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Multi-omics has the promise to provide a detailed molecular picture of biological systems. Although obtaining multi-omics data is relatively easy, methods that analyze such data have been lagging. In this paper, we present an algorithm that uses probabilistic graph representations and external knowledge to perform optimal structure learning and deduce a multifarious interaction network for multi-omics data from a bacterial community. Kefir grain, a microbial community that ferments milk and creates kefir, represents a self-renewing, stable, natural microbial community. Kefir has been shown to have a wide range of health benefits. We obtained a controlled bacterial community using the two most abundant and well-studied species in kefir grains: Lentilactobacillus kefiri and Lactobacillus kefiranofaciens. We applied growth temperatures of 30 °C and 37 °C and obtained transcriptomic, metabolomic, and proteomic data for the same 20 samples (10 samples per temperature). We obtained a multi-omics interaction network, which generated insights that would not have been possible with single-omics analysis. We identified interactions among transcripts, proteins, and metabolites, suggesting active toxin/antitoxin systems. We also observed multifarious interactions that involved the shikimate pathway. These observations helped explain bacterial adaptation to different stress conditions, co-aggregation, and increased activation of L. kefiranofaciens at 37 °C.
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Peeling back the layers of coral holobiont multi-omics data
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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- Award ID(s):
- 2128073
- PAR ID:
- 10472748
- Publisher / Repository:
- iScience
- Date Published:
- Journal Name:
- iScience
- Volume:
- 26
- Issue:
- 9
- ISSN:
- 2589-0042
- Page Range / eLocation ID:
- 107623
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.isci.2023.107623
