Attila Gyenesei

Transcriptome Profiling of the Murine Testis during the First Wave of Spermatogenesis

Correct gene expression patterns form the basis for male germ cell differentiation and male fertility. Although previous studies have elucidated the importance of testis specific gene expression, the exact transcripts and comprehensive gene expression patterns remain unknown. Large scale sequencing techniques have enabled cost effective analysis of gene expression and isoform studies. Using the SOLiD 4 next-generation sequencing platform we have investigated the gene expression patterns at five different time points during the first wave on murine spermatogenesis. Our results highlight the upregulation of spermatogenesis related biological processes and associated cellular components. Elucidation of differential gene expression at important time points during the sperm development emphasizes the importance of correct timing of gene expression within biological processes. Differential gene level expression was analyzed with R/Bioconductor’s Limma package and isoform analysis was conducted with the Cufflinks pipeline. At gene level total of 2494 differentially expressed genes were identified and Cufflinks characterized over 160 000 gene isoforms, of which 29% were novel transcripts assigned to known genes. Isoforms were detected for 57% of expressed genes and in a total over 26 000 genes were expressed in the testis. Differential promoter and transcription start site usage appears also to play a role in regulation of gene expression during spermatogenesis. Furthermore, we identified 947 upregulated long non-coding RNAs during the first wave of spermatogenesis. These RNAs appeared to be highly specific to different time points. Transcriptomic analysis of testis tissue samples is highly informative due to the large number of expressed genes and identified isoforms. Our study provides a very valuable basis for investigation of gene isoforms and regulation and factors contributing to male fertility.

Gene expression differences between Noccaea caerulescens ecotypes help to identify candidate genes for metal phytoremediation.

Populations of Noccaea caerulescens show tremendous differences in their capacity to hyperaccumulate and hypertolerate metals. To explore the differences that could contribute to these traits, we undertook SOLiD high-throughput sequencing of the root transcriptomes of three phenotypically well-characterized N. caerulescens accessions, i.e., Ganges, La Calamine, and Monte Prinzera. Genes with possible contribution to zinc, cadmium, and nickel hyperaccumulation and hypertolerance were predicted. The most significant differences between the accessions were related to metal ion (di-, trivalent inorganic cation) transmembrane transporter activity, iron and calcium ion binding, (inorganic) anion transmembrane transporter activity, and antioxidant activity. Analysis of correlation between the expression profile of each gene and the metal-related characteristics of the accessions disclosed both previously characterized (HMA4, HMA3) and new candidate genes (e.g., for nickel IRT1, ZIP10, and PDF2.3) as possible contributors to the hyperaccumulation/tolerance phenotype. A number of unknown Noccaea-specific transcripts also showed correlation with Zn(2+), Cd(2+), or Ni(2+) hyperaccumulation/tolerance. This study shows that N. caerulescens populations have evolved great diversity in the expression of metal-related genes, facilitating adaptation to various metalliferous soils. The information will be helpful in the development of improved plants for metal phytoremediation.

Compact fuzzy association rule-based classifier

Classification is one of the most popular data mining techniques applied to many scientific and industrial problems. The efficiency of a classification model is evaluated by two parameters, namely the accuracy and the interpretability of the model. While most of the existing methods claim their accurate superiority over others, their models are usually complex and hardly understandable for the users. In this paper, we propose a novel classification model that is based on easily interpretable fuzzy association rules and fulfils both efficiency criteria. Since the accuracy of a classification model can be largely affected by the partitioning of numerical attributes, this paper discusses several fuzzy and crisp partitioning techniques. The proposed classification method is compared to 15 previously published association rule-based classifiers by testing them on five benchmark data sets. The results show that the fuzzy association rule-based classifier presented in this paper, offers a compact, understandable and accurate classification model.

High-throughput analysis of epistasis in genome-wide association studies with BiForce

Motivation: Gene–gene interactions (epistasis) are thought to be important in shaping complex traits, but they have been under-explored in genome-wide association studies (GWAS) due to the computational challenge of enumerating billions of single nucleotide polymorphism (SNP) combinations. Fast screening tools are needed to make epistasis analysis routinely available in GWAS. Results: We present BiForce to support high-throughput analysis of epistasis in GWAS for either quantitative or binary disease (case–control) traits. BiForce achieves great computational efficiency by using memory efficient data structures, Boolean bitwise operations and multithreaded parallelization. It performs a full pair-wise genome scan to detect interactions involving SNPs with or without significant marginal effects using appropriate Bonferroni-corrected significance thresholds. We show that BiForce is more powerful and significantly faster than published tools for both binary and quantitative traits in a series of performance tests on simulated and real datasets. We demonstrate BiForce in analysing eight metabolic traits in a GWAS cohort (323 697 SNPs, >4500 individuals) and two disease traits in another (>340 000 SNPs, >1750 cases and 1500 controls) on a 32-node computing cluster. BiForce completed analyses of the eight metabolic traits within 1 day, identified nine epistatic pairs of SNPs in five metabolic traits and 18 SNP pairs in two disease traits. BiForce can make the analysis of epistasis a routine exercise in GWAS and thus improve our understanding of the role of epistasis in the genetic regulation of complex traits. Availability and implementation: The software is free and can be downloaded from http://bioinfo.utu.fi/BiForce/. Contact: wenhua.wei@igmm.ed.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Genome-wide analysis of epistasis in body mass index using multiple human populations

We surveyed gene–gene interactions (epistasis) in human body mass index (BMI) in four European populations (n<1200) via exhaustive pair-wise genome scans where interactions were computed as F ratios by testing a linear regression model fitting two single-nucleotide polymorphisms (SNPs) with interactions against the one without. Before the association tests, BMI was corrected for sex and age, normalised and adjusted for relatedness. Neither single SNPs nor SNP interactions were genome-wide significant in either cohort based on the consensus threshold (P=5.0E−08) and a Bonferroni corrected threshold (P=1.1E−12), respectively. Next we compared sub genome-wide significant SNP interactions (P<5.0E−08) across cohorts to identify common epistatic signals, where SNPs were annotated to genes to test for gene ontology (GO) enrichment. Among the epistatic genes contributing to the commonly enriched GO terms, 19 were shared across study cohorts of which 15 are previously published genome-wide association loci, including CDH13 (cadherin 13) associated with height and SORCS2 (sortilin-related VPS10 domain containing receptor 2) associated with circulating insulin-like growth factor 1 and binding protein 3. Interactions between the 19 shared epistatic genes and those involving BMI candidate loci (P<5.0E−08) were tested across cohorts and found eight replicated at the SNP level (P<0.05) in at least one cohort, which were further tested and showed limited replication in a separate European population (n>5000). We conclude that genome-wide analysis of epistasis in multiple populations is an effective approach to provide new insights into the genetic regulation of BMI but requires additional efforts to confirm the findings.

Differential Promoter Methylation of Macrophage Genes Is Associated With Impaired Vascular Growth in Ischemic Muscles of Hyperlipidemic and Type 2 Diabetic Mice Genome-Wide Promoter Methylation Study

RATIONALE Hyperlipidemia and type 2 diabetes mellitus (T2DM) severely impair adaptive vascular growth responses in ischemic muscles. This is largely attributed to dysregulated gene expression, although details of the changes are unknown. OBJECTIVE To define the role of promoter methylation in adaptive vascular growth in hyperlipidemia (LDLR(-/-)ApoB(100/100)) and T2DM (IGF-II/LDLR(-/-)ApoB(100/100)) mouse models of hindlimb ischemia. METHODS AND RESULTS Unilateral hindlimb ischemia was induced by ligating femoral artery. Perfusion was assessed using ultrasound, and capillary and arteriole parameters were assessed using immunohistochemistry. Genome-wide methylated DNA sequencing was performed with DNA isolated from ischemic muscle, tissue macrophages (Mϕs), and endothelial cells. Compared with the controls, hyperlipidemia and T2DM mice showed impaired perfusion recovery, which was associated with impaired angiogenesis and arteriogenesis. Genome-wide proximal promoter DNA methylation analysis suggested differential patterns of methylation in Mϕ genes in ischemic muscles. Classically activated M1-Mϕ gene promoters, including Cfb, Serping1, and Tnfsf15, were significantly hypomethylated, whereas alternatively activated M2-Mϕ gene promoters, including Nrp1, Cxcr4, Plxnd1, Arg1, Cdk18, and Fes, were significantly hypermethylated in Mϕs isolated from hyperlipidemia and T2DM ischemic muscles compared with controls. These results combined with mRNA expression and immunohistochemistry showed the predominance of proinflammatory M1-Mϕs, compared with anti-inflammatory and proangiogenic M2-Mϕs in hyperlipidemia and T2DM ischemic muscles. CONCLUSIONS We found significant promoter hypomethylation of genes typical for proinflammatory M1-Mϕs and hypermethylation of anti-inflammatory, proangiogenic M2-Mϕ genes in hyperlipidemia and T2DM ischemic muscles. Epigenetic alterations modify Mϕ phenotype toward proinflammatory M1 as opposed to anti-inflammatory, proangiogenic, and tissue repair M2 phenotype, which may contribute to the impaired adaptive vascular growth under these pathological conditions.

Whole transcriptome characterization of the effects of dehydration and rehydration on Cladonia rangiferina, the grey reindeer lichen

BackgroundLichens are symbiotic organisms with a fungal and an algal or a cyanobacterial partner. Lichens inhabit some of the harshest climates on earth and most lichen species are desiccation-tolerant. Lichen desiccation-tolerance has been studied at the biochemical level and through proteomics, but the underlying molecular genetic mechanisms remain largely unexplored. The objective of our study was to examine the effects of dehydration and rehydration on the gene expression of Cladonia rangiferina.ResultsSamples of C. rangiferina were collected at several time points during both the dehydration and rehydration process and the gene expression intensities were measured using a custom DNA microarray. Several genes, which were differentially expressed in one or more time points, were identified. The microarray results were validated using qRT-PCR analysis. Enrichment analysis of differentially expressed transcripts was also performed to identify the Gene Ontology terms most associated with the rehydration and dehydration process.ConclusionsOur data identify differential expression patterns for hundreds of genes that are modulated during dehydration and rehydration in Cladonia rangiferina. These dehydration and rehydration events clearly differ from each other at the molecular level and the largest changes to gene expression are observed within minutes following rehydration. Distinct changes are observed during the earliest stage of rehydration and the mechanisms not appear to be shared with the later stages of wetting or with drying. Several of the most differentially expressed genes are similar to genes identified in previous studies that have investigated the molecular mechanisms of other desiccation-tolerant organisms. We present here the first microarray experiment for any lichen species and have for the first time studied the genetic mechanisms behind lichen desiccation-tolerance at the whole transcriptome level.

BiForce Toolbox: powerful high-throughput computational analysis of gene–gene interactions in genome-wide association studies

Genome-wide association studies (GWAS) have discovered many loci associated with common disease and quantitative traits. However, most GWAS have not studied the gene–gene interactions (epistasis) that could be important in complex trait genetics. A major challenge in analysing epistasis in GWAS is the enormous computational demands of analysing billions of SNP combinations. Several methods have been developed recently to address this, some using computers equipped with particular graphical processing units, most restricted to binary disease traits and all poorly suited to general usage on the most widely used operating systems. We have developed the BiForce Toolbox to address the demand for high-throughput analysis of pairwise epistasis in GWAS of quantitative and disease traits across all commonly used computer systems. BiForce Toolbox is a stand-alone Java program that integrates bitwise computing with multithreaded parallelization and thus allows rapid full pairwise genome scans via a graphical user interface or the command line. Furthermore, BiForce Toolbox incorporates additional tests of interactions involving SNPs with significant marginal effects, potentially increasing the power of detection of epistasis. BiForce Toolbox is easy to use and has been applied in multiple studies of epistasis in large GWAS data sets, identifying interesting interaction signals and pathways.

Promoter-specific alterations of APC are a rare cause for mutation-negative familial adenomatous polyposis.

In familial adenomatous polyposis (FAP), 20% of classical and 70% of attenuated/atypical (AFAP) cases remain mutation‐negative after routine testing; yet, allelic expression imbalance may suggest an APC alteration. Our aim was to determine the proportion of families attributable to genetic or epigenetic changes in the APC promoter region. We studied 51 unrelated families/cases (26 with classical FAP and 25 with AFAP) with no point mutations in the exons and exon/intron borders and no rearrangements by multiplex ligation‐dependent probe amplification (MLPA, P043‐B1). Promoter‐specific events of APC were addressed by targeted resequencing, MLPA (P043‐C1), methylation‐specific MLPA, and Sanger sequencing of promoter regions. A novel 132‐kb deletion encompassing the APC promoter 1B and upstream sequence occurred in a classical FAP family with allele‐specific APC expression. No promoter‐specific point mutations or hypermethylation were present in any family. In conclusion, promoter‐specific alterations are a rare cause for mutation‐negative FAP (1/51, 2%). The frequency and clinical correlations of promoter 1B deletions are poorly defined. This investigation provides frequencies of 1/26 (4%) for classical FAP, 0/25 (0%) for AFAP, and 1/7 (14%) for families with allele‐specific expression of APC. Clinically, promoter 1B deletions may associate with classical FAP without extracolonic manifestations.

Expression of small nucleolar RNAs in leukemic cells

PurposeSmall nucleolar RNAs (snoRNAs) direct sequence-specific modifications to ribosomal RNA. We hypothesized that the expression of snoRNAs may be altered in leukemic cells.MethodsThe expression of snoRNAs was analyzed in various leukemic cell lines by massive parallel sequencing (SOLiD). Quantitative real-time PCR (RT-qPCR) was used to validate the expression profiles.ResultsOur results show characteristic differences in the expression patterns of snoRNAs between cell lines representing the main subgroups of leukemia, AML, pre-B-ALL and T-ALL, respectively. In RT-qPCR analyses, several snoRNAs were found to be differentially expressed in T-ALL as compared to pre-B-ALL cell lines.ConclusionssnoRNAs are differentially expressed in various leukemic cell lines and could, therefore, be potentially useful in the classification of leukemia subgroups.