Multi-omics Integration Research Articles

Spatial multiomics atlas reveals smooth muscle phenotypic transformation and metabolic reprogramming in diabetic macroangiopathy

BackgroundDiabetic macroangiopathy has been the main cause of death and disability in diabetic patients. The mechanisms underlying smooth muscle cell transformation and metabolic reprogramming other than abnormal glucose and lipid metabolism remain to be further explored.MethodSingle-cell transcriptome, spatial transcriptome and spatial metabolome sequencing were performed on anterior tibial artery from 11 diabetic patients with amputation. Multi-omics integration, cell communication analysis, time series analysis, network analysis, enrichment analysis, and gene expression analysis were performed to elucidate the potential molecular features.ResultWe constructed a spatial multiomics map of diabetic blood vessels based on multiomics integration, indicating single-cell and spatial landscape of transcriptome and spatial landscape of metabolome. At the same time, the characteristics of cell composition and biological function of calcified regions were obtained by integrating spatial omics and single cell omics. On this basis, our study provides favorable evidence for the cellular fate of smooth muscle cells, which can be transformed into pro-inflammatory chemotactic smooth muscle cells, macrophage-like smooth muscle cells/foam-like smooth muscle cells, and fibroblast/chondroblast smooth muscle cells in the anterior tibial artery of diabetic patients. The smooth muscle cell phenotypic transformation is driven by transcription factors net including KDM5B, DDIT3, etc. In addition, in order to focus on metabolic reprogramming apart from abnormal glucose and lipid metabolism, we constructed a metabolic network of diabetic vascular activation, and found that HNMT and CYP27A1 participate in diabetic vascular metabolic reprogramming by combining public data.ConclusionThis study constructs the spatial gene-metabolism map of the whole anterior tibial artery for the first time and reveals the characteristics of vascular calcification, the phenotypic transformation trend of SMCs, and the transcriptional driving network of SMCs phenotypic transformation of diabetic macrovascular disease. In the perspective of combining the transcriptome and metabolome, the study demonstrates the activated metabolic pathways in diabetic blood vessels and the key genes involved in diabetic metabolic reprogramming.Graphical abstract

Integrative multi-omic and machine learning approach for prognostic stratification and therapeutic targeting in lung squamous cell carcinoma.

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi-omic data obtained from multi-level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi-omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high- and low-risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype-specific machine learning model, LUSC multi-omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward "cold" tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies.

Interpretable multi-omics integration with UMAP embeddings and density-based clustering.

Integrating high-dimensional cellular multi-omics data is crucial for understanding various layers of biological control. Single 'omic methods provide important insights, but often fall short in handling the complex relationships between genes, proteins, metabolites and beyond. Here, we present a novel, non-linear, and unsupervised method called GAUDI (Group Aggregation via UMAP Data Integration) that leverages independent UMAP embeddings for the concurrent analysis of multiple data types. GAUDI uncovers non-linear relationships among different omics data better than several state-of-the-art methods. This approach not only clusters samples by their multi-omic profiles but also identifies latent factors across each omics dataset, thereby enabling interpretation of the underlying features contributing to each cluster. Consequently, GAUDI facilitates more intuitive, interpretable visualizations to identify novel insights and potential biomarkers from a wide range of experimental designs.

A comprehensive proteogenomic pipeline for neoantigen discovery to advance personalized cancer immunotherapy.

The accurate identification and prioritization of antigenic peptides is crucial for the development of personalized cancer immunotherapies. Publicly available pipelines to predict clinical neoantigens do not allow direct integration of mass spectrometry immunopeptidomics data, which can uncover antigenic peptides derived from various canonical and noncanonical sources. To address this, we present an end-to-end clinical proteogenomic pipeline, called NeoDisc, that combines state-of-the-art publicly available and in-house software for immunopeptidomics, genomics and transcriptomics with in silico tools for the identification, prediction and prioritization of tumor-specific and immunogenic antigens from multiple sources, including neoantigens, viral antigens, high-confidence tumor-specific antigens and tumor-specific noncanonical antigens. We demonstrate the superiority of NeoDisc in accurately prioritizing immunogenic neoantigens over recent prioritization pipelines. We showcase the various features offered by NeoDisc that enable both rule-based and machine-learning approaches for personalized antigen discovery and neoantigen cancer vaccine design. Additionally, we demonstrate how NeoDisc's multiomics integration identifies defects in the cellular antigen presentation machinery, which influence the heterogeneous tumor antigenic landscape.

Integrative multiomics analysis identifies key genes regulating intramuscular fat deposition during development

Intramuscular fat (IMF) content is an important indicator of livestock and poultry meat quality. Enhancing IMF deposition can significantly improve meat quality. Focusing on the core process of IMF deposition, this study used the Jingxing Yellow (JXY) chickens as a model organism and employed multi-omics approaches, including RNA-sequencing (RNA-seq), Whole-genome bisulfite sequencing (WGBS), and metabolomics, to identify the key genes influencing IMF deposition in chickens during development. The results indicated that the contents of triglycerides (TG) and phospholipids (PLIP) exhibited an upward trend. The TG content did not differ significantly between day 1 (D1) and day 7 (D7), but increased significantly after 35 days (D35) of age. The WGBS results revealed that CpG methylation was the predominant methylation type in the breast muscle tissue of JXY chickens. Integrative analysis of RNA-seq and WGBS identified 50 genes, including PLA2G4F, PALMD, PLSCR5, ARHGEF26, LUM, DCN, TNRC6B, CACNA1C, ROBO1, and MBTPS2, whose methylation levels were significantly negatively correlated with their expression levels. In addition, the combined Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially-expressed metabolites (DEM) and differentially-expressed genes (DEG) converged on the glycerophospholipid metabolism pathway, which was significantly enriched in DEGs such as PLA2G4F, PLA2G15, LPIN1, MBOAT2, DGKH, AGPAT2, and CHKA, as well as DEM like glycerophosphocholine and phosphocholine. Notably, PLA2G4F was identified as a DEG by DNA methylation, suggesting that PLA2G4F could be a key candidate gene influencing IMF deposition during chicken development. These findings are expected to provide a solid theoretical foundation for improving meat quality through targeted genetic and epigenetic interventions.

Leveraging Multi-Omics Approaches and Advanced Technologies to Unravel the Molecular Complexities, Modifiers, and Precision Medicine Strategies for Hemoglobin H Disease.

Hemoglobin H (HbH) disease, a form of alpha-thalassemia, poses significant clinical challenges due to its complex molecular underpinnings. It is characterized by reduced synthesis of the alpha-globin chain. The integration of multi-omics and precision medicine holds immense potential to comprehensively understand and capture interactions at the molecular and genetic levels. This review integrates current multi-omics approaches and advanced technologies in HbH research. Furthermore, it delves into detailed pathophysiology and possible therapeutics in the upcoming future. We explore the role of genomics, transcriptomics, proteomics, and metabolomics studies, alongside bioinformatics tools and gene-editing technologies like CRISPR/Cas9, to identify genetic modifiers, decipher molecular pathways, and discover therapeutic targets. Recent advancements are unveiling novel genetic and epigenetic modifiers impacting HbH disease severity, paving the way for personalized precision medicine interventions. The significance of multi-omics research in unraveling the complexities of rare diseases like HbH is underscored, highlighting its potential to revolutionize clinical practice through precision medicine approaches. This paradigm shift can pave the way for a deeper understanding of HbH complexities and improved disease management.

Multi-omics integration analysis reveals the role of N6-methyladenosine in lncRNA translation during glioma stem cell differentiation.

Glioblastoma is one of the most lethal brain diseases in humans. Although recent studies have shown reciprocal interactions between N6-methyladenosine (m6A) modifications and long noncoding RNAs (lncRNAs) in gliomagenesis and malignant progression, the mechanism of m6A-mediated lncRNA translational regulation in glioblastoma remains unclear. Herein, we profiled the transcriptomes, translatomes, and epitranscriptomics of glioma stem cells and differentiated glioma cells to investigate the role of m6A in lncRNA translation comprehensively. We found that lncRNAs with numerous m6A peaks exhibit reduced translation efficiency. Transcript-level expression analysis demonstrates an enrichment of m6A around short open reading frames (sORFs) of translatable lncRNA transcripts. Further comparison analysis of m6A modifications in different RNA regions indicates that m6A peaks downstream of sORFs inhibit lncRNA translation more than those upstream. Observations in glioma-associated lncRNAs H19, LINC00467, and GAS5 further confirm the negative effect of m6A methylation on lncRNA translation. Overall, these findings elucidate the dynamic profiles of the m6A methylome and enhance the understanding of the complexity of lncRNA translational regulation.

Multiomics of the intestine-liver-adipose axis in multiple studies unveils a consistent link of the gut microbiota and the antiviral response with systemic glucose metabolism

BackgroundThe microbiota is emerging as a key factor in the predisposition to insulin resistance and obesity.ObjectiveTo understand the interplay among gut microbiota and insulin sensitivity in multiple tissues.DesignIntegrative multiomics and...

Interplay between Lipids and Complement Proteins—How Multiomics Data Integration Can Help Unravel Age-related Macular Degeneration Pathophysiology: A Proof-of-concept Study

RationaleWe hypothesized that the integration of multi-omics data sets can help unravel some complex aspects of AMD pathophysiology. Our objectives were to identify correlation patterns between genetic variants, complement proteins and lipids using a multi-omics data integration approach and to determine how these interconnections affect AMD. Materials and methodsThe analyses were performed in a subset of the Singapore Indian Eye Study (SINDI) study. We randomly selected 155 AMD cases and age- and sex-matched them with 155 controls. We measured in serum samples 35 complement proteins and 66 lipids, and used nine genetic variants. Firstly, a multi-omics data integration method was used to identify correlation patterns between the omics data. Then, we tested possible interactions between the lipids and complement proteins using logistic regression models. ResultsAmong the 155 AMD cases, 93 (60.0%) had early and 62 (40.0%) intermediate AMD. Firstly, we identified two clusters between complement proteins and lipids involving (1) MASP1 and several different HDL particles, and (2) FHR1, CPN2 and CO8G, and sphingomyelin and different cholesterol. Secondly, we identified one interaction between C1R and sphingomyelin with an odds of AMD 2 times higher for individuals with low levels of sphingomyelin and C1R (OR=2.13 [1.09, 4.17]). ConclusionWe report here, using a cutting-edge multi-omics integration approach, the complex interconnections between genetic, metabolomics and proteomic data. This method permitted us to obtain a holistic picture and identify multi-omics signature of AMD pathophysiology. These results advocate for a personalized therapeutic approach that accounts for multiple pathways. However, these results need to be validated in larger studies with different ethnic groups.

Dissecting the lung transcriptome of pulmonary fibrosis-associated pulmonary hypertension.

Integrative multiomics can help elucidate the pathophysiology of pulmonary fibrosis (PF)-associated pulmonary hypertension (PH) (PF-PH). Weighted gene coexpression network analysis (WGCNA) was performed on a transcriptomic dataset of explanted lung tissue from 116 patients with PF. Patients were stratified by pulmonary vascular resistance (PVR), and differential gene expression analysis was conducted. Gene modules were correlated with hemodynamics at the time of transplantation and tested for enrichment in the lung transcriptomics signature of an independent pulmonary arterial hypertension (PAH) cohort. We found 1,250 differentially expressed genes between high and low PVR groups. WGCNA identified that black and yellowgreen modules negatively correlated with PVR, whereas the tan and darkgrey modules are positively correlated with PVR in PF-PH. In addition, the tan module showed the strongest enrichment for an independent PAH gene signature, suggesting shared gene expression patterns between PAH and PF-PH. Pharmacotranscriptomic analysis using the Connectivity Map implicated the tan and darkgrey modules as potentially pathogenic in PF-PH, given their combined module signature demonstrated a high negative connectivity score for treprostinil, a medication used in the treatment of PF-PH, and a high positive connectivity score for bone morphogenetic protein (BMP) loss of function. Pathway enrichment analysis revealed that inflammatory pathways and oxidative phosphorylation were downregulated, whereas epithelial-mesenchymal transition was upregulated in modules associated with increased PVR. Our integrative systems biology approach to the lung transcriptome of PF with and without PH identified several PH-associated coexpression modules and gene targets with shared molecular features with PAH warranting further investigation to uncover potential new therapies for PF-PH.NEW & NOTEWORTHY An integrative systems biology approach that included transcriptomic analysis of explanted lung tissue from patients with pulmonary fibrosis (PF) with and without pulmonary hypertension (PH) undergoing lung transplantation, combined with hemodynamic correlation and pharmacotranscriptomics, identified modules of genes associated with pulmonary vascular disease severity. Comparison with an independent pulmonary arterial hypertension (PAH) dataset identified shared gene expression patterns between PAH and PF-PH.

Integrative single-cell multi-omics of CD19-CARpos and CARneg Tcells suggest drivers of immunotherapy response in B cell neoplasias.

The impact of phenotypic, clonal, and functional heterogeneity of chimeric antigen receptor (CAR)-T cells on clinical outcome remains understudied. Here, we integrate clonal kinetics with transcriptomic heterogeneity resolved by single-cell omics to interrogate cellular dynamics of non-transduced (CARneg) and transduced (CARpos) Tcells, in the infusion product (IP) and at the CAR-T cell expansion peak in five B cell acute lymphoblastic leukemia (B-ALL) patients treated with CD19CAR-T cells (varni-cel). We identify significant differences in cellular dynamics in response to therapy. CARpos Tcells at IP of complete response patients exhibit a significantly higher CD4:CD8 ratio, validated in a larger cohort B-ALL patients (n= 47). Conversely, at the expansion peak, there is a clonal expansion of CD8+ effector memory and cytotoxic Tcells. Cytotoxic CARpos γδ-T cells expansion correlates with treatment efficacy validated in a cohort of B-ALL (n= 18) and diffuse large B cell lymphoma (DLBCL) patients (n= 58). Our data provide insights into the complexity of Tcell responses following CAR-T cell therapy and suggest drivers of immunotherapy response.

DeepFusionCDR: Employing Multi-Omics Integration and Molecule-Specific Transformers for Enhanced Prediction of Cancer Drug Responses.

Deep learning approaches have demonstrated remarkable potential in predicting cancer drug responses (CDRs), using cell line and drug features. However, existing methods predominantly rely on single-omics data of cell lines, potentially overlooking the complex biological mechanisms governing cell line responses. This paper introduces DeepFusionCDR, a novel approach employing unsupervised contrastive learning to amalgamate multi-omics features, including mutation, transcriptome, methylome, and copy number variation data, from cell lines. Furthermore, we incorporate molecular SMILES-specific transformers to derive drug features from their chemical structures. The unified multi-omics and drug signatures are combined, and a multi-layer perceptron (MLP) is applied to predict IC50 values for cell line-drug pairs. Moreover, this MLP can discern whether a cell line is resistant or sensitive to a particular drug. We assessed DeepFusionCDR's performance on the GDSC dataset and juxtaposed it against cutting-edge methods, demonstrating its superior performance in regression and classification tasks. We also conducted ablation studies and case analyses to exhibit the effectiveness and versatility of our proposed approach. Our results underscore the potential of DeepFusionCDR to enhance CDR predictions by harnessing the power of multi-omics fusion and molecular-specific transformers. The prediction of DeepFusionCDR on TCGA patient data and case study highlight the practical application scenarios of DeepFusionCDR in real-world environments.

Metabolic Landscape of Naturally Occurring CKD in Cats: An Integrated Multiomics Study

Metabolic Landscape of Naturally Occurring CKD in Cats: An Integrated Multiomics Study

Enhancing Non-Small Cell Lung Cancer Survival Prediction through Multi-Omics Integration Using Graph Attention Network.

Background: Cancer survival prediction is vital in improving patients' prospects and recommending therapies. Understanding the molecular behavior of cancer can be enhanced through the integration of multi-omics data, including mRNA, miRNA, and DNA methylation data. In light of these multi-omics data, we proposed a graph attention network (GAT) model in this study to predict the survival of non-small cell lung cancer (NSCLC). Methods: The different omics data were obtained from The Cancer Genome Atlas (TCGA) and preprocessed and combined into a single dataset using the sample ID. We used the chi-square test to select the most significant features to be used in our model. We used the synthetic minority oversampling technique (SMOTE) to balance the dataset and the concordance index (C-index) to measure the performance of our model on different combinations of omics data. Results: Our model demonstrated superior performance, with the highest value of the C-index obtained when we used both mRNA and miRNA data. This demonstrates that the multi-omics approach could be effective in predicting survival. Further pathway analysis conducted with KEGG showed that our GAT model provided high weights to the features that are associated with the viral entry pathways, such as the Epstein-Barr virus and Influenza A pathways, which are involved in lung cancer development. From our findings, it can be observed that the proposed GAT model leads to a significantly improved prediction of survival by exploiting the strengths of multiple omics datasets and the findings from the enriched pathways. Our GAT model outperforms other state-of-the-art methods that are used for NSCLC prediction. Conclusions: In this study, we developed a new model for the survival prediction of NSCLC using the GAT based on multi-omics data. Our model showed outstanding predictive values, and the KEGG analysis of the selected significant features showed that they were implicated in pivotal biological processes underlying pathways such as Influenza A and the Epstein-Barr virus infection, which are linked to lung cancer progression.

Harnessing Multi-Omics Strategies and Bioinformatics Innovations for Advancing Soybean Improvement: A Comprehensive Review.

Soybean improvement has entered a new era with the advent of multi-omics strategies and bioinformatics innovations, enabling more precise and efficient breeding practices. This comprehensive review examines the application of multi-omics approaches in soybean-encompassing genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics. We first explore pre-breeding and genomic selection as tools that have laid the groundwork for advanced trait improvement. Subsequently, we dig into the specific contributions of each -omics field, highlighting how bioinformatics tools and resources have facilitated the generation and integration of multifaceted data. The review emphasizes the power of integrating multi-omics datasets to elucidate complex traits and drive the development of superior soybean cultivars. Emerging trends, including novel computational techniques and high-throughput technologies, are discussed in the context of their potential to revolutionize soybean breeding. Finally, we address the challenges associated with multi-omics integration and propose future directions to overcome these hurdles, aiming to accelerate the pace of soybean improvement. This review serves as a crucial resource for researchers and breeders seeking to leverage multi-omics strategies for enhanced soybean productivity and resilience.

Systemic dysregulation and molecular insights into poor influenza vaccine response in the aging population.

Vaccination-induced protection against influenza is greatly diminished and increasingly heterogeneous with age. We investigated longitudinally (up to five time points) a cohort of 234 vaccinated >65-year-old vaccinees with adjuvanted vaccine FluAd across two independent seasons. System-level analyses of multiomics datasets measuring six modalities and serological data revealed that poor responders lacked time-dependent changes in response to vaccination as observed in responders, suggestive of systemic dysregulation in poor responders. Multiomics integration revealed key molecules and their likely role in vaccination response. High prevaccination plasma interleukin-15 (IL-15) concentrations negatively associated with antibody production, further supported by experimental validation in mice revealing an IL-15-driven natural killer cell axis explaining the suppressive role in vaccine-induced antibody production as observed in poor responders. We propose a subset of long-chain fatty acids as modulators of persistent inflammation in poor responders. Our findings provide a potential link between low-grade chronic inflammation and poor vaccination response and open avenues for possible pharmacological interventions to enhance vaccine responses.

Benchmarking algorithms for single-cell multi-omics prediction and integration.

The development of single-cell multi-omics technology has greatly enhanced our understanding of biology, and in parallel, numerous algorithms have been proposed to predict the protein abundance and/or chromatin accessibility of cells from single-cell transcriptomic information and to integrate various types of single-cell multi-omics data. However, few studies have systematically compared and evaluated the performance of these algorithms. Here, we present a benchmark study of 14 protein abundance/chromatin accessibility prediction algorithms and 18 single-cell multi-omics integration algorithms using 47 single-cell multi-omics datasets. Our benchmark study showed overall totalVI and scArches outperformed the other algorithms for predicting protein abundance, and LS_Lab was the top-performing algorithm for the prediction of chromatin accessibility in most cases. Seurat, MOJITOO and scAI emerge as leading algorithms for vertical integration, whereas totalVI and UINMF excel beyond their counterparts in both horizontal and mosaic integration scenarios. Additionally, we provide a pipeline to assist researchers in selecting the optimal multi-omics prediction and integration algorithm.

Contemporary Update on Clinical and Experimental Prostate Cancer Biomarkers: A Multi-Omics-Focused Approach to Detection and Risk Stratification.

Prostate cancer remains a significant health challenge, being the most prevalent non-cutaneous cancer in men worldwide. This review discusses the critical advancements in biomarker discovery using single-omics and multi-omics approaches. Multi-omics, integrating genomic, transcriptomic, proteomic, metabolomic, and epigenomic data, offers a comprehensive understanding of the molecular heterogeneity of prostate cancer, leading to the identification of novel biomarkers and therapeutic targets. This holistic approach not only enhances the specificity and sensitivity of prostate cancer detection but also supports the development of personalized treatment strategies. Key studies highlighted include the identification of novel genes, genetic mutations, peptides, metabolites, and potential biomarkers through multi-omics analyses, which have shown promise in improving prostate cancer management. The integration of multi-omics in clinical practice can potentially revolutionize prostate cancer prognosis and treatment, paving the way for precision medicine. This review underscores the importance of continued research and the application of multi-omics to overcome current challenges in prostate cancer diagnosis and therapy.

A Review on Integrating Bioinformatics Tools in Modern Plant Breeding

Bioinformatics has become integral to modern plant breeding, facilitating the analysis of large-scale genomic data, identifying key genetic markers, and enabling precision breeding techniques such as genomic selection. Advances in sequencing technologies and computational tools have accelerated the pace of crop improvement, allowing for the rapid identification of genes associated with important agronomic traits, including yield, stress tolerance, and disease resistance. The integration of bioinformatics in plant breeding also presents significant challenges, particularly in terms of data management, multi-omics integration, and the interpretation and validation of complex datasets. The role of emerging trends such as pangenomics, metagenomics, and epigenomics in expanding the scope of plant breeding, as well as the increasing importance of artificial intelligence and machine learning in enhancing predictive accuracy and optimizing breeding strategies. Personalized plant breeding and precision agriculture are identified as promising approaches for tailoring crop varieties to specific environments and farming practices, driven by bioinformatics tools that enable detailed analysis of genomic and phenotypic data. Ethical considerations and data privacy issues are also addressed, emphasizing the need for transparent and equitable practices in the collection, sharing, and use of genomic data. The importance of collaborative efforts and global initiatives in advancing bioinformatics-driven plant breeding, with a focus on fostering international cooperation, building capacity in developing regions, and ensuring open access to bioinformatics resources. As the field continues to evolve, bioinformatics will play a Important role in developing sustainable agricultural systems capable of meeting the demands of a growing global population while mitigating the impacts of climate change. The current applications, challenges, and future prospects of bioinformatics in plant breeding, offering insights into the critical role of this field in shaping the future of agriculture.

Multi-omics analysis reveals regime shifts in the gastrointestinal ecosystem in chickens following anticoccidial vaccination and Eimeria tenella challenge.

Coccidiosis, caused by Eimeria parasites, significantly impacts poultry farm economics and animal welfare. Beyond its direct impact on health, Eimeria infection disrupts enteric microbial populations leading to dysbiosis and increases vulnerability to secondary diseases such as necrotic enteritis, caused by Clostridium perfringens. The impact of Eimeria infection or anticoccidial vaccination on host gastrointestinal phenotypes and enteric microbiota remains understudied. In this study, the metabolomic profiles and microbiota composition of chicken caecal tissue and contents were evaluated concurrently during a controlled experimental vaccination and challenge trial. Cobb500 broilers were vaccinated with a Saccharomyces cerevisiae-vectored anticoccidial vaccine and challenged with 15,000 Eimeria tenella oocysts. Assessment of caecal pathology and quantification of parasite load revealed correlations with alterations to caecal microbiota and caecal metabolome linked to infection and vaccination status. Infection heightened microbiota richness with increases in potentially pathogenic species, while vaccination elevated beneficial Bifidobacterium. Using a multi-omics factor analysis, data on caecal microbiota and metabolome were integrated and distinct profiles for healthy, infected, and recovering chickens were identified. Healthy and recovering chickens exhibited higher vitamin B metabolism linked to short-chain fatty acid-producing bacteria, whereas essential amino acid and cell membrane lipid metabolisms were prominent in infected and vaccinated chickens. Notably, vaccinated chickens showed distinct metabolites related to the enrichment of sphingolipids, important components of nerve cells and cell membranes. Our integrated multi-omics model revealed latent biomarkers indicative of vaccination and infection status, offering potential tools for diagnosing infection, monitoring vaccination efficacy, and guiding the development of novel treatments or controls.IMPORTANCEAdvances in anticoccidial vaccines have garnered significant attention in poultry health management. However, the intricacies of vaccine-induced alterations in the chicken gut microbiome and its subsequent impact on host metabolism remain inadequately explored. This study delves into the metabolic and microbiotic shifts in chickens post-vaccination, employing a multi-omics integration analysis. Our findings highlight a notable synergy between the microbiome composition and host-microbe interacted metabolic pathways in vaccinated chickens, differentiating them from infected or non-vaccinated cohorts. These insights pave the way for more targeted and efficient approaches in poultry disease control, enhancing both the efficacy of vaccines and the overall health of poultry populations.