Biomedical subjects

Tim Beissbarth

Publications and source records attributed to Tim Beissbarth.

8 recordsLinked to original sources

PubMed2006

Nedd4-WW domain-binding protein 5 (Ndfip1) is associated with neuronal survival after acute cortical brain injury.

Understanding the transcriptional response to neuronal injury after trauma is a necessary prelude to formulation of therapeutic strategies. We used Serial Analysis of Gene Expression (SAGE) to identify 50,000 sequence tags representing 18,000 expressed genes in the cortex 2 h after traumatic brain injury (TBI). A similar tag library was obtained from sham-operated cortex. The SAGE data were validated on biological replicates using quantitative real-time-PCR on multiple samples at 2, 6, 12, and 24 h after TBI. This analysis revealed that the vast majority of genes showed a downward trend in their pattern of expression over 24 h. This was confirmed for a subset of genes using in situ hybridization and immunocytochemistry on brain sections. Of the overexpressed genes in the trauma library, Nedd4-WW (neural precursor cell expressed, developmentally downregulated) domain-binding protein 5 (N4WBP5) (also known as Ndfip1) is strongly expressed in surviving neurons around the site of injury. Overexpression of N4WBP5 in cultured cortical neurons increased the number of surviving neurons after gene transfection and growth factor starvation compared with control transfections. These results identify N4WBP5 as a neuroprotective protein and, based on its known interaction with the ubiquitin ligase Nedd4, would suggest protein ubiquitination as a possible survival strategy in neuronal injury.

Acute Disease↗

PubMed2006

Interpreting experimental results using gene ontologies.

High-throughput experimental techniques, such as microarrays, produce large amounts of data and knowledge about gene expression levels. However, interpretation of these data and turning it into biologically meaningful knowledge can be challenging. Frequently the output of such an analysis is a list of significant genes or a ranked list of genes. In the case of DNA microarray studies, data analysis often leads to lists of hundreds of differentially expressed genes. Also, clustering of gene expression data may lead to clusters of tens to hundreds of genes. These data are of little use if one is not able to interpret the results in a biological context. The Gene Ontology Consortium provides a controlled vocabulary to annotate the biological knowledge we have or that is predicted for a given gene. The Gene Ontologies (GOs) are organized as a hierarchy of annotation terms that facilitate an analysis and interpretation at different levels. The top-level ontologies are molecular function, biological process, and cellular component. Several annotation databases for genes of different organisms exist. This chapter describes how to use GO in order to help biologically interpret the lists of genes resulting from high-throughput experiments. It describes some statistical methods to find significantly over- or underrepresented GO terms within a list of genes and describes some tools and how to use them in order to do such an analysis. This chapter focuses primarily on the tool GOstat (http://gostat.wehi.edu.au). Other tools exist that enable similar analyses, but are not described in detail here.

Animals↗

PubMed2005

A systematic approach for comprehensive T-cell epitope discovery using peptide libraries.

MOTIVATION: T-cell response to peptides bound on MHC Class I or Class II molecules is essential for immune recognition of pathogens. T-cells are activated by specific peptide epitopes that are determined within the antigen processing pathways and presented on the surface of other cells bound to MHC molecules. To determine which part of allergenic or pathogenic proteins can stimulate T-cells is important for the treatment of diseases. We sought to take advantage of the falling cost of synthetic, screening grade peptides, and devise a comprehensive, non-hypothesis-driven screen for T-cell epitopes. We were interested in the study of celiac disease (CD) and used the ELISPOT technique to perform a large number of T-cell assays. We therefore needed to compensate for the lack of statistical data analysis methods for ELISPOT assays. RESULTS: We describe a method to comprehensively screen for T-cell epitopes within a family or a group of proteins. We have implemented an algorithm to generate a set of unique short peptide sequences that incorporate all possible epitopes within a group of proteins. T-cell assays were performed in 96-well plates using the ELISPOT assay to screen for responses in CD patients against any epitopes in glutens. We describe a statistical model to fit the data and an Expectation Maximization algorithm to estimate the parameters of interest and analyze the resulting data. AVAILABILITY: Implementations of our algorithms in R or Perl are available at http://bioinf.wehi.edu.au/folders/immunol.

Algorithms↗

PubMed2004

A comparative analysis of transcribed genes in the mouse hypothalamus and neocortex reveals chromosomal clustering.

The hypothalamus and neocortex are subdivisions of the mammalian forebrain, and yet, they have vastly different evolutionary histories, cytoarchitecture, and biological functions. In an attempt to define these attributes in terms of their genetic activity, we have compared their genetic repertoires by using the Serial Analysis of Gene Expression database. From a comparison of 78,784 hypothalamus tags with 125,296 neocortical tags, we demonstrate that each structure possesses a different transcriptional profile in terms of gene ontological characteristics and expression levels. Despite its more recent evolutionary history, the neocortex has a more complex pattern of gene activity. Gene identities and levels of gene expression were mapped to their chromosomal positions by using in silico definition of GC-rich and GC-poor genome bands. This analysis shows contrasting views of gene activity on a genome scale that is unique to each brain substructure. We show that genes that are more highly expressed in one tissue tend to be clustered together on a chromosomal scale, further defining the genetic identity of either the hypothalamus or neocortex. We propose that physical proximity of coregulated genes may facilitate transcriptional access to the genetic substrates of evolutionary selection that ultimately shape the functional subdivisions of the mammalian brain.

Animals↗

PubMed2004

Statistical modeling of sequencing errors in SAGE libraries.

MOTIVATION: Sequencing errors may bias the gene expression measurements made by Serial Analysis of Gene Expression (SAGE). They may introduce non-existent tags at low abundance and decrease the real abundance of other tags. These effects are increased in the longer tags generated in LongSAGE libraries. Current sequencing technology generates quite accurate estimates of sequencing error rates. Here we make use of the sequence neighborhood of SAGE tags and error estimates from the base-calling software to correct for such errors. RESULTS: We introduce a statistical model for the propagation of sequencing errors in SAGE and suggest an Expectation-Maximization (EM) algorithm to correct for them given observed sequences in a library and base-calling error estimates. We tested our method using simulated and experimental SAGE libraries. When comparing SAGE libraries, we found that sequencing errors can introduce considerable bias. High abundance tags may be falsely called as significantly differentially expressed, especially when comparing libraries with different levels of sequencing errors and/or of different size. Truly, differentially expressed tags have decreased significance as 'true'-tag counts are generally underestimated. This may alter if tags near the threshold of differential expression are called significant. Moreover, the number of different transcripts present in a library is overestimated as false tags are introduced at low abundance. Our correction method adjusts the tag counts to be closer to the true counts and is able to partly correct for biases introduced by sequencing errors. AVAILABILITY: An implementation using R is distributed as an R package. An online version is available at http://tagcalling.mbgproject.org

Algorithms↗

PubMed2004

GOstat: find statistically overrepresented Gene Ontologies within a group of genes.

SUMMARY: Modern experimental techniques, as for example DNA microarrays, as a result usually produce a long list of genes, which are potentially interesting in the analyzed process. In order to gain biological understanding from this type of data, it is necessary to analyze the functional annotations of all genes in this list. The Gene-Ontology (GO) database provides a useful tool to annotate and analyze the functions of a large number of genes. Here, we introduce a tool that utilizes this information to obtain an understanding of which annotations are typical for the analyzed list of genes. This program automatically obtains the GO annotations from a database and generates statistics of which annotations are overrepresented in the analyzed list of genes. This results in a list of GO terms sorted by their specificity. AVAILABILITY: Our program GOstat is accessible via the Internet at http://gostat.wehi.edu.au

Abstracting and Indexing↗

PubMed2003

Analysis of CREM-dependent gene expression during mouse spermatogenesis.

The transcription factors CREM, CREB, and ATF-1 constitute a subfamily of beta-Zip transcription factors. Several different kinase cascades regulate the activity of these proteins. The activator splice-isoform CREMtau is specifically and highly expressed in post-meiotic germ cells during mouse spermatogenesis. Male mice lacking CREMtau expression are sterile because of stage-specific arrest of sperm maturation as the spermatids undergo apoptosis. In order to characterize the genes that are controlled by CREM during post-meiotic differentiation of round spermatids, we compared the expression levels of mRNA prepared from testes of wild-type and CREM-deficient mice by suppression subtractive hybridization (SSH) and affymetrix oligonucleotide arrays. A set of 956 unique sequences found in the CREM SSH library was further characterized by generating stage-specific expression profiles during spermatogenesis by hybridization with cDNA from pre-pubertal mice at defined stages of spermatogenesis using nylon DNA arrays. The resulting expression profiles were arranged in a linear order according to similarity in their profile shapes to find co-regulation of functionally related genes. Our data shows that a large number of genes are transcriptionally activated in round spermatids when CREM activity is maximal, including functional groups like transcription factors, proteins involved in signal transduction, and metabolic enzymes, therefore providing novel information of post-meiotic expression of many known as well as novel genes that are either directly or indirectly influenced by CREM expression.

Animals↗

PubMed2001

Monitoring the switch from housekeeping to pathogen defense metabolism in Arabidopsis thaliana using cDNA arrays.

Plants respond to pathogen attack by deploying several defense reactions. Some rely on the activation of preformed components, whereas others depend on changes in transcriptional activity. Using cDNA arrays comprising 13,000 unique expressed sequence tags, changes in the transcriptome of Arabidopsis thaliana were monitored after attempted infection with the bacterial plant pathogen Pseudomonas syringae pv. tomato carrying the avirulence gene avrRpt2. Sampling at four time points during the first 24 h after infiltration revealed significant changes in the steady state transcript levels of approximately 650 genes within 10 min and a massive shift in gene expression patterns by 7 h involving approximately 2,000 genes representing many cellular processes. This shift from housekeeping to defense metabolism results from changes in regulatory and signaling circuits and from an increased demand for energy and biosynthetic capacity in plants fighting off a pathogenic attack. Concentrating our detailed analysis on the genes encoding enzymes in glycolysis, the Krebs cycle, the pentose phosphate pathway, the biosynthesis of aromatic amino acids, phenylpropanoids, and ethylene, we observed interesting differential regulation patterns. Furthermore, our data showed potentially important changes in areas of metabolism, such as the glyoxylate metabolism, hitherto not suspected to be components of plant defense.

Arabidopsis↗