Signaling Metabolites in Transcriptional Regulation

Metabolomics
Transcriptomics
Regulatory Networks
Systems Biology
Author

Andrés Arredondo

Metabolomics and transcriptional regulation


Integrating metabolomics and transcriptomics to uncover regulatory signals


This project aimed to identify signaling metabolites involved in transcriptional regulation by integrating metabolomic and transcriptomic data. The main model system was Escherichia coli, used as a reference regulatory network, with a conceptual extension toward symbiotic regulation in Rhizobium phaseoli–Phaseolus vulgaris.

The central hypothesis was that metabolites correlated with transcription factor (TF) activity can be detected through ranked metabolite–gene correlations and contextualized using pathway enrichment analysis (PEA). This approach enables the systematic discovery of metabolite–TF–gene relationships under physiologically relevant conditions.

Transcription Factors as Metabolic Sensors

Transcription factors (TFs) are regulatory proteins that bind DNA to control gene expression. In bacteria, many TFs also bind small metabolites, which act as environmental signals that activate or repress transcription.

In E. coli K-12, approximately 300 TFs have been identified, and nearly 50% contain metabolite-binding domains, suggesting a large number of regulatory interactions remain undiscovered. This regulatory logic provides a powerful framework to explore signal transduction through metabolism.

E. coli regulatory network

E. coli regulatory network

Objective

To apply and validate the correlation-based framework in E. coli by:

  • Ranking metabolites based on their average correlation with TF-regulated genes
  • Performing pathway enrichment analysis using KEGG compound annotations
  • Identifying pathways recurrently enriched across multiple transcription factors

This objective resulted in a prioritized set of metabolic pathways and candidate signaling metabolites linked to transcriptional regulation.

Results Overview

The pipeline was first applied to a reference dataset involving three well-characterized transcription factors (TFs), each associated with a defined set of regulated genes:

  • ArgR
  • TyrR
  • CysB

Subsequently, the same analytical workflow was applied to a second, independent experiment involving a broader and more diverse set of transcription factors, including:

  • PgrR
  • MarA
  • DecR
  • CecR
  • MngR
  • MlrA
  • MetJ
  • Lrp
  • Cra

This step was critical to demonstrate that the pipeline is transferable across experimental conditions and regulatory contexts, reinforcing its potential applicability beyond the initial proof-of-concept system.

E. coli regulatory network

Enrichment Analysis of ArgR correlated metabolites

Key Outcomes

  • Systematic ranking of metabolites associated with TF activity
  • Identification of enriched metabolic pathways linked to transcriptional regulation
  • Comparative analysis between full databases and experimentally measured metabolites
  • A scalable computational framework adaptable to non-model organisms

Perspectives

Phaseolus vulgaris (common bean) is the most widely consumed grain legume worldwide and the most important legume for human consumption in Mexico. It contributes 22–25% protein content and represents a substantial fraction of daily protein intake.

Like most legumes, P. vulgaris establishes a symbiotic association with nitrogen-fixing bacteria, reducing dependence on chemical fertilizers and improving soil fertility.

As a perspective, the validated framework can be extrapolated to Rhizobium phaseoli, a symbiotic bacterium associated with Phaseolus vulgaris. By integrating transcriptomic and metabolomic profiles under symbiosis-related conditions, this approach could reveal metabolites involved in early signaling events during nodulation.

This extension would leverage homology-based regulatory networks and comparative genomics to adapt the E. coli model to a symbiotic context.

A further perspective is the identification of symbiosis-specific regulatory mechanisms, where metabolite–TF interactions drive the expression of genes involved in nodulation and nitrogen fixation.

By mapping correlated metabolite–gene pairs onto transcriptional networks, this strategy could uncover regulatory signals beyond well-characterized flavonoid–NodD interactions, expanding our understanding of metabolic control in symbiosis.

Bean–Rhizobium symbiosis

Symbiotic interaction between Phaseolus vulgaris and Rhizobium spp.

Academic Context and Role

E. coli regulatory network

Functional regulation lab at CCG, UNAM

This project was carried out during my research stay at the Centro de Ciencias Genómicas (CCG–UNAM) as part of the Computational Genomics Program. The work was conducted within the Laboratory of Functional Regulation in Bacteria, under the supervision of Dr. Daniela Ledezma-Tejeida.

In particular, my contribution focused on the application of an already validated computational pipeline for metabolite–TF association analysis. Rather than developing the methodology from scratch, my work consisted of deploying, adapting, and evaluating the pipeline across independent experimental datasets, ensuring its robustness and reproducibility.

My role in this project was as a research assistant, contributing primarily to:

  • Computational analysis and pipeline development
  • Metabolite–gene correlation analysis
  • Pathway enrichment analysis
  • Interpretation of results within regulatory network frameworks

Conclusion

This project demonstrates how multi-omics integration can uncover hidden layers of transcriptional regulation mediated by metabolism. Beyond E. coli, the framework provides a foundation for studying regulatory signaling in symbiotic systems such as Rhizobium–legume interactions, with potential applications in sustainable agriculture and microbial engineering.

References

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  • Aguirre-Noyola, J. L., M. Rosenblueth, M. G. Santiago-Martínez, and E. Martínez-Romero. Transcriptomic Responses of Rhizobium phaseoli** to Root Exudates Reflect Its Capacity to Colonize Maize and Common Bean in an Intercropping System.** Frontiers in Microbiology 12 (2021). https://doi.org/10.3389/fmicb.2021.740818.

  • Martínez, E., R. Palacios, and F. Sánchez. Nitrogen-fixing nodules induced by Agrobacterium tumefaciens** harboring *****Rhizobium phaseoli***** plasmids.** Journal of Bacteriology 169 (1987): 2828–2834. https://doi.org/10.1128/jb.169.6.2828-2834.1987.

  • Hernández-Benítez, Ericka M., Esperanza Martínez-Romero, José Luis Aguirre-Noyola, and Daniela Ledezma-Tejeida. Putrescine Acts as a Signaling Metabolite in the Transition from Nodulation to Nitrogen Fixation in Rhizobium phaseoli. bioRxiv, January 29, 2024. https://doi.org/10.1101/2024.01.29.577838.

  • Kochanowski, Karl. Few Regulatory Metabolites Coordinate Expression of Central Metabolic Genes in Escherichia coli. Molecular Systems Biology (2017).