Data Reduction Approaches for Dissecting Transcriptional Effects on Metabolism.

The availability of high-throughput data from transcriptomics and metabolomics technologies provides the opportunity to characterize the transcriptional effects on metabolism. Here we propose and evaluate two computational approaches rooted in data reduction techniques to identify and categorize transcriptional effects on metabolism by combining data on gene expression and metabolite levels. The approaches determine the partial correlation between two metabolite data profiles upon control of given principal components extracted from transcriptomics data profiles. Therefore, they allow us to investigate both data types with all features simultaneously without doing preselection of genes. The proposed approaches allow us to categorize the relation between pairs of metabolites as being under transcriptional or post-transcriptional regulation. The resulting classification is compared to existing literature and accumulated evidence about regulatory mechanism of reactions and pathways in the cases of Escherichia coli, Saccharomycies cerevisiae, and Arabidopsis thaliana.

Genomic dissection of enhancers uncovers principles of combinatorial regulation and cell type-specific wiring of enhancer-promoter contacts.

Genomic binding of transcription factors, like the glucocorticoid receptor (GR), is linked to the regulation of genes. However, as we show here, GR binding is a poor predictor of GR-dependent gene regulation even when taking the 3D organization of the genome into account. To connect GR binding sites to the regulation of genes in the endogenous genomic context, we turned to genome editing. By deleting GR binding sites, individually or in combination, we uncovered how cooperative interactions between binding sites contribute to the regulation of genes. Specifically, for the GR target gene GILZ, we show that the simultaneous presence of a cluster of GR binding sites is required for the activity of an individual enhancer and that the GR-dependent regulation of GILZ depends on multiple GR-bound enhancers. Further, by deleting GR binding sites that are shared between different cell types, we show how cell type-specific genome organization and enhancer-blocking can result in cell type-specific wiring of promoter-enhancer contacts. This rewiring allows an individual GR binding site shared between different cell types to direct the expression of distinct transcripts and thereby contributes to the cell type-specific consequences of glucocorticoid signaling.

Role of the chromatin landscape and sequence in determining cell type-specific genomic glucocorticoid receptor binding and gene regulation.

The genomic loci bound by the glucocorticoid receptor (GR), a hormone-activated transcription factor, show little overlap between cell types. To study the role of chromatin and sequence in specifying where GR binds, we used Bayesian modeling within the universe of accessible chromatin. Taken together, our results uncovered that although GR preferentially binds accessible chromatin, its binding is biased against accessible chromatin located at promoter regions. This bias can only be explained partially by the presence of fewer GR recognition sequences, arguing for the existence of additional mechanisms that interfere with GR binding at promoters. Therefore, we tested the role of H3K9ac, the chromatin feature with the strongest negative association with GR binding, but found that this correlation does not reflect a causative link. Finally, we find a higher percentage of promoter-proximal GR binding for genes regulated by GR across cell types than for cell type-specific target genes. Given that GR almost exclusively binds accessible chromatin, we propose that cell type-specific regulation by GR preferentially occurs via distal enhancers, whose chromatin accessibility is typically cell type-specific, whereas ubiquitous target gene regulation is more likely to result from binding to promoter regions, which are often accessible regardless of cell type examined.

Stoichiometric Correlation Analysis: Principles of Metabolic Functionality from Metabolomics Data.

Recent advances in metabolomics technologies have resulted in high-quality (time-resolved) metabolic profiles with an increasing coverage of metabolic pathways. These data profiles represent read-outs from often non-linear dynamics of metabolic networks. Yet, metabolic profiles have largely been explored with regression-based approaches that only capture linear relationships, rendering it difficult to determine the extent to which the data reflect the underlying reaction rates and their couplings. Here we propose an approach termed Stoichiometric Correlation Analysis (SCA) based on correlation between positive linear combinations of log-transformed metabolic profiles. The log-transformation is due to the evidence that metabolic networks can be modeled by mass action law and kinetics derived from it. Unlike the existing approaches which establish a relation between pairs of metabolites, SCA facilitates the discovery of higher-order dependence between more than two metabolites. By using a paradigmatic model of the tricarboxylic acid cycle we show that the higher-order dependence reflects the coupling of concentration of reactant complexes, capturing the subtle difference between the employed enzyme kinetics. Using time-resolved metabolic profiles from Arabidopsis thaliana and Escherichia coli, we show that SCA can be used to quantify the difference in coupling of reactant complexes, and hence, reaction rates, underlying the stringent response in these model organisms. By using SCA with data from natural variation of wild and domesticated wheat and tomato accession, we demonstrate that the domestication is accompanied by loss of such couplings, in these species. Therefore, application of SCA to metabolomics data from natural variation in wild and domesticated populations provides a mechanistic way to understanding domestication and its relation to metabolic networks.

Highlights from the ISCB Student Council Symposia in 2016.

This editorial provides a brief overview of the 12th International Society for Computational Biology (ISCB) Student Council Symposium and the 4th European Student Council Symposium held in Florida, USA and The Hague, Netherlands, respectively. Further, the role of the ISCB Student Council in promoting education and networking in the field of computational biology is also highlighted.

Observability of Plant Metabolic Networks Is Reflected in the Correlation of Metabolic Profiles.

Understanding whether the functionality of a biological system can be characterized by measuring few selected components is key to targeted phenotyping techniques in systems biology. Methods from observability theory have proven useful in identifying sensor components that have to be measured to obtain information about the entire system. Yet, the extent to which the data profiles reflect the role of components in the observability of the system remains unexplored. Here we first identify the sensor metabolites in the model plant Arabidopsis (Arabidopsis thaliana) by employing state-of-the-art genome-scale metabolic networks. By using metabolic data profiles from a set of seven environmental perturbations as well as from natural variability, we demonstrate that the data profiles of sensor metabolites are more correlated than those of nonsensor metabolites. This pattern was confirmed with in silico generated metabolic profiles from a medium-size kinetic model of plant central carbon metabolism. Altogether, due to the small number of identified sensors, our study implies that targeted metabolite analyses may provide the vast majority of relevant information about plant metabolic systems.

Unravelling the in vivo regulation and metabolic role of the alternative oxidase pathway in C3 species under photoinhibitory conditions.

The mitochondrial alternative oxidase pathway (AOP) has been suggested to act as a sink for excess reducing power generated in the chloroplast under high-light (HL) stress and thus may reduce photoinhibition. The aim of this study was to compare different species to investigate the in vivo regulation and role of AOP under HL stress. The in vivo activities of AOP (νalt ) and the cytochrome oxidase pathway, chlorophyll fluorescence, metabolite profiles, alternative oxidase (AOX) capacity and protein amount were determined in leaves of five C3 species under growth light and after HL treatment. Differences in respiration and metabolite levels were observed among species under growth light conditions. The HL response of νalt was highly species dependent, correlated with the AOP capacity and independent of AOX protein content. Nevertheless, significant correlations were observed between νalt , levels of key metabolites and photosynthetic parameters. The results show that the species-specific response of νalt is caused by the differential post-translational regulation of AOX. Significant correlations between respiration, metabolites and photosynthetic performance across species suggest that AOP may permit stress-related amino acid synthesis, whilst maintaining photosynthetic activity under HL stress.

Starch Granule Re-Structuring by Starch Branching Enzyme and Glucan Water Dikinase Modulation Affects Caryopsis Physiology and Metabolism.

Starch is of fundamental importance for plant development and reproduction and its optimized molecular assembly is potentially necessary for correct starch metabolism. Re-structuring of starch granules in-planta can therefore potentially affect plant metabolism. Modulation of granule micro-structure was achieved by decreasing starch branching and increasing starch-bound phosphate content in the barley caryopsis starch by RNAi suppression of all three Starch Branching Enzyme (SBE) isoforms or overexpression of potato Glucan Water Dikinase (GWD). The resulting lines displayed Amylose-Only (AO) and Hyper-Phosphorylated (HP) starch chemotypes, respectively. We studied the influence of these alterations on primary metabolism, grain composition, starch structural features and starch granule morphology over caryopsis development at 10, 20 and 30 days after pollination (DAP) and at grain maturity. While HP showed relatively little effect, AO showed significant reduction in starch accumulation with re-direction to protein and ß-glucan (BG) accumulation. Metabolite profiling indicated significantly higher sugar accumulation in AO, with re-partitioning of carbon to accumulate amino acids, and interestingly it also had high levels of some important stress-related metabolites and potentially protective metabolites, possibly to elude deleterious effects. Investigations on starch molecular structure revealed significant increase in starch phosphate and amylose content in HP and AO respectively with obvious differences in starch granule morphology at maturity. The results demonstrate that decreasing the storage starch branching resulted in metabolic adjustments and re-directions, tuning to evade deleterious effects on caryopsis physiology and plant performance while only little effect was evident by increasing starch-bound phosphate as a result of overexpressing GWD.

Metabolomics-assisted refinement of the pathways of steroidal glycoalkaloid biosynthesis in the tomato clade.

Steroidal glycoalkaloids (SGAs) are nitrogen-containing secondary metabolites of the Solanum species, which are known to have large chemical and bioactive diversity in nature. While recent effort and development on LC/MS techniques for SGA profiling have elucidated the main pathways of SGA metabolism in tomato, the problem of peak annotation still remains due to the vast diversity of chemical structure and similar on overlapping of chemical formula. Here we provide a case study of peak classification and annotation approach by integration of species and tissue specificities of SGA accumulation for provision of comprehensive pathways of SGA biosynthesis. In order to elucidate natural diversity of SGA biosynthesis, a total of 169 putative SGAs found in eight tomato accessions (Solanum lycopersicum, S. pimpinellifolium, S. cheesmaniae, S. chmielewskii, S. neorickii, S. peruvianum, S. habrochaites, S. pennellii) and four tissue types were used for correlation analysis. The results obtained in this study contribute annotation and classification of SGAs as well as detecting putative novel biosynthetic branch points. As such this represents a novel strategy for peak annotation for plant secondary metabolites.