A statistical model for HIV-1 sequence classification using the subtype analyser (STAR)

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Preexposure Prophylaxis for Women Across the Criminal Justice System: Implications for Policy and Practice

Data that inform preexposure prophylaxis (PrEP) implementation for women involved in criminal justice (WICJ) systems are scarce. In a survey of PrEP attitudes, acceptability, and barriers across the criminal justice system, incarcerated women (n = 48) were more likely than WICJ on probation (n = 125) to be eligible for PrEP (29% vs. 15%; p = .04) and willing to take PrEP if offered (94% vs. 78%; p = .01). In multivariate models, PrEP eligibility directly correlated with being incarcerated (adjusted odds ratio [aOR] 4.81, 95% confidence interval [CI] 1.76-13.1) and inversely correlated with Hispanic/Latina ethnicity (aOR 0.31; 95% CI 0.10-0.96). Recent partner violence exposure was associated with PrEP eligibility (aOR 3.29; 95% CI 1.54-7.02) and discordant risk perception (aOR 2.36; 95% CI 1.18-4.70). Findings demonstrate high potential for PrEP for all WICJ, though implementation efforts will need to address partner violence.

SNAPPy: A snakemake pipeline for scalable HIV-1 subtyping by phylogenetic pairing

Human immunodeficiency virus 1 (HIV-1) genome sequencing is routinely done for drug resistance monitoring in hospitals worldwide. Subtyping these extensive datasets of HIV-1 sequences is a critical first step in molecular epidemiology and evolution studies. The clinical relevance of HIV-1 subtypes is increasingly recognized. Several studies suggest subtype-related differences in disease progression, transmission route efficiency, immune evasion, and even therapeutic outcomes. HIV-1 subtyping is mainly done using web-servers. These tools have limitations in scalability and potential noncompliance with data protection legislation. Thus, the aim of this work was to develop an efficient method for large-scale local HIV-1 subtyping. We designed SNAPPy: a snakemake pipeline for scalable HIV-1 subtyping by phylogenetic pairing. It contains several tasks of phylogenetic inference and BLAST queries, which can be executed sequentially or in parallel, taking advantage of multiple-core processing units. Although it was built for subtyping, SNAPPy is also useful to perform extensive HIV-1 alignments. This tool facilitates large-scale sequence-based HIV-1 research by providing a local, resource efficient and scalable alternative for HIV-1 subtyping. It is capable of analyzing full-length genomes or partial HIV-1 genomic regions (GAG, POL, and ENV) and recognizes more than ninety circulating recombinant forms. SNAPPy is freely available at: https://github.com/PMMAraujo/snappy/releases.

Bioinformatics Applications in Advancing Animal Virus Research

Viruses serve as infectious agents for all living entities. There have been various research groups that focus on understanding the viruses in terms of their host-viral relationships, pathogenesis and immune evasion. However, with the current advances in the field of science, now the research field has widened up at the ‘omics’ level. Apparently, generation of viral sequence data has been increasing. There are numerous bioinformatics tools available that not only aid in analysing such sequence data but also aid in deducing useful information that can be exploited in developing preventive and therapeutic measures. This chapter elaborates on bioinformatics tools that are specifically designed for animal viruses as well as other generic tools that can be exploited to study animal viruses. The chapter further provides information on the tools that can be used to study viral epidemiology, phylogenetic analysis, structural modelling of proteins, epitope recognition and open reading frame (ORF) recognition and tools that enable to analyse host-viral interactions, gene prediction in the viral genome, etc. Various databases that organize information on animal and human viruses have also been described. The chapter will converse on overview of the current advances, online and downloadable tools and databases in the field of bioinformatics that will enable the researchers to study animal viruses at gene level.

Assessing the Paradox Between Transmitted and Acquired HIV Type 1 Drug Resistance Mutations in the Swiss HIV Cohort Study From 1998 to 2012

BACKGROUND

Transmitted human immunodeficiency virus type 1 (HIV) drug resistance (TDR) mutations are transmitted from nonresponding patients (defined as patients with no initial response to treatment and those with an initial response for whom treatment later failed) or from patients who are naive to treatment. Although the prevalence of drug resistance in patients who are not responding to treatment has declined in developed countries, the prevalence of TDR mutations has not. Mechanisms causing this paradox are poorly explored.

METHODS

We included recently infected, treatment-naive patients with genotypic resistance tests performed ≤ 1 year after infection and before 2013. Potential risk factors for TDR mutations were analyzed using logistic regression. The association between the prevalence of TDR mutations and population viral load (PVL) among treated patients during 1997-2011 was estimated with Poisson regression for all TDR mutations and individually for the most frequent resistance mutations against each drug class (ie, M184V/L90M/K103N).

RESULTS

We included 2421 recently infected, treatment-naive patients and 5399 patients with no response to treatment. The prevalence of TDR mutations fluctuated considerably over time. Two opposing developments could explain these fluctuations: generally continuous increases in the prevalence of TDR mutations (odds ratio, 1.13; P = .010), punctuated by sharp decreases in the prevalence when new drug classes were introduced. Overall, the prevalence of TDR mutations increased with decreasing PVL (rate ratio [RR], 0.91 per 1000 decrease in PVL; P = .033). Additionally, we observed that the transmitted high-fitness-cost mutation M184V was positively associated with the PVL of nonresponding patients carrying M184V (RR, 1.50 per 100 increase in PVL; P < .001). Such association was absent for K103N (RR, 1.00 per 100 increase in PVL; P = .99) and negative for L90M (RR, 0.75 per 100 increase in PVL; P = .022).

CONCLUSIONS

Transmission of antiretroviral drug resistance is temporarily reduced by the introduction of new drug classes and driven by nonresponding and treatment-naive patients. These findings suggest a continuous need for new drugs, early detection/treatment of HIV-1 infection.

Unraveling the web of viroinformatics: computational tools and databases in virus research

The beginning of the second century of research in the field of virology (the first virus was discovered in 1898) was marked by its amalgamation with bioinformatics, resulting in the birth of a new domain--viroinformatics. The availability of more than 100 Web servers and databases embracing all or specific viruses (for example, dengue virus, influenza virus, hepatitis virus, human immunodeficiency virus [HIV], hemorrhagic fever virus [HFV], human papillomavirus [HPV], West Nile virus, etc.) as well as distinct applications (comparative/diversity analysis, viral recombination, small interfering RNA [siRNA]/short hairpin RNA [shRNA]/microRNA [miRNA] studies, RNA folding, protein-protein interaction, structural analysis, and phylotyping and genotyping) will definitely aid the development of effective drugs and vaccines. However, information about their access and utility is not available at any single source or on any single platform. Therefore, a compendium of various computational tools and resources dedicated specifically to virology is presented in this article.

COMET: adaptive context-based modeling for ultrafast HIV-1 subtype identification

Viral sequence classification has wide applications in clinical, epidemiological, structural and functional categorization studies. Most existing approaches rely on an initial alignment step followed by classification based on phylogenetic or statistical algorithms. Here we present an ultrafast alignment-free subtyping tool for human immunodeficiency virus type one (HIV-1) adapted from Prediction by Partial Matching compression. This tool, named COMET, was compared to the widely used phylogeny-based REGA and SCUEAL tools using synthetic and clinical HIV data sets (1,090,698 and 10,625 sequences, respectively). COMET's sensitivity and specificity were comparable to or higher than the two other subtyping tools on both data sets for known subtypes. COMET also excelled in detecting and identifying new recombinant forms, a frequent feature of the HIV epidemic. Runtime comparisons showed that COMET was almost as fast as USEARCH. This study demonstrates the advantages of alignment-free classification of viral sequences, which feature high rates of variation, recombination and insertions/deletions. COMET is free to use via an online interface.

Automated subtyping of HIV-1 genetic sequences for clinical and surveillance purposes: performance evaluation of the new REGA version 3 and seven other tools

BACKGROUND

To investigate differences in pathogenesis, diagnosis and resistance pathways between HIV-1 subtypes, an accurate subtyping tool for large datasets is needed. We aimed to evaluate the performance of automated subtyping tools to classify the different subtypes and circulating recombinant forms using pol, the most sequenced region in clinical practice. We also present the upgraded version 3 of the Rega HIV subtyping tool (REGAv3).

METHODOLOGY

HIV-1 pol sequences (PR+RT) for 4674 patients retrieved from the Portuguese HIV Drug Resistance Database, and 1872 pol sequences trimmed from full-length genomes retrieved from the Los Alamos database were classified with statistical-based tools such as COMET, jpHMM and STAR; similarity-based tools such as NCBI and Stanford; and phylogenetic-based tools such as REGA version 2 (REGAv2), REGAv3, and SCUEAL. The performance of these tools, for pol, and for PR and RT separately, was compared in terms of reproducibility, sensitivity and specificity with respect to the gold standard which was manual phylogenetic analysis of the pol region.

RESULTS

The sensitivity and specificity for subtypes B and C was more than 96% for seven tools, but was variable for other subtypes such as A, D, F and G. With regard to the most common circulating recombinant forms (CRFs), the sensitivity and specificity for CRF01_AE was ~99% with statistical-based tools, with phylogenetic-based tools and with Stanford, one of the similarity based tools. CRF02_AG was correctly identified for more than 96% by COMET, REGAv3, Stanford and STAR. All the tools reached a specificity of more than 97% for most of the subtypes and the two main CRFs (CRF01_AE and CRF02_AG). Other CRFs were identified only by COMET, REGAv2, REGAv3, and SCUEAL and with variable sensitivity. When analyzing sequences for PR and RT separately, the performance for PR was generally lower and variable between the tools. Similarity and statistical-based tools were 100% reproducible, but this was lower for phylogenetic-based tools such as REGA (~99%) and SCUEAL (~96%).

CONCLUSIONS

REGAv3 had an improved performance for subtype B and CRF02_AG compared to REGAv2 and is now able to also identify all epidemiologically relevant CRFs. In general the best performing tools, in alphabetical order, were COMET, jpHMM, REGAv3, and SCUEAL when analyzing pure subtypes in the pol region, and COMET and REGAv3 when analyzing most of the CRFs. Based on this study, we recommend to confirm subtyping with 2 well performing tools, and be cautious with the interpretation of short sequences.

Classification of HIV-1 sequences using profile Hidden Markov Models

Accurate classification of HIV-1 subtypes is essential for studying the dynamic spatial distribution pattern of HIV-1 subtypes and also for developing effective methods of treatment that can be targeted to attack specific subtypes. We propose a classification method based on profile Hidden Markov Model that can accurately identify an unknown strain. We show that a standard method that relies on the construction of a positive training set only, to capture unique features associated with a particular subtype, can accurately classify sequences belonging to all subtypes except B and D. We point out the drawbacks of the standard method; namely, an arbitrary choice of threshold to distinguish between true positives and true negatives, and the inability to discriminate between closely related subtypes. We then propose an improved classification method based on construction of a positive as well as a negative training set to improve discriminating ability between closely related subtypes like B and D. Finally, we show how the improved method can be used to accurately determine the subtype composition of Common Recombinant Forms of the virus that are made up of two or more subtypes. Our method provides a simple and highly accurate alternative to other classification methods and will be useful in accurately annotating newly sequenced HIV-1 strains.

Global analysis of sequence diversity within HIV-1 subtypes across geographic regions

AIMS: HIV-1 sequence diversity can affect host immune responses and phenotypic characteristics such as antiretroviral drug resistance. Current HIV-1 sequence diversity classification uses phylogeny-based methods to identify subtypes and recombinants, which may overlook distinct subpopulations within subtypes. While local epidemic studies have characterized sequence-level clustering within subtypes using phylogeny, identification of new genotype - phenotype associations are based on mutational correlations at individual sequence positions. We perform a systematic, global analysis of position-specific pol gene sequence variation across geographic regions within HIV-1 subtypes to characterize subpopulation differences that may be missed by standard subtyping methods and sequence-level phylogenetic clustering analyses. MATERIALS #ENTITYSTARTX00026; METHODS: Analysis was performed on a large, globally diverse, cross-sectional pol sequence dataset. Sequences were partitioned into subtypes and geographic subpopulations within subtypes. For each subtype, we identified positions that varied according to geography using VESPA (viral epidemiology signature pattern analysis) to identify sequence signature differences and a likelihood ratio test adjusted for multiple comparisons to characterize differences in amino acid (AA) frequencies, including minority mutations. Synonymous nonsynonymous analysis program (SNAP) was used to explore the role of evolutionary selection witihin subtype C. RESULTS: In 7693 protease (PR) and reverse transcriptase (RT) sequences from untreated patients in multiple geographic regions, 11 PR and 11 RT positions exhibited sequence signature differences within subtypes. Thirty six PR and 80 RT positions exhibited within-subtype geography-dependent differences in AA distributions, including minority mutations, at both conserved and variable loci. Among subtype C samples from India and South Africa, nine PR and nine RT positions had significantly different AA distributions, including one PR and five RT positions that differed in consensus AA between regions. A selection analysis of subtype C using SNAP demonstrated that estimated rates of nonsynonymous and synonymous mutations are consistent with the possibility of positive selection across geographic subpopulations within subtypes. CONCLUSION: We characterized systematic genotypic pol differences across geographic regions within subtypes that are not captured by the subtyping nomenclature. Awareness of such differences may improve the interpretation of future studies determining the phenotypic consequences of genetic backgrounds.

Patterns of HIV-1 drug resistance after first-line antiretroviral therapy (ART) failure in 6 sub-Saharan African countries: implications for second-line ART strategies

BACKGROUND

Human immunodeficiency virus type 1 (HIV-1) drug resistance may limit the benefits of antiretroviral therapy (ART). This cohort study examined patterns of drug-resistance mutations (DRMs) in individuals with virological failure on first-line ART at 13 clinical sites in 6 African countries and predicted their impact on second-line drug susceptibility.

METHODS

A total of 2588 antiretroviral-naive individuals initiated ART consisting of different nucleoside reverse transcriptase inhibitor (NRTI) backbones (zidovudine, stavudine, tenofovir, or abacavir, plus lamivudine or emtricitabine) with either efavirenz or nevirapine. Population sequencing after 12 months of ART was retrospectively performed if HIV RNA was >1000 copies/mL. The 2010 International Antiviral Society-USA list was used to score major DRMs. The Stanford algorithm was used to predict drug susceptibility.

RESULTS

HIV-1 sequences were generated for 142 participants who virologically failed ART, of whom 70% carried ≥1 DRM and 49% had dual-class resistance, with an average of 2.4 DRMs per sequence (range, 1-8). The most common DRMs were M184V (53.5%), K103N (28.9%), Y181C (15.5%), and G190A (14.1%). Thymidine analogue mutations were present in 8.5%. K65R was frequently selected by stavudine (15.0%) or tenofovir (27.7%). Among participants with ≥1 DRM, HIV-1 susceptibility was reduced in 93% for efavirenz/nevirapine, in 81% for lamivudine/emtricitabine, in 59% for etravirine/rilpivirine, in 27% for tenofovir, in 18% for stavudine, and in 10% for zidovudine.

CONCLUSIONS

Early failure detection limited the accumulation of resistance. After stavudine failure in African populations, zidovudine rather than tenofovir may be preferred in second-line ART. Strategies to prevent HIV-1 resistance are a global priority.

Multifractal analysis of HIV-1 genomes

Pathogens like HIV-1, which evolve into many closely related variants displaying differential infectivity and evolutionary dynamics in a short time scale, require fast and accurate classification. Conventional whole genome sequence alignment-based methods are computationally expensive and involve complex analysis. Alignment-free methodologies are increasingly being used to effectively differentiate genomic variations between viral species. Multifractal analysis, which explores the self-similar nature of genomes, is an alignment-free methodology that has been applied to study such variations. However, whether multifractal analysis can quantify variations between closely related genomes, such as the HIV-1 subtypes, is an open question. Here we address the above by implementing the multifractal analysis on four retroviral genomes (HIV-1, HIV-2, SIVcpz, and HTLV-1), and demonstrate that individual multifractal properties can differentiate between different retrovirus types easily. However, the individual multifractal measures do not resolve within-group variations for different known subtypes of HIV-1 M group. We show here that these known subtypes can instead be classified correctly using a combination of the crucial multifractal measures. This method is simple and computationally fast in comparison to the conventional alignment-based methods for whole genome phylogenetic analysis.

The role of migration and domestic transmission in the spread of HIV-1 non-B subtypes in Switzerland

BACKGROUND

By analyzing human immunodeficiency virus type 1 (HIV-1) pol sequences from the Swiss HIV Cohort Study (SHCS), we explored whether the prevalence of non-B subtypes reflects domestic transmission or migration patterns.

METHODS

Swiss non-B sequences and sequences collected abroad were pooled to construct maximum likelihood trees, which were analyzed for Swiss-specific subepidemics, (subtrees including ≥80% Swiss sequences, bootstrap >70%; macroscale analysis) or evidence for domestic transmission (sequence pairs with genetic distance <1.5%, bootstrap ≥98%; microscale analysis).

RESULTS

Of 8287 SHCS participants, 1732 (21%) were infected with non-B subtypes, of which A (n = 328), C (n = 272), CRF01_AE (n = 258), and CRF02_AG (n = 285) were studied further. The macroscale analysis revealed that 21% (A), 16% (C), 24% (CRF01_AE), and 28% (CRF02_AG) belonged to Swiss-specific subepidemics. The microscale analysis identified 26 possible transmission pairs: 3 (12%) including only homosexual Swiss men of white ethnicity; 3 (12%) including homosexual white men from Switzerland and partners from foreign countries; and 10 (38%) involving heterosexual white Swiss men and females of different nationality and predominantly nonwhite ethnicity.

CONCLUSIONS

Of all non-B infections diagnosed in Switzerland, <25% could be prevented by domestic interventions. Awareness should be raised among immigrants and Swiss individuals with partners from high prevalence countries to contain the spread of non-B subtypes.

Effect of pretreatment HIV-1 drug resistance on immunological, virological, and drug-resistance outcomes of first-line antiretroviral treatment in sub-Saharan Africa: a multicentre cohort study

BACKGROUND

The effect of pretreatment HIV-1 drug resistance on the response to first-line combination antiretroviral therapy (ART) in sub-Saharan Africa has not been assessed. We studied pretreatment drug resistance and virological, immunological, and drug-resistance treatment outcomes in a large prospective cohort.

METHODS

HIV-1 infected patients in the PharmAccess African Studies to Evaluate Resistance Monitoring (PASER-M) cohort started non-nucleoside reverse transcriptase inhibitor-based ART at 13 clinical sites in six countries, from 2007 to 2009. We used the International Antiviral Society-USA drug resistance mutation list and the Stanford algorithm to classify participants into three pretreatment drug resistance categories: no pretreatment drug resistance, pretreatment drug resistance with fully active ART prescribed, or pretreatment drug resistance with reduced susceptibility to at least one prescribed drug. We assessed risk factors of virological failure (≥400 copies per mL) and acquired drug resistance after 12 months of ART by use of multilevel logistic regression with multiple imputations for missing data. CD4 cell count increase was estimated with linear mixed models.

FINDINGS

Pretreatment drug resistance results were available for 2579 (94%) of 2733 participants; 2404 (93%) had no pretreatment drug resistance, 123 (5%) had pretreatment drug resistance to at least one prescribed drug, and 52 (2%) had pretreatment drug resistance and received fully active ART. Compared with participants without pretreatment drug resistance, the odds ratio (OR) for virological failure (OR 2·13, 95% CI 1·44-3·14; p<0·0001) and acquired drug-resistance (2·30, 1·55-3·40; p<0·0001) was increased in participants with pretreatment drug resistance to at least one prescribed drug, but not in those with pretreatment drug resistance and fully active ART. CD4 count increased less in participants with pretreatment drug resistance than in those without (35 cells per μL difference after 12 months; 95% CI 13-58; p=0·002).

INTERPRETATION

At least three fully active antiretroviral drugs are needed to ensure an optimum response to first-line regimens and to prevent acquisition of drug resistance. Improved access to alternative combinations of antiretroviral drugs in sub-Saharan Africa is warranted.

FUNDING

The Netherlands Ministry of Foreign Affairs.

HIV-1 drug resistance in antiretroviral-naive individuals in sub-Saharan Africa after rollout of antiretroviral therapy: a multicentre observational study

BACKGROUND

There are few data on the epidemiology of primary HIV-1 drug resistance after the roll-out of antiretroviral treatment (ART) in sub-Saharan Africa. We aimed to assess the prevalence of primary resistance in six African countries after ART roll-out and if wider use of ART in sub-Saharan Africa is associated with rising prevalence of drug resistance.

METHODS

We did a cross-sectional study in antiretroviral-naive adults infected with HIV-1 who had not started first-line ART, recruited between 2007 and 2009 from 11 regions in Kenya, Nigeria, South Africa, Uganda, Zambia, and Zimbabwe. We did population-based sequencing of the pol gene on plasma specimens with greater than 1000 copies per mL of HIV RNA. We identified drug-resistance mutations with the WHO list for transmitted resistance. The prevalence of sequences containing at least one drug-resistance mutation was calculated accounting for the sampling weights of the sites. We assessed the risk factors of resistance with multilevel logistic regression with random coefficients.

FINDINGS

2436 (94.1%) of 2590 participants had a pretreatment genotypic resistance result. 1486 participants (57.4%) were women, 1575 (60.8%) had WHO clinical stage 3 or 4 disease, and the median CD4 count was 133 cells per μL (IQR 62-204). Overall sample-weighted drug-resistance prevalence was 5.6% (139 of 2436; 95% CI 4.6-6.7), ranging from 1.1% (two of 176; 0.0-2.7) in Pretoria, South Africa, to 12.3% (22 of 179; 7.5-17.1) in Kampala, Uganda. The pooled prevalence for all three Ugandan sites was 11.6% (66 of 570; 8.9-14.2), compared with 3.5% (73 of 1866; 2.5-4.5) for all other sites. Drug class-specific resistance prevalence was 2.5% (54 of 2436; 1.8-3.2) for nucleoside reverse-transcriptase inhibitors (NRTIs), 3.3% (83 of 2436; 2.5-4.2) for non-NRTIs (NNRTIs), 1.3% (31 of 2436; 0.8-1.8) for protease inhibitors, and 1.2% (25 of 2436; 0.7-1.7) for dual-class resistance to NRTIs and NNRTIs. The most common drug-resistance mutations were K103N (43 [1.8%] of 2436), thymidine analogue mutations (33 [1.6%] of 2436), M184V (25 [1.2%] of 2436), and Y181C/I (19 [0.7%] of 2436). The odds ratio for drug resistance associated with each additional year since the start of the ART roll-out in a region was 1.38 (95% CI 1.13-1.68; p=0.001).

INTERPRETATION

The higher prevalence of primary drug resistance in Uganda than in other African countries is probably related to the earlier start of ART roll-out in Uganda. Resistance surveillance and prevention should be prioritised in settings where ART programmes are scaled up.

FUNDING

Ministry of Foreign Affairs of the Netherlands.

A classification approach for genotyping viral sequences based on multidimensional scaling and linear discriminant analysis

BACKGROUND

Accurate classification into genotypes is critical in understanding evolution of divergent viruses. Here we report a new approach, MuLDAS, which classifies a query sequence based on the statistical genotype models learned from the known sequences. Thus, MuLDAS utilizes full spectra of well characterized sequences as references, typically of an order of hundreds, in order to estimate the significance of each genotype assignment.

RESULTS

MuLDAS starts by aligning the query sequence to the reference multiple sequence alignment and calculating the subsequent distance matrix among the sequences. They are then mapped to a principal coordinate space by multidimensional scaling, and the coordinates of the reference sequences are used as features in developing linear discriminant models that partition the space by genotype. The genotype of the query is then given as the maximum a posteriori estimate. MuLDAS tests the model confidence by leave-one-out cross-validation and also provides some heuristics for the detection of 'outlier' sequences that fall far outside or in-between genotype clusters. We have tested our method by classifying HIV-1 and HCV nucleotide sequences downloaded from NCBI GenBank, achieving the overall concordance rates of 99.3% and 96.6%, respectively, with the benchmark test dataset retrieved from the respective databases of Los Alamos National Laboratory.

CONCLUSIONS

The highly accurate genotype assignment coupled with several measures for evaluating the results makes MuLDAS useful in analyzing the sequences of rapidly evolving viruses such as HIV-1 and HCV. A web-based genotype prediction server is available at http://www.muldas.org/MuLDAS/.

HIV-1 protease mutations and protease inhibitor cross-resistance

The effects of many protease inhibitor (PI)-selected mutations on the susceptibility to individual PIs are unknown. We analyzed in vitro susceptibility test results on 2,725 HIV-1 protease isolates. More than 2,400 isolates had been tested for susceptibility to fosamprenavir, indinavir, nelfinavir, and saquinavir; 2,130 isolates had been tested for susceptibility to lopinavir; 1,644 isolates had been tested for susceptibility to atazanavir; 1,265 isolates had been tested for susceptibility to tipranavir; and 642 isolates had been tested for susceptibility to darunavir. We applied least-angle regression (LARS) to the 200 most common mutations in the data set and identified a set of 46 mutations associated with decreased PI susceptibility of which 40 were not polymorphic in the eight most common HIV-1 group M subtypes. We then used least-squares regression to ascertain the relative contribution of each of these 46 mutations. The median number of mutations associated with decreased susceptibility to each PI was 28 (range, 19 to 32), and the median number of mutations associated with increased susceptibility to each PI was 2.5 (range, 1 to 8). Of the mutations with the greatest effect on PI susceptibility, I84AV was associated with decreased susceptibility to eight PIs; V32I, G48V, I54ALMSTV, V82F, and L90M were associated with decreased susceptibility to six to seven PIs; I47A, G48M, I50V, L76V, V82ST, and N88S were associated with decreased susceptibility to four to five PIs; and D30N, I50L, and V82AL were associated with decreased susceptibility to fewer than four PIs. This study underscores the greater impact of nonpolymorphic mutations compared with polymorphic mutations on decreased PI susceptibility and provides a comprehensive quantitative assessment of the effects of individual mutations on susceptibility to the eight clinically available PIs.

Short communication: antiretroviral therapy resistance mutations present in the HIV type 1 subtype C pol and env regions from therapy-naive patients in Zambia

The prevalence of antiretroviral therapy (ART) resistance mutations present in HIV-1 subtype C pol and env regions of the proviral DNA was analyzed and compared from therapy-naive individuals before (Cohort A) and after (Cohort B) the availability of free ART in Zambia. Mutations present in sequences published in a previous study from Zambian ART-naive individuals infected with subtype C were analyzed using current parameters for the classification of ART drug resistance and compared with Cohorts A and B. No statistically significant differences were observed when comparing mutations present in the pol and env of these cohorts. However, an increase in the number of minor, borderline, or partial resistance mutations as well as the presence of major resistance mutations were observed in Cohort B. These results suggest there is an increasing trend of drug resistance-associated mutations that could be a result of the availability of free ART in Zambia. Moreover, the high prevalence of resistance mutations observed for maraviroc and vicriviroc in both cohorts may suggest a limited efficacy of entry inhibitors on HIV-1 subtype C.

Using genomic signatures for HIV-1 sub-typing

Background

Human Immunodeficiency Virus type 1 (HIV-1), the causative agent of Acquired Immune Deficiency Syndrome (AIDS), exhibits very high genetic diversity with different variants or subtypes prevalent in different parts of the world. Proper classification of the HIV-1 subtypes, displaying differential infectivity, plays a major role in monitoring the epidemic and is also a critical component for effective treatment strategy. The existing methods to classify HIV-1 sequence subtypes, based on phylogenetic analysis focusing only on specific genes/regions, have shown inconsistencies as they lack the capability to analyse whole genome variations. Several isolates are left unclassified due to unresolved sub-typing. It is apparent that classification of subtypes based on complete genome sequences, rather than sub-genomic regions, is a more robust and comprehensive approach to address genome-wide heterogeneity. However, no simple methodology exists that directly computes HIV-1 subtype from the complete genome sequence.

Results

We use Chaos Game Representation (CGR) as an approach to identify the distinctive genomic signature associated with the DNA sequence organisation in different HIV-1 subtypes. We first analysed the effect of nucleotide word lengths (k = 2 to 8) on whole genomes of the HIV-1 M group sequences, and found the optimum word length of k = 6, that could classify HIV-1 subtypes based on a Test sequence set. Using the optimised word length, we then showed accurate classification of the HIV-1 subtypes from both the Reference Set sequences and from all available sequences in the database. Finally, we applied the approach to cluster the five unclassified HIV-1 sequences from Africa and Europe, and predict their possible subtypes.

Conclusion

We propose a genomic signature-based approach, using CGR with suitable word length optimisation, which can be applied to classify intra-species variations, and apply it to the complex problem of HIV-1 subtype classification. We demonstrate that CGR is a simple and computationally less intensive method that not only accurately segregates the HIV-1 subtype and sub-subtypes, but also aid in the classification of the unclassified sequences. We hope that it will be useful in subtype annotation of the newly sequenced HIV-1 genomes.

An evolutionary model-based algorithm for accurate phylogenetic breakpoint mapping and subtype prediction in HIV-1

Genetically diverse pathogens (such as Human Immunodeficiency virus type 1, HIV-1) are frequently stratified into phylogenetically or immunologically defined subtypes for classification purposes. Computational identification of such subtypes is helpful in surveillance, epidemiological analysis and detection of novel variants, e.g., circulating recombinant forms in HIV-1. A number of conceptually and technically different techniques have been proposed for determining the subtype of a query sequence, but there is not a universally optimal approach. We present a model-based phylogenetic method for automatically subtyping an HIV-1 (or other viral or bacterial) sequence, mapping the location of breakpoints and assigning parental sequences in recombinant strains as well as computing confidence levels for the inferred quantities. Our Subtype Classification Using Evolutionary ALgorithms (SCUEAL) procedure is shown to perform very well in a variety of simulation scenarios, runs in parallel when multiple sequences are being screened, and matches or exceeds the performance of existing approaches on typical empirical cases. We applied SCUEAL to all available polymerase (pol) sequences from two large databases, the Stanford Drug Resistance database and the UK HIV Drug Resistance Database. Comparing with subtypes which had previously been assigned revealed that a minor but substantial (≈5%) fraction of pure subtype sequences may in fact be within- or inter-subtype recombinants. A free implementation of SCUEAL is provided as a module for the HyPhy package and the Datamonkey web server. Our method is especially useful when an accurate automatic classification of an unknown strain is desired, and is positioned to complement and extend faster but less accurate methods. Given the increasingly frequent use of HIV subtype information in studies focusing on the effect of subtype on treatment, clinical outcome, pathogenicity and vaccine design, the importance of accurate, robust and extensible subtyping procedures is clear.

There are nine different subtypes of the main group of HIV-1, each originating as a distinct subepidemic of HIV-1. The distribution of subtypes is often unique to a given geographic region of the world and constitutes a useful epidemiological and surveillance resource. The effects of viral subtype on disease progression, treatment outcome and vaccine design are being actively researched, and the importance of accurate subtyping procedures is clear. In HIV-1, subtype assignment is complicated by frequent recombination among co-circulating strains, creating new genetic mosaics or recombinant forms: 43 have been characterized to date, and many more likely exist. We present an automated phylogenetic method (SCUEAL) to accurately characterize both simple and complex HIV-1 mosaics. Using computer simulations and biological data we demonstrate that SCUEAL performs very well under various conditions, especially when some of the existing classification procedures fail. Furthermore, we show that a small, but noticeable proportion of subtype characterization stored in public databases may be incomplete or incorrect. The computational technique introduced here should provide a much more accurate characterization of HIV-1 strains, especially novel recombinants, and lead to new insights into molecular history, epidemiology and geographical distribution of the virus.

African descent is associated with slower CD4 cell count decline in treatment-naive patients of the Swiss HIV Cohort Study

OBJECTIVE

We investigated the effect of descent (African versus European) on the progression of untreated HIV infections in a prospective cohort study of HIV-1-infected individuals.

METHODS

We estimated the linear rate of decline of the CD4 cell count and the setpoint viral load in patients with sufficient data points. The effect of descent was assessed by microltivariate regression models including descent, sex, viral subtype, the earliest date of confirmed infection, age, and the baseline CD4 cell count; the rate of CD4 cell count decline was also analyzed with mixed-effect models and with matched comparisons between patients of African and European descent based on the baseline CD4 cell count.

RESULTS

We found that the decline slope of the CD4 cell count was significantly less steep (+26.6 cells/microl per year; 95% confidence interval, 12.3-41.0; P < 0.001) in patients of African descent (n = 123) compared with patients of European descent (n = 463), and this effect was independent of differences in the infecting viral subtypes. Matched comparisons confirmed the effect of African descent (P < 0.001). Remarkably, the rate of CD4 cell count decline depended strongly on the viral setpoint in patients of European descent (-46.3 cells/microl per year/log10 RNA copies/ml; 95% confidence interval, -55.8 to -36.7; P < 0.001) but not in patients of African descent.

CONCLUSION

Slower disease progression in patients of African descent might be related to host factors allowing better tolerance of high virus levels in patients of African descent compared with patients of European descent.

The calibrated population resistance tool: standardized genotypic estimation of transmitted HIV-1 drug resistance

Summary: The calibrated population resistance (CPR) tool is a web-accessible program for performing standardized genotypic estimation of transmitted HIV-1 drug resistance. The program is linked to the Stanford HIV drug resistance database and can additionally perform viral genotyping and algorithmic estimation of resistance to specific antiretroviral drugs.

Availability:http://cpr.stanford.edu/cpr/index.html

Contact:robjgiff@gmail.com

Drug resistance mutations for surveillance of transmitted HIV-1 drug-resistance: 2009 update

Programs that monitor local, national, and regional levels of transmitted HIV-1 drug resistance inform treatment guidelines and provide feedback on the success of HIV-1 treatment and prevention programs. To accurately compare transmitted drug resistance rates across geographic regions and times, the World Health Organization has recommended the adoption of a consensus genotypic definition of transmitted HIV-1 drug resistance. In January 2007, we outlined criteria for developing a list of mutations for drug-resistance surveillance and compiled a list of 80 RT and protease mutations meeting these criteria (surveillance drug resistance mutations; SDRMs). Since January 2007, several new drugs have been approved and several new drug-resistance mutations have been identified. In this paper, we follow the same procedures described previously to develop an updated list of SDRMs that are likely to be useful for ongoing and future studies of transmitted drug resistance. The updated SDRM list has 93 mutations including 34 NRTI-resistance mutations at 15 RT positions, 19 NNRTI-resistance mutations at 10 RT positions, and 40 PI-resistance mutations at 18 protease positions.

Analysis of natural sequence variation and covariation in human immunodeficiency virus type 1 integrase

Human immunodeficiency virus type 1 (HIV-1) integrase inhibitors are in clinical trials, and raltegravir and elvitegravir are likely to be the first licensed drugs of this novel class of HIV antivirals. Understanding resistance to these inhibitors is important to maximize their efficacy. It has been shown that natural variation and covariation provide valuable insights into the development of resistance for established HIV inhibitors. Therefore, we have undertaken a study to fully characterize natural polymorphisms and amino acid covariation within an inhibitor-naïve sequence set spanning all defined HIV-1 subtypes. Inter- and intrasubtype variation was greatest in a 50-amino-acid segment of HIV-1 integrase incorporating the catalytic aspartic acid codon 116, suggesting that polymorphisms affect inhibitor binding and pathways to resistance. The critical mutations that determine the resistance pathways to raltegravir and elvitegravir (N155H, Q148K/R/H, and E92Q) were either rare or absent from the 1,165-sequence data set. However, 25 out of 41 mutations associated with integrase inhibitor resistance were present. These mutations were not subtype associated and were more prevalent in the subtypes that had been sampled frequently within the database. A novel modification of the Jaccard index was used to analyze amino acid covariation within HIV-1 integrase. A network of 10 covarying resistance-associated mutations was elucidated, along with a further 15 previously undescribed mutations that covaried with at least two of the resistance positions. The validation of covariation as a predictive tool will be dependent on monitoring the evolution of HIV-1 integrase under drug selection pressure.

Molecular epidemiology and divergence of HIV type 1 protease codon 35 inserted strains among treatment-naive patients in Hong Kong

This study reported the prevalence and pattern of viral replication-associated HIV-1 protease codon 35 amino acid insertions among treatment-naive patients in Hong Kong. The transmission and divergence date of these inserted strains was also investigated. The pol gene of 264 local HIV-1 isolates was sequenced and phylogenetic analysis was performed. The transmission history of protease codon 35-inserted HIV-1 strains in Hong Kong was estimated by the Bayesian coalescent method. This insertion was detected in 12 (4.55%) among 264 treatment-naive subtype B HIV-1 patients in Hong Kong, which was 20-times higher than the prevalence in the western countries. Among these strains, eight carried a glutamic acid (GAA) insertion (E35E_E), two carried an aspartic acid (GAC) insertion (E35E_D), and two carried a glycine (GGA) insertion (E35E_G). E35E_D and E35E_E insertions were the first to be reported. All the 12 inserted sequences clustered in the same lineage of the phylogenetic tree, indicating the possibility of transmission of this insertion. Epidemiological investigation revealed the major route of infection for this inserted strain in Hong Kong was associated mainly among homosexual Chinese males. The evolutionary rate of these inserted strains was similar to other subtype B HIV-1 strains. Through coalescent-based analysis, the divergence date of the protease codon 35-inserted strains in Hong Kong was 1995. Our findings demonstrate the epidemic pathways of viral fitness-related HIV-1 protease codon 35-inserted isolates in Hong Kong. The effect of these novel insertions on viral fitness and drug susceptibility requires further investigation.

Sequence editing by Apolipoprotein B RNA-editing catalytic component [corrected] and epidemiological surveillance of transmitted HIV-1 drug resistance

DESIGN

Promiscuous guanine (G) to adenine (A) substitutions catalysed by apolipoprotein B RNA-editing catalytic component (APOBEC) enzymes are observed in a proportion of HIV-1 sequences in vivo and can introduce artifacts into some genetic analyses. The potential impact of undetected lethal editing on genotypic estimation of transmitted drug resistance was assessed.

METHODS

Classifiers of lethal, APOBEC-mediated editing were developed by analysis of lentiviral pol gene sequence variation and evaluated using control sets of HIV-1 sequences. The potential impact of sequence editing on genotypic estimation of drug resistance was assessed in sets of sequences obtained from 77 studies of 25 or more therapy-naive individuals, using mixture modelling approaches to determine the maximum likelihood classification of sequences as lethally edited as opposed to viable.

RESULTS

Analysis of 6437 protease and reverse transcriptase sequences from therapy-naive individuals using a novel classifier of lethal, APOBEC3G-mediated sequence editing, the polypeptide-like 3G (APOBEC3G)-mediated defectives (A3GD) index', detected lethal editing in association with spurious 'transmitted drug resistance' in nearly 3% of proviral sequences obtained from whole blood and 0.2% of samples obtained from plasma.

CONCLUSION

Screening for lethally edited sequences in datasets containing a proportion of proviral DNA, such as those likely to be obtained for epidemiological surveillance of transmitted drug resistance in the developing world, can eliminate rare but potentially significant errors in genotypic estimation of transmitted drug resistance.

Nucleotide composition string selection in HIV-1 subtyping using whole genomes

MOTIVATION

The availability of the whole genomic sequences of HIV-1 viruses provides an excellent resource for studying the HIV-1 phylogenies using all the genetic materials. However, such huge volumes of data create computational challenges in both memory consumption and CPU usage.

RESULTS

We propose the complete composition vector representation for an HIV-1 strain, and a string scoring method to extract the nucleotide composition strings that contain the richest evolutionary information for phylogenetic analysis. In this way, a large-scale whole genome phylogenetic analysis for thousands of strains can be done both efficiently and effectively. By using 42 carefully curated strains as references, we apply our method to subtype 1156 HIV-1 strains (10.5 million nucleotides in total), which include 825 pure subtype strains and 331 recombinants. Our results show that our nucleotide composition string selection scheme is computationally efficient, and is able to define both pure subtypes and recombinant forms for HIV-1 strains using the 5000 top ranked nucleotide strings.

AVAILABILITY

The Java executable and the HIV-1 datasets are accessible through 'http://www.cs.ualberta.ca/~ghlin/src/WebTools/hiv.php.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Genotypic variation in the pol gene of HIV type 1 in an antiretroviral treatment-naive population in rural southwestern Uganda

The majority of studies of HIV-1 drug resistance have involved subtype B viruses. Here we have characterized subtype distribution and determined the levels of polymorphism at protease (PR) and reverse transcriptase (RT) drug resistance positions, in antiretroviral treatment-naive HIV-positive Ugandan patients. We have also investigated codon usage variability at these positions and assessed intersubtype recombination within the pol gene. The study population consisted of 187 patients, from a cohort established by the UK Medical Research Council Programme on AIDS in Uganda in 1990. Results indicate that 28.3% of patients were infected with subtype A (n = 53), 64.2% subtype D (n = 120), 6.4% A/D recombinant (n = 12), and 1.1% subtype C (n = 2). Variation in amino acid usage at drug resistance-associated positions was minimal between the two main subtypes (A and D) in RT, but there was appreciable variation in PR. Codon usage, however, was considerably more variable between subtypes A and D in both PR and RT. Thus, while no natural high-level resistance to antiretroviral therapy was detected in this cohort, subtypes A and D may possess different genetic barriers to be overcome in order to achieve resistance. With the increasing introduction of antiretroviral therapy into Africa, such information will be vital in our understanding and evaluation of the development of drug resistance as it occurs, and how to interpret resistance data the type of which has rarely previously been seen. This analysis also significantly increases the number of Ugandan PR and RT sequences characterized to date.

Assessment of automated genotyping protocols as tools for surveillance of HIV-1 genetic diversity

BACKGROUND

The routine use of drug resistance testing provides an abundant source of HIV-1 sequence data. However, it is not clear how reliable standard genotyping of these sequences is for describing HIV-1 genetic variation and for detecting novel genetic variants and epidemiological trends.

OBJECTIVES

To compare assignment of HIV-1 resistance test sequences to reference strains across commonly used genotyping protocols.

METHODS

Subtype assignments were compared across three standard genotyping protocols for 10 537 resistance test sequences, representing approximately one-fifth of all reported infections in the United Kingdom. Sequences that were inconsistently genotyped across methods, or that were unassigned by at least one method, were examined for evidence of recombination using sliding-window-based approaches.

RESULTS

Although agreement across methods was high for subtypes B, C and H, it was generally much lower (< 50%) for other subtypes. Disagreement between methods typically involved closely related, but epidemiologically distinct, groups or involved a significant proportion ( approximately 12%) of divergent sequences in which analysis revealed widespread evidence of recombination and a remarkable diversity of unusual recombinant forms.

CONCLUSIONS

With frequent long-distance transfer of viral strains and widespread recombination between them, genetic and epidemiological relationships within HIV-1 are becoming increasingly complex. Current methods of subtype assignment vary in their ability to identify novel genetic variants and to distinguish epidemiologically distinct strains. Capturing meaningful epidemiological information from resistance test data will require a critical understanding of the methodologies used in order to appreciate the possible sources of error and misclassification.

Genotyping Hepatitis B virus from whole- and sub-genomic fragments using position-specific scoring matrices in HBV STAR

Hepatitis B virus (HBV) genomes have been classified into eight genotypes based on phylogenetic analysis of sequence variation. Identifying and tracking the movement of HBV genotypes is important in terms of both monitoring infection rates and predicting disease and treatment. An HBV genotyping tool has been developed that compares query sequences with position-specific scoring matrices representing the eight HBV genotypes. This tool (hbv star) is rapid, robust and accurate and assigns genotype based on a statistically defined scoring model. hbv star confidently assigned 90% of 590 full-length HBV genomes to an HBV genotype (Z score >2.0). Thirty-two of the residual 48 sequences were identified as non-human primate viruses and 16 sequences were identified as recombinant or putative recombinants. Receiver-Operated Characteristic (ROC) analysis was used to compare the accuracy of genotype prediction using basal core promoter sequences and surface and core genes with the accuracy achieved by using full-length sequences. A web interface to hbv star is available at http://www.vgb.ucl.ac.uk/starn.shtml.

Web resources for HIV type 1 genotypic-resistance test interpretation

Interpreting the results of plasma human immunodeficiency virus type 1 (HIV-1) genotypic drug-resistance tests is one of the most difficult tasks facing clinicians caring for HIV-1-infected patients. There are many drug-resistance mutations, and they arise in complex patterns that cause varying levels of drug resistance. In addition, HIV-1 exists in vivo as a virus population containing many genomic variants. Genotypic-resistance testing detects the drug-resistance mutations present in the most common plasma virus variants but may not detect drug-resistance mutations present in minor virus variants. Therefore, interpretation systems are necessary to determine the phenotypic and clinical significance of drug-resistance mutations found in a patient's plasma virus population. We describe the scientific principles of HIV-1 genotypic-resistance test interpretation and the most commonly used Web-based resources for clinicians ordering genotypic drug-resistance tests.