Biomedical subjects

Janick Mathys

Publications and source records attributed to Janick Mathys.

8 recordsLinked to original sources

PubMed2026

Revealing Hidden Variables in DESI-Based Spatial Metabolomics: Solvent Composition and Tissue Type as Critical Drivers.

In the development of a desorption electrospray ionization (DESI) workflow for spatial metabolomics, we investigated the impact of two commonly used solvent systems, 90% acetonitrile (ACN) and 90% methanol (MeOH), on the spatial metabolomic profiling of various murine tissues. The performance of both solvents was evaluated across several metabolite classes (central carbon metabolites, amino acids, and fatty acids). Although the ACN-based solvent system led to higher signal intensities for small polar metabolites involved in glycolysis, the tricarboxylic acid (TCA) cycle, and amino acid metabolism, the MeOH-based solvent system provided superior signal intensities for fatty acids. These findings demonstrate that the solvent composition differentially influences metabolite extraction and ionization processes in DESI and should be carefully matched to the biological questions and metabolite classes of interest. As a proof-of-principle, the ACN solvent system was applied to a pilot study based on a rat model of renal ischemic injury, revealing region-specific metabolic changes between normoxic and ischemic conditions. Together, these results demonstrate the importance of solvent selection in DESI-based spatial metabolomics and showcase the ability of this approach to uncover spatially resolved metabolic adaptations associated with tissue injury.

Animals↗

PubMed2005

PKA and Sch9 control a molecular switch important for the proper adaptation to nutrient availability.

In the yeast Saccharomyces cerevisiae, PKA and Sch9 exert similar physiological roles in response to nutrient availability. However, their functional redundancy complicates to distinguish properly the target genes for both kinases. In this article, we analysed different phenotypic read-outs. The data unequivocally showed that both kinases act through separate signalling cascades. In addition, genome-wide expression analysis under conditions and with strains in which either PKA and/or Sch9 signalling was specifically affected, demonstrated that both kinases synergistically or oppositely regulate given gene targets. Unlike PKA, which negatively regulates stress-responsive element (STRE)- and post-diauxic shift (PDS)-driven gene expression, Sch9 appears to exert additional positive control on the Rim15-effector Gis1 to regulate PDS-driven gene expression. The data presented are consistent with a cyclic AMP (cAMP)-gating phenomenon recognized in higher eukaryotes consisting of a main gatekeeper, the protein kinase PKA, switching on or off the activities and signals transmitted through primary pathways such as, in case of yeast, the Sch9-controlled signalling route. This mechanism allows fine-tuning various nutritional responses in yeast cells, allowing them to adapt metabolism and growth appropriately.

Adaptation, Physiological↗

PubMed2004

TXTGate: profiling gene groups with text-based information.

We implemented a framework called TXTGate that combines literature indices of selected public biological resources in a flexible text-mining system designed towards the analysis of groups of genes. By means of tailored vocabularies, term- as well as gene-centric views are offered on selected textual fields and MEDLINE abstracts used in LocusLink and the Saccharomyces Genome Database. Subclustering and links to external resources allow for in-depth analysis of the resulting term profiles.

Animals↗

PubMed2004

Annotation of the pRhico plasmid of Azospirillum brasilense reveals its role in determining the outer surface composition.

The plant growth-promoting soil bacterium Azospirillum brasilense enhances growth of economically important crops, such as wheat, corn and rice. In order to improve plant growth, a close bacterial association with the plant roots is needed. Genes encoded on a 90-MDa plasmid, denoted pRhico plasmid, present in A. brasilense Sp7, play an important role in plant root interaction. Sequencing, annotation and in silico analysis of this 90-MDa plasmid revealed the presence of a large collection of genes encoding enzymes involved in surface polysaccharide biosynthesis. Analysis of the 90-MDa plasmid genome provided evidence for its essential role in the viability of the bacterial cell.

Azospirillum brasilense↗

PubMed2004

Microarray screening for target genes of the proto-oncogene PLAG1.

PLAG1 is a proto-oncogene whose ectopic expression can trigger the development of pleomorphic adenomas of the salivary glands and of lipoblastomas. As PLAG1 is a transcription factor, able to activate transcription through the binding to the consensus sequence GRGGC(N)(6-8)GGG, its ectopic expression presumably results in the deregulation of target genes, leading to uncontrolled cell proliferation. The identification of PLAG1 target genes is therefore a crucial step in understanding the molecular mechanisms involved in PLAG1-induced tumorigenesis. To this end, we analysed the changes in gene expression caused by the conditional induction of PLAG1 expression in fetal kidney 293 cell lines. Using oligonucleotide microarray analyses of about 12 000 genes, we consistently identified 47 genes induced and 12 genes repressed by PLAG1. One of the largest classes identified as upregulated PLAG1 targets consists of growth factors such as the insulin-like growth factor II and the cytokine-like factor 1. The in silico search for PLAG1 consensus sequences in the promoter of the upregulated genes reveals that a large proportion of them harbor several copies of the PLAG1-binding motif, suggesting that they represent direct PLAG1 targets. Our approach was complemented by the comparison of the expression profiles of pleomorphic adenomas induced by PLAG1 versus normal salivary glands. Concordance between these two sets of experiments pinpointed 12 genes that were significantly and consistently upregulated in pleomorphic adenomas and in PLAG1-expressing cells, identifying them as putative PLAG1 targets in these tumors.

Adenoma↗

PubMed2003

INCLUSive: A web portal and service registry for microarray and regulatory sequence analysis.

INCLUSive is a suite of algorithms and tools for the analysis of gene expression data and the discovery of cis-regulatory sequence elements. The tools allow normalization, filtering and clustering of microarray data, functional scoring of gene clusters, sequence retrieval, and detection of known and unknown regulatory elements using probabilistic sequence models and Gibbs sampling. All tools are available via different web pages and as web services. The web pages are connected and integrated to reflect a methodology and facilitate complex analysis using different tools. The web services can be invoked using standard SOAP messaging. Example clients are available for download to invoke the services from a remote computer or to be integrated with other applications. All services are catalogued and described in a web service registry. The INCLUSive web portal is available for academic purposes at http://www.esat.kuleuven.ac.be/inclusive.

Algorithms↗

PubMed2002

INCLUSive: integrated clustering, upstream sequence retrieval and motif sampling.

INCLUSive allows automatic multistep analysis of microarray data (clustering and motif finding). The clustering algorithm (adaptive quality-based clustering) groups together genes with highly similar expression profiles. The upstream sequences of the genes belonging to a cluster are automatically retrieved from GenBank and can be fed directly into Motif Sampler, a Gibbs sampling algorithm that retrieves statistically over-represented motifs in sets of sequences, in this case upstream regions of co-expressed genes.

Algorithms↗

PubMed2002

Adaptive quality-based clustering of gene expression profiles.

MOTIVATION: Microarray experiments generate a considerable amount of data, which analyzed properly help us gain a huge amount of biologically relevant information about the global cellular behaviour. Clustering (grouping genes with similar expression profiles) is one of the first steps in data analysis of high-throughput expression measurements. A number of clustering algorithms have proved useful to make sense of such data. These classical algorithms, though useful, suffer from several drawbacks (e.g. they require the predefinition of arbitrary parameters like the number of clusters; they force every gene into a cluster despite a low correlation with other cluster members). In the following we describe a novel adaptive quality-based clustering algorithm that tackles some of these drawbacks. RESULTS: We propose a heuristic iterative two-step algorithm: First, we find in the high-dimensional representation of the data a sphere where the "density" of expression profiles is locally maximal (based on a preliminary estimate of the radius of the cluster-quality-based approach). In a second step, we derive an optimal radius of the cluster (adaptive approach) so that only the significantly coexpressed genes are included in the cluster. This estimation is achieved by fitting a model to the data using an EM-algorithm. By inferring the radius from the data itself, the biologist is freed from finding an optimal value for this radius by trial-and-error. The computational complexity of this method is approximately linear in the number of gene expression profiles in the data set. Finally, our method is successfully validated using existing data sets. AVAILABILITY: http://www.esat.kuleuven.ac.be/~thijs/Work/Clustering.html

Algorithms↗