Biology subjects

Cannistraci, C. V.

Publications and source records attributed to Cannistraci, C. V..

6 recordsLinked to original sources

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↗

bioRxiv2021

De novo identification of universal cell mechanics regulators

Cell mechanical properties determine many physiological functions, such as cell fate specification, migration, or circulation through vasculature. Identifying factors that govern the mechanical properties is therefore a subject of great interest. Here we present a mechanomics approach for establishing links between single-cell mechanical phenotype changes and the genes involved in driving them. We combine mechanical characterization of cells across a variety of mouse and human systems with machine learning-based discriminative network analysis of associated transcriptomic profiles to infer a conserved network module of five genes with putative roles in cell mechanics regulation. We validate in silico that the identified gene markers are universal, trustworthy and specific to the mechanical phenotype across the studied mouse and human systems, and demonstrate experimentally that a selected target, CAV1, changes the mechanical phenotype of cells accordingly when silenced or overexpressed. Our data-driven approach paves the way towards engineering cell mechanical properties on demand to explore their impact on physiological and pathological cell functions.

biophysics↗

bioRxiv2020

Medial prefrontal activity at encoding determines recognition of threatening faces after 1.5 years

Studies demonstrated that faces with threatening emotional expressions are better remembered than non-threatening faces. However, whether this memory advantage persists over years and which neural systems underlie such an effect remains unknown. Here, we investigated recognition of incidentally encoded faces with angry, fearful, happy, sad and neutral expressions over >1.5 years (N= 89). Univariate analyses showed that threatening faces (angry, fearful) were better recognized than happy and neutral faces after >1.5 years, and that the threat-related memory enhancement was driven by forgetting of non-threatening faces. Multivariate principal component analysis (PCA) confirmed the discriminative performance between threatening and non-threatening faces. With an innovative Behavioral Pattern Similarity Analysis (BPSA) approach and functional magnetic resonance imaging (fMRI) acquisition during encoding, we further found that the long-term memory advantage for threatening faces were underpinned by differential neural encoding in the left inferior occipital gyrus (IOG) and right ventromedial prefrontal/orbitofrontal cortex (vmPFC/OFC). Our study provides the first evidence that threatening facial expressions lead to persistent face recognition over periods of >1.5 years and differential encoding-related activity in the visual cortex and medial prefrontal cortex may underlie this effect.

neuroscience↗

bioRxiv2020

Machine learning pattern recognition and differential network analysis of gastric microbiome in the presence of proton pump inhibitor treatment or Helicobacter pylori infection

Although long thought to be a sterile and inhospitable environment, the stomach is inhabited by diverse microbial communities, co-existing in a dynamic balance. Long-term use of orally administered drugs such as Proton Pump Inhibitors (PPIs), or bacterial infection such as Helicobacter pylori, cause significant microbial alterations. Yet, studies revealing how the commensal bacteria re-organize, due to these perturbations of the gastric environment, are in the early phase. They mainly focus on the most prevalent taxa and rely on linear techniques for multivariate analysis. Here we disclose the importance of complementing linear dimensionality reduction techniques such as Principal Component Analysis and Multidimensional Scaling with nonlinear approaches derived from the physics of complex systems. Then, we show the importance to complete multivariate pattern analysis with differential network analysis, to reveal mechanisms of re-organizations which emerge from combinatorial microbial variations induced by a medical treatment (PPIs) or an infectious state (H. pylori).

systems biology↗

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↗

bioRxiv2018

LIPEA: Lipid Pathway Enrichment Analysis

MotivationAnalyzing associations among multiple omic variables to infer mechanisms that meaningfully link them is a crucial step in systems biology. Gene Set Enrichment Analysis (GSEA) was conceived to pursue this aim in computational genomics, unveiling significant pathways associated to certain gene signatures under investigation. Lipidomics is a rapidly growing omic field, and absolute quantification of lipid abundance by shotgun mass spectrometry is generating high-throughput datasets that depict lipid metabolism in a plethora of conditions and organisms. In addition, high-throughput lipidomics represents a new important ally to develop personalized medicine approaches, investigate the causes and predict effective biomarkers in metabolic diseases, and not only.\n\nResultsHere, we present Lipid Pathway Enrichment Analysis (LIPEA), a web-tool for over-representation analysis of lipid signatures and detection of the biological pathways in which they are enriched. LIPEA is a new valid resource for biologists and physicians to mine pathways significantly associated to a set of lipids, helping them to discover whether common and collective mechanisms are hidden behind those lipids. LIPEA was extensively tested and we provide two examples where our system gave successfully results related with Major Depression Disease (MDD) and insulin re-sistance.\n\nAvailabilityThe tool is available as web platform at https://lipea.biotec.tu-dresden.de.

bioinformatics↗