The gaseous plant hormone ethylene is involved in many physiological processes including climacteric fruit ripening, in which it is a key determinant of fruit quality. A detailed model that describes ethylene biochemistry dynamics is missing. Often, kinetic modeling is used to describe metabolic networks or signaling cascades, mostly ignoring the link with transcriptomic data.We have constructed an elegant kinetic model that describes the transfer of genetic information into abundance and metabolic activity of proteins for the entire ethylene biosynthesis pathway during fruit development and ripening of tomato (Solanum lycopersicum).Our model was calibrated against a vast amount of transcriptomic, proteomic and metabolic data and showed good descriptive qualities. Subsequently it was validated successfully against several ripening mutants previously described in the literature. The model was used as a predictive tool to evaluate novel and existing hypotheses regarding the regulation of ethylene biosynthesis.This bottom-up kinetic network model was used to indicate that a side-branch of the ethylene pathway, the formation of the dead-end product 1-(malonylamino)-1-aminocyclopropane-1-carboxylic acid (MACC), might have a strong effect on eventual ethylene production. Furthermore, our in silico analyses indicated potential (post-) translational regulation of the ethylene-forming enzyme ACC oxidase.
C4 photosynthesis has higher light, nitrogen, and water use efficiencies than C3 photosynthesis. Although the basic anatomical, cellular, and biochemical features of C4 photosynthesis are well understood, the quantitative significance of each element of C4 photosynthesis to the high photosynthetic efficiency are not well defined. Here, we addressed this question by developing and using a systems model of C4 photosynthesis, which includes not only the Calvin-Benson cycle, starch synthesis, sucrose synthesis, C4 shuttle, and CO2 leakage, but also photorespiration and metabolite transport between the bundle sheath cells and mesophyll cells. The model effectively simulated the CO2 uptake rates, and the changes of metabolite concentrations under varied CO2 and light levels. Analyses show that triose phosphate transport and CO2 leakage can help maintain a high photosynthetic rate by balancing ATP and NADPH amounts in bundle sheath cells and mesophyll cells. Finally, we used the model to define the optimal enzyme properties and a blueprint for C4 engineering. As such, this model provides a theoretical framework for guiding C4 engineering and studying C4 photosynthesis in general.
This paper demonstrates that CRISPR/Cas gene editing can be heritable and is highly specific in plants with no off-target effects detected by deep sequencing. A useful tool for modifying gene function.
The authors carried out detailed analysis of the components of a key transcriptional complex regulating anthocyanin production in Petunia. The resulting gene regulatory model captures the network hierarchy, transcriptional activation and repression as well as feedforward and feedback mechanisms. Fundamental features of the network are conserved in Arabidopsis and it provides a framework for understanding the specifics of anthocyanin pigmentation in response to particular developmental or environmental cues.
Wigwams is a simple and efficient method to identify gene modules showing evidence for co-regulation in multiple time series of gene expression data. Wigwams analyzes similarities of gene expression patterns within each time series (condition) and directly tests the dependence or independence of these across different conditions. The expression pattern of each gene in each subset of conditions is tested statistically as a potential signature of a condition-dependent regulatory mechanism regulating multiple genes. Wigwams does not require particular time points and can process datasets that are on different time scales. Differential expression relative to control conditions can be taken into account. The output is succinct and non-redundant, enabling gene network reconstruction to be focused on those gene modules and combinations of conditions that show evidence for shared regulatory mechanisms. Wigwams was run using six Arabidopsis time series expression datasets, producing a set of biologically significant modules spanning different combinations of conditions.
Katherine Denby's insight:
A useful tool for identifying statistically significant sets of genes likely to be co-regulated across multiple time series data sets.
Abiotic and biotic stress responses are traditionally thought to be regulated by discrete signaling mechanisms. Recent experimental evidence revealed a more complex picture where these mechanisms are highly entangled and can have synergistic and antagonistic effects on each other. In this study, we identified shared stress-responsive genes between abiotic and biotic stresses in rice (Oryza sativa) by performing meta-analyses of microarray studies. About 70% of the 1,377 common differentially expressed genes showed conserved expression status, and the majority of the rest were down-regulated in abiotic stresses and up-regulated in biotic stresses. Using dimension reduction techniques, principal component analysis, and partial least squares discriminant analysis, we were able to segregate abiotic and biotic stresses into separate entities. The supervised machine learning model, recursive-support vector machine, could classify abiotic and biotic stresses with 100% accuracy using a subset of differentially expressed genes. Furthermore, using a random forests decision tree model, eight out of 10 stress conditions were classified with high accuracy. Comparison of genes contributing most to the accurate classification by partial least squares discriminant analysis, recursive-support vector machine, and random forests revealed 196 common genes with a dynamic range of expression levels in multiple stresses. Functional enrichment and coexpression network analysis revealed the different roles of transcription factors and genes responding to phytohormones or modulating hormone levels in the regulation of stress responses. We envisage the top-ranked genes identified in this study, which highly discriminate abiotic and biotic stresses, as key components to further our understanding of the inherently complex nature of multiple stress responses in plants.
Studying the control properties of complex networks provides insight into how designers and engineers can influence these systems to achieve a desired behavior. Topology of a network has been shown to strongly correlate with certain control properties; here we uncover the fundamental structures that explain the basis of this correlation. We develop the control profile, a statistic that quantifies the different proportions of control-inducing structures present in a network. We find that standard random network models do not reproduce the kinds of control profiles that are observed in real-world networks. The profiles of real networks form three well-defined clusters that provide insight into the high-level organization and function of complex systems.