Björn Olsson

Combining functional and topological properties to identify core modules in protein interaction networks

Advances in large‐scale technologies in proteomics, such as yeast two‐hybrid screening and mass spectrometry, have made it possible to generate large Protein Interaction Networks (PINs). Recent methods for identifying dense sub‐graphs in such networks have been based solely on graph theoretic properties. Therefore, there is a need for an approach that will allow us to combine domain‐specific knowledge with topological properties to generate functionally relevant sub‐graphs from large networks. This article describes two alternative network measures for analysis of PINs, which combine functional information with topological properties of the networks. These measures, called weighted clustering coefficient and weighted average nearest‐neighbors degree, use weights representing the strengths of interactions between the proteins, calculated according to their semantic similarity, which is based on the Gene Ontology terms of the proteins. We perform a global analysis of the yeast PIN by systematically comparing the weighted measures with their topological counterparts. To show the usefulness of the weighted measures, we develop an algorithm for identification of functional modules, called SWEMODE (Semantic WEights for MODule Elucidation), that identifies dense sub‐graphs containing functionally similar proteins. The proposed method is based on the ranking of nodes, i.e., proteins, according to their weighted neighborhood cohesiveness. The highest ranked nodes are considered as seeds for candidate modules. The algorithm then iterates through the neighborhood of each seed protein, to identify densely connected proteins with high functional similarity, according to the chosen parameters. Using a yeast two‐hybrid data set of experimentally determined protein–protein interactions, we demonstrate that SWEMODE is able to identify dense clusters containing proteins that are functionally similar. Many of the identified modules correspond to known complexes or subunits of these complexes. Proteins 2006.

Molecular Signature of Cardiomyocyte Clusters Derived from Human Embryonic Stem Cells

Human embryonic stem cells (hESCs) can differentiate in vitro into spontaneously contracting cardiomyocytes (CMs). These cells may prove extremely useful for various applications in basic research, drug discovery, and regenerative medicine. To fully use the potential of the cells, they need to be extensively characterized, and the regulatory mechanisms that control hESC differentiation toward the cardiac lineage need to be better defined. In this study, we used microarrays to analyze, for the first time, the global gene expression profile of isolated hESC‐derived CM clusters. By comparing the clusters with undifferentiated hESCs and using stringent selection criteria, we identified 530 upregulated and 40 downregulated genes in the contracting clusters. To further characterize the family of upregulated genes in the hESC‐derived CM clusters, the genes were classified according to their Gene Ontology annotation. The results indicate that the hESC‐derived CM clusters display high similarities, on a molecular level, to human heart tissue. Moreover, using the family of upregulated genes, we created protein interaction maps that revealed topological characteristics. We also searched for cellular pathways among the upregulated genes in the hESC‐derived CM clusters and identified eight significantly upregulated pathways. Real‐time quantitative polymerase chain reaction and immunohistochemical analysis confirmed the expression of a subset of the genes identified by the microarrays. Taken together, the results presented here provide a molecular signature of hESC‐derived CM clusters and further our understanding of the biological processes that are active in these cells.

Generation and analysis of 9792 EST sequences from cold acclimated oat, Avena sativa

BackgroundOat is an important crop in North America and northern Europe. In Scandinavia, yields are limited by the fact that oat cannot be used as a winter crop. In order to develop such a crop, more knowledge about mechanisms of cold tolerance in oat is required.ResultsFrom an oat cDNA library 9792 single-pass EST sequences were obtained. The library was prepared from pooled RNA samples isolated from leaves of four-week old Avena sativa (oat) plants incubated at +4°C for 4, 8, 16 and 32 hours. Exclusion of sequences shorter than 100 bp resulted in 8508 high-quality ESTs with a mean length of 710.7 bp. Clustering and assembly identified a set of 2800 different transcripts denoted the Avena sativa cold induced UniGene set (AsCIUniGene set). Taking advantage of various tools and databases, putative functions were assigned to 1620 (58%) of these genes. Of the remaining 1180 unclassified sequences, 427 appeared to be oat-specific since they lacked any significant sequence similarity (Blast E values > 10-10) to any sequence available in the public databases. Of the 2800 UniGene sequences, 398 displayed significant homology (BlastX E values ≤ 10-10) to genes previously reported to be involved in cold stress related processes. 107 novel oat transcription factors were also identified, out of which 51 were similar to genes previously shown to be cold induced. The CBF transcription factors have a major role in regulating cold acclimation. Four oat CBF sequences were found, belonging to the monocot cluster of DREB family ERF/AP2 domain proteins. Finally in the total EST sequence data (5.3 Mbp) approximately 400 potential SSRs were found, a frequency similar to what has previously been identified in Arabidopsis ESTs.ConclusionThe AsCIUniGene set will now be used to fabricate an oat biochip, to perform various expression studies with different oat cultivars incubated at varying temperatures, to generate molecular markers and provide tools for various genetic transformation experiments in oat. This will lead to a better understanding of the cellular biology of this important crop and will open up new ways to improve its agronomical properties.

Differentiating human embryonic stem cells express a unique housekeeping gene signature.

Housekeeping genes (HKGs) are involved in basic functions needed for the sustenance of the cell and are assumed to be constitutively expressed at a constant level. Based on these features, HKGs are frequently used for normalization of gene expression data. In the present study, we used the CodeLink Gene Expression Bioarray system to interrogate changes in gene expression occurring during differentiation of human ESCs (hESCs). Notably, in the three hESC lines used for the study, we observed that the RNA levels of 56 frequently used HKGs varied to a degree that rendered them inappropriate as reference genes. Therefore, we defined a novel set of HKGs specifically for hESCs. Here we present a comprehensive list of 292 genes that are stably expressed (coefficient of variation <20%) in differentiating hESCs. These genes were further grouped into high‐, medium‐, and low‐expressed genes. The expression patterns of these novel HKGs show very little overlap with results obtained from somatic cells and tissues. We further explored the stability of this novel set of HKGs in independent, publicly available gene expression data from hESCs and observed substantial similarities with our results. Gene expression was confirmed by real‐time quantitative polymerase chain reaction analysis. Taken together, these results suggest that differentiating hESCs have a unique HKG signature and underscore the necessity to validate the expression profiles of putative HKGs. In addition, this novel set of HKGs can preferentially be used as controls in gene expression analyses of differentiating hESCs.

A miRNA expression signature that separates between normal and malignant prostate tissues.

BackgroundMicroRNAs (miRNAs) constitute a class of small non-coding RNAs that post-transcriptionally regulate genes involved in several key biological processes and thus are involved in various diseases, including cancer. In this study we aimed to identify a miRNA expression signature that could be used to separate between normal and malignant prostate tissues.ResultsNine miRNAs were found to be differentially expressed (p <0.00001). With the exception of two samples, this expression signature could be used to separate between the normal and malignant tissues. A cross-validation procedure confirmed the generality of this expression signature. We also identified 16 miRNAs that possibly could be used as a complement to current methods for grading of prostate tumor tissues.ConclusionsWe found an expression signature based on nine differentially expressed miRNAs that with high accuracy (85%) could classify the normal and malignant prostate tissues in patients from the Swedish Watchful Waiting cohort. The results show that there are significant differences in miRNA expression between normal and malignant prostate tissue, indicating that these small RNA molecules might be important in the biogenesis of prostate cancer and potentially useful for clinical diagnosis of the disease.

Gene expression variation to predict 10-year survival in lymph-node-negative breast cancer

BackgroundIt is of great significance to find better markers to correctly distinguish between high-risk and low-risk breast cancer patients since the majority of breast cancer cases are at present being overtreated.Methods46 tumours from node-negative breast cancer patients were studied with gene expression microarrays. A t-test was carried out in order to find a set of genes where the expression might predict clinical outcome. Two classifiers were used for evaluation of the gene lists, a correlation-based classifier and a Voting Features Interval (VFI) classifier. We then evaluated the predictive accuracy of this expression signature on tumour sets from two similar studies on lymph-node negative patients. They had both developed gene expression signatures superior to current methods in classifying node-negative breast tumours. These two signatures were also tested on our material.ResultsA list of 51 genes whose expression profiles could predict clinical outcome with high accuracy in our material (96% or 89% accuracy in cross-validation, depending on type of classifier) was developed. When tested on two independent data sets, the expression signature based on the 51 identified genes had good predictive qualities in one of the data sets (74% accuracy), whereas their predictive value on the other data set were poor, presumably due to the fact that only 23 of the 51 genes were found in that material. We also found that previously developed expression signatures could predict clinical outcome well to moderately well in our material (72% and 61%, respectively).ConclusionThe list of 51 genes derived in this study might have potential for clinical utility as a prognostic gene set, and may include candidate genes of potential relevance for clinical outcome in breast cancer. According to the predictions by this expression signature, 30 of the 46 patients may have benefited from different adjuvant treatment than they recieved.Trial registrationThe research on these tumours was approved by the Medical Faculty Research Ethics Committee (Medicinska fakultetens forskningsetikkommitté, Göteborg, Sweden (S164-02)).

Co-evolutionary search in asymmetric spaces

Abstract Co-evolutionary models have received increasing attention in the research on evolutionary algorithms (EAs), since they seem to offer novel ideas for how to overcome some of the problems inherent in “classical” EAs. This article is concerned with one type of co-evolutionary model, based on the use of hosts and parasites, which allows co-evolution of a population of candidate problem solutions and a population of fitness cases. Although host-parasite algorithms have shown promising results in previous research, there is a lack of work aimed at formalizing this class of algorithms, and studying aspects of their behaviour in more detail. To help remedy this situation, this article explores the role of problem asymmetry in limiting the progress of host-parasite search, and shows how a suite of co-evolutionary function optimization problems can be used to study the behaviour of host-parasite algorithms under varying levels of asymmetry. The article also presents the asymmetry-handling host-parasite algorithm (AHPA) and a set of experiments aimed at identifying the conditions under which AHPA consistently gives improvements over simple host-parasite algorithms.

Validation of suitable endogenous control genes for expression studies of miRNA in prostate cancer tissues

When performing quantitative polymerase chain reaction analysis, there is a need for correction of technical variation between experiments. This correction is most commonly performed by using endogenous control genes, which are stably expressed across samples, as reference genes for normal expression in a specific tissue. In microRNA (miRNA) studies, two types of control genes are commonly used: small nuclear RNAs and small nucleolar RNAs. In this study, six different endogenous control genes for miRNA studies were investigated in prostate tissue material from the Swedish Watchful Waiting cohort. The stability of the controls was investigated using two different software applications, NormFinder and BestKeeper. RNU24 was the most suitable endogenous control gene for miRNA studies in prostate tissue materials.

Potential predictive markers of chemotherapy resistance in stage III ovarian serous carcinomas

BackgroundChemotherapy resistance remains a major obstacle in the treatment of women with ovarian cancer. Establishing predictive markers of chemoresponse would help to individualize therapy and improve survival of ovarian cancer patients. Chemotherapy resistance in ovarian cancer has been studied thoroughly and several non-overlapping single genes, gene profiles and copy number alterations have been suggested as potential markers. The objective of this study was to explore genetic alterations behind chemotherapy resistance in ovarian cancer with the ultimate aim to find potential predictive markers.MethodsTo create the best opportunities for identifying genetic alterations of importance for resistance, we selected a homogenous tumor material concerning histology, stage and chemotherapy. Using high-resolution whole genome array comparative genomic hybridization (CGH), we analyzed the tumor genomes of 40 fresh-frozen stage III ovarian serous carcinomas, all uniformly treated with combination therapy paclitaxel/carboplatin. Fishers exact test was used to identify significant differences. Subsequently, we examined four genes in the significant regions (EVI1, MDS1, SH3GL2, SH3KBP1) plus the ABCB1 gene with quantitative real-time polymerase chain reaction (QPCR) to evaluate the impact of DNA alterations on the transcriptional level.ResultsWe identified gain in 3q26.2, and losses in 6q11.2-12, 9p22.3, 9p22.2-22.1, 9p22.1-21.3, Xp22.2-22.12, Xp22.11-11.3, and Xp11.23-11.1 to be significantly associated with chemotherapy resistance. In the gene expression analysis, EVI1 expression differed between samples with gain versus without gain, exhibiting higher expression in the gain group.ConclusionIn conclusion, we detected specific genetic alterations associated with resistance, of which some might be potential predictive markers of chemotherapy resistance in advanced ovarian serous carcinomas. Thus, further studies are required to validate these findings in an independent ovarian tumor series.

Putative cold acclimation pathways in Arabidopsis thaliana identified by a combined analysis of mRNA co-expression patterns, promoter motifs and transcription factors

BackgroundWith the advent of microarray technology, it has become feasible to identify virtually all genes in an organism that are induced by developmental or environmental changes. However, relying solely on gene expression data may be of limited value if the aim is to infer the underlying genetic networks. Development of computational methods to combine microarray data with other information sources is therefore necessary. Here we describe one such method.ResultsBy means of our method, previously published Arabidopsis microarray data from cold acclimated plants at six different time points, promoter motif sequence data extracted from ~24,000 Arabidopsis promoters and known transcription factor binding sites were combined to construct a putative genetic regulatory interaction network. The inferred network includes both previously characterised and hitherto un-described regulatory interactions between transcription factor (TF) genes and genes that encode other TFs or other proteins. Part of the obtained transcription factor regulatory network is presented here. More detailed information is available in the additional files.ConclusionThe rule-based method described here can be used to infer genetic networks by combining data from microarrays, promoter sequences and known promoter binding sites. This method should in principle be applicable to any biological system. We tested the method on the cold acclimation process in Arabidopsis and could identify a more complex putative genetic regulatory network than previously described. However, it should be noted that information on specific binding sites for individual TFs were in most cases not available. Thus, gene targets for the entire TF gene families were predicted. In addition, the networks were built solely by a bioinformatics approach and experimental verifications will be necessary for their final validation. On the other hand, since our method highlights putative novel interactions, more directed experiments could now be performed.