Arabidopsis GERANYLGERANYL DIPHOSPHATE SYNTHASE 11 is a hub isozyme required for the production of most photosynthesis-related isoprenoids.

Most plastid isoprenoids, including photosynthesis-related metabolites such as carotenoids and the side chain of chlorophylls, tocopherols (vitamin E), phylloquinones (vitamin K), and plastoquinones, derive from geranylgeranyl diphosphate (GGPP) synthesized by GGPP synthase (GGPPS) enzymes. Seven out of 10 functional GGPPS isozymes in Arabidopsis thaliana reside in plastids. We aimed to address the function of different GGPPS paralogues for plastid isoprenoid biosynthesis. We constructed a gene co-expression network (GCN) using GGPPS paralogues as guide genes and genes from the upstream and downstream pathways as query genes. Furthermore, knock-out and/or knock-down ggpps mutants were generated and their growth and metabolic phenotypes were analyzed. Also, interacting protein partners of GGPPS11 were searched for. Our data showed that GGPPS11, encoding the only plastid isozyme essential for plant development, functions as a hub gene among GGPPS paralogues and is required for the production of all major groups of plastid isoprenoids. Furthermore, we showed that the GGPPS11 protein physically interacts with enzymes that use GGPP for the production of carotenoids, chlorophylls, tocopherols, phylloquinone, and plastoquinone. GGPPS11 is a hub isozyme required for the production of most photosynthesis-related isoprenoids. Both gene co-expression and protein-protein interaction likely contribute to the channeling of GGPP by GGPPS11.

A flexible protocol for targeted gene co-expression network analysis.

The inference of gene co-expression networks is a valuable resource for novel hypotheses in experimental research. Routine high-throughput microarray transcript profiling experiments and the rapid development of next-generation sequencing (NGS) technologies generate a large amount of publicly available data, enabling in silico reconstruction of regulatory networks. Analysis of the transcriptome under various experimental conditions proved that genes with an overall similar expression pattern often have similar functions. Consistently, genes involved in the same metabolic pathway are found in co-expressed modules. In this chapter, we describe a detailed workflow for analyzing gene co-expression networks using large-scale gene expression data and explain critical steps from design and data analysis to prediction of functionally related modules. This protocol is platform independent and can be used for data generated by ATH1 arrays, tiling arrays, or RNA sequencing for any organism. The most important feature of this workflow is that it can infer statistically significant gene co-expression networks for any number of genes and transcriptome data sets and it does not involve any particular hardware requirements.

Controlling false positive selections in high-dimensional regression and causal inference.

Guarding against false positive selections is important in many applications. We discuss methods based on subsampling and sample splitting for controlling the expected number of false positives and assigning p-values. They are generic and especially useful for high-dimensional settings. We review encouraging results for regression, and we discuss new adaptations and remaining challenges for selecting relevant variables, based on observational data, having a causal or interventional effect on a response of interest.