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PubMed2026

Pseudomonas aeruginosa adaptation and persistence in the aspergilloma microbiome revealed by integrated multi-omics.

Chronic pulmonary aspergillosis involves the formation of a fungal ball (aspergilloma) in lung cavities. Pseudomonas aeruginosa commonly co-colonizes these lesions; however, the in vivo mechanisms underlying its persistence are unknown. Using a multi-omics approach on resected aspergillomas, we defined the genomic, transcriptional, and metabolic adaptations of P. aeruginosa within this polymicrobial niche. We reconstructed high-quality P. aeruginosa genomes and identified a conserved core genome, along with accessory genes for secondary metabolism, virulence, and antimicrobial resistance. Phylogenomics revealed heterogeneous evolutionary paths among co-colonizing strains. Metatranscriptomics showed stark physiological heterogeneity, from metabolically aggressive to stress-adapted states. High expression of phenazine, quorum-sensing (PQS), siderophore, and secretion-system operons was corroborated by metabolomic detection of phenazine-1-carboxylic acid and 2-heptylquinolin-4(1H)-one, confirming active bacterial antagonism in vivo. Concurrent Aspergillus fumigatus transcriptomics revealed the activation of oxidative stress responses, secondary metabolism (eg fumagillin), and iron scavenging, demonstrating reciprocal competition. Host transcriptomics revealed patient-specific immune signatures that correlated with the metabolic activity of the co-colonizers. This work provides an integrated systems-level analysis of the tri-kingdom aspergilloma ecosystem. P. aeruginosa persistence is driven by genomic plasticity and context-dependent expression of competitive pathways, shaped within a chronic inflammatory environment. These findings redefine aspergillomas as active polymicrobial consortia, establishing a framework for targeting resilient microbial communities in chronic lung disease.

Multiomics↗
PubMed2025

OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.

Cell deconvolution estimates cell type proportions from bulk omics data, enabling insights into tissue microenvironments and disease. However, practical applications are often hindered by batch effects between bulk data and referenced single-cell data, a challenge that is frequently overlooked. To address this discrepancy, we developed OmicsTweezer, a distribution-independent cell deconvolution model. By integrating optimal transport with deep learning, OmicsTweezer aligns simulated and real data in a shared latent space, effectively mitigating data shifts and inter-omics distribution differences. OmicsTweezer is versatile, capable of deconvolving bulk RNA-seq, bulk proteomics, and spatial transcriptomics. Extensive evaluations on simulated and real-world datasets demonstrate its robustness and accuracy. Furthermore, applications in prostate and colon cancer showcase OmicsTweezer's ability to identify biologically meaningful cell types. As a unified deconvolution framework for multi-omics data, OmicsTweezer offers an efficient and powerful tool for studying disease microenvironments.

Humans↗
PubMed2025

Establishing the ELIXIR Microbiome Community.

Microbiome research has grown substantially over the past decade in terms of the range of biomes sampled, identified taxa, and the volume of data derived from the samples. In particular, experimental approaches such as metagenomics, metabarcoding, metatranscriptomics and metaproteomics have provided profound insights into the vast, hitherto unknown, microbial biodiversity. The ELIXIR Marine Metagenomics Community, initiated amongst researchers focusing on marine microbiomes, has concentrated on promoting standards around microbiome-derived sequence analysis, as well as understanding the gaps in methods and reference databases, and identifying solutions to the computational overheads of performing such analyses. Nevertheless, the methods used and the challenges faced are not confined to marine microbiome studies, but are broadly applicable to other biomes. Thus, expanding this Marine Metagenomics Community to a more inclusive ELIXIR Microbiome Community will enable it to encompass a broader range of biomes and link expertise across 'omics technologies. Furthermore, engaging with a large number of researchers will improve the efficiency and sustainability of bioinformatics infrastructure and resources for microbiome research (standards, data, tools, workflows, training), which will enable a deeper understanding of the function and taxonomic composition of the different microbial communities.

Computational Biology↗
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

Integrative multi-omics reveals a fibroblast-centered, ZFHX3-prioritized regulatory framework linking sick sinus syndrome and atrial fibrillation.

OBJECTIVE: To define shared genetic and multi-scale mechanisms underlying comorbidity between sick sinus syndrome (SSS) and atrial fibrillation (AF). METHODS: We integrated genome-wide association study (GWAS) summary statistics for SSS and AF with Genotype-Tissue Expression (GTEx) expression and splicing quantitative trait loci (eQTL/sQTL), atrial single-cell and spatial transcriptomics, and epigenomics. We identified trait-relevant tissues and pathways, prioritized shared cell types, quantified genome-wide and local genetic sharing, detected joint loci by cross-trait meta-analysis, and linked loci to regulatory programs via colocalization and cell-prioritized co-expression networks. RESULTS: Both traits showed strongest enrichment in cardiac tissue, especially Heart Atrial Appendage. Fibroblasts from the left atrial appendage were consistently prioritized as the key shared cell population. SSS and AF displayed significant positive genome-wide genetic correlation, with multiple locally shared regions, including six major loci. Cross-trait meta-analysis identified eight joint-phenotype SNPs implicating four susceptibility genes. ZFHX3 was the leading tissue-cell-gene candidate, acting as a hub in fibroblast co-expression modules and colocalizing with cardiac regulatory signals. CONCLUSION: Shared liability for SSS and AF is highly tissue- and cell-specific, converging on regulatory networks in atrial appendage fibroblasts, with ZFHX3 serving as a central mechanistic and biomarker node.

Humans↗
PubMed2006

Concept of sample in OMICS technology.

Fundamental biological processes can now be studied by applying the full range of OMICS technologies (genomics, transcriptomics, proteomics, metabolomics, and beyond) to the same biological sample. Clearly, it would be desirable if the concept of sample were shared among these technologies, especially as up until the time a biological sample is prepared for use in a specific OMICS assay, its description is inherently technology independent. Sharing a common informatic representation would encourage data sharing (rather than data replication), thereby reducing redundant data capture and the potential for error. This would result in a significant degree of harmonization across different OMICS data standardization activities, a task that is critical if we are to integrate data from these different data sources. Here, we review the current concept of sample in OMICS technologies as it is being dealt with by different OMICS standardization initiatives and discuss the special role that the newly formed Genomic Standards Consortium (GSC) might have to play in this domain.

Animals↗
PubMed2004

Systems approaches to understanding cell signaling and gene regulation.

The age of 'omics' is upon us, and scientific papers that reflect this are starting to appear at an ever-increasing rate. The amount of information generated in any 'omics' program is daunting and often overwhelms plant scientists whose main interests relate to cell or developmental biology. For this revolution in data generation to have any impact in plant signaling studies, we must have great confidence in both the quality of the data and our ability to represent it in ways that are meaningful to general plant biologists. Systems biology has begun to address these issues and to provide examples in which the analysis of large data sets has led to biological insights into cell signaling and gene regulation.

Arabidopsis↗
PubMed2026

Multi-omics insights into the physiological mechanisms of bile acid accumulation in the gallbladder in brumation-like snakes.

Hibernation/brumation represents an important physiological adaptation for animals to cope with seasonal environmental changes. Field observations suggested increased gallbladder weight in the Five-pacer viper (Deinagkistrodon acutus) during brumation, and our quantitative measurements confirmed this increase together with bile acid accumulation. By integrating a multi-omic approach, this study elucidates the regulatory mechanisms of bile acid accumulation in the gallbladder during brumation. Results showed that taurocholic acid (TCA) and taurodeoxycholic acid (TDCA) were the major components in the gallbladder of the brumation-like group, with significantly elevated concentrations of bile acids, whereas bile acid concentrations in serum and intestinal contents were markedly reduced, indicating suppression of the enterohepatic circulation and consequent accumulation of bile acids in the gallbladder. Hepatic transcriptomic analysis revealed significant downregulation of bile acid synthesis and regulatory genes in brumation-like snakes. In contrast, the alternative synthesis pathway gene sterol 27-hydroxylase (CYP27A1) and some transporter genes were slightly upregulated. Further, some modification genes and regulatory genes showed no significant differences between active and brumation-like states. Gut microbiota analysis demonstrated Akkermansia muciniphila, Bacteroides fragilis, and Citrobacter freundii were more enriched in the active group, which were common microbes related to bile acid metabolism, and the correlation analysis confirmed this relationship. Taken together, these findings indicate that the "physiological bile acid accumulation" observed in snakes during brumation-like state is jointly driven by suppressed hepatic synthesis, reduced enterohepatic circulation, and remodeled microbial community structure. The study provides novel comparative physiological insights into extreme metabolic homeostasis in animals.

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

Multi-omics identification of therapeutic targets of compound sappan decoction in hepatocellular carcinoma.

BACKGROUND: Compound sappan decoction (CSD) is a multi-herbal traditional Chinese medicine formulation with clinical relevance in hepatocellular carcinoma (HCC). However, its therapeutic mechanisms remain unclear. METHODS: Bioactive compounds of CSD were identified and standardized using pharmacological and chemical databases. Potential targets were predicted via multiple target inference platforms. HCC-related genes were curated from comprehensive disease databases. Summary-data-based Mendelian randomization (SMR) was conducted to infer causal relationships between compound targets and HCC risk using large-scale quantitative trait loci (QTL) datasets and HCC genome-wide association study data. Colocalization analysis, protein-protein interaction (PPI) network construction, and GO/KEGG enrichment were performed on SMR-identified targets. Molecular docking evaluated binding affinities of representative compounds to prioritized targets. RESULTS: A total of 784 overlapping genes between predicted CSD targets and HCC-related genes were subjected to SMR analysis. Among these, 22 targets were significantly associated with HCC risk based on transcriptomic or proteomic QTLs and showed colocalization evidence. Notably, four targets (ADRB2, APOE, SYK, and PGF) were supported by both replication in an independent cohort and strong colocalization. These 22 targets were enriched in apoptosis, PI3K-Akt signaling, redox metabolism, and detoxification pathways. PPI analysis revealed central hubs including MMP9, BCL2, CASP1, and MCL1. Molecular docking demonstrated strong binding of APOE to quercetin, PGF to luteolin-7-olate, and SYK to kaempferol. CONCLUSIONS: CSD may exert therapeutic effects on HCC through modulation of genetically validated targets involved in tumor progression, inflammation, and metabolic reprogramming, supporting its potential clinical utility as an adjunctive treatment strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s12672-026-04740-8.

Caesalpinia↗
PubMed2006

Bridging biological scales by state-space analysis and modeling using molecular, tissue cytometric and physiological data.

Combining data streams across different levels of biological organization such as molecular, cellular, and physiological responses support to a system-wide view in biology. Recently, an unbiased analysis of tissues that provides data-rich descriptors of tissue architecture, cell types, and cell states has become available. As tissues are centrally located in the biological hierarchy, these advancements give rise to a new class of state variables that are critical to elucidate both underlying cellular, molecular and emergent physiological properties. Concepts to statistically identify, correlate, and model relationships across scales are introduced, which rely on a state-space matrix derived by multi-omics data aggregation.

Computational Biology↗
PubMed2026

Multi-omics integration and colocalization analyses prioritize candidate molecular loci associated with hypothermia.

BACKGROUND: Hypothermia is a life-threatening condition lacking specific pharmacological treatments. This study aimed to prioritize genetically supported molecular loci associated with hypothermia and to explore their pharmacological tractability using multi-omics data. METHODS: Initially, 2532 druggable genes were curated from the Drug-Gene Interaction Database and established literature. These were cross-referenced with cis-eQTL and cis-pQTL datasets, encompassing 870,655 and 114,281 SNPs for blood, respectively, alongside 2379 shared SNPs across adipose, skeletal muscle, and heart tissues. Matched instrumental variables were integrated with hypothermia GWAS summary statistics for two-sample Mendelian randomization (MR) and Bayesian colocalization. Transcriptomic differential expression analysis (DEA) was subsequently conducted as an exploratory analysis of cold-exposure-associated expression changes. Database-derived compound annotations were systematically re-evaluated according to target specificity, established pharmacological mechanism, and concordance with the direction of the MR estimates. RESULTS: Among 671 gene-level MR tests, 36 genes reached nominal significance, whereas only ABCC8 remained significant after FDR correction. Colocalization was evaluable for 8 of these 36 genes, and 4 loci (COL18A1, SLC1A7, ADIPOQ, and MERTK) met the prespecified PP.H4>0.90 threshold. The remaining 28 loci were not evaluable because sufficient overlapping regional variants were unavailable after harmonization. Transcriptomic analysis identified altered expression of SLC1A3 and SLCO4A1 under cold exposure, although these findings did not directly validate the colocalization-supported loci. Re-evaluation of database-derived compound annotations did not identify any direct, selective, and directionally concordant drug-repurposing candidate for hypothermia. CONCLUSIONS: COL18A1, SLC1A7, ADIPOQ, and MERTK showed colocalization support among the 8 evaluable nominal MR-associated loci. Because colocalization coverage was limited, these genes should be regarded as preliminary candidate loci rather than established therapeutic targets. The pharmacological annotations were indirect, non-selective, unsupported, or directionally inconsistent and should be interpreted solely as hypothesis-generating information.

Bayesian colocalization↗
PubMed2004

Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana.

Plant metabolism is a complex set of processes that produce a wide diversity of foods, woods, and medicines. With the genome sequences of Arabidopsis and rice in hands, postgenomics studies integrating all "omics" sciences can depict precise pictures of a whole-cellular process. Here, we present, to our knowledge, the first report of investigation for gene-to-metabolite networks regulating sulfur and nitrogen nutrition and secondary metabolism in Arabidopsis, with integration of metabolomics and transcriptomics. Transcriptome and metabolome analyses were carried out, respectively, with DNA macroarray and several chemical analytical methods, including ultra high-resolution Fourier transform-ion cyclotron MS. Mathematical analyses, including principal component analysis and batch-learning self-organizing map analysis of transcriptome and metabolome data suggested the presence of general responses to sulfur and nitrogen deficiencies. In addition, specific responses to either sulfur or nitrogen deficiency were observed in several metabolic pathways: in particular, the genes and metabolites involved in glucosinolate metabolism were shown to be coordinately modulated. Understanding such gene-to-metabolite networks in primary and secondary metabolism through integration of transcriptomics and metabolomics can lead to identification of gene function and subsequent improvement of production of useful compounds in plants.

Arabidopsis↗
PubMed2025

Multi‑omics identification of a novel signature for serous ovarian carcinoma in the context of 3P medicine and based on twelve programmed cell death patterns: a multi-cohort machine learning study.

BACKGROUND: Predictive, preventive, and personalized medicine (PPPM/3PM) is a strategy aimed at improving the prognosis of cancer, and programmed cell death (PCD) is increasingly recognized as a potential target in cancer therapy and prognosis. However, a PCD-based predictive model for serous ovarian carcinoma (SOC) is lacking. In the present study, we aimed to establish a cell death index (CDI)-based model using PCD-related genes. METHODS: We included 1254 genes from 12 PCD patterns in our analysis. Differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were screened. Subsequently, 14 PCD-related genes were included in the PCD-gene-based CDI model. Genomics, single-cell transcriptomes, bulk transcriptomes, spatial transcriptomes, and clinical information from TCGA-OV, GSE26193, GSE63885, and GSE140082 were collected and analyzed to verify the prediction model. RESULTS: The CDI was recognized as an independent prognostic risk factor for patients with SOC. Patients with SOC and a high CDI had lower survival rates and poorer prognoses than those with a low CDI. Specific clinical parameters and the CDI were combined to establish a nomogram that accurately assessed patient survival. We used the PCD-genes model to observe differences between high and low CDI groups. The results showed that patients with SOC and a high CDI showed immunosuppression and hardly benefited from immunotherapy; therefore, trametinib_1372 and BMS-754807 may be potential therapeutic agents for these patients. CONCLUSIONS: The CDI-based model, which was established using 14 PCD-related genes, accurately predicted the tumor microenvironment, immunotherapy response, and drug sensitivity of patients with SOC. Thus this model may help improve the diagnostic and therapeutic efficacy of PPPM.

Humans↗
PubMed2026

Astrocyte reactivity by alcohol dependence in the central amygdala.

Astrocytes play essential roles in maintaining brain homeostasis and in contributing to synaptic functions, but, in response to injury, infection, or disease, astrocytes can downregulate their homeostatic and physiological functions while increasing neuroinflammatory responses. The central amygdala (CeA) is important for stress responsivity and the development of alcohol (ethanol) dependence. Using a multi-omics approach in Aldh1l1-EGFP/Rpl10a mice and the chronic intermittent ethanol two-bottle choice (CIE-2BC) model, we have characterized the translational response of CeA astrocytes, as well as the proteomic and phosphoproteomic changes in ethanol dependent, non-dependent, and naïve mice. We identified astrocyte-specific alterations in neuroimmune functions and antioxidant/oxidative stress pathways in ethanol dependent mice as well as cytoskeletal plasticity related pathways in non-dependent mice. Proteomic analysis showed down-regulation of astrocyte physiological functions in dependent animals while phosphoproteomic analysis identified pathways associated with cytoskeleton remodeling in both dependent and non-dependent mice. Reconstructions of astrocyte morphologies demonstrated increased CeA astrocyte complexity in dependent and non-dependent groups compared to naïve mice. The astrocyte-specific activation of neuroimmune and antioxidant pathways, down-regulation of homeostatic functions, alteration in protein phosphorylation-mediated cytoskeleton remodeling, and increased astrocyte morphological complexity demonstrate that ethanol dependence induces astrocyte reactivity in the CeA consistent with both adaptive and maladaptive changes. These findings highlight the role of CeA astrocytes in the progression from alcohol intake to dependence and represent a first step toward identifying astrocyte-specific therapeutic strategies to treat Alcohol Use Disorder (AUD) aimed at potentiating reactive astrocyte adaptive changes and inhibiting maladaptive responses.

Animals↗
PubMed2024

Cardiovascular Risk Factors and Genetic Risk in Transthyretin V142I Carriers.

BACKGROUND: Nearly 3% to 4% of Black individuals in the United States carry the transthyretin V142I variant, which increases their risk of heart failure. However, the role of cardiovascular (CV) risk factors (RFs) in influencing the risk of clinical outcomes among V142I variant carriers is unknown. OBJECTIVES: This study aimed to assess the impact of CV RFs on the risk of heart failure in V142I carriers. METHODS: This study included self-identified Black individuals without prevalent heart failure from 6 TOPMed (Trans-Omics for Precision Medicine) cohorts, the REGARDS (Reasons for Geographic And Racial Differences in Stroke) study, and the All of Us Research Program. The cohort was stratified based on the V142I genotype and the number of CV RFs (hypertension, diabetes, obesity, and hypercholesterolemia). Adjusted Cox models were used to assess the association of heart failure with the V142I genotype and CV RF profile, taking noncarriers with a favorable CV RF profile as reference. RESULTS: The cross-sectional analysis, including 1,625 V142I carriers among 48,365 Black individuals, found that the prevalence of CV RFs did not vary by V142I carrier status. In the longitudinal analysis, there were 587 (3.2%) V142I carriers among 18,407 Black individuals (median age: 60 years [Q1-Q3: 52-68 years], 63.0% female). Among carriers, the heart failure risk was attenuated with a favorable (0 or 1 RF) CV RF profile (adjusted HR: 2.26; 95% CI: 1.58-3.23) compared with an unfavorable (3 or 4 RFs) CV RF profile (adjusted HR: 4.14; 95% CI: 2.79-6.14). CONCLUSIONS: A favorable CV RF profile lowers but does not abrogate V142I variant-associated heart failure risk. This study highlights the importance of having a favorable CV RF profile among V142I carriers for risk reduction of heart failure.

Aged↗
PubMed2026

The pseudouridine epitranscriptomic landscape of advanced prostate cancer therapeutic resistance identifies TIMM17A as a key player.

BACKGROUND: Resistance to androgen receptor signaling inhibitors (ARSIs) remains a major barrier of advanced prostate cancer (PCa) treatment. While RNA epitranscriptomic modifications are increasingly recognized as key regulators of tumor biology, the role of pseudouridine (Ψ) in therapeutic resistance is largely unexplored. METHODS: A darolutamide-resistant PCa cell model was established and subjected to integrated multi-omics profiling using bulk RNA sequencing and photo-crosslinking-assisted Ψ sequencing (PA-Ψ-seq). Differential expression and pseudouridylation analyses were combined to identify Ψ-associated genes. Public datasets validated expression and prognosis. Functional assays including RNA knockdown, cell proliferation, colony formation, and xenograft models were conducted. Single-cell RNA sequencing investigated tumor microenvironment (TME) interactions. RESULTS: We identified extensive transcriptomic and pseudouridylation alterations associated with ARSI resistance, with a significant positive correlation between Ψ modification and mRNA expression. Integrated analysis highlighted a subset of "hyper-up" genes enriched in resistance-related pathways. Thus, TIMM17A was identified as a novel candidate. TIMM17A expression was significantly elevated in PCa and correlated with disease progression and poor prognosis. Experimental validations demonstrated that TIMM17A promoted tumor growth and resistance, while its knockdown restored sensitivity to darolutamide both in vitro and in vivo. Mechanistically, TIMM17A expression may be regulated by PUS1‑mediated pseudouridylation. Single-cell analysis further revealed that TIMM17A is enriched in malignant epithelial cells and associated with enhanced cell-cell communication within the TME. CONCLUSIONS: This study delineates the pseudouridine epitranscriptomic landscape in advanced PCa and identifies TIMM17A as a key mediator of therapeutic resistance. Targeting the Ψ-TIMM17A axis may offer a novel strategy to overcome ARSI resistance.

Advanced prostate cancer↗
PubMed2026

Characterization of age-related changes in the gut microbiome and metabolome of Kunming dogs and their associations with police performance.

BACKGROUND: Gut microbiota plays a pivotal role in regulating the host's central nervous system (CNS) activity and behavior. However, its influence on the police performance of Kunming dogs and the underlying mechanisms remain largely unexplored. This study was the first to apply multi-omics technologies to investigate the dynamic variations in gut microbiota and their metabolic profiles across different ages of Kunming dogs. Furthermore, we systematically examined the associations between these microbial alterations and police performance metrics, providing a theoretical foundation for enhancing the working capabilities of Kunming dogs through targeted modulation of intestinal microecology. RESULTS: The study showed that puppies, young dogs and adult dogs had significantly better police performance than elderly dogs, with young dogs exhibiting the highest scores. Analysis of 16S rRNA sequencing demonstrated that gut microbial diversity and stability were highest during the young dog stage, gradually declining with age. Metagenomic analysis revealed that the abundance of Lactobacillus acidophilus, Lactobacillus johnsonii, Limosilactobacillus reuteri, Ligilactobacillus animalis and Muribaculum gordoncarteri were strongly correlated with police performance. The results of metagenome-assembled genomes (MAGs) indicated that the above species have functional genes involved in GABAergic and glutamatergic synapse pathways. Furthermore, metabolomic analysis showed that differential metabolites were enriched in the neuroactive ligand-receptor interaction pathway, in which GABA (γ-aminobutyric acid), histamine and tyramine metabolites were positively correlated with the above species and police performance. CONCLUSION: The species L. acidophilus, L. johnsonii, L. reuteri, L. animalis, and M. gordoncarteri, which were enriched in the gut of puppies and young Kunming dogs, may potentially influence the nervous system through the production of neurotransmitters and neuromodulators, suggesting a possible association with police performance. Video Abstract.

Animals↗