Biology subjects

Lizana, L.

Publications and source records attributed to Lizana, L..

6 recordsLinked to original sources

bioRxiv2026

A network approach to DNA methylation clocks

Biological age predicts health and lifespan better than chronological age, but remains difficult to measure. One leading molecular proxy for biological age is DNA methylation, which underlies age predictors known as "clocks". These clocks use penalized linear regression to predict chronological age from methylation levels using selected cytosine-guanine pairs (CpGs) along DNA. Although they predict chronological age within a few years and track mortality risk, there are several issues. Different clocks share a vanishingly small number of CpG sites, many of which show weak associations with age. Also, the clocks often do not transfer across methylation array platforms. This paper takes a network approach to better understand these issues. By using 12 public datasets from human blood, we build a co-methylation network of the sites that show the strongest age correlation. After pruning weak links, we find that it has a small number of large modules of covarying CpGs surrounded by many small modules and singleton sites. These modules are biologically interpretable, as they are associated with CpG island contexts and enriched for distinct Gene Ontology functions. We also map five established clocks onto this network (Horvath, Hannum, AltumAge, Skin & Blood, and Han) and find that they select some CpGs from the same module. This suggests that they are more similar than they appear. The network structure also suggests new ways to build clocks. A simple clock that retains one CpG per module matches the performance of established clocks. A second one, built from module-level principal components, outperforms all five established clocks in three validation cohorts and is transferable across array platforms (Illumina Infinium Methylation 450K or EPIC arrays). Overall, the network perspective shifts attention from individual CpG sites to modules of covarying sites. This perspective helps explain why DNA methylation clocks perform so well despite their differences and provides a more systematic approach for developing the next generation of aging biomarkers.

bioinformatics↗

bioRxiv2025

Extending cis-regulatory networks using chromatin-RNA interactions

Cis-regulatory networks are essential for determining gene transcriptional states, yet the mechanisms that mediate cellular signaling and enhancer-promoter communications are not yet well understood. Here, we integrate high-resolution enhancer-promoter interactions obtained using targeted chromosome conformation capture and RNA-DNA contacts from RADICL-seq to uncover the role of RNA in modulating enhancer-promoter interactions. Both datasets were generated from human induced pluripotent cells at different stages across neural differentiation. As expected, most enhancer-promoter interactions were dynamic with only 38% shared across all cell states and 68% of promoter interacting regions overlapped at least one annotated neural enhancer. Integration with RADICL-seq data revealed a 9.3-fold enrichment for cis-interacting DNA regions to associate with at least one RNA. Among 18,346 cis-interacting promoters, only 1,170 (6.3%) lacked any RNA association, whereas 3,702 (28%) showed stable RNA association and 13,579 (74%) displayed dynamic RNA association across differentiation. Promoters associated with RNA engaged with higher number of enhancers and exhibited higher expression levels, while enhancers with RNA associations showed higher levels of chromatin activation marks. Promoters with dynamic RNA association were also more likely to be differentially expressed and enriched for relevant biological processes, phenotypes and diseases. Importantly, RNA-DNA association dynamics correlated strongly with DNA-DNA interaction dynamics; gain of RNA association on enhancers was typically accompanied by enhancer-promoter interaction gain, whereas concurrent RNA association gain on both promoter and enhancers frequently led to interaction loss. The correlation of RNA association on cis-interaction dynamics across differentiation were also captured in the community structure of the DNA-RNA network. Together, our results reveal extensive coupling between RNA-DNA and DNA-DNA networks supporting a coordinating role of RNA in gene regulation.

genomics↗

bioRxiv2024

Competition and cooperation: The plasticity of bacteria interactions across environments

Bacteria live in diverse communities, forming complex networks of interacting species. A central question in bacterial ecology is why some species engage in cooperative interactions, whereas others compete. But this question often neglects the role of the environment. Here, we use genome-scale metabolic networks from two different open-access collections (AGORA and CarveMe) to assess pairwise interactions of different microbes in varying environmental conditions (provision of different environmental compounds). By scanning thousands of environments for 10,000 pairs of bacteria from each collection, we found that most pairs were able to both compete and cooperate depending on the availability of environmental resources. This approach allowed us to determine commonalities between environments that could facilitate the potential for cooperation or competition between a pair of species. Namely, cooperative interactions, especially obligate, were most common in less diverse environments. Further, as compounds were removed from the environment, we found interactions tended to degrade towards obligacy. However, we also found that on average at least one compound could be removed from an environment to switch the interaction from competition to facultative cooperation or vice versa. Together our approach indicates a high degree of plasticity in microbial interactions to the availability of environmental resources.

microbiology↗

bioRxiv2023

Forecasting histone methylation by Polycomb complexes with minute-scale precision

All animals use the Polycomb system to epigenetically repress developmental genes. The repression requires tri-methylation of Lysine 27 of histone H3 (H3K27me3) by Polycomb Repressive Complex 2 (PRC2), but the dynamics of this process is poorly understood. To bridge the gap, we developed a computational model that forecasts H3K27 methylation in Drosophila with high temporal resolution and spatial accuracy of contemporary experimental techniques. Using this model, we show that pools of methylated H3K27 in dividing cells are defined by the effective concentration of PRC2 and the replication frequency. We find that the allosteric stimulation by pre-existing H3K27me3 makes PRC2 better in methylating developmental genes as opposed to indiscriminate methylation throughout the genome. Applied to Drosophila development, our model argues that, in this organism, the intergenerationally inherited H3K27me3 does not "survive" rapid cycles of embryonic chromatin replication and is unlikely to transmit the memory of epigenetic repression to the offspring. We foresee that our model is adaptable to other organisms, including mice and humans.

developmental biology↗

bioRxiv2022

Mapping the semi-nested community structure of 3D chromosome contact networks

Mammalian DNA folds into 3D structures that facilitate and regulate genetic processes such as transcription, DNA repair, and epigenetics. Several insights derive from chromosome capture methods, such as Hi-C, which allow researchers to construct contact maps depicting 3D interactions among all DNA segment pairs. These maps show a complex cross-scale organization spanning megabase-pair compartments to short-ranged DNA loops. To better understand the organizing principles, several groups analyzed Hi-C data assuming a Russian-doll-like nested hierarchy where DNA regions of similar sizes merge into larger and larger structures. Apart from being a simple and appealing description, this model explains, e.g., the omnipresent chequerboard pattern seen in Hi-C maps, known as A/B compartments, and foreshadows the co-localization of some functionally similar DNA regions. However, while successful, this model is incompatible with the two competing mechanisms that seem to shape a significant part of the chromosomes 3D organization: loop extrusion and phase separation. This paper aims to map out the chromosomes actual folding hierarchy from empirical data. To this end, we take advantage of Hi-C experiments and treat the measured DNA-DNA interactions as a weighted network. From such a network, we extract 3D communities using the generalized Louvain algorithm. This algorithm has a resolution parameter that allows us to scan seamlessly through the community size spectrum, from A/B compartments to topologically associated domains (TADs). By constructing a hierarchical tree connecting these communities, we find that chromosomes are more complex than a perfect hierarchy. Analyzing how communities nest relative to a simple folding model, we found that chromosomes exhibit a significant portion of nested and non-nested community pairs alongside considerable randomness. In addition, by examining nesting and chromatin types, we discovered that nested parts are often associated with active chromatin. These results highlight that crossscale relationships will be essential components in models aiming to reach a deep understanding of the causal mechanisms of chromosome folding.

genetics↗

bioRxiv2022

Polycomb proteins translate histone methylation to chromatin folding

Epigenetic repression often involves covalent histone modifications. Yet, how the presence of a histone mark translates into changes in chromatin structure that ultimately benefit the repression is largely unclear. Polycomb group proteins comprise a family of evolutionarily conserved epigenetic repressors. They act as multi-subunit complexes one of which trimethylates histone H3 at Lysine 27 (H3K27). Here we describe a novel Monte Carlo - Molecular Dynamics simulation framework, which we employed to discover that stochastic interaction of the Polycomb Repressive Complex 1 (PRC1) with tri-methylated H3K27 is sufficient to fold the methylated chromatin. Unexpectedly, such chromatin folding leads to spatial clustering of the DNA elements bound by PRC1.

developmental biology↗