Biology subjects

Jimenez, N. E.

Publications and source records attributed to Jimenez, N. E..

3 recordsLinked to original sources

bioRxiv2025

A community-consensus reconstruction of Chinese Hamster metabolism enables structural systems biology analyses to decipher metabolic rewiring in lactate-free CHO cells

Genome-scale metabolic models (GEMs) are indispensable for studying and engineering cellular metabolism. Here, we present iCHO3K, a community-consensus, manually-curated reconstruction of the Chinese Hamster metabolic network. In addition to accounting for 11004 reactions associated with 3597 genes, iCHO3K includes 3489 protein structures and structural descriptors for >70% of its 7377 metabolites, enabling deeper exploration of the link between molecular structure and cellular metabolism. We used iCHO3K to contextualize transcriptomics and metabolomics data from a CHO cell line in which lactate secretion is abolished. We found the reduced glycolytic flux and enhanced TCA cycle flux were accompanied by an elevated NADH and PEP levels in these cells, consistent with experimental measurements. Leveraging iCHO3Ks structural annotations, we identified candidate binding interactions of NADH and PEP with glycolytic enzymes showing model-predicted differential flux, suggesting novel allosteric regulation associated with the observed decrease in glucose uptake and glycolysis. Overall, iCHO3K offers a valuable framework for systematic integration of omics data, improved flux predictions, and structure-guided insights, thus advancing CHO cell engineering and enhancing biomanufacturing efficiency.

systems biology↗

bioRxiv2024

Modeling the emergent metabolic potential of soil microbiomes in Atacama landscapes

Soil microbiomes harbor complex communities and exhibit important ecological roles resulting from biochemical transformations and microbial interactions. Difficulties in characterizing the mechanisms and consequences of such interactions together with the multidimensionality of niches hinder our understanding of these ecosystems. The Atacama Desert is an extreme environment that includes unique combinations of stressful abiotic factors affecting microbial life. In particular, the Talabre Lejia transect has been proposed as a unique natural laboratory for understanding adaptation mechanisms. We propose a systems biology-based computational framework for the reconstruction and simulation of community-wide and genome-resolved metabolic models, in order to provide an overview of the metabolic potential as a proxy of how microbial communities are prepared to respond to the environment. Through a multifaceted approach that includes taxonomic and functional profiling of microbiomes, simulation of the metabolic potential, and multivariate analyses, we were able to identify key species and functions from six contrasting soil samples across the Talabre Lejia transect. We highlight the functional redundancy of whole metagenomes, which act as a gene reservoir from which site-specific functions emerge at the species level. We also link the physicochemistry from the puna and the lagoon samples to specific metabolic machineries that could be associated with their adaptation to the unique environmental conditions found there. We further provide an abstraction of community composition and structure for each site that allows to describe them as sensitive or resilient to environmental shifts through putative cooperation events. Our results show that the study of community-wide and genome-resolved metabolic potential, together with targeted modeling, may help to elucidate the role of producible metabolites in the adaptation of microbial communities. Our framework was designed to handle non-model microorganisms, making it suitable for any (meta)genomic dataset that includes nucleotide sequence data and high-quality environmental metadata for different samples.

systems biology↗

bioRxiv2023

Unveiling abundance-dependent metabolic phenotypes of microbial communities

Constraint-based modeling has risen as an alternative for characterizing the metabolism of communities. Adaptations of Flux Balance Analysis have been proposed to model metabolic interactions in most cases, considering a unique optimal flux distribution derived from the maximization of biomass production. However, these approaches do not consider the development of other potentially novel essential functions not directly related to cell growth which forces them to display suboptimal growth rates in nature. Additionally, suboptimal states allow a degree of plasticity in the metabolism, thus allowing quick shifts between alternative flux distributions as an initial response to environmental changes. In this work, we present a method to explore the abundance-growth space as a representation of metabolic flux distributions of a community. This space is defined by the composition of a community, represented by its members relative abundance and their growth rate. The analysis of this space allows us to represent the whole set of feasible fluxes without needing a complete description of the solution space unveiling abundance-dependent metabolic phenotypes displayed in a given environment. As an illustration, we consider a community composed of two bioleaching bacteria, Acidithiobacillus ferrooxidans Wenelen and Sulfobacillus thermosulfidooxidans Cutipay, finding that changes in the composition of their available resources significantly affects their metabolic plasticity. IMPORTANCEIn nature, organisms live in communities and not as isolated species. Their interactions provide a source of resilience to environmental disturbances. Despite their importance in ecology, human health, and industry, understanding how organisms interact in different environments remains an open question. In this work, we provide a novel approach which, only using genomics information, studies the metabolic phenotype exhibited by communities, where the exploration of suboptimal growth flux distributions and the composition of a community allows to unveil its capacity to respond to environmental changes, shedding the light of the degree of metabolic plasticity inherent to the community.

bioinformatics↗