Network-based prediction of metabolic enzymes' subcellular localization

Motivation: Revealing the subcellular localization of proteins within membrane-bound compartments is of a major importance for inferring protein function. Though current high-throughput localization experiments provide valuable data, they are costly and time-consuming, and due to technical difficulties not readily applicable for many Eukaryotes. Physical characteristics of proteins, such as sequence targeting signals and amino acid composition are commonly used to predict subcellular localizations using computational approaches. Recently it was shown that protein–protein interaction (PPI) networks can be used to significantly improve the prediction accuracy of protein subcellular localization. However, as high-throughput PPI data depend on costly high-throughput experiments and are currently available for only a few organisms, the scope of such methods is yet limited

Functional characterization of hexokinases in the moss Physcomitrella patens

Carbohydrates are important nutrients and structural components in all living organisms. In plants they affect the developmental and metabolic processes throughout the plant life cycle. However, the mechanisms by which plants recognise and respond to carbohydrates are mainly unknown. Hexokinase, an enzyme that mediates the first catalytic step in hexose metabolism, has recently been suggested to be involved in sugar sensing and signalling in plants. The moss Physcomitrella patens has recently emerged as a powerful model system in plant functional genomics following the discovery that gene targeting works in it with frequencies comparable to those in yeast. The aim of this thesis was to learn more about the function of plant hexokinases, both as key metabolic enzymes and in their putative role as sensors in sugar signalling, using Physcomitrella patens as a model system. Five hexokinases from Physcomitrella patens were cloned and studied with respect to their subcellular localizations. PpHxk1 and PpHxk5 are located in the stroma of chloroplasts and are dependent on N-terminal transit peptides for correct localization. PpHxk2 and PpHxk3 both contain hydrophobic membrane anchors that localize the proteins to the outer envelope of chloroplasts. PpHxk4 contain neither a transit peptide nor an anchor, and is found in the cytosol. A targeted knockout revealed that PpHxk1 is the major hexokinase in Physcomitrella, accounting for 80% of the glucose phosphorylating activity. Consistent with this, the knockout mutant exhibits an artificial starvation phenotype with altered sensitivities to plant hormones and a disturbed development

Reciprocal regulation of protein synthesis and carbon metabolism for thylakoid membrane biogenesis

Metabolic control of gene expression coordinates the levels of specific gene products to meet cellular demand for their activities. This control can be exerted by metabolites acting as regulatory signals and/or a class of metabolic enzymes with dual functions as regulators of gene expression. However, little is known about how metabolic signals affect the balance between enzymatic and regulatory roles of these dual functional proteins. We previously described the RNA binding activity of a 63 kDa chloroplast protein from Chlamydomonas reinhardtii, which has been implicated in expression of the psbA mRNA, encoding the D1 protein of photosystem II. Here, we identify this factor as dihydrolipoamide acetyltransferase (DLA2), a subunit of the chloroplast pyruvate dehydrogenase complex (cpPDC), which is known to provide acetyl-CoA for fatty acid synthesis. Analyses of RNAi lines revealed that DLA2 is involved in the synthesis of both D1 and acetyl-CoA. Gel filtration analyses demonstrated an RNP complex containing DLA2 and the chloroplast psbA mRNA specifically in cells metabolizing acetate. An intrinsic RNA binding activity of DLA2 was confirmed by in vitro RNA binding assays. Results of fluorescence microscopy and subcellular fractionation experiments support a role of DLA2 in acetate-dependent localization of the psbA mRNA to a translation zone within the chloroplast. Reciprocally, the activity of the cpPDC was specifically affected by binding of psbA mRNA. Beyond that, in silico analysis and in vitro RNA binding studies using recombinant proteins support the possibility that RNA binding is an ancient feature of dihydrolipoamide acetyltransferases. Our results suggest a regulatory function of DLA2 in response to growth on reduced carbon energy sources. This raises the intriguing possibility that this regulation functions to coordinate the synthesis of lipids and proteins for the biogenesis of photosynthetic membranes

Studies of molecular mechanisms integrating carbon metabolism and growth in plants

Plants use light energy, carbon dioxide and water to produce sugars and other carbohydrates, which serve as stored energy reserves and as building blocks for biosynthetic reactions. Supply of light is variable and plants have evolved means to adjust their growth and development accordingly. An increasing body of evidence suggests that the basic mechanisms for sensing and signaling energy availability in eukaryotes are evolutionary conserved and thus shared between plants, animals and fungi. I have used different experimental approaches that take advantage of findings from other eukaryotes in studying carbon and energy metabolism in plants. In the first part, I developed a novel screening procedure in yeast aimed at isolating cDNAs from other organisms encoding proteins with a possible function in sugar sensing or signaling. The feasibility of the method was confirmed by the cloning of a cDNA from Arabidopsis thaliana encoding a new F-box protein named AtGrh1, which is related to the yeast Grr1 protein that is involved in glucose repression. In the second part of the study, plant homologues of key components in the yeast glucose repression pathway were cloned and characterized in the moss Physcomitrella patens, in which gene function can be studied by gene targeting. We first cloned PpHXK1 which was shown to encode a chloroplast localized hexokinase representing a previously overlooked class of plant hexokinases with an N-terminal chloroplast transit peptide. Significantly, PpHxk1 is the major hexokinase in Physcomitrella, accounting for 80% of the glucose phosphorylating activity. A knockout mutant deleted for PpHXK1 exhibits a complex phenotype affecting growth, development and sensitivities to plant hormones. I also cloned and characterized two closely related Physcomitrella genes, PpSNF1a and PpSNF1b, encoding type 1 Snf1-related kinases. A double knockout mutant for these genes was viable even though it lacks detectable Snf1-like kinase activity. The mutant suffers from pleiotropic phenotypes which may reflect a constitutive high energy growth mode. Significantly, the double mutant requires constant high light and is therefore unable to grow in a normal day/night light cycle. These findings are consistent with the proposed role of the Snf1-related kinases as energy gauges which are needed to recognize and respond to low energy conditions

Metric learning pairwise kernel for graph inference

Much recent work in bioinformatics has focused on the inference of various types of biological networks, representing gene regulation, metabolic processes, protein-protein interactions, etc. A common setting involves inferring network edges in a supervised fashion from a set of high-confidence edges, possibly characterized by multiple, heterogeneous data sets (protein sequence, gene expression, etc.). Here, we distinguish between two modes of inference in this setting: direct inference based upon similarities between nodes joined by an edge, and indirect inference based upon similarities between one pair of nodes and another pair of nodes. We propose a supervised approach for the direct case by translating it into a distance metric learning problem. A relaxation of the resulting convex optimization problem leads to the support vector machine (SVM) algorithm with a particular kernel for pairs, which we call the metric learning pairwise kernel (MLPK). We demonstrate, using several real biological networks, that this direct approach often improves upon the state-of-the-art SVM for indirect inference with the tensor product pairwise kernel

Challenges in experimental data integration within genome-scale metabolic models

A report of the meeting "Challenges in experimental data integration within genome-scale metabolic models", Institut Henri Poincar\'e, Paris, October 10-11 2009, organized by the CNRS-MPG joint program in Systems Biology.Comment: 5 page

Genome-wide functional annotation and structural verification of metabolic ORFeome of Chlamydomonas reinhardtii

<p>Abstract</p> <p>Background</p> <p>Recent advances in the field of metabolic engineering have been expedited by the availability of genome sequences and metabolic modelling approaches. The complete sequencing of the <it>C. reinhardtii</it> genome has made this unicellular alga a good candidate for metabolic engineering studies; however, the annotation of the relevant genes has not been validated and the much-needed metabolic ORFeome is currently unavailable. We describe our efforts on the functional annotation of the ORF models released by the Joint Genome Institute (JGI), prediction of their subcellular localizations, and experimental verification of their structural annotation at the genome scale.</p> <p>Results</p> <p>We assigned enzymatic functions to the translated JGI ORF models of <it>C. reinhardtii</it> by reciprocal BLAST searches of the putative proteome against the UniProt and AraCyc enzyme databases. The best match for each translated ORF was identified and the EC numbers were transferred onto the ORF models. Enzymatic functional assignment was extended to the paralogs of the ORFs by clustering ORFs using BLASTCLUST.</p> <p>In total, we assigned 911 enzymatic functions, including 886 EC numbers, to 1,427 transcripts. We further annotated the enzymatic ORFs by prediction of their subcellular localization. The majority of the ORFs are predicted to be compartmentalized in the cytosol and chloroplast. We verified the structure of the metabolism-related ORF models by reverse transcription-PCR of the functionally annotated ORFs. Following amplification and cloning, we carried out 454FLX and Sanger sequencing of the ORFs. Based on alignment of the 454FLX reads to the ORF predicted sequences, we obtained more than 90% coverage for more than 80% of the ORFs. In total, 1,087 ORF models were verified by 454 and Sanger sequencing methods. We obtained expression evidence for 98% of the metabolic ORFs in the algal cells grown under constant light in the presence of acetate.</p> <p>Conclusions</p> <p>We functionally annotated approximately 1,400 JGI predicted metabolic ORFs that can facilitate the reconstruction and refinement of a genome-scale metabolic network. The unveiling of the metabolic potential of this organism, along with structural verification of the relevant ORFs, facilitates the selection of metabolic engineering targets with applications in bioenergy and biopharmaceuticals. The ORF clones are a resource for downstream studies.</p