Computational Identification of Novel Transcriptional Regulators and Functional Gene Clusters in Lactococcus lactis...

Lactococcus lactis is an essential industrial "cell factory" used extensively in food fermentation and biotechnology. However, a critical biological question regarding the regulatory mechanisms of the microorganism's adaptation process remains unresolved: how does the bacterium transcriptionally coordinate the trade-off between primary metabolism and cell-surface remodeling during environmental stress and competence? To date, a unified, global model of its gene regulatory networks (GRNs) that accounts for this transition remains lacking. To address this fragmentation and eliminate selection bias, we integrated the complete compendium of publicly available transcriptomic datasets for L. lactis deposited in the NCBI database as of the summer of 2025. This exhaustive dataset encompasses a wide range of conditions, including thermal, acid, and phage-induced stress, as well as natural competence, providing the necessary transcriptional variance for robust network inference. We implemented an integrated bioinformatics pipeline using the GENIE3 algorithm to infer a core regulatory network common to all tested conditions, complemented by an ensemble of DeepTFactor, Entraf, and p2TF tools to predict strain-specific potential transcription factors (TFs) for L. lactis. The co-expression network partitioned into 50 functional clusters, notably highlighting putative regulators for a unique WxL operon potentially involved in cell-surface modifications. Furthermore, we proposed candidate regulatory targets for the master competence regulator, ComX, and computationally predicted CpsY as a potential LysR-family regulator of branched-chain amino acid metabolism. These findings provide a transcriptomics-based computational model of L. lactis regulation. By clearly distinguishing between established regulatory pathways and purely computational predictions, we suggest several uncharacterized proteins as putative key nodes in the bacterial response to environmental challenges. While requiring direct experimental validation to establish physical interactions, this computational approach generates high-confidence hypotheses and offers a curated resource of candidates for targeted metabolic engineering.