Omics A Journal of Integrative Biology最新文献

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 Changing the entrenched assumptions that are long ossified, outdated or uncritically internalized by a knowledge community and profession can have multiplying revolutionary effects on downstream knowledge production.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.

The study of longevity and its determinants has been revitalized with the rise of microbiome scholarship. The gut microbiota have been established to play essential protective, metabolic, and physiological roles in human health and disease. The gut dysbiosis has been identified as an important factor contributing to the development of multiple diseases. Accordingly, it is reasonable to hypothesize that the gut microbiota of long-living individuals have healthy antiaging-associated gut microbes, which, by extension, might provide specific molecular targets for antiaging treatments and interventions. In the present study, we compared the gut microbiota of Chinese individuals in two different age groups, long-living adults (aged over 90 years) and elderly adults (aged 65-74 years) who were free of major diseases. We found significantly lower relative abundances of bacteria in the genera Sutterella and Megamonas in the long-living individuals. Furthermore, we established that while biological processes such as autophagy (GO:0006914) and telomere maintenance through semiconservative replication (GO:0032201) were enhanced in the long-living group, response to lipopolysaccharide (GO:0032496), nicotinamide adenine dinucleotide oxidation (GO:0006116), and S-adenosyl methionine metabolism (GO:0046500) were weakened. Moreover, the two groups were found to differ with respect to amino acid metabolism. We suggest that these compositional and functional differences in the gut microbiota may potentially be associated with mechanisms that contribute to determining longevity or aging.

Precision oncology promises individually tailored drugs and clinical care for patients with cancer: That is, "the right drug, for the right patient, at the right dose, and at the right time." Although stratification of the risk for treatment resistance and toxicity is key to precision oncology, there are multiple ways in which such stratification can be achieved, for example, genetic, functional pathway based, among others. Moving toward precision oncology is sorely needed in the case of acute lymphoblastic leukemia (ALL) wherein adult patients display survival rates ranging from 30% to 70%. The present study reports on the pathway activity signature of adult B-ALL, with an eye to precision oncology. Transcriptome profiles from three different expression datasets, comprising 346 patients who were adolescents or adults with B-ALL, were harnessed to determine the activity of signaling pathways commonly disrupted in B-ALL. Pathway activity analyses revealed that Ph-like ALL closely resembles Ph-positive ALL. Although this was the case at the average pathway activity level, the pathway activity patterns in B-ALL differ from genetic subtypes. Importantly, clustering analysis revealed that five distinct clusters exist in B-ALL patients based on pathway activity, with each cluster displaying a unique pattern of pathway activation. Identifying pathway-based subtypes thus appears to be crucial, considering the inherent heterogeneity among patients with the same genetic subtype. In conclusion, a pathway-based stratification of the B-ALL could potentially allow for simultaneously targeting highly active pathways within each ALL subtype, and thus might open up new avenues of innovation for personalized/precision medicine in this cancer that continues to have poor prognosis in adult patients compared with the children.

Digital health, an emerging scientific domain, attracts increasing attention as artificial intelligence and relevant software proliferate. Pharmacogenomics (PGx) is a core component of precision/personalized medicine driven by the overarching motto "the right drug, for the right patient, at the right dose, and the right time." PGx takes into consideration patients' genomic variations influencing drug efficacy and side effects. Despite its potentials for individually tailored therapeutics and improved clinical outcomes, adoption of PGx in clinical practice remains slow. We suggest that e-health tools such as clinical decision support systems (CDSSs) can help accelerate the PGx, precision/personalized medicine, and digital health emergence in everyday clinical practice worldwide. Herein, we present a systematic review that examines and maps the PGx-CDSSs used in clinical practice, including their salient features in both technical and clinical dimensions. Using Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines and research of the literature, 29 relevant journal articles were included in total, and 19 PGx-CDSSs were identified. In addition, we observed 10 technical components developed mostly as part of research initiatives, 7 of which could potentially facilitate future PGx-CDSSs implementation worldwide. Most of these initiatives are deployed in the United States, indicating a noticeable lack of, and the veritable need for, similar efforts globally, including Europe.