Latent Interacting Variable Effects (LIVE) modeling is a framework to integrate different types of microbiome multi-omics data by combining latent variables from single-omic models into a structured meta-model to determine discriminative, interacting multi-omics features driving disease status. We implemented and tested LIVE modeling in publicly available metagenomics and metabolomics datasets from Crohn’s Disease and Ulcerative Colitis patients. Here, LIVE modeling reduced the number of feature correlations from the original data set for CD and UC to tractable numbers and facilitated prioritization of biological associations between microbes, metabolites, enzymes and IBD status through the application of stringent thresholds on generated inferential statistics. We determined LIVE modeling confirmed previously reported IBD biomarkers and uncovered potentially novel disease mechanisms in IBD. LIVE modeling makes a distinct and complementary contribution to the current methods to integrate microbiome data to predict IBD status because of its flexibility to adapt to different types of microbiome multi-omics data, scalability for large and small cohort studies via reliance on latent variables and dimensionality reduction, and the intuitive interpretability of the linear meta-model integrating -omic data types. The results of LIVE modeling and the biological relationships can be represented in networks that connect local correlation structure of single omic data types with global community and omic structure in the latent variable VIP scores. This model arises as novel tool that allows researchers to be more selective about omic feature interaction without disrupting the structural correlation framework provided by sPLS-DA interaction effects modeling. It will lead to form testable hypothesis by identifying potential and unique interactions between metabolome and microbiome that must be considered for future studies.
more »
« less
This content will become publicly available on January 23, 2025
Poly-omic risk scores predict inflammatory bowel disease diagnosis
Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and selection operator regression to select features for each omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between data sets. Individually, metabolomic and viromic scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke’s
Complex traits are characterized by many biological and environmental factors, such that multi-omic data sets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each omic data set in single-omic versus multi-omic models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple omic data types to disentangle complex disease etiologies and biological signatures.
- Award ID(s):
- 2022138
- PAR ID:
- 10522024
- Editor(s):
- Beiko, Robert G
- Publisher / Repository:
- mSystems
- Date Published:
- Journal Name:
- mSystems
- Volume:
- 9
- Issue:
- 1
- ISSN:
- 2379-5077
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
more » « less
Abstract Inflammatory bowel disease (IBD) includes Crohn’s disease and ulcerative colitis. Each disease is characterized by a diverse set of potential manifestations, which determine patients’ disease phenotype. Current understanding of phenotype determinants is limited, despite increasing prevalence and healthcare costs. Diagnosis and monitoring of disease requires invasive procedures, such as endoscopy and tissue biopsy. Here we report signatures of heterogeneity between disease diagnoses and phenotypes. Using mass cytometry, we analyze leukocyte subsets, characterize their function(s), and examine gut-homing molecule expression in blood and intestinal tissue from healthy and/or IBD subjects. Some signatures persist in IBD despite remission, and many signatures are highly represented by leukocytes that express gut trafficking molecules. Moreover, distinct systemic and local immune signatures suggest patterns of cell localization in disease. Our findings highlight the importance of gut tropic leukocytes in circulation and reveal that blood-based immune signatures differentiate clinically relevant subsets of IBD.
-
Abstract A large number of genetic variations have been identified to be associated with Alzheimer’s disease (AD) and related quantitative traits. However, majority of existing studies focused on single types of omics data, lacking the power of generating a community including multi-omic markers and their functional connections. Because of this, the immense value of multi-omics data on AD has attracted much attention. Leveraging genomic, transcriptomic and proteomic data, and their backbone network through functional relations, we proposed a modularity-constrained logistic regression model to mine the association between disease status and a group of functionally connected multi-omic features, i.e. single-nucleotide polymorphisms (SNPs), genes and proteins. This new model was applied to the real data collected from the frontal cortex tissue in the Religious Orders Study and Memory and Aging Project cohort. Compared with other state-of-art methods, it provided overall the best prediction performance during cross-validation. This new method helped identify a group of densely connected SNPs, genes and proteins predictive of AD status. These SNPs are mostly expression quantitative trait loci in the frontal region. Brain-wide gene expression profile of these genes and proteins were highly correlated with the brain activation map of ‘vision’, a brain function partly controlled by frontal cortex. These genes and proteins were also found to be associated with the amyloid deposition, cortical volume and average thickness of frontal regions. Taken together, these results suggested a potential pathway underlying the development of AD from SNPs to gene expression, protein expression and ultimately brain functional and structural changes.more » « less
-
Wei, Yanjie ; Li, Min ; Skums, Pavel ; Cai, Zhipeng (Ed.)Long-time evolution has shaped a harmonious host-microbiota symbiosis consisting of intestinal microbiota in conjunction with the host immune system. Inflammatory bowel disease (IBD) is a result of the dysbiotic microbial composition together with aberrant mucosal immune responses, while the underlying mechanism is far from clear. In this report, we creatively proposed that when correlating with the host metabolism, functional microbial communities matter more than individual bacteria. Based on this assumption, we performed a systematic analysis to characterize the co-metabolism of host and gut microbiota established on a set of newly diagnosed Crohn’s disease (CD) samples and healthy controls. From the host side, we applied gene set enrichment analysis on host mucosal proteome data to identify those host pathways associated with CD. At the same time, we applied community detection analysis on the metagenomic data of mucosal microbiota to identify those microbial communities, which were assembled for a functional purpose. Then, the correlation analysis between host pathways and microbial communities was conducted. We discovered two microbial communities negatively correlated with IBD enriched host pathways. The dominant genera for these two microbial communities are known as health-benefits and could serve as a reference for designing complex beneficial microorganisms for IBD treatment. The correlated host pathways are all relevant to MHC antigen presentation pathways, which hints toward a possible mechanism of immune-microbiota cross talk underlying IBD.more » « less
-
more » « less
Abstract Psoriasis is an immune‐mediated chronic inflammatory skin disease. Although its pathogenesis is not fully understood, Th17 cells and the cytokines they produce, such as IL‐17, IL‐22 and IL‐23, play critical roles in the pathogenesis of psoriasis. Evidence has demonstrated that psoriasis has some common features, including immune responses (due to Th17 cells) and inflammatory cytokine profiles, with systematic diseases including inflammatory bowel diseases (IBDs) and obesity. Recently, studies have demonstrated that the gut microbiota plays a crucial role in host homoeostasis and immune response, particular in Th17 cells, but the role of the gut microbiota in psoriasis remains unclear. To study the relationship between gut microbiota and psoriasis, we analysed microbiota profiles in psoriasis using a 16S rDNA sequencing platform, and we found that the abundance of
Akkermansia muciniphila was significantly reduced in patients with psoriasis.A. muciniphila is believed to have an important function in the pathogenesis of IBD and obesity; therefore,A. muciniphila , which is an indicator of health status, may be a key node for psoriasis as well as IBD and obesity. Taken together, our study identified that gut microbiota signature and function are significantly altered in the gut of patients with psoriasis, which provides a novel angle to understanding the pathogenesis of psoriasis.
