Longitudinal study of epigenetic aging and its relationship with brain aging and cognitive skills in young adulthood.

Introduction: The proportion of older adults within society is sharply increasing and a better understanding of how we age starts to be critical. However, given the paucity of longitudinal studies with both neuroimaging and epigenetic data, it remains largely unknown whether the speed of the epigenetic clock changes over the life course and whether any such changes are proportional to changes in brain aging and cognitive skills. To fill these knowledge gaps, we conducted a longitudinal study of a prenatal birth cohort, studied epigenetic aging across adolescence and young adulthood, and evaluated its relationship with brain aging and cognitive outcomes. Methods: DNA methylation was assessed using the Illumina EPIC Platform in adolescence, early and late 20 s, DNA methylation age was estimated using Horvath's epigenetic clock, and epigenetic age gap (EpiAGE) was calculated as DNA methylation age residualized for batch, chronological age and the proportion of epithelial cells. Structural magnetic resonance imaging (MRI) was acquired in both the early 20 s and late 20 s using the same 3T Prisma MRI scanner and brain age was calculated using the Neuroanatomical Age Prediction using R (NAPR) platform. Cognitive skills were assessed using the Wechsler Adult Intelligence Scale (WAIS) in the late 20 s. Results: The EpiAGE in adolescence, the early 20 s, and the late 20 s were positively correlated (r = 0.34-0.47), suggesting that EpiAGE is a relatively stable characteristic of an individual. Further, a faster pace of aging between the measurements was positively correlated with EpiAGE at the end of the period (r = 0.48-0.77) but negatively correlated with EpiAGE at the earlier time point (r = -0.42 to -0.55), suggesting a compensatory mechanism where late matures might be catching up with the early matures. Finally, higher positive EpiAGE showed small (Adj R2 = 0.03) but significant relationships with a higher positive brain age gap in all participants and lower full-scale IQ in young adult women in the late 20 s. Discussion: We conclude that the EpiAGE is a relatively stable characteristic of an individual across adolescence and early adulthood, but that it shows only a small relationship with accelerated brain aging and a women-specific relationship with worse performance IQ.

IntOMICS: A Bayesian Framework for Reconstructing Regulatory Networks Using Multi-Omics Data.

Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components. We present a new comprehensive R/Bioconductor-package, IntOMICS, which implements a Bayesian framework for multi-omics data integration. IntOMICS adopts a Markov Chain Monte Carlo sampling scheme to systematically analyze gene expression, copy number variation, DNA methylation, and biological prior knowledge to infer regulatory networks. The unique feature of IntOMICS is an empirical biological knowledge estimation from the available experimental data, which complements the missing biological prior knowledge. IntOMICS has the potential to be a powerful resource for exploratory systems biology.

Using empirical biological knowledge to infer regulatory networks from multi-omics data.

BACKGROUND: Integration of multi-omics data can provide a more complex view of the biological system consisting of different interconnected molecular components, the crucial aspect for developing novel personalised therapeutic strategies for complex diseases. Various tools have been developed to integrate multi-omics data. However, an efficient multi-omics framework for regulatory network inference at the genome level that incorporates prior knowledge is still to emerge. RESULTS: We present IntOMICS, an efficient integrative framework based on Bayesian networks. IntOMICS systematically analyses gene expression, DNA methylation, copy number variation and biological prior knowledge to infer regulatory networks. IntOMICS complements the missing biological prior knowledge by so-called empirical biological knowledge, estimated from the available experimental data. Regulatory networks derived from IntOMICS provide deeper insights into the complex flow of genetic information on top of the increasing accuracy trend compared to a published algorithm designed exclusively for gene expression data. The ability to capture relevant crosstalks between multi-omics modalities is verified using known associations in microsatellite stable/instable colon cancer samples. Additionally, IntOMICS performance is compared with two algorithms for multi-omics regulatory network inference that can also incorporate prior knowledge in the inference framework. IntOMICS is also applied to detect potential predictive biomarkers in microsatellite stable stage III colon cancer samples. CONCLUSIONS: We provide IntOMICS, a framework for multi-omics data integration using a novel approach to biological knowledge discovery. IntOMICS is a powerful resource for exploratory systems biology and can provide valuable insights into the complex mechanisms of biological processes that have a vital role in personalised medicine.

Epigenetic clock as a correlate of anxiety.

DNA methylation changes consistently throughout life and age-dependent alterations in DNA methylation can be used to estimate one's epigenetic age. Post-mortem studies revealed higher epigenetic age in brains of patients with major depressive disorder, as compared with controls. Since MDD is highly correlated with anxiety, we hypothesized that symptoms of anxiety, as well as lower volume of grey matter (GM) in depression-related cortical regions, will be associated with faster epigenetic clock in a community-based sample of young adults. Participants included 88 young adults (53% men; 23-24 years of age) from the European Longitudinal Study of Pregnancy and Childhood (ELSPAC) who participated in its neuroimaging follow-up and provided saliva samples for epigenetic analysis. Epigenetic age was calculated according to Horvath (Horvath, 2013). Women had slower epigenetic clock than men (Cohen's d = 0.48). In women (but not men), slower epigenetic clock was associated with less symptoms of anxiety. In the brain, women (but not men) with slower epigenetic clock had greater GM volume in the cerebral cortex (brain size-corrected; R2 = 0.07). Lobe-specific analyses showed that in women (but not men), slower epigenetic clock was associated with greater GM volume in frontal lobe (R2 = 0.16), and that GM volume in frontal lobe mediated the relationship between the speed of epigenetic clock and anxiety trait (ab = 0.15, SE = 0.15, 95% CI [0.007; 0.369]). These findings were not replicated, however, in a community-based sample of adolescents (n = 129; 49% men; 12-19 years of age), possibly due to the different method of tissue collection (blood vs. saliva) or additional sources of variability in the cohort of adolescents (puberty stages, socioeconomic status, prenatal exposure to maternal smoking during pregnancy).

Cross-platform Data Analysis Reveals a Generic Gene Expression Signature for Microsatellite Instability in Colorectal Cancer.

The dysfunction of the DNA mismatch repair system results in microsatellite instability (MSI). MSI plays a central role in the development of multiple human cancers. In colon cancer, despite being associated with resistance to 5-fluorouracil treatment, MSI is a favourable prognostic marker. In gastric and endometrial cancers, its prognostic value is not so well established. Nevertheless, recognising the MSI tumours may be important for predicting the therapeutic effect of immune checkpoint inhibitors. Several gene expression signatures were trained on microarray data sets to understand the regulatory mechanisms underlying microsatellite instability in colorectal cancer. A wealth of expression data already exists in the form of microarray data sets. However, the RNA-seq has become a routine for transcriptome analysis. A new MSI gene expression signature presented here is the first to be valid across two different platforms, microarrays and RNA-seq. In the case of colon cancer, its estimated performance was (i) AUC = 0.94, 95% CI = (0.90 - 0.97) on RNA-seq and (ii) AUC = 0.95, 95% CI = (0.92 - 0.97) on microarray. The 25-gene expression signature was also validated in two independent microarray colon cancer data sets. Despite being derived from colorectal cancer, the signature maintained good performance on RNA-seq and microarray gastric cancer data sets (AUC = 0.90, 95% CI = (0.85 - 0.94) and AUC = 0.83, 95% CI = (0.69 - 0.97), respectively). Furthermore, this classifier retained high concordance even when classifying RNA-seq endometrial cancers (AUC = 0.71, 95% CI = (0.62 - 0.81). These results indicate that the new signature was able to remove the platform-specific differences while preserving the underlying biological differences between MSI/MSS phenotypes in colon cancer samples.