Improving automated model reconstruction across phylogenetically diverse genome-scale metabolic models

info:eu-repo/semantics/publishedVersio

Improved Evidence-based Genome-scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes

High-throughput Comparison, Functional Annotation, and Metabolic Modeling of Plant Genomes using the PlantSEED Resource

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes

Systems-level analysis of the plasticity of the maize metabolic network reveals novel hypotheses in the nitrogen-use efficiency of maize roots

This article comments on:Chowdhury NB, Schroeder WL, Sarkar D, Amiour N, Quilleré I, Hirel B, Maranas CD, Saha R. 2022. Dissecting the metabolic reprogramming of maize root under nitrogen-deficient stress conditions. Journal of Experimental Botany 73, 275–291.</jats:p

Metabolomic Profiling of the Nectars of <i>Aquilegia pubescens</i> and <i>A</i>. <i>Canadensis</i>

To date, variation in nectar chemistry of flowering plants has not been studied in detail. Such variation exerts considerable influence on pollinator–plant interactions, as well as on flower traits that play important roles in the selection of a plant for visitation by specific pollinators. Over the past 60 years the Aquilegia genus has been used as a key model for speciation studies. In this study, we defined the metabolomic profiles of flower samples of two Aquilegia species, A. Canadensis and A. pubescens. We identified a total of 75 metabolites that were classified into six main categories: organic acids, fatty acids, amino acids, esters, sugars, and unknowns. The mean abundances of 25 of these metabolites were significantly different between the two species, providing insights into interspecies variation in floral chemistry. Using the PlantSEED biochemistry database, we found that the majority of these metabolites are involved in biosynthetic pathways. Finally, we explored the annotated genome of A. coerulea, using the PlantSEED pipeline and reconstructed the metabolic network of Aquilegia. This network, which contains the metabolic pathways involved in generating the observed chemical variation, is now publicly available from the DOE Systems Biology Knowledge Base (KBase; http://kbase.us).</p

Model selection.

<p>We consider logistic models with different number of pathways <i>P</i> and of pairs of pathways <i>z</i> (see text and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002762#s4" target="_blank">Methods</a>). <b>A</b>, Model accuracy. We calculate the true positive (<i>TP</i>) and true negative (<i>TN</i>) rates for the different models. <i>TP</i> reflects whether the model correctly predicts <i>G</i> nutrients, whilst the <i>TN</i> reflects whether the model correctly predicts <i>NG</i> nutrients. <b>B</b>, Area under the ROC curve (<i>AUC</i>) for the 10 models. The higher the <i>AUC</i>, the better the model is at separating <i>G</i> nutrients from <i>NG</i> nutrients. <b>C</b>, Akaike information criterion (AIC) and Bayesian information criterion (BIC) of the 10 models. The lower the information criterion, the more parsimonious the model. We could not identify any additional pathways and/or pathway pairs that improved the AIC and BIC of the model with <i>P</i> = 8, <i>z</i> = 2 (pathways and pathway pairs are listed in the upper right panel of the figure). In the case of <i>TP</i>, <i>TN</i>, and <i>AUC</i>, we apply our complete model including both nutrient classes and KEGG pathways to the training set of organisms, and to the test set of organisms (see text and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002762#s4" target="_blank">Methods</a>). When <i>P</i> = 0, <i>z</i> = 0, there are more <i>NG</i> nutrients than <i>G</i> nutrients that are not included in a nutrient class, therefore all of these nutrients are considered <i>i</i>∈<i>NG</i>; hence, the initially low <i>TN</i> rate. When <i>P</i>≥4, the <i>TP</i> in the test set is similar to the <i>TP</i> in the training set. This means that our model is successful at identifying <i>G</i> nutrients. However, the <i>TN</i> for the test set is slightly lower than the TN for the training set. This occurs because there are more <i>NG</i> nutrients in the test set that are also found in the Sugar and Sugar derivative classes, or in <i>G</i> pathways in the linear model, which we could not account for because of the small sample size of the training set. The difference between the <i>TN</i> rates of the two test sets has an impact on the overall accuracy of the model for the training and test sets.</p

Nutrients uptaken and that stimulate growth in the presence of minimal media for the organisms in the training set.

<p><b>A</b>, <i>E. coli</i> and <i>B</i>. subtilis have the largest number of uptaken nutrients whereas <i>M. barkeri</i> has the fewest. This reflects the current understanding of <i>M. barkeri</i> as a specialized methanogen <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002762#pcbi.1002762-Liu1" target="_blank">[68]</a>. <b>B</b>, <i>E. coli</i> and <i>B. subtilis</i> are able to catabolize half of the nutrients they uptake whereas <i>M. barkeri</i> can catabolize less than 10% of the nutrients it uptakes. <b>C</b>, Number of uptaken nutrients by nutrient class. Within each class, the four organisms uptake approximately the same number of nutrients. Exceptions are <i>M. barkeri</i>—which does not uptake neither Fatty acids nor Sugars, and only one Sugar derivative—and <i>B. subtilis</i>—which is not assumed to uptake Fatty acids in the <i>in silico</i> reconstruction. <b>D</b>, Fraction of uptaken nutrients that stimulate growth by nutrient class. There is a consistent pattern of growth stimulation across all four species for six nutrient classes: Sugars, Sugar derivatives, and Purines are catabolized whilst Inorganic compounds, Pyrimidines, Cofactors, and compounds involved in the formation of the cell membrane or cell wall (Cell boundary class) are not catabolized.</p

Compositions of compounds in all metabolic categories.

<p>A) Percentage of each type of compound in <i>A</i>. <i>canadensis</i> and <i>A</i>. <i>pubescens</i>, relative to the total number metabolites. B-C) Pie chart representation of the percentages shown in A.</p