Epigenetic alterations during aging have been proposed to contribute to decline in physical and cognitive functions, and accelerated epigenetic aging has been associated with disease and all-cause mortality later in life. In this study, we estimated epigenetic age dynamics in groups with different memory trajectories (maintained high performance, average decline, and accelerated decline) over a 15-year period. Epigenetic (DNA-methylation [DNAm]) age was assessed, and delta age (DNAm age - chronological age) was calculated in blood samples at baseline (age: 55-65 years) and 15 years later in 52 age- and gender-matched individuals from the Betula study in Sweden. A lower delta DNAm age was observed for those with maintained memory functions compared with those with average (p = 0.035) or accelerated decline (p = 0.037). Moreover, separate analyses revealed that DNAm age at follow-up, but not chronologic age, was a significant predictor of dementia (p = 0.019). Our findings suggest that young epigenetic age contributes to maintained memory in aging.

Background Despite increased knowledge about genetic aberrations in pediatric T‐cell acute lymphoblastic leukemia (T‐ALL), no clinically feasible treatment‐stratifying marker exists at diagnosis. Instead patients are enrolled in intensive induction therapies with substantial side effects. In modern protocols, therapy response is monitored by minimal residual disease (MRD) analysis and used for postinduction risk group stratification. DNA methylation profiling is a candidate for subtype discrimination at diagnosis and we investigated its role as a prognostic marker in pediatric T‐ALL. Procedure Sixty‐five diagnostic T‐ALL samples from Nordic pediatric patients treated according to the Nordic Society of Pediatric Hematology and Oncology ALL 2008 (NOPHO ALL 2008) protocol were analyzed by HumMeth450K genome wide DNA methylation arrays. Methylation status was analyzed in relation to clinical data and early T‐cell precursor (ETP) phenotype. Results Two distinct CpG island methylator phenotype (CIMP) groups were identified. Patients with a CIMP‐negative profile had an inferior response to treatment compared to CIMP‐positive patients (3‐year cumulative incidence of relapse (CIR3y) rate: 29% vs. 6%, P = 0.01). Most importantly, CIMP classification at diagnosis allowed subgrouping of high‐risk T‐ALL patients (MRD ≥0.1% at day 29) into two groups with significant differences in outcome (CIR3y rates: CIMP negative 50% vs. CIMP positive 12%; P = 0.02). These groups did not differ regarding ETP phenotype, but the CIMP‐negative group was younger (P = 0.02) and had higher white blood cell count at diagnosis (P = 0.004) compared with the CIMP‐positive group. Conclusions CIMP classification at diagnosis in combination with MRD during induction therapy is a strong candidate for further risk classification and could confer important information in treatment decision making.

BackgroundCluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization.ResultWe evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is preferable, in particular if the gene selection is successful. However, this is an area that needs to be studied further in order to draw any general conclusions.ConclusionsThe choice of cluster analysis, and in particular gene selection, has a large impact on the ability to cluster individuals correctly based on expression profiles. Normalization has a positive effect, but the relative performance of different normalizations is an area that needs more research. In summary, although clustering, gene selection and normalization are considered standard methods in bioinformatics, our comprehensive analysis shows that selecting the right methods, and the right combinations of methods, is far from trivial and that much is still unexplored in what is considered to be the most basic analysis of genomic data.

Classification of pediatric T‐cell acute lymphoblastic leukemia (T‐ALL) patients into CIMP (CpG Island Methylator Phenotype) subgroups has the potential to improve current risk stratification. To investigate the biology behind these CIMP subgroups, diagnostic samples from Nordic pediatric T‐ALL patients were characterized by genome‐wide methylation arrays, followed by targeted exome sequencing, telomere length measurement, and RNA sequencing. The CIMP subgroups did not correlate significantly with variations in epigenetic regulators. However, the CIMP+ subgroup, associated with better prognosis, showed indicators of longer replicative history, including shorter telomere length (P = 0.015) and older epigenetic (P < 0.001) and mitotic age (P < 0.001). Moreover, the CIMP+ subgroup had significantly higher expression of ANTP homeobox oncogenes, namely TLX3, HOXA9, HOXA10, and NKX2‐1, and novel genes in T‐ALL biology including PLCB4, PLXND1, and MYO18B. The CIMP− subgroup, with worse prognosis, was associated with higher expression of TAL1 along with frequent STIL‐TAL1 fusions (2/40 in CIMP+ vs 11/24 in CIMP−), as well as stronger expression of BEX1. Altogether, our findings suggest different routes for leukemogenic transformation in the T‐ALL CIMP subgroups, indicated by different replicative histories and distinct methylomic and transcriptomic profiles. These novel findings can lead to new therapeutic strategies.

Tularemia is a febrile disease caused by the highly contagious bacterium Francisella tularensis. We undertook an analysis of the transcriptional response in peripheral blood during the course of ulceroglandular tularemia by use of Affymetrix microarrays comprising 14 500 genes. Samples were obtained from seven individuals at five occasions during 2 weeks after the first hospital visit and convalescent samples 3 months later. In total, 265 genes were differentially expressed, 95 of which at more than one time point. The differential expression was verified with real-time quantitative polymerase chain reaction for 36 genes (R 2 ¼ 0.590). The most prominent changes were noted in samples drawn on days 2-3 and a considerable proportion of the upregulated genes appeared to represent an interferon-g-induced response and also a proapoptotic response. Genes involved in the generation of innate and acquired immune responses were found to be downregulated, presumably a pathogen-induced event. A logistic regression analysis revealed that seven genes were good predictors of the early phase of tularemia. This is the first description of the transcriptional host response to ulceroglandular tularemia and the study has identified gene subsets relevant to the pathogenesis of the disease and subsets that may serve as early diagnostic biomarkers.