Omics A Journal of Integrative Biology最新文献

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.

Microtubule-associated serine/threonine-protein kinase 3 (MAST3) is a member of the MAST kinase family implicated in neuronal and immune pathways and is predicted to associate with cytoskeletal regulation. However, insights into its functional role in cytoskeletal organization remain unexplored. In this study, we performed a large-scale phosphoproteomic analysis of MAST3 using 562 datasets to delineate its functional network. We identified four predominant phosphosites, S134, S146, S792, and S793, based on the frequency of detection and differential regulation, with S134 and S146 localized within the Domain of Unknown Function domain, a noncatalytic region. These phosphosites exhibited distinct coregulatory profiles, suggesting regulation through noncatalytic domains. Coregulated phosphosites were enriched for cytoskeleton-associated functions, including actin filament organization, microtubule organization, and spindle assembly. Additionally, predicted downstream substrates such as KIF15, EPB41L1, CP110, and HNRNPU, and binary interactors including LMNA, CKAP4, and CAMSAP2, further support the involvement of MAST3 in cytoskeletal regulation. The convergence of these cytoskeletal partners across phosphosites, substrates, and interactors suggests that MAST3 may act as a key modulator of cytoskeletal organization through phosphorylation-dependent protein-protein interactions. Notably, frequent phosphorylation of S146 across cancer types points to a potential tumor-specific regulatory role. Together, these findings provide the first systems-level insight into the role of MAST3 in cytoskeletal regulation and disease relevance.

The increasing accessibility of high-throughput omics technologies has represented a paradigm change in systems biology, facilitating the systematic exploration of biological complexity at genomic, transcriptomic, proteomic, and metabolomic levels. Contemporary systems biology more and more depends on integrative multi-omics strategies to unravel the sophisticated, dynamic networks of cellular function and organismal phenotypes. Such methodologies enable scientists to clarify molecular interactions, decipher disease pathology, identify strong biomarkers, and guide precision medicine and synthetic biology initiatives. Recent technological breakthroughs in computational tools, ranging from early or late data integration, network analysis, and machine learning, have overcome obstacles of high-dimensionality, heterogeneity, and perturbations restricted to specific contexts. In this review, we critically assess the principles, methods, and applications of multi-omics integration, with an emphasis on cancer biology, microbial engineering, and synthetic biology. We showcase case studies in which integrative omics provided actionable findings. Finally, we address current limitations (e.g., data heterogeneity, interpretability) and forthcoming solutions (artificial intelligence, single-cell omics, cloud platforms). By closing the gap between molecular layers, multi-omics integration is moving toward predictive models of biological systems and revolutionary biotechnological applications.

Lung adenocarcinoma (LUAD) remains the most common subtype of lung cancer, characterized by high heterogeneity and poor survival outcomes. Although transcriptomic and metabolomic alterations have been individually studied, integrated multi-omics analyses are needed to uncover the convergent pathways that drive tumor progression. Differentially expressed genes (DEGs) were identified from the GSE229253 transcriptomic dataset comprising LUAD tumor and adjacent normal tissues, while significantly altered metabolites were obtained from the Lung Cancer Metabolome Database. The top 10 DEGs and metabolites were analyzed using the search tool for interacting chemicals (STITCH) to construct gene-metabolite networks, and Integrated Molecular Pathway Level Analysis (IMPaLA) was employed for integrated pathway enrichment to identify overlapping molecular processes. Transcriptomic profiling revealed 973 DEGs (410 upregulated and 563 downregulated), and metabolomic analysis identified significant alterations in metabolites linked to redox balance, amino acid derivatives, and nucleotide metabolism. Integration through STITCH generated a network of 16 nodes and 9 edges, highlighting gene-metabolite associations of probable biological relevance. Joint pathway enrichment analysis using IMPaLA consistently identified glycosylation-related pathways, particularly O-linked glycosylation of mucins, as major axes of convergence between transcriptomic and metabolomic alterations in LUAD (joint p = 0.00129-0.00434). Several genes (B3GNT6, FEZF1-AS1, and LCAL1) and metabolites (isoleucylleucine, leucylleucine, and isoleucylvaline) are probable novel candidates, warranting further investigation. These findings provide systems-level evidence that aberrant glycosylation is likely a central hallmark of LUAD, underscore the potential of glycosylation pathways as biomarkers and therapeutic targets, and demonstrate the utility of cross-omics approaches to unpack the molecular complexity of lung cancer.