Aging is often perceived as a degenerative process caused by random accrual of cellular damage over time. In spite of this, age can be accurately estimated by epigenetic clocks based on DNA methylation profiles from almost any tissue of the body. Since such pan-tissue epigenetic clocks have been successfully developed for several different species, it is difficult to ignore the likelihood that a defined and shared mechanism instead, underlies the aging process. To address this, we generated 10,000 methylation arrays, each profiling up to 37,000 cytosines in highly-conserved stretches of DNA, from over 59 tissue-types derived from 128 mammalian species. From these, we identified and characterized specific cytosines, whose methylation levels change with age across mammalian species. Genes associated with these cytosines are greatly enriched in mammalian developmental processes and implicated in age-associated diseases. From the methylation profiles of these age-related cytosines, we successfully constructed three highly accurate universal mammalian clocks for eutherians, and one universal clock for marsupials. The universal clocks for eutherians are similarly accurate for estimating ages (r>0.96) of any mammalian species and tissue with a single mathematical formula. Collectively, these new observations support the notion that aging is indeed evolutionarily conserved and coupled to developmental processes across all mammalian species - a notion that was long-debated without the benefit of this new and compelling evidence.

Cognitive deficits are a core feature of psychiatric disorders like schizophrenia and bipolar disorder. Evidence supports a genome-wide polygenic score (GPS) for educational attainment (GPS EDU ) can be used to explain variability in cognitive performance. We aimed to identify different cognitive domains associated with GPS EDU in a transdiagnostic clinical cohort of chronic psychiatric patients with known cognitive deficits. Bipolar and schizophrenia patients from the PsyCourse cohort ( N = 730; 43% female) were used. Likewise, we tested whether GPSs for schizophrenia (GPS SZ ) and bipolar disorder (GPS BD ) were associated with cognitive outcomes. GPS EDU explained 1.5% of variance in the backward verbal digit span, 1.9% in the number of correctly recalled words of the Verbal Learning and Memory Test, and 1.1% in crystallized intelligence. These effects were robust to the influences of treatment and diagnosis. No significant associations between GPS SZ or GPS BD with cognitive outcomes were found. Furthermore, these risk scores did not confound the effect of GPS EDU on cognitive outcomes. GPS EDU explains a small fraction of cognitive performance in adults with psychiatric disorders, specifically for domains related to linguistic learning and working memory. Investigating such a proxy-phenotype longitudinally, could give intriguing insight into the disease course, highlighting at what time genes play a more influential role on cognitive performance. Better understanding the origin of these deficits might help identify those patients at risk for lower levels of functioning and poor social outcomes. Polygenic estimates may in the future be part of predictive models for more personalized interventions.

Protein interaction networks are widely used in computational biology as a graphical means of representing higher-level systemic functions in a computable form. Although, many algorithms exist that seamlessly collect and measure protein interaction information in network models, they often do not provide novel mechanistic insights using quantitative criteria. Measuring information content and knowledge representation in network models about disease mechanisms becomes crucial particularly when exploring new target candidates in a well-defined functional context of a potential disease mechanism. To this end, we have developed a knowledge-based scoring approach that uses literature-derived protein interaction features to quantify protein interaction confidence. Thereby, we introduce the novel concept of knowledge cliffs, regions of the interaction network where a significant gap between high scoring and low scoring interactions is observed, representing a divide between established and emerging knowledge on disease mechanism. To show the application of this approach, we constructed and assessed reliability of a protein-protein interaction model specific to Alzheimer’s disease, which led to screening, and prioritization of four novel protein candidates. Evaluation of the identified candidates showed that two of them are already followed in clinical trials for testing potential AD drugs.

Objective: We examine the trajectories of and the dynamic interplay between cognitive functioning and depressive symptoms in patients with Parkinson's disease (PD) in comparison to healthy controls (HC) from an intraindividual perspective.Method: The DeNoPa study is a single-center, observational, longitudinal study with biennial follow-ups over 8 years. The present analyses are based on 123 PD (79 male) and 107 HC (64 male) with a mean age of 64.1. PD and HC completed a comprehensive battery of neurological tests and scales assessing depressive symptoms. To study their trajectories and the dynamic interplay we used a random-intercept cross-lagged panel model.Results: Cognitive abilities of PD were on average d = -0.56 worse at baseline and d = -0.93 at 8-years follow-up in comparison to HC. Depressive symptoms in PD showed a large variability and followed a U-shaped trajectory. From an intraindividual perspective, stronger impairments in cognitive abilities were subsequently associated with increased depressive symptoms (b = -0.52, p = .01), whereas the effect in the opposite direction was not significant.Conclusions: We found no indication that depressive symptoms can be seen as precursors of dementia. On the contrary, to counter cognitive losses and the subsequent mood deterioration, patient education and early cognitive (and behavioral) enrichment seem promising candidates for treatment.