RNA Virus Evolution: a Cross-scale, Disease Dynamic Perspective

RNA viruses are an important cause of global morbidity and mortality. The rapid evolutionary rates of RNA virus pathogens, caused by high replication rates and error-prone polymerases, can make the pathogens difficult to control. RNA viruses can undergo immune escape within their hosts and develop resistance to the treatment and vaccines we design to fight them. Understanding the spread and evolution of RNA pathogens is essential for reducing human suffering. In this dissertation, I make use of the rapid evolutionary rate of viral pathogens to answer several questions about how RNA viruses spread and evolve. To address each of the questions, I link mathematical techniques for modeling viral population dynamics with phylogenetic and coalescent techniques for analyzing and modeling viral genetic sequences and evolution. The first project uses multi-scale mechanistic modeling to show that decreases in viral substitution rates over the course of an acute infection, combined with the timing of infectious hosts transmitting new infections to susceptible individuals, can account for discrepancies in viral substitution rates in different host populations. The second project combines coalescent models with within-host mathematical models to identify driving evolutionary forces in chronic hepatitis C virus infection. The third project compares the effects of intrinsic and extrinsic viral transmission rate variation on viral phylogenies.

Shared monocyte subset phenotypes in HIV-1 infection and in uninfected subjects with acute coronary syndrome.

The mechanisms responsible for increased cardiovascular risk associated with HIV-1 infection are incompletely defined. Using flow cytometry, in the present study, we examined activation phenotypes of monocyte subpopulations in patients with HIV-1 infection or acute coronary syndrome to find common cellular profiles. Nonclassic (CD14(+)CD16(++)) and intermediate (CD14(++)CD16(+)) monocytes are proportionally increased and express high levels of tissue factor and CD62P in HIV-1 infection. These proportions are related to viremia, T-cell activation, and plasma levels of IL-6. In vitro exposure of whole blood samples from uninfected control donors to lipopolysaccharide increased surface tissue factor expression on all monocyte subsets, but exposure to HIV-1 resulted in activation only of nonclassic monocytes. Remarkably, the profile of monocyte activation in uncontrolled HIV-1 disease mirrors that of acute coronary syndrome in uninfected persons. Therefore, drivers of immune activation and inflammation in HIV-1 disease may alter monocyte subpopulations and activation phenotype, contributing to a pro-atherothrombotic state that may drive cardiovascular risk in HIV-1 infection.

Host gene expression classifiers diagnose acute respiratory illness etiology.

Acute respiratory infections caused by bacterial or viral pathogens are among the most common reasons for seeking medical care. Despite improvements in pathogen-based diagnostics, most patients receive inappropriate antibiotics. Host response biomarkers offer an alternative diagnostic approach to direct antimicrobial use. This observational cohort study determined whether host gene expression patterns discriminate noninfectious from infectious illness and bacterial from viral causes of acute respiratory infection in the acute care setting. Peripheral whole blood gene expression from 273 subjects with community-onset acute respiratory infection (ARI) or noninfectious illness, as well as 44 healthy controls, was measured using microarrays. Sparse logistic regression was used to develop classifiers for bacterial ARI (71 probes), viral ARI (33 probes), or a noninfectious cause of illness (26 probes). Overall accuracy was 87% (238 of 273 concordant with clinical adjudication), which was more accurate than procalcitonin (78%, P < 0.03) and three published classifiers of bacterial versus viral infection (78 to 83%). The classifiers developed here externally validated in five publicly available data sets (AUC, 0.90 to 0.99). A sixth publicly available data set included 25 patients with co-identification of bacterial and viral pathogens. Applying the ARI classifiers defined four distinct groups: a host response to bacterial ARI, viral ARI, coinfection, and neither a bacterial nor a viral response. These findings create an opportunity to develop and use host gene expression classifiers as diagnostic platforms to combat inappropriate antibiotic use and emerging antibiotic resistance.