# Exploring the relation between evolutionary gene age, gene expression and chromatin 3D structure in cancer

Flavien Raynal, Benoît Aliaga, Kaustav Sengupta, Dariusz Plewczynski, Vera Pancaldi

► To cite this version:

Flavien Raynal, Benoît Aliaga, Kaustav Sengupta, Dariusz Plewczynski, Vera Pancaldi. Exploring the relation between evolutionary gene age, gene expression and chromatin 3D structure in cancer. ISMB/ECCB 2023, Jul 2023, Lyon, France. hal-04987967

HAL Id: hal-04987967

https://hal.science/hal-04987967v1

Submitted on 12 Mar 2025

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# Exploring the relation between evolutionary gene age, gene expression and chromatin 3D structure in cancer

Flavien Raynal*, Benoît Aliaga*, Kaustav Sengupta, Dariusz Plewczynski and Vera Pancaldi

**Keywords:** 3D chromatin structure, 3D network, cancer, evolution

## Introduction

The human genome is composed of genes that appeared at different evolutionary ages for 3.5 billion years. Those genes have progressively been integrated across time, they acquired new functions and made species' genomes more and more sophisticated. Genes of specific age classes were found to preferentially interact in protein-protein networks. The availability of unprecedented datasets detailing the organization of chromatin across species, developmental stages, cell types and diseases invites an integrated analysis of genome organization in 3D and its biological significance. Recently developed methods to represent chromatin structure as a network of contacts between genomic regions facilitate the comparison between nuclear organization across conditions, adding a 4th dimension. Here we attempt to formally hypothesize that the topological properties of the chromatin network can be shaped by the context of the cell, impacting its phenotype. Our observations lead us to reconsider the atavistic theory, according to which cancer cells re-acquire pluripotency and become insensitive to their tissue context through a reactivation of early evolutionary programs characteristic of species lacking complex body plans or even multicellularity¹. Whether genes with the same evolutionary age are associated within the genome, have similar expression or share the same chromatin structure is still not well characterized.

## Datasets and methods

We used a gene ages dataset which has estimated evolutionary ages of genes by phylostratigraphy². 17.318 Genes are classified into 16 age classes from unicellular genes to Homo sapiens. Those classes can be reassigned to 3 main gene age categories: Unicellular Ancestor genes (UC), Early Metazoan genes (EM) and Mammal-Specific genes (MM). We have integrated multiple epigenetic and transcription factors ChIP-Seq as well as DNA methylation and gene expression datasets in order to study genomic environments and gene expression variability across individuals related to their respective evolutionary ages. For this analysis, we used several datasets from human embryonic stem cells (hESC), B cells and their malignants counterparts in Chronic Lymphocytic Leukaemia (CLL). Firstly, we used H3K27me3 and PolII ChIP-Seq datasets to study regulatory processes involving polycomb complexes and paused RNA polymerase II through cell differentiation and cancerogenesis. Secondly, in order to study chromatin 3D structure changes, we used promoter capture HiC (PChi-C) datasets, a sequencing-based chromosome conformation capture technique, which describes 3D contacts amongst promoters, and between promoters and regulatory regions³⁻⁵.

We turned such data into chromatin networks by considering DNA restriction fragments as nodes and significant identified 3D interactions between fragments as edges. We analyzed chromatin networks by computing topological measurements, such as node degrees and coreness. We then computed Chromatin Assortativity (ChAs), which is a global network measure identifying to what extent chromatin fragments preferentially interact with other fragments that share a specific property⁶. If a feature appears to be localized in specific well-connected areas of the network, forming clusters, it will be characterized by having high ChAs. In previous works, we have observed assortativity of gene expression, DNA methylation, many histone marks, replication timing and also replication origin efficiency, suggesting strong associations between chromatin structure and different cellular processes⁷. To assess whether the ChAs values are significant we have performed 100 randomizations of the chromatin networks, which preserve abundance and genomics distance spanned by the contacts, to then calculate a Z-score. Gene ages' ChAs values were computed considering them as categorical classes.

## Results and conclusion

We found that genes show specific epigenomic characteristics depending on their respective evolutionary age classes. Oldest genes are highly enriched in active epigenetic marks. Genes from the early metazoans class tend to be the most targeted by polycomb proteins. Newest genes are the most highly enriched in DNA methylation. We observed that the newest genes are more expressed through cell differentiation whereas oldest genes, involved mostly in house-keeping functions, proliferation and basic cellular functions, are more expressed
through cancerogenesis. We also found reverse effects of some regulatory processes like Paused RNA Polymerase II and Polycomb complexes depending on gene evolutionary ages, suggesting that cancerogenesis involves a return to a less differentiated cell state and more primordial phenotypes.

In terms of chromatin 3D structure, evolutionary older genes are found to be highly interacting together, forming age-specific gene clusters, distributed in a set of cores, also broadly corresponding to the least variable genes. On the contrary, we found a higher proportion of recent genes interacting with Lamina domains, at the nucleus periphery. We identify consistent changes during oncogenesis in the spatial organization of genes from different evolutionary classes, reinforcing the important role that old genes and their organization in the nucleus play in cancer phenotypes. These findings suggest a potential atavistic theory confirmation at the level of chromatin organization.

## Pipeline:

<figure><img src="image_1.png" /></figure>

## Overview:

<figure><img src="image_2.png" /><figcaption>Figure 1: Overview of chromatin 3D structure analyses through cell differentiation and carcinogenesis. 3D networks were reconstructed from PCHiC data⁸.</figcaption></figure>
1. Lineweaver et al. *Cancer progression as a sequence of atavistic reversions*. *BioEssays* **43**, 2000305 (2021).

2. Trigos et al., *Altered interactions between unicellular and multicellular genes drive hallmarks of transformation in a diverse range of solid tumors*. *Proc. Natl. Acad. Sci. U. S. A.* **114**, 6406–6411 (2017).

3. Beekman, R. et al. *The reference epigenome and regulatory chromatin landscape of chronic lymphocytic leukemia*. *Nat. Med.* **24**, 868–880 (2018).

4. Freire-Pritchett, P. et al. *Global reorganisation of cis-regulatory units upon lineage commitment of human embryonic stem cells*. *eLife* **6**, e21926 (2017).

5. Javierre, B. M. et al. *Lineage-Specific Genome Architecture Links Enhancers and Non-coding Disease Variants to Target Gene Promoters*. *Cell* **167**, 1369-1384.e19 (2016).

6. Madrid-Mencía, M., Raineri, E., Cao, T. B. N. & Pancaldi, V. *Using GARDEN-NET and ChAseR to explore human haematopoietic 3D chromatin interaction networks*. *Nucleic Acids Res.* **48**, 4066–4080 (2020).

7. Pancaldi, V. *Chromatin Network Analyses: Towards Structure-Function Relationships in Epigenomics*. *Front. Bioinforma.* **1**, 742216 (2021).

**Short abstract (200 words)**

**Instructions:** The 200 word abstract should be a brief and concise summary of the background/motivation for the study, method and result(s), as well as conclusion/statement of significance.

**Abstract (200 words):**

The human genome is composed of genes that appeared at different evolutionary ages for 3.5 billion years. Those genes have progressively been integrated across time, they acquired new functions and made species genomes more sophisticated. Evolutionary scientists have been able to precisely estimate current human gene ages by studying duplication events through time. Whether genes with the same evolutionary age are associated within the genome, have similar expression or share a same chromatin structure is still not well characterized. Inspired by the atavistic theory of cancer, which relates malignancy to the expression of evolutionary ancient phenotypes, we investigate whether cell differentiation and cancer can alter the associations between gene age and other (epi)genomic characteristics. We therefore investigate whether genes with different evolutionary ages could show specific epigenome properties, expression regulation, variability and location in 3D chromatin structures that can be potentially altered in cancer and during differentiation. We identify consistent changes during oncogenesis in the spatial organization of genes from different evolutionary classes, correlated with changes in their expression variability across individuals, reinforcing the important role that old genes and their organization in the nucleus play in cancer phenotypes.