Omics A Journal of Integrative Biology最新文献

Drug delivery innovation is an important pillar of systems pharmacology wherein nanotechnology offers significant prospects. This expert review examines and unpacks how core-shell nanoparticles (NPs) could revolutionize drug delivery systems and play a key role in advancing personalized and precision medicine. Core-shell NPs have gained attention as flexible tools for drug delivery due to their distinct structure, which features a core material enclosed by a protective shell. This setup offers multiple benefits, such as effective drug encapsulation, shielding the drug from degradation, and allowing for controlled release. Accordingly, the core serves as a safe storage area for the drug while the shell manages the release speed, providing added stability and supporting sustained delivery. By enabling targeted drug release, this controlled mechanism can help improve treatment outcomes and reduce side effects. Various materials, including polymers, lipids, and inorganic substances create these NPs. Biodegradable polymers, such as poly(lactic-co-glycolic acid) and poly(lactic acid), are popular choices because they offer adjustable degradation rates, which further control how the drug is released. These materials can be tailored for better drug loading, compatibility with the host organism, and specific chemical properties to suit different therapeutic needs. Research into core-shell NPs has been advancing in many therapeutic areas, highlighting their potential for drug delivery innovations. The potential of core-shell NPs to revolutionize drug delivery is not just a possibility but a promising reality that could significantly advance the field of personalized/precision medicine.

Interleukin-2-inducible T-cell kinase (ITK) is a critical tyrosine kinase enzyme that is involved in the activation and differentiation of T cells. ITK is mainly found in T cells, which plays an essential role in controlling T-cell receptor signaling and downstream pathways. ITK regulates the synthesis of cytokines, particularly interleukin-2 (IL-2), and the development of Th2 cells. ITK is of interest for drug discovery and molecular targeting in immunology, autoimmune diseases, and cancer. Here, we report a structure-based virtual screening utilizing a collection of small molecules obtained from the PubChem database with an eye on the discovery of drugs targeting ITK. The compounds were selected according to compliance with the Lipinski's rule of five. The molecular docking investigation focused on prioritizing binding affinity and specific interaction toward the kinase domain. The highest-ranking search results were subjected to identification of possible pan-assay interference compounds (PAINS), assessment of pharmacokinetic properties, and estimation of pharmacological activity using Prediction of Activity Spectra of Substances (PASS) analysis. The interactions among these chemicals and the salient residues in the interleukin-2-inducible T-cell kinase (ITK) kinase domain were unpacked using a two-dimensional approach. The reference inhibitor ITK-Inhibitor-2 (IMM) and four elucidated compounds with PubChem CIDs, namely, 90442621 (PFB), 141764004 (FTP), 149213796 (FPP), and 145983307 (MBD), showed significant binding affinity of -8, -10.4, -9.8, -10.2, and -10.7 kcal/mol, respectively, and high selectivity for the ITK binding pocket. In conclusion, this study reports on the potential of several compounds for therapeutic targeting of ITK. Furthermore, structural analysis revealed the interaction of proposed compounds and active site residues within the ATP-binding pocket is highly similar to known inhibitors but shares distinct interaction patterns that could improve specificity. This specificity and optimization hold potential for the development of next-generation ITK inhibitors with possible applications in the treatment of immune-related disorders and cancers. Further in vitro, in vivo, and translational clinical research are called for.

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.

Innovation in drug discovery for human diseases stands to benefit from systems science and next-generation phenomics approaches. An example is idiopathic pulmonary fibrosis (IPF) that is a chronic pulmonary disorder leading to respiratory failure and for which preventive and therapeutic medicines are sorely needed. Matrix metalloproteinases (MMPs), particularly MMP1 and MMP7, have been associated with IPF pathogenesis and are thus relevant to IPF drug discovery. This study evaluates the comparative therapeutic potentials of doxycycline, pirfenidone, and nintedanib in relation to MMP1 and MMP7 using molecular docking, molecular dynamics simulations, and a next-generation phenomics approach. Adsorption, distribution, metabolism, excretion, and toxicity analysis revealed that doxycycline and nintedanib adhered to Lipinski's rule of five, while pirfenidone exhibited no violations. The toxicity analysis revealed favorable safety profiles, with lethal dose 50 values of doxycycline, pirfenidone, and nintedanib being 2240kg, 580, and 500 mg/kg, respectively. Homology modeling validated the accuracy of the structures of the target proteins, that is, MMP1 and MMP7. The Protein Contacts Atlas tool, a next-generation phenomics platform that broadens the scope of phenomics research, was employed to visualize protein contacts at atomic levels, revealing interaction surfaces in MMP1 and MMP7. Docking studies revealed that nintedanib exhibited superior binding affinities with the candidate proteins (-6.9 kcal/mol for MMP1 and -7.9 kcal/mol for MMP7) compared with doxycycline and pirfenidone. Molecular dynamics simulations further demonstrated the stability of protein-ligand complexes. These findings highlight the notable potential of nintedanib in relation to future IPF therapeutics innovation. By integrating in silico and a next-generation phenomics approach, this study opens up new avenues for drug discovery and development for IPF and possibly, for precision/personalized medicines that consider the molecular signatures of therapeutic candidates for each patient.

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.