Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning

Jonathan Josephs-Spaulding, Akanksha Rajput, Ying Hefner, Richard Szubin, Archana Balasubramanian, Gaoyuan Li, Daniel C. Zielinski, Leonie Jahn, Morten Sommer, Patrick Phaneuf, Bernhard O. Palsson

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Limosilactobacillus reuteri, a probiotic microbe instrumental to human health and sustainable food production, adapts to diverse environmental shifts via dynamic gene expression. We applied the independent component analysis (ICA) to 117 RNA-seq data sets to decode its transcriptional regulatory network (TRN), identifying 35 distinct signals that modulate specific gene sets. Our findings indicate that the ICA provides a qualitative advancement and captures nuanced relationships within gene clusters that other methods may miss. This study uncovers the fundamental properties of L. reuteri's TRN and deepens our understanding of its arginine metabolism and the co-regulation of riboflavin metabolism and fatty acid conversion. It also sheds light on conditions that regulate genes within a specific biosynthetic gene cluster and allows for the speculation of the potential role of isoprenoid biosynthesis in L. reuteri's adaptive response to environmental changes. By integrating transcriptomics and machine learning, we provide a system-level understanding of L. reuteri's response mechanism to environmental fluctuations, thus setting the stage for modeling the probiotic transcriptome for applications in microbial food production.
Original languageEnglish
Article numbere0125723
JournalmSystems
ISSN2379-5077
DOIs
Publication statusAccepted/In press - 2024

Keywords

  • L. reuteri
  • machine learning
  • probiotic
  • systems biology
  • transcriptome

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