Aims During virally-suppressed chronic HIV infection, persistent inflammation contributes to the development of cardiovascular disease (CVD), a major comorbidity in people living with HIV (LWH). Classical blood monocytes (CMs) remain activated during antiretroviral therapy and are a major source of pro-inflammatory and pro-thrombotic factors that contribute to atherosclerotic plaque development and instability. Methods and Results Here we identify transcriptomic changes in circulating CMs in peripheral blood mononuclear cell samples from participants of the Women’s Interagency HIV Study, selected by HIV and subclinical CVD (sCVD) status. We flow-sorted CM from participants of the Women’s Interagency HIV Study and deep-sequenced their mRNA (n = 92). CMs of HIV+ participants showed elevated IL-6, IL-1β, and IL-12β, overlapping with many transcripts identified in sCVD+ participants. In sCVD+ participants LWH, those reporting statin use showed reduced pro-inflammatory gene expression to a level comparable with healthy (HIV-sCVD-) participants. Statin non-users maintained an elevated inflammatory profile and increased cytokine production. Conclusion Statin therapy has been associated with a lower risk of cardiac events, such as myocardial infarction in the general population, but not in those LWH. Our data suggest that women LWH may benefit from statin therapy even in the absence of overt CVD. Translational perspective Monocytes from women living with HIV express many more pro-inflammatory genes than uninfected controls. An overlapping list of genes is expressed in samples from women with ultrasound evidence of carotid plaque. The inflammatory burden is enhanced in women with both HIV and carotid plaque, and this is mitigated by statin treatment, almost to the level of healthy participants. Thus, the present monocyte transcriptome data from 92 women support the idea that participants with HIV may specifically benefit from statin treatment, perhaps more so than seronegative subjects.

Background Bacterial vaginosis (BV) treatment failures and recurrences are common. To identify features associated with treatment response, we compared vaginal microbiota and host ectocervical transcriptome before and after oral metronidazole therapy. Methods Women with BV (Bronx, NY and Thika, Kenya) received 7 days of oral metronidazole at enrollment (Day 0) and underwent genital tract sampling of microbiome (16S rRNA gene sequencing), transcriptome (RNAseq), and immune mediator concentrations on Day 0, 15 and 35. Results Bronx participants were more likely than Thika participants to clinically respond to metronidazole (19/20 vs 10/18, respectively, p=0.0067) and by changes in microbiota composition and diversity. After dichotomizing the cohort into responders and non-responders by change in alpha diversity between Day 35 and Day 0, we identified transcription differences associated with chemokine signaling (q=0.002) and immune system process (q=2.5e-8) that differentiated responders from non-responders were present at enrollment. Responders had significantly lower levels of CXCL9 in cervicovaginal lavage on Day 0 (p< 0.007) and concentrations of CXCL9, CXCL10 and MCP-1 increased significantly between Day 0 and Day 35 in responders versus non-responders. Conclusions Response to metronidazole is characterized by significant changes in chemokines and related transcripts suggesting that treatments that promote these pathways may prove beneficial.

Linear regression is a standard approach to identify genetic variants associated with continuous traits in genome-wide association studies (GWAS). In a standard epidemiology study, linear regression is often performed with adjustment for covariates to estimate the independent effect of a predictor variable or to improve statistical power by reducing residual variability. However, it is problematic to adjust for heritable covariates in genetic association analysis. Here, we propose a new method that utilizes summary statistics of the covariate from additional samples for reducing the residual variability and hence improves statistical power. Our simulation study showed that the proposed methodology can maintain a good control of type I error and can achieve much higher power than a simple linear regression. The method is illustrated by an application to the GWAS results from the GIANT consortium.