Monte Carlo simulation of signals in digital diaphanoscopy of the maxillary sinuses

Digital diaphanoscopy method has potential to separate normal and pathological conditions of the maxillary sinuses. The entirety of all the features of the investigated area (the presence or absence of pathology, its etiology and morphological features) affects the resulting images of the maxillary sinuses by the digital diaphanoscopy. In this work, the MonteCarlo numerical simulation method was used to determine the patterns of propagation of light radiation in biological tissue. A biologically heterogeneous environment, represented by structures of the skull and maxillary sinuses, as well as pathological changes in them was modelled in the TracePro software

DNA methylation epigenotypes in breast cancer molecular subtypes

12 páginas, 3 figuras, 3 tablas.-- et al.[Introduction]: Identification of gene expression-based breast cancer subtypes is considered a critical means of prognostication. Genetic mutations along with epigenetic alterations contribute to gene-expression changes occurring in breast cancer. So far, these epigenetic contributions to sporadic breast cancer subtypes have not been well characterized, and only a limited understanding exists of the epigenetic mechanisms affected in those particular breast cancer subtypes. The present study was undertaken to dissect the breast cancer methylome and to deliver specific epigenotypes associated with particular breast cancer subtypes. [Methods]: By using a microarray approach, we analyzed DNA methylation in regulatory regions of 806 cancer-related genes in 28 breast cancer paired samples. We subsequently performed substantial technical and biologic validation by pyrosequencing, investigating the top qualifying 19 CpG regions in independent cohorts encompassing 47 basal-like, 44 ERBB2+ overexpressing, 48 luminal A, and 48 luminal B paired breast cancer/adjacent tissues. With the all-subset selection method, we identified the most subtype-predictive methylation profiles in multivariable logistic regression analysis. [Results]: The approach efficiently recognized 15 individual CpG loci differentially methylated in breast cancer tumor subtypes. We further identified novel subtype-specific epigenotypes that clearly demonstrate the differences in the methylation profiles of basal-like and human epidermal growth factor 2 (HER2)-overexpressing tumors. [Conclusions]: Our results provide evidence that well-defined DNA methylation profiles enable breast cancer subtype prediction and support the utilization of this biomarker for prognostication and therapeutic stratification of patients with breast cancer.This work was supported by grants from project CGL2008-01131 (Departamento de Sanidad del Gobierno Vasco), S-PE08UN45 and PE09BF02 (Departamento de Ciencia y Tecnologia del Gobierno Vasco), BIO2008-04212, and RD06/0020/1019 (Red Tematica de Investigacion Cooperativa en Cancer, RTICC) from the MICINN. The CIBER de Enfermedades Raras is an initiative of the ISCIII. NGB had a doctoral fellowship from the Basque Government (Departamento de Educacion, Universidades e Investigacion).Peer reviewe

A Tissue Biomarker Panel Predicting Systemic Progression after PSA Recurrence Post-Definitive Prostate Cancer Therapy

Many men develop a rising PSA after initial therapy for prostate cancer. While some of these men will develop a local or metastatic recurrence that warrants further therapy, others will have no evidence of disease progression. We hypothesized that an expression biomarker panel can predict which men with a rising PSA would benefit from further therapy.A case-control design was used to test the association of gene expression with outcome. Systemic (SYS) progression cases were men post-prostatectomy who developed systemic progression within 5 years after PSA recurrence. PSA progression controls were matched men post-prostatectomy with PSA recurrence but no evidence of clinical progression within 5 years. Using expression arrays optimized for paraffin-embedded tissue RNA, 1021 cancer-related genes were evaluated-including 570 genes implicated in prostate cancer progression. Genes from 8 previously reported marker panels were included. A systemic progression model containing 17 genes was developed. This model generated an AUC of 0.88 (95% CI: 0.84-0.92). Similar AUCs were generated using 3 previously reported panels. In secondary analyses, the model predicted the endpoints of prostate cancer death (in SYS cases) and systemic progression beyond 5 years (in PSA controls) with hazard ratios 2.5 and 4.7, respectively (log-rank p-values of 0.0007 and 0.0005). Genes mapped to 8q24 were significantly enriched in the model.Specific gene expression patterns are significantly associated with systemic progression after PSA recurrence. The measurement of gene expression pattern may be useful for determining which men may benefit from additional therapy after PSA recurrence

Histone deacetylase controls adult stem cell aging by balancing the expression of polycomb genes and jumonji domain containing 3

Aging is linked to loss of the self-renewal capacity of adult stem cells. Here, we observed that human multipotent stem cells (MSCs) underwent cellular senescence in vitro. Decreased expression of histone deacetylases (HDACs), followed by downregulation of polycomb group genes (PcGs), such as BMI1, EZH2 and SUZ12, and by upregulation of jumonji domain containing 3 (JMJD3), was observed in senescent MSCs. Similarly, HDAC inhibitors induced cellular senescence through downregulation of PcGs and upregulation of JMJD3. Regulation of PcGs was associated with HDAC inhibitor-induced hypophosphorylation of RB, which causes RB to bind to and decrease the transcriptional activity of E2F. JMJD3 expression regulation was dependant on histone acetylation status at its promoter regions. A histone acetyltransferase (HAT) inhibitor prevented replicative senescence of MSCs. These results suggest that HDAC activity might be important for MSC self-renewal by balancing PcGs and JMJD3 expression, which govern cellular senescence by p16INK4A regulation

DNA Methylation and Gene Expression Changes in Monozygotic Twins Discordant for Psoriasis: Identification of Epigenetically Dysregulated Genes

Monozygotic (MZ) twins do not show complete concordance for many complex diseases; for example, discordance rates for autoimmune diseases are 20%–80%. MZ discordance indicates a role for epigenetic or environmental factors in disease. We used MZ twins discordant for psoriasis to search for genome-wide differences in DNA methylation and gene expression in CD4+ and CD8+ cells using Illumina's HumanMethylation27 and HT-12 expression assays, respectively. Analysis of these data revealed no differentially methylated or expressed genes between co-twins when analyzed separately, although we observed a substantial amount of small differences. However, combined analysis of DNA methylation and gene expression identified genes where differences in DNA methylation between unaffected and affected twins were correlated with differences in gene expression. Several of the top-ranked genes according to significance of the correlation in CD4+ cells are known to be associated with psoriasis. Further, gene ontology (GO) analysis revealed enrichment of biological processes associated with the immune response and clustering of genes in a biological pathway comprising cytokines and chemokines. These data suggest that DNA methylation is involved in an epigenetic dysregulation of biological pathways involved in the pathogenesis of psoriasis. This is the first study based on data from MZ twins discordant for psoriasis to detect epigenetic alterations that potentially contribute to development of the disease

Batch effect correction for genome-wide methylation data with Illumina Infinium platform

<p>Abstract</p> <p>Background</p> <p>Genome-wide methylation profiling has led to more comprehensive insights into gene regulation mechanisms and potential therapeutic targets. Illumina Human Methylation BeadChip is one of the most commonly used genome-wide methylation platforms. Similar to other microarray experiments, methylation data is susceptible to various technical artifacts, particularly batch effects. To date, little attention has been given to issues related to normalization and batch effect correction for this kind of data.</p> <p>Methods</p> <p>We evaluated three common normalization approaches and investigated their performance in batch effect removal using three datasets with different degrees of batch effects generated from HumanMethylation27 platform: quantile normalization at average β value (QNβ); two step quantile normalization at probe signals implemented in "lumi" package of R (lumi); and quantile normalization of A and B signal separately (ABnorm). Subsequent Empirical Bayes (EB) batch adjustment was also evaluated.</p> <p>Results</p> <p>Each normalization could remove a portion of batch effects and their effectiveness differed depending on the severity of batch effects in a dataset. For the dataset with minor batch effects (Dataset 1), normalization alone appeared adequate and "lumi" showed the best performance. However, all methods left substantial batch effects intact in the datasets with obvious batch effects and further correction was necessary. Without any correction, 50 and 66 percent of CpGs were associated with batch effects in Dataset 2 and 3, respectively. After QNβ, lumi or ABnorm, the number of CpGs associated with batch effects were reduced to 24, 32, and 26 percent for Dataset 2; and 37, 46, and 35 percent for Dataset 3, respectively. Additional EB correction effectively removed such remaining non-biological effects. More importantly, the two-step procedure almost tripled the numbers of CpGs associated with the outcome of interest for the two datasets.</p> <p>Conclusion</p> <p>Genome-wide methylation data from Infinium Methylation BeadChip can be susceptible to batch effects with profound impacts on downstream analyses and conclusions. Normalization can reduce part but not all batch effects. EB correction along with normalization is recommended for effective batch effect removal.</p

Influence of Genetic Background and Tissue Types on Global DNA Methylation Patterns

Recent studies have shown a genetic influence on gene expression variation, chromatin, and DNA methylation. However, the effects of genetic background and tissue types on DNA methylation at the genome-wide level have not been characterized extensively. To study the effect of genetic background and tissue types on global DNA methylation, we performed DNA methylation analysis using the Affymetrix 500K SNP array on tumor, adjacent normal tissue, and blood DNA from 30 patients with esophageal squamous cell carcinoma (ESCC). The use of multiple tissues from 30 individuals allowed us to evaluate variation of DNA methylation states across tissues and individuals. Our results demonstrate that blood and esophageal tissues shared similar DNA methylation patterns within the same individual, suggesting an influence of genetic background on DNA methylation. Furthermore, we showed that tissue types are important contributors of DNA methylation states

Highly Parallel Genome-Wide Expression Analysis of Single Mammalian Cells

We have developed a high-throughput amplification method for generating robust gene expression profiles using single cell or low RNA inputs.The method uses tagged priming and template-switching, resulting in the incorporation of universal PCR priming sites at both ends of the synthesized cDNA for global PCR amplification. Coupled with a whole-genome gene expression microarray platform, we routinely obtain expression correlation values of R(2)~0.76-0.80 between individual cells and R(2)~0.69 between 50 pg total RNA replicates. Expression profiles generated from single cells or 50 pg total RNA correlate well with that generated with higher input (1 ng total RNA) (R(2)~0.80). Also, the assay is sufficiently sensitive to detect, in a single cell, approximately 63% of the number of genes detected with 1 ng input, with approximately 97% of the genes detected in the single-cell input also detected in the higher input.In summary, our method facilitates whole-genome gene expression profiling in contexts where starting material is extremely limiting, particularly in areas such as the study of progenitor cells in early development and tumor stem cell biology