HEALTH AND DEPRIVATION: Inequality and the north

A plethora of epigenetic modifications have been described in the human genome and shown to play diverse roles in gene regulation, cellular differentiation and the onset of disease. Although individual modifications have been linked to the activity levels of various genetic functional elements, their combinatorial patterns are still unresolved and their potential for systematic de novo genome annotation remains untapped. Here, we use a multivariate Hidden Markov Model to reveal chromatin states in human T cells, based on recurrent and spatially coherent combinations of chromatin marks.We define 51 distinct chromatin states, including promoter-associated, transcription-associated, active intergenic, largescale repressed and repeat-associated states. Each chromatin state shows specific enrichments in functional annotations, sequence motifs and specific experimentally observed characteristics, suggesting distinct biological roles. This approach provides a complementary functional annotation of the human genome that reveals the genome-wide locations of diverse classes of epigenetic function.

Discovery and characterization of chromatin states for systematic annotation of the human genome

BACKGROUND
Depression, anxiety and somatization are the most common mental disorders in primary care as well as medical specialty populations; each is present in at least 5-10% of patients and frequently comorbid with one another. An efficient means for measuring and monitoring all three conditions would be desirable.


METHODS
Evidence regarding the psychometric and pragmatic characteristics of the Patient Health Questionnaire (PHQ)-9 depression, generalized anxiety disorder (GAD)-7 anxiety and PHQ-15 somatic symptom scales are synthesized from two sources: (1) four multisite cross-sectional studies (three conducted in primary care and one in obstetric-gynecology practices) comprising 9740 patients, and (2) key studies from the literature that have studied these scales.


RESULTS
The PHQ-9 and its abbreviated eight-item (PHQ-8) and two-item (PHQ-2) versions have good sensitivity and specificity for detecting depressive disorders. Likewise, the GAD-7 and its abbreviated two-item (GAD-2) version have good operating characteristics for detecting generalized anxiety, panic, social anxiety and post-traumatic stress disorder. The optimal cutpoint is > or = 10 on the parent scales (PHQ-9 and GAD-7) and > or = 3 on the ultra-brief versions (PHQ-2 and GAD-2). The PHQ-15 is equal or superior to other brief measures for assessing somatic symptoms and screening for somatoform disorders. Cutpoints of 5, 10 and 15 represent mild, moderate and severe symptom levels on all three scales. Sensitivity to change is well-established for the PHQ-9 and emerging albeit not yet definitive for the GAD-7 and PHQ-15.


CONCLUSIONS
The PHQ-9, GAD-7 and PHQ-15 are brief well-validated measures for detecting and monitoring depression, anxiety and somatization.

Iconography : The Patient Health Questionnaire Somatic, Anxiety, and Depressive Symptom Scales: a systematic review

Evaluation of incidence and prognostic significance of newly identified hotspot mutations in DNA polymerase epsilon (POLE) in endometrial cancer: Contextualizing findings from The Cancer Genome Atlas Research Network

Importance Heterogeneous evidence exists for the association between COVID-19 and the clinical outcomes of patients with mental health disorders. It remains unknown whether patients with COVID-19 and mental health disorders are at increased risk of mortality and should thus be targeted as a high-risk population for severe forms of COVID-19. Objective To determine whether patients with mental health disorders were at increased risk of COVID-19 mortality compared with patients without mental health disorders. Data sources For this systematic review and meta-analysis, MEDLINE, Web of Science, and Google Scholar were searched from inception to February 12, 2021. Bibliographies were also searched, and the corresponding authors were directly contacted. The search paradigm was based on the following combination: (mental, major[MeSH terms]) AND (COVID-19 mortality[MeSH terms]). To ensure exhaustivity, the term mental was replaced by psychiatric, schizophrenia, psychotic, bipolar disorder, mood disorders, major depressive disorder, anxiety disorder, personality disorder, eating disorder, alcohol abuse, alcohol misuse, substance abuse, and substance misuse. Study selection Eligible studies were population-based cohort studies of all patients with identified COVID-19 exploring the association between mental health disorders and mortality. Data extraction and synthesis Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline was used for abstracting data and assessing data quality and validity. This systematic review is registered with PROSPERO. Main outcomes and measures Pooled crude and adjusted odds ratios (ORs) for the association of mental health disorders with mortality were calculated using a 3-level random-effects (study/country) approach with a hierarchical structure to assess effect size dependency. Results In total, 16 population-based cohort studies (data from medico-administrative health or electronic/medical records databases) across 7 countries (1 from Denmark, 2 from France, 1 from Israel, 3 from South Korea, 1 from Spain, 1 from the UK, and 7 from the US) and 19 086 patients with mental health disorders were included. The studies covered December 2019 to July 2020, were of good quality, and no publication bias was identified. COVID-19 mortality was associated with an increased risk among patients with mental health disorders compared with patients without mental health disorders according to both pooled crude OR (1.75 [95% CI, 1.40-2.20]; P Conclusions and relevance In this systematic review and meta-analysis of 16 observational studies in 7 countries, mental health disorders were associated with increased COVID-19-related mortality. Thus, patients with mental health disorders should have been targeted as a high-risk population for severe forms of COVID-19, requiring enhanced preventive and disease management strategies. Future studies should more accurately evaluate the risk for patients with each mental health disorder. However, the highest risk seemed to be found in studies including individuals with schizophrenia and/or bipolar disorders.

/pdf/association-between-mental-health-disorders-and-mortality-3npjpdagtm.pdf

Association Between Mental Health Disorders and Mortality Among Patients With COVID-19 in 7 Countries: A Systematic Review and Meta-analysis.

Socioeconomic Deprivation Index Is Associated With Psychiatric Disorders: An Observational and Genome-wide Gene-by-Environment Interaction Analysis in the UK Biobank Cohort.

Estimating cell type compositions for complex diseases is an important step to investigate the cellular heterogeneity for understanding disease etiology and potentially facilitate early disease diagnosis and prevention. Here, we developed a computationally statistical method, referring to Multi-Omics Matrix Factorization (MOMF), to estimate the cell-type compositions of bulk RNA sequencing (RNA-seq) data by leveraging cell type-specific gene expression levels from single-cell RNA sequencing (scRNA-seq) data. MOMF not only directly models the count nature of gene expression data, but also effectively accounts for the uncertainty of cell type-specific mean gene expression levels. We demonstrate the benefits of MOMF through three real data applications, i.e., Glioblastomas (GBM), colorectal cancer (CRC) and type II diabetes (T2D) studies. MOMF is able to accurately estimate disease-related cell type proportions, i.e., oligodendrocyte progenitor cells and macrophage cells, which are strongly associated with the survival of GBM and CRC, respectively.

https://www.mdpi.com/2073-4409/8/10/1161/pdf

An Efficient and Flexible Method for Deconvoluting Bulk RNA-Seq Data with Single-Cell RNA-Seq Data

Extensive studies have shown that gene expression levels are strongly affected by chromatin mark combinations via at least two mechanisms, i.e., activation or repression. But their combinatorial patterns are still unclear. To further understand the relationship between histone modifications and gene expression levels, here in this paper, we introduce a purely geometric higher-order representation, tensor (also called multidimensional array), which might borrow more unknown interactions in chromatin states to predicting gene expression levels. The prediction models were learned from regions around upstream 10k base pairs and downstream 10k base pairs of the transcriptional start sites (TSSs) on three species (i.e., Human, Rhesus Macaque, and Chimpanzee) with five histone modifications (i.e., H3K4me1, H3K4me3, H3K27ac, H3K27me3, and Pol II). Experimental results demonstrate that the proposed method is more powerful to predicting gene expression levels than several other popular methods. Specifically, our method enable to get more powerful performance on both commonly used criteria, R and RMSE, as high as 1.7% and 11%, respectively. The overall aim of this work is to show that the higher-order representation is able to include more unknown interaction information between histone modifications across different species.

/pdf/higher-order-partial-least-squares-for-predicting-gene-1fe1tcr0h2.pdf

Higher-order partial least squares for predicting gene expression levels from chromatin states.

Multiple publications have indicated that gene expression levels are strongly affected by chromatin mark combinations via at least two mechanisms, i.e., activation or repression. But their combinatorial patterns remain unresolved. To further understand the relationship between histone modifications and gene expression levels, here in this paper, we introduce a purely geometric higher-order representation, tensor (also called multidimensional array), which might contain more hidden information from chromatin states to predicting gene expression levels. The prediction models were learned from regions around upstream 10k base pairs and downstream 10k base pairs of the transcriptional start sites (TSSs) over three species (i.e., Human, Rhesus Macaque, and Chimpanzee) with five histone modifications (i.e., H3K4me1, H3K4me3, H3K27ac, H3K27me3, and Pol II). Experimental results demonstrate that the proposed method is more powerful for predicting gene expression levels than several commonly used methods. Specifically, our method improves the performance on both criteria, R and RMSE as high as 1.7% and 11%, respectively.

/pdf/higher-order-partial-least-squares-for-predicting-gene-57qmknzt50.pdf

Xifang Sun

Papers

Socioeconomic Deprivation Index Is Associated With Psychiatric Disorders: An Observational and Genome-wide Gene-by-Environment Interaction Analysis in the UK Biobank Cohort.

An Efficient and Flexible Method for Deconvoluting Bulk RNA-Seq Data with Single-Cell RNA-Seq Data

Higher-order partial least squares for predicting gene expression levels from chromatin states.

Higher-order partial least squares for predicting gene expression levels from chromatin states