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

Aug 5, 2026

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Limosilactobacillus reuteri is a probiotic that helps human health and sustainable food production by adapting to environmental changes through dynamic gene expression. The study applies independent component analysis to 117 RNA-seq datasets to reconstruct its transcriptional regulatory network, uncovering 35 distinct signals that modulate specific gene sets. The method improves qualitative insight and reveals nuanced gene-cluster relationships missed by other approaches. Results connect the network to arginine metabolism and coordinated riboflavin and fatty-acid conversion, while highlighting biosynthetic gene-cluster regulation and potential links to isoprenoid biosynthesis. By integrating transcriptomics and machine learning, it enables system-level modeling of probiotic responses to environmental fluctuations and supports future microbial food production applications.

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| Applied and Industrial Microbiology | Research Article Reconstructing the transcriptional regulatory network of probiotic L. reuteri is enabled by transcriptomics and machine learning Jonathan Josephs-Spaulding,1 Akanksha Rajput,2 Ying Hefner,2 Richard Szubin,2 Archana Balasubramanian,2 Gaoyuan Li,2 Daniel C. Zielinski,2 Leonie Jahn,1 Morten Sommer,1 Patrick Phaneuf,1 Bernhard O. Palsson1,2 AUTHOR AFFILIATIONS See affiliation list on p. 19. 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. IMPORTANCE We have studied Limosilactobacillus reuteri, a beneficial probiotic microbe that plays a significant role in our health and production of sustainable foods, a typeof foods that are nutritionally dense and healthier and have low-carbon emissions compared to traditional foods. Similar to how humans adapt their lifestyles to different environments, this microbe adjusts its behavior by modulating the expression of genes. We applied machine learning to analyze large-scale data sets on how these genes behave across diverse conditions. From this, we identified 35 unique patterns demonstrating how L. reuteri adjusts its genes based on 50 unique environmental conditions (such as various sugars, salts, microbial cocultures, human milk, and fruit juice) . This research helps us understand better how L. reuteri functions, especially in processes like breaking down certain nutrients and adapting to stressful changes. More importantly, with our findings, we become closer to using this knowledge to improve how we produce more sustainable and healthier foods with the help of microbes. KEYWORDS L. reuteri, probiotic, machine learning, transcriptome, systems biology L imosilactobacillus reuteri (L. reuteri) has become a focal point in probiotic research due to its versatile roles in human health and various commercial applications. Recognizing that reuterin is an intermediate in glycerol metabolism, its function extends beyond fermentation byproduct to a potent antimicrobial and its broader efficacy in food preservation (1) . Reuterin’s active form, 3-hydroxypropionaldehyde, has been validated for antimicrobial activity in an array of food matrices, combating pathogens Editor Danilo Ercolini, Universita degli Studi di Napoli Federico II, Italy Address correspondence to Bernhard O. Palsson, [palsson@ucsd.edu](palsson@ucsd.edu), or Patrick Phaneuf, [phaneuf@biosustain.dtu.dk](phaneuf@biosustain.dtu.dk). The authors declare no conflict of interest. See the funding table on p. 19. Received 27 November 2023 Accepted 9 January 2024 Published 13 February 2024 Copyright © 2024 Josephs-Spaulding et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license. March 2024 Volume 9 Issue

FAQs

How was the transcriptional regulatory network of L. reuteri reconstructed?

Independent component analysis was applied to 117 RNA-seq datasets to decode the transcriptional regulatory network and identify regulatory signals.

What key regulatory signals and biological processes were identified?

The analysis identified 35 distinct signals that modulate gene sets, highlighting links to arginine metabolism and the co-regulation of riboflavin metabolism and fatty acid conversion.

How does this work relate to environmental adaptation and food production applications?

The study integrates transcriptomics and machine learning to explain how L. reuteri responds to environmental fluctuations, supporting system-level modeling that can guide applications in microbial, more sustainable food production.

Aug 5, 2026

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