Biomedical subjects

Carl Virtanen

Publications and source records attributed to Carl Virtanen.

6 recordsLinked to original sources

PubMed2006

Intra- and interindividual epigenetic variation in human germ cells.

Epigenetics represents a secondary inheritance system that has been poorly investigated in human biology. The objective of this study was to perform a comprehensive analysis of DNA methylation variation between and within the germlines of normal males. First, methylated cytosines were mapped using bisulphite modification-based sequencing in the promoter regions of the following disease genes: presenilins (PSEN1 and PSEN2), breast cancer (BRCA1 and BRCA2), myotonic dystrophy (DM1), and Huntington disease (HD). Major epigenetic variation was detected within samples, since the majority of sperm cells of the same individual exhibited unique DNA methylation profiles. In the interindividual analysis, 41 of 61 pairwise comparisons revealed distinct DNA methylation profiles (P=.036 to 6.8 x 10(-14)). Second, a microarray-based epigenetic profiling of the same sperm samples was performed using a 12,198-feature CpG island microarray. The microarray analysis has identified numerous DNA methylation-variable positions in the germ cell genome. The largest degree of variation was detected within the promoter CpG islands and pericentromeric satellites among the single-copy DNA fragments and repetitive elements, respectively. A number of genes, such as EED, CTNNA2, CALM1, CDH13, and STMN2, exhibited age-related DNA methylation changes. Finally, allele-specific methylation patterns in CDH13 were detected. This study provides evidence for significant epigenetic variability in human germ cells, which warrants further research to determine whether such epigenetic patterns can be efficiently transmitted across generations and what impact inherited epigenetic individuality may have on phenotypic outcomes in health and disease.

Confounding Factors, Epidemiologic↗

PubMed2006

Microarray-based DNA methylation profiling: technology and applications.

This work is dedicated to the development of a technology for unbiased, high-throughput DNA methylation profiling of large genomic regions. In this method, unmethylated and methylated DNA fractions are enriched using a series of treatments with methylation sensitive restriction enzymes, and interrogated on microarrays. We have investigated various aspects of the technology including its replicability, informativeness, sensitivity and optimal PCR conditions using microarrays containing oligonucleotides representing 100 kb of genomic DNA derived from the chromosome 22 COMT region in addition to 12 192 element CpG island microarrays. Several new aspects of methylation profiling are provided, including the parallel identification of confounding effects of DNA sequence variation, the description of the principles of microarray design for epigenomic studies and the optimal choice of methylation sensitive restriction enzymes. We also demonstrate the advantages of using the unmethylated DNA fraction versus the methylated one, which substantially improve the chances of detecting DNA methylation differences. We applied this methodology for fine-mapping of methylation patterns of chromosomes 21 and 22 in eight individuals using tiling microarrays consisting of over 340 000 oligonucleotide probe pairs. The principles developed in this work will help to make epigenetic profiling of the entire human genome a routine procedure.

Chromosome Mapping↗

PubMed2005

Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and non-negative matrix factorization.

Current clinical and histopathological criteria used to define lung squamous cell carcinomas (SCCs) are insufficient to predict clinical outcome. To make a clinically useful classification by gene expression profiling, we used a 40 386 element cDNA microarray to analyse 48 SCC, nine adenocarcinoma, and 30 normal lung samples. Initial analysis by hierarchical clustering (HC) allowed division of SCCs into two distinct subclasses. An additional independent round of HC induced a similar partition and consensus clustering with the non-negative matrix factorization approach indicated the robustness of this classification. Kaplan-Meier analysis with the log-rank test pointed to a nonsignificant difference in survival (P = 0.071), but the likelihood of survival to 6 years was significantly different between the two groups (40.5 vs 81.8%, P = 0.014, Z-test). Biological process categories characteristic for each subclass were identified statistically and upregulation of cell-proliferation-related genes was evident in the subclass with poor prognosis. In the subclass with better survival, genes involved in differentiated intracellular functions, such as the MAPKKK cascade, ceramide metabolism, or regulation of transcription, were upregulated. This work represents an important step toward the identification of clinically useful classification for lung SCC.

Biomarkers, Tumor↗

PubMed2004

Identification of MEF2-regulated genes during muscle differentiation.

Although a great deal has been elucidated concerning the mechanisms regulating muscle differentiation, little is known about transcription factor-specific gene regulation. Our understanding of the genetic mechanisms regulating cell differentiation is quite limited. Much of what has been defined centers on regulatory signaling cascades and transcription factors. Surprisingly few studies have investigated the association of genes with specific transcription factors. To address these issues, we have utilized a method coupling chromatin immunoprecipitation and CpG microarrays to characterize the genes associated with MEF2 in differentiating C(2)C(12) cells. Results demonstrated a defined binding pattern over the course of differentiation. Filtered data demonstrated 9 clones to be elevated at 0 h, 792 at 6 h, 163 by 1 day, and 316 at 3 days. Using unbiased selection parameters, we selected a subset of 291 prospective candidates. Clones were sequenced and filtered for removal of redundancy between clones and for the presence of repetitive elements. We were able to place 50 of these on the mouse genome, and 20 were found to be located near well-annotated genes. From this list, previously undefined associations with MEF2 were discovered. Many of these genes represent proteins involved in neurogenesis, neuromuscular junctions, signaling and metabolism. The remaining clones include many full-length cDNA and represent novel gene targets. The results of this study provides for the first time, a unique look at gene regulation at the level of transcription factor binding in differentiating muscle.

Actins↗

PubMed2004

Two prognostically significant subtypes of high-grade lung neuroendocrine tumours independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles.

BACKGROUND: Classification of high-grade neuroendocrine tumours (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40?386 elements to analyse the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumours and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analysed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumour samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analysed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterised markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumours, and provide important insights into their underlying biology.

Biomarkers, Tumor↗

PubMed2002

Integrated classification of lung tumors and cell lines by expression profiling.

The utility of cancer cell lines depends largely on their accurate classification, commonly based on histopathological diagnosis of the cancers from which they were derived. However, because cancer is often heterogeneous, the cell line, which also has the opportunity to alter in vitro, may not be representative. Yet without the overall architecture used in histopathological diagnosis of fresh samples, reclassification of cell lines has been difficult. Gene-expression profiling accurately reproduces histopathological classification and is readily applicable to cell lines. Here, we compare the gene-expression profiles of 41 cell lines with 44 tumors from lung cancer. These profiles were generated after hybridization of samples to four replicate 7,685-element cDNA microarrays. After removal of genes that were uniformly up- or down-regulated in fresh compared with cell-line samples, cluster analysis produced four major branch groups. Within these major branches, fresh tumor samples essentially clustered according to pathological type, and further subclusters were seen for both adenocarcinoma (AC) and small cell lung carcinoma (SCLC). Four of eight squamous cell carcinoma (SCC) cell lines clustered with fresh SCC, and 11 of 13 SCLC cell lines grouped with fresh SCLC. In contrast, although none of the 11 AC cell lines clustered with AC tumors, three clustered with SCC tumors and six with SCLC tumors. Although it is possible that preexisting SCC or SCLC cells are being selected from AC tumors after establishment of cell lines, we propose that, even in situ, AC will ultimately progress toward one of two poorly differentiated phenotypes with expression profiles resembling SCC or SCLC.

Adenocarcinoma↗