Longitudinal changes of telomere length and epigenetic age related to traumatic stress and post-traumatic stress disorder

Several studies have reported an association between traumatic stress and telomere length suggesting that traumatic stress has an impact on ageing at the cellular level. A newly derived tool provides an additional means to investigate cellular ageing by estimating epigenetic age based on DNA methylation profiles. We therefore hypothesise that in a longitudinal study of traumatic stress both indicators of cellular ageing will show increased ageing. We expect that particularly in individuals that developed symptoms of post-traumatic stress disorder (PTSD) increases in these ageing parameters would stand out.From an existing longitudinal cohort study, ninety-six male soldiers were selected based on trauma exposure and the presence of symptoms of PTSD. All military personnel were deployed in a combat zone in Afghanistan and assessed before and 6 months after deployment. The Self-Rating Inventory for PTSD was used to measure the presence of PTSD symptoms, while exposure to combat trauma during deployment was measured with a 19-item deployment experiences checklist. These groups did not differ for age, gender, alcohol consumption, cigarette smoking, military rank, length, weight, or medication use. In DNA from whole blood telomere length was measured and DNA methylation levels were assessed using the Illumina 450K DNA methylation arrays. Epigenetic ageing was estimated using the DNAm age estimator procedure.The association of trauma with telomere length was in the expected direction but not significant (. B=. -10.2, p=. 0.52). However, contrary to our expectations, development of PTSD symptoms was associated with the reverse process, telomere lengthening (. B=. 1.91, p=. 0.018). In concordance, trauma significantly accelerated epigenetic ageing (. B=. 1.97, p=. 0.032) and similar to the findings in telomeres, development of PTSD symptoms was inversely associated with epigenetic ageing (. B=. -0.10, p=. 0.044). Blood cell count, medication and premorbid early life trauma exposure did not confound the results.Overall, in this longitudinal study of military personnel deployed to Afghanistan we show an acceleration of ageing by trauma. However, development of PTSD symptoms was associated with telomere lengthening and reversed epigenetic ageing. These findings warrant further study of a perhaps dysfunctional compensatory cellular ageing reversal in PTSD

Від коваріацій до каузальності. Відкриття структур залежностей у даних

Проаналізовано сучасну методологію виводу каузальних моделей та структур систем імовірнісних залежностей із статистичних даних пасивних спостережень. Висвітлено можливості, проблеми, застереження та обмеження методів індуктивної ідентифікації каузальних відношень в апараті марковських властивостей та баєсових мереж. Виділено кілька ступенів каузальних моделей згідно з рівнем їх обґрунтованості та адекватності джерелу даних. Сформульовано статистичний паттерн, який зводить обґрунтування висновку про каузальний характер зв’язку двох змінних до тестування набору статистичних фактів (не)залежності.Проанализирована современная методология вывода каузальных моделей и структур систем вероятностных зависимостей из статистических данных пассивных наблюдений. Освещены возможности, проблемы, оговорки и ограничения методов индуктивной идентификации каузальных отношений в аппарате марковских свойств и байесовых сетей. Выделены несколько ступеней каузальных моделей согласно уровню их обоснованности и адекватности источнику данных. Сформулирован статистический паттерн, который сводит обоснование вывода о каузальном характере связи двух переменных к тестированию набора статистических фактов (не)зависимости.The current methodology of output casual models and structures of systems of probabilistic dependencies of stafistical data of passive observation is analysed. The problems, features, traps and limitations of the methods of the inductive identification of casual relation in the unit of marcov properties and bayesias nets are highlighted. Several stages of casual models according to the level of their validity and adequacy of the data source are emphasized. The statistical pattern, which brings the justification of a finding about casual nature of the connections between two variables to the test of a set of statistical facts of (in)dependency is formulated

Internet based vascular risk factor management for patients with clinically manifest vascular disease: randomised controlled trial

Objective To investigate whether an internet based, nurse led vascular risk factor management programme promoting self management on top of usual care is more effective than usual care alone in reducing vascular risk factors in patients with clinically manifest vascular disease

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Childhood abuse v. neglect and risk for major psychiatric disorders

Background. Childhood maltreatment (CM) is a strong risk factor for psychiatric disorders but serves in its current definitions as an umbrella for various fundamentally different childhood experiences. As first step toward a more refined analysis of the impact of CM, our objective is to revisit the relation of abuse and neglect, major subtypes of CM, with symptoms across disorders.Methods. Three longitudinal studies of major depressive disorder (MDD, N = 1240), bipolar disorder (BD, N = 1339), and schizophrenia (SCZ, N = 577), each including controls (N = 881), were analyzed. Multivariate regression models were used to examine the relation between exposure to abuse, neglect, or their combination to the odds for MDD, BD, SCZ, and symptoms across disorders. Bidirectional Mendelian randomization (MR) was used to probe causality, using genetic instruments of abuse and neglect derived from UK Biobank data (N = 143 473).Results. Abuse was the stronger risk factor for SCZ (OR 3.51, 95% CI 2.17-5.67) and neglect for BD (OR 2.69, 95% CI 2.09-3.46). Combined CM was related to increased risk exceeding additive effects of abuse and neglect for MDD (RERI = 1.4) and BD (RERI = 1.1). Across disorders, abuse was associated with hallucinations (OR 2.16, 95% CI 1.55-3.01) and suicide attempts (OR 2.16, 95% CI 1.55-3.01) whereas neglect was associated with agitation (OR 1.24, 95% CI 1.02-1.51) and reduced need for sleep (OR 1.64, 95% CI 1.08-2.48). MR analyses were consistent with a bidirectional causal effect of abuse with SCZ (IVWforward = 0.13, 95% CI 0.01-0.24).Conclusions. Childhood abuse and neglect are associated with different risks to psychiatric symptoms and disorders. Unraveling the origin of these differences may advance understanding of disease etiology and ultimately facilitate development of improved personalized treatment strategies

'Aspirin resistance' or treatment non-compliance: Which is to blame for cardiovascular complications?

Aspirin is one of the 'cornerstone' drugs in our current management of cardiovascular disorders. However, despite the prescription of aspirin recurrent vascular events still occur in 10–20% of patients. These, data together with the observations of diminished antiaggregatory response to aspirin in some subjects have provided the basis of the current debate on the existence of so-called "aspirin resistance". Unfortunately, many of the tests employed to define 'aspirin resistance' lack sufficient sensitivity, specificity, and reproducibility. The prevalence of 'aspirin resistance' as defined by each test varies widely, and furthermore, the value of a single point estimate measure of aspirin resistance is questionable. The rate of 'aspirin resistance' is law if patients observed to ingest aspirin, with large proportion of patients to be pseudo-'aspirin resistant', due to non-compliance. What are the implications for clinical practice? Possible non-adherence to aspirin prescription should also be carefully considered before changing to higher aspirin doses, other antiplatelet drugs (e.g. clopidogrel) or even combination antiplatelet drug therapy. Given the multifactorial nature of atherothrombotic disease, it is not surprising that only about 25% of all cardiovascular complications can usually be prevented by any single medication. We would advocate against routine testing of platelet sensitivity to aspirin (as an attempt to look for 'aspirin resistance') but rather, to highlight the importance of clinicians and public attention to the problem of treatment non-compliance

Correlations of blood and brain biochemistry in phenylketonuria: results from the Pah-enu2 PKU mouse

Background: In phenylketonuria (PKU), treatment monitoring is based on frequent blood phenylalanine (Phe) measurements, as this is the predictor of neurocognitive and behavioural outcome by reflecting brain Phe con-centrations and brain biochemical changes. Despite clinical studies describing the relevance of blood Phe to out-come in PKU patients, blood Phe does not explain the variance in neurocognitive and behavioural outcome completely. Methods: In a PKU mouse model we investigated 1) the relationship between plasma Phe and brain biochemistry (Brain Phe and monoaminergic neurotransmitter concentrations), and 2) whether blood non-Phe Large Neutral Amino Acids (LNAA) would be of additional value to blood Phe concentrations to explain brain biochemistry. To this purpose, we assessed blood amino acid concentrations and brain Phe as well as monoaminergic neuro -transmitter levels in in 114 Pah-Enu2 mice on both B6 and BTBR backgrounds using (multiple) linear regression analyses. Results: Plasma Phe concentrations were strongly correlated to brain Phe concentrations, significantly negatively correlated to brain serotonin and norepinephrine concentrations and only weakly correlated to brain dopamine concentrations. From all blood markers, Phe showed the strongest correlation to brain biochemistry in PKU mice. Including non-Phe LNAA concentrations to the multiple regression model, in addition to plasma Phe, did not help explain brain biochemistry. Conclusion: This study showed that blood Phe is still the best amino acid predictor of brain biochemistry in PKU. Nevertheless, neurocognitive and behavioural outcome cannot fully be explained by blood or brain Phe concen-trations, necessitating a search for other additional parameters. Take-home message: Blood Phe is still the best amino acid predictor of brain biochemistry in PKU. Nevertheless, neurocognitive and behavioural outcome cannot fully be explained by blood or brain Phe concentrations, neces-sitating a search for other additional parameters. (c) 2021 Published by Elsevier Inc.Education and Child Studie

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment