Biology subjects

Anastos, K.

Publications and source records attributed to Anastos, K..

5 recordsLinked to original sources

bioRxiv2024

A hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data

BackgroundMethylation Quantitative Trait Loci (meQTLs) are chromosomal regions that harbor genetic variants affecting DNA methylation levels. The identification of meQTLs can be accomplished through quantifying the effects of single nucleotide polymorphisms (SNPs) on DNA methylation levels, and these inferred meQTLs can shed light on the complex interplay between the genome and methylome. However, most meQTL studies to date utilize bulk methylation datasets composed of different cell types that may have distinct methylation patterns in each cell type. Current technological challenges hinder the comprehensive collection of large-scale, cell-type-specific (CTS) methylation data, which limits our understanding of CTS methylation regulation. To address this challenge, we propose a hierarchical Bayesian interaction model (HBI) to infer CTS meQTLs from bulk methylation data. ResultsOur HBI method integrates bulk methylations data from a large number of samples and CTS methylation data from a small number of samples to estimate CTS meQTLs. Through simulations, we show that HBI improves the estimation (accuracy and power) of CTS genetic effects on DNA methylation. To systematically characterize genome-wide SNP-methylation level associations in multiple cell types, we apply HBI to bulk methylation data measured in peripheral blood mononuclear cells (PBMC) from a cohort of 431 individuals together with flow-sorted cell-derived methylation sequencing (MC-seq) data measured in isolated white blood cells (CD4+ T-cells, CD8+ T-cells, CD16+ monocytes) for 47 individuals. We demonstrate that HBI can identify CTS meQTLs and improve the functional annotation of SNPs. ConclusionsHBI can incorporate strong and robust signals from MC-seq data to improve the estimation of CTS meQTLs. Applying HBI to link the methylome and genome data helps to identify biologically relevant cell types for complex traits.

genetics↗

bioRxiv2020

Evaluation of the Abbott ARCHITECT HIV Ag/Ab Combo Assay for Determining Recent HIV-1 Infection

BackgroundGiven the challenges and costs associated with implementing HIV-1 incidence assay testing, there is great interest in evaluating the use of commercial HIV diagnostic tests for determining recent HIV infection. A diagnostic test with the capability of providing reliable data for the determination of recent HIV infection without substantial modifications to the test protocol would have a significant impact on HIV surveillance. The Abbott ARCHITECT HIV Ag/Ab Combo Assay is an antigen/antibody immunoassay, which meets the criteria as the first screening test in the recommended HIV laboratory diagnostic algorithm for the United States. MethodsIn this study, we evaluated the performance characteristics of the ARCHITECT HIV Ag/Ab Combo signal-to-cutoff ratio (S/Co) for determining recent infection, including estimation of the mean duration of recent infection (MDRI) and false recent rate (FRR), and selection of recency cutoffs. ResultsThe MDRI estimates for the S/Co recency cutoff of 400 is within the 4 to 12 months range recommended for HIV incidence assays, and the FRR rate for this cutoff was 1.5%. Additionally, ARCHITECT Combo S/Co values were compared relative to diagnostic test results from two prior prospective HIV-1 diagnostic studies in order to validate the use of the S/Co for both diagnostic and recency determination. ConclusionDual-use of the ARCHITECT Combo assay data for diagnostic and incidence purposes would reduce the need for separate HIV incidence testing and allow for monitoring of recent infection for incidence estimation and other public health applications.

microbiology↗

bioRxiv2019

Mortality among adults living with HIV treated for tuberculosis based on positive, negative, or no bacteriologic test results for tuberculosis: the IeDEA consortium

BackgroundIn resource-constrained settings, people living with HIV (PLWH) treated for tuberculosis (TB) despite negative bacteriologic tests have a higher mortality than those treated with positive tests. Many PLWH are treated without bacteriologic testing; their mortality compared to those with bacteriologic testing is uncertain.\n\nMethodsWe conducted an observational cohort study among PLWH [≥] 15 years of age who initiated TB treatment at clinical sites affiliated with four regions of the International epidemiology Databases to Evaluate AIDS (IeDEA) consortium from 2012-2014: Caribbean, Central and South America, and Central, East, and West Africa. The primary exposure of interest was the TB bacteriologic test status at TB treatment initiation: positive, negative, or no test result. The hazard for death in the 12 months following TB treatment initiation was estimated using the Cox proportional hazard model, adjusted for patient- and site-level factors. Missing covariates were multiply imputed.\n\nResultsAmong 2,091 PLWH included, the median age at TB treatment initiation was 36 years, 44% were female, 53% had CD4 counts [≤] 200 cells/mm3, and 52% were on antiretroviral treatment (ART). Compared to patients with positive bacteriologic tests, the adjusted hazard for death was higher among patients with no test results (HR 1.56, 95% CI 1.08-2.26) but not different than those with negative tests (HR 1.28, 95% CI 0.91-1.81). Older age was also associated with a higher hazard for death, while being on ART, having a higher CD4 count, West Africa region, and tertiary facility level were associated with lower hazards for death.\n\nConclusionPLWH treated for TB with no bacteriologic test results were more likely to die than those treated with positive tests, underscoring the importance of TB bacteriologic diagnosis in resource-constrained settings. Research is needed to understand the causes of death among PLWH treated for TB in the absence of positive bacteriologic tests.

epidemiology↗

bioRxiv2018

Evolution of the HIV-1 RRE during natural infection reveals key nucleotide changes that correlate with altered structure and increased activity over time

AbstractThe HIV-1 Rev Response Element (RRE) is a cis-acting RNA element characterized by multiple stem-loops. Binding and multimerization of the HIV Rev protein on the RRE promotes nucleocytoplasmic export of incompletely spliced mRNAs, an essential step in HIV replication. Most of our understanding of the Rev-RRE regulatory axis comes from studies of lab-adapted HIV clones. However, in human infection, HIV evolves rapidly and mechanistic studies of naturally occurring Rev and RRE sequences are essential to understanding this system. We previously described the functional activity of two RREs found in circulating viruses in a patient followed during the course of HIV infection. The "early" RRE was less functionally active than the "late" RRE despite differing in sequence by only four nucleotides. In this study, we describe the sequence, function, and structural evolution of circulating RREs in this patient using plasma samples collected over six years of untreated infection. RRE sequence diversity varied over the course of infection with evidence of selection pressure that led to sequence convergence as disease progressed. An increase in RRE functional activity was observed over time, and a key mutation was identified that correlates with a major conformational change in the RRE and increased functional activity. Additional mutations were found that may have contributed to increased activity as a result of greater Shannon entropy in RRE stem-loop II, which is key to primary Rev binding. ImportanceHIV-1 replication requires interaction of the viral Rev protein with a cis-acting regulatory RNA, the Rev Response Element (RRE), whose sequence changes over time during infection within a single host. In this study, we show that the RRE is subject to selection pressure and that RREs from later time points in infection tend to have higher functional activity. Differences in RRE functional activity are attributable to specific changes in RNA structure. Our results suggest that RRE evolution during infection may be important for HIV pathogenesis and that efforts to develop therapies acting on this viral pathway should take this into account.

molecular biology↗

bioRxiv2018

Repeated Measures Regression In Laboratory, Clincal And Enviromental Research - Different Between/Within Subject Slopes And Common Misconceptions

When using repeated measures linear regression models to make causal inference in laboratory, clinical and environmental research, it is often assumed that the Within Subject association of differences (or changes) in predictor value across replicates is the same as the Between Subject association of differences in those predictor values. But this is often false, for example with body weight as the predictor and blood cholesterol the outcome i) a 10 pound weight increase in the same adult more greatly a higher increase in cholesterol in that adult than does ii) one adult weighing 10 pounds more than a second reflect increased cholesterol levels in the first adult as the weigh difference in i) more closely tracks higher body fat while that in ii) is also influenced by heavier adults being taller. Hence to make causal inferences, different Within and Between subject slopes should be separately modeled. A related misconception commonly made using generalized estimation equations (GEE) and mixed models (MM) on repeated measures (i.e. for fitting Cross Sectional Regression) is that the working correlation structure used only influences variance of model parameter estimates. But only independence working correlation guarantees the modeled parameters have any interpretability. We illustrate this with an example where changing working correlation from independence to equicorrelation qualitatively biases parameters of GEE models and show this happens because Between and Within Subject slopes for the predictor variables differ. We then describe several common mechanisms that cause Within and Between Subject slopes to differ as; change effects, lag/reverse lag and spillover causality, shared within subject measurement bias or confounding, and predictor variable measurement error. The misconceptions noted here should be better publicized in laboratory, clinical and environmental research. Repeated measures analyses should compare Within and Between subject slopes of predictors and when they differ, investigate the reasons this has happened.\n\nHIGHLIGHTSWhen using repeated measures with time varying predictors variables in laboratory, clinical and environmental research: O_LICross sectional regressions with any working correlation structure other than independence often give non-meaningful results\nC_LIO_LIBetween/Within subject decomposition of slopes should be undertaken when making causal inferences\nC_LIO_LIInvestigators should investigate the reasons Between and Within Subject slopes differ if this occurs\nC_LI

epidemiology↗