Biology subjects

Menichetti, G.

Publications and source records attributed to Menichetti, G..

10 recordsLinked to original sources

bioRxiv2025

Revealing Interactions between Microbes, Metabolites, and Dietary Compounds using Genome-scale Analysis

BackgroundThe role of gut microbiome in predicting diet response and developing personalized dietary recommendations has been increasingly recognized. Yet, we still lack comprehensive, genome-based insights into which gut microbes metabolize specific dietary compounds. ResultsHere, we leveraged the metabolic networks constructed from well-annotated microbial genomes to characterize the potential interactions between microbes and metabolites, specifically emphasizing the interactions between microbes and dietary compounds. We revealed a substantial, approximately four-fold variation in both the number of metabolites and dietary compounds in the microbial genome-scale metabolic networks across different genera, whereas species within the same genus showed a high metabolic similarity (mean coefficient of variation in microbial network degree CV = 0.023 for metabolites and 0.015 for dietary compounds). We found that the number of species that can utilize a metabolite drastically varies, ranging from 1 to 818 species, with some metabolites being used by a wide range of species (211 out of 1390 metabolites used by more than 95% of species) and others only by a few species (435 metabolites used by less than 5% of species). Leveraging a longitudinal microbiome study, we observed that microbial taxa with similar metabolic capacity tend to have positively correlated abundances, and the gut microbiomes capacity to process dietary compounds is functionally stable. Finally, we propose a network-based method to identify the dietary compounds that are specific to no more than 10 microbial species, offering a new strategy for combining a dietary compound and its linked microbial species to design synbiotics. ConclusionsOur results quantitatively reveal large-scale variation and redundancy in gut microbial metabolism and identify dietary compounds linked to only a few microbial species. These findings improve understanding of microbe-metabolite interactions and provide a foundation for the rational design of microbiome-based interventions for healthy benefits.

bioinformatics↗

bioRxiv2025

Physical Network Constraints Define the Lognormal Architecture of the Brain's Connectome

The brain has long been conceptualized as a network of neurons connected by synapses. However, attempts to describe the connectome using established network science models have yielded conflicting outcomes, leaving the architecture of neural networks unresolved. Here, by performing a comparative analysis of eight experimentally mapped connectomes, we find that their degree distributions cannot be captured by the well-established random or scale-free models. Instead, the node degrees and strengths are well approximated by lognormal distributions, although these lack a mechanistic explanation in the context of the brain. By acknowledging the physical network nature of the brain, we show that neuron size is governed by a stochastic multiplicative process, which allows us to analytically derive the lognormal nature of the neuron length distribution. Our framework not only predicts the degree and strength distributions across each of the eight connectomes, but also yields a series of empirically falsifiable relationships between different neuron characteristics. The resulting multiplicative network represents a novel architecture for network science, whose distinctive quantitative features bridge critical gaps between neural structure and function, with implications for brain dynamics, robustness, and synchronization.

neuroscience↗

bioRxiv2024

NetMedPy: A Python package for Large-Scale Network Medicine Screening

SummaryNetwork medicine leverages the quantification of information flow within sub-cellular networks to elucidate disease etiology and comorbidity, as well as to predict drug efficacy and identify potential therapeutic targets. However, current Network Medicine toolsets often lack computationally efficient data processing pipelines that support diverse scoring functions, network distance metrics, and null models. These limitations hamper their application in large-scale molecular screening, hypothesis testing, and ensemble modeling. To address these challenges, we introduce NetMedPy, a highly efficient and versatile computational package designed for comprehensive Network Medicine analyses. AvailabilityNetMedPy is an open-source Python package under an MIT license. Source code, documentation, and installation instructions can be downloaded from https://github.com/menicgiulia/NetMedPy and https://pypi.org/project/NetMedPy. The package can run on any standard desktop computer or computing cluster.

bioinformatics↗

bioRxiv2024

CPIExtract: A software package to collect and harmonize small molecule and protein interactions

SummaryThe binding interactions between small molecules and proteins are the basis of cellular functions. Yet, experimental data available regarding compound-protein interactions (CPIs) are not harmonized into a single entity but rather scattered across multiple institutions, each maintaining databases with different formats. Extracting information from these multiple sources remains challenging due to data heterogeneity. Here, we present CPIExtract (Compound-Protein Interaction Extract), a Python package that automatically retrieves CPI data from nine major repositories, filters non-human and low-quality records, harmonizes chemical and protein identifiers, and computes unified pChEMBL binding values. Compared with MINER, a state-of-the-art CPI extraction algorithm, CPIExtract retrieves 85.5% more compounds, 16-fold more experimentally supported interactions, and over four times more proteins, substantially increasing the availability of strong and weak binders. The resulting harmonized dataset enables custom filtering and export in standard tabular formats for downstream applications such as network medicine, drug repurposing, and training of deep learning models. AvailabilityCPIExtract is an open-source Python package under an MIT license. CPIExtract can be downloaded from https://github.com/menicgiulia/CPIExtract and https://pypi.org/project/cpiextract. The package can run on any standard desktop computer or computing cluster.

bioinformatics↗

bioRxiv2022

Estimating Nutrient Concentration in Food Using Untargeted Metabolomics

Untargeted metabolomics can detect hundreds of biochemicals in food, yet without standards, it cannot quantify them. Here we show that we can take advantage of the universal scaling of nutrient concentrations to estimate the concentration of all biochemicals detected by untargeted metabolomics. We validate our method on 20 raw foods, finding an excellent agreement between the predicted and the experimentally observed concentrations.

biochemistry↗

bioRxiv2022

The fidelity of genetic information transfer with aging segregates according to biological processes

Maintenance of organismal function requires tightly regulated biomolecular communication. However, with aging, communication deteriorates, thereby disrupting effective information flow. Using information theory applied to skeletal muscle single cell RNA-seq data from young, middle-aged, and aged animals, we quantified the loss of communication efficiency over time. We considered communication channels between transcription factors (TF; input message) and corresponding target genes (TG; output message). Mutual information (MI), defined as the information effectively transmitted between TFs and TGs, declined with age. This decline was attributed to escalating biological noise and loss of precision with which TFs regulate TGs (i.e., channel capacity). When we ranked TF:TG pairs by MI, pairs associated with fatty acid oxidation displayed the greatest loss of communication with aging, while the system preserved communication between pairs related to RNA synthesis. These data suggest ineffective communication with aging against a backdrop of resource reallocation to support essential cellular functions.

bioengineering↗

bioRxiv2022

Genomics-based annotations help unveil the molecular composition of edible plants

Given the important role food plays in health and well-being, the past decades have seen considerable experimental efforts dedicated to mapping the chemical composition of food ingredients. As the composition of raw food is genetically predetermined, here we ask, to what degree can we rely on genome-associated metabolic annotations to predict the chemical composition of natural ingredients? To that end, we developed an approach to unveil the chemical composition of 75 edible plants genomes, finding that genome-associated functional annotations substantially increase the number of compounds linked to specific plants. To account for the gap between the metabolic potential represented by functional annotations and the compounds likely to be accumulated and detected experimentally, we employed a predictive thermodynamic feasibility approach to systematically identify compounds that are likely to accumulate in plants. To quantify the accuracy of our predictions, we performed untargeted metabolomics on 13 plants, allowing us to experimentally confirm the detectability of the predicted compounds. For example, we observed 59 potentially novel compounds predicted and experimentally detected in corn. Our study shows that the application of genome-associated functional annotations can lead to an integrated metabologenomics platform capable of unveiling the chemical composition of edible plants and the biochemical pathways responsible for the observed compounds. Author SummaryNutrition and well-being take a central role in todays high pace life, but how much do we really know about the food we eat? Here, we harness existing metabolic knowledge encrypted in staple food ingredients genome to help us explore the composition of raw edible plants. We first show the benefit and value of looking into genome-associated functional annotations on a wide scale. Next, we rely on new experimental data to develop a framework that helps us reveal new, potentially bioactive compounds in staple food ingredients. This has significance in, first, extending current food composition knowledge and second in discovering newly detected bioactive compounds, shedding light on the potential impacts of common food ingredients beyond their nutritional value as described in food labels. Finally, we show that staple foods that are already included in our daily diets might have the potential to contribute to our well-being.

bioinformatics↗

bioRxiv2021

Multidose evaluation of 6,710 drug repurposing library identifies potent SARS-CoV-2 infection inhibitors In Vitro and In Vivo.

Identification of host factors contributing to replication of viruses and resulting disease progression remains a promising approach for development of new therapeutics. Here, we evaluated 6710 clinical and preclinical compounds targeting 2183 host proteins by immunocytofluorescence-based screening to identify SARS-CoV-2 infection inhibitors. Computationally integrating relationships between small molecule structure, dose-response antiviral activity, host target and cell interactome networking produced cellular networks important for infection. This analysis revealed 389 small molecules, >12 scaffold classes and 813 host targets with micromolar to low nanomolar activities. From these classes, representatives were extensively evaluated for mechanism of action in stable and primary human cell models, and additionally against Beta and Delta SARS-CoV-2 variants and MERS-CoV. One promising candidate, obatoclax, significantly reduced SARS-CoV-2 viral lung load in mice. Ultimately, this work establishes a rigorous approach for future pharmacological and computational identification of novel host factor dependencies and treatments for viral diseases.

microbiology↗

bioRxiv2019

FoodMine: Exploring Food Contents in Scientific Literature

Thanks to the many chemical and nutritional components it carries, diet critically affects human health. However, the currently available comprehensive databases on food composition cover only a tiny fraction of the total number of chemicals present in our food, focusing on the nutritional components essential for our health. Indeed, thousands of other molecules, many of which have well documented health implications, remain untracked. To explore the body of knowledge available on food composition, we built FoodMine, an algorithm that uses natural language processing to identify papers from PubMed that potentially report on the chemical composition of garlic and cocoa. After extracting from each paper information on the reported quantities of chemicals, we find that the scientific literature carries extensive information on the detailed chemical components of food that is currently not integrated in databases. Finally, we use unsupervised machine learning to create chemical embeddings, finding that the chemicals identified by FoodMine tend to have direct health relevance, reflecting the scientific communitys focus on health-related chemicals in our food.

bioinformatics↗

bioRxiv2017

Network integration of multi-tumour omics data suggests novel targeting strategies

We characterize different tumour types in the search for multi-tumour drug targets, in particular aiming for drug repurposing or novel drug combinations. Starting from 11 tumour types from The Cancer Genome Atlas, we obtain three clusters based on transcriptomic correlation profiles. A network-based analysis, integrating gene expression profiles and protein interactions of cancer-related genes, allowed us to define three cluster-specific signatures, with genes belonging to NF-B signaling, chromosomal instability, ubiquitin-proteasome system, DNA metabolism, and apoptosis biological processes. These signatures have been characterized by different approaches based on mutational, pharmacological and clinical evidences, demonstrating the validity of our selection. Moreover, we defined new pharmacological strategies validated by in vitro experiments that showed inhibition of cell growth in two tumour cell lines, with significant synergistic effect. Our study thus provides a list of genes and pathways with the potential to be used, singularly or in combination, for the design of novel treatment strategies.

systems biology↗