Designing a genome-based HIV incidence assay with high sensitivity and specificity

Using viral sequence diversity to estimate time of HIV infection in infants

Age at HIV acquisition may influence viral pathogenesis in infants, and yet infection timing (i.e. date of infection) is not always known. Adult studies have estimated infection timing using rates of HIV RNA diversification, however, it is unknown whether adult-trained models can provide accurate predictions when used for infants due to possible differences in viral dynamics. While rates of viral diversification have been well defined for adults, there are limited data characterizing these dynamics for infants. Here, we performed Illumina sequencing of gag and pol using longitudinal plasma samples from 22 Kenyan infants with well-characterized infection timing. We used these data to characterize viral diversity changes over time by designing an infant-trained Bayesian hierarchical regression model that predicts time since infection using viral diversity. We show that diversity accumulates with time for most infants (median rate within pol = 0.00079 diversity/month), and diversity accumulates much faster than in adults (compare previously-reported adult rate within pol = 0.00024 diversity/month [1]). We find that the infant rate of viral diversification varies by individual, gene region, and relative timing of infection, but not by set-point viral load or rate of CD4+ T cell decline. We compare the predictive performance of this infant-trained Bayesian hierarchical regression model with simple linear regression models trained using the same infant data, as well as existing adult-trained models [1]. Using an independent dataset from an additional 15 infants with frequent HIV testing to define infection timing, we demonstrate that infant-trained models more accurately estimate time since infection than existing adult-trained models. This work will be useful for timing HIV acquisition for infants with unknown infection timing and for refining our understanding of how viral diversity accumulates in infants, both of which may have broad implications for the future development of infant-specific therapeutic and preventive interventions.

Precision detection of recent HIV infections using high-throughput genomic incidence assay

HIV incidence is a key measure for tracking disease spread and identifying populations and geographic regions where new infections are most concentrated. The HIV sequence population provides a robust signal for the stage of infection. Large-scale and high-precision HIV sequencing is crucial for effective genomic incidence surveillance. We produced 1,034 full-length envelope gene sequences from a seroconversion cohort by conducting HIV microdrop sequencing and measuring the genomic incidence assay's genome similarity index (GSI) dynamics. The measured dynamics of 9 of 12 individuals aligned with the GSI distribution estimated independently using 417 publicly available incident samples. We enhanced the capacity to identify individuals with recent infections, achieving predicted detection accuracies of 92% (89%-94%) for cases within 6 months and 81% (74%-87%) for cases within 9 months. These accuracy levels agreed with the observed detection accuracy intervals of an independent validation data set. Additionally, we produced 131 full-length envelope gene sequences from eight individuals with chronic HIV infection. This analysis confirmed a false recency rate (FRR) of 0%, which was consistent with 162 publicly available chronic samples. The mean duration of recent infection (MDRI) was 238 (209-267) days, indicating an 83% improvement in performance compared to current recent infection testing algorithms. The shifted Poisson mixture model was then used to estimate the time since infection, and the model estimates showed an 88% consistency with the days post infection derived from HIV RNA test dates and/or seroconversion dates. HIV microdrop sequencing provides unique prospects for large-scale incidence surveillance using high-throughput sequencing. IMPORTANCE Accurate identification of recently infected individuals is vital for prioritizing specific populations for interventions, reducing onward transmission risks, and optimizing public health services. However, current HIV-specific antibody-based methods have not been satisfactory in accurately identifying incident cases, hindering the use of HIV recency testing for prevention efforts and partner protection. Genomic incidence assays offer a promising alternative for identifying recent infections. In our study, we used microdroplet technologies to produce a large number of complete HIV envelope gene sequences, enabling the accurate detection of early infection signs. We assessed the dynamics of the incidence assay's metrics and compared them with statistical models. Our approach demonstrated high accuracy in identifying individuals with recent infections, achieving predicted detection rates exceeding 90% within 6 months and over 80% within 9 months of infection. This high-resolution method holds significant potential for enhancing the effectiveness of HIV incidence screening for case-based surveillance in public health initiatives.

Microdrop Human Immunodeficiency Virus Sequencing for Incidence and Drug Resistance Surveillance

BACKGROUND

Precise and cost-efficient human immunodeficiency virus (HIV) incidence and drug resistance surveillances are in high demand for the advancement of the 90-90-90 "treatment for all" target.

METHODS

We developed microdrop HIV sequencing for the HIV incidence and drug resistance assay (HIDA), a single-blood-draw surveillance tool for incidence and drug resistance mutation (DRM) detection. We amplified full-length HIV envelope and pol gene sequences within microdroplets, and this compartmental amplification with long-read high-throughput sequencing enabled us to recover multiple unique sequences.

RESULTS

We achieved greater precision in determining the stage of infection than current incidence assays, with a 1.2% false recency rate (proportion of misclassified chronic infections) and a 262-day mean duration of recent infection (average time span of recent infection classification) from 83 recently infected and 81 chronically infected individuals. Microdrop HIV sequencing demonstrated an increased capacity to detect minority variants and linked DRMs. By screening all 93 World Health Organization surveillance DRMs, we detected 6 pretreatment drug resistance mutations with 2.6%-13.2% prevalence and cross-linked mutations.

CONCLUSIONS

HIDA with microdrop HIV sequencing may promote global HIV real-time surveillance by serving as a precise and high-throughput cross-sectional survey tool that can be generalized for surveillance of other pathogens.

Viral Diversity Based on Next-Generation Sequencing of HIV-1 Provides Precise Estimates of Infection Recency and Time Since Infection

BACKGROUND

Human immunodeficiency virus type 1 (HIV-1) genetic diversity increases over the course of infection and can be used to infer the time since infection and, consequently, infection recency, which are crucial for HIV-1 surveillance and the understanding of viral pathogenesis.

METHODS

We considered 313 HIV-infected individuals for whom reliable estimates of infection dates and next-generation sequencing (NGS)-derived nucleotide frequency data were available. Fractions of ambiguous nucleotides, obtained by population sequencing, were available for 207 samples. We assessed whether the average pairwise diversity calculated using NGS sequences provided a more exact prediction of the time since infection and classification of infection recency (<1 year after infection), compared with the fraction of ambiguous nucleotides.

RESULTS

NGS-derived average pairwise diversity classified an infection as recent with a sensitivity of 88% and a specificity of 85%. When considering only the 207 samples for which fractions of ambiguous nucleotides were available, the NGS-derived average pairwise diversity exhibited a higher sensitivity (90% vs 78%) and specificity (95% vs 67%) than the fraction of ambiguous nucleotides. Additionally, the average pairwise diversity could be used to estimate the time since infection with a mean absolute error of 0.84 years, compared with 1.03 years for the fraction of ambiguous nucleotides.

CONCLUSIONS

Viral diversity based on NGS data is more precise than that based on population sequencing in its ability to predict infection recency and provides an estimated time since infection that has a mean absolute error of <1 year.

Molecular dating and viral load growth rates suggested that the eclipse phase lasted about a week in HIV-1 infected adults in East Africa and Thailand

Most HIV-1 infected individuals do not know their infection dates. Precise infection timing is crucial information for studies that document transmission networks or drug levels at infection. To improve infection timing, we used the prospective RV217 cohort where the window when plasma viremia becomes detectable is narrow: the last negative visit occurred a median of four days before the first detectable HIV-1 viremia with an RNA test, referred below as diagnosis. We sequenced 1,280 HIV-1 genomes from 39 participants at a median of 4, 32 and 170 days post-diagnosis. HIV-1 infections were dated by using sequence-based methods and a viral load regression method. Bayesian coalescent and viral load regression estimated that infections occurred a median of 6 days prior to diagnosis (IQR: 9–3 and 11–4 days prior, respectively). Poisson-Fitter, which analyzes the distribution of hamming distances among sequences, estimated a median of 7 days prior to diagnosis (IQR: 15–4 days) based on sequences sampled 4 days post-diagnosis, but it did not yield plausible results using sequences sampled at 32 days. Fourteen participants reported a high-risk exposure event at a median of 8 days prior to diagnosis (IQR: 12 to 6 days prior). These different methods concurred that HIV-1 infection occurred about a week before detectable viremia, corresponding to 20 days (IQR: 34–15 days) before peak viral load. Together, our methods comparison helps define a framework for future dating studies in early HIV-1 infection.

HIV-1 infected individuals rarely know when they became infected but knowing when an infection occurred provides critical information regarding HIV-1 pathogenesis and epidemiology. Using a unique cohort in which infection was known to have occurred in a narrow interval, we investigated methods to estimate the timing of infections. Several methods suggested that HIV-1 infection typically occurs a median of one week before the infection can be detected by HIV-1 RNA testing. Going forward, we provide a strategy that can be used to elucidate the origin of an acute/early infection.

Using Primer-ID Deep Sequencing to Detect Recent Human Immunodeficiency Virus Type 1 Infection

Intrahost viral sequence diversity can be evaluated over multiple genomic regions using next-generation sequencing (NGS) and scaled to population-level diversity to identify recent human immunodeficiency virus type 1 infection. Using Primer-ID NGS, we sequenced the reverse transcriptase (RT) and env V1-V3 regions from persons with known infection dates, and assessed the mean (π) and first quintile of pairwise diversity distributions over time. The receiver operating characteristic area under the curve (AUC) of RT and V1-V3 combined showed excellent discrimination of recent infection <9 months: using π (only single transmitted variants: AUC, 0.98; threshold <1.03%; sensitivity, 97%; specificity, 89%) and the first quintile (including all variants: AUC, 0.90; threshold <0.60%; sensitivity, 91%; specificity, 92%).

HIITE: HIV-1 incidence and infection time estimator

Motivation

Around 2.1 million new HIV-1 infections were reported in 2015, alerting that the HIV-1 epidemic remains a significant global health challenge. Precise incidence assessment strengthens epidemic monitoring efforts and guides strategy optimization for prevention programs. Estimating the onset time of HIV-1 infection can facilitate optimal clinical management and identify key populations largely responsible for epidemic spread and thereby infer HIV-1 transmission chains. Our goal is to develop a genomic assay estimating the incidence and infection time in a single cross-sectional survey setting.

Results

We created a web-based platform, HIV-1 incidence and infection time estimator (HIITE), which processes envelope gene sequences using hierarchical clustering algorithms and informs the stage of infection, along with time since infection for incident cases. HIITE's performance was evaluated using 585 incident and 305 chronic specimens' envelope gene sequences collected from global cohorts including HIV-1 vaccine trial participants. HIITE precisely identified chronically infected individuals as being chronic with an error less than 1% and correctly classified 94% of recently infected individuals as being incident. Using a mixed-effect model, an incident specimen's time since infection was estimated from its single lineage diversity, showing 14% prediction error for time since infection. HIITE is the first algorithm to inform two key metrics from a single time point sequence sample. HIITE has the capacity for assessing not only population-level epidemic spread but also individual-level transmission events from a single survey, advancing HIV prevention and intervention programs.

Availability and implementation

Web-based HIITE and source code of HIITE are available at http://www.hayounlee.org/software.html.

Supplementary information

Supplementary data are available at Bioinformatics online.

Diagnosis of Human Immunodeficiency Virus Infection

HIV diagnostics have played a central role in the remarkable progress in identifying, staging, initiating, and monitoring infected individuals on life-saving antiretroviral therapy. They are also useful in surveillance and outbreak responses, allowing for assessment of disease burden and identification of vulnerable populations and transmission "hot spots," thus enabling planning, appropriate interventions, and allocation of appropriate funding. HIV diagnostics are critical in achieving epidemic control and require a hybrid of conventional laboratory-based diagnostic tests and new technologies, including point-of-care (POC) testing, to expand coverage, increase access, and positively impact patient management. In this review, we provide (i) a historical perspective on the evolution of HIV diagnostics (serologic and molecular) and their interplay with WHO normative guidelines, (ii) a description of the role of conventional and POC testing within the tiered laboratory diagnostic network, (iii) information on the evaluations and selection of appropriate diagnostics, (iv) a description of the quality management systems needed to ensure reliability of testing, and (v) strategies to increase access while reducing the time to return results to patients. Maintaining the central role of HIV diagnostics in programs requires periodic monitoring and optimization with quality assurance in order to inform adjustments or alignment to achieve epidemic control.

HIV-1 envelope sequence-based diversity measures for identifying recent infections

Identifying recent HIV-1 infections is crucial for monitoring HIV-1 incidence and optimizing public health prevention efforts. To identify recent HIV-1 infections, we evaluated and compared the performance of 4 sequence-based diversity measures including percent diversity, percent complexity, Shannon entropy and number of haplotypes targeting 13 genetic segments within the env gene of HIV-1. A total of 597 diagnostic samples obtained in 2013 and 2015 from recently and chronically HIV-1 infected individuals were selected. From the selected samples, 249 (134 from recent versus 115 from chronic infections) env coding regions, including V1-C5 of gp120 and the gp41 ectodomain of HIV-1, were successfully amplified and sequenced by next generation sequencing (NGS) using the Illumina MiSeq platform. The ability of the four sequence-based diversity measures to correctly identify recent HIV infections was evaluated using the frequency distribution curves, median and interquartile range and area under the curve (AUC) of the receiver operating characteristic (ROC). Comparing the median and interquartile range and evaluating the frequency distribution curves associated with the 4 sequence-based diversity measures, we observed that the percent diversity, number of haplotypes and Shannon entropy demonstrated significant potential to discriminate recent from chronic infections (p<0.0001). Using the AUC of ROC analysis, only the Shannon entropy measure within three HIV-1 env segments could accurately identify recent infections at a satisfactory level. The env segments were gp120 C2_1 (AUC = 0.806), gp120 C2_3 (AUC = 0.805) and gp120 V3 (AUC = 0.812). Our results clearly indicate that the Shannon entropy measure represents a useful tool for predicting HIV-1 infection recency.

Estimating time of HIV-1 infection from next-generation sequence diversity

Estimating the time since infection (TI) in newly diagnosed HIV-1 patients is challenging, but important to understand the epidemiology of the infection. Here we explore the utility of virus diversity estimated by next-generation sequencing (NGS) as novel biomarker by using a recent genome-wide longitudinal dataset obtained from 11 untreated HIV-1-infected patients with known dates of infection. The results were validated on a second dataset from 31 patients. Virus diversity increased linearly with time, particularly at 3rd codon positions, with little inter-patient variation. The precision of the TI estimate improved with increasing sequencing depth, showing that diversity in NGS data yields superior estimates to the number of ambiguous sites in Sanger sequences, which is one of the alternative biomarkers. The full advantage of deep NGS was utilized with continuous diversity measures such as average pairwise distance or site entropy, rather than the fraction of polymorphic sites. The precision depended on the genomic region and codon position and was highest when 3rd codon positions in the entire pol gene were used. For these data, TI estimates had a mean absolute error of around 1 year. The error increased only slightly from around 0.6 years at a TI of 6 months to around 1.1 years at 6 years. Our results show that virus diversity determined by NGS can be used to estimate time since HIV-1 infection many years after the infection, in contrast to most alternative biomarkers. We provide the regression coefficients as well as web tool for TI estimation.

HIV-1 establishes a chronic infection, which may last for many years before the infected person is diagnosed. The resulting uncertainty in the date of infection leads to difficulties in estimating the number of infected but undiagnosed persons as well as the number of new infections, which is necessary for developing appropriate public health policies and interventions. Such estimates would be much easier if the time since HIV-1 infection for newly diagnosed cases could be accurately estimated. Three types of biomarkers have been shown to contain information about the time since HIV-1 infection, but unfortunately, they only distinguish between recent and long-term infections (concentration of HIV-1-specific antibodies) or are imprecise (immune status as measured by levels of CD4+ T-lymphocytes and viral sequence diversity measured by polymorphisms in Sanger sequences). In this paper, we show that recent advances in sequencing technologies, i.e. the development of next generation sequencing, enable significantly more precise determination of the time since HIV-1 infection, even many years after the infection event. This is a significant advance which could translate into more effective HIV-1 prevention.

The HIV Genomic Incidence Assay Meets False Recency Rate and Mean Duration of Recency Infection Performance Standards

HIV incidence is a primary metric for epidemic surveillance and prevention efficacy assessment. HIV incidence assay performance is evaluated via false recency rate (FRR) and mean duration of recent infection (MDRI). We conducted a meta-analysis of 438 incident and 305 chronic specimens' HIV envelope genes from a diverse global cohort. The genome similarity index (GSI) accurately characterized infection stage across diverse host and viral factors. All except one chronic specimen had GSIs below 0.67, yielding a FRR of 0.33 [0-0.98] %. We modeled the incidence assay biomarker dynamics with a logistic link function assuming individual variabilities in a Beta distribution. The GSI probability density function peaked close to 1 in early infection and 0 around two years post infection, yielding MDRI of 420 [361, 467] days. We tested the assay by newly sequencing 744 envelope genes from 59 specimens of 21 subjects who followed from HIV negative status. Both standardized residuals and Anderson-Darling tests showed that the test dataset was statistically consistent with the model biomarker dynamics. This is the first reported incidence assay meeting the optimal FRR and MDRI performance standards. Signatures of HIV gene diversification can allow precise cross-sectional surveillance with a desirable temporal range of incidence detection.

Pairwise diversity and tMRCA as potential markers for HIV infection recency

Intrahost human immunodeficiency virus (HIV)-1 diversity increases linearly over time. We assessed the extent to which mean pairwise distances and the time to the most recent common ancestor (tMRCA) inferred from intrahost HIV-1C env sequences were associated with the estimated time of HIV infection. Data from a primary HIV-1C infection study in Botswana were used for this analysis (N = 42). A total of 2540 HIV-1C env gp120 variable loop region 1 to conserved region 5 (V1C5) of the HIV-1 envelope gp120 viral sequences were generated by single genome amplification and sequencing, with an average of 61 viral sequences per participant and 11 sequences per time point per participant. Raw pairwise distances were calculated for each time point and participant using the ape package in R software. The tMRCA was estimated using phylogenetic inference implemented in Bayesian Evolutionary Analysis by Sampling Trees v1.8.2. Pairwise distances and tMRCA were significantly associated with the estimated time since HIV infection (both P < 0.001). Taking into account multiplicity of HIV infection strengthened these associations. HIV-1C env-based pairwise distances and tMRCA can be used as potential markers for HIV recency. However, the tMRCA estimates demonstrated no advantage over the pairwise distances estimates.

Enhancement of viral escape in HIV-1 Nef by STEP vaccination

OBJECTIVE

Properly priming cytotoxic T-lymphocyte (CTL) responses is an important task in HIV-1 vaccination. However, the STEP trial showed no efficacy even though the vaccine elicited HIV-specific CTL responses. Our study is to investigate whether or not the STEP vaccine enhanced viral escape in infected volunteers.

METHODS

The signature of viral escape, the presence of multiple escape variants, could be falsely represented by the existence of multiple founder viruses. Therefore, we use a mathematical model to designate STEP study patients with infections from a single founder virus. We then conduct permutation tests on each of 9988 Gag, Pol, and Nef overlapping peptides to identify epitopes with significant differences in diversity between the vaccine and placebo groups using previously published STEP trial sequence data.

RESULTS

We identify signatures of vaccine-enhanced viral escape within HIV-1 Nef from the STEP trial. Vaccine-treated patients showed a greater level of epitope diversity in one of the immunodomiant epitopes, EVGFPVRPQVPL (Nef65-76), compared with placebo-treated patients (P = 0.0038). In the other three Nef epitopes, there is a marginally significant difference in the epitope diversity between the vaccine and placebo group (P < 0.1). This greater epitope diversity was neither due to any difference in infection duration nor overall nef gene diversity between the two groups, suggesting that the increase in viral escape was likely mediated by vaccine-induced T-cell responses.

CONCLUSION

Viral escape in Nef is elevated preferentially in STEP vaccine-treated individuals, suggesting that vaccination primarily modulated initial CTL responses. Our observations provide important insights into improving vaccine-primed first immune control.

Molecular clock of HIV-1 envelope genes under early immune selection

Background

The molecular clock hypothesis that genes or proteins evolve at a constant rate is a key tool to reveal phylogenetic relationships among species. Using the molecular clock, we can trace an infection back to transmission using HIV-1 sequences from a single time point. Whether or not a strict molecular clock applies to HIV-1’s early evolution in the presence of immune selection has not yet been fully examined.

Results

We identified molecular clock signatures from 1587 previously published HIV-1 full envelope gene sequences obtained since acute infection in 15 subjects. Each subject’s sequence diversity linearly increased during the first 150 days post infection, with rates ranging from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$1.54 \times 10^{ - 5}$$\end{document}1.54×10-5 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$3.91 \times 10^{ - 5}$$\end{document}3.91×10-5 with a mean of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.69 \times 10^{ - 5}$$\end{document}2.69×10-5 per base per day. The rate of diversification for 12 out of the 15 subjects was comparable to the neutral evolution rate. While temporal diversification was consistent with evolution patterns in the absence of selection, mutations from the founder virus were highly clustered on statistically identified selection sites, which diversified more than 65 times faster than non-selection sites. By mathematically quantifying deviations from the molecular clock under various selection scenarios, we demonstrate that the deviation from a constant clock becomes negligible as multiple escape lineages emerge. The most recent common ancestor of a virus pair from distinct escape lineages is most likely the transmitted founder virus, indicating that HIV-1 molecular dating is feasible even after the founder viruses are no longer detectable.

Conclusions

The ability of HIV-1 to escape from immune surveillance in many different directions is the driving force of molecular clock persistence. This finding advances our understanding of the robustness of HIV-1’s molecular clock under immune selection, implying the potential for molecular dating.

Electronic supplementary material

The online version of this article (doi:10.1186/s12977-016-0269-6) contains supplementary material, which is available to authorized users.

SPMM: estimating infection duration of multivariant HIV-1 infections

MOTIVATION

Illustrating how HIV-1 is transmitted and how it evolves in the following weeks is an important step for developing effective vaccination and prevention strategies. It is currently possible through DNA sequencing to account for the diverse array of viral strains within an infected individual. This provides an unprecedented opportunity to pinpoint when each patient was infected and which viruses were transmitted.

RESULTS

Here we develop a mathematical tool for early HIV-1 evolution within a subject whose infection originates either from a single or multiple viral variants. The shifted Poisson mixture model (SPMM) provides a quantitative guideline for segregating viral lineages, which in turn enables us to assess when a subject was infected. The infection duration estimated by SPMM showed a statistically significant linear relationship with that by Fiebig laboratory staging (P = 0.00059) among 37 acutely infected subjects. Our tool provides a functional approach to understanding early genetic diversity, one of the most important parameters for deciphering HIV-1 transmission and predicting the rate of disease progression.

AVAILABILITY AND IMPLEMENTATION

SPMM, webserver, is available at http://www.hayounlee.org/web-tools.html.

CONTACT

hayoun@usc.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Identifying Recent HIV Infections: From Serological Assays to Genomics

In this paper, we review serological and molecular based methods to identify HIV infection recency. The accurate identification of recent HIV infection continues to be an important research area and has implications for HIV prevention and treatment interventions. Longitudinal cohorts that follow HIV negative individuals over time are the current gold standard approach, but they are logistically challenging, time consuming and an expensive enterprise. Methods that utilize cross-sectional testing and biomarker information have become an affordable alternative to the longitudinal approach. These methods use well-characterized biological makers to differentiate between recent and established HIV infections. However, recent results have identified a number of limitations in serological based assays that are sensitive to the variability in immune responses modulated by HIV subtypes, viral load and antiretroviral therapy. Molecular methods that explore the dynamics between the timing of infection and viral evolution are now emerging as a promising approach. The combination of serological and molecular methods may provide a good solution to identify recent HIV infection in cross-sectional data. As part of this review, we present the advantages and limitations of serological and molecular based methods and their potential complementary role for the identification of HIV infection recency.

Assessing Biases in the Evaluation of Classification Assays for HIV Infection Recency

Identifying recent HIV infection cases has important public health and clinical implications. It is essential for estimating incidence rates to monitor epidemic trends and evaluate the effectiveness of interventions. Detecting recent cases is also important for HIV prevention given the crucial role that recently infected individuals play in disease transmission, and because early treatment onset can improve the clinical outlook of patients while reducing transmission risk. Critical to this enterprise is the development and proper assessment of accurate classification assays that, based on cross-sectional samples of viral sequences, help determine infection recency status. In this work we assess some of the biases present in the evaluation of HIV recency classification algorithms that rely on measures of within-host viral diversity. Particularly, we examine how the time since infection (TSI) distribution of the infected subjects from which viral samples are drawn affect performance metrics (e.g., area under the ROC curve, sensitivity, specificity, accuracy and precision), potentially leading to misguided conclusions about the efficacy of classification assays. By comparing the performance of a given HIV recency assay using six different TSI distributions (four simulated TSI distributions representing different epidemic scenarios, and two empirical TSI distributions), we show that conclusions about the overall efficacy of the assay depend critically on properties of the TSI distribution. Moreover, we demonstrate that an assay with high overall classification accuracy, mainly due to properly sorting members of the well-represented groups in the validation dataset, can still perform notoriously poorly when sorting members of the less represented groups. This is an inherent issue of classification and diagnostics procedures that is often underappreciated. Thus, this work underscores the importance of acknowledging and properly addressing evaluation biases when proposing new HIV recency assays.

A Generalized Entropy Measure of Within-Host Viral Diversity for Identifying Recent HIV-1 Infections

There is a need for incidence assays that accurately estimate HIV incidence based on cross-sectional specimens. Viral diversity-based assays have shown promises but are not particularly accurate. We hypothesize that certain viral genetic regions are more predictive of recent infection than others and aim to improve assay accuracy by using classification algorithms that focus on highly informative regions (HIRs).We analyzed HIV gag sequences from a cohort in Botswana. Forty-two subjects newly infected by HIV-1 Subtype C were followed through 500 days post-seroconversion. Using sliding window analysis, we screened for genetic regions within gag that best differentiate recent versus chronic infections. We used both nonparametric and parametric approaches to evaluate the discriminatory abilities of sequence regions. Segmented Shannon Entropy measures of HIRs were aggregated to develop generalized entropy measures to improve prediction of recency. Using logistic regression as the basis for our classification algorithm, we evaluated the predictive power of these novel biomarkers and compared them with recently reported viral diversity measures using area under the curve (AUC) analysis.Change of diversity over time varied across different sequence regions within gag. We identified the top 50% of the most informative regions by both nonparametric and parametric approaches. In both cases, HIRs were in more variable regions of gag and less likely in the p24 coding region. Entropy measures based on HIRs outperformed previously reported viral-diversity-based biomarkers. These methods are better suited for population-level estimation of HIV recency.The patterns of diversification of certain regions within the gag gene are more predictive of recency of infection than others. We expect this result to apply in other HIV genetic regions as well. Focusing on these informative regions, our generalized entropy measure of viral diversity demonstrates the potential for improving accuracy when identifying recent HIV-1 infections.

Decreasing Proportion of Recent Infections among Newly Diagnosed HIV-1 Cases in Switzerland, 2008 to 2013 Based on Line-Immunoassay-Based Algorithms

Background

HIV surveillance requires monitoring of new HIV diagnoses and differentiation of incident and older infections. In 2008, Switzerland implemented a system for monitoring incident HIV infections based on the results of a line immunoassay (Inno-Lia) mandatorily conducted for HIV confirmation and type differentiation (HIV-1, HIV-2) of all newly diagnosed patients. Based on this system, we assessed the proportion of incident HIV infection among newly diagnosed cases in Switzerland during 2008-2013.

Methods and Results

Inno-Lia antibody reaction patterns recorded in anonymous HIV notifications to the federal health authority were classified by 10 published algorithms into incident (up to 12 months) or older infections. Utilizing these data, annual incident infection estimates were obtained in two ways, (i) based on the diagnostic performance of the algorithms and utilizing the relationship ‘incident = true incident + false incident’, (ii) based on the window-periods of the algorithms and utilizing the relationship ‘Prevalence = Incidence x Duration’. From 2008—2013, 3’851 HIV notifications were received. Adult HIV-1 infections amounted to 3’809 cases, and 3’636 of them (95.5%) contained Inno-Lia data. Incident infection totals calculated were similar for the performance- and window-based methods, amounting on average to 1’755 (95% confidence interval, 1588—1923) and 1’790 cases (95% CI, 1679—1900), respectively. More than half of these were among men who had sex with men. Both methods showed a continuous decline of annual incident infections 2008—2013, totaling -59.5% and -50.2%, respectively. The decline of incident infections continued even in 2012, when a 15% increase in HIV notifications had been observed. This increase was entirely due to older infections. Overall declines 2008—2013 were of similar extent among the major transmission groups.

Conclusions

Inno-Lia based incident HIV-1 infection surveillance proved useful and reliable. It represents a free, additional public health benefit of the use of this relatively costly test for HIV confirmation and type differentiation.

High-accuracy identification of incident HIV-1 infections using a sequence clustering based diversity measure

Accurate estimates of HIV-1 incidence are essential for monitoring epidemic trends and evaluating intervention efforts. However, the long asymptomatic stage of HIV-1 infection makes it difficult to effectively distinguish incident infections from chronic ones. Current incidence assays based on serology or viral sequence diversity are both still lacking in accuracy. In the present work, a sequence clustering based diversity (SCBD) assay was devised by utilizing the fact that viral sequences derived from each transmitted/founder (T/F) strain tend to cluster together at early stage, and that only the intra-cluster diversity is correlated with the time since HIV-1 infection. The dot-matrix pairwise alignment was used to eliminate the disproportional impact of insertion/deletions (indels) and recombination events, and so was the proportion of clusterable sequences (P_c) as an index to identify late chronic infections with declined viral genetic diversity. Tested on a dataset containing 398 incident and 163 chronic infection cases collected from the Los Alamos HIV database (last modified 2/8/2012), our SCBD method achieved 99.5% sensitivity and 98.8% specificity, with an overall accuracy of 99.3%. Further analysis and evaluation also suggested its performance was not affected by host factors such as the viral subtypes and transmission routes. The SCBD method demonstrated the potential of sequencing based techniques to become useful for identifying incident infections. Its use may be most advantageous for settings with low to moderate incidence relative to available resources. The online service is available at http://www.bioinfo.tsinghua.edu.cn:8080/SCBD/index.jsp.

Assessment of ambiguous base calls in HIV-1 pol population sequences as a biomarker for identification of recent infections in HIV-1 incidence studies

An increase in the proportion of ambiguous base calls in HIV-1 pol population sequences during the course of infection has been demonstrated in different study populations, and sequence ambiguity thresholds to classify infections as recent or nonrecent have been suggested. The aim of our study was to evaluate sequence ambiguities as a candidate biomarker for use in an HIV-1 incidence assay using samples from antiretroviral treatment-naive seroconverters with known durations of infection (German HIV-1 Seroconverter Study). We used 2,203 HIV-1 pol population sequences derived from 1,334 seroconverters to assess the sequence ambiguity method (SAM). We then compared the serological incidence BED capture enzyme immunoassay (BED-CEIA) with the SAM for a subset of 723 samples from 495 seroconverters and evaluated a multianalyte algorithm that includes BED-CEIA results, SAM results, viral loads, and CD4 cell counts for 453 samples from 325 seroconverters. We observed a significant increase in the proportion of sequence ambiguities with the duration of infection. A sequence ambiguity threshold of 0.5% best identified recent infections with 76.7% accuracy. The mean duration of recency was determined to be 208 (95% confidence interval, 196 to 221) days. In the subset analysis, BED-CEIA achieved a significantly higher accuracy than the SAM (84.6 versus 75.5%, P < 0.001) and results were concordant for 64.2% (464/723) of the samples. Also, the multianalyte algorithm did not show better accuracy than the BED-CEIA (83.4 versus 84.3%, P = 0.786). In conclusion, the SAM and the multianalyte algorithm including SAM were inferior to the BED-CEIA, and the proportion of sequence ambiguities is therefore not a preferable biomarker for HIV-1 incidence testing.

Short Communication: Defining optimality of a test for recent infection for HIV incidence surveillance

The estimation of HIV incidence from cross-sectional surveys using tests for recent infection has attracted much interest. It is increasingly recognized that the lack of high performance recent infection tests is hindering the implementation of this surveillance approach. With growing funding opportunities, test developers are currently trying to fill this gap. However, there is a lack of consensus and clear guidance for developers on the evaluation and optimization of candidate tests. A fundamental shift from conventional thinking about test performance is needed: away from metrics relevant in typical public health settings where the detection of a condition in individuals is of primary interest (sensitivity, specificity, and predictive values) and toward metrics that are appropriate when estimating a population-level parameter such as incidence (accuracy and precision). The inappropriate use of individual-level diagnostics performance measures could lead to spurious assessments and suboptimal designs of tests for incidence estimation. In some contexts, such as population-level application to HIV incidence, bias of estimates is essentially negligible, and all that remains is the maximization of precision. The maximization of the precision of incidence estimates provides a completely general criterion for test developers to assess and optimize test designs. Summarizing the test dynamics into the properties relevant for incidence estimation, high precision estimates are obtained when (1) the mean duration of recent infection is large, and (2) the false-recent rate is small. The optimal trade-off between these two test properties will produce the highest precision, and therefore the most epidemiologically useful incidence estimates.

Developing high-throughput HIV incidence assay with pyrosequencing platform

Human immunodeficiency virus (HIV) incidence is an important measure for monitoring the epidemic and evaluating the efficacy of intervention and prevention trials. This study developed a high-throughput, single-measure incidence assay by implementing a pyrosequencing platform. We devised a signal-masking bioinformatics pipeline, which yielded a process error rate of 5.8 × 10(-4) per base. The pipeline was then applied to analyze 18,434 envelope gene segments (HXB2 7212 to 7601) obtained from 12 incident and 24 chronic patients who had documented HIV-negative and/or -positive tests. The pyrosequencing data were cross-checked by using the single-genome-amplification (SGA) method to independently obtain 302 sequences from 13 patients. Using two genomic biomarkers that probe for the presence of similar sequences, the pyrosequencing platform correctly classified all 12 incident subjects (100% sensitivity) and 23 of 24 chronic subjects (96% specificity). One misclassified subject's chronic infection was correctly classified by conducting the same analysis with SGA data. The biomarkers were statistically associated across the two platforms, suggesting the assay's reproducibility and robustness. Sampling simulations showed that the biomarkers were tolerant of sequencing errors and template resampling, two factors most likely to affect the accuracy of pyrosequencing results. We observed comparable biomarker scores between AIDS and non-AIDS chronic patients (multivariate analysis of variance [MANOVA], P = 0.12), indicating that the stage of HIV disease itself does not affect the classification scheme. The high-throughput genomic HIV incidence marks a significant step toward determining incidence from a single measure in cross-sectional surveys.

IMPORTANCE

Annual HIV incidence, the number of newly infected individuals within a year, is the key measure of monitoring the epidemic's rise and decline. Developing reliable assays differentiating recent from chronic infections has been a long-standing quest in the HIV community. Over the past 15 years, these assays have traditionally measured various HIV-specific antibodies, but recent technological advancements have expanded the diversity of proposed accurate, user-friendly, and financially viable tools. Here we designed a high-throughput genomic HIV incidence assay based on the signature imprinted in the HIV gene sequence population. By combining next-generation sequencing techniques with bioinformatics analysis, we demonstrated that genomic fingerprints are capable of distinguishing recently infected patients from chronically infected patients with high precision. Our high-throughput platform is expected to allow us to process many patients' samples from a single experiment, permitting the assay to be cost-effective for routine surveillance.

Molecular characterization of ambiguous mutations in HIV-1 polymerase gene: implications for monitoring HIV infection status and drug resistance

Detection of recent HIV infections is a prerequisite for reliable estimations of transmitted HIV drug resistance (t-HIVDR) and incidence. However, accurately identifying recent HIV infection is challenging due partially to the limitations of current serological tests. Ambiguous nucleotides are newly emerged mutations in quasispecies, and accumulate by time of viral infection. We utilized ambiguous mutations to establish a measurement for detecting recent HIV infection and monitoring early HIVDR development. Ambiguous nucleotides were extracted from HIV-1 pol-gene sequences in the datasets of recent (HIVDR threshold surveys [HIVDR-TS] in 7 countries; n=416) and established infections (1 HIVDR monitoring survey at baseline; n=271). An ambiguous mutation index of 2.04×10^-3 nts/site was detected in HIV-1 recent infections which is equivalent to the HIV-1 substitution rate (2×10^-3 nts/site/year) reported before. However, significantly higher index (14.41×10^-3 nts/site) was revealed with established infections. Using this substitution rate, 75.2% subjects in HIVDR-TS with the exception of the Vietnam dataset and 3.3% those in HIVDR-baseline were classified as recent infection within one year. We also calculated mutation scores at amino acid level at HIVDR sites based on ambiguous or fitted mutations. The overall mutation scores caused by ambiguous mutations increased (0.54×10^-23.48×10^-2/DR-site) whereas those caused by fitted mutations remained stable (7.50-7.89×10^-2/DR-site) in both recent and established infections, indicating that t-HIVDR exists in drug-naïve populations regardless of infection status in which new HIVDR continues to emerge. Our findings suggest that characterization of ambiguous mutations in HIV may serve as an additional tool to differentiate recent from established infections and to monitor HIVDR emergence.

Simple estimation of incident HIV infection rates in notification cohorts based on window periods of algorithms for evaluation of line-immunoassay result patterns

Background

Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods.

Methods

We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship ‘Prevalence  =  Incidence x Duration’ in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship ‘incident  =  true incident + false incident’ and also to the IIR derived from the BED incidence assay.

Results

Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R² = 0.962; P<0.0001). Among the 25 algorithms, the mean window-based IIR among the 748 notifications of 2005/06 was 0.457 compared to 0.453 obtained for performance-based IIR with a model not correcting for selection bias. Evaluation of BED results using a window of 153 days yielded an IIR of 0.669. Window-based IIR and performance-based IIR increased by 22.4% and respectively 30.6% in 2008, while 2009 and 2010 showed a return to baseline for both methods.

Conclusions

IIR estimations by window- and performance-based evaluations of Inno-Lia algorithm results were similar and can be used together to assess IIR changes between annual HIV notification cohorts.

Future of phylogeny in HIV prevention

The success of the HIV Prevention Trials Network 052 trial has led to revisions in HIV-1 treatment guidelines. Antiretroviral therapy may reduce the risk of HIV-1 transmissions at the population level. The design of successful treatment as prevention interventions will be predicated on a comprehensive understanding of the spatial, temporal, and biological dynamics of heterosexual men who have sex with men and intravenous drug user epidemics. Viral phylogenetics can capture the underlying structure of transmission networks based on the genetic interrelatedness of viral sequences and cluster networks that could not be otherwise identified. This article describes the phylogenetic expansion of the Montreal men who have sex with men epidemic over the last decade. High rates of coclustering of primary infections are associated with 1 infection leading to 13 onward transmissions. Phylogeny substantiates the role of primary and recent stage infection in transmission dynamics, underlying the importance of timely diagnosis and immediate antiretroviral therapy initiation to avert transmission cascades.

Towards estimation of HIV-1 date of infection: a time-continuous IgG-model shows that seroconversion does not occur at the midpoint between negative and positive tests

Estimating date of infection for HIV-1-infected patients is vital for disease tracking and informed public health decisions, but is difficult to obtain because most patients have an established infection of unknown duration at diagnosis. Previous studies have used HIV-1-specific immunoglobulin G (IgG) levels as measured by the IgG capture BED enzyme immunoassay (BED assay) to indicate if a patient was infected recently, but a time-continuous model has not been available. Therefore, we developed a logistic model of IgG production over time. We used previously published metadata from 792 patients for whom the HIV-1-specific IgG levels had been longitudinally measured using the BED assay. To account for patient variability, we used mixed effects modeling to estimate general population parameters. The typical patient IgG production rate was estimated at r = 6.72[approximate 95% CI 6.17,7.33]×10⁻³ OD-n units day⁻¹, and the carrying capacity at K = 1.84[1.75,1.95] OD-n units, predicting how recently patients seroconverted in the interval ^∧t = (31,711) days. Final model selection and validation was performed on new BED data from a population of 819 Swedish HIV-1 patients diagnosed in 2002–2010. On an appropriate subset of 350 patients, the best model parameterization had an accuracy of 94% finding a realistic seroconversion date. We found that seroconversion on average is at the midpoint between last negative and first positive HIV-1 test for patients diagnosed in prospective/cohort studies such as those included in the training dataset. In contrast, seroconversion is strongly skewed towards the first positive sample for patients identified by regular public health diagnostic testing as illustrated in the validation dataset. Our model opens the door to more accurate estimates of date of infection for HIV-1 patients, which may facilitate a better understanding of HIV-1 epidemiology on a population level and individualized prevention, such as guidance during contact tracing.

Heterogeneity in neutralization sensitivities of viruses comprising the simian immunodeficiency virus SIVsmE660 isolate and vaccine challenge stock

The sooty mangabey-derived simian immunodeficiency virus (SIV) strain E660 (SIVsmE660) is a genetically heterogeneous, pathogenic isolate that is commonly used as a vaccine challenge strain in the nonhuman primate (NHP) model of human immunodeficiency virus type 1 (HIV-1) infection. Though it is often employed to assess antibody-based vaccine strategies, its sensitivity to antibody-mediated neutralization has not been well characterized. Here, we utilize single-genome sequencing and infectivity assays to analyze the neutralization sensitivity of the uncloned SIVsmE660 isolate, individual viruses comprising the isolate, and transmitted/founder (T/F) viruses arising from low-dose mucosal inoculation of macaques with the isolate. We found that the SIVsmE660 isolate overall was highly sensitive to neutralization by SIV-infected macaque plasma samples (50% inhibitory concentration [IC50] < 10(-5)) and monoclonal antibodies targeting V3 (IC50 < 0.01 μg/ml), CD4-induced (IC50 < 0.1 μg/ml), CD4 binding site (IC50 ~ 1 μg/ml), and V4 (IC50, ~5 μg/ml) epitopes. In comparison, SIVmac251 and SIVmac239 were highly resistant to neutralization by these same antibodies. Differences in neutralization sensitivity between SIVsmE660 and SIVmac251/239 were not dependent on the cell type in which virus was produced or tested. These findings indicate that in comparison to SIVmac251/239 and primary HIV-1 viruses, SIVsmE660 generally exhibits substantially less masking of antigenically conserved Env epitopes. Interestingly, we identified a minor population of viruses (~10%) in both the SIVsmE660 isolate and T/F viruses arising from it that were substantially more resistant (>1,000-fold) to antibody neutralization and another fraction (~20%) that was intermediate in neutralization resistance. These findings may explain the variable natural history and variable protection afforded by heterologous Env-based vaccines in rhesus macaques challenged by high-dose versus low-dose SIVsmE660 inoculation regimens.

A likelihood estimation of HIV incidence incorporating information on past prevalence

The prevalence and incidence of an epidemic are basic characteristics that are essential for monitoring its impact, determining public health priorities, assessing the effect of interventions, and for planning purposes. A direct approach for estimating incidence is to undertake a longitudinal cohort study where a representative sample of disease free individuals are followed for a specified period of time and new cases of infection are observed and recorded. This approach is expensive, time consuming and prone to bias due to loss-to-follow-up. An alternative approach is to estimate incidence from cross sectional surveys using biomarkers to identify persons recently infected as in (Brookmeyer and Quinn, 1995; Janssen et al., 1998). This paper builds on the work of Janssen et al. (1998) and extends the theoretical framework proposed by Balasubramanian and Lagakos (2010) by incorporating information on past prevalence and deriving maximum likelihood estimators of incidence. The performance of the proposed method is evaluated through a simulation study, and its use is illustrated using data from the Botswana AIDS Impact (BAIS) III survey of 2008.

HIV-1 amino acid changes among participants with virologic failure: associations with first-line efavirenz or atazanavir plus ritonavir and disease status

BACKGROUND

Although specific human immunodeficiency virus type 1 (HIV-1) drug resistance mutations are well studied, little is known about cumulative amino acid changes, or how regimen and participant characteristics influence these changes.

METHODS

In the AIDS Clinical Trials Group randomized study A5202 of treatment-naive HIV-infected participants, cumulative HIV-1 amino acid changes from pretreatment to virologic failure were evaluated in protease and reverse transcriptase (RT) gene sequences.

RESULTS

Among 265 participants with virologic failure, those assigned atazanavir plus ritonavir (ATV/r) did not have significantly more protease changes compared with those assigned efavirenz (EFV) (P ≥ .13). In contrast, participants with virologic failure assigned EFV had more RT changes, including and excluding known resistance codons (P < .001). At pretreatment, lower CD4 cell count, major resistance, more amino acid mixtures (all P < .001), hepatitis C antibody negativity (P = .05), and black race/ethnicity (P = .02) were associated with more HIV-1 amino acid changes.

CONCLUSIONS

Virologic failure following EFV-containing treatment was associated with more HIV-1 amino acid changes compared to failure of ATV/r-containing treatment. Furthermore, we show that non-drug resistance mutations occurred more frequently among those failing EFV, the clinical relevance of which warrants further investigation. Pretreatment immunologic status may play a role in viral evolution during treatment, as evidenced by increased amino acid changes among those with lower pretreatment CD4 count.

CLINICAL TRIALS REGISTRATION

NCT00118898.

Workshop summary: Novel biomarkers for HIV incidence assay development

Reliable methods for measuring human immunodeficiency virus (HIV) incidence are a high priority for HIV prevention. They are particularly important to assess the population-level effectiveness of new prevention strategies, to evaluate the community-wide impact of ongoing prevention programs, and to assess whether a proposed prevention trial can be performed in a timely and cost-efficient manner in a particular population and setting. New incidence assays and algorithms that are accurate, rapid, cost-efficient, and can be performed on easily-obtained specimens are urgently needed. On May 4, 2011, the Division of AIDS (DAIDS), National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), sponsored a 1-day workshop to examine strategies for developing new assays to distinguish recent from chronic HIV infections. Participants included leading investigators, clinicians, public health experts, industry, regulatory specialists, and other stakeholders. Immune-based parameters, markers of viral sequence diversity, and other biomarkers such as telomere length were evaluated. Emerging nanotechnology and chip-based diagnostics, including algorithms for performing diverse assays on a single platform, were also reviewed. This report summarizes the presentations, panel discussions, and the consensus reached for pursuing the development of a new generation of HIV incidence assays.

Diagnostic performance of line-immunoassay based algorithms for incident HIV-1 infection

Background

Serologic testing algorithms for recent HIV seroconversion (STARHS) provide important information for HIV surveillance. We have previously demonstrated that a patient's antibody reaction pattern in a confirmatory line immunoassay (INNO-LIA™ HIV I/II Score) provides information on the duration of infection, which is unaffected by clinical, immunological and viral variables. In this report we have set out to determine the diagnostic performance of Inno-Lia algorithms for identifying incident infections in patients with known duration of infection and evaluated the algorithms in annual cohorts of HIV notifications.

Methods

Diagnostic sensitivity was determined in 527 treatment-naive patients infected for up to 12 months. Specificity was determined in 740 patients infected for longer than 12 months. Plasma was tested by Inno-Lia and classified as either incident (< = 12 m) or older infection by 26 different algorithms. Incident infection rates (IIR) were calculated based on diagnostic sensitivity and specificity of each algorithm and the rule that the total of incident results is the sum of true-incident and false-incident results, which can be calculated by means of the pre-determined sensitivity and specificity.

Results

The 10 best algorithms had a mean raw sensitivity of 59.4% and a mean specificity of 95.1%. Adjustment for overrepresentation of patients in the first quarter year of infection further reduced the sensitivity. In the preferred model, the mean adjusted sensitivity was 37.4%. Application of the 10 best algorithms to four annual cohorts of HIV-1 notifications totalling 2'595 patients yielded a mean IIR of 0.35 in 2005/6 (baseline) and of 0.45, 0.42 and 0.35 in 2008, 2009 and 2010, respectively. The increase between baseline and 2008 and the ensuing decreases were highly significant. Other adjustment models yielded different absolute IIR, although the relative changes between the cohorts were identical for all models.

Conclusions

The method can be used for comparing IIR in annual cohorts of HIV notifications. The use of several different algorithms in combination, each with its own sensitivity and specificity to detect incident infection, is advisable as this reduces the impact of individual imperfections stemming primarily from relatively low sensitivities and sampling bias.

HIV testing: the cornerstone of HIV prevention efforts in the USA

An estimated 1.2 million persons in the USA are infected with HIV, of whom approximately 20% are unaware they are infected. HIV testing and knowledge of HIV serostatus have important individual and public health benefits, including reduction of morbidity, mortality and HIV transmission. Although testing is the necessary first step to prevention, more than half of the US adult population has never been tested for HIV. However, this proportion is increasing due to revised national recommendations to make HIV testing a routine part of healthcare, expansion of testing efforts at local, state and national levels, and progress in the development and adoption of new testing technologies. In this article, we describe the essential role of HIV testing as a public health prevention strategy, examine recent advances in HIV testing technologies and testing implementation, and identify future directions for HIV testing in the USA.

Biophysical methods for the study of microbial surfaces

The challenge in studying the surface architecture of different microbial pathogens is to integrate the most current biochemical, spectroscopic, microscopic, and processing techniques. Individually these methods have insufficient sensitivity to reveal complex structures, such as branched, large, viscous polymers with a high structure hydration, size, and complexity. However, when used in combination biophysical techniques are our primary source of information for understanding polydisperse molecules and complex microbial surfaces. Biophysical methods seek to explain biological function in terms of the molecular structures and properties of specific molecules. The sizes of the molecules found in microbial surfaces vary greatly from small fatty acids and sugars to macromolecules like proteins, polysaccharides, and pigments, such as melanin. These molecules, which comprise the building blocks of living organisms, assemble into cells, tissues, and whole organisms by forming complex individual structures with dimensions from 10 to 10,000 nm and larger. Biophysics is directed to determining the structure of specific biological molecules and of the larger structures into which they assemble. Some of this effort involves developing new methods, adapting old methods and building new instruments for viewing these structures. The description of biophysical properties in an experimental model where, properties such as flexibility, hydrodynamic characteristics, and size can be precisely determined is of great relevance to study the affinity of the surfaces with biologically active and inert substrates and the interaction with host molecules. Furthermore, this knowledge could establish the abilities of different molecules and their structures to differentially activate cellular responses. Recent studies in the fungal pathogen Cryptococcus neoformans have demonstrated that the physical properties of its unique polysaccharide capsule correlate with the biological functions associated with the intact capsule and the components comprising the capsule. In this review, we describe the application of biophysical techniques to study and characterize this highly hydrated and fragile fungal surface structure.