Giulia Menichetti

The role of gut microbiome in predicting diet response and developing personalized dietary recommendations has been increasingly recognized. Yet, we still

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 (TFs; “input message”) and corresponding target genes (TGs; “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 (ie, channel capacity). When we ranked TF:TG pairs by MI, pairs associated with fatty acid oxidation displayed the greatest loss of communication with aging, whereas 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.

Over the past two decades, network medicine (NM) has evolved to help define disease mechanisms, identify drug targets, and guide increasingly precise therapies. In recent years, the integration of NM with artificial intelligence (AI), particularly deep learning techniques, has evolved with increasing applications. AI techniques help elucidate complex disease mechanisms and define precise therapies. The depth of useful, mechanistic information implicit in molecular interaction networks and prior deep learning successes provide a rational basis for combining NM and AI in the analyses of large multiomic datasets to enhance the speed, predictive precision, and biological insights of the computational process. In this review, we provide a summary of concepts related to the combined use of AI and NM as a path to precision medicine, illustrating the success of this joint approach to biomedical complexity and its ongoing challenges.

Summary: Network 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. Availability and implementation: NetMedPy 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.

Background/Objectives/Methods: Current research on the link between diet and stroke or myocardial infarction primarily focuses on individual food items. However, people’s eating habits involve complex combinations of various foods. By employing an innovative approach known as the Gaussian graphical model to identify dietary patterns along with the Cox proportional model, the study aimed to identify dietary networks and explore their relationship with the incidence of stroke and/or myocardial infarction in the Korean population. The research utilized data from 84,729 participants in the Korean Genome and Epidemiological Study (KoGES), including the HEXA cohort (61,140 participants), CAVAS cohort (15,419 participants), and Ansan-Ansung cohort (8170 participants). Results: The network identified five dietary patterns or communities consisting of different food groups, while nine food groups did not belong to any community. The High-Protein and Green Tea Community consistently reduced the risk of stroke and myocardial infarction (MI), particularly among females. In most communities, no significant associations with stroke risk were noted in males, and the Rice and High-Calorie Beverages Community was linked to an increased risk of MI in both the total population and females. Conclusions: Dietary patterns derived from network analysis revealed distinct dietary habits in the Korean population, offering new insights into the relationship between diet and the risk of stroke and MI.

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.

The offering of grocery stores is a strong driver of consumer decisions. While highly processed foods such as packaged products, processed meat and sweetened soft drinks have been increasingly associated with unhealthy diets, information on the degree of processing characterizing an item in a store is not straightforward to obtain, limiting the ability of individuals to make informed choices. GroceryDB, a database with over 50,000 food items sold by Walmart, Target and Whole Foods, shows the degree of processing of food items and potential alternatives in the surrounding food environment. The extensive data gathered on ingredient lists and nutrition facts enables a large-scale analysis of ingredient patterns and degrees of processing, categorized by store, food category and price range. Furthermore, it allows the quantification of the individual contribution of over 1,000 ingredients to ultra-processing. GroceryDB makes this information accessible, guiding consumers toward less processed food choices.

Summary Network 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. Availability NetMedPy 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.

Summary The binding interactions between small molecules and proteins are the basis of cellular functions. Yet, experimental data available regarding compound-protein interaction is 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 tool to interactively extract experimental binding interaction data from multiple databases, perform filtering, and harmonize the resulting information, thus providing a gain of compound-protein interaction data. When compared to a single source, DrugBank, we show that it can collect more than 10 times the amount of annotations. The end-user can apply custom filtering to the aggregated output data and save it in any generic tabular file suitable for further downstream tasks such as network medicine analyses for drug repurposing and cross-validation of deep learning models. Availability CPIExtract 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.