Biology subjects

Moreno, E. L.

Publications and source records attributed to Moreno, E. L..

4 recordsLinked to original sources

bioRxiv2024

fluxTrAM: Integration of tracer-based metabolomics data into atomically resolved genome-scale metabolic networks for metabolic flux analysis

Quantitative inference of intracellular reaction rates is essential for characterising metabolic phenotypes. The classical experimental method for measuring metabolic fluxes makes use of stable-isotope tracing of metabolites through the metabolic network, followed by mass spectrometry analysis. The most common 13C-based metabolic flux analysis requires multidisciplinary knowledge in analytical chemistry, cell biology, and mathematical modelling, as well as the use of multiple independent tools for handling mass spectrometry data. Besides, flux analysis is usually carried out within a small network to validate a specific biological hypothesis. To overcome interdisciplinary barriers and extend flux interpretation towards a genome-scale level, we developed fluxTrAM, a semi-automated pipeline for processing tracer- based metabolomics data and integrating it with atomically resolved genome-scale metabolic networks to enable flux predictions at genome-scale. fluxTrAM integrates different software packages inside and outside of the COBRA Toolbox v3.4 for the generation of metabolite structure and reaction databases for a genome-scale model, labelled mass spectrometry data processing into standardised mass isotopologue distribution data (MID), and metabolic flux analysis. To demonstrate the utility of this pipeline, we generated 13C-labeled metabolomics data on an in vitro human induced pluripotent stem cell (iPSC)-derived dopaminergic neuronal culture and processed 13C-labeled MID datasets. In parallel, we generated a cheminformatic database of standardised and context-specific metabolite structures, and atom-mapped reactions for a genome-scale dopaminergic neuronal metabolic model. MID data could be exported into established flux inference software for conventional flux inference on a core model scale. It could also be integrated into the atomically resolved metabolic model for flux inference at genome-scale using moiety fluxomics method. The core model flux solution and moiety flux solution were then compared to two additional flux solutions predicted via flux balance analysis and entropic flux balance analysis. The extensive computational flux analysis and comparison helped to better evaluate the obtained flux feasibility of the neuron-specific genome-scale model and suggested new tracer-based metabolomics experiments with novel labeling configurations, such as labelling a moiety within the thymidine metabolite. Overall, fluxTrAM enables the automation of labelled liquid chromatography (LC)-mass spectrometry (MS) data processing into MID datasets and atom mapping for any given genome-scale metabolic model. It contributes to the standardisation and high throughput of metabolic flux analysis at genome- scale.

systems biology↗

bioRxiv2024

Increasing intraspecific plant chemical diversity at plot and plant level affects herbivorous, predatory, and pollinating arthropod communities

1. Plant chemistry mediates interactions between plants and their environment. While intraspecific chemodiversity at the plant level is well-studied, the effects of chemodiversity at the community level on arthropod interactions need more attention. We conducted a field experiment to test how intraspecific chemodiversity affects plant-arthropod interactions. 2. We manipulated plots of Tanacetum vulgare L., varying in chemotype richness and composition, and monitored four arthropod groups (herbivores, flower visitors, predators, and ants) over three seasons. We hypothesized that higher plot-level chemotype richness would enhance occurrence across all studied arthropod groups but have functional group-specific effects on abundance, resulting in reduced herbivore and ant abundance and increased flower visitor and predator abundance with increased chemotype richness. 3. Using mixed models, we found that increasing plot-level chemotype richness had a limited effect on most arthropod group occurrences but led to significant changes in abundance. Herbivore abundance decreased over time, and flower visitor abundance increased, while predatory arthropods and ants remained largely unaffected. Furthermore, we found that chemotype presence within a plot showed year-to-year variation in its effects, particularly on herbivores in which positive effects turned negative over time, and on flower visitors in which the presence of specific chemotypes positively affected their abundances. Predators and ants, on the other hand, showed weaker and more variable responses to specific chemotypes. 4. These results suggest that different arthropod groups may respond to plant chemicals through different ecological mechanisms. Our research underscores the role of plant chemical diversity in shaping insect communities and contributing to ecosystem dynamics.

ecology↗

bioRxiv2022

Coupling comprehensive pesticide-wide association study to iPSC dopaminergic screening identifies and classifies Parkinson-relevant pesticides

Parkinsons disease (PD) is a complex, multi-factorial neurodegenerative disease, known to involve genetic, aging-related components, but also to be highly sensitive to environmental factors. In particular, ample evidence links pesticides to PD etiology. Here, establishing a field-to-bench paradigm, we have combined record-based exposure assessment in a population-based epidemiologic study of PD with testing in dopaminergic neurons produced from iPSCs to further identify and classify PD-relevant pesticides. First, agricultural pesticide-application records in California enabled us to investigate exposure to nearly 300 specific pesticides and PD risk in a comprehensive, pesticide-wide association study (PWAS). We implicated long-term exposure to 53 pesticide active ingredients in PD risk and identified their relevant co-exposure profiles. Second, to identify which of these pesticides might contribute to PD through direct effects on dopaminergic neurons, we employed a live-cell imaging screening paradigm in which neurons, definitively identified with a tyrosine hydroxylase reporter, were exposed to 43 of the high-risk pesticides. Using detailed morphometric measures, we found 10 pesticides were directly toxic to these neurons. Further, we analyzed pesticides typically used in combinations in cotton farming. Among these "cotton cluster" pesticides, co-exposures resulted in markedly greater toxicity than any single pesticide. Trifluralin was a pivotal driver of toxicity to dopaminergic neurons and led to marked mitochondrial dysfunction. Our field-to-bench paradigm may prove useful to mechanistically dissect pesticide exposure implicated in PD risk, and guide agricultural policy in the future.

neuroscience↗

bioRxiv2021

Mechanistic model-driven exometabolomic characterisation of human dopaminergic neuronal metabolism

Starting with a comprehensive generic reconstruction of human metabolism, we generated high-quality, constraint-based, genome-scale, cell-type and condition specific models of metabolism in human dopaminergic neurons, the cell type most vulnerable to degeneration in Parkinsons disease. They are a synthesis of extensive manual curation of the biochemical literature on neuronal metabolism, together with novel, quantitative, transcriptomic and targeted exometabolomic data from human stem cell-derived, midbrainspecific, dopaminergic neurons in vitro. Thermodynamic constraint-based modelling enabled qualitatively accurate and moderately quantitatively accurate prediction of dopaminergic neuronal metabolite exchange fluxes, including predicting the consequences of metabolic perturbations in a manner also consistent with literature on monogenic mitochondrial Parkinsons disease. These dopaminergic neurons models provide a foundation for a quantitative systems biochemistry approach to metabolic dysfunction in Parkinsons disease. Moreover, the plethora of novel mathematical and computational approaches required to develop them are generalisable to study any other disease associated with metabolic dysfunction.

systems biology↗