The recent data flood has required greater and greater reliance on computational usage in plant biology. This Research Topic will focus on the utility of computational approaches across the breadth of modern plant biology with particular focus on the following areas: (i) Comparative genomics- gene family size in the green lineage (ii) Adaptive evolution - specifics of development (iii) Adaptive evolution - specifics of secondary metabolism (iv) Translational biology- co-response analysis from arabidopsis outwards (v) Conserved(and differential) transcriptional response to stress (vi) Transcriptomics databases (vii) Translatomics (ix) Proteomics- abundance (x) Proteomics- location, (xi) Proteomics- interactions (xii) Proteomics databases (xiii) he activome (xiv)Metabolite-abundance (xv) metabolite- location (xvi) Experimental flux calculations, (xvii)dvanced metabolomic technologies (xviii) metabolite databases (xix) Genome wide metabolic modelling

Max Planck Institute of Molecular Plant Physiology

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Frontiers in Plant Science

Plant Physiology

Frontiers in Physiology

Computational approaches in aid of advancing understanding in Plant Physiology

Evidence that adult humans have functional brown adipose tissue has stirred interest in the possibility that the impressive effectiveness of induction of brown adipocytes to reduce obesity in mice may be translated to the human condition. A major focus recently on the identification of signaling and transcription factor that stimulate the induction of brown adipocytes has come from transgenic and gene KO models. However, these models have created a very complex picture of the regulatory mechanisms for brown fat induction. In this review insights into the critical regulatory pathways involved in brown adipocyte induction in the retroperitoneal fat depot of mice are described from quantitative trait locus (QTL) analysis of allelic variability determining Ucp1 levels and brown adipocyte induction in A/J vs. B6 mice. The key observation is that recombinant genotypes, found in recombinant inbred stains and backcross and intercross progeny, show transgressive variation for Ucp1 mRNA levels. These genetic crosses also show that the levels of Ucp1 mRNA are determined by interactions that control the levels of PPARα, PGC-1α, and type 2 deiodinase (DIO2) and that each factor is controlled by a subset of QTLs that also control Ucp1 expression. These results indicate that induction of Ucp1 in the retroperitoneal fat depot involves synergy between signaling and transcription factors that vary depending upon the environmental conditions. Inherent in this model is the idea that there is a high level of redundancy that can involve any factor with the potential to influence expression of the core factors, PPARα, PGC-1a, and DIO2.

The Genetics of Brown Adipocyte Induction in White Fat Depots

Holistic analysis of lipids is becoming increasingly popular in the life sciences. Recently, several interesting, mass spectrometry-based studies have been conducted, especially in plant biology. However, while great advancements have been made we are still far from detecting all the lipids species in an organism. In this study we developed an ultra performance liquid chromatography-based method using a high resolution, accurate mass, mass spectrometer for the comprehensive profiling of more than 260 polar and non-polar Arabidopsis thaliana leaf lipids. The method is fully compatible to the commonly used lipid extraction protocols and provides a viable alternative to the commonly used direct infusion-based shotgun lipidomics approaches. The whole process is described in detail and compared to alternative lipidomic approaches. Next to the developed method we also introduce an in-house developed database search software (GoBioSpace), which allows one to perform targeted or un-targeted lipidomic and metabolomic analysis on mass spectrometric data of every kind.

Holistic analysis of lipids is becoming increasingly popular in the life sciences. Recently, several interesting, mass spectrometry-based studies have been conducted, especially in plant biology. However, while great advancements have been made we are still far from detecting all the lipids species in an organism. In this study we developed an ultra performance liquid chromatography-based method using a high resolution, accurate mass, mass spectrometer for the comprehensive profiling of more than 260 polar and non-polar Arabidopsis thaliana leaf lipids. The method is fully compatible to the commonly used lipid extraction protocols and provides a viable alternative to the commonly used direct infusion-based shotgun lipidomics approaches. The whole process is described in detail and compared to alternative lipidomic approaches. Next to the developed method we also introduce an in-house developed database search software (GoBioSpace), which allows one to perform targeted or un-targeted lipidomic and metabolomic analysis on mass spectrometric data of every kind.

Ultra Performance Liquid Chromatography and High Resolution Mass Spectrometry for the Analysis of Plant Lipids

Plant cell walls are complex composites largely consisting of carbohydrate-based polymers, and are generally divided into primary and secondary walls based on content and characteristics. Cellulose microfibrils constitute a major component of both primary and secondary cell walls and are synthesized at the plasma membrane by cellulose synthase (CESA) complexes. Several studies in Arabidopsis have demonstrated the power of co-expression analyses to identify new genes associated with secondary wall cellulose biosynthesis. However, across-species comparative co-expression analyses remain largely unexplored. Here, we compared co-expressed gene vicinity networks of primary and secondary wall CESAsin Arabidopsis, barley, rice, poplar, soybean, Medicago, and wheat, and identified gene families that are consistently co-regulated with cellulose biosynthesis. In addition to the expected polysaccharide acting enzymes, we also found many gene families associated with cytoskeleton, signaling, transcriptional regulation, oxidation, and protein degradation. Based on these analyses, we selected and biochemically analyzed T-DNA insertion lines corresponding to approximately twenty genes from gene families that re-occur in the co-expressed gene vicinity networks of secondary wall CESAs across the seven species. We developed a statistical pipeline using principal component analysis and optimal clustering based on silhouette width to analyze sugar profiles. One of the mutants, corresponding to a pinoresinol reductase gene, displayed disturbed xylem morphology and held lower levels of lignin molecules. We propose that this type of large-scale co-expression approach, coupled with statistical analysis of the cell wall contents, will be useful to facilitate rapid knowledge transfer across plant species.

Plant cell walls are complex composites largely consisting of carbohydrate-based polymers, and are generally divided into primary and secondary walls based on content and characteristics. Cellulose microfibrils constitute a major component of both primary and secondary cell walls and are synthesized at the plasma membrane by cellulose synthase (CESA) complexes. Several studies in Arabidopsis have demonstrated the power of co-expression analyses to identify new genes associated with secondary wall cellulose biosynthesis. However, across-species comparative co-expression analyses remain largely unexplored. Here, we compared co-expressed gene vicinity networks of primary and secondary wall CESAsin Arabidopsis, barley, rice, poplar, soybean, Medicago, and wheat, and identified gene families that are consistently co-regulated with cellulose biosynthesis. In addition to the expected polysaccharide acting enzymes, we also found many gene families associated with cytoskeleton, signaling, transcriptional regulation, oxidation, and protein degradation. Based on these analyses, we selected and biochemically analyzed T-DNA insertion lines corresponding to approximately twenty genes from gene families that re-occur in the co-expressed gene vicinity networks of secondary wall CESAs across the seven species. We developed a statistical pipeline using principal component analysis and optimal clustering based on silhouette width to analyze sugar profiles. One of the mutants, corresponding to a pinoresinol reductase gene, displayed disturbed xylem morphology and held lower levels of lignin molecules. We propose that this type of large-scale co-expression approach, coupled with statistical analysis of the cell wall contents, will be useful to facilitate rapid knowledge transfer across plant species.