantitatively differentially expressed), we identified 63 phosphorylation sites on RAF1. Among these, 29 sites demonstrated distinct regulatory effects in various contexts, including cancer, infections, and signaling-related studies. Notably, our analysis revealed that the most prevalent phosphorylation sites, S259, S621, S642, S296, S301, and S43 primarily regulate kinase-independent RAF1 signaling. This observation suggests a complex interplay between phosphorylation events and RAF1 function, beyond its canonical kinase activity. By elucidating these regulatory mechanisms, our study provides valuable insights into the intricate regulation of RAF1 and its potential impact on cancer-related signaling pathways. These findings not only advance the current understanding of RAF1 regulation but also open new possibilities for the development of targeted therapeutic interventions for cancer treatment. Further investigation of these phosphorylation sites and their functional consequences may lead to novel strategies for cancer treatment innovation by modulating RAF1 activity in cancer cells.