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bioRxiv · 10.1101/2023.07.03.547516

Reconstructing the Transcriptional Regulatory Network of Probiotic L. reuteri is Enabled by Transcriptomics and Machine Learning

Josephs-Spaulding, J.·Rajput, A.·Hefner, Y.·Szubin, R.·Balasubramanian, A.·Li, G.·Zielinski, D. C.·Jahn, L.·Sommer, M.·Phaneuf, P.·Palsson, B. O.
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Abstract

ILimosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied independent component analysis to 117 high-quality RNA-seq datasets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. This study uncovers the fundamental properties of L. reuteris TRN, deepens our understanding of its arginine metabolism, and the co-regulation of riboflavin metabolism and fatty acid biosynthesis. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and the role of isoprenoid biosynthesis in L. reuteris adaptive response to environmental changes. Through the integration of transcriptomics and machine learning, we provide a systems-level understanding of L. reuteris response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/547516v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@118ec4corg.highwire.dtl.DTLVardef@141b0b7org.highwire.dtl.DTLVardef@1b8cf6forg.highwire.dtl.DTLVardef@3ab181_HPS_FORMAT_FIGEXP M_FIG C_FIG Comprehensive iModulon Workflow Overview. Our innovative workflow is grounded in the analysis of the LactoPRECISE compendium, a curated dataset containing 117 internally sequenced RNA-seq samples derived from a diversity of 50 unique conditions, encompassing an extensive range of 13 distinct condition types. We employ the power of Independent Component Analysis (ICA), a cutting-edge machine learning algorithm, to discern the underlying structure of iModulons within this wealth of data. In the subsequent stage of our workflow, the discovered iModulons undergo detailed scrutiny to uncover media-specific regulatory mechanisms governing metabolism, illuminate the context-dependent intricacies of gene expression, and predict pathways leading to the biosynthesis of probiotic secondary metabolites. Our workflow offers an invaluable and innovative lens through which to view probiotic strain design while simultaneously highlighting transformative approaches to data analytics in the field.

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Josephs-Spaulding, J., Rajput, A., Hefner, Y., Szubin, R., Balasubramanian, A., Li, G., Zielinski, D. C., Jahn, L., Sommer, M., Phaneuf, P., Palsson, B. O.. 2023-07-03. Reconstructing the Transcriptional Regulatory Network of Probiotic L. reuteri is Enabled by Transcriptomics and Machine Learning. https://doi.org/10.1101/2023.07.03.547516

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