Biology subjects

De Domenico, M.

Publications and source records attributed to De Domenico, M..

4 recordsLinked to original sources

bioRxiv2025

Structural Connectome Dimension Shapes Brain Dynamics in Health and Disease

The structural connectome serves as the foundation for neural information signaling, playing a primary constraint on brain functionality. Yet, its influence on emergent dynamical properties is not fully understood. Generally, a key measure of a systems structural impact on dynamical phenomena is its dimension. By tracking the temporal evolution of diffusive perturbations, we estimate a scale-dependent measure of dimension of empirical connectomes. At the local scale, it is highly heterogeneous and follows a gradient from sensory-motor to high-cognitive areas. At the global scale, it encapsulates mesoscale topological information related to the balance between segregation and integration. Furthermore, by comparing connectomes from stroke patients, we find that dimension captures the local effects of lesions and, at a global level, is linked to impaired critical patterns and decreased cognitive performance. Overall, the dimension of the connectome may serve as a powerful descriptor for bridging the gap between structure and function in the human brain.

neuroscience↗

bioRxiv2025

Species interactions determine plasmid persistence in a 3-member bacterial community

Microbial communities are shaped by multiple forces, including interspecies interactions and the effects of mobile genetic elements such as plasmids. How these forces interact to affect community responses to environmental perturbations remains unclear, particularly considering the qualitatively different natures and the complexity of competitive and plasmid transfer interactions. We investigated the role of bacterial and plasmid interaction networks in community responses to single and multiple environmental perturbations, using a model community of three bacteria and two plasmids grown in rich media over five 2-day transfers. Bacterial interactions were the primary driver of community response, to the extent that plasmids were not always retained in the community even when they carried relevant resistance genes. Overall, our results indicate that while bacterial and plasmid interactions both shape community responses to environmental perturbations, bacteria-bacteria interactions may be the primary driver of community dynamics and their response to perturbations.

ecology↗

bioRxiv2025

Unraveling the network signatures of oncogenicity in virus-human protein-protein interactions

Climate change, increasing urbanization and global human mobility have increased the risk of emergent infectious diseases and the risk for their pandemic potential. Consequently, there is a pressing need for methods that can provide rapid insights about the potential enduring effects on the human hosts. In-silico methods can be more suitable than in-vitro and in-vivo methods to identify effects potentially manifesting after a long time, under the hypothesis that the underlying causes can be ascribed to perturbed biomolecular processes. Here we focus on oncogenicity, i.e., the risk of developing cancer subsequent to a viral infection, a phenomenon estimated to account for approximately 15% of global cancer prevalence. We characterize viruses in terms of their oncogenic potential by analyzing a multilayer representation of protein-protein interaction networks reconstructed from the human interactome. Our frame-work facilitates classification and allows us to identify sub-sets of proteins in-volved in oncogenesis, highlighting the mechanisms underpinning viral oncogenicity, including factors like chromatin structure. Our framework serves as a foundational resource for medical research, paving the way for the study of other potential long-term effects associated with emerging viruses.

systems biology↗

bioRxiv2017

The multiplex network of human diseases

Untangling the complex interplay between phenotype and genotype is crucial to the effective characterization and subtyping of diseases. Here we build and analyze the multiplex network of 779 human diseases, which consists of a genotype-based layer and a phenotype-based layer. We show that diseases with common genetic constituents tend to share symptoms, and uncover how phenotype information helps boost genotype information. Moreover, we offer a flexible classification of diseases that considers their molecular underpinnings alongside their clinical manifestations. We detect cohesive groups of diseases that have high intra-group similarity at both the molecular and the phenotypic level. Inspecting these disease classes, we demonstrate the underlying pathways that connect diseases mechanistically. We observe monogenic disorders grouped together with complex diseases for which they increase the risk factor. We propose potentially new disease associations that arise as a unique feature of the information flow within and across the two layers.

bioinformatics↗