Emerging next-generation sequencing technologies have revolutionized the collection of genomic data for applications in bioforensics, biosurveillance, and for use in clinical settings. However, to make the most of these new data, new methodology needs to be developed that can accommodate large volumes of genetic data in a computationally efficient manner. We present a statistical framework to analyze raw next-generation sequence reads from purified or mixed environmental or targeted infected tissue samples for rapid species identification and strain attribution against a robust database of known biological agents. Our method, Pathoscope, capitalizes on a Bayesian statistical framework that accommodates information on sequence quality, mapping quality, and provides posterior probabilities of matches to a known database of target genomes. Importantly, our approach also incorporates the possibility that multiple species can be present in the sample and considers cases when the sample species/strain is not in the reference database. Furthermore, our approach can accurately discriminate between very closely related strains of the same species with very little coverage of the genome and without the need for multiple alignment steps, extensive homology searches, or genome assembly-which are time-consuming and labor-intensive steps. We demonstrate the utility of our approach on genomic data from purified and in silico ''environmental'' samples from known bacterial agents impacting human health for accuracy assessment and comparison with other approaches.

Significance We present our Universal exPression Code (UPC) approach for deriving “barcodes,” which estimate the active/inactive state of genes in a sample. UPCs normalize for technological variance and standardize data so they can be combined across microarray and RNA-sequencing experiments with high concordance. Because our method is applied to one sample at a time and thus bypasses the need to standardize samples together, it is distinctively suitable for situations in which samples arrive serially rather than in batches. We demonstrate our method’s utility in various biomedical research applications and compare against technology-specific approaches.

Serial limiting dilution (SLD) assays are used in many areas of infectious disease related research. This paper presents SLDAssay, a free and publicly available R software package and web tool for analyzing data from SLD assays. SLDAssay computes the maximum likelihood estimate (MLE) for the concentration of target cells, with corresponding exact and asymptotic confidence intervals. Exact and asymptotic goodness of fit p-values, and a bias-corrected (BC) MLE are also provided. No other publicly available software currently implements the BC MLE or the exact methods. For validation of SLDAssay, results from Myers et al. (1994) are replicated. Simulations demonstrate the BC MLE is less biased than the MLE. Additionally, simulations demonstrate that exact methods tend to give better confidence interval coverage and goodness-of-fit tests with lower type I error than the asymptotic methods. Additional advantages of using exact methods are also discussed.

Background HIV may amplify immunologic, physiologic, and functional changes of aging. We determined associations of frailty phenotype, a T-cell senescence marker (p16INK4a expression), age, and demographics with exposures of the intracellular metabolites (IM) and endogenous nucleotides (EN) of tenofovir/emtricitabine (TFV/FTC), efavirenz (EFV), atazanavir (ATV), and ritonavir (RTV). Materials and Methods Plasma and PBMC samples for drug, IM, and EN concentrations were collected at 4 time points in HIV+ adults receiving TFV/FTC with EFV or ATV/RTV. Subjects underwent frailty phenotyping and p16INK4a expression analysis. Noncompartmental analysis generated an area under the curve (AUC) for each analyte. Spearman rank correlation and Kruskal-Wallis tests were used to assess associations between AUC, demographics, and aging markers, adjusting for multiple comparisons with the Holm procedure. Results Subjects (n=79) ranged in age from 22–73yr (median 48yr). Forty-eight were African-American, 24 were female, 54 received EFV. Three subjects (range 51–60yr) demonstrated frailty, with 17 subjects (range 26–60yr) demonstrating pre-frailty. Negative associations were observed between p16INK4a expression and each of FTC-triphosphate (r= −0.45), deoxyadenosine triphosphate (dATP) (r= −0.47), and deoxycytidine triphosphate (dCTP) (r= −0.57) AUCs (p-values<0.02). TFV and FTC AUCs were larger among subjects with lower renal function or higher chronologic age (p-values ≤0.05). No associations were observed for EFV, ATV, or RTV AUCs. Conclusions Associations of IM/EN exposure and p16INK4a expression observed here suggest that senescence may alter drug phosphorylation, metabolism, or transport. This finding warrants further mechanistic study to ensure optimal treatment in the aging HIV+ population.

ObjectivesAs the HIV-infected population ages, the role of cellular senescence and inflammation on co-morbid conditions and pharmacotherapy is increasingly of interest. p16INK4a expression, a marker for aging and senescence in T-cells, is associated with lower intracellular concentrations of endogenous nucleotides (EN) and nucleos(t)ide reverse transcriptase inhibitors (NRTIs). This study expands on these findings by determining whether inflammation is contributing to the association of p16INK4a expression with intracellular metabolite (IM) exposure and endogenous nucleotide concentrations.MethodsSamples from 73 HIV-infected adults receiving daily tenofovir/emtricitabine (TFV/FTC) with either efavirenz (EFV) or atazanavir/ritonavir (ATV/r) were tested for p16INK4a expression, and plasma cytokine and intracellular drug concentrations. Associations between p16INK4a expression and cytokine concentrations were assessed using maximum likelihood methods, and elastic net regression was applied to assess whether cytokines were predictive of intracellular metabolite/endogenous nucleotide exposures.ResultsEnrolled participants had a median age of 48 years (range 23–73). There were no significant associations between p16INK4a expression and cytokines. Results of the elastic net regression showed weak relationships between IL-1Ra and FTC-triphosphate and deoxyadenosine triphosphate exposures, and MIP-1β, age and TFV-diphosphate exposures.ConclusionsIn this clinical evaluation, we found no relationships between p16INK4a expression and cytokines, or cytokines and intracellular nucleotide concentrations. While inflammation is known to play a role in this population, it is not a major contributor to the p16INK4a association with decreased IM/EN exposures in these HIV-infected participants.