Biology subjects

Guzzi, P. H.

Publications and source records attributed to Guzzi, P. H..

12 recordsLinked to original sources

bioRxiv2026

FibrilNet maps conserved and tissue-specific molecular environments across systemic amyloidoses

Systemic amyloidoses are initiated by distinct amyloidogenic precursor proteins but frequently contain recurrent extracellular, complement, lipid-transport and matrix-remodelling components. Whether these recurrent proteins form a conserved systems-level environment across amyloid diseases, and how strongly that environment depends on precursor and tissue context, remains unresolved. We developed FibrilNet, a network framework that integrates experimentally defined amyloid proteomes with a human protein protein interaction graph and Gene Ontology derived semantic information. FibrilNet compares topology-only random walk with restart (RWR) with ontology aware semantic RWR in frozen leave-one-out module reconstruction and precursor-seeded prioritization tasks. The human graph contains 17,997 proteins and 925,977 physical interactions, with a 9-dimensional semantic representation of interaction context. In expanded cardiac transthyretin amyloidosis (ATTR), semantic-RWR increased mean reciprocal rank (MRR) from 0.00167 to 0.05015 and Recall@100 from 0.0199 to 0.3377, improving 132 of 151 held-out targets. Significant semantic gains were also observed in renal serum amyloid A amyloidosis (AA) and leukocyte chemotactic factor 2 amyloidosis (ALECT2). Across compact ATTR, light-chain amyloidosis (AL), AA and ALECT2 modules, APCS, VTN and TIMP3 formed a direct four-disease recurrent core, while APOE occurred in three of four modules. A tissue-aware ATTR analysis showed limited overlap between cardiac and neurologic modules (19 shared proteins; Jaccard 0.0569). In the hTTR-A97S peripheral-nerve model, semantic-RWR significantly improved reconstruction of the 202-protein mapped neurologic module, with the strongest evidence concentrated in the downregulated proteomic program. TTR-seeded propagation improved with semantic information but remained weak in absolute terms, separating precursor identity from the distributed downstream molecular environment. These results support a multilayer model in which a restricted conserved amyloid environment coexists with precursor-, tissue- and disease-specific organization

bioinformatics↗

bioRxiv2026

Sex-Dimorphic Aging of Cardiovascular Disease Genes: A Network-Based Multi-Omics Analysis

Sex differences influence the incidence, timing, clinical presentation, and outcomes of cardiovascular disease (CVD), yet the molecular programs through which aging interacts with biological sex remain insufficiently understood. To address this gap, we integrated basal gene expression profiles from multiomics data across 981 donors and 17 CVD-relevant tissues with regulatory, genetic, network, disease-expression, and druggability information to characterize sex-dimorphic aging patterns in 1,176 candidate CVD genes. Using a two-step expression analysis, we identified 4,404 genes with significant age-associated expression trends (BH-FDR < 0.05), including 2,718 male-specific, 202 female-specific, and 742 shared trends. Concordant evidence across complementary statistical approaches highlighted 35 high-confidence sex-dimorphic genes, including REN, APOE, GUCY1A2, and SRD5A2. Regulatory analysis showed that most CVD genes were influenced by nearby genetic variants, with 96.2 Network-based analyses further suggested that CVD genes are organized within hierarchical biological structures, with curated protein-interaction data showing stronger geometric organization than broader interaction resources. Integration with Open Targets identified 289 genes already linked to approved drugs and 48 of the top 50 biomarker candidates supported by GWAS-eQTL colocalisation evidence. A final composite ranking prioritized NTRK1, TUBB4A, PTGS2, IL6, and PDE5A, and identified 19 actionable biomarkers supported by convergent expression, regulatory, genetic, and therapeutic evidence. Among these, a dedicated sex-specific evidence score nominated GUCY1A2, CACNA1D, PGR, PDE5A, and LEPR as the strongest candidates for sex-stratified validation, with GUCY1A2 and PDE5A converging on a nitric oxide-cGMP signaling axis. This study provides an integrative framework for discovering sex-dependent molecular signatures of cardiovascular aging and for prioritizing biologically supported, potentially actionable targets for precision cardiovascular medicine.

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bioRxiv2026

Dynamic consensus pocket detection across molecular dynamics ensembles reveals persistent and transient druggable sites

The traditional "one drug, one target" paradigm assumes that drugs interact with a single specific binding site. Modern pharmacology has proven this definition overly simplistic and, instead, recognizes that drugs operate within complex biological systems and often interact with multiple targets. In this context, proteins cannot be viewed as possessing a single functional binding site, but rather as dynamic entities capable of accommodating ligands at multiple regions, including transient and cryptic pockets. Here, we review and repurpose representative pocket detection tools across geometry-based, energy-based, and machine/deep learning approaches, originally designed to work on static conformations, to evaluate their agreement on molecular dynamics-derived conformational ensembles. Using GLUT1 protein as a dynamic transporter model and Aldose reductase as a cryptic-pocket reference system, we combine inter-tool concordance, HDBSCAN-based spatial clustering, volumetric IoU analysis, and temporal persistence scoring. Our results show that different algorithmic classes capture complementary aspects of pocket dynamics, with energy-based methods showing stronger sensitivity to transient cryptic regions and geometry-based approaches depending more strongly on pre-formed cavities. This work proposes a consensus-oriented framework for identifying conserved and transient druggable pockets in dynamic protein systems.

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bioRxiv2025

NetMD: Unsupervised Synchronization of Molecular Dynamics Trajectories via Graph Embedding and Time Warping

Molecular dynamics (MD) simulations yield detailed atomistic views of biomolecular processes, yet comparing independent trajectories is hindered by stochastic divergence. Here, we introduce NetMD, a computational approach that synchronizes and analyzes MD trajectories by combining graph-based representations with dynamic time warping. Frames are transformed into residue-contact graphs, entropy-filtered to retain variable interactions, and embedded as low-dimensional vectors. NetMD then uses time-warping barycenter averaging to align these vector trajectories, yielding a consensus "average" trajectory while pruning the outlier simulations. Applied to diverse systems, such as transporters, demethylases, and protein complexes, NetMD revealed shared multiphase dynamics and pinpointed mutation- or ligand-specific deviations. Thus, this method enables an unsupervised, time-resolved comparison of MD ensembles across conditions. It is robust, broadly applicable, and available as an open-source software, offering a powerful tool for uncovering common patterns and critical divergences in biomolecular dynamics.

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bioRxiv2025

Enhancing Clinical Classification of Protein Variants using ESM2 and UMAP

Protein sequences may vary due to mutations in their coding DNA sequence, leading to differences in structure and function. The same protein may exist in multiple variant forms, each potentially leading to distinct phenotypic consequences depending on how the alterations affect its structure, function, or expression. Missense variants are single nucleotide substitutions in the DNA sequence that result in the replacement of one amino acid with another in the corresponding protein, potentially altering its structure, stability, or function. The clinical interpretation of missense variants in protein-coding regions remains a fundamental challenge in genomic medicine. Recent advances in protein language models and manifold learning provide new opportunities for unsupervised extraction of biologically relevant information from protein sequences. In this work, we integrate representations derived from ESM2 (spiegare) with nonlinear dimensionality reduction via UMAP (spiegare) to improve the classification of variants of uncertain significance (VUS) in disease-associated proteins. Our results suggest that this approach improves separability of benign and pathogenic variants, offering a scalable and interpretable strategy for variant prioritization in precision medicine.

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bioRxiv2024

Non Parametric Differential Network Analysis: A Tool for Unveiling Specific Molecular Signatures.

The rewiring of molecular interactions in various conditions leads to distinct phenotypic outcomes. Differential Network Analysis (DNA) is dedicated to exploring these rewirings within gene and protein networks. Leveraging statistical learning and graph theory, DNA algorithms scrutinize alterations in interaction patterns derived from experimental data. Introducing a novel approach to differential network analysis, we incorporate differential gene expression based on sex and gender attributes. We hypothesize that gene expression can be accurately represented through non-Gaussian processes. Our methodology involves quantifying changes in non-parametric correlations among gene pairs and expression levels of individual genes. Applying our method to public expression datasets concerning diabetes mellitus and atherosclerosis in liver tissue, we identify gender-specific differential networks. Results underscore the biological relevance of our approach in uncovering meaningful molecular distinctions. Author summaryThis paper explores a novel technique for Differential Network Analysis (DNA) that considers sex-based variations. DNA compares biological networks under different conditions, like healthy vs. diseased states. Our method tackles the limitations of traditional DNA approaches, which often assume specific data distributions. We propose a non-parametric DNA methodology that integrates sex differences and identifies differential edges between networks. This approach utilizes data on gene expression levels and sex to construct a more accurate picture of the molecular mechanisms underlying diseases, particularly those exhibiting sex-dependent variations. Our method paves the way for a deeper understanding of how sex and age influence disease processes at the molecular level.

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bioRxiv2023

Non Parametric Differential Network Analysis for Biological Data

Rewiring of molecular interactions under different conditions causes different phenotypic responses. Differential Network Analysis (also indicated as DNA) aims to investigate the rewiring of gene and protein networks. DNA algorithms combine statistical learning and graph theory to explore the changes in the interaction patterns starting from experimental observation. Despite there exist many methods to model rewiring in networks, we propose to use age and gender factors to guide rewiring algorithms. We present a novel differential network analysis method that consider the differential expression of genes by means of sex and gender attributes. We hypothesise that the expression of genes may be represented by using a non-gaussian process. We quantify changes in nonparametric correlations between gene pairs and changes in expression levels for individual genes. We apply our method to identify the differential networks between males and females in public expression datasets related to mellitus diabetes in liver tissue. Results show that this method can find biologically relevant differential networks.

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bioRxiv2023

Investigating mitochondrial gene expression patterns in Drosophila melanogaster using network analysis to understand aging mechanisms

The process of aging is a complex phenomenon that involves a progressive decline in physiological functions required for survival and fertility. To better understand the mechanisms underlying this process, the scientific community has utilized several tools. Among them, mitochondrial DNA has emerged as a crucial factor in biological aging, given that mitochondrial dysfunction is thought to significantly contribute to this phenomenon. Additionally, Drosophila melanogaster has proven to be a valuable model organism for studying aging due to its low cost, capacity to generate large populations, and ease of genetic manipulation and tissue dissection. Moreover, graph theory has been employed to understand the dynamic changes in gene expression patterns associated with aging and to investigate the interactions between aging and aging-related diseases. In this study, we have integrated these approaches to examine the patterns of gene co-expression in Drosophila melanogaster at various stages of development. By applying graph-theory techniques, we have identified modules of co-expressing genes, highlighting those that contain a significantly high number of mitochondrial genes. We found important mitochondrial genes involved in aging and age-related diseases in Drosophila melanogaster, including UQCR-C1, ND-B17.2, ND-20, and Pdhb. Our findings shed light on the role of mitochondrial genes in the aging process and demonstrate the utility of Drosophila melanogaster as a model organism and graph theory in aging research.

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bioRxiv2023

SARS-CoV-2 protein structure and sequence mutations: evolutionary analysis and effects on virus variants SARS-CoV-2 protein structure and sequence mutations:

Proteins sequence, structure, and function are related, so that any changes in the protein sequence may cause modifications in its structure and function. Thanks to the exponential growth of data availability, many studies have addressed different questions such as: (i) how structure evolves based on the sequence changes, (ii) how structure and function change over time. Computational experiments have contributed to the study of viral protein structures. For instance the Spike (S) protein has been investigated for its role in binding receptors and infection activity in COVID-19, hence the interest of scientific researchers in studying the effects of virus mutations due to sequence, structure and vaccination effects. Protein Contact Networks (PCNs) can be used for investigating protein structures to detect biological properties thorough network topology. We apply topological studies based on graph theory of the PCNs to compare the structural changes with sequence changes, and find that both node centrality and community extraction analysis play a relevant role in changes in protein stability and functionality caused by mutations. We compare the structural evolution to sequence changes and study mutations from a temporal perspective focusing on virus variants. We finally highlight a timeline correlation between Omicron variant identification and the vaccination campaign.

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bioRxiv2020

Structural Genetics of circulating variants affecting the SARS CoV-2 Spike / human ACE2 complex

SARS-CoV-2 entry in human cells is mediated by the interaction between the viral Spike protein and the human ACE2 receptor. This mechanism evolved from the ancestor bat coronavirus and is currently one of the main targets for antiviral strategies. However, there currently exist several Spike protein variants in the SARS-CoV-2 population as the result of mutations, and it is unclear if these variants may exert a specific effect on the affinity with ACE2 which, in turn, is also characterized by multiple alleles in the human population. In the current study, the GBPM analysis, originally developed for highlighting host-guest interaction features, has been applied to define the key amino acids responsible for the Spike/ACE2 molecular recognition, using four different crystallographic structures. Then, we intersected these structural results with the current mutational status, based on more than 295,000 sequenced cases, in the SARS-CoV-2 population. We identified several Spike mutations interacting with ACE2 and mutated in at least 20 distinct patients: S477N, N439K, N501Y, Y453F, E484K, K417N, S477I and G476S. Among these, mutation N501Y in particular is one of the events characterizing SARS-CoV-2 lineage B.1.1.7, which has recently risen in frequency in Europe. We also identified five ACE2 rare variants that may affect interaction with Spike and susceptibility to infection: S19P, E37K, M82I, E329G and G352V. Significance StatementWe developed a method to identify key amino acids responsible for the initial interaction between SARS-CoV-2 (the COVID-19 virus) and human cells, through the analysis of Spike/ACE2 complexes. We further identified which of these amino acids show variants in the viral and human populations. Our results will facilitate scientists and clinicians alike in identifying the possible role of present and future Spike and ACE2 sequence variants in cell entry and general susceptibility to infection.

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bioRxiv2020

Analysis of Virus-Host Interactomes through a network-centric approach.

AO_SCPLOWBSTRACTC_SCPLOWViruses are small microorganisms that invade living cells and use them to replicate themselves. Viruses cause many common human infections (such as cold flu) as well as many lethal diseases. Therefore the comprehensive knowledge of mechanisms used by viruses to infect living cells, also known as host-pathogen interactions, is crucial. Mechanisms of infections of viruses are mediated by Protein-protein interactions (PPIs). PPIs are often modelled using graphs, thus the use of such a model may also explain mechanisms of infection of viruses. In this work, we propose a methodology to model and analyse host-pathogen interactions and a supporting tool able to analyse such data. We also analyse host-pathogen interactions of some common viruses demonstrating common mechanism and differences.

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bioRxiv2020

Master Regulator Analysis of the SARS-CoV-2/Human interactome

the recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 and causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. In this paper, we set out to shed light on the SARS-CoV-2/host receptor recognition, a crucial factor for successful virus infection. Based on the current knowledge of the interactome between SARS-CoV-2 and host cell proteins, we performed Master Regulator Analysis to detect which parts of the human interactome are most affected by the infection. We detected, amongst others, affected apoptotic and mitochondrial mechanisms, and a downregulation of the ACE2 protein receptor, notions that can be used to develop specific therapies against this new virus.

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