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PubMed2026

Multi-omics-based study on the biological characteristics of kidney renal deficiency and blood stasis in ankylosing spondylitis.

OBJECIVE: To explore the objective biological evidence for the classification and diagnosis of Traditional Chinese Medicine (TCM) syndromes in ankylosing spondylitis (AS) using multiomics analysis. METHODS: Patients with AS were categorized into kidney deficiency and blood stasis syndrome (SX group) and damp-heat stasis syndrome (SR group). Transcriptomic sequencing and quantitative plasma proteomics were performed on patients with AS and healthy volunteers. Multiomics integration was used to characterize the biological basis of AS with renal deficiency and blood stasis syndrome. Specific proteins were validated by quantitative reverse transcription-polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA). RESULTS: Transcriptomic sequencing identified 31 significantly upregulated genes in patients with AS compared to healthy controls. These genes were primarily involved in tumor necrosis factor, interleukin-17, and nuclear factor kappa-B signaling pathways, as well as osteoblast differentiation and various viral infection pathways. Differentially expressed genes, including intercellular adhesion molecule 1 (ICAM1), 6-phosphofructo-2-kinase, cyclin-dependent kinase inhibitor 1A, interleukin 1 receptor antagonist, integrin alpha IIb, and myosin light chain 9 were more upregulated in the SX group than in the SR group. Quantitative proteomics identified 723 differential proteins associated with the disease and 788 differential proteins between the SX and SR groups. Notable proteins such as myeloperoxidase, cluster of differentiation 14, macrophage simulating 1 (MST1), and Ras homolog enriched in brain may serve as characteristic proteins of the SX group. By integrating transcriptomic and proteomic data, 45 associated differential molecules involved in platelet activation, pathogenic intestinal flora infection, glycolysis/gluconeogenesis, and T-cell receptor signaling pathways were identified in patients with AS compared to healthy controls. Additionally, ICAM1, MST1, C-X-C motif chemokine ligand 8 (CXCL8), suppressor of cytokine signaling 3 (SOCS3), and insulin-like growth factor binding protein 1 (IGFBP1) were detected in TCM syndromes by RT-qPCR and ELISA, showing upregulation in AS renal deficiency and blood stasis syndromes, which is consistent with the proteomic and transcriptomic results. CONCLUSIONS: ICAM1, MST1, CXCL8, SOCS3, and IGFBP1 were identified as biomarkers of renal deficiency and blood stasis syndrome in AS. This study provides a biological basis for the differential diagnosis of TCM syndromes in AS, offering new insights into Chinese medicine evidence and more precise Chinese medicine treatments for AS.

Humans↗
PubMed2006

Systems approaches to the networks of aging.

The aging of an organism is the result of complex changes in structure and function of molecules, cells, tissues, and whole body systems. To increase our understanding of how aging works, we have to analyze and integrate quantitative evidence from multiple levels of biological organization. Here, we define a broader conceptual framework for a quantitative, computational systems biology approach to aging. Initially, we consider fractal supply networks that give rise to scaling laws relating body mass, metabolism and lifespan. This approach provides a top-down view of constrained cellular processes. Concomitantly, multi-omics data generation build such a framework from the bottom-up, using modeling strategies to identify key pathways and their physiological capacity. Multiscale spatio-temporal representations finally connect molecular processes with structural organization. As aging manifests on a systems level, it emerges as a highly networked process regulated through feedback loops between levels of biological organization.

Aging↗
PubMed2026

Can host genetics transform the sustainable control of tropical theileriosis? Insights from the Tick-Theileria interface.

Tropical theileriosis, caused by the tick-transmitted apicomplexan parasite Theileria annulata, remains a major constraint on cattle production across North Africa, the Mediterranean basin, the Middle East and South Asia. Current control depends on acaricides, the theilericidal drug buparvaquone and live attenuated schizont vaccines, but acaricide resistance, buparvaquone-resistance mutations and the logistical demands of vaccination are eroding the sustainability of these tools. Host genetics offers a complementary and durable alternative. Indigenous Bos indicus breeds are consistently more resistant to ticks and tolerate T. annulata infection better than exotic Bos taurus cattle, and this advantage has a measurable heritable component. Unlike previous reviews, which treat tick resistance, T. annulata immunobiology and livestock genomic selection as separate subjects, we integrate all three and assess host genetics specifically against the failure modes of current control. We review the tick, parasite and host interface, the evidence for natural resistance, and the genetic and immunological mechanisms involved, including signal-regulatory protein, bovine major histocompatibility complex class II and inflammatory pathway genes. We then assess whether genomic selection, multi-omics, machine learning and gene editing can translate these mechanisms into resistant cattle, and we weigh the biological, economic and infrastructural barriers to implementation. The evidence indicates that host genetics will not replace existing control but could reduce reliance on acaricides and chemotherapy. That contribution remains prospective rather than demonstrated: no resistance marker for T. annulata has yet been validated, prediction accuracies are moderate and transfer poorly between breeds, and no endemic production system has implemented selection for resistance.

Animals↗
PubMed2026

The molecular landscape of chordoma: Current frontiers from multi-omics to artificial intelligence.

Chordoma is a rare and aggressive malignant bone tumor of the axial skeleton that has historically challenged clinicians due to its complex anatomical locations and a high recurrence rate of up to 85%. This review synthesizes the most recent advances in chordoma research and offers an overview of how multi-omics, advanced immunology, and artificial intelligence are reshaping the treatment paradigm. Central to its pathogenesis is the T-box transcription factor Brachyury, which this review highlights as both the pathognomonic diagnostic marker and the primary therapeutic vulnerability. Cutting-edge innovations targeting this driver include covalent small-molecule binders, targeted protein degradation, and peptide-centric CAR-T cells designed to attack the intracellular oncoprotein. The tumor immune microenvironment is functionally dynamic, and new dimensions in cellular therapy, such as dual-specific CAR constructs and NK-cell platforms, are being engineered to neutralize immunosuppressive factors. Beyond biological insights, the review emphasizes the role of computational biology, specifically how deep-learning and machine-learning models achieve expert-level precision in tumor segmentation and personalized survival forecasting. By integrating genomic, transcriptomic, epigenomic, and proteomic data, multiomics approaches can fully elucidate chordoma subtypes and underlying resistance mechanisms, ultimately paving the way for more precise and personalized therapeutic strategies.

Humans↗
PubMed2025

Integrating Imaging-Derived Clinical Endotypes with Plasma Proteomics and External Polygenic Risk Scores Enhances Coronary Microvascular Disease Risk Prediction.

Coronary microvascular disease (CMVD) is an underdiagnosed but significant contributor to the burden of ischemic heart disease, characterized by angina and myocardial infarction. The development of risk prediction models such as polygenic risk scores (PRS) for CMVD has been limited by a lack of large-scale genome-wide association studies (GWAS). However, there is significant overlap between CMVD and enrollment criteria for coronary artery disease (CAD) GWAS. In this study, we developed CMVD PRS models by selecting variants identified in a CMVD GWAS and applying weights from an external CAD GWAS, using CMVD-associated loci as proxies for the genetic risk. We integrated plasma proteomics, clinical measures from perfusion PET imaging, and PRS to evaluate their contributions to CMVD risk prediction in comprehensive machine and deep learning models. We then developed a novel unsupervised endotyping framework for CMVD from perfusion PET-derived myocardial blood flow data, revealing distinct patient subgroups beyond traditional case-control definitions. This imaging-based stratification substantially improved classification performance alongside plasma proteomics and PRS, achieving AUROCs between 0.65 and 0.73 per class, significantly outperforming binary classifiers and existing clinical models, highlighting the potential of this stratification approach to enable more precise and personalized diagnosis by capturing the underlying heterogeneity of CMVD. This work represents the first application of imaging-based endotyping and the integration of genetic and proteomic data for CMVD risk prediction, establishing a framework for multimodal modeling in complex diseases.

Cardiovascular Disease↗
PubMed2026

Osteoarthritis Year in Review 2026: Genetics, genomics and epigenetics.

OBJECTIVE: The purpose of this narrative review is to highlight advances made over the past 12 months in the field of osteoarthritis (OA) genetics, genomics and epigenomics, with a particular focus on the interpretation of OA risk loci through functional genomic and regulatory approaches. DESIGN: PubMed and Europe PMC were searched to identify studies relevant to OA genetics, genomics and epigenomics published between 1st March 2025 and 30th April 2026. Searches used combinations of terms relating to genetics, genomics, epigenomics, functional genomics, molecular quantitative trait loci, chromatin accessibility and enhancer biology. Studies were limited to human subjects and English-language publications, with additional articles identified through citation screening and expert knowledge of the field. RESULTS: Over the past year, the field has continued to transition from large-scale locus discovery towards biological interpretation of OA genetic risk. Major advances included the largest OA genome-wide association study to date, further development of polygenic risk score approaches, and increasing integration of molecular quantitative trait loci, chromatin accessibility, and enhancer biology datasets to prioritise effector genes and elucidate regulatory mechanisms. Several studies highlighted the highly context-dependent nature of OA genetic risk mechanisms, demonstrating that distinct tissues, cell types, and regulatory layers can identify different candidate effector genes at the same locus. Additional developments included increasing application of singlecell and multi-omic technologies to study OA-relevant tissues. CONCLUSION: Recent advances in OA genetics have shifted the field from locus discovery towards mechanistic interpretation. Emerging evidence demonstrates that the biological consequences of genetic variation are highly dependent upon tissue, cell state and disease context, with different functional genomic approaches often prioritising distinct candidate genes and regulatory mechanisms at the same susceptibility locus. Together, these findings suggest that OA risk loci should increasingly be viewed as dynamic regulatory systems rather than simple variant-to-gene relationships, providing a framework for future studies aimed at resolving causal mechanisms, defining disease endotypes, and identifying therapeutic targets.

Genetics↗
PubMed2025

Integrated proteomic network analysis reveals PTPRC as a central hub protein orchestrating co-expression modules and metabolic dysregulation in renal carcinoma: PTPRC protein molecular action.

The occurrence of renal carcinoma is closely related to a variety of molecular mechanisms and metabolic disorders. PTPRC (protein tyrosine phosphatase receptor C), as an important regulatory protein, was studied to reveal the role of PTPRC in renal carcinoma through comprehensive proteomic network analysis, especially its core position in the coordination of co-expression modules and metabolic disorders. This study was the first to download and process multiple publicly available renal cancer transcriptome data to conduct differential gene expression analysis across datasets. Functional enrichment and disease ontology analysis were performed on the transcriptome of renal cancer, and weighted gene co-expression network (WGCNA) was constructed. The results showed that comprehensive principal component analysis revealed significant differences in the transcriptome of renal cancer, and functional annotation revealed specific pathways associated with renal cancer. WGCNA analysis identified tumor-associated co-expression modules, while multi-omics analysis further identified core regulatory networks including PTPRC. As a central hub protein, PTPRC plays an important coordinating role in the co-expression module and metabolic dysregulation of renal carcinoma. This discovery provides a new perspective for understanding the molecular mechanism of kidney cancer.

Humans↗
PubMed2025

Microbiology Galaxy Lab: The first community-driven gateway for reproducible and FAIR analysis of microbial data.

The explosion of microbial omics data has outpaced the ability of many researchers to analyze it, with complex tools and limited computational resources creating barriers to discovery. To address this gap, we present the Microbiology Galaxy Lab: a free, globally accessible, community-supported platform that combines state-of-the-art analytical power with user-friendly accessibility. Supported by the Galaxy and global microbiology communities, this platform integrates over 315 tool suites and 115 curated workflows, enabling comprehensive metabarcoding, (meta)genomic, (meta)transcriptomic, and (meta)proteomic data analysis within a FAIR-aligned environment. It also supports research in the health and infectious disease sectors, as well as in environmental microbiology. The platform's utility is exemplified through various use cases, including antimicrobial resistance tracking, biomarker prediction, microbiome classification, and functional annotation of key microbes. Built on reproducibility and community engagement, it supports creation, sharing, and updating of best-practice workflows. Over 35 tutorials and learning paths empower scientists, fostering an ecosystem that keeps resources at the forefront of microbial science. The Microbiology Galaxy Lab enables collective analysis, democratising research, thereby accelerating discovery across the global microbiology community (microbiology.usegalaxy.org, .eu, .org.au, .fr).

Journal Article↗
PubMed2026

Genomic characterization of aggressiveness in pituitary neuroendocrine tumors.

BACKGROUND: Aggressive evolution of PitNETs is rare; metastatic spread is even more. Defining aggressiveness and malignancy is challenging, subsequently hard to predict, and to understand. The aim was to provide a molecular definition of aggressiveness using genomic approaches. METHODS: PitNETs from 206 patients were included. Associations between 9 clinicopathological features of aggressiveness and PitNETs' omics were explored. Omics included transcriptome, DNA methylation, chromosomal alterations, and mutations. Clonal tumor evolution was monitored in 7 patients. RESULTS: Among the 9 clinicopathological features of aggressiveness, only rapid progression, progression after radiotherapy, Ki67/MIB1 proliferation index ≥10%, temozolomide treatment, metastases, and specific death were associated with specific omics signatures, while tumour maximal diameter ≥40 mm, cavernous, and sphenoid invasion were not. The omic signatures associated with these features of aggressiveness overlapped but remained distinct between corticotroph and mammo-somato-thyrotroph lineages. For each lineage, a common signature of aggressiveness was identified, associating a proliferative transcriptome signature and DNA hypermethylation. Alterations in specific genes were associated with aggressive features, including a novel PitNET gene, LRP1B, and known cancer genes (TP53, CDKN2A), while USP8 and GNAS alterations were not. Integration of gene alterations with methylome and transcriptome signatures isolated a subset of molecularly aggressive PitNETs. Molecular signatures were stable during the course of the disease, despite evolution toward aggressiveness and potential clonal divergence. CONCLUSION: This systematic analysis of clinicopathological features of aggressiveness using an integrated multiomic approach establishes a histomolecular definition of aggressiveness in PitNETs. Prospective cohort studies are needed to validate these molecular signatures and establish their prognostic value.

Humans↗
PubMed2006

Cytomics: A multiparametric, dynamic approach to cell research.

Cytomics aims to determine the molecular phenotype of single cells. Within the context of the -omics, cytomics allows the investigation of multiple biochemical features of the heterogeneous cellular systems known as the cytomes. Cytomics can be considered as the science of single cell-based analyses that links genomics and proteomics with the dynamics of cell and tissue function, as modulated by external influences. Inherent to cytomics are the use of sensitive, scarcely invasive, fluorescence-based multiparametric methods and the event-integrating concept of individual cells to understand the complexity and behaviour of tissues and organisms. Among cytomic technologies, flow cytometry, confocal laser scanning microscopy and laser capture microdissection are of great relevance. Other recent technologies based on single cell bioimaging and bioinformatic tools become important in drug discovery and toxicity testing, because of both high-content and high-troughput. The multiparametric capacity of cytomics is very useful for the identification, characterization and isolation of stem cell populations. In our experience, flow cytometry is a powerful and versatile tool that allows quantitative analysis of single molecules, prokaryotic and eukaryotic cells for basic, biotechnological, environmental and clinical studies. The dynamic nature of cytomic assays leads to a real-time kinetic approach based on sequential examination of different single cells from a population undergoing a dynamic process, the in fluxo level. Finally, cytomic technologies may provide in vitro methods alternative to laboratory animals for toxicity assessment.

Animals↗
PubMed2004

Primer on medical genomics. Part XIV: Introduction to systems biology--a new approach to understanding disease and treatment.

The advent of the "-omics revolution" has forced us to reevaluate our ability to acquire, measure, and handle large data sets. Omic platforms such as expression arrays and mass spectrometry, with their exquisite selectivity, sensitivity, and specificity, are unrivaled technologies for detection, quantitation, and identification of DNA, messenger RNA, proteins, and metabolites derived from complex body tissue and fluids. More recently, attempts have been made to capture the utility of these platform technologies and combine them under the umbrella of systems biology, also referred to as pathway, network, or integrative biology. Applied systems biology is the integrated analysis of genetic, genomic, protein, metabolite, cellular, and pathway events that are in flux and interdependent. It necessitates the use of a variety of analytic platforms as well as biostatistics, bioinformatics, data integration, computational biology, modeling, and knowledge assembly protocols. Such sophisticated analyses may provide new insight into the understanding of disease processes and mechanisms of action of pharmaceutical agents. Ultimately, this requires a perspective on how complex systems behave and are modulated. In this regard, systems biology, more appropriately considered as a process containing a series of modules, aims to provide tools and capabilities to carry out such tasks. We describe the essentials required to carry out systems biology experiments, the method in which integrated data in the form of a systems biology correlation network affords new insight into understanding disease, and the vista of developing more efficient biomarkers and therapeutic agents.

Animals↗
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

G-quadruplex upstream of PAX9 TSS acts as a 3D-genome scaffold to remotely silence X-linked genes and modulate cell-cycle progression.

G-quadruplexes (G4s) are non-canonical DNA secondary structures that act as local replication barriers and transcriptional regulators. Whether G4 can simultaneously influence splicing, DNA replication, and long-range, trans-chromosomal gene regulation remains untested. Here we combined in vitro biophysics, CRISPR mutagenesis and multi-omics to dissect a conserved G4 motif (QS1) located ~173 bp upstream of the PAX9 transcription start site. CD spectroscopy confirmed that the wild-type, but not the G-to-T mutant sequence, folds into a stable parallel G4 under physiological K+. In human cells, disruption of the QS1 G4 changed chromatin accessibility, remotely down-regulated a cohort of X-linked genes, accelerated migration and delayed G1/S progression. Integrative analysis of ATAC-seq, RNA-seq profiling reveals that the QS1 G4 acts as a three-dimensional genome scaffold linking PAX9 to cell-cycle and metabolic networks. Our findings establish a pleiotropic role for a single promoter G4 in coordinating DNA replication stress, chromatin architecture and trans-chromosomal transcriptional control.

G-Quadruplexes↗
PubMed2026

A Comprehensive Review of Radiomics in Pulmonary Nodule Management: Clinical Applications and Standardization Dilemmas.

Lung cancer is the most common and fatal malignant tumour. Early detection and treatment are likely to reduce mortality, but most pulmonary nodules identified during routine health checks are harmless. Consequently, a clear distinction between benign and malignant nodules is vital to improve early detection and reduce unnecessary interventions. Radiomics, a new omics technology, can be used to extract high-dimensional quantitative features from medical images, providing a profound understanding of tumour pathophysiology. Radiomics has attracted the attention of medical researchers since its formal definition by the Dutch researcher Lambin et al. in 2012. The number of research papers on radiomics has grown tremendously over the past few years. At present, it is used to predict pulmonary nodule malignancy, for noninvasive risk stratification, for integration with genomics to identify genetic mutations associated with lung cancer, and for evaluation of therapeutic responses. With this review, we summarise the literature on radiomics of pulmonary nodules, discuss how it could be used in nodule management, and address the current challenges and future directions for improving precision oncology.

Humans↗
PubMed2026

Multi-dimensional profiling of primary metabolites in Heuchera micrantha varieties reveals potential for functional food development.

Heuchera micrantha is a horticultural plant with emerging pharmacological value, yet its primary metabolites remain underexplored. This study comprehensively profiled nutrient metabolites in four H. micrantha varieties using LC-MS/MS. We identified 285 metabolites, with amino acid derivatives being predominant. Multivariate analysis revealed distinct varietal accumulation patterns and 204 differential accumulated metabolites (DAMs). Integrative network pharmacology and molecular docking suggested γ-glutamyltyrosine and L-prolyl-L-phenylalanine as potential bioactive dipeptides that may interact with core hubs (MAPK1, EGFR, SRC) involved in cancer and inflammation pathways, though these predictions require experimental validation. Transcriptomics identified 39 differentially expressed genes regulating the biosynthesis of their precursor amino acids. Antioxidant assays showed varietal differences: some excelled in free radical scavenging (DPPH/ABTS) while others demonstrated superior reducing power (FRAP). This multi-omics study suggests that H. micrantha may be a rich source of therapeutically relevant primary metabolites, providing a preliminary scientific basis for its development as a functional food or nutraceutical pending further validation.

Functional Food↗
PubMed2026

BioMedGraphica: an all-in-one platform for joint textual biomedical prior knowledge and numeric graph generation.

MOTIVATION: Multiomics data analysis is essential for scientific discovery in precision medicine. However, translating analysis results of omics data analysis into novel scientific hypotheses remains a significant challenge. Human experts must manually review analysis results and generate new hypotheses based on extensive and interconnected biomedical prior knowledge, which is subjective and not scalable. While large language models can accelerate the discovery, their reasoning improves when grounded in structured, auditable, and comprehensive biomedical prior knowledge. However, biomedical knowledge is scattered across heterogeneous databases that use diverse and inconsistent nomenclature systems, making it difficult to integrate resources into a unified format for scalable analysis. This fragmentation limits the ability of artificial intelligence systems to fully leverage biomedical data for scientific discovery. RESULTS: We developed BioMedGraphica, a novel all-in-one platform that harmonizes fragmented biomedical resources by integrating 11 entity types and 30 relation types from 43 databases into a unified textual prior knowledge graph containing 2 306 921 entities and 27 232 091 relations. In addition, we present a novel textual-numeric graph (TNG) data structure concept, where textual information captures prior biological knowledge (e.g. transcription start sites, functions, mechanisms), numeric values represent quantitative biomedical features, and the integrated relations can help uncover mechanisms. By bridging prior knowledge with user-specific data, TNG is a novel and ideal data structure for developing novel graph analysis models. AVAILABILITY AND IMPLEMENTATION: The code is available at: https://github.com/FuhaiLiAiLab/BioMedGraphica and BioMedGraphica knowledge graph database can be downloaded from huggingface dataset: https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica.

Humans↗
PubMed2025

Spatial Metabolomics Reveals the Role of Penicillic Acid in Cheese Rind Microbiome Disruption by a Spoilage Fungus.

Microbial interactions in cheese rinds influence community structure, food safety, and product quality. But the chemical mechanisms that mediate microbial interactions in cheeses and other fermented foods are generally not known. Here, we investigate how the spoilage mold Aspergillus westerdijkiae chemically inhibits beneficial cheese-rind bacteria using a combination of omics technologies. In cheese rind community and co-culture experiments, A. westerdijkiae strongly inhibited most cheese rind community members. In co-culture with Staphylococcus equorum, A. westerdijkiae strongly affected bacterial gene expression, including upregulation of a putative bceAB gene cluster that is associated with resistance to antimicrobial compounds in other bacteria. Mass spectrometry imaging (MSI) revealed spatially localized production of secondary metabolites, including penicillic acid and ochratoxin B at the fungal-bacterial interface. Integration of LC-MS/MS and genome annotations confirmed the presence of additional bioactive metabolites, such as notoamides and circumdatins. Fungal metabolic responses varied by bacterial partner, suggesting species-specific chemical strategies. Notably, penicillic acid levels increased 2.5-fold during interaction with Brachybacterium, and experiments with purified penicillic acid showed inhibition of a range of cheese rind bacteria. These findings show that A. westerdijkiae deploys a context-dependent arsenal of mycotoxins and other metabolites, disrupting microbial community assembly in cheese rinds.

Aspergillus westerdijkiae↗
PubMed2026

Integrated transcriptomic and metabolomic analysis of fluoride tolerance-related pathways and differentially expressed genes in silkworm strain XSKD.

XueSong KD (XSKD) silkworm strain exhibits prominent fluoride tolerance, yet the underlying molecular mechanisms of fluoride tolerance remains unclear. In the present study, fourth-instar pre-molting XSKD silkworms were used as experimental materials for integrated transcriptomic and untargeted metabolomic analyses. In total, 572 differentially expressed genes and 90 differential metabolites were screened. GO enrichment and KEGG enrichment based on the hypergeometric distribution model revealed that 13-Hydroxy-9Z,11E-octadecadienoic acid (13-(S)-HODE) acts as the core differential metabolite, which is significantly enriched in the linoleic acid metabolism pathway. Within this pathway, LOC101737302 and CYP338A1 display opposite expression trends and show correlations with pathway metabolites. Based on multi-omics data, this study preliminarily characterizes the lipid metabolic response under fluoride stress, providing omics dataset support for further in-depth exploration of the molecular mechanism of fluoride tolerance in silkworms.

Animals↗