Omics A Journal of Integrative Biology最新文献

Diabesity is a comorbidity of type 2 diabetes mellitus and obesity. Diabesity is a major global epidemic and a veritable planetary health burden. With diabesity, several clinical signs are present such as excess accumulation of fat, altered lipid metabolism, chronic inflammation, insulin resistance, disordered pancreatic β-cell metabolism, and hyperglycemia. We report here new potential candidate genes for diabesity, and the structural and functional effects of non-synonymous single nucleotide polymorphisms (nsSNPs) in these genes using a computational biology approach. A protein-protein interaction (PPI) network was constructed using Human Integrated Protein-Protein Interaction rEference (HIPPIE') data for 186 diabesity-associated genes from the Disease Gene Network (DisGeNET). Subsequently, the top 2% of nine centrality-ranked genes were identified as hub genes. Gene ontology enrichment analysis was performed with the same gene list using the Gene Ontology enRIchment anaLysis and visuaLizAtion (GORILLA) tool, and importantly, 63 enriched hub genes with no prior disease association were selected and their differential expressions in adipose, skeletal, and hepatic tissues were analyzed using Gene Expression Omnibus (GEO) profiles. Finally, the nsSNPs in the top five prioritized genes (EGFR, SRC, SQSTM1, CCND1, and RELA) were retrieved from Database of Single Nucleotide Polymorphisms (dbSNP) and subjected to deleterious variant analysis. The significant variants were subjected to structural prediction using AlphaFold, stability analysis, and molecular dynamics simulation using GROningen MAchine for Chemical Simulations (GROMACS). Taken together, the present computational biology research reports new molecular insights on diabesity candidate genes and the role of nsSNPs that may potentially contribute to diabesity. As diabesity and diabetes continue to be major planetary health challenges, these findings warrant further in vitro and clinical translation research with an eye to precision medicine and therapeutics innovation. Understanding the differences between wild type and variant proteins is crucial for developing interventions aimed at stabilizing these proteins in the prevention and treatment of diabesity.

Immunoinformatics, an integrative field consisting of bioinformatics and immunology, has showcased its potential in addressing zoonotic diseases, as evidenced during the Coronavirus disease 2019 (COVID-19) pandemic. However, its application in livestock health remains largely untapped. This opinion commentary explores how immunoinformatics, combined with advancements in genomics, multi-omics integration, and genome editing technologies, can revolutionize livestock management by enhancing disease resistance, vaccine development, and productivity. We examine the current and critical challenges, such as limited breed-specific data, computational barriers, and economic constraints, while highlighting the transformative potential of immunoinformatics in addressing these issues. We underscore the importance of leveraging immunoinformatics to bridge the phenotype-genotype gap, develop effective diagnostics and vaccines, and tackle emerging pathogens with zoonotic potential. By emphasizing interdisciplinary collaboration and the need for accessible veterinomics solutions, particularly in low- and middle-income countries, we outline herein the actionable steps to harness immunoinformatics for sustainable livestock management and global food security. In all, this opinion commentary aims to inspire a renewed focus on and veritable innovations in planetary health by unpacking and recognizing immunoinformatics' key role in shaping the future of veterinomics, the convergence of veterinary medicine with omics systems science, in the pursuit of agricultural sustainability.

There is a growing interest in harnessing natural compounds and bioactive phytochemicals to accelerate drug discovery and development, including in the treatment of human cancers. Receptor tyrosine kinases (RTKs) are critical regulators of many fundamental cellular processes and have been implicated in cancer pathogenesis as well as targets for anticancer drug development. The members of TAM, Tyro3, Axl, and MERTK subfamily RTKs, especially Mer, affect immune homeostasis in the tumor microenvironment. Hence, tyrosine-protein kinase Mer has emerged as one of the key factors in cancer susceptibility and metastasis and, by extension, as a potential target of relevance for cancer drug resistance. Here, we report, using an integrated virtual screening and simulation of phytochemicals from the IMPPAT 2.0 library, phytochemicals withanolide N and dryobalanolide as potential bioactive leads for developing anticancer drugs targeting tyrosine-protein kinase Mer. The study employed an integrated design, including physicochemical property analyses, binding affinity calculations, pan-assay interference compounds filtering, absorption, distribution, metabolism, excretion, and toxicity, and PASS analyses, in silico molecular dynamics simulations, followed by principal component analysis and free energy landscape. We call for further evaluation, validation, and translational medical research on these two phytochemicals in vitro and in vivo, with an eye to their putative therapeutic efficacy and safety in the field of oncology and anticancer drug discovery and development.

A systems medicine understanding of the regulatory molecular circuits that underpin breast cancer is essential for early cancer detection and precision/personalized medicine in clinical oncology. Transcription factors (TFs), microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) control gene expression and cell biology, and by extension, serve as pillars of the regulatory circuits that determine human health and disease. We report here the development of a regulatory circuit analysis program, miRCuit, constructing 10 different types of regulatory elements involving messenger RNA, miRNA, lncRNA, and TFs. Using the miRCuit, we analyzed expression profiling data from 179 invasive ductal breast carcinoma and 51 normal tissue samples from the Gene Expression Omnibus database. We identified eight circuit types along with two special types of circuits, one of which highlighted the significant roles of lncRNA CASC15, miR-130b-3p, and TF KLF5 in breast cancer development and progression. These findings advance our understanding of the regulatory molecules associated with breast cancer. Moreover, miRCuit offers a new avenue for users to construct circuits from regulatory molecules for potential applications to decipher disease pathogenesis.

UN Women is the United Nations "entity dedicated to gender equality and the empowerment of women". UN Women is an example of the institutions of global governance that followed the gender turn in women's rights over the past 2 decades. This opinion commentary unpacks a brief history of UN Women, and the ongoing disparities in gender diversity, equity, and inclusion (DEI) in science, engineering, and medicine, not to mention in science communication, with the aim to shed light on the adverse impacts of gender essentialism and gender binary. First, I argue that another world and liberatory structural change are indeed possible by resisting and refusing empty platitudes for band-aid solutions, disingenuous pleasantries and cultures of scheming for professional ladder-climbing that cloak the systemic causes-of-causes and sustain DEI inequities. Second, I argue for systems thinking and reflexive change in research cultures through queering global science, and rethinking everyday hegemonic assumptions and the prevailing blind spots in sex, gender, science, and society. Third, queer theory is not limited to studies of gender and sexuality. When used as a verb, "queering," its meaning broadens so as to mean critical examination of the unchecked assumptions and norms in a given field of scholarly inquiry. The DEI inequities in science, engineering, and medicine are real, harmful to individuals and communities in the present historical moment, and undermine intergenerational justice, not to mention hinder science and innovation. Going forward in the current decade amid uncertainty and polycrisis in world affairs and global democracy, the systemic gaps in gender equity in everyday laboratory life and on the streets ought to be remedied for global science and planetary health to be just, responsible, democratic, and innovative.

Structural variation (SV) typically refers to alterations in DNA fragments at least 50 base pairs long in the human genome. It can alter thousands of DNA nucleotides and thus significantly influence human health, disease, and clinical phenotypes. There is a shared and growing recognition that the emergence of effective computational tools and high-throughput technologies such as short-read sequencing and long-read sequencing offers novel insight into SV and, by extension, diseases affecting planetary health. However, numerous available SV tools exist with varying strengths and weaknesses. This is currently hampering the abilities of scholars to select the optimal tools to study SVs. Here, we reviewed 175 tools developed in the past two decades for SV detection, annotation, visualization, and downstream analysis of human genomics. In this expert review, we provide a comprehensive catalog of SV-related tools across different technology platforms and summarize their features, strengths, and limitations with an eye to accelerate systems science and planetary health innovations.

The current decade 2021-2030 was designated by the United Nations as the decade of healthy aging, which underlines the need for public health innovation for arthritis clinical care. The triglyceride-glucose (TyG) index is a novel and emerging parameter closely associated with diabetes and cardiovascular diseases and has been suggested to indicate the risk of arthritis. This study examined the longitudinal changes of TyG levels in relation to arthritis among a nationwide cohort of older Chinese adults. We recruited 1257 participants from a national cohort of older Chinese adults, the Chinese Longitudinal Healthy Longevity Survey. On the basis of the longitudinal changes in TyG between 2012 and 2014, we performed a k-means clustering analysis to classify the participants into four TyG groups: Class 1 with moderate and stable levels of TyG; Class 2 with low but rising level of TyG; Class 3 with consistently high TyG; and Class 4 with high and TyG-level rise compared with the baseline. After a 2-year follow-up, logistic regression was used to identify the association between TyG and the onset of arthritis. Compared with individuals in Class 1, those in Class 3 and Class 4 experienced a higher risk of arthritis, with an odds ratio (OR) of 2.823 (95% confidence interval [CI]: 1.113-7.160) and 2.848 (95% CI: 1.299-6.246), respectively. To the best of our knowledge, this is the first study exploring the association between dynamic longitudinal changes in TyG and arthritis. Further studies on world populations are called for.

The journey of a biomarker from analytical and clinical validation to clinical and public health utility is laden with a host of challenges. This opinion piece and innovation analysis presents an approach to biomarker discovery and development with a focus on tissue specificity and disease causality, using the case of hepatic disease.

Next-generation cancer phenomics by deployment of multiple molecular endophenotypes coupled with high-throughput analyses of gene expression offer veritable opportunities for triangulation of discovery findings in non-small cell lung cancer (NSCLC) research. This study reports differentially expressed genes in NSCLC using publicly available datasets (GSE18842 and GSE229253), uncovering 130 common genes that may potentially represent crucial molecular signatures of NSCLC. Additionally, network analyses by GeneMANIA and STRING revealed significant coexpression and interaction patterns among these genes, with four notable hub genes-GRK5, CAV1, PPARG, and CXCR2-identified as pivotal in NSCLC progression. Validation of these hub genes indicated their consistent downregulation in tumor tissues compared to normal counterparts. Gene expression across the endophenotypes representing pathological stages revealed distinct downregulation trends, emphasizing their putative roles as biomarkers for cancer progression. Moreover, three miRNAs (hsa-miR-429, hsa-miR-335-5p, and hsa-miR-126-3p) showed strong associations with these hub genes, while SREBF1 emerged as a relevant transcription factor. Pathway enrichment analysis identified the chemokine signaling pathway as significantly associated with these genes, highlighting its role in tumor progression and immune evasion. Cell-type enrichment analysis indicated that endothelial cells may play a significant role in NSCLC pathogenesis. Finally, survival analysis demonstrated that GRK5 is a potential oncogenic marker, whereas CAV1 may have a protective effect. These findings collectively underscore the critical molecular interactions in NSCLC and suggest novel paths for translational research, targeted therapies, and prognostic markers in clinical settings. They also attest to the promises of next-generation cancer phenomics using multiple endophenotypes for discovery and triangulation of novel findings.

Artificial intelligence (AI) and its applications in digital health, bioengineering, and society have significant material impacts on the environment owing to AI's vast energy demands and energy consumption, carbon footprints, and water usage to cool data centers and generate electricity to power the data centers. Yet, the environmental footprints of AI remain underappreciated and inadequately acknowledged. This is significant, particularly in this era of climate emergency and ongoing threats to planetary energy and water supplies. The vocabulary attached to AI often aims to mimic positive human capacities such as "warmness" and "care." However, these attempts to humanize AI and digital technology come with an anthropocentric gaze and blind spots that bracket out the environmental impacts and footprints of AI and privilege humans and technology over nonhuman animals and planetary ecological limits. In medicine, the environmental impacts of large language models range from water consumption and carbon emission to rare mineral usage. This commentary and innovation analysis question and queer the popular imagination of AI and digital technology as things that only exist in the immaterial world of cyberspace. In the course of research on AI in planetary health, we must be cognizant of its materiality, ecological impacts, and massive energy and water demands. We argue that moving away from anthropocentric narratives and vocabulary in AI design and praxis would bode well to live within planetary ecological limits so that AI and emerging digital technologies best serve robust and responsible science and all life on the planet Earth.