Hazardous alcohol use and HIV indicators in six African countries: results from the Population-based HIV Impact Assessments, 2015-2017.

INTRODUCTION: Hazardous alcohol use (HAU), defined as a pattern of alcohol consumption that increases the risk of harmful consequences for the user or others, is associated with an elevated risk of human immunodeficiency virus (HIV) infection and poor health outcomes. We describe the association between people living with HIV (PLHIV) who report HAU and key HIV indicators. Gaps in current literature in estimating HAU on HIV outcomes at the regional level of Eastern and Southern Africa still exist and our analysis aims to address this issue. METHODS: We used weighted pooled data (2015-2017) from the nationally representative Population-based HIV Impact Assessments among adults who provided written consent aged 18-59 years from Eswatini, Malawi, Namibia, Tanzania, Zambia and Zimbabwe. We estimated differences in the prevalence of HIV infection and The Joint United Nations Programme on HIV and AIDS (UNAIDS) 90-90-90 indicators between PLHIV by HAU status using log-binomial regression, stratified by sex. HAU was determined using the Alcohol Use Identification Test-Consumption. RESULTS: Among the 9755 women and 4444 men who tested HIV positive, 6.6% of women and 21.8% of men engaged in HAU. Women who reported HAU were more likely to be HIV positive (adjusted prevalence ratio [aPR] = 1.31, 95% CI: 1.18-1.46) compared to those who did not report HAU. For the UNAIDS 90-90-90 targets, women who engaged in HAU were more likely to be unaware of their HIV-positive status (aPR = 1.22, 95% CI: 1.01-1.47) and not on antiretroviral therapy (ART) (aPR = 1.73, 95% CI: 1.26-2.37). Men who engaged in HAU were more likely to be unaware of their HIV-positive status (aPR = 1.56, 95% CI 1.39-1.76) and not on ART (aPR = 1.72, 95% CI: 1.30-2.29). No difference in viral load suppression, defined as <1000 copies/ml of HIV RNA, was seen by sex. CONCLUSIONS: PLHIV who engage in HAU were more likely to have suboptimal outcomes along the HIV care continuum when compared to those who did not engage in HAU. Targeted interventions, such as alcohol screening for HAU in HIV testing and treatment settings and HIV prevention efforts in alcohol-based venues, may help countries reach HIV epidemic control by 2030.

Systematic Evaluation of Whole Genome Sequence-Based Predictions of Salmonella Serotype and Antimicrobial Resistance.

Whole-genome sequencing (WGS) is used increasingly in public-health laboratories for typing and characterizing foodborne pathogens. To evaluate the performance of existing bioinformatic tools for in silico prediction of antimicrobial resistance (AMR) and serotypes of Salmonella enterica, WGS-based genotype predictions were compared with the results of traditional phenotyping assays. A total of 111 S. enterica isolates recovered from a Canadian baseline study on broiler chicken conducted in 2012-2013 were selected based on phenotypic resistance to 15 different antibiotics and isolates were subjected to WGS. Both SeqSero2 and SISTR accurately determined S. enterica serotypes, with full matches to laboratory results for 87.4 and 89.2% of isolates, respectively, and partial matches for the remaining isolates. Antimicrobial resistance genes (ARGs) were identified using several bioinformatics tools including the Comprehensive Antibiotic Resistance Database - Resistance Gene Identifier (CARD-RGI), Center for Genomic Epidemiology (CGE) ResFinder web tool, Short Read Sequence Typing for Bacterial Pathogens (SRST2 v 0.2.0), and k-mer alignment method (KMA v 1.17). All ARG identification tools had ≥ 99% accuracy for predicting resistance to all antibiotics tested except streptomycin (accuracy 94.6%). Evaluation of ARG detection in assembled versus raw-read WGS data found minimal observable differences that were gene- and coverage- dependent. Where initial phenotypic results indicated isolates were sensitive, yet ARGs were detected, repeat AMR testing corrected discrepancies. All tools failed to find resistance-determining genes for one gentamicin- and two streptomycin-resistant isolates. Further investigation found a single nucleotide polymorphism (SNP) in the nuoF coding region of one of the isolates which may be responsible for the observed streptomycin-resistant phenotype. Overall, WGS-based predictions of AMR and serotype were highly concordant with phenotype determination regardless of computational approach used.

ConFindr: rapid detection of intraspecies and cross-species contamination in bacterial whole-genome sequence data.

Whole-genome sequencing (WGS) of bacterial pathogens is currently widely used to support public-health investigations. The ability to assess WGS data quality is critical to underpin the reliability of downstream analyses. Sequence contamination is a quality issue that could potentially impact WGS-based findings; however, existing tools do not readily identify contamination from closely-related organisms. To address this gap, we have developed a computational pipeline, ConFindr, for detection of intraspecies contamination. ConFindr determines the presence of contaminating sequences based on the identification of multiple alleles of core, single-copy, ribosomal-protein genes in raw sequencing reads. The performance of this tool was assessed using simulated and lab-generated Illumina short-read WGS data with varying levels of contamination (0-20% of reads) and varying genetic distance between the designated target and contaminant strains. Intraspecies and cross-species contamination was reliably detected in datasets containing 5% or more reads from a second, unrelated strain. ConFindr detected intraspecies contamination with higher sensitivity than existing tools, while also being able to automatically detect cross-species contamination with similar sensitivity. The implementation of ConFindr in quality-control pipelines will help to improve the reliability of WGS databases as well as the accuracy of downstream analyses. ConFindr is written in Python, and is freely available under the MIT License at github.com/OLC-Bioinformatics/ConFindr.

Improved detection of CXCR4-using HIV by V3 genotyping: application of population-based and "deep" sequencing to plasma RNA and proviral DNA.

BACKGROUND: Tropism testing should rule out CXCR4-using HIV before treatment with CCR5 antagonists. Currently, the recombinant phenotypic Trofile assay (Monogram) is most widely utilized; however, genotypic tests may represent alternative methods. METHODS: Independent triplicate amplifications of the HIV gp120 V3 region were made from either plasma HIV RNA or proviral DNA. These underwent standard, population-based sequencing with an ABI3730 (RNA n = 63; DNA n = 40), or "deep" sequencing with a Roche/454 Genome Sequencer-FLX (RNA n = 12; DNA n = 12). Position-specific scoring matrices (PSSMX4/R5) (-6.96 cutoff) and geno2pheno[coreceptor] (5% false-positive rate) inferred tropism from V3 sequence. These methods were then independently validated with a separate, blinded dataset (n = 278) of screening samples from the maraviroc MOTIVATE trials. RESULTS: Standard sequencing of HIV RNA with PSSM yielded 69% sensitivity and 91% specificity, relative to Trofile. The validation dataset gave 75% sensitivity and 83% specificity. Proviral DNA plus PSSM gave 77% sensitivity and 71% specificity. "Deep" sequencing of HIV RNA detected >2% inferred-CXCR4-using virus in 8/8 samples called non-R5 by Trofile, and <2% in 4/4 samples called R5. CONCLUSIONS: Triplicate analyses of V3 standard sequence data detect greater proportions of CXCR4-using samples than previously achieved. Sequencing proviral DNA and "deep" V3 sequencing may also be useful tools for assessing tropism.

Trofile HIV co-receptor usage assay.

BACKGROUND: The introduction of CCR5 antagonists increases the options available for constructing therapeutic drug regimens for HIV-positive patients. However, as these drugs do not inhibit HIV variants that use the CXCR4 co-receptor, a pretreatment test is required to determine accurately HIV co-receptor usage (tropism) before initiating CCR5 antagonist-based therapy. OBJECTIVE/METHOD: To discuss the Monogram Trofile assay as a diagnostic tool for determining HIV tropism by critically reviewing reported literature and available data. CONCLUSIONS: Monogram Trofile has become, largely by default, the de facto standard for HIV tropism assay. However, there is significant room for improvement in the speed, cost and availability of the test. Furthermore, the test is not quantitative, requires high-input HIV RNA viral loads, and produces results that are less biologically stable than expected. These technical considerations may limit the use of CCR5 antagonists in therapy. Nevertheless, this test is likely to remain the most widely used tropism diagnostic for the short term. We expect that a more practical and possibly more accurate method for measuring HIV tropism can be developed.

HIV coreceptor phenotyping in the clinical setting.

The introduction of CCR5 antagonists increases the options available for constructing antiretroviral regimens. However, this option is coupled with the caveat that patients should be tested for HIV coreceptor tropism prior to initiating CCR5 antagonist-based therapy. Failure to screen for CXCR4 usage increases the risk of using an ineffective drug, thus reducing the likelihood of viral suppression and increasing their risk for developing antiretroviral resistance. This review discusses current and future methods of determining HIV tropism, with a focus on their utility in the clinical setting for screening purposes. Some of these methods include recombinant phenotypic tests, such as the Monogram Trofile assay, as well as genotype-based predictors, heteroduplex tracking assays, and flow cytometry based methods. Currently, the best evidence supports the use of phenotypic methods, although other methods of screening for HIV coreceptor usage prior to the administration of CCR5 antagonists may reduce costs and increase turnaround time over phenotypic methods. The presence of low levels of X4 virus is a challenge to all assay methods, resulting in reduced sensitivity in clinical, patient-derived samples when compared to clonally derived samples. Gaining a better understanding of the output of these assays and correlating them with clinical progression and therapy response will provide some indication on how both genotype-based, and phenotypic assays for determining HIV coreceptor usage can be improved. In addition, leveraging new technologies capable of detecting low-level minority species may provide the most significant advances in ensuring that individuals with low levels of dual/mixed tropic virus are not inadvertently prescribed CCR5 antagonists.

CD4-dependent characteristics of coreceptor use and HIV type 1 V3 sequence in a large population of therapy-naive individuals.

We investigated the associations between coreceptor use, V3 loop sequence, and CD4 count in a cross-sectional analysis of a large cohort of chronically HIV-infected, treatment-naive patients. HIV coreceptor usage was determined in the last pretherapy plasma sample for 977 individuals initiating HAART in British Columbia, Canada using the Monogram Trofile Tropism assay. Relative light unit (RLU) readouts from the Trofile assay, as well as HIV V3 loop sequence data, were examined as a function of baseline CD4 cell count for 953 (97%) samples with both phenotype and genotype data available. Median CCR5 RLUs were high for both R5 and X4-capable samples, while CXCR4 RLUs were orders of magnitude lower for X4 samples (p < 0.001). CCR5 RLUs in R5 samples (N = 799) increased with decreasing CD4 count (p < 0.001), but did not vary with plasma viral load (pVL) (p = 0.74). In X4 samples (N = 178), CCR5 RLUs decreased with decreasing CD4 count (p = 0.046) and decreasing pVL (p = 0.097), while CXCR4 RLUs increased with decreasing pVL (p = 0.0008) but did not vary with CD4 (p = 0.96). RLUs varied with the presence of substitutions at V3 loop positions 11, 25, and 6-8. The prevalence and impact of substitutions at codons 25 and 6-8 were CD4 dependent as was the presence of amino acid mixtures in the V3; substitutions at position 11 were CD4 independent. Assay RLU measures predictably vary with both immunological and virological parameters. The ability to predict X4 virus using genotypic determinants at positions 25 and 6-8 of the V3 loop is CD4 dependent, while position 11 appears to be CD4 independent.

Predicting HIV coreceptor usage on the basis of genetic and clinical covariates.

BACKGROUND: We compared several statistical learning methods for the prediction of HIV coreceptor use from clonal HIV third hypervariable (V3) loop sequences, and evaluated and improved their effectiveness on clinical samples. METHODS: Support vector machines (SVM), artificial neural networks, position-specific scoring matrices (PSSM) and mixtures of localized rules were estimated and tested using 10x ten-fold cross-validation on a clonal dataset consisting of 1,100 matched clonal genotype-phenotype pairs from 332 patients. Different SVMs were also trained and tested on a clinically derived dataset, representing 920 patient samples from British Columbia, Canada. Methods were evaluated using receiver operating characteristic (ROC) curves. RESULTS: In the clonal analysis, the sensitivity of the 11/25 rule at 92.5% specificity was 59.5%. PSSMs and SVMs increased sensitivity to 71.9% and 76.4%, respectively, at the same specificity (P < < 0.05). In clinical samples, the sensitivity of the 11/25 rule and SVM decreased to 25.9% (specificity 93.9%) and 39.8% (specificity 93.5%), respectively. However, the integration of clinical data resulted in a further 2.4-fold increase in sensitivity over the 11/25 rule (63%). Univariate analyses identified 41 V3 mutations significantly associated with coreceptor usage. CONCLUSION: For all methods tested, a substantial sensitivity decrease is observed on clinical data, probably owing to the heterogeneity of the viral population in vivo. In response to these complications, we present an SVM-based approach that integrates sequence information with clinical and host data, resulting in improved performance and sensitivity compared with purely sequence-based approaches.

Current V3 genotyping algorithms are inadequate for predicting X4 co-receptor usage in clinical isolates.

OBJECTIVE: Integrating CCR5 antagonists into clinical practice would benefit from accurate assays of co-receptor usage (CCR5 versus CXCR4) with fast turnaround and low cost. DESIGN: Published HIV V3-loop based predictors of co-receptor usage were compared with actual phenotypic tropism results in a large cohort of antiretroviral naive individuals to determine accuracy on clinical samples and identify areas for improvement. METHODS: Aligned HIV envelope V3 loop sequences (n = 977), derived by bulk sequencing were analyzed by six methods: the 11/25 rule; a neural network (NN), two support vector machines, and two subtype-B position specific scoring matrices (PSSM). Co-receptor phenotype results (Trofile Co-receptor Phenotype Assay; Monogram Biosciences) were stratified by CXCR4 relative light unit (RLU) readout and CD4 cell count. RESULTS: Co-receptor phenotype was available for 920 clinical samples with V3 genotypes having fewer than seven amino acid mixtures (n = 769 R5; n = 151 X4-capable). Sensitivity and specificity for predicting X4 capacity were evaluated for the 11/25 rule (30% sensitivity/93% specificity), NN (44%/88%), PSSM(sinsi) (34%/96%), PSSM(x4r5) (24%/97%), SVMgenomiac (22%/90%) and SVMgeno2pheno (50%/89%). Quantitative increases in sensitivity could be obtained by optimizing the cut-off for methods with continuous output (PSSM methods), and/or integrating clinical data (CD4%). Sensitivity was directly proportional to strength of X4 signal in the phenotype assay (P < 0.05). CONCLUSIONS: Current default implementations of co-receptor prediction algorithms are inadequate for predicting HIV X4 co-receptor usage in clinical samples, particularly those X4 phenotypes with low CXCR4 RLU signals. Significant improvements can be made to genotypic predictors, including training on clinical samples, using additional data to improve predictions and optimizing cutoffs and increasing genotype sensitivity.

Structural descriptors of gp120 V3 loop for the prediction of HIV-1 coreceptor usage.

HIV-1 cell entry commonly uses, in addition to CD4, one of the chemokine receptors CCR5 or CXCR4 as coreceptor. Knowledge of coreceptor usage is critical for monitoring disease progression as well as for supporting therapy with the novel drug class of coreceptor antagonists. Predictive methods for inferring coreceptor usage based on the third hypervariable (V3) loop region of the viral gene coding for the envelope protein gp120 can provide us with these monitoring facilities while avoiding expensive phenotypic tests. All simple heuristics (such as the 11/25 rule) as well as statistical learning methods proposed to date predict coreceptor usage based on sequence features of the V3 loop exclusively. Here, we show, based on a recently resolved structure of gp120 with an untruncated V3 loop, that using structural information on the V3 loop in combination with sequence features of V3 variants improves prediction of coreceptor usage. In particular, we propose a distance-based descriptor of the spatial arrangement of physicochemical properties that increases discriminative performance. For a fixed specificity of 0.95, a sensitivity of 0.77 was achieved, improving further to 0.80 when combined with a sequence-based representation using amino acid indicators. This compares favorably with the sensitivities of 0.62 for the traditional 11/25 rule and 0.73 for a prediction based on sequence information as input to a support vector machine and constitutes a statistically significant improvement. A detailed analysis and interpretation of structural features important for classification shows the relevance of several specific hydrogen-bond donor sites and aliphatic side chains to coreceptor specificity towards CCR5 or CXCR4. Furthermore, an analysis of side chain orientation of the specificity-determining residues suggests a major role of one side of the V3 loop in the selection of the coreceptor. The proposed method constitutes the first approach to an improved prediction of coreceptor usage based on an original integration of structural bioinformatics methods with statistical learning.

Determining human immunodeficiency virus coreceptor use in a clinical setting: degree of correlation between two phenotypic assays and a bioinformatic model.

Two recombinant phenotypic assays for human immunodeficiency virus (HIV) coreceptor usage and an HIV envelope genotypic predictor were employed on a set of clinically derived HIV type 1 (HIV-1) samples in order to evaluate the concordance between measures. Previously genotyped HIV-1 samples derived from antiretroviral-naïve individuals were tested for coreceptor usage using two independent phenotyping methods. Phenotypes were determined by validated recombinant assays that incorporate either an approximately 2,500-bp ("Trofile" assay) or an approximately 900-bp (TRT assay) fragment of the HIV envelope gp120. Population-based HIV envelope V3 loop sequences ( approximately 105 bp) were derived by automated sequence analysis. Genotypic coreceptor predictions were performed using a support vector machine model trained on a separate genotype-Trofile phenotype data set. HIV coreceptor usage was obtained from both phenotypic assays for 74 samples, with an overall 85.1% concordance. There was no evidence of a difference in sensitivity between the two phenotypic assays. A bioinformatic algorithm based on a support vector machine using HIV V3 genotype data was able to achieve 86.5% and 79.7% concordance with the Trofile and TRT assays, respectively, approaching the degree of agreement between the two phenotype assays. In most cases, the phenotype assays and the bioinformatic approach gave similar results. However, in cases where there were differences in the tropism results, it was not clear which of the assays was "correct." X4 (CXCR4-using) minority species in clinically derived samples likely complicate the interpretation of both phenotypic and genotypic assessments of HIV tropism.