Accelerated global sensitivity analysis of genome-wide constraint-based metabolic models

The limitations of phenotype prediction in metabolism

Phenotype prediction is at the center of many questions in biology. Prediction is often achieved by determining statistical associations between genetic and phenotypic variation, ignoring the exact processes that cause the phenotype. Here, we present a framework based on genome-scale metabolic reconstructions to reveal the mechanisms behind the associations. We calculated a polygenic score (PGS) that identifies a set of enzymes as predictors of growth, the phenotype. This set arises from the synergy of the functional mode of metabolism in a particular setting and its evolutionary history, and is suitable to infer the phenotype across a variety of conditions. We also find that there is optimal genetic variation for predictability and demonstrate how the linear PGS can still explain phenotypes generated by the underlying nonlinear biochemistry. Therefore, the explicit model interprets the black box statistical associations of the genotype-to-phenotype map and helps to discover what limits the prediction in metabolism.

Enhancing control systems of higher plant culture chambers via multilevel structural mechanistic modelling

Modelling higher plant growth is of strategic interest for modern agriculture as well as for the development of bioregenerative life support systems for space applications, where crop growth is expected to play an essential role. The capability of constraint-based metabolic models to cope the diel dynamics of plants growth is integrated into a multilevel modelling approach including mass and energy transfer and enzyme kinetics. Lactuca sativa is used as an exemplary crop to validate, with experimental data, the approach presented as well as to design a novel model-based predictive control strategy embedding metabolic information. The proposed modelling strategy predicts with high accuracy the dynamics of gas exchange and the distribution of fluxes in the metabolic network whereas the control architecture presented can be useful to manage higher plants chambers and open new ways of merging metabolome and control algorithms.

Mycoplasmas as Host Pantropic and Specific Pathogens: Clinical Implications, Gene Transfer, Virulence Factors, and Future Perspectives

Mycoplasmas as economically important and pantropic pathogens can cause similar clinical diseases in different hosts by eluding host defense and establishing their niches despite their limited metabolic capacities. Besides, enormous undiscovered virulence has a fundamental role in the pathogenesis of pathogenic mycoplasmas. On the other hand, they are host-specific pathogens with some highly pathogenic members that can colonize a vast number of habitats. Reshuffling mycoplasmas genetic information and evolving rapidly is a way to avoid their host's immune system. However, currently, only a few control measures exist against some mycoplasmosis which are far from satisfaction. This review aimed to provide an updated insight into the state of mycoplasmas as pathogens by summarizing and analyzing the comprehensive progress, current challenge, and future perspectives of mycoplasmas. It covers clinical implications of mycoplasmas in humans and domestic and wild animals, virulence-related factors, the process of gene transfer and its crucial prospects, the current application and future perspectives of nanotechnology for diagnosing and curing mycoplasmosis, Mycoplasma vaccination, and protective immunity. Several questions remain unanswered and are recommended to pay close attention to. The findings would be helpful to develop new strategies for basic and applied research on mycoplasmas and facilitate the control of mycoplasmosis for humans and various species of animals.

INTEGRATE: Model-based multi-omics data integration to characterize multi-level metabolic regulation

Metabolism is directly and indirectly fine-tuned by a complex web of interacting regulatory mechanisms that fall into two major classes. On the one hand, the expression level of the catalyzing enzyme sets the maximal theoretical flux level (i.e., the net rate of the reaction) for each enzyme-controlled reaction. On the other hand, metabolic regulation controls the metabolic flux through the interactions of metabolites (substrates, cofactors, allosteric modulators) with the responsible enzyme. High-throughput data, such as metabolomics and transcriptomics data, if analyzed separately, do not accurately characterize the hierarchical regulation of metabolism outlined above. They must be integrated to disassemble the interdependence between different regulatory layers controlling metabolism. To this aim, we propose INTEGRATE, a computational pipeline that integrates metabolomics and transcriptomics data, using constraint-based stoichiometric metabolic models as a scaffold. We compute differential reaction expression from transcriptomics data and use constraint-based modeling to predict if the differential expression of metabolic enzymes directly originates differences in metabolic fluxes. In parallel, we use metabolomics to predict how differences in substrate availability translate into differences in metabolic fluxes. We discriminate fluxes regulated at the metabolic and/or gene expression level by intersecting these two output datasets. We demonstrate the pipeline using a set of immortalized normal and cancer breast cell lines. In a clinical setting, knowing the regulatory level at which a given metabolic reaction is controlled will be valuable to inform targeted, truly personalized therapies in cancer patients.