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Gene regulatory networks in disease and ageing

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Abstract

The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene–gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.

Key points

  • Computational models of gene regulatory networks (GRNs) can aid the interpretation of several types of molecular high-throughput (omics) data; the integration of GRNs with omics data can provide insights into disease mechanisms and the physiological consequences of ageing.

  • Different types of GRNs need to be distinguished depending on whether the focus is on regulatory molecules or the expression levels of target genes.

  • GRNs have been compiled in publicly accessible resources or they can be computationally derived (‘learned’) from omics data.

  • The structure of gene coexpression networks is surprisingly invariant across cell types and tissues.

  • GRNs are becoming routine tools for the mechanistic interpretation of single-cell data and will probably have an important role in the context of spatial omics data.

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Fig. 1: Alterations in gene regulatory networks.
Fig. 2: Using gene expression to monitor pathway activity.
Fig. 3: Data classes that can be used for the construction of transcriptional regulatory networks.
Fig. 4: Scenarios that link causal relationships between genes with correlated expression.

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Acknowledgements

We acknowledge funding from the Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy—CECAD, EXC 2030—390661388 (to A.B.), KA 3217/5-1 (to M.K.), BE 2603/10-2 (to A.B.), and CRU 329 (A.B., M.K.). P.U.A. and A.C.L. received support from the Cologne Graduate School of Ageing Research.

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Authors and Affiliations

  1. Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany

    Paula Unger Avila, Tsimafei Padvitski, Ana Carolina Leote, He Chen & Andreas Beyer

  2. Department II of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

    He Chen & Martin Kann

  3. Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany

    Julio Saez-Rodriguez

  4. Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany

    Martin Kann & Andreas Beyer

  5. Institute for Genetics, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany

    Andreas Beyer

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Contributions

The initial draft manuscript was written by P.U.A., T.P., M.K. and A.B. P.U.A. and T.P. created the figures. All authors contributed to all other aspects of the manuscript.

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Correspondence to Andreas Beyer.

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Competing interests

J.S.-R. reports funding from GSK, Pfizer and Sanofi, and honoraria from Travere Therapeutics, Stadapharm, Astex, Owkin, Pfizer and Grunenthal. The other authors declare no competing interests.

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Glossary

Bayesian networks

Probabilistic graphical models that represent a set of variables and their conditional dependencies via a directed acyclic (no loops) graph. Inference of feedback loops is possible in an extended framework called a dynamic Bayesian network.

Causal edge

An edge that represents a direct causal effect.

Causal path

A path in a network that causally links two nodes (for example, two genes), where the two nodes may not be directly linked, but indirectly connected via multiple intermediate nodes. For example, in the case of transcriptional regulatory networks these may be cascades of transcription factors. In the case of a signalling network this could be an extracellular ligand that affects the activity of a transcription factor via intermediate signalling proteins.

Causality

X causes Y if, were we to intervene and change the value of X, then the distribution of Y would also change as a result.

Gene coexpression networks

Undirected graphs, where each node corresponds to a gene, and a pair of nodes is connected to an edge if a significant coexpression relationship exists between them.

Gene regulatory network

A collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of RNA and proteins, which, in turn, determine the function of the cell.

Probabilistic graphical models

Probabilistic models for which a graph expresses the conditional dependence structure between random variables (that is, between the nodes).

Signal transduction networks

Molecular networks that depict the transmission of signals from cell external or cell internal cues to cellular ‘action points’ such as inducing the expression of specific genes or triggering a cellular state change such as inducing apoptosis. Typically, signals are transmitted via protein post-translational modifications such as phosphorylation.

Sparse networks

Network with much fewer edges than the possible maximum number of edges within that network.

Transcription factor networks

Also known as transcriptional regulatory networks; transcription factors and their direct target genes, that is, genes that are regulated through the binding of transcription factors to their regulatory DNA regions (enhancers and promoters).

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Unger Avila, P., Padvitski, T., Leote, A.C. et al. Gene regulatory networks in disease and ageing. Nat Rev Nephrol (2024). https://doi.org/10.1038/s41581-024-00849-7

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