Beta cell function in type 1 diabetes determined from clinical and fasting biochemical variables

AIMS/HYPOTHESIS: Beta cell function in type 1 diabetes is commonly assessed as the average plasma C-peptide concentration over 2 h following a mixed-meal test (CPAVE). Monitoring of disease progression and response to disease-modifying therapy would benefit from a simpler, more convenient and less costly measure. Therefore, we determined whether CPAVE could be reliably estimated from routine clinical variables. METHODS: Clinical and fasting biochemical data from eight randomised therapy trials involving participants with recently diagnosed type 1 diabetes were used to develop and validate linear models to estimate CPAVE and to test their accuracy in estimating loss of beta cell function and response to immune therapy. RESULTS: A model based on disease duration, BMI, insulin dose, HbA1c, fasting plasma C-peptide and fasting plasma glucose most accurately estimated loss of beta cell function (area under the receiver operating characteristic curve [AUROC] 0.89 [95% CI 0.87, 0.92]) and was superior to the commonly used insulin-dose-adjusted HbA1c (IDAA1c) measure (AUROC 0.72 [95% CI 0.68, 0.76]). Model-estimated CPAVE (CPEST) reliably identified treatment effects in randomised trials. CPEST, compared with CPAVE, required only a modest (up to 17%) increase in sample size for equivalent statistical power. CONCLUSIONS/INTERPRETATION: CPEST, approximated from six variables at a single time point, accurately identifies loss of beta cell function in type 1 diabetes and is comparable to CPAVE for identifying treatment effects. CPEST could serve as a convenient and economical measure of beta cell function in the clinic and as a primary outcome measure in trials of disease-modifying therapy in type 1 diabetes

Genome-wide analysis reveals no evidence of trans chromosomal regulation of mammalian immune development.

It has been proposed that interactions between mammalian chromosomes, or transchromosomal interactions (also known as kissing chromosomes), regulate gene expression and cell fate determination. Here we aimed to identify novel transchromosomal interactions in immune cells by high-resolution genome-wide chromosome conformation capture. Although we readily identified stable interactions in cis, and also between centromeres and telomeres on different chromosomes, surprisingly we identified no gene regulatory transchromosomal interactions in either mouse or human cells, including previously described interactions. We suggest that advances in the chromosome conformation capture technique and the unbiased nature of this approach allow more reliable capture of interactions between chromosomes than previous methods. Overall our findings suggest that stable transchromosomal interactions that regulate gene expression are not present in mammalian immune cells and that lineage identity is governed by cis, not trans chromosomal interactions

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

Genome-wide analysis reveals no evidence of trans chromosomal regulation of mammalian immune development.

It has been proposed that interactions between mammalian chromosomes, or transchromosomal interactions (also known as kissing chromosomes), regulate gene expression and cell fate determination. Here we aimed to identify novel transchromosomal interactions in immune cells by high-resolution genome-wide chromosome conformation capture. Although we readily identified stable interactions in cis, and also between centromeres and telomeres on different chromosomes, surprisingly we identified no gene regulatory transchromosomal interactions in either mouse or human cells, including previously described interactions. We suggest that advances in the chromosome conformation capture technique and the unbiased nature of this approach allow more reliable capture of interactions between chromosomes than previous methods. Overall our findings suggest that stable transchromosomal interactions that regulate gene expression are not present in mammalian immune cells and that lineage identity is governed by cis, not trans chromosomal interactions

Identification of transchromosomal interactions in mammalian immune cells.

<p><b>(A)</b> Promoter capture HiC contact matrix in human CD4⁺ T cells confirming intrachromosomal interactions previously reported in the mouse Th2 locus control region <b>(B)</b> Heatmaps of chromosomes involved in detected transchromosomal interactions in mouse B cells, CD4⁺ and CD8⁺ T cells before and after exclusion of interactions associated with blacklisted regions. <b>(C)</b> Numbers of transchromosomal (upper panels) and intrachromosomal interactions (lower panels) common, or unique to murine B cells, CD4⁺ or CD8⁺ T cells before (white) and after (black) exclusion of interactions associated with blacklisted regions. <b>(D)</b> Circos plot of transchromosomal interactions in human B cells. Insets show examples of interactions associated with centromeres and telomeres. Centromeres are shown in red. <b>(E)</b> Association of mouse or human B cell specific transchromosomal (black histogram) and intrachromosomal interactions (grey line) with telomeres or centromeres. The x-axis is normalised to chromosome length starting from the telomere.</p

Reported transchromosomal interactions are not detected by <i>in situ</i> HiC.

<p><b>(A)</b> HiC contact matrix of regions on chromosome 10 and 11 in mouse CD4⁺ T cells previously reported to interact. Dotted squares show regions reported to interact. Colour intensity represents interaction with white being absence of detected interaction and black being intense interaction. Pixels are 20kB. <b>(B)</b> HiC contact matrix of regions on chromosome 1 and 11 in mouse CD4⁺ T cells previously reported to interact. Dotted square shows regions reported to interact. <b>(C)</b> Expanded HiC contact matrix of regions on chromosome 10 and 11 in mouse CD4⁺ T cells previously reported to interact. Dotted square encloses the region shown in Fig 2A. <b>(D)</b> Expanded HiC contact matrix of regions on chromosome 1 and 11 in mouse CD4⁺ T cells previously reported to interact. Dotted square encloses the region shown in Fig 2B. <b>(E)</b> HiC contact matrices showing the detected intrachromosomal interactions in mouse CD4⁺ T cells in the two regions on chromosome 10 and 11 reported to interact in <i>trans</i>. <b>(F)</b> HiC contact matrices showing the detected intrachromosomal interactions in mouse CD4⁺ T cells in the two regions on chromosome 1 and 11 reported to interact in <i>trans</i>. <b>(G)</b> HiC contact matrix of regions on chromosome 12 and 5 in human CD4⁺ T cells previously reported to interact in mouse CD4⁺ T cells. <b>(H)</b> HiC contact matrix of regions on chromosome 6 and 5 in human CD4⁺ T cells previously reported to interact in mouse CD4⁺ T cells. <b>(I)</b> Expanded HiC contact matrix of regions on chromosome 12 and 5 in human CD4⁺ T cells previously reported to interact. Dotted square encloses the region shown in Fig 2G. <b>(J)</b> Expanded HiC contact matrix of regions on chromosome 6 and 5 in human CD4⁺ T cells previously reported to interact in mouse CD4⁺ T cells. Dotted square encloses the region shown in Fig 2H. <b>(K)</b> HiC contact matrices showing the detected intrachromosomal interactions in human CD4⁺ T cells in the two regions on chromosome 12 and 5 reported to interact in <i>trans</i>. <b>(L)</b> HiC contact matrices showing the detected intrachromosomal interactions in human CD4⁺ T cells in the two regions on chromosome 6 and 5 reported to interact in <i>trans</i>.</p