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

Area of Science:

• Microbiology
• Systems Biology
• Bioinformatics

Background:

• Lactococcus lactis is a vital industrial microorganism for food fermentation and biotechnology.
• Understanding its adaptation mechanisms, particularly the balance between metabolism and cell-surface remodeling under stress, is crucial but lacks a unified model.
• Existing knowledge of Lactococcus lactis gene regulatory networks (GRNs) is fragmented, hindering a global understanding.

Purpose of the Study:

• To develop a comprehensive, transcriptomics-based computational model of Lactococcus lactis GRNs.
• To elucidate the regulatory coordination between primary metabolism and cell-surface remodeling during environmental stress and competence.
• To identify novel regulators and targets for metabolic engineering.

Main Methods:

• Integrated analysis of all publicly available Lactococcus lactis transcriptomic datasets (NCBI, summer 2025).
• Applied bioinformatics pipeline including GENIE3 for core network inference and DeepTFactor, Entraf, p2TF for strain-specific transcription factor prediction.
• Co-expression network analysis to identify functional clusters and potential regulators.

Main Results:

• Inferred a core regulatory network common across diverse conditions (stress, competence).
• Identified 50 functional clusters, highlighting potential regulators for a WxL operon involved in cell-surface modification.
• Proposed candidate targets for the competence regulator ComX and predicted CpsY as a potential regulator of amino acid metabolism.

Conclusions:

• Generated a high-confidence, transcriptomics-based computational model for Lactococcus lactis regulation.
• Distinguished between known and computationally predicted regulatory elements, offering hypotheses for uncharacterized proteins.
• Provided a valuable resource for experimental validation and targeted metabolic engineering of Lactococcus lactis.