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1. Latent Interacting Variable-Effects Modeling of Gut Microbiome Multi-Omics in Inflammatory Bowel Disease.

   https://doi.org/10.1101/2022.07.08.499280  

   Munoz, J. E. (July 2022, bioRxiv)

   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. 

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2. Standardized multi-omics of Earth’s microbiomes reveals microbial and metabolite diversity

   https://doi.org/10.1038/s41564-022-01266-x  

   Shaffer, Justin P.; Nothias, Louis-Félix; Thompson, Luke R.; Sanders, Jon G.; Salido, Rodolfo A.; Couvillion, Sneha P.; Brejnrod, Asker D.; Lejzerowicz, Franck; Haiminen, Niina; Huang, Shi; et al (December 2022, Nature Microbiology)

   Despite advances in sequencing, lack of standardization makes comparisons across studies challenging and hampers insights into the structure and function of microbial communities across multiple habitats on a planetary scale. Here we present a multi-omics analysis of a diverse set of 880 microbial community samples collected for the Earth Microbiome Project. We include amplicon (16S, 18S, ITS) and shotgun metagenomic sequence data, and untargeted metabolomics data (liquid chromatography-tandem mass spectrometry and gas chromatography mass spectrometry). We used standardized protocols and analytical methods to characterize microbial communities, focusing on relationships and co-occurrences of microbially related metabolites and microbial taxa across environments, thus allowing us to explore diversity at extraordinary scale. In addition to a reference database for metagenomic and metabolomic data, we provide a framework for incorporating additional studies, enabling the expansion of existing knowledge in the form of an evolving community resource. We demonstrate the utility of this database by testing the hypothesis that every microbe and metabolite is everywhere but the environment selects. Our results show that metabolite diversity exhibits turnover and nestedness related to both microbial communities and the environment, whereas the relative abundances of microbially related metabolites vary and co-occur with specific microbial consortia in a habitat-specific manner. We additionally show the power of certain chemistry, in particular terpenoids, in distinguishing Earth’s environments (for example, terrestrial plant surfaces and soils, freshwater and marine animal stool), as well as that of certain microbes including Conexibacter woesei (terrestrial soils), Haloquadratum walsbyi (marine deposits) and Pantoea dispersa (terrestrial plant detritus). This Resource provides insight into the taxa and metabolites within microbial communities from diverse habitats across Earth, informing both microbial and chemical ecology, and provides a foundation and methods for multi-omics microbiome studies of hosts and the environment. 

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3. Multitask knowledge-primed neural network for predicting missing metadata and host phenotype based on human microbiome

   https://doi.org/10.1093/bioadv/vbae203  

   Monshizadeh, Mahsa; Hong, Yuhui; Ye, Yuzhen (December 2024, Bioinformatics Advances)

   Lengauer, Thomas (Ed.)

   Abstract MotivationMicrobial signatures in the human microbiome are closely associated with various human diseases, driving the development of machine learning models for microbiome-based disease prediction. Despite progress, challenges remain in enhancing prediction accuracy, generalizability, and interpretability. Confounding factors, such as host’s gender, age, and body mass index, significantly influence the human microbiome, complicating microbiome-based predictions. ResultsTo address these challenges, we developed MicroKPNN-MT, a unified model for predicting human phenotype based on microbiome data, as well as additional metadata like age and gender. This model builds on our earlier MicroKPNN framework, which incorporates prior knowledge of microbial species into neural networks to enhance prediction accuracy and interpretability. In MicroKPNN-MT, metadata, when available, serves as additional input features for prediction. Otherwise, the model predicts metadata from microbiome data using additional decoders. We applied MicroKPNN-MT to microbiome data collected in mBodyMap, covering healthy individuals and 25 different diseases, and demonstrated its potential as a predictive tool for multiple diseases, which at the same time provided predictions for the missing metadata. Our results showed that incorporating real or predicted metadata helped improve the accuracy of disease predictions, and more importantly, helped improve the generalizability of the predictive models. Availability and implementationhttps://github.com/mgtools/MicroKPNN-MT. 

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4. Poly-omic risk scores predict inflammatory bowel disease diagnosis

   https://doi.org/10.1128/msystems.00677-23  

   Arehart, Christopher H; Sterrett, John D; Garris, Rosanna L; Quispe-Pilco, Ruth E; Gignoux, Christopher R; Evans, Luke M; Stanislawski, Maggie A (January 2024, mSystems)

   Beiko, Robert G (Ed.)

   ABSTRACT 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’sR²of 0.46 and an area under the curve of 0.80 (95% confidence interval: 0.63, 0.98). Our work supports that some single-omic models for complex traits are more predictive than others, that incorporating multiple omic data sets may improve prediction, and that each omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic data sets. IMPORTANCEComplex 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. 

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5. Peeling back the layers of coral holobiont multi-omics data

   https://doi.org/10.1016/j.isci.2023.107623  

   Williams, Amanda; Stephens, Timothy G.; Shumaker, Alexander; Bhattacharya, Debashish (September 2023, iScience)

   The integration of multiple ‘omics’ datasets is a promising avenue for answering many important and challenging questions in biology, particularly those relating to complex ecological systems. Whereas, multi-omics was developed using data from model organisms with significant prior knowledge and resources, its application to non-model organisms, such as coral holobionts, is less clear-cut. We explore, in the emerging rice coral model Montipora capitata, the intersection of holobiont transcriptomic, proteomic, metabolomic, and microbiome amplicon data and investigate how well they correlate under high temperature treatment. Using a typical thermal stress regime, we show that transcriptomic and proteomic data broadly capture the stress response of the coral, whereas the metabolome and microbiome datasets show patterns that likely reflect stochastic and homeostatic processes associated with each sample. These results provide a framework for interpreting multi-omics data generated from non-model systems, particularly those with complex biotic interactions among microbial partners. 

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