Omics A Journal of Integrative Biology最新文献

SLC6A15, (sodium-dependent neutral amino acid transporter B(0)AT2), plays a crucial role in amino acid homeostasis and neuronal signaling, and it has been genetically and transcriptionally associated with major depressive disorder (MDD). However, the post-translational regulation of SLC6A15, particularly through phosphorylation, remains poorly understood. To address this knowledge gap, we report here curation of 3825 global phosphoproteomic datasets and layered statistical and bioinformatic analyses to uncover predominant phosphosites, co-phospho-regulated proteins, upstream kinases, and binary interactors of SLC6A15. Importantly, after stringent filtration, three sites, pS701, pS699, and pS687, were identified as the predominant sites of phosphorylation. Predicted upstream kinases included mitogen-activated protein kinase 3 (MAPK3) and cyclin-dependent kinase 12 (CDK12), suggesting regulatory control at these phosphorylation sites. Binary interactors such as tyrosine-protein kinase Lyn, epidermal growth factor receptor, and calnexin were found to have direct associations with stress-related pathways, indicating potential roles in stress-responsive signaling mechanisms. Pathway enrichment analysis of the high-confidence phosphosites in other proteins revealed significant enrichment of the ErbB signaling pathway, which is frequently dysregulated in MDD. Collectively, this study presents a comprehensive co-phospho-regulation-based catalog of SLC6A15, systematically mapping its key phosphorylation sites, regulatory kinases, and interaction partners. Identification of upstream kinases such as MAPK3 and CDK12 and enrichment of the ErbB signaling axis indicate a potential role of SLC6A15 in synaptic plasticity, neuronal signaling, and stress response mechanisms associated with depression. These findings uncover novel protein-protein relationships and phosphorylation-driven interactions, offering new insights into transporter regulation in neuropsychiatric disorders and potential therapeutic targets for MDD.

Checkpoint kinase 1 (CHEK1/CHK1) is a serine/threonine kinase that is pivotal in maintaining genomic stability by regulating DNA replication, mitotic progression, and DNA damage response (DDR). Phosphorylation at distinct regulatory sites of CHK1 serves as a central signaling switch that tightly coordinates checkpoint control and DNA repair pathways. However, the broad phosphorylation network associated with the DNA repair pathway and CHK1 phosphorylation remains relatively underexplored, representing an untapped avenue with profound therapeutic potential. To bridge this discovery gap, we systematically analyzed global phosphoproteome datasets to visualize CHK1 phosphosites and their coregulated protein phosphosites, providing new insights into the functional networks governed by DDR signaling. The integrative analysis of 577 qualitative and 120 quantitative cellular phosphoproteomic datasets identified signatures of the CHK1 phosphorylation landscape. Our study visualized a strong co-occurrence of DDR-associated phosphosites in proteins, particularly with S280, and the Ataxia telangiectasia and Rad3-related protein-dependent S317 phosphosites in CHK1 located outside its kinase domain. Their coregulation analysis across CHK1 substrates, kinase regulators, and protein interactions uncovered connectivity between CHK1 phosphosites and DDR regulators. Collectively, our phosphosite-concordance approach reported here provides a regulatory map of CHK1 phosphorylation patterns, uncovering unexplored regulatory layers, and highlights new opportunities to explore mechanistic insights into CHK1 phosphoregulation as a target for therapeutic interventions in cancer.

Glioblastoma (GBM) remains one of the most aggressive human malignancies, and its biological diversity is largely shaped by the isocitrate dehydrogenase (IDH) mutation status. Although apoptosis, autophagy, and ferroptosis have each been implicated in GBM pathophysiology, their coordinated regulation through the immunoproteasome (i-PSM) axis has not been systematically explored. Here, we integrated transcriptomic datasets from The Cancer Genome Atlas and The Chinese Glioma Genome Atlas with immune deconvolution and network analyses to map the IDH-specific crosstalk between i-PSM components and cell death programs. We identified a nine-gene signature (i.e., CD14, HMOX1, CTSB, CTSS, RRAS, BAK1, FTH1, PRKCD, and CYBB) that captures IDH-dependent immune and metabolic divergence. IDH-mutant (IDHmt) astrocytomas exhibited coordinated upregulation of HMOX1 and CD14, suggesting an actively maintained, immunosuppressive niche rather than a merely "immune-cold" state. Receiver operating characteristic analyses demonstrated strong discrimination of IDH status (area under the curve ≥ 0.92 for key genes), whereas structure-guided docking nominated rational, multipathway combinations such as temozolomide + bortezomib, luteolin, or lovastatin. Collectively, this integrative omics approach reframes IDHmt GBM as a redox- and myeloid-driven suppressive ecosystem and provides a systems-level rationale for personalized, IDH-informed therapeutic strategies.

Glioma remains a major clinical challenge due to its molecular heterogeneity and limited therapeutic options. While numerous biomarker and drug discovery efforts exist, most are restricted by small sample sizes, subtype-agnostic analyses, or limited integration of computational strategies. Here, we present an integrative machine learning-based systems pipeline for the identification of subtype-specific biomarkers and repurposed therapeutics for glioblastoma (GBM) and low-grade glioma (LGG). We report high-confidence, subtype-specific biomarker candidates by harnessing publicly available gene expression datasets and systematic analyses with oversampling strategies to balance class distributions, followed by feature selection algorithms. Specifically, 10 candidate genes with strong diagnostic potential were identified, including RAB11FIP4, TYRO3, THEM5, SST, SMIM32, MIGA1, ARFGEF3, and ANK3 for GBM and GUCA1A and CES4A for LGG. Repurposed drug candidates were then predicted via signature-based prioritization and evaluated using molecular docking simulations, revealing six promising compounds for GBM (vandetanib, capecitabine, melatonin, agomelatine, ramelteon, and tasimelteon) and one for LGG (ambroxol). This study demonstrates the utility of combining class-balancing, feature selection, and drug repurposing pipelines to uncover clinically relevant glioma biomarkers and therapeutic candidates, thus providing a computational foundation for future experimental and translational validation in these brain cancers and neuro-oncology.

Chronic shoulder pain/disability is a significant cause of morbidity among breast cancer survivors, which can persist for several years postsurgery, thus markedly impacting their quality of life. The condition is a multifactorial and polygenic trait. In this overarching context, we report here on the polygenic effects through polymorphisms in opioid signaling and pain pathways, specifically, the (1) ATP-binding cassette subfamily B, member 1 gene-catechol-O-methyltransferase (ABCB1-COMT) and (2) ABCB1-opioid receptor Mu 1 (OPRM1)-COMT genes. Using TaqMan™ assays, we genotyped the polymorphisms in the candidate genes in a sample of South African breast cancer survivors (N = 252) reporting chronic shoulder pain/disability. The Shoulder Pain and Disability Index was used to evaluate pain/disability symptoms, with total scores converted to percentages and participants categorized as no-low (< 30%) or moderate-high (≥ 30%). The ABCB1 (rs1128503)-COMT (rs4680) G-A allele combination was significantly associated with increased pain (p = 0.005, odds ratio [OR]: 2.08, 95% confidence interval [CI]: 1.12-3.84) and combined (p = 0.008, OR: 1.94, 95% CI: 1.02-3.69) symptoms. Furthermore, the ABCB1 (rs1045642)-OPRM1 (rs1799971)-COMT (rs4680) G-A-A allele combination was associated with increased pain (p < 0.001, OR: 1.93, 95% CI: 1.01-3.69) and combined (p < 0.001, OR: 1.60, 95% CI: 0.81-3.19) symptoms. Collectively, these findings suggest that chronic shoulder pain/disability in breast cancer survivors in this sample of South African patients is influenced by the combined effects of polymorphisms within the ABCB1-OPRM1-COMT genes. These observations present the potential for further translational research, personalized medicine, and pain management strategies to improve the long-term quality of life in breast cancer patients.

N6-methyladenosine (m6A) is an abundant post-transcriptional RNA modification that critically regulates brain function. Dysregulation of m6A signaling has been implicated in several neurological diseases, including Alzheimer's disease (AD). However, whether genetic variation associated with the risk of AD is mediated via m6A-dependent gene regulation is currently unknown. Here we investigated the association of m6A with the risk of AD using the summary-data-based Mendelian randomization (SMR) approach. By integrating m6A quantitative trait loci (m6A-QTLs) and genome-wide association study (GWAS) summary data for AD, we identified six nominally significant m6A-AD associations (uncorrected P_SMR < 0.05, P_HEIDI ≥ 0.01 with ≥5 SNPs), although none remained significant after false discovery rate (FDR) correction. We performed targeted SMR analyses for AD using brain- and blood-based expression QTL summary data, restricting instrumental variables to a set of 18,606 single nucleotide polymorphisms (SNPs) previously identified as m6A-related sites. This analysis identified 75 FDR-significant genes associated with the risk of AD via changes in gene expression (FDR < 0.05, P_HEIDI ≥ 0.01 with ≥5 SNPs); however, the instrumental SNPs for these genes showed no enrichment for m6A-QTLs. In summary, we found limited evidence for the direct association of m6A genetic variation with the risk of AD. Larger m6A-QTL datasets will be required to establish whether m6A variation is associated with the risk of AD.

Lung adenocarcinoma (LUAD) is the most prevalent subtype of nonsmall cell lung cancer. Cigarette smoking, the primary etiological factor, introduces mutagenic and epigenetic changes that promote tumorigenesis, with nicotine acting as a key bioactive component modulating cellular signaling rather than directly causing mutations. In this study, differential transcriptomic profiling of smoker and nonsmoker LUAD samples from the PanCancer Atlas identified neurotensin (NTS) and calcitonin-related polypeptide alpha (CALCA) as the most significantly upregulated genes in smokers. The analysis included 495 LUAD tumor samples with annotated smoking history, comprising 209 never smokers and 286 current smokers. A dataset from the NCBI Gene Expression Omnibus (GSE10072) was used to validate the results. Only samples from current smokers and never smokers were considered to unravel direct molecular impact of active smoking, and the analysis confirmed the observed differential expression patterns of key genes, including NTS and CALCA, between smoker- and nonsmoker-derived LUAD samples. Pathway enrichment analysis revealed G protein-coupled receptor-mediated neuroendocrine signaling activation, suggesting a nicotine-driven reprogramming of tumor cells toward a secretory phenotype. Molecular docking simulations demonstrated stable interactions of (S)-nicotine with NTS and CALCA, suggesting these proteins as potential mediators of nicotine-induced oncogenic signaling. Kaplan-Meier analysis indicated that high expression of NTS and CALCA was associated with poorer overall survival, warranting further investigation in independent cohorts. Collectively, this integrative bioinformatics and structural study informs the molecular consequences of smoking in LUAD, identifies nicotine-responsive neuropeptides as potential signatures of tumor aggressiveness, and can provide a foundation for drug repurposing strategies to mitigate smoking-associated malignancy.

Artificial intelligence (AI) marks an era in systems science when digital technologies are transforming big data-driven knowledge production and their applications toward public policy and governance including health care innovation, be they in internal medicine, surgery, biotechnology, or public health. The anticipations for an increase in throughput and efficiency of science and medicine are also accompanied by political and moral corollaries of AI. There is a need to explore and better understand the role of AI within the conceptual frames of the information society, knowledge society, and innovation ecosystems, as well as governance guided by critical policy studies. This article reviews and explores the political and normative implications of AI for a systems science audience and in relation to AI's generative nature, which can redirect human behavior and, to a certain extent, shape societies, not to mention cultures and practices in science and innovation ecosystems in the 21st century.