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Austin G Meyer

Publications and source records attributed to Austin G Meyer.

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Analysis of infection biomarkers within a Bayesian framework reveals their role in pneumococcal pneumonia diagnosis in HIV patients

BackgroundHIV patients are more likely to contract bacterial pneumonia and more likely to die from the infection. Unfortunately, there are few tests to quickly diagnosis the etiology of these dangerous infections. Several biomarkers may be useful for diagnosing the most common pneumonia-causing organism, S. pneumoniae, but studies utilizing the standard statistical approach provide little concrete guidance for the HIV-infected population.\n\nMethodology and FindingsUsing a Bayesian approach, I analyze data from a cohort of 280 HIV patients with x-ray confirmed community acquired pneumonia. First, I use a variety of techniques to establish predictor significance and to identify their optimal cutoffs. Next, in lieu of cutoffs, I find the continuous and combined likelihood ratios for every value of each biomarker, and I compute the associated posttest probabilities. As expected, I find the three biomarkers with good clinical yield and a statistically significant association with S. pneumoniae are C-reactive protein (CRP), procalcitonin (PCT), and lytA gene PCR (lytA). Based on Bayesian clinical yield, optimal cutoffs are largely equivocal. The optimal dichotomous cutoff for CRP is essentially any value between 10 mg/dL and 30 mg/dL ({bigtriangleup}pPosttest {approx} 0.49). The optimal cutoff for PCT is any value between 2 ng/mL and 40 ng/mL ({bigtriangleup}pposttest {approx} 0.35). The optimal cutoff for lytA is any value less than 6 log10 copies/mL ({bigtriangleup}pposttest {approx} 0.45). Further, I find that continuous likelihood ratios provide much more accurate posttest probabilities than dichotomous cutoffs. For example, starting with the empirical pretest probability, a lytA approaching 0 copies/mL lowers the probability of S. pneumoniae infection to less than 15%, while a result of 10 copies/mL raises the probability to greater than 65%. However, a lytA value just above or below the suggested cutoff of 8000 copies/mL or my new optimal cutoff of 30,000 copies/mL leaves the posttest probability of infection essentially unchanged from the pretest probability.\n\nConclusionCRP, PCT, and lytA all provide significant value in diagnosing the etiology of pneumonia in HIV patients. The optimal dichotomous cutoffs for lytA, CRP, and PCT need to be adjusted for pneumococcal diagnosis in this population. However, continuous and combined likelihood ratios avoid discarding valuable quantitative information, and a combined likelihood ratio can be easily computed without the need for prior logistic regression. Importantly, there is significant overlap between these biomarkers such that only one of the three biomarkers at a time should be used to update clinical probabilities. Thus, it is ill-advised to combine the likelihood ratios of different biomarkers to produce a posttest probability. Finally, I provide a simple web application to quantitatively calculate the posttest probability of S. pneumoniae infection in HIV patients with x-ray confirmed pneumonia: http://meyerapps.org/pneumococcal_etiology_hiv.

Epidemiology

Geometric constraints dominate the antigenic evolution of influenza H3N2 hemagglutinin

We have carried out a comprehensive analysis of the determinants of human influenza A H3 hemagglutinin evolution, considering three distinct predictors of evolutionary variation at individual sites: solvent accessibility (as a proxy for protein fold stability and/or conservation), experimental epitope sites (as a proxy for host immune bias), and proximity to the receptor-binding region (as a proxy for protein function). We found that these three predictors individually explain approximately 15% of the variation in site-wise dN/dS. The solvent accessibility and proximity predictors were largely independent of each other, while the epitope sites were not. In combination, solvent accessibility and proximity explained 32% of the variation in dN/dS. Incorporating experimental epitope sites into the model added only an additional 2 percentage points. We also found that the historical H3 epitope sites, which date back to the 1980s and 1990s, showed only weak overlap with the latest experimental epitope data. Finally, sites with dN/dS > 1, i.e., the sites most likely driving seasonal immune escape, are not correctly predicted by either historical or experimental epitope sites, but only by proximity to the receptor-binding region. In summary, proximity to the receptor-binding region, and not host immune bias, seems to be the primary determinant of H3 evolution.\n\nAuthor summaryThe influenza virus is one of the most rapidly evolving human viruses. Every year, it accumulates mutations that allow it to evade the host immune response of previously infected individuals. Which sites in the virus genome allow this immune escape and the manner of escape is not entirely understood, but conventional wisdom states that specific \"immune epitope sites\" in the protein hemagglutinin are preferentially attacked by host antibodies and that these sites mutate to directly avoid host recognition; as a result, these sites are commonly targeted by vaccine development efforts. Here, we combine influenza hemagglutinin sequence data, protein structural information, experimental immune epitope data, and historical epitopes to demonstrate that neither the historical epitope groups nor epitopes based on experimental data are crucial for predicting the rate of influenza evolution. Instead, we find that a simple geometrical model works best: sites that are closest to the location where the virus binds the human receptor are the primary driver of hemagglutinin evolution. There are two possible explanations for this result. First, the existing historical and experimental epitope sites may not be the real antigenic sites in hemagglutinin. Second, alternatively, hemagglutinin antigenicity may not the primary driver of influenza evolution.

Evolutionary Biology