Genome-wide non-CpG methylation of the host genome during M. tuberculosis infection

A mammalian cell utilizes DNA methylation to modulate gene expression in response to environmental changes during development and differentiation. Aberrant DNA methylation changes as a correlate to diseased states like cancer, neurodegenerative conditions and cardiovascular diseases have been documented. Here we show genome-wide DNA methylation changes in macrophages infected with the pathogen M. tuberculosis. Majority of the affected genomic loci were hypermethylated in M. tuberculosis infected THP1 macrophages. Hotspots of differential DNA methylation were enriched in genes involved in immune response and chromatin reorganization. Importantly, DNA methylation changes were observed predominantly for cytosines present in non-CpG dinucleotide context. This observation was consistent with our previous finding that the mycobacterial DNA methyltransferase, Rv2966c, targets non-CpG dinucleotides in the host DNA during M. tuberculosis infection and reiterates the hypothesis that pathogenic bacteria use non-canonical epigenetic strategies during infection

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

Background: Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021. Methods: The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk–outcome pairs. Pairs were included on the basis of data-driven determination of a risk–outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk–outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk–outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws. Findings: Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7–9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4–9·2]), smoking (5·7% [4·7–6·8]), low birthweight and short gestation (5·6% [4·8–6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8–6·0]). For younger demographics (ie, those aged 0–4 years and 5–14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9–27·7]) and environmental and occupational risks (decrease of 22·0% [15·5–28·8]), coupled with a 49·4% (42·3–56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9–21·7] for high BMI and 7·9% [3·3–12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6–1·9) for high BMI and 1·3% (1·1–1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4–78·8) for child growth failure and 66·3% (60·2–72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP). Interpretation: Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions

Design and Implementation of Cognitive Assessment Tool for Working Memory and Attention based on PGI Memory Scale

983-988Cognitive function is one of the most fundamental psychological functions that play a significant role in person’s daily life. Impairment in cognitive function can impacts the daily functioning and overall performance of the person. A digital application could be an accessible and convenient method for the effective evaluation of cognition. The proposed Cognitive Assessment Digital Smart Tool (CADST) evaluates the Attention (ATT) and Working Memory (WM) parameters of cognition. The outcome measures of CADST were evaluated against PGI Memory Scale (PGIMS) and Montreal Cognitive Assessment (MoCA). Usability testing for the CADST tool was performed using the Post‒Study System Usability Questionnaire (PSSUQ). A total of 30 healthy participants were recruited (women = 12, men = 18; age (M ± SD) = 35.6 ± 10.63 y. o.). The feasibility study analysis revealed a significant moderate to strong correlation between the total scores of CADST and PGIMS (r = 0.75; p < 0.001) and a low to moderate correlation between the total scores of CADST and MoCA (r = 0.44; p < 0.001). Subtests of CADST and PGIMS showed strong correlation for ATT (r = 0.81; p < 0.001) and moderate correlation for WM (r = 0.51; p < 0.001). Similarly, subtests of CADST and MoCA showed moderate correlation for ATT (r = 0.63; p < 0.001) and low correlation for WM (r = 0.24; p = 1.82). CADST showed a high correlation with PGIMS for evaluating ATT and WM symptoms of cognition provide evidence of convergent validity. CADST is the first digital smart screening tool based on PGIMS for ATT and WM using web‒based technology. The overall usability ratings showed high acceptance for system usage, interface and information quality

The interaction of mycobacterial protein Rv2966c with host chromatin is mediated through non-CpG methylation and histone H₃/H₄ binding

To effectively modulate the gene expression within an infected mammalian cell, the pathogen Mycobacterium tuberculosis would need to bring about epigenetic modifications at appropriate genomic loci. Working on this hypothesis, we show in this study that the mycobacterial protein Rv2966c is a 5-methylcytosine-specific DNA methyltransferase that is secreted out from the mycobacterium and gets localized to the nucleus in addition to the cytoplasm inside the host cell. Importantly, Rv2966c binds to specific DNA sequences, methylates cytosines predominantly in a non-CpG context and its methylation activity is positively influenced by phosphorylation. Interestingly, like the mammalian DNA methyltransferase, DNMT3L, Rv2966c can also interact with histone proteins. Ours is the first study that identifies a protein from a pathogenic bacteria with potential to influence host DNA methylation in a non-canonical manner providing the pathogen with a novel mechanism to alter the host epigenetic machinery. This contention is supported by repression of host genes upon M. tuberculosis infection correlated with Rv2966c binding and non-CpG methylation