Biology subjects

Martinelli, F.

Publications and source records attributed to Martinelli, F..

8 recordsLinked to original sources

bioRxiv2025

StereoMapper: Clarifying Metabolite Identity Through Stereochemically Aware Relationship Assignment

Inaccuracies in metabolite cross-mapping frequently introduce errors into genome-scale metabolic models (GEMs). A key challenge lies in distinguishing truly identical metabolites from closely related structural variants, such as stereoisomers, across biochemical databases. StereoMapper addresses this issue by establishing stereochemically aware relationships that clarify metabolite identity. It operates entirely on structural information, using molecular structures to infer equivalence and define relationships. StereoMapper was implemented using RDKit (2025.3.3) and Open Babel-wheel (3.1.1.22) which provides the core functionality of the Open Babel software, and benchmarked across four curated control datasets to evaluate performance in relationship classification (enantiomer, diastereomer, stereo-resolution pairs, and protomer). The pipeline was subsequently applied to 1.3 million molecular structures from multiple metabolic databases to generate equivalent isomeric sets (groups of structures identified as equivalent by StereoMapper). These sets were compared against the human subset of MetaNetX, a context-aware reference, to assess concordance and investigate disagreements. On curated positive control sets containing only positive examples across multiple relationship classes, StereoMapper achieved mean precision, recall, and F1-scores of 92.6, 98.0, and 95.3, respectively; following optimisation, these improved to 98.9, 98.2, and 98.5. Application to the 1.3 million structures produced over 339,000 stereochemically defined relationships between molecular structures. Comparison with the human subset of MetaNetX assignments showed an overall grouping concordance of 93.5%. The remaining 6.5% of differences were mainly associated with protomer relationships (52.3%) and stereo-resolution pairs (37.4%), while 21.6% involved complex or ambiguous cases. Scientific contribution statementStereoMapper provides a stereochemically aware pipeline for metabolite relationship assignment that shows high performance across multiple benchmark datasets, resolving key ambiguities in metabolite identity and providing a complementary approach to existing context-aware frameworks. Its integration will enhance reaction-level cross-mapping and improve the accuracy of future GEM reconstructions.

systems biology↗

bioRxiv2025

Elucidating a potential role of the infant gut microbiome on the bioavailability of L-tyrosine in phenylketonuria

BackgroundPhenylketonuria (PKU) is an inherited metabolic disorder caused by phenylalanine hydroxylase (PAH) deficiency, leading to elevated L-phenylalanine and severe neurological damage if untreated. While phenylalanine-based biomarkers are diagnostic and phenylalanine levels correlate with disease severity, the clinical manifestations of PKU are heterogeneous. ResultsTo identify additional reliable, potentially novel biomarkers, we used germ-free sex-specific, organ-resolved infant whole-body metabolic models (infant-WBMs) to simulate PAH deficiency and predicted elevated L-phenylalanine and its derivatives, alongside reduced L-tyrosine fluxes, as the product of phenylalanine hydroxylation. To test the reliability of these predictions, we combined the infant-WBMs with gut microbiome models from 48 healthy infants. Upon integrating microbiome data, we found that microbial metabolism significantly increased L-tyrosine availability, obscuring its utility as a universal biomarker. In [~]23% of microbiome-PKU models, L-tyrosine fluxes remained low, indicating insufficient microbial compensation. These cases were enriched in Firmicutes and lacked specific Bifidobacterium and Escherichia strains linked to L-tyrosine biosynthesis via the pretyrosine pathway. Shadow price analysis identified microbial species critical for host L-tyrosine metabolism. However, some, such as Bifidobacterium dentium, also contributed to L-phenylalanine synthesis, potentially worsening the PKU phenotype. In contrast, L-phenylalanine, phenylpyruvate, and hydroxyphenylacetic acid remained reliably elevated across all models, validating their diagnostic relevance. ConclusionsOur study demonstrates that microbiome composition can modulate biomarker reliability in PKU, particularly for L-tyrosine. Integrating microbial metabolic models with whole-body physiology enables assessment of biomarker reliability and reveals subpopulations for whom secondary biomarkers or targeted probiotics may be beneficial. This approach offers a powerful framework for refining diagnostics and therapy monitoring in rare metabolic diseases and the development of possible targeted microbiome therapies.

systems biology↗

bioRxiv2025

The transcriptional mechanism behind Mimosa pudica leaf-folding in response to mechanical disturbance

Mimosa pudica is a plant known for its ability to fold leaves in response to mechanical disturbances, which serves as a visible phenotypic stress marker. Leaf folding occurs with a response timing and intensity that varies depending on the stimulus. This adaptive behavior may function as a defense mechanism, helping plant resist herbivores and environmental stressors. However, the molecular and genetic mechanisms that are involved in leaf folding are still not totally understood. In this study, the gene regulatory networks underlying M. pudica leaf closure following single and multiple mechanical disturbances (whole pot drops) were investigated. Chlorophyll fluorescence was measured as fast phenotypic indicator of transient or permanent photochemical damage, and transcriptional responses were measured to identify the key genes regulating phenotypic changes after single or multiple drops. A progressive reduction of the quantum yield of photosystem 2 revealed a lower electron transport rate in leaves subjected to one or more drops, which may indicate the onset of energy shortage, perhaps caused by low ATP availability, limiting both leaf movement and photosynthesis. The transcriptomic profiles revealed larger differences when plants were subjected to multiple drops than to a single drop, with respect to unstressed controls. Interestingly, following a single drop, the majority of up-regulated genes were associated with the flavonoid biosynthetic pathway. After multiple drops, however, genes associated with biotic and abiotic stress resistance pathways were predominantly up-regulated. These results provide a basis for developing a gene regulatory network model of stress-induced movements in M. pudica leaves, which may help design sustainable strategies of plant stress defense. Main ConclusionsRepeated stress in Mimosa pudica reduces photosystem efficiency, alters gene expression, shifting from flavonoid biosynthesis to stress resistance pathways, offering insights for sustainable plant stress defense strategies.

plant biology↗

bioRxiv2025

The MicroMap is a network visualisation resource for microbiome metabolism

The human microbiome plays a crucial role in metabolism and thereby influences health and disease. Constraint-based reconstruction and analysis (COBRA) has proven an attractive framework to generate mechanism-derived hypotheses along the nutrition-host-microbiome-disease axis within the computational systems biology community. Unlike for human, no large-scale visualisation resource for microbiome metabolism has been available to date. To address this gap, we created the MicroMap, a manually curated microbiome metabolic network visualisation, which captures the metabolic content of over a quarter million microbial genome-scale metabolic reconstructions. The MicroMap contains 5,064 unique reactions and 3,499 unique metabolites, including for 98 drugs. The MicroMap allows users to intuitively explore microbiome metabolism, inspect microbial metabolic capabilities, and visualise computational modelling results. Further, the MicroMap shall serve as an educational tool to make microbiome metabolism accessible to broader audiences beyond computational modellers. For example, we utilised the MicroMap to generate a comprehensive collection of 257,429 visualisations, corresponding to the entire scope of our current microbiome reconstruction resources, to enable users to visually compare and contrast the metabolic capabilities for diaerent microbes. The MicroMap seamlessly integrates with the Virtual Metabolic Human (VMH, www.vmh.life) and the COBRA Toolbox (opencobra.github.io), and is freely accessible at the MicroMap dataverse (https://dataverse.harvard.edu/dataverse/micromap), in addition to all the generated reconstruction visualisations.

systems biology↗

bioRxiv2023

APOLLO: A genome-scale metabolic reconstruction resource of 247,092 diverse human microbes spanning multiple continents, age groups, and body sites

Computational modelling of microbiome metabolism has proved instrumental to catalyse our understanding of diet-host-microbiome-disease interactions through the interrogation of mechanistic, strain- and molecule-resolved metabolic models. We present APOLLO, a resource of 247,092 human microbial genome-scale metabolic reconstructions spanning 19 phyla and accounting for microbial genomes from 34 countries, all age groups, and five body sites. We explored the metabolic potential of the reconstructed strains and developed a machine learning classifier able to predict with high accuracy the taxonomic strain assignments. We also built 14,451 sample-specific microbial community models, which could be stratified by body site, age, and disease states. Finally, we predicted faecal metabolites enriched or depleted in gut microbiomes of people with Crohns disease, Parkinson disease, and undernourished children. APOLLO is compatible with the human whole-body models, and thus, provide unprecedented opportunities for systems-level modelling of personalised host-microbiome co-metabolism. APOLLO will be freely available under https://www.vmh.life/.

systems biology↗

bioRxiv2020

AGORA2: Large scale reconstruction of the microbiome highlights wide-spread drug-metabolising capacities.

The human microbiome influences the efficacy and safety of a wide variety of commonly prescribed drugs, yet comprehensive systems-level approaches to interrogate drug-microbiome interactions are lacking. Here, we present a computational resource of human microbial genome-scale reconstructions, deemed AGORA2, which accounts for 7,206 strains, includes microbial drug degradation and biotransformation, and was extensively curated based on comparative genomics and literature searches. AGORA2 serves as a knowledge base for the human microbiome and as a metabolic modelling resource. We demonstrate the latter by mechanistically modelling microbial drug metabolism capabilities in single strains and pairwise models. Moreover, we predict the individual-specific drug conversion potential in a cohort of 616 colorectal cancer patients and controls. This analysis reveals that some drug activation capabilities are present in only a subset of individuals, moreover, drug conversion potential correlate with clinical parameters. Thus, AGORA2 paves the way towards personalised, predictive analysis of host-drug-microbiome interactions.

systems biology↗

bioRxiv2020

Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacteria species on community butyrate production

Integrating constraint-based community modelling with population statistics, we introduce new theoretical concepts for interrogating the metabolic functions of the microbiome, applying them to a public metagenomic dataset consisting of 365 colorectal cancer cases (CRC) and 251 healthy controls. We found that 1) glutarate production capability was significantly enriched in CRC microbiomes and mechanistically linked to lysine fermentation in Fusobacteria species, 2) acetate and butyrate production potentials were lowered in CRC, 3) Fusobacteria presence had large negative ecological effects on community butyrate production in CRC and healthy controls. Validating the model predictions against faecal metabolomics, our in silico frameworks correctly predicted in vivo species metabolite correlations with high accuracy. In conclusion, highlighting the value of combining statistical association studies with in silico modelling, this study delivers insights on the metabolic role of Fusobacteria in the gut, while providing a proof of concept for the validity of constraint-based community modelling.

systems biology↗

bioRxiv2018

Confidence Analysis of DEER Data and its Structural Interpretation with Ensemble-Biased Metadynamics

Given its ability to measure multicomponent distance distributions between electron-spin probes, Double Electron-Electron Resonance spectroscopy (DEER) has become a leading technique to assess the structural dynamics of biomolecules. However, methodologies to evaluate the statistical error of these distributions are not standard, often hampering a rigorous interpretation of the experimental results. Distance distributions are often determined from the experimental DEER data through a mathematical method known as Tikhonov regularization, but this approach makes rigorous error estimates difficult. Here, we build upon an alternative model-based approach in which the distance probability distribution is represented as a sum of Gaussian components and use propagation of errors to calculate an associated confidence band. Our approach considers all sources of uncertainty, including the experimental noise, the uncertainty in the fitted background signal, and the limited time-span of the data collection. The resulting confidence band reveals the most and least reliable features of the probability distribution, thereby informing the structural interpretation of DEER experiments. To facilitate this interpretation, we also generalize the molecular-simulation method known as Ensemble-Biased Metadynamics. This method, originally designed to generate maximum-entropy structural ensembles consistent with one or more probability distributions, now also accounts for the uncertainty in those target distributions, exactly as dictated by their confidence bands. After careful benchmarks, we demonstrate the proposed techniques using DEER results from spin-labeled T4 lysozyme.

biophysics↗