Biomedical subjects

Sven Bergmann

Publications and source records attributed to Sven Bergmann.

10 recordsLinked to original sources

PubMed2006

Common and divergent roles for members of the mouse DCX superfamily.

The doublecortin-like (DCX) domains serve as protein-interaction platforms. DCX tandem domains appear in the product of the X-linked doublecortin (DCX) gene, in retinitis pigmentosa-1 (RP1), as well as in other gene products. Mutations in the human DCX gene are associated with abnormal neuronal migration, epilepsy, and mental retardation; mutations in RP1 are associated with a form of inherited blindness, while DCDC2 has been associated with dyslectic reading disabilities. Motivated by the possible importance of this gene family, a thorough analysis to detect all family members in the mouse was conducted. The DCX-repeat gene superfamily is composed of eleven paralogs, and we cloned the DCX domains from nine different genes. Our study questioned which functions attributed to the DCX domain, are conserved among the different members. Our results suggest that the proteins with the DCX-domain have conserved and unique roles in microtubule regulation and signal transduction. All the tested proteins stimulated microtubule assembly in vitro. Proteins with tandem repeats stabilized the microtubule cytoskeleton in transfected cells, while those with single repeats localized to actin-rich subcellular structures, or the nucleus. All tested proteins interacted with components of the JNK/MAP-kinase pathway, while only a subset interacted with Neurabin 2, and a nonoverlapping group demonstrated actin association. The sub-specialization of some members due to confined intracellular localization, and protein interactions may explain the success of this superfamily.

Amino Acid Sequence↗

PubMed2005

Rewiring of the yeast transcriptional network through the evolution of motif usage.

Recent experiments revealed large-scale differences in the transcription programs of related species, yet little is known about the genetic basis underlying the evolution of gene expression and its contribution to phenotypic diversity. Here we describe a large-scale modulation of the yeast transcription program that is connected to the emergence of the capacity for rapid anaerobic growth. Genes coding for mitochondrial and cytoplasmic ribosomal proteins display a strongly correlated expression pattern in Candida albicans, but this correlation is lost in the fermentative yeast Saccharomyces cerevisiae. We provide evidence that this change in gene expression is connected to the loss of a specific cis-regulatory element from dozens of genes following the apparent whole-genome duplication event. Our results shed new light on the genetic mechanisms underlying the large-scale evolution of transcriptional networks.

Aerobiosis↗

PubMed2005

Comparative genome hybridization reveals widespread aneuploidy in Candida albicans laboratory strains.

Clinical strains of Candida albicans are highly tolerant of aneuploidies and other genome rearrangements. We have used comparative genome hybridization (CGH), in an array format, to analyse the copy number of over 6000 open reading frames (ORFs) in the genomic DNA of C. albicans laboratory strains carrying one (CAI-4) to three (BWP17) auxotrophies. We find that during disruption of the HIS1 locus all genes telomeric to HIS1 were deleted and telomeric repeats were added to a 9 nt sequence within the transforming DNA. This deletion occurred in approximately 10% of transformants analysed and was stably maintained through two additional rounds of transformation and counterselection of the transformation marker. In one example, the deletion was repaired, apparently via break-induced replication. Furthermore, all CAI-4 strains tested were trisomic for chromosome 2 although this trisomy appears to be unstable, as it is not detected in strains subsequently derived from CAI-4. Our data indicate CGH arrays can be used to detect monosomies and trisomies, to predict the sites of chromosome breaks, and to identify chromosomal aberrations that have not been detected with other approaches in C. albicans strains. Furthermore, they highlight the high level of genome instability in C. albicans laboratory strains exposed to the stress of transformation and counterselection on 5-fluoro-orotic acid.

Aneuploidy↗

PubMed2005

Comparative gene expression analysis by differential clustering approach: application to the Candida albicans transcription program.

Differences in gene expression underlie many of the phenotypic variations between related organisms, yet approaches to characterize such differences on a genome-wide scale are not well developed. Here, we introduce the "differential clustering algorithm" for revealing conserved and diverged co-expression patterns. Our approach is applied at different levels of organization, ranging from pair-wise correlations within specific groups of functionally linked genes, to higher-order correlations between such groups. Using the differential clustering algorithm, we systematically compared the transcription program of the fungal pathogen Candida albicans with that of the model organism Saccharomyces cerevisiae. Many of the identified differences are related to the differential requirement for mitochondrial function in the two yeasts. Distinct regulation patterns of cell cycle genes and of amino acid metabolic genes were also revealed and, in some cases, could be linked to the differential appearance of cis-regulatory elements in the gene promoter regions. Our study provides a comprehensive framework for comparative gene expression analysis and a rich source of hypotheses for uncharacterized open reading frames and putative cis-regulatory elements in C. albicans.

Candida albicans↗

PubMed2004

Defining transcription modules using large-scale gene expression data.

MOTIVATION: Large-scale gene expression data comprising a variety of cellular conditions hold the promise of a global view on the transcription program. While conventional clustering algorithms have been successfully applied to smaller datasets, the utility of many algorithms for the analysis of large-scale data is limited by their inability to capture combinatorial and condition-specific co-regulation. In addition, there is an increasing need to integrate the rapidly accumulating body of other high-throughput biological data with the expression analysis. In a previous work, we introduced the signature algorithm, which overcomes the problems of conventional clustering and allows for intuitive integration of additional biological data. However, this approach is constrained by the comprehensiveness of relevant external data and its lacking ability to capture hierarchical modularity. METHODS: We present a novel method for the analysis of large-scale expression data, which assigns genes into context-dependent and potentially overlapping regulatory units. We introduce the notion of a transcription module as a self-consistent regulatory unit consisting of a set of co-regulated genes as well as the experimental conditions that induce their co-regulation. Self-consistency is defined by a rigorous mathematical criterion. We propose an efficient algorithm to identify such modules, which is based on the iterative application of the signature algorithm. A threshold parameter that determines the resolution of the modular decomposition is introduced. RESULTS: The method is applied systematically to over 1000 expression profiles of the yeast Saccharomyces cerevisiae, and the results are presented using two complementary visualization schemes we developed. The average biological coherence, as measured by the conservation of putative cis-regulatory motifs between four related yeast species, is higher for transcription modules than for clusters identified by other methods applied to the same dataset. Our method is related to singular value decomposition (SVD) and to the pairwise average linkage clustering algorithm. It extends SVD by filtering out noise in the expression data and offering variable resolution to reveal hierarchical organization. It furthermore has the advantage over both methods of capturing overlapping modules in the presence of combinatorial regulation. SUPPLEMENTARY INFORMATION: http://www.weizmann.ac.il/~barkai/modules

Algorithms↗

PubMed2004

Investigations on the ORF 167L of lymphocystis disease virus (Iridoviridae).

The predicted open reading frame 167L (ORF 167L) of lymphocystis disease virus (LCDV, Iridoviridae ) isolated from plaice, dab and flounder was investigated. The ORF 167L corresponding genes of the three LCDV isolates were amplified, cloned and sequenced. A comparison of the LCDV strains showed that the nucleotide sequence of ORF 167L and its deduced amino acid sequence were highly conserved in the genus lymphocystivirus (a homology of 80% in dab and flounder/plaice, 97% in plaice and flounder). The N-terminus protein predicted from the ORF 167L suggests similarities to the tumor necrosis factor receptor (TNFR)-family, and to TNFR-like proteins, which play an important role in various poxvirus species. Further, homology to the CUB-domain was shown at the C-terminus of the LCDV protein. Phylogenetic analyses of partial LCDV protein sequences identified two clusters: one cluster containing the flounder and plaice LCDV isolate (LCDV-1), and another cluster, containing the dab LCDV isolate (LCDV-2). The ORF 167L of plaice LCDV was expressed in Escherichia coli, and in fish cells. The expressed ORF resulted in a 30-kDa cytoplasmic protein lacking a signal peptide. An established monoclonal antibody (mAb 18) was used to detect LCDV proteins in skin explants of flounders and cryosections of dab skin. Specific fluorescence was found in the cytoplasm of intact epitheloid cells of the lymphocystis capsule and in the epidermis skin covering the lymphocystic nodules. LCDV-specific labelling of mAb 18 was also shown in spleen and liver tissue of LCDV-positive flounders. The ORF 167L protein seemed not to have the extracellular receptor function predicted from the usual cellular TNFR. The myxomavirus M-T2 protein, a poxviral TNFR homologue, was also shown not to have TNFR-like functions but to be involved in the apoptosis signal cascade.

Amino Acid Sequence↗

PubMed2004

Challenges and prospects in the analysis of large-scale gene expression data.

Large heterogeneous expression data comprising a variety of cellular conditions hold the promise of a global view of transcriptional regulation. While standard analysis methods have been successfully applied to smaller data sets, large-scale data pose specific challenges that have prompted the development of new and more sophisticated approaches. This paper focuses on one such approach (the Signature Algorithm) and discusses the central challenges in the analysis of large data sets, and how they might be overcome. Biological questions that have been addressed using the Signature Algorithm are highlighted and a summary of other important methods from the literature is provided.

Algorithms↗

PubMed2003

Similarities and differences in genome-wide expression data of six organisms.

Comparing genomic properties of different organisms is of fundamental importance in the study of biological and evolutionary principles. Although differences among organisms are often attributed to differential gene expression, genome-wide comparative analysis thus far has been based primarily on genomic sequence information. We present a comparative study of large datasets of expression profiles from six evolutionarily distant organisms: S. cerevisiae, C. elegans, E. coli, A. thaliana, D. melanogaster, and H. sapiens. We use genomic sequence information to connect these data and compare global and modular properties of the transcription programs. Linking genes whose expression profiles are similar, we find that for all organisms the connectivity distribution follows a power-law, highly connected genes tend to be essential and conserved, and the expression program is highly modular. We reveal the modular structure by decomposing each set of expression data into coexpressed modules. Functionally related sets of genes are frequently coexpressed in multiple organisms. Yet their relative importance to the transcription program and their regulatory relationships vary among organisms. Our results demonstrate the potential of combining sequence and expression data for improving functional gene annotation and expanding our understanding of how gene expression and diversity evolved.

Animals↗

PubMed2003

Iterative signature algorithm for the analysis of large-scale gene expression data.

We present an approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, which searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of singular value decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast Saccharomyces cerevisiae.

Algorithms↗

PubMed2002

Revealing modular organization in the yeast transcriptional network.

Standard clustering methods can classify genes successfully when applied to relatively small data sets, but have limited use in the analysis of large-scale expression data, mainly owing to their assignment of a gene to a single cluster. Here we propose an alternative method for the global analysis of genome-wide expression data. Our approach assigns genes to context-dependent and potentially overlapping 'transcription modules', thus overcoming the main limitations of traditional clustering methods. We use our method to elucidate regulatory properties of cellular pathways and to characterize cis-regulatory elements. By applying our algorithm systematically to all of the available expression data on Saccharomyces cerevisiae, we identify a comprehensive set of overlapping transcriptional modules. Our results provide functional predictions for numerous genes, identify relations between modules and present a global view on the transcriptional network.

Algorithms↗