RegaDB: community-driven data management and analysis for infectious diseases.

SUMMARY: RegaDB is a free and open source data management and analysis environment for infectious diseases. RegaDB allows clinicians to store, manage and analyse patient data, including viral genetic sequences. Moreover, RegaDB provides researchers with a mechanism to collect data in a uniform format and offers them a canvas to make newly developed bioinformatics tools available to clinicians and virologists through a user friendly interface. AVAILABILITY AND IMPLEMENTATION: Source code, binaries and documentation are available on http://rega.kuleuven.be/cev/regadb. RegaDB is written in the Java programming language, using a web-service-oriented architecture.

A collaborative environment allowing clinical investigations on integrated biomedical databases.

In order to perform clinical investigations on integrated biomedical data sets and to predict virological and epidemiological outcome, medical experts require an IT-based collaborative environment that provides them a user-friendly space for building and executing their complex studies and workflows on largely available and high-quality data repositories. In this paper, the authors introduce such a novel collaborative working environment a so-called virtual laboratory for clinicians and medical researchers, which allows users to interactively access and browse several biomedical research databases and re-use relevant data sets within own designed experiments. Firstly, technical details on the integration of relevant data resources into the virtual laboratory infrastructure and specifically developed user interfaces are briefly explained. The second part describes research possibilities for medical scientists including potential application fields and benefits as using the virtual laboratory functionalities for a particular exemplary study.

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.

Antiretroviral drug resistance in HIV-1 therapy-naive patients in Cuba.

In Cuba, antiretroviral therapy rollout started in 2001 and antiretroviral therapy coverage has reached almost 40% since then. The objectives of this study were therefore to analyze subtype distribution, and level and patterns of drug resistance in therapy-naive HIV-1 patients. Four hundred and one plasma samples were collected from HIV-1 therapy-naive patients in 2003 and in 2007-2011. HIV-1 drug resistance genotyping was performed in the pol gene and drug resistance was interpreted according to the WHO surveillance drug-resistance mutations list, version 2009. Potential impact on first-line therapy response was estimated using genotypic drug resistance interpretation systems HIVdb version 6.2.0 and Rega version 8.0.2. Phylogenetic analysis was performed using Neighbor-Joining. The majority of patients were male (84.5%), men who have sex with men (78.1%) and from Havana City (73.6%). Subtype B was the most prevalent subtype (39.3%), followed by CRF20-23-24_BG (19.5%), CRF19_cpx (18.0%) and CRF18_cpx (10.3%). Overall, 29 patients (7.2%) had evidence of drug resistance, with 4.0% (CI 1.6%-4.8%) in 2003 versus 12.5% (CI 7.2%-14.5%) in 2007-2011. A significant increase in drug resistance was observed in recently HIV-1 diagnosed patients, i.e. 14.8% (CI 8.0%-17.0%) in 2007-2011 versus 3.8% (CI 0.9%-4.7%) in 2003 (OR 3.9, CI 1.5-17.0, p=0.02). The majority of drug resistance was restricted to a single drug class (75.8%), with 55.2% patients displaying nucleoside reverse transcriptase inhibitor (NRTI), 10.3% non-NRTI (NNRTI) and 10.3% protease inhibitor (PI) resistance mutations. Respectively, 20.7% and 3.4% patients carried viruses containing drug resistance mutations against NRTI+NNRTI and NRTI+NNRTI+PI. The first cases of resistance towards other drug classes than NRTI were only detected from 2008 onwards. The most frequent resistance mutations were T215Y/rev (44.8%), M41L (31.0%), M184V (17.2%) and K103N (13.8%). The median genotypic susceptibility score for the commonly prescribed first-line therapies was 2.5. This analysis emphasizes the need to perform additional surveillance studies to accurately assess the level of transmitted drug resistance in Cuba, as the extent of drug resistance might jeopardize effectiveness of first-line regimens prescribed in Cuba and might necessitate the implementation of baseline drug resistance testing.

Clinical evaluation of Rega 8: an updated genotypic interpretation system that significantly predicts HIV-therapy response.

INTRODUCTION: Clinically evaluating genotypic interpretation systems is essential to provide optimal guidance in designing potent individualized HIV-regimens. This study aimed at investigating the ability of the latest Rega algorithm to predict virological response on a short and longer period. MATERIALS METHODS: 9231 treatment changes episodes were extracted from an integrated patient database. The virological response after 8, 24 and 48 weeks was dichotomized to success and failure. Success was defined as a viral load below 50 copies/ml or alternatively, a 2 log decrease from the baseline viral load at 8 weeks. The predictive ability of Rega version 8 was analysed in comparison with that of previous evaluated version Rega 5 and two other algorithms (ANRS v2011.05 and Stanford HIVdb v6.0.11). A logistic model based on the genotypic susceptibility score was used to predict virological response, and additionally, confounding factors were added to the model. Performance of the models was compared using the area under the ROC curve (AUC) and a Wilcoxon signed-rank test. RESULTS: Per unit increase of the GSS reported by Rega 8, the odds on having a successful therapy response on week 8 increased significantly by 81% (OR = 1.81, CI = [1.76-1.86]), on week 24 by 73% (OR = 1.73, CI = [1.69-1.78]) and on week 48 by 85% (OR = 1.85, CI = [1.80-1.91]). No significant differences in AUC were found between the performance of Rega 8 and Rega 5, ANRS v2011.05 and Stanford HIVdb v6.0.11, however Rega 8 had the highest sensitivity: 76.9%, 76.5% and 77.2% on 8, 24 and 48 weeks respectively. Inclusion of additional factors increased the performance significantly. CONCLUSION: Rega 8 is a significant predictor for virological response with a better sensitivity than previously, and with rules for recently approved drugs. Additional variables should be taken into account to ensure an effective regimen.

HIV-1 fitness landscape models for indinavir treatment pressure using observed evolution in longitudinal sequence data are predictive for treatment failure.

We previously modeled the in vivo evolution of human immunodeficiency virus-1 (HIV-1) under drug selective pressure from cross-sectional viral sequences. These fitness landscapes (FLs) were made by using first a Bayesian network (BN) to map epistatic substitutions, followed by scaling the fitness landscape based on an HIV evolution simulator trying to evolve the sequences from treatment naïve patients into sequences from patients failing treatment. In this study, we compared four FLs trained with different sequence populations. Epistatic interactions were learned from three different cross-sectional BNs, trained with sequence from patients experienced with indinavir (BNT), all protease inhibitors (PIs) (BNP) or all PI except indinavir (BND). Scaling the fitness landscape was done using cross-sectional data from drug naïve and indinavir experienced patients (Fcross using BNT) and using longitudinal sequences from patients failing indinavir (FlongT using BNT, FlongP using BNP, FlongD using BND). Evaluation to predict the failing sequence and therapy outcome was performed on independent sequences of patients on indinavir. Parameters included estimated fitness (LogF), the number of generations (GF) or mutations (MF) to reach the fitness threshold (average fitness when a major resistance mutation appeared), the number of generations (GR) or mutations (MR) to reach a major resistance mutation and compared to genotypic susceptibility score (GSS) from Rega and HIVdb algorithms. In pairwise FL comparisons we found significant correlation between fitness values for individual sequences, and this correlation improved after correcting for the subtype. Furthermore, FLs could predict the failing sequence under indinavir-containing combinations. At 12 and 48 weeks, all parameters from all FLs and indinavir GSS (both for Rega and HIVdb) were predictive of therapy outcome, except MR for FlongT and FlongP. The fitness landscapes have similar predictive power for treatment response under indinavir-containing regimen as standard rules-based algorithms, and additionally allow predicting genetic evolution under indinavir selective pressure.

High frequency of antiviral drug resistance and non-B subtypes in HIV-1 patients failing antiviral therapy in Cuba.

BACKGROUND: Emergence of HIV-1 drug resistance may limit the sustained benefits of antiretroviral therapy (ART) in settings with limited laboratory monitoring and drug options. OBJECTIVES: Surveillance of drug resistance and subtypes in HIV-1 patients failing ART in Cuba. STUDY DESIGN: This study compiled data of ART-experienced HIV-1 patients attending a clinical center in Havana in 2003 and 2009-2011. The first period included results of a cross-sectional study, whereas in the second period genotyping was performed as part of routine care. Drug resistance mutations and levels were determined using HIVdb version 6.0.9. RESULTS: Seventy-six percent received solely ART containing at least 3 drugs, of which 79.1% ever receiving unboosted protease inhibitors (PI). Patients from 2009 to 2011 were longer treated and exposed to more ART regimens. Subtype B (39%) and CRF19_cpx (18%) were the most prevalent genetic forms. Subtype distribution did not change significantly between both periods, except for BG recombinants that increased from 6% to 14%. Nucleoside reverse transcriptase inhibitor (NRTI), non-nucleoside RTI (NNRTI) and PI mutations were present in 69.5%, 54.8% and 44.4%. Full-class resistance (FCR) to NRTI, NNRTI, PI and multidrug resistance (MDR) were detected in 31.8%, 37.9%, 18.5% and 15.4%. FCR to NRTI, NNRTI, PI and MDR were present in 9.8%, 14.1%, 0%, 0% after first-line failure and in 19.8%, 20.8%, 2.9% and 2.9% after second-line failure. CONCLUSIONS: Our study found a high prevalence of drug resistance and supports the need for appropriate laboratory monitoring in clinical practice and access to drug options in case of virological failure.

Appearance of a single amino acid insertion at position 33 in HIV type 1 protease under a lopinavir-containing regimen, associated with reduced protease inhibitor susceptibility.

HIV drug resistance is a multifactorial phenomenon and constitutes a major concern as it results in therapy failure. The aim of this study was to assess the impact of an amino acid insertion identified at position 33 of the protease gene, derived from samples from three patients under lopinavir therapy, on viral fitness and protease inhibitor (PI) resistance. Successive samples were available from one of the patients for genotypic and phenotypic testing in order to investigate the role of this insertion. The patient had been pretreated with various antiretroviral drugs and showed poor virological response from the point of the acquisition of the mutation onward. The insertion was acquired in the context of a number of other PI mutations and was stable following acquisition. Phenotypic testing revealed reduced susceptibility to various PIs and a reduction of the replicative capacity (RC) of the virus. In the presence of the insertion alone, a decrease of the RC was observed, which seemed to be compensated by the presence of other mutations. The L33ins might have a potential role in PI resistance pathways but further investigation in a larger number of clinical samples is required in order to elucidate this resistance mechanism.

Comparative performance of the REGA subtyping tool version 2 versus version 1.

The REGA HIV-1 subtyping tool is a phylogenetic-based method for subtyping HIV-1 genomic sequences that was published in 2005. The subtyping tool combines phylogenetic approaches with recombination detection methods. Recently, version 2 was released (http://www.bioafrica.net/rega-genotype/html/index.html) as an improvement of version 1. Version 2 implements a Decision-Tree-based algorithm that was not implemented in version 1. We wanted to compare the two versions on a large sequence dataset to assess the improvements of version 2 and to verify whether features lost during updating the tool needed to be recovered. We analysed the results of the two versions in the genotyping of 4676 HIV-1 pol sequences. We compared those results to a manual approach, used in previous studies. Our results show that version 2 has an overall better sensitivity but especially for the detection of subtypes A, B, D, F, G and CRF14_BG and CRF06_CPX. For the other subtypes, no significant differences were observed in the sensitivity of versions 1 and 2. The overall increase in sensitivity was however accompanied by a decrease in the specificity for the detection of subtype B. This is the main limitation of version 2. However, while the number of false negatives decreased by 53 samples, the number of false positives increased only by 5 samples from version 1 to 2. The performance of the REGA HIV-1 subtyping tool was considerably improved from one version to the other. Our results are very valuable and allow us to make suggestions for further improvement of the tool for a version 3 release.