Omics A Journal of Integrative Biology最新文献

The crosstalk between cytokines and immunoglobulin G (IgG) N-glycosylation forms a bidirectional regulatory network that significantly impacts inflammation and immune function. This review examines how various cytokines, both pro- and anti-inflammatory, modulate IgG N-glycosylation, shaping antibody activity and influencing inflammatory responses. In addition, we explore how altered IgG N-glycosylation patterns affect cytokine production and immune signaling, either promoting or reducing inflammation. Through a comprehensive analysis of current studies, this review underscores the dynamic relationship between cytokines and IgG N-glycosylation. These insights enhance our understanding of the mechanisms underlying inflammatory diseases and contribute to improved strategies for disease prevention, diagnosis, monitoring, prognosis, and the exploration of novel treatment options. By focusing on this crosstalk, we identify new avenues for developing innovative diagnostic tools and therapies to improve patient outcomes in inflammatory diseases.

Liver cirrhosis is a severe chronic disease that results from various etiological factors and leads to substantial morbidity and mortality. Alcoholic cirrhosis (AC) and non-AC (NAC) arise from prolonged and excessive consumption of alcohol and metabolic syndromes, respectively. Precise molecular mechanisms of AC and NAC are yet to be comprehensively understood for diagnostics and therapeutic advances to materialize. This study reports novel findings to this end by utilizing high-throughput RNA sequencing and microarray data from the Gene Expression Omnibus (GEO). We performed a meta-analysis of transcriptomics data to identify the differentially expressed genes specific to AC and NAC. Functional enrichment and protein-protein interaction network analyses uncovered novel hub genes and transcription factors (TFs) critical to AC and NAC. We found that AC is primarily driven by metabolic dysregulation and oxidative stress, with key TFs such as RELA, NFKB1, and STAT3. NAC was characterized by fibrosis and tissue remodeling associated with metabolic dysfunction, with TFs including USF1, MYCN, and HIF1A. Key hub genes such as ESR1, JUN, FOS, and PKM in AC, and CD8A, MAPK3, CCND1, and CXCR4 in NAC were identified, along with their associated TFs, pointing to potential therapeutic targets. Our results underscore the unique and shared molecular mechanisms that underlie AC and NAC and inform the efforts toward precision medicine and improved patient outcomes in liver cirrhosis.

It is estimated that approximately one in every 100 children is diagnosed with autism spectrum disorder (ASD) around the globe. Currently, there are no curative pharmacological treatments for ASD. Discoveries on key molecular mechanisms of ASD are essential for precision medicine strategies. Considering that atypical brain connectivity patterns have been observed in individuals with ASD, this study examined the brain connectivity-associated genes and their putatively distinct expression patterns in brain samples from individuals diagnosed with ASD and using an iterative strategy based on random forest and support vector machine algorithms. We discovered a potential gene signature capable of differentiating ASD from control samples with a 92% accuracy. This gene signature comprised 14 brain connectivity-associated genes exhibiting enrichment in synapse-related terms. Of these genes, 11 were previously associated with ASD in varying degrees of evidence. Notably, NFKBIA, WNT10B, and IFT22 genes were identified as ASD-related for the first time in this study. Subsequent clustering analysis revealed the existence of two distinct ASD subtypes based on our gene signature. The expression levels of signature genes have the potential to influence brain connectivity patterns, potentially contributing to the manifestation of ASD. Further studies on the omics of ASD are called for so as to elucidate the molecular basis of ASD and for diagnostic and therapeutic innovations. Finally, we underscore that advances in ASD research can benefit from integrative bioinformatics and data science approaches.

Hurler-Scheie syndrome (MPS IH/S), also known as mucopolysaccharidosis type I-H/S (MPS IH/S), is a lysosomal storage disorder caused by deficiency of the enzyme alpha-L-iduronidase (IDUA) leading to the accumulation of glycosaminoglycans (GAGs) in various tissues, resulting in a wide range of symptoms affecting different organ systems. Postgenomic omics technologies offer the promise to understand the changes in proteome, phosphoproteome, and phosphorylation-based signaling in MPS IH/S. Accordingly, we report here a large dataset and the proteomic and phosphoproteomic analyses of fibroblasts derived from patients with MPS IH/S (n = 8) and healthy individuals (n = 8). We found that protein levels of key lysosomal enzymes such as cathepsin D, prosaposin, arylsulfatases (arylsulfatase A and arylsulfatase B), and IDUA were downregulated. We identified 16,693 unique phosphopeptides, corresponding to 4,605 proteins, in patients with MPS IH/S. We found that proteins related to the cell cycle, mitotic spindle assembly, apoptosis, and cytoskeletal organization were differentially phosphorylated in MPS IH/S. We identified 12 kinases that were differentially phosphorylated, including hyperphosphorylation of cyclin-dependent kinases 1 and 2, hypophosphorylation of myosin light chain kinase, and calcium/calmodulin-dependent protein kinases. Taken together, the findings of the present study indicate significant alterations in proteins involved in cytoskeletal changes, cellular dysfunction, and apoptosis. These new observations significantly contribute to the current understanding of the pathophysiology of MPS IH/S specifically, and the molecular mechanisms involved in the storage of GAGs in MPS more generally. Further translational clinical omics studies are called for to pave the way for diagnostics and therapeutics innovation for patients with MPS IH/S.

Protein kinases are key targets for cancer therapies, with the c-Met receptor tyrosine kinase (MET) and its ligand, hepatocyte growth factor, playing a role in various cancers, including non-small cell lung cancer, gastric cancer, and hepatocellular carcinoma. Although small-molecule inhibitors have been designed to target MET, the development of drug resistance remains a significant challenge to advancing therapeutic strategies. In this study, we employed virtual screening of plant-based compounds sourced from the IMPPAT 2.0 databank to identify potent inhibitors of MET. Preliminary filtering based on the physicochemical parameters following Lipinski's rule of five and pan-assay interference compounds criteria were applied to prioritize hits. Subsequent molecular docking, pharmacokinetic evaluation, prediction of activity spectra for biologically active substances, and specificity assessments facilitated the identification of two promising phytochemicals, neogitogenin and samogenin. Both phytochemicals exhibited considerable drug-like properties with notable binding affinity and selectivity toward MET. Molecular dynamics simulation studies showed the conformational stability of MET with neogitogenin and samogenin. Taken together, these findings suggest that neogitogenin and samogenin hold potential as lead molecules for the development of MET-targeted therapeutics. We call for further evaluations of these phytochemicals in preclinical and experimental studies for anticancer drug discovery and development.

This paper defines a revolution as an orthogonal change in direction, a 90-degree perpendicular turn from the status quo ways of thinking, being and doing, so as to create a complete break, an abolitionist rupture with current and past ways of producing knowledge. David Bowie was a relatable example of a revolutionary and orthogonal innovator who completely and courageously broke with the past and the present and opened up new vistas in music and performing arts. The late anthropologist and public intellectual David Graeber also argued that a revolution fundamentally changes the assumptions in a given field of inquiry. Changing the entrenched assumptions that are long ossified, outdated or uncritically internalized by a knowledge community and profession can have multiplying revolutionary effects on downstream knowledge production. Thinking orthogonally to change the prevailing assumptions is indeed a revolutionary act. Orthogonal innovation as described in this paper is not a repackaging of an innovation in a different field. An orthogonal innovation is proposed as coalescence of ideas drawn from orthogonal domains, e.g., epistemologically speaking as in medicine and political theory, with an eye to pave the way for unprecedented social change and innovation. Grounding systems medicine in political determinants of planetary health, to link two fields of inquiry that have remained isolated and orthogonal since the 17th century, is nothing short of a revolution and orthogonal innovation in the making. For systems medicine to be a truly revolutionary field, it ought to acknowledge that there is no single-issue health nor single-issue politics.

Cattle breed identification is crucial for livestock research and sustainable food systems, and advances in genomics and artificial intelligence present new opportunities to address these challenges. This study investigates the identification of the Tharparkar cattle breed using genomics tools combined with machine learning (ML) techniques. By leveraging data from the Bovine SNP 50K chip, we developed a breed-specific panel of single nucleotide polymorphisms (SNPs) for Tharparkar cattle and integrated data from seven other Indian cattle populations to enhance panel robustness. Genome-wide association studies (GWAS) and principal component analysis were employed to identify 500 SNPs, which were then refined using ML models-AdaBoost, bagging tree, gradient boosting machines, and random forest-to determine the minimal number of SNPs needed for accurate breed identification. Panels of 23 and 48 SNPs achieved accuracy rates of 95.2-98.4%. Importantly, the identified SNPs were associated with key productive and adaptive traits, thus attesting to the value and potentials of digital transformation in livestock genomics. The ML-aided ultra-low-density SNP panel approach reported here not only facilitates breed identification but also contributes to preserving genetic diversity and guiding future breeding programs.

Identifying genomic regions under selection is essential for understanding the genetic mechanisms driving species evolution and adaptation. Traditional methods often fall short in detecting complex, spatially varying selection signals. Recent advances in deep learning, however, present promising new approaches for uncovering subtle selection signals that traditional methods might miss. In this study, we utilized the deep learning framework DeepGenomeScan to detect spatially varying selection signatures across 15 bovine populations worldwide. Our analysis uncovered novel insights into selective sweep hotspots within the bovine genome, revealing key genes associated with physiological and adaptive traits that were previously undetected. We identified significant quantitative trait loci linked to milk protein and fat percentages. By comparing the selection signatures identified in this study with those reported in the Bovine Genome Variation Database, we discovered 38 novel genes under selection that were not identified through traditional methods. These genes are primarily associated with milk and meat yield and quality. Our findings enhance our understanding of spatially varying selection's impact on bovine genomic diversity, laying a foundation for future research in genetic improvement and conservation. This is the first deep learning-based study of selection signatures in cattle, offering new insights for evolutionary and livestock genomics research.