Biology subjects

Abdelhamid, I.

Publications and source records attributed to Abdelhamid, I..

3 recordsLinked to original sources

bioRxiv2026

The human RNA-DNA interactome is cell type-specific and dynamic

More than twenty years ago, the FANTOM consortium uncovered that mammalian genomes are pervasively transcribed, revealing multitudes of RNAs with unknown functions. A subset of these transcripts has since then been linked to transcriptional control and to chromatin organization via their ability to interact with DNA, suggesting that chromatin-associated RNAs could be key players in genome regulation. Although recent technological advances now enable the mapping of genome-wide RNA-DNA contacts, a lack of analyses integrating these methods with other genomic features and across multiple cellular contexts hinders our comprehensive understanding of the principles underlying RNA-DNA interactions and of their biological importance. As part of the FANTOM6 project, we thus generated RNA-DNA interaction maps in 16 different human cell types, then combined these contacts with multiple layers of other genomic data to investigate how patterns of interaction between RNA and DNA relate to chromatin organization and function. We show that the RNA-DNA interactome is highly dynamic yet reproducibly organized in cell-type specific networks, constituted of a great diversity of interactions that vary in function of their distance, the nature of their sources and the chromatin state of their targets. In particular, we detected numerous regulatory elements that exhibit marked changes in activity when differentially bound by transcripts, implying that thousands of RNA-DNA interactions can play a mechanistic role in gene expression. This regulatory function correlates with RNA-protein interactions and significantly associates with cell type-relevant and disorder-related traits. In addition to providing essential resources for future research in RNA-mediated chromatin regulation, cellular biology and human diseases, our study thus establishes the RNA-DNA interactome as a new genome regulatory layer that defines and maintains cellular identity and behavior.

genomics↗

bioRxiv2023

Network shape intelligence outperforms AlphaFold2 intelligence in vanilla protein interaction prediction.

For decades, scientists and engineers have been working to predict protein interactions, and network topology methods have emerged as extensively studied techniques. Recently, approaches based on AlphaFold2 intelligence, exploiting 3D molecular structural information, have been proposed for protein interaction prediction, they are promising as potential alternatives to traditional laboratory experiments, and their design and performance evaluation is compelling. Here, we introduce a new concept of intelligence termed Network Shape Intelligence (NSI). NSI is modelled via network automata rules which minimize external links in local communities according to a brain-inspired principle, as it draws upon the local topology and plasticity rationales initially devised in brain network science and then extended to any complex network. We show that by using only local network information and without the need for training, these network automata designed for modelling and predicting network connectivity can outperform AlphaFold2 intelligence in vanilla protein interactions prediction. We find that the set of interactions mispredicted by AlphaFold2 predominantly consists of proteins whose amino acids exhibit higher probability of being associated with intrinsically disordered regions. Finally, we suggest that the future advancements in AlphaFold intelligence could integrate principles of NSI to further enhance the modelling and structural prediction of protein interactions.

bioinformatics↗

bioRxiv2018

Local-community network automata modelling based on length-three-paths for prediction of complex network structures in protein interactomes, food webs and more

From nests to nets intricate wiring diagrams surround the birth and the death of life. Here we show that the same rule of complex network self-organization is valid across different physical scales and allows to predict protein interactions, food web trophic relations and world trade network transitions. This rule, which we named CH2-L3, is a network automaton that is based on paths of length-three and that maximizes internal links in local communities and minimizes external ones, according to a mechanistic model essentially driven by topological neighbourhood information.

systems biology↗