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PubMed2026

Geometric mechanogenomics: engineering boundary conditions for deterministic cell fate control.

In tissue development and regeneration, cellular behavior has traditionally been interpreted through biochemical signaling frameworks. However, cells exist within physically defined environments, where geometric boundary conditions - including confinement, curvature, anisotropy, and multicellular architecture - define the mechanical state space in which mechanical forces are generated, transmitted, and interpreted. Here, we introduce geometric mechanogenomics, a conceptual framework that positions geometry as an upstream spatial regulator linking tissue-scale boundary conditions to nuclear mechanics, chromatin organization, and genome regulation. We propose a boundary-to-nucleus axis through which geometric information is decoded by adhesion-mediated mechanotransduction, cytoskeletal force transmission, and nuclear mechanoregulation to regulate chromatin accessibility, epigenetic remodeling, and transcriptional programs. Rather than introducing new mechanotransduction pathways, this framework emphasizes that geometry spatially organizes conserved mechanotransductive machinery to generate context-dependent mechanogenomic outcomes. We further discuss how engineered geometries reduce morphogenetic stochasticity, coordinate multicellular organization, and establish mechanical memory that influences long-term cell fate. Finally, we highlight current challenges in establishing predictive geometry-to-genome relationships and discuss emerging opportunities enabled by spatial omics, artificial intelligence-assisted inverse design, and dynamic biomaterials for programmable mechanobiology, regenerative medicine, developmental biology, and disease modeling.

genome organization↗

PubMed2026

Induced pluripotent stem cell reprogramming: methodological evolution and challenges in clinical translation.

Cell reprogramming can transform somatic cells into induced pluripotent stem cells providing a platform for patient-specific disease modeling, drug screening and regenerative medicine research. Since the advent of OKSM-mediated reprogramming, the system of technical approaches has evolved continuously - from integrated viral vectors to non-integrated episomal systems and, more recently, chemical reprogramming and CRISPR approaches. The simultaneous advances in single-cell multi-omics, biomaterials engineering, and artificial intelligence have further refined the controllability and precision of the reprogramming process. Despite these innovations, problems persist that hinder clinical translation: incomplete epigenetic resetting, ongoing clonal heterogeneity, genomic instability in long-term culture, and the lack of standardized Good Manufacturing Practice protocols for large-scale manufacturing. This review summarizes the trajectory of iPSC reprogramming technologies, with special emphasis on the translational applicability of each modality. We evaluated viral and nonviral delivery systems, chemical reprogramming, strategies that aid gene editing, and emerging engineering platforms, including microfluidics, smart biomaterials, and artificial-intelligence-driven process optimization. We further identify the core "translational triltrilas", namely, the inherent tradeoffs between security, homogeneity, and scalability, and propose a comprehensive strategy to overcome these bottlenecks. By linking basic mechanistic understandings with industrial and regulatory considerations, this review aims to provide a route for transitioning iPSC technology from a laboratory tool to a clinically viable manufacturing platform.

clinical translation↗

PubMed2026

Brucellar spondylitis is associated with disturbance in gut microbiota and histamine metabolism associated inflammation.

BACKGROUND: The pathogenesis of brucellar spondylitis (BLS) has traditionally been considered to be primarily limited to local osteoarticular lesions. With the proposal of the "gut-spine axis" concept, the role of intestinal microecological dysbiosis in inflammatory spinal diseases has attracted in an increase of attention. The overactivated inflammatory cytokine network not only mediates bone destruction and intervertebral disc damage, but also forms a bidirectional interaction with gut microbiota dysbiosis through the "gut-spine axis," collectively driving disease progression. However, the inflammatory mechanism by which gut microbiota participates in the pathological process of BLS remains largely unclear. METHODS: This study recruited 20 BLS patients and 20 healthy donors. Multi-omics analysis including metagenomics, untargeted metabolomics, and targeted short-chain fatty acids (SCFAs) analysis, were used to compare the structural differences in gut microbiota between the two groups and screen for signature differential bacterial species. Plasma levels of histamine and histidine decarboxylase were measured by ELISA to clarify the role of differential histidine metabolic pathway in the disease. Additionally, plasma levels of lipopolysaccharide (LPS) and inflammatory cytokines (IL-1β, IL-6, IL-10, IL-17A, TNF-α) were detected by ELISA. The correlation between gut microbiota and inflammatory indicators was further analyzed. RESULTS: Compared to the healthy control group, the α-diversity of the gut microbiota in BLS patients was significantly reduced, with the microbial community structure exhibiting increased homogeneity. Beta diversity analysis revealed significant differences, suggesting that disease progression is associated with an overall imbalance in the gut microbiota and the deterioration of its specific structural composition. At the phylum level, the abundances of Actinomycetota, unclassified_d_Viruses, and Fusobacteriota were significantly increased in the gut microbiota of BLS patients compared to the control group, while the abundances of Bacillota and Pseudomonadota were significantly decreased. Further analysis revealed that, compared to the control group, the generic abundance of Enterococcus was significantly increased, while the proportions of Blautia, Faecalibacterium, Ruminococcus, Agathobacter, Roseburia, Clostridium, Eubacterium, Alistipes and Anaerobutyricum were significantly decreased. At the species level, the abundances of Enterococcus sp and Enterococcus-faecium were increased, whereas Blautia sp, Ruminococcus sp, Faecalibacterium sp, Faecalibacterium prausnitzii, Agathobacter rectalis, Eubacterium sp, Agathobacter sp, and Roseburia sp were decreased. Furthermore, untargeted metabolomics revealed that metabolites were enriched in the histidine metabolic pathway, and the levels of SCFAs including butyrate, isobutyrate, valerate, and 4-methylvalerate in the intestinal contents were reduced in BLS. Functional KEGG profiling revealed that key KOs involved in butyrate synthesis (e.g., K00074, K00172, K01640) and transport were globally downregulated in the patient group, whereas histidine decarboxylase KOs (K01693, K11755, K19787) that convert histidine to pro-inflammatory histamine were significantly enriched. The loss of butyrate-producing symbionts led to SCFAs deficiency and mucosal barrier disruption, creating ecological niches for facultatively anaerobic Enterococcus, which further exacerbated local inflammation via proteolytic fermentation and histamine production. Compared with the control group, BLS patients showed decreased plasma levels of IL-10, while levels of IL-1β, IL-6, IL-17A, and TNF-α were increased, and LPS levels were elevated. In addition, significantly elevated plasma pro-inflammatory LPS levels in patients with BLS suggest disruption of intestinal integrity and permeability. Correlation analysis indicated a close relationship between gut microbiota and inflammation. CONCLUSION: BLS is associated with gut microbiota dysbiosis and alterations in microbial metabolites, which may be linked to inflammatory responses and histamine metabolism. The differential microbial taxa identified in this study could be developed into a stool-based non-invasive diagnostic panel to facilitate early differentiation of BLS from other spinal disorders. Furthermore, restoring gut microbial balance through probiotic supplementation or dietary modulation may represent a promising adjunctive strategy to enhance the efficacy of standard antibiotic therapy and reduce disease recurrence.

Humans↗

PubMed2026

Freely available genomic datasets for atrial fibrillation research: current resources and analytical pipeline.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, characterized by clinical and genetic heterogeneity. Increasing use of genomics and other omics approaches has driven reliance on publicly available AF datasets to advance biological discovery. Thus, this systematic review aimed to identify freely available genomic AF datasets through Mendeley Data and its interconnected repositories, and to characterize the most common analyses performed on these data. The search was conducted in adherence to the PRISMA 2020 guideline. Nineteen freely available genomic AF datasets were identified: Summary statistics for 'Biobank-driven genomic discovery yields new insight into atrial fibrillation biology', hum0014.v8.58qt.v1, AF GWAS in UK Biobank, UK Biobank (Publication 9659), GWAS summary statistics from a 2025 multi-ancestry AF meta-analysis, GSE115574, GSE128188, GSE14975, GSE2240, GSE238242, GSE254133, GSE261170, GSE271748, GSE271839, GSE293813, GSE294456, GSE31821, GSE41177, and GSE79768. The GEO datasets were further examined using differential gene expression, functional enrichment, protein-protein interaction networks, hub gene analysis, microRNA target prediction, and gene clustering, as well as, for the more recently deposited datasets, eQTL colocalization, single-cell/single-nucleus clustering, cell-cell communication analysis, and gene-dosage-dependent transcriptional and electrophysiological profiling. These analyses show some consistency but also considerable heterogeneity in initial conditions, data normalization, and analytical methodological settings. In conclusion, only a limited number of datasets are freely available, so additional, well-characterized and standardized datasets are needed to provide a complete picture of the AF pathology.

Mendeley Data↗

PubMed2024

Developing a machine learning-based prognosis and immunotherapeutic response signature in colorectal cancer: insights from ferroptosis, fatty acid dynamics, and the tumor microenvironment.

INSTRUCTION: Colorectal cancer (CRC) poses a challenge to public health and is characterized by a high incidence rate. This study explored the relationship between ferroptosis and fatty acid metabolism in the tumor microenvironment (TME) of patients with CRC to identify how these interactions impact the prognosis and effectiveness of immunotherapy, focusing on patient outcomes and the potential for predicting treatment response. METHODS: Using datasets from multiple cohorts, including The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), we conducted an in-depth multi-omics study to uncover the relationship between ferroptosis regulators and fatty acid metabolism in CRC. Through unsupervised clustering, we discovered unique patterns that link ferroptosis and fatty acid metabolism, and further investigated them in the context of immune cell infiltration and pathway analysis. We developed the FeFAMscore, a prognostic model created using a combination of machine learning algorithms, and assessed its predictive power for patient outcomes and responsiveness to treatment. The FeFAMscore signature expression level was confirmed using RT-PCR, and ACAA2 progression in cancer was further verified. RESULTS: This study revealed significant correlations between ferroptosis regulators and fatty acid metabolism-related genes with respect to tumor progression. Three distinct patient clusters with varied prognoses and immune cell infiltration were identified. The FeFAMscore demonstrated superior prognostic accuracy over existing models, with a C-index of 0.689 in the training cohort and values ranging from 0.648 to 0.720 in four independent validation cohorts. It also responses to immunotherapy and chemotherapy, indicating a sensitive response of special therapies (e.g., anti-PD-1, anti-CTLA4, osimertinib) in high FeFAMscore patients. CONCLUSION: Ferroptosis regulators and fatty acid metabolism-related genes not only enhance immune activation, but also contribute to immune escape. Thus, the FeFAMscore, a novel prognostic tool, is promising for predicting both the prognosis and efficacy of immunotherapeutic strategies in patients with CRC.

Ferroptosis↗

PubMed2026

Advanced and underlying therapeutic strategies in transformed small cell lung cancer.

Transformed small-cell lung cancer (T-SCLC) is a clinically important form of histologic transformation and a mechanism of acquired resistance in non-small-cell lung cancer (NSCLC). It is associated with poor prognosis, with a median overall survival of only about 9-13 months. This review summarizes recent advances in the mechanisms, diagnosis, monitoring, and treatment of T-SCLC. Repeat biopsy remains the gold standard for confirming histologic transformation, whereas molecular profiling and liquid biopsy may facilitate early detection and longitudinal disease monitoring. Platinum-etoposide remains the most commonly used clinical standard after transformation, but its benefit is typically transient and durable disease control remains uncommon. Continuation of EGFR tyrosine kinase inhibitors combined with chemotherapy may prolong progression-free survival in selected patients but has not consistently improved overall survival. Anti-angiogenic therapy, particularly anlotinib, and chemo-immunotherapy have shown encouraging activity in selected patients, while emerging strategies targeting DLL3, MYC, SOX2, and epigenetic regulators may broaden the therapeutic landscape. Prospective studies integrating repeat tissue sampling, comprehensive genomic profiling, biomarker-guided patient stratification, pharmacogenomics, functional drug-sensitivity testing where feasible, and integrated multi-omics approaches are needed to advance molecularly guided and individualized treatment for T-SCLC.

advanced therapy↗

PubMed2026

Spatial niche remodeling of senescent liver-resident immune cells and its role in chronic liver diseases.

The liver serves the triple functions of metabolism, detoxification, and immune surveillance. Its unique immune microenvironment is shaped by continuous exposure to gut-derived antigens, pathogen-associated molecular patterns (PAMPs), and metabolites arriving via the portal vein, necessitating a delicate equilibrium between immune tolerance and effector activation. This equilibrium relies on the coordinated activities of diverse liver-resident immune cell populations-including Kupffer cells (KCs), liver sinusoidal endothelial cells (LSECs), hepatic stellate cells (HSCs), dendritic cells (DCs), tissue-resident memory T cells (TRM), innate-like T cells, including mucosal-associated invariant T (MAIT) cells, natural killer T (NKT) cells, and γδ T cells, innate lymphoid cells (ILCs, encompassing conventional NK cells and helper ILC subsets), and neutrophils. With advancing age and chronic injury, these resident immune cell populations undergo profound senescence-associated phenotypic reprogramming that is spatially organized along the portal-to-central axis of the hepatic lobule. Key mechanisms include: telomere dysfunction and DNA damage accumulation driving persistent activation of p53/p21 and p16/Rb pathways; mitochondrial dysfunction with mitochondrial DNA (mtDNA) leakage fueling the senescence-associated secretory phenotype (SASP) via the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway; epigenetic age acceleration, including genome-wide H3K27me3 heterochromatinization; and metabolic reprogramming toward glycolysis and lipid accumulation. This review proposes a "spatial niche remodeling" framework to integrate these cell-intrinsic senescence programs with their lobular context, intercellular communication network rewiring, and pathogenic roles across the spectrum of chronic liver disease-from steatosis through steatohepatitis, fibrosis, cirrhosis, to hepatocellular carcinoma. We critically evaluate emerging senotherapeutic strategies targeting specific liver-resident immune cell subsets, discuss the barriers to clinical translation, and identify priority areas for future investigation, including the application of spatial multi-omics, humanized models, and epigenetic clock-guided clinical trials.

Kupffer cells↗

PubMed2026

Thermophilic bacteria of the Arabian Gulf and their emerging biotechnological applications: current insights and future prospects.

Thermophilic bacteria represent a powerful class of extremophiles whose ability to thrive at elevated temperatures makes them indispensable to modern biotechnology. The Arabian Gulf characterized by extreme heat, geothermal systems, hot springs, oil reservoirs, and hypersaline habitats hosts a rich yet understudied reservoir of these organisms. This review consolidates current insights into the diversity, ecological niches, and biotechnological relevance of thermophilic bacteria isolated from the region. Dominant genera such as Bacillus, Geobacillus, Thermus, Anoxybacillus, and Brevibacillus exhibit remarkable physiological and molecular strategies that enable survival under intense thermal, saline, and pH stress. Their capacity to produce thermostable enzymes, including proteases, amylases, lipases, cellulases, and DNA polymerases, positions them as high-value contributors to sectors spanning bioenergy, pharmaceuticals, food processing, agriculture, and environmental remediation. Beyond enzyme production, emerging applications such as antimicrobial compound discovery, hydrocarbon bioremediation, wastewater treatment, and sustainable bioprocessing highlight the region's untapped biotechnological potential. However, systematic exploration remains limited, hindered by sparse isolation efforts, incomplete physiological profiling, and a lack of genomic and omics-driven studies. The review underscores the need for integrated approaches that merge classical microbiology with advanced molecular and systems-level tools. Collectively, thermophilic bacteria from the Arabian Gulf constitute a promising yet underutilized biological resource poised to drive sustainable industrial innovation and environmental solutions.

Arabian Gulf↗

PubMed2026

Integrative cross-tissue transcriptome-wide association and metabolomic analysis reveals novel genetic risk loci for aortic aneurysm.

BACKGROUND: Aortic aneurysm (AA) is a life-threatening cardiovascular condition with a strong genetic component, however, its molecular mechanisms remain poorly understood. Although genome-wide association studies (GWAS) have identified numerous risk loci, most prior studies have investigated genetic and metabolic factors separately, leaving the causal pathways from genetic variants to disease largely unexplored. METHODS: We established an integrative framework combining cross-tissue transcriptome-wide association studies (TWAS) with metabolomic mediation analysis. First, we integrated GWAS data from FinnGen R12 with multi-tissue expression quantitative trait loci (eQTL) data from Genotype-Tissue Expression Project (GTEx) V8, then performed cross-tissue TWAS using the Unified Test for MOlecular SignaTures (UTMOST) and single-tissue validation with the Functional Summary-based Imputation (FUSION) to prioritize susceptibility genes. Second, we applied Mendelian randomization (MR), colocalization, and Fine-mapping Of CaUsal gene Sets (FOCUS) to assess causality and identify high-confidence genes. Third, we performed metabolite mediation analysis to uncover metabolic pathways linking genetic variants to disease risk. Finally, we validated key findings in mouse models of thoracic aortic aneurysm (TAA) and abdominal aortic aneurysm (AAA) using Quantitative Real-Time Reverse Transcription Polymerase Chain Reaction (RT-qPCR) and Western blotting. RESULTS: We identified multiple novel susceptibility genes for AA and its subtypes. Key genes included ADH family members (ADH1A, ADH1B, ADH4, ADH6) and ZNF827, which showed cross-subtype associations with strong colocalization evidence in vascular tissues. Metabolite mediation analysis revealed significant pathways involving N-acetylphenylalanine and methionine sulfoxide. Functional enrichment revealed distinct biological mechanisms: AA and AAA were primarily associated with metabolic pathways, whereas TAA-related genes were enriched in developmental and contractile processes. PheWAS indicated no significant off-target associations. Critically, experimental validation in mouse models confirmed significant upregulation of ZNF827 in TAA and ADH6 in AAA at both mRNA and protein levels, corroborating the genetic predictions. CONCLUSION: This integrated cross-omics analysis identifies novel genetic loci and, crucially, uncovers specific nutrient-related metabolic pathways that mediate genetic risk. These findings provide a mechanistic basis for future nutritional and metabolic intervention studies in AA and its subtypes.

MAGMA↗

PubMed2026

Epigenetic profiling of circulating cell-free DNA for early detection and minimal residual disease assessment in lung cancer: a focus on DNA methylation.

Lung Cancer (LC) continues to be the biggest cause of cancer-related deaths around the world, mostly because of delayed diagnosis. Even if tissue biopsies and circulating tumor DNA (ctDNA) tests have revolutionized clinical management of LC patients, their effectiveness is restricted in settings with lower tumor burden, molecular heterogeneity, and bias in sampling approaches. In this scenario, the epigenetic profiling of cell-free DNA (cfDNA) stands out as a promising, less invasive approach, accurately detect cancer traces. Evidence from stage I-II disease and CT-detected pulmonary nodules supports the diagnostic potential of cfDNA methylation, although further validation in prospective screening cohorts remains necessary. Beyond genomic alterations, cfDNA epigenetic changes, including DNA methylation, chromatin organization, nucleosome positioning, and fragmentation patterns, reflect multi-dimensional complexity of tumor biology. These properties convey both the functional status and the origin of the circulating DNA fragments, accelerating for tumor integrating genomic analysis. Within this group, DNA methylation is the biologically robust and clinically well-established epigenetic marker, as alterations in methylation linked to cancer often occur in the early stages of tumorigenesis and are commonly found across different cancer cell types. Here, we explored the biological and clinical relevance of the epigenetic landscape of cfDNA in LC patients, particularly focusing on DNA methylation-based biomarkers and their evolving applications towards early diagnosis and post-surgical monitoring of minimal residual disease (MRD). We aimed to comprehensively overview analytical approaches for cfDNA methylation analysis, including targeted and genome-wide profiling strategies, and discuss their integration with machine learning (ML) and multi-omics frameworks in order to improve diagnostic performance and clinical applicability in LC management.

DNA methylation↗

PubMed2026

A voyage of reprogrammable metabolic bioengineering reshapes plant defense: from editing tools to synthetic systems.

Metabolic bioengineering has emerged as a transformative approach for reshaping plant defense by targeting intrinsic biosynthetic pathways to enhance immunity in modern agriculture. Moving beyond proof-of-concept metabolomics to broad-spectrum programmable pathway engineering addresses gaps in plant rational design and optimizes resilience in response to diverse environmental cues. This review aims to comprehensively highlight the transition of innovative approaches to phenolics, alkaloids, flavonoids, terpenoids, and benzoxazinoids, inferring adaptive reprogramming that mediates the growth-defense balance and functions as molecular sentinels in plants. Furthermore, decoding the volatile metabolome reveals a dynamic signaling interface that influences defense responses and stress-induced plant-microbe interactions, with the shikimate, jasmonate, and salicylate pathways functioning as central hubs for microbial deterrence and priming immune memory. Recent developments in multi-scalar genome-editing strategies, including CRISPR-driven combinatorial edits, enzyme orthogonalization, fluxomics, and spatially resolved multi-omics, reconfigure central and specialized metabolic fluxes toward improved defense function and regulation. Additionally, emerging tools, such as WUSCHEL2 and BABY BOOM transcriptional modules, and artificial engineering strategies integrating deep learning model-driven predictions facilitate rapid development of synthetic genetic circuits and support a predictive engineering of plants. Moreover, Mass spectrometry imaging (MSI) in spatial metabolomics enables to obtain structures and locations of unidentified endogenous metabolites within cells and tissues. Overall, this review emphasizes a diverse array of primary and secondary metabolites, spanning molecular concepts to recent advances in plant immune mechanisms. It also illustrates new frontiers in programmable metabolic engineering that accelerate the understanding of plant-microbe-metabolite cross-talks, offering strategies to improve plant resistance and advance sustainable agricultural solutions.

metabolic bioengineering↗

PubMed2026

Exogenous ABA enhances cold tolerance of Rhododendron yedoense var. poukhanense under subzero temperature: integrating physiology, transcriptome, and proteome.

Low temperature limits the growth and ornamental value of evergreen shrubs. Rhododendron yedoense var. poukhanense, an important ornamental shrub from Northeast China, frequently suffers freezing damage during winter. While exogenous abscisic acid (ABA) enhances cold tolerance in many plants, its molecular mechanisms at subzero temperatures remain poorly understood in non-model species lacking chromosome-level reference genomes. This study investigated the effects of exogenous ABA on freezing tolerance in R. yedoense var. poukhanense at -4 °C using an integrated physiological, transcriptomic, and proteomic approach. Cutting seedlings were subjected to four treatments: CK (22°C control), A (22°C + ABA), LT (-4°C), and ALT (-4°C + ABA). Photosynthetic pigments, osmotic regulation substances, antioxidant enzyme activities, and malondialdehyde (MDA) content were measured. Transcriptome sequencing and quantitative proteomics were performed, and transcriptome data were validated by quantitative real-time PCR (qRT-PCR) of 15 selected genes. ABA pretreatment reduced visible cold injury severity, partially preserved photosynthetic pigments, decreased MDA content by 28.7%, and promoted recovery of catalase (+43.6%), superoxide dismutase (+31.1%), and peroxidase (+20.0%) activities under freezing stress. Transcriptome analysis revealed 8, 444 differentially expressed genes (DEGs) in LT versus CK and 6, 481 DEGs in ALT versus CK, representing a 23% reduction in transcriptional reprogramming scope attributable to ABA priming. The ALT versus LT comparison identified only 1, 690 additional DEGs, indicating that most cold-responsive genes were pre-activated during the ABA priming phase. Proteome analysis identified 1, 461 differentially expressed proteins (DEPs) in ALT versus CK. Integrated analysis revealed extensive post-transcriptional regulation, with transcript-protein concordance of only 1.0-4.1%, and co-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in both omics layers. qRT-PCR validation confirmed high reliability of the transcriptome data (R2 = 0.8500). These findings demonstrate that exogenous ABA enhances freezing tolerance through multi-layered molecular regulation encompassing transcriptional buffering, translational reprogramming, and functional reallocation from photosynthesis to stress protection. This study provides the first integrated physiology-transcriptome-proteome framework for ABA-mediated freezing tolerance in an evergreen ornamental shrub and offers theoretical support for ABA-based winter protection strategies.

Rhododendron yedoense var. Poukhanense↗

PubMed2022

Systems Analysis Reveals Contraceptive-Induced Alteration of Cervicovaginal Gene Expression in a Randomized Trial.

Hormonal contraceptives (HCs) are vital in managing the reproductive health of women. However, HC usage has been linked to perturbations in cervicovaginal immunity and increased risk of sexually transmitted infections. Here, we evaluated the impact of three HCs on the cervicovaginal environment using high-throughput transcriptomics. From 2015 to 2017, 130 adolescent females aged 15-19 years were enrolled into a substudy of UChoose, a single-site, open-label randomized, crossover trial (NCT02404038) and randomized to injectable norethisterone-enanthate (Net-En), combined oral contraceptives (COC), or etonorgesterol/ethinyl-estradiol-combined contraceptive vaginal ring (CCVR). Cervicovaginal samples were collected after 16 weeks of randomized HC use and analyzed by RNA-Seq, 16S rRNA gene sequencing, and Luminex analysis. Participants in the CCVR arm had a significant elevation of transcriptional networks driven by IL-6, IL-1, and NFKB, and lower expression of genes supporting epithelial barrier integrity. An integrated multivariate analysis demonstrated that networks of microbial dysbiosis and inflammation best discriminated the CCVR arm from the other contraceptive groups, while genes involved in epithelial cell differentiation were predictive of the Net-En and COC arms. Collectively, these data from a randomized trial represent the most comprehensive "omics" analyses of the cervicovaginal response to HCs and provide important mechanistic guidelines for the provision of HCs in sub-Saharan Africa.

HIV↗

PubMed2026

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

big data analytics↗

PubMed2026

Integrated Metabolomic and Transcriptomic Analysis Reveals Tissue-Specific Secondary Metabolic Differentiation and Indole Alkaloid Accumulation in Evodia rutaecarpa.

Evodia rutaecarpa is a valuable medicinal plant, yet its non-medicinal tissues remain largely underexplored. Here, we integrated ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS)-based widely targeted metabolomics and RNA sequencing (RNA-seq) transcriptomics to systematically profile the metabolic and transcriptional landscapes of roots, stems, leaves, and flowers of Evodia rutaecarpa (Juss.) Benth. Our aim was to characterize tissue-specific metabolic differentiation and its underlying transcriptional regulatory mechanisms. Metabolomic analysis, employing principal component analysis (PCA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) with robust model parameters (R2Y > 0.9, Q2 > 0.5), identified 3090 differential metabolite features (variable importance in projection, VIP > 1.0; p < 0.05) across the four tissues, which exhibited distinct tissue-specific clustering patterns. Integrated Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and weighted gene co-expression network analysis (WGCNA) revealed that roots specifically accumulated quinolone alkaloids and flavonoid glycosides, accompanied by the coordinated upregulation of genes involved in flavonoid and phenylpropanoid biosynthetic pathways. In contrast, stems, leaves, and flowers were enriched in indole alkaloids (evodiamine and rutaecarpine) and volatile oil precursors, with concurrent upregulation of genes involved in tryptophan metabolism and indole alkaloid biosynthesis (e.g., tryptophan decarboxylase, TDC; s N-methyltransferase, NMT). Notably, leaves and flowers displayed particularly high accumulation levels of these bioactive alkaloids, suggesting their potential as alternative sources for industrial and pharmaceutical applications. WGCNA further identified multiple transcription factors and structural gene modules tightly correlated with evodiamine accumulation, offering promising candidate regulators for future biosynthetic pathway engineering. Collectively, this multi-omics integration study systematically elucidates the tissue-partitioned secondary metabolism of Evodia rutaecarpa (Juss.) Benth. and provides a solid scientific foundation for full-plant resource utilization, targeted development of non-medicinal tissues, and future metabolic engineering of indole alkaloid production.

Evodia rutaecarpa↗

PubMed2026

A Landscape of Drosophila melanogaster Disease Models: From Genetic Platforms to Cross-Disease Mechanisms and Translational Research.

Modeling human diseases using the fruit fly (Drosophila melanogaster) has established itself as a cornerstone of functional genomics and preclinical medicine. Despite its anatomical simplicity, the Drosophila genome shares remarkable functional conservation with human disease-related genes, enabling the study of complex physiological traits through accessible tissue models. Furthermore, beyond individual disease models, we propose a framework demonstrating how these diseases converge at common molecular centers, such as the breakdown of protein homeostasis, mitochondrial dysfunction, chronic inflammation, and organ-to-organ communication. Finally, we discuss strategies for integrating the Drosophila platform into drug development pipelines and establishing standards to enhance inter-laboratory reproducibility. Overall, this review highlights the enduring value of fruit flies as a model system, particularly when combined with AI-omics approaches to transform complex biological datasets into actionable therapeutic strategies.

Drosophila↗

PubMed2026

Mechanism of Action of Hedyotis diffusa Extract in a Rat Model of Acute Lung Injury Based on Transcriptomic Analysis.

OBJECTIVE: This study established a rat model of lipopolysaccharide (LPS)-induced acute lung injury (ALI) to evaluate pathological damage, collagen deposition, inflammatory cytokine levels, and key gene/protein expression following Hedyotis diffusa water extract (HDWE) intervention. Combined with ultra-high-performance liquid chromatography-quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS), transcriptomic analysis, and molecular simulation, this study identified the bioactive components of HDWE, evaluated their potential interactions with ALI-related targets, and explored the multi-omics-based protective mechanisms of HDWE. METHODS: Thirty-six Sprague-Dawley (SD) rats were randomly divided into six groups: Control group, ALI group, DXMS group, HDWE-L group (100 mg/kg), HDWE-M group (200 mg/kg), and HDWE-H group (300 mg/kg). Hematoxylin and eosin (H&E) and Masson's trichrome staining were used to evaluate lung pathological changes and collagen deposition. Enzyme-linked immunosorbent assay (ELISA) was used to measure serum tumor necrosis factor-α TNF-α interleukin-1β IL-1β, erleukin-6 (IL-6), and interleukin-10 (IL-10) levels. Transcriptomic analysis identified differentially expressed genes (DEGs), followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), receiver operating characteristic (ROC), and immune infiltration analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) detected the mRNA expression levels of SPHK1, RELA, and NFKBIA. Immunohistochemistry evaluated the expression of eight hub targets, including endothelin-1 (EDN1), sphingosine kinase 1 (SPHK1), intercellular adhesion molecule 1 (ICAM1), interleukin-17 (IL-17), prostaglandin-endoperoxide synthase 2 (PTGS2/COX-2), NF-κB p65 (encoded by RELA), WT1-associated protein (WTAP), and myeloperoxidase (MPO). UHPLC-Q-Orbitrap HRMS characterized HDWE constituents. Molecular docking analysis was performed between 22 compounds and eight hub targets, followed by 100 ns molecular dynamics simulations and molecular mechanics-Poisson-Boltzmann surface area (MM/PBSA) binding free energy calculations for five core targets. Compared with the control group, the ALI group showed increased levels of TNF-α (86%), IL-1β (107%), and IL-6 (66%), accompanied by a 43% reduction in IL-10 and a 300% increase in lung collagen deposition. All HDWE doses alleviated inflammatory responses, with medium-dose HDWE showing the most pronounced effects. Specifically, medium-dose HDWE increased IL-10 levels by 52% and reduced IL-6, TNF-α, and IL-1β levels by 18%, 22%, and 11%, respectively. Transcriptomic analysis identified 2512 DEGs between the control group and ALI groups, 832 exclusive DEGs between the ALI group and HDWE-M groups, and 876 overlapping DEGs enriched in TNF, IL-17, and NF-κB signaling pathways. The eight-hub-gene diagnostic model achieved an area under the curve (AUC) of 0.969. RELA, SPHK1, and four other hub genes showed positive correlations with Th1, Th17, and neutrophil infiltration. In the ALI group, SPHK1, RELA, and NFKBIA mRNA expression levels were 1.30-, 0.96-, and 0.71-fold of those in the control group, respectively. Compared with the ALI group, high-dose HDWE treatment and low-dose HDWE treatment reduced SPHK1 expression to 0.62- and 0.57-fold, respectively, and increased NFKBIA expression to 1.68- and 1.58-fold, respectively. High-dose HDWE treatment reduced RELA expression to 0.43-fold. The expression levels of inflammation-related proteins were increased in the ALI group and were reduced after HDWE treatment. Twenty-two HDWE components were identified, 16 of which met the docking criteria. Asperulosidic acid exhibited favorable predicted binding affinities with all eight targets, with calculated binding free energies of -14.74, -14.92, -17.58, -23.04, and -16.10 kcal/mol for MPO, IL-17, NF-κB p65, PTGS2/COX-2, and SPHK1, respectively. CONCLUSIONS: This study provides systematic in vivo pharmacodynamic and in silico component-target evidence regarding the protective effects of HDWE against LPS-induced ALI. HDWE treatment increased NFKBIA expression and reduced SPHK1, RELA, and multiple inflammatory protein levels, suggesting that HDWE may regulate the IL-17/NF-κB-associated inflammatory network, although direct causal relationships require further validation. Asperulosidic acid may represent a key bioactive component with broad target-binding potential. This study was limited by the use of an LPS-induced rat ALI model without gene knockout or target inhibitor validation; therefore, further functional experiments are required to confirm the proposed regulatory mechanisms.

Hedyotis diffusa↗

PubMed2025

CRISPR as a Tool to Uncover Gene Function in Polycystic Ovary Syndrome: A Literature Review of Experimental Models Targeting Ovarian and Metabolic Genes.

Polycystic ovary syndrome (PCOS) is a complex disorder characterized by reproductive abnormalities such as hyperandrogenism, ovulatory dysfunction, and polycystic ovarian morphology, and is frequently accompanied by metabolic disturbances such as insulin resistance, obesity and dyslipidemia. Genome-wide association studies (GWASs) have identified several susceptibility loci, yet little is known about their functional implications. Clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (CRISPR/Cas9) has emerged as a powerful gene editing tool in bridging this gap by allowing researchers to directly target candidate genes in ovarian and metabolic pathways. For instance, experimental models have highlighted the role of CYP17A1 and DENND1A.V2 in androgen excess, anti-Müllerian hormone (AMH) in follicular arrest, and insulin receptor substrate 1 (IRS1) and PPARγ in insulin signaling and adipogenesis. To highlight the multifactorial nature of PCOS, animal models, including zebrafish and rodents, have been used to reveal interactions between reproductive and metabolic phenotypes. Nevertheless, most studies remain restricted to single-gene models, and dual-gene models or combined gene editing and hormonal induction models remain underexplored. Future research integrating precision editing, multi-omic platforms, and patient-derived organoids may provide more accurate disease models and novel therapeutic strategies.

Polycystic Ovary Syndrome↗