Kris Laukens

Gradual Soil Water Depletion Results in Reversible Changes of Gene Expression, Protein Profiles, Ecophysiology, and Growth Performance in Populus euphratica , a Poplar Growing in Arid Regions

The responses of Populus euphratica Oliv. plants to soil water deficit were assessed by analyzing gene expression, protein profiles, and several plant performance criteria to understand the acclimation of plants to soil water deficit. Young, vegetatively propagated plants originating from an arid, saline field site were submitted to a gradually increasing water deficit for 4 weeks in a greenhouse and were allowed to recover for 10 d after full reirrigation. Time-dependent changes and intensity of the perturbations induced in shoot and root growth, xylem anatomy, gas exchange, and water status were recorded. The expression profiles of approximately 6,340 genes and of proteins and metabolites (pigments, soluble carbohydrates, and oxidative compounds) were also recorded in mature leaves and in roots (gene expression only) at four stress levels and after recovery. Drought successively induced shoot growth cessation, stomatal closure, moderate increases in oxidative stress-related compounds, loss of CO2 assimilation, and root growth reduction. These effects were almost fully reversible, indicating that acclimation was dominant over injury. The physiological responses were paralleled by fully reversible transcriptional changes, including only 1.5% of the genes on the array. Protein profiles displayed greater changes than transcript levels. Among the identified proteins for which expressed sequence tags were present on the array, no correlation was found between transcript and protein abundance. Acclimation to water deficit involves the regulation of different networks of genes in roots and shoots. Such diverse requirements for protecting and maintaining the function of different plant organs may render plant engineering or breeding toward improved drought tolerance more complex than previously anticipated.

Targeted interactomics reveals a complex core cell cycle machinery in Arabidopsis thaliana.

Cell proliferation is the main driving force for plant growth. Although genome sequence analysis revealed a high number of cell cycle genes in plants, little is known about the molecular complexes steering cell division. In a targeted proteomics approach, we mapped the core complex machinery at the heart of the Arabidopsis thaliana cell cycle control. Besides a central regulatory network of core complexes, we distinguished a peripheral network that links the core machinery to up‐ and downstream pathways. Over 100 new candidate cell cycle proteins were predicted and an in‐depth biological interpretation demonstrated the hypothesis‐generating power of the interaction data. The data set provided a comprehensive view on heterodimeric cyclin‐dependent kinase (CDK)–cyclin complexes in plants. For the first time, inhibitory proteins of plant‐specific B‐type CDKs were discovered and the anaphase‐promoting complex was characterized and extended. Important conclusions were that mitotic A‐ and B‐type cyclins form complexes with the plant‐specific B‐type CDKs and not with CDKA;1, and that D‐type cyclins and S‐phase‐specific A‐type cyclins seem to be associated exclusively with CDKA;1. Furthermore, we could show that plants have evolved a combinatorial toolkit consisting of at least 92 different CDK–cyclin complex variants, which strongly underscores the functional diversification among the large family of cyclins and reflects the pivotal role of cell cycle regulation in the developmental plasticity of plants.

A Tandem Affinity Purification-based Technology Platform to Study the Cell Cycle Interactome in Arabidopsis thaliana

Defining protein complexes is critical to virtually all aspects of cell biology because many cellular processes are regulated by stable protein complexes, and their identification often provides insights into their function. We describe the development and application of a high throughput tandem affinity purification/mass spectrometry platform for cell suspension cultures to analyze cell cycle-related protein complexes in Arabidopsis thaliana. Elucidation of this protein-protein interaction network is essential to fully understand the functional differences between the highly redundant cyclin-dependent kinase/cyclin modules, which are generally accepted to play a central role in cell cycle control, in all eukaryotes. Cell suspension cultures were chosen because they provide an unlimited supply of protein extracts of actively dividing and undifferentiated cells, which is crucial for a systematic study of the cell cycle interactome in the absence of plant development. Here we report the mapping of a protein interaction network around six known core cell cycle proteins by an integrated approach comprising generic Gateway-based vectors with high cloning flexibility, the fast generation of transgenic suspension cultures, tandem affinity purification adapted for plant cells, matrix-assisted laser desorption ionization tandem mass spectrometry, data analysis, and functional assays. We identified 28 new molecular associations and confirmed 14 previously described interactions. This systemic approach provides new insights into the basic cell cycle control mechanisms and is generally applicable to other pathways in plants.

PROTEOME ANALYSIS OF NON-MODEL PLANTS: A CHALLENGING BUT POWERFUL APPROACH

Biological research has focused in the past on model organisms and most of the functional genomics studies in the field of plant sciences are still performed on model species or species that are characterized to a great extent. However, numerous non-model plants are essential as food, feed, or energy resource. Some features and processes are unique to these plant species or families and cannot be approached via a model plant. The power of all proteomic and transcriptomic methods, that is, high-throughput identification of candidate gene products, tends to be lost in non-model species due to the lack of genomic information or due to the sequence divergence to a related model organism. Nevertheless, a proteomics approach has a great potential to study non-model species. This work reviews non-model plants from a proteomic angle and provides an outline of the problems encountered when initiating the proteome analysis of a non-model organism. The review tackles problems associated with (i) sample preparation, (ii) the analysis and interpretation of a complex data set, (iii) the protein identification via MS, and (iv) data management and integration. We will illustrate the power of 2DE for non-model plants in combination with multivariate data analysis and MS/MS identification and will evaluate possible alternatives.

A Hormone and Proteome Approach to Picturing the Initial Metabolic Events During Plasmodiophora brassicae Infection on Arabidopsis

We report on the early response of Arabidopsis thaliana to the obligate biotrophic pathogen Plasmodiophora brassicae at the hormone and proteome level. Using a CYCB1;1::GUS construct, the re-initiation of infection-related cell division is shown from 4 days after inoculation on. Sensitivity to cytokinins and auxins as well as the endogenous hormone levels are evaluated. Both an enhanced cytokinin gene response and an accumulation of isopentenyl adenine and adenosine precede this re-initiation of cell division, whereas an enhanced auxin gene response is observed from 6 days after inoculation on. The alhl mutant, impaired in the cross talk between ethylene and auxins, is resistant to P. brassicae. A differential protein analysis of infected versus noninfected roots and hypocotyls was performed using two-dimensional gel electrophoresis and quantitative image analysis, coupled to matrix-assisted laser desorption ionization time of flight-time of flight mass spectrometry-based protein identification. Of the visualized proteins, 12% show altered abundance compared with the noninfected plants, including proteins involved in metabolism, cell defense, cell differentiation, and detoxification. Combining the hormone and proteome data, we postulate that, at the very first stages of Plasmodiophora infection, plasmodial-produced cytokinins trigger a local re-initiation of cell division in the root cortex. Consequently, a de novo meristematic area is established that acts as a sink for host-derived indole-3-acetic acid, carbohydrates, nitrogen, and energy to maintain the pathogen and to trigger gall development.

1H NMR based metabolomics of CSF and blood serum: A metabolic profile for a transgenic rat model of Huntington disease

Huntington disease (HD) is a hereditary brain disease. Although the causative gene has been found, the exact mechanisms of the pathogenesis are still unknown. Recent investigations point to metabolic and energetic dysfunctions in HD neurons. Both univariate and multivariate analyses were used to compare proton nuclear magnetic resonance spectra of serum and cerebrospinal fluid (CSF) taken from presymptomatic HD transgenic rats and their wild-type littermates. N-acetylaspartate (NAA), was found to be significantly decreased in the serum of HD rats compared to wild-type littermates. Moreover, in the serum their levels of glutamine, succinic acid, glucose and lactate are significantly increased as well. An increased concentration of lactate and glucose is also found in CSF. There is a 1:1 stoichiometry coupling glucose utilization and glutamate cycling. The observed increase in the glutamine concentration, which indicates a shutdown in the neuronal-glial glutamate-glutamine cycling, results therefore in an increased glucose concentration. The elevated succinic acid concentration might be due to an inhibition of succinate dehydrogenase, an enzyme linked to the mitochondrial respiratory chain and TCA cycle. Moreover, reduced levels of NAA may reflect an impairment of mitochondrial energy production. In addition, the observed difference in lactate supports a deficiency of oxidative energy metabolism in rats transgenic for HD as well. The observed metabolic alterations seem to be more profound in serum than in CSF in presymptomatic rats. All findings suggest that even in presymptomatic rats, a defect in energy metabolism is already apparent. These results support the hypothesis of mitochondrial energy dysfunction in HD.

Prediction of kinase-specific phosphorylation sites using conditional random fields

Motivation: Phosphorylation is a crucial post-translational protein modification mechanism with important regulatory functions in biological systems. It is catalyzed by a group of enzymes called kinases, each of which recognizes certain target sites in its substrate proteins. Several authors have built computational models trained from sets of experimentally validated phosphorylation sites to predict these target sites for each given kinase. All of these models suffer from certain limitations, such as the fact that they do not take into account the dependencies between amino acid motifs within protein sequences in a global fashion. Results: We propose a novel approach to predict phosphorylation sites from the protein sequence. The method uses a positive dataset to train a conditional random field (CRF) model. The negative training dataset is used to specify the decision threshold corresponding to a desired false positive rate. Application of the method on experimentally verified benchmark phosphorylation data (Phospho.ELM) shows that it performs well compared to existing methods for most kinases. This is to our knowledge that the first report of the use of CRFs to predict post-translational modification sites in protein sequences. Availability: The source code of the implementation, called CRPhos, is available from http://www.ptools.ua.ac.be/CRPhos/ Contact: kris.laukens@ua.ac.be Suplementary Information: Supplementary data are available at http://www.ptools.ua.ac.be/CRPhos/

Functional genomics in a non-model crop: transcriptomics or proteomics?

There is no question that protein- and RNA-based measurements are complementary, but which approach has the highest return in the case of a non-model crop and what is the correlation between mRNA and proteins? We describe and evaluate in detail the advantages and pitfalls of both a proteomics and a transcriptomics approach. The information on the abundance of transcripts was obtained by serial analysis of gene expression (SAGE), while information on the abundance of proteins was obtained via two-dimensional gel electrophoresis.

A primer to frequent itemset mining for bioinformatics

Over the past two decades, pattern mining techniques have become an integral part of many bioinformatics solutions. Frequent itemset mining is a popular group of pattern mining techniques designed to identify elements that frequently co-occur. An archetypical example is the identification of products that often end up together in the same shopping basket in supermarket transactions. A number of algorithms have been developed to address variations of this computationally non-trivial problem. Frequent itemset mining techniques are able to efficiently capture the characteristics of (complex) data and succinctly summarize it. Owing to these and other interesting properties, these techniques have proven their value in biological data analysis. Nevertheless, information about the bioinformatics applications of these techniques remains scattered. In this primer, we introduce frequent itemset mining and their derived association rules for life scientists. We give an overview of various algorithms, and illustrate how they can be used in several real-life bioinformatics application domains. We end with a discussion of the future potential and open challenges for frequent itemset mining in the life sciences.

Use of structural DNA properties for the prediction of transcription-factor binding sites in Escherichia coli

Recognition of genomic binding sites by transcription factors can occur through base-specific recognition, or by recognition of variations within the structure of the DNA macromolecule. In this article, we investigate what information can be retrieved from local DNA structural properties that is relevant to transcription factor binding and that cannot be captured by the nucleotide sequence alone. More specifically, we explore the benefit of employing the structural characteristics of DNA to create binding-site models that encompass indirect recognition for the Escherichia coli model organism. We developed a novel methodology [Conditional Random fields of Smoothed Structural Data (CRoSSeD)], based on structural scales and conditional random fields to model and predict regulator binding sites. The value of relying on local structural-DNA properties is demonstrated by improved classifier performance on a large number of biological datasets, and by the detection of novel binding sites which could be validated by independent data sources, and which could not be identified using sequence data alone. We further show that the CRoSSeD-binding-site models can be related to the actual molecular mechanisms of the transcription factor DNA binding, and thus cannot only be used for prediction of novel sites, but might also give valuable insights into unknown binding mechanisms of transcription factors.