Rapid epidemic expansion of the SARS-CoV-2 Omicron variant in southern Africa

The SARS-CoV-2 epidemic in southern Africa has been characterized by three distinct waves. The first was associated with a mix of SARS-CoV-2 lineages, while the second and third waves were driven by the Beta (B.1.351) and Delta (B.1.617.2) variants, respectively1-3. In November 2021, genomic surveillance teams in South Africa and Botswana detected a new SARS-CoV-2 variant associated with a rapid resurgence of infections in Gauteng province, South Africa. Within three days of the first genome being uploaded, it was designated a variant of concern (Omicron, B.1.1.529) by the World Health Organization and, within three weeks, had been identified in 87 countries. The Omicron variant is exceptional for carrying over 30 mutations in the spike glycoprotein, which are predicted to influence antibody neutralization and spike function4. Here we describe the genomic profile and early transmission dynamics of Omicron, highlighting the rapid spread in regions with high levels of population immunity.

Behavioral changes before lockdown and decreased retail and recreation mobility during lockdown contributed most to controlling COVID-19 in Western countries

BACKGROUND

The COVID-19 pandemic has prompted a lockdown in many countries to control the exponential spread of the SARS-CoV-2 virus, hereby reducing the time-varying basic reproduction number (Rt) to below one. Governments are looking for evidence to balance the demand of their citizens to ease some of the restriction, against the fear of a new peak in infections. In this study, we wanted to quantify the relative contribution of mobility restrictions, and that of behavioral changes that occurred already before the lockdowns, on the reduction of transmission during lockdowns in Western countries in early 2020.

METHODS

Incidence data of cases and deaths from the first wave of infections for 35 Western countries (32 European, plus Israel, USA and Canada) were analyzed using epidemiological compartment models in a Bayesian framework. Mobility data was used to estimate the timing of changes associated with a lockdown, and was correlated with estimated reductions of Rt.

RESULTS

Across all countries, the initial median estimate for Rt was 3.6 (95% IQR 2.4-5.2), and it was reduced to 0.78 (95% IQR 0.58-1.01) during lockdown. 48% (18-65%) of the reduction occurred already in the week before lockdown, with lockdown itself causing the remaining drop in transmission. A lower Rt during lockdown was independently associated with an increased time spent at home (0.21 per 10% more time, p < 0.007), and decreased mobility related to retail and recreation (0.07 per 10% less mobility, p < 0.008).

CONCLUSIONS

In a Western population unaware of the risk, SARS-CoV-2 can be highly contagious with a reproduction number R0 > 5. Our results are consistent with evidence that recreational activities (including restaurant and bar visits) enable super-spreading events. Exiting from lockdown therefore requires continued physical distancing and tight control on this kind of activities.

Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes

Summary

Genome detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next-generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present the Genome Detective Coronavirus Typing Tool that can accurately identify the novel severe acute respiratory syndrome (SARS)-related coronavirus (SARS-CoV-2) sequences isolated in China and around the world. The tool can accept up to 2000 sequences per submission and the analysis of a new whole-genome sequence will take approximately 1 min. The tool has been tested and validated with hundreds of whole genomes from 10 coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for SARS-CoV-2. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines to stop the COVID-19 disease.

Availability and implementation

https://www.genomedetective.com/app/typingtool/cov

Contact

koen@emweb.be or deoliveira@ukzn.ac.za

Supplementary information

Supplementary data are available at Bioinformatics online.

VIRULIGN: fast codon-correct alignment and annotation of viral genomes

Summary

Virus sequence data are an essential resource for reconstructing spatiotemporal dynamics of viral spread as well as to inform treatment and prevention strategies. However, the potential benefit of these applications critically depends on accurate and correctly annotated alignments of genetically heterogeneous data. VIRULIGN was built for fast codon-correct alignments of large datasets, with standardized and formalized genome annotation and various alignment export formats.

Availability and implementation

VIRULIGN is freely available at https://github.com/rega-cev/virulign as an open source software project.

Supplementary information

Supplementary data is available at Bioinformatics online.

Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes

Genome Detective is a web-based, user-friendly software application to quickly and accurately assemble all known virus genomes from next generation sequencing datasets. This application allows the identification of phylogenetic clusters and genotypes from assembled genomes in FASTA format. Since its release in 2019, we have produced a number of typing tools for emergent viruses that have caused large outbreaks, such as Zika and Yellow Fever Virus in Brazil. Here, we present The Genome Detective Coronavirus Typing Tool that can accurately identify novel coronavirus (2019-nCoV) sequences isolated in China and around the world. The tool can accept up to 2,000 sequences per submission and the analysis of a new whole genome sequence will take approximately one minute. The tool has been tested and validated with hundreds of whole genomes from ten coronavirus species, and correctly classified all of the SARS-related coronavirus (SARSr-CoV) and all of the available public data for 2019-nCoV. The tool also allows tracking of new viral mutations as the outbreak expands globally, which may help to accelerate the development of novel diagnostics, drugs and vaccines.

SANTA-SIM: simulating viral sequence evolution dynamics under selection and recombination

Simulations are widely used to provide expectations and predictive distributions under known conditions against which to compare empirical data. Such simulations are also invaluable for testing and comparing the behaviour and power of inference methods. We describe SANTA-SIM, a software package to simulate the evolution of a population of gene sequences forwards through time. It models the underlying biological processes as discrete components: replication, recombination, point mutations, insertion-deletions, and selection under various fitness models and population size dynamics. The software is designed to be intuitive to work with for a wide range of users and executable in a cross-platform manner.

Genome Detective: an automated system for virus identification from high-throughput sequencing data

SUMMARY

Genome Detective is an easy to use web-based software application that assembles the genomes of viruses quickly and accurately. The application uses a novel alignment method that constructs genomes by reference-based linking of de novo contigs by combining amino-acids and nucleotide scores. The software was optimized using synthetic datasets to represent the great diversity of virus genomes. The application was then validated with next generation sequencing data of hundreds of viruses. User time is minimal and it is limited to the time required to upload the data.

AVAILABILITY AND IMPLEMENTATION

Available online: http://www.genomedetective.com/app/typingtool/virus/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Possible footprints of APOBEC3F and/or other APOBEC3 deaminases, but not APOBEC3G, on HIV-1 from patients with acute/early and chronic infections

Members of the apolipoprotein B mRNA-editing enzyme-catalytic polypeptide-like-3 (APOBEC3) innate cellular cytidine deaminase family, particularly APOBEC3F and APOBEC3G, can cause extensive and lethal G-to-A mutations in HIV-1 plus-strand DNA (termed hypermutation). It is unclear if APOBEC3-induced mutations in vivo are always lethal or can occur at sublethal levels that increase HIV-1 diversification and viral adaptation to the host. The viral accessory protein Vif counteracts APOBEC3 activity by binding to APOBEC3 and promoting proteasome degradation; however, the efficiency of this interaction varies, since a range of hypermutation frequencies are observed in HIV-1 patient DNA. Therefore, we examined "footprints" of APOBEC3G and APOBEC3F activity in longitudinal HIV-1 RNA pol sequences from approximately 3,000 chronically infected patients by determining whether G-to-A mutations occurred in motifs that were favored or disfavored by these deaminases. G-to-A mutations were more frequent in APOBEC3G-disfavored than in APOBEC3G-favored contexts. In contrast, mutations in APOBEC3F-disfavored contexts were relatively rare, whereas mutations in contexts favoring APOBEC3F (and possibly other deaminases) occurred 16% more often than average G-to-A mutations. These results were supported by analyses of >500 HIV-1 env sequences from acute/early infection.

IMPORTANCE

Collectively, our results suggest that APOBEC3G-induced mutagenesis is lethal to HIV-1, whereas mutagenesis caused by APOBEC3F and/or other deaminases may result in sublethal mutations that might facilitate viral diversification. Therefore, Vif-specific cytotoxic T lymphocyte (CTL) responses and drugs that manipulate the interplay between Vif and APOBEC3 may have beneficial or detrimental clinical effects depending on how they affect the binding of Vif to various members of the APOBEC3 family.

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.

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.

Contact:pieter.libin@rega.kuleuven.be

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.

Treatment-associated polymorphisms in protease are significantly associated with higher viral load and lower CD4 count in newly diagnosed drug-naive HIV-1 infected patients

BACKGROUND

The effect of drug resistance transmission on disease progression in the newly infected patient is not well understood. Major drug resistance mutations severely impair viral fitness in a drug free environment, and therefore are expected to revert quickly. Compensatory mutations, often already polymorphic in wild-type viruses, do not tend to revert after transmission. While compensatory mutations increase fitness during treatment, their presence may also modulate viral fitness and virulence in absence of therapy and major resistance mutations. We previously designed a modeling technique that quantifies genotypic footprints of in vivo treatment selective pressure, including both drug resistance mutations and polymorphic compensatory mutations, through the quantitative description of a fitness landscape from virus genetic sequences.

RESULTS

Genotypic correlates of viral load and CD4 cell count were evaluated in subtype B sequences from recently diagnosed treatment-naive patients enrolled in the SPREAD programme. The association of surveillance drug resistance mutations, reported compensatory mutations and fitness estimated from drug selective pressure fitness landscapes with baseline viral load and CD4 cell count was evaluated using regression techniques. Protease genotypic variability estimated to increase fitness during treatment was associated with higher viral load and lower CD4 cell counts also in treatment-naive patients, which could primarily be attributed to well-known compensatory mutations at highly polymorphic positions. By contrast, treatment-related mutations in reverse transcriptase could not explain viral load or CD4 cell count variability.

CONCLUSIONS

These results suggest that polymorphic compensatory mutations in protease, reported to be selected during treatment, may improve the replicative capacity of HIV-1 even in absence of drug selective pressure or major resistance mutations. The presence of this polymorphic variation may either reflect a history of drug selective pressure, i.e. transmission from a treated patient, or merely be a result of diversity in wild-type virus. Our findings suggest that transmitted drug resistance has the potential to contribute to faster disease progression in the newly infected host and to shape the HIV-1 epidemic at a population level.

APOBEC3G-induced hypermutation of human immunodeficiency virus type-1 is typically a discrete "all or nothing" phenomenon

The rapid evolution of Human Immunodeficiency Virus (HIV-1) allows studies of ongoing host-pathogen interactions. One key selective host factor is APOBEC3G (hA3G) that can cause extensive and inactivating Guanosine-to-Adenosine (G-to-A) mutation on HIV plus-strand DNA (termed hypermutation). HIV can inhibit this innate anti-viral defense through binding of the viral protein Vif to hA3G, but binding efficiency varies and hypermutation frequencies fluctuate in patients. A pivotal question is whether hA3G-induced G-to-A mutation is always lethal to the virus or if it may occur at sub-lethal frequencies that could increase viral diversification. We show in vitro that limiting-levels of hA3G-activity (i.e. when only a single hA3G-unit is likely to act on HIV) produce hypermutation frequencies similar to those in patients and demonstrate in silico that potentially non-lethal G-to-A mutation rates are ∼10-fold lower than the lowest observed hypermutation levels in vitro and in vivo. Our results suggest that even a single incorporated hA3G-unit is likely to cause extensive and inactivating levels of HIV hypermutation and that hypermutation therefore is typically a discrete "all or nothing" phenomenon. Thus, therapeutic measures that inhibit the interaction between Vif and hA3G will likely not increase virus diversification but expand the fraction of hypermutated proviruses within the infected host.

An automated genotyping tool for enteroviruses and noroviruses

BACKGROUND

Molecular techniques are established as routine in virological laboratories and virus typing through (partial) sequence analysis is increasingly common. Quality assurance for the use of typing data requires harmonization of genotype nomenclature, and agreement on target genes, depending on the level of resolution required, and robustness of methods.

OBJECTIVE

To develop and validate web-based open-access typing-tools for enteroviruses and noroviruses.

STUDY DESIGN

An automated web-based typing algorithm was developed, starting with BLAST analysis of the query sequence against a reference set of sequences from viruses in the family Picornaviridae or Caliciviridae. The second step is phylogenetic analysis of the query sequence and a sub-set of the reference sequences, to assign the enterovirus type or norovirus genotype and/or variant, with profile alignment, construction of phylogenetic trees and bootstrap validation. Typing is performed on VP1 sequences of Human enterovirus A to D, and ORF1 and ORF2 sequences of genogroup I and II noroviruses. For validation, we used the tools to automatically type sequences in the RIVM and CDC enterovirus databases and the FBVE norovirus database.

RESULTS

Using the typing-tools, 785(99%) of 795 Enterovirus VP1 sequences, and 8154(98.5%) of 8342 norovirus sequences were typed in accordance with previously used methods. Subtyping into variants was achieved for 4439(78.4%) of 5838 NoV GII.4 sequences.

DISCUSSION AND CONCLUSIONS

The online typing-tools reliably assign genotypes for enteroviruses and noroviruses. The use of phylogenetic methods makes these tools robust to ongoing evolution. This should facilitate standardized genotyping and nomenclature in clinical and public health laboratories, thus supporting inter-laboratory comparisons.

Estimating the individualized HIV-1 genetic barrier to resistance using a nelfinavir fitness landscape

Background

Failure on Highly Active Anti-Retroviral Treatment is often accompanied with development of antiviral resistance to one or more drugs included in the treatment. In general, the virus is more likely to develop resistance to drugs with a lower genetic barrier. Previously, we developed a method to reverse engineer, from clinical sequence data, a fitness landscape experienced by HIV-1 under nelfinavir (NFV) treatment. By simulation of evolution over this landscape, the individualized genetic barrier to NFV resistance may be estimated for an isolate.

Results

We investigated the association of estimated genetic barrier with risk of development of NFV resistance at virological failure, in 201 patients that were predicted fully susceptible to NFV at baseline, and found that a higher estimated genetic barrier was indeed associated with lower odds for development of resistance at failure (OR 0.62 (0.45 - 0.94), per additional mutation needed, p = .02).

Conclusions

Thus, variation in individualized genetic barrier to NFV resistance may impact effective treatment options available after treatment failure. If similar results apply for other drugs, then estimated genetic barrier may be a new clinical tool for choice of treatment regimen, which allows consideration of available treatment options after virological failure.

Resistance pathways of human immunodeficiency virus type 1 against the combination of zidovudine and lamivudine

A better understanding of human immunodeficiency virus type 1 drug-resistance evolution under the selective pressure of combination treatment is important for the design of long-term effective treatment strategies. We applied Bayesian network learning to sequences from patients treated with the reverse transcriptase inhibitor combination of zidovudine (AZT) and lamivudine (3TC) to identify the role of many treatment-selected mutations in the development of resistance. Based on the Bayesian network structure, an in vivo fitness landscape was built, reflecting the necessary selective pressure under treatment, to evolve naive sequences to sequences obtained from patients treated with the combination. This landscape, combined with an evolutionary model, was used to predict resistance evolution in longitudinal sequence pairs. In our analysis, mutations 41L, 70R, 184V and 215F/Y were identified as major resistance mutations to the combination of AZT and 3TC, as they were associated directly with treatment experience. The network also suggested a possible role in resistance development for a number of novel mutations. Estimated fitness, using the landscape, correlated significantly with in vitro resistance phenotype in genotype-phenotype pairs (R(2)=0.70). Variation in predicted evolution under selective pressure correlated significantly with observed in vivo evolution during AZT plus 3CT treatment. In conclusion, we confirmed current knowledge on resistance development to the combination of AZT and 3CT, but additional novel mutations were identified. Moreover, a model to predict resistance evolution during AZT and 3CT treatment has been built and validated.

A standardized framework for accurate, high-throughput genotyping of recombinant and non-recombinant viral sequences

Human immunodeficiency virus type-1 (HIV-1), hepatitis B and C and other rapidly evolving viruses are characterized by extremely high levels of genetic diversity. To facilitate diagnosis and the development of prevention and treatment strategies that efficiently target the diversity of these viruses, and other pathogens such as human T-lymphotropic virus type-1 (HTLV-1), human herpes virus type-8 (HHV8) and human papillomavirus (HPV), we developed a rapid high-throughput-genotyping system. The method involves the alignment of a query sequence with a carefully selected set of pre-defined reference strains, followed by phylogenetic analysis of multiple overlapping segments of the alignment using a sliding window. Each segment of the query sequence is assigned the genotype and sub-genotype of the reference strain with the highest bootstrap (>70%) and bootscanning (>90%) scores. Results from all windows are combined and displayed graphically using color-coded genotypes. The new Virus-Genotyping Tools provide accurate classification of recombinant and non-recombinant viruses and are currently being assessed for their diagnostic utility. They have incorporated into several HIV drug resistance algorithms including the Stanford (http://hivdb.stanford.edu) and two European databases (http://www.umcutrecht.nl/subsite/spread-programme/ and http://www.hivrdb.org.uk/) and have been successfully used to genotype a large number of sequences in these and other databases. The tools are a PHP/JAVA web application and are freely accessible on a number of servers including: http://bioafrica.mrc.ac.za/rega-genotype/html/, http://lasp.cpqgm.fiocruz.br/virus-genotype/html/, http://jose.med.kuleuven.be/genotypetool/html/.

The rise and fall of K65R in a Portuguese HIV-1 Drug Resistance database, despite continuously increasing use of tenofovir

OBJECTIVE

The overall prevalence of the K65R mutation in HIV-1 reverse transcriptase has increased in treatment-experienced patients, mostly attributed to the increasing use of tenofovir (TDF). A number of TDF-based regimens are associated with high rate of early virological failure. In this study, we evaluated the impact of these combinations on K65R selection over time.

METHODS

Treatment-experienced patients who had a genotypic resistance test at time of failure in the period 2002-2005 in a hospital in Lisbon were included. Incidence of K65R was calculated and compared with proportions of failing therapies in the respective year of sampling including TDF or didanosine plus stavudine and their respective frequency of K65R selection were analyzed using classical statistics and Bayesian Network Learning.

RESULTS

The overall rate of K65R in the database was consistent with earlier reports. The incidence of K65R increased in the period 2002-2004 but showed a sharp, significant decrease in 2005, despite a continuous increase of patients failing TDF-based regimens. The proportion of patients failing certain not-recommended regimens decreased sharply in 2005 and therefore correlated well with K65R incidence trend.

CONCLUSIONS

This study suggests that the use of certain TDF-based regimens caused the increase in K65R incidence over the last years.

Modelled in vivo HIV Fitness under drug Selective Pressure and Estimated Genetic Barrier Towards Resistance are Predictive for Virological Response

Background

A method has been developed to estimate a fitness landscape experienced by HIV-1 under treatment selective pressure as a function of the genotypic sequence thereby also estimating the genetic barrier to resistance.

Methods

We evaluated the performance of two estimated fitness landscapes (nelfinavir [NFV] and zidovudine [AZT] plus lamivudine [3TC]) to predict week 12 viral load (VL) change for 176 treatment change episodes (TCEs) and probability of week 48 virological failure for 90 TCEs, in treatment experienced patients starting these drugs in combination.

Results

A higher genetic barrier for AZT plus 3TC, (quantified per additional mutation required to develop resistance against these drugs) was associated with a 0.54 (95% confidence interval [CI] 0.30–0.77) larger log10 VL reduction at 12 weeks ( P<0.0001) and a 0.39 (95% CI 0.23–0.66) lower odds of virological failure at 48 weeks ( P=0.0005), in analyses adjusting for the pre-TCE VL and the exact time-lag between the TCE and the date of determining response VL. The strength of these associations was comparable with those seen with expert interpretation systems (Rega, ANRS and HIVDB). A higher genetic barrier to NFV resistance was the only genotypic predictor that tended to be associated with a 0.19 (95% CI 0–0.39) higher log10 VL reduction at 12 weeks ( P=0.05) and a 0.63 (95% CI 0.36–1.09) lower odds of virological failure at 48 weeks ( P=0.10) per additional mutation.

Conclusions

These results suggest that an estimated genetic barrier derived from fitness landscapes may contribute to an improvement of predicted treatment outcome for NFV and this approach should be explored for other drugs.

Prevalence and epidemiology of HIV type 1 drug resistance among newly diagnosed therapy-naive patients in Belgium from 2003 to 2006

This study is the first prospective study to assess the prevalence, epidemiology, and risk factors of HIV-1 drug resistance in newly diagnosed HIV-infected patients in Belgium. In January 2003 it was initiated as part of the pan-European SPREAD program, and continued thereafter for four inclusion rounds until December 2006. Epidemiological, clinical, and behavioral data were collected using a standardized questionnaire and genotypic resistance testing was done on a sample taken within 6 months of diagnosis. Two hundred and eighty-five patients were included. The overall prevalence of transmitted HIV-1 drug resistance in Belgium was 9.5% (27/285, 95% CI: 6.6-13.4). Being infected in Belgium, which largely coincided with harboring a subtype B virus, was found to be significantly associated with transmission of drug resistance. The relatively high rate of baseline resistance might jeopardize the success of first line treatment as more than 1 out of 10 (30/285, 10.5%) viruses did not score as fully susceptible to one of the recommended first-line regimens, i.e., zidovudine, lamivudine, and efavirenz. Our results support the implementation of genotypic resistance testing as a standard of care in all treatment-naive patients in Belgium.

The incidence of multidrug and full class resistance in HIV-1 infected patients is decreasing over time (2001-2006) in Portugal

Despite improvements in HIV treatment, the prevalence of multidrug resistance and full class resistance is still reported to be increasing. However, to investigate whether current treatment strategies are still selecting for multidrug and full class resistance, the incidence, instead of the prevalence, is more informative. Temporal trends in multidrug resistance (MDR defined as at most 1 drug fully susceptible) and full class resistance (FCR defined as no drug in this class fully susceptible) in Portugal based on 3394 viral isolates genotyped from 2000 to 2006 were examined using the Rega 6.4.1 interpretation system. From July 2001 to July 2006 there was a significant decreasing trend of MDR with 5.7%, 5.2%, 3.8%, 3.4% and 2.7% for the consecutive years (P = 0.003). Multivariate analysis showed that for every consecutive year the odds of having a new MDR case decreased with 20% (P = 0.003). Furthermore, a decline was observed for NRTI- and PI-FCR (both P < 0.001), whereas for NNRTI-FCR a parabolic trend over time was seen (P < 0.001), with a maximum incidence in 2003–'04. Similar trends were obtained when scoring resistance for only one drug within a class or by using another interpretation system. In conclusion, the incidence of multidrug and full class resistance is decreasing over time in Portugal, with the exception of NNRTI full class resistance which showed an initial rise, but subsequently also a decline. This is most probably reflecting the changing drug prescription, the increasing efficiency of HAART and the improved management of HIV drug resistance. This work was presented in part at the Eighth International Congress on Drug Therapy in HIV Infection, Glasgow (UK), 12-16 November 2006 (PL5.5); and at the Fifth European HIV Drug Resistance Workshop, Cascais (Portugal), 28-30 March 2007 (Abstract 1).

HIV-1 gp120 N-linked glycosylation differs between plasma and leukocyte compartments

Background

N-linked glycosylation is a major mechanism for minimizing virus neutralizing antibody response and is present on the Human Immunodeficiency Virus (HIV) envelope glycoprotein. Although it is known that glycosylation changes can dramatically influence virus recognition by the host antibody, the actual contribution of compartmental differences in N-linked glycosylation patterns remains unclear.

Methodology and Principal Findings

We amplified the env gp120 C2-V5 region and analyzed 305 clones derived from plasma and other compartments from 15 HIV-1 patients. Bioinformatics and Bayesian network analyses were used to examine N-linked glycosylation differences between compartments. We found evidence for cellspecific single amino acid changes particular to monocytes, and significant variation was found in the total number of N-linked glycosylation sites between patients. Further, significant differences in the number of glycosylation sites were observed between plasma and cellular compartments. Bayesian network analyses showed an interdependency between N-linked glycosylation sites found in our study, which may have immense functional relevance.

Conclusion

Our analyses have identified single cell/compartment-specific amino acid changes and differences in N-linked glycosylation patterns between plasma and diverse blood leukocytes. Bayesian network analyses showed associations inferring alternative glycosylation pathways. We believe that these studies will provide crucial insights into the host immune response and its ability in controlling HIV replication in vivo. These findings could also have relevance in shielding and evasion of HIV-1 from neutralizing antibodies.

Modelled in vivo HIV fitness under drug selective pressure and estimated genetic barrier towards resistance are predictive for virological response

BACKGROUND

A method has been developed to estimate a fitness landscape experienced by HIV-1 under treatment selective pressure as a function of the genotypic sequence thereby also estimating the genetic barrier to resistance.

METHODS

We evaluated the performance of two estimated fitness landscapes (nelfinavir [NFV] and zidovudine [AZT] plus lamivudine [3TC]) to predict week 12 viral load (VL) change for 176 treatment change episodes (TCEs) and probability of week 48 virological failure for 90 TCEs, in treatment experienced patients starting these drugs in combination.

RESULTS

A higher genetic barrier for AZT plus 3TC, (quantified per additional mutation required to develop resistance against these drugs) was associated with a 0.54 (95% confidence interval [CI] 0.30-0.77) larger log10 VL reduction at 12 weeks (P < 0.0001) and a 0.39 (95%/ CI 0.23-0.66) lower odds of virological failure at 48 weeks (P = 0.0005), in analyses adjusting for the pre-TCE VL and the exact time-lag between the TCE and the date of determining response VL. The strength of these associations was comparable with those seen with expert interpretation systems (Rega, ANRS and HIVDB). A higher genetic barrier to NFV resistance was the only genotypic predictor that tended to be associated with a 0.19 (95% CI 0-0.39) higher log10 VL reduction at 12 weeks (P = 0.05) and a 0.63 (95% CI 0.36-1.09) lower odds of virological failure at 48 weeks ( P = 0.10) per additional mutation.

CONCLUSIONS

These results suggest that an estimated genetic barrier derived from fitness landscapes may contribute to an improvement of predicted treatment outcome for NFV and this approach should be explored for other drugs.

Estimating the relative contribution of dNTP pool imbalance and APOBEC3G/3F editing to HIV evolution in vivo

The human immunodeficiency virus (HIV) has a genome that is rich in adenine, and its rapid evolution shows an observed bias of guanine (G) to adenine (A) mutations. Two mechanisms have been proposed to explain these properties: (1) an imbalance in dNTP pool concentrations which drives the misincorporation process during reverse transcription, and (2) cytidine deamination by the APOBEC3G/3F restriction factor, causing G to A mutations most notably in specific dinucleotide contexts. Although crucial to understanding HIV evolution, current estimates on misincorporation bias during the replication cycle are based on scarce in vitro measurements. In this work, HIV partial pol sequences obtained for drug resistance testing purposes are analyzed using likelihood methods to estimate various models of HIV misincorporation bias in vivo. The technique is robust to selection on the amino acid sequence and selection against CpG dinucleotides. A model where misincorporations are explained only by an imbalance in dNTP pool concentrations, together with a preference for transitions versus transversions, explained 98% (95% confidence interval [C.I.] 93-100) of the observed variation in freely estimated misincorporation rates. Although dinucleotide context was responsible for variation in misincorporation probabilities, this variation was not specific for G to A mutations implying that the footprint of APOBEC3G/3F editing could not be detected. These results indicate that an imbalance in dNTP pool concentrations explains most of the bias in HIV nucleotide misincorporations, while the effect of editing by APOBEC3G/3F on HIV evolution, based on its dinucleotide specificity, could not be observed in this study.

Estimation of an in vivo fitness landscape experienced by HIV-1 under drug selective pressure useful for prediction of drug resistance evolution during treatment

MOTIVATION

HIV-1 antiviral resistance is a major cause of antiviral treatment failure. The in vivo fitness landscape experienced by the virus in presence of treatment could in principle be used to determine both the susceptibility of the virus to the treatment and the genetic barrier to resistance. We propose a method to estimate this fitness landscape from cross-sectional clinical genetic sequence data of different subtypes, by reverse engineering the required selective pressure for HIV-1 sequences obtained from treatment naive patients, to evolve towards sequences obtained from treated patients. The method was evaluated for recovering 10 random fictive selective pressures in simulation experiments, and for modeling the selective pressure under treatment with the protease inhibitor nelfinavir.

RESULTS

The estimated fitness function under nelfinavir treatment considered fitness contributions of 114 mutations at 48 sites. Estimated fitness correlated significantly with the in vitro resistance phenotype in 519 matched genotype-phenotype pairs (R(2) = 0.47 (0.41 - 0.54)) and variation in predicted evolution under nelfinavir selective pressure correlated significantly with observed in vivo evolution during nelfinavir treatment for 39 mutations (with FDR = 0.05).

AVAILABILITY

The software is available on request from the authors, and data sets are available from http://jose.med.kuleuven.be/~kdforc0/nfv-fitness-data/.

Bayesian network analysis of resistance pathways against HIV-1 protease inhibitors

Interpretation of Human Immunodeficiency Virus 1 (HIV-1) genotypic drug resistance is still a major challenge in the follow-up of antiviral therapy in infected patients. Because of the high degree of HIV-1 natural variation, complex interactions and stochastic behaviour of evolution, the role of resistance mutations is in many cases not well understood. Using Bayesian network learning of HIV-1 sequence data from diverse subtypes (A, B, C, F and G), we could determine the specific role of many resistance mutations against the protease inhibitors (PIs) nelfinavir (NFV), indinavir (IDV), and saquinavir (SQV). Such networks visualize relationships between treatment, selection of resistance mutations and presence of polymorphisms in a graphical way. The analysis identified 30N, 88S, and 90M for nelfinavir, 90M for saquinavir, and 82A/T and 46I/L for indinavir as most probable major resistance mutations. Moreover we found striking similarities for the role of many mutations against all of these drugs. For example, for all three inhibitors, we found that the novel mutation 89I was minor and associated with mutations at positions 90 and 71. Bayesian network learning provides an autonomous method to gain insight in the role of resistance mutations and the influence of HIV-1 natural variation. We successfully applied the method to three protease inhibitors. The analysis shows differences with current knowledge especially concerning resistance development in several non-B subtypes.

Analysis of HIV-1 pol sequences using Bayesian Networks: implications for drug resistance

Human Immunodeficiency Virus-1 (HIV-1) antiviral resistance is a major cause of antiviral therapy failure and compromises future treatment options. As a consequence, resistance testing is the standard of care. Because of the high degree of HIV-1 natural variation and complex interactions, the role of resistance mutations is in many cases insufficiently understood. We applied a probabilistic model, Bayesian networks, to analyze direct influences between protein residues and exposure to treatment in clinical HIV-1 protease sequences from diverse subtypes. We can determine the specific role of many resistance mutations against the protease inhibitor nelfinavir, and determine relationships between resistance mutations and polymorphisms. We can show for example that in addition to the well-known major mutations 90M and 30N for nelfinavir resistance, 88S should not be treated as 88D but instead considered as a major mutation and explain the subtype-dependent prevalence of the 30N resistance pathway.

Investigation of Baseline Susceptibility to Protease Inhibitors in HIV-1 Subtypes C, F, G and Crf02_Ag

Objective

To compare baseline susceptibility to protease inhibitors among HIV-1 isolates of subtypes C, F, G and CRF02_AG, and to identify polymorphisms that determine the differences in susceptibility.

Methods

A total of 42 samples of drug-naive patients infected with subtypes G ( n=19), CRF02_AG ( n=10), F ( n=6) and C ( n=7) were phenotyped and genotyped with the Antivirogram and the ViroSeq 2.0 genotyping system, respectively. A Bayesian network approach was used for a preliminary analysis of the collected data and the dependencies indicated by the network were statistically confirmed.

Results

CRF02_AG samples were found to be more susceptible to nelfinavir and ritonavir than other subtypes. Hypersusceptibility to these drugs was associated with the 70R polymorphism. 37D/S/T was associated with reduced susceptibility to indinavir and 89M with reduced susceptibility to lopinavir. Susceptibility to tipranavir was the lowest among the subtype F samples and the highest for subtype G samples, with samples carrying 57R being more susceptible than samples carrying 57K.

Conclusions

Our study suggests that there are baseline susceptibility differences between subtypes and these differences are due to naturally occurring polymorphisms in these subtypes. The predictive value for phenotype of these polymorphisms was even valid in subtypes where these polymorphisms are less prevalent. Taking into account such polymorphisms should improve current algorithms for interpretation of genotyping results in a subtype-independent way.

Discordances between interpretation algorithms for genotypic resistance to protease and reverse transcriptase inhibitors of human immunodeficiency virus are subtype dependent

The major limitation of drug resistance genotyping for human immunodeficiency virus remains the interpretation of the results. We evaluated the concordance in predicting therapy response between four different interpretation algorithms (Rega 6.3, HIVDB-08/04, ANRS [07/04], and VGI 8.0). Sequences were gathered through a worldwide effort to establish a database of non-B subtype sequences, and demographic and clinical information about the patients was gathered. The most concordant results were found for nonnucleoside reverse transcriptase (RT) inhibitors (93%), followed by protease inhibitors (84%) and nucleoside RT inhibitor (NRTIs) (76%). For therapy-naive patients, for nelfinavir, especially for subtypes C and G, the discordances were driven mainly by the protease (PRO) mutational pattern 82I/V + 63P + 36I/V for subtype C and 82I + 63P + 36I + 20I for subtype G. Subtype F displayed more discordances for ritonavir in untreated patients due to the combined presence of PRO 20R and 10I/V. In therapy-experienced patients, subtype G displayed a lot of discordances for saquinavir and indinavir due to mutational patterns involving PRO 90 M and 82I. Subtype F had more discordance for nelfinavir attributable to the presence of PRO 88S and 82A + 54V. For the NRTIs lamivudine and emtricitabine, CRF01_AE had more discordances than subtype B due to the presence of RT mutational patterns 65R + 115 M and 118I + 215Y, respectively. Overall, the different algorithms agreed well on the level of resistance scored, but some of the discordances could be attributed to specific (subtype-dependent) combinations of mutations. It is not yet known whether therapy response is subtype dependent, but the advice given to clinicians based on a genotypic interpretation algorithm differs according to the subtype.

Investigation of baseline susceptibility to protease inhibitors in HIV-1 subtypes C, F, G and CRF02_AG

OBJECTIVE

To compare baseline susceptibility to protease inhibitors among HIV-1 isolates of subtypes C, F, G and CRF02_AG, and to identify polymorphisms that determine the differences in susceptibility.

METHODS

A total of 42 samples of drug-naive patients infected with subtypes G (n=19), CRF02_AG (n = 10), F (n = 6) and C (n = 7) were phenotyped and genotyped with the Antivirogram and the ViroSeq 2.0 genotyping system, respectively. A Bayesian network approach was used for a preliminary analysis of the collected data and the dependencies indicated by the network were statistically confirmed.

RESULTS

CRF02_AG samples were found to be more susceptible to nelfinavir and ritonavir than other subtypes. Hypersusceptibility to these drugs was associated with the 70R polymorphism. 37D/S/T was associated with reduced susceptibility to indinavir and 89M with reduced susceptibility to lopinavir. Susceptibility to tipranavir was the lowest among the subtype F samples and the highest for subtype G samples, with samples carrying 57R being more susceptible than samples carrying 57K.

CONCLUSIONS

Our study suggests that there are baseline susceptibility differences between subtypes and these differences are due to naturally occurring polymorphisms in these subtypes. The predictive value for phenotype of these polymorphisms was even valid in subtypes where these polymorphisms are less prevalent. Taking into account such polymorphisms should improve current algorithms for interpretation of genotyping results in a subtype-independent way.

Protease mutation M89I/V is linked to therapy failure in patients infected with the HIV-1 non-B subtypes C, F or G

OBJECTIVE

To investigate whether and how mutations at position 89 of HIV-1 protease were associated with protease inhibitor (PI) failure, and what is the impact of the HIV-1 subtype.

METHODS

In a database containing pol nucleotide sequences and treatment history, the correlation between PI experience and mutations at codon 89 was determined separately for subtype B and several non-B subtypes. A Bayesian network model was used to map the resistance pathways in which M89I/V is involved for subtype G. The phenotypic effect of M89I/V for several PIs was also measured.

RESULTS

The analysis showed that for the subtypes C, F and G in which the wild-type codon at 89 was M compared to L for subtype B, M89I/V was significantly more frequently observed in PI-treated patients displaying major resistance mutations to PIs than in drug-naive patients. M89I/V was strongly associated with PI resistance mutations at codons 71, 74 and 90. Phenotypically, M89I/V alone did not confer a reduced susceptibility to PIs. However, when combined with L90M, a significantly reduced susceptibility to nelfinavir was observed (P < 0.05) in comparison with strains with L90M alone.

CONCLUSIONS

The results of the present study show that M89I/V is associated with PI experience in subtypes C, F and G but not in subtype B. M89I/V should be considered a secondary PI mutation with an important effect on nelfinavir susceptibility in the presence of L90M.

A genotypic assay for the amplification and sequencing of gag and protease from diverse human immunodeficiency virus type 1 group M subtypes

In human immunodeficiency virus type 1 (HIV-1), an interaction exists between the in vivo evolution of Gag protein and protease to escape from antiretroviral drug selective pressure. Therefore, it was decided to develop a genotypic assay for the amplification and sequencing of HIV-1 gag and protease. As the HIV-1 pandemic is characterised by a large genetic diversity, the assay developed was evaluated on a panel of 28 genetically divergent samples belonging to the following subtypes A1, B, C, D, F1, F2, G, H, J, CRF01-AE, CRF02-AG and CRF13-cpx. The assay displayed a detection limit ranging between 500 RNA copies/ml and 5000 RNA copies/ml plasma. Full-length sequences could be obtained for 25 samples. The population sequences of the three other samples lacked a part of the sequence because of heterogeneous signal, probably due to the presence of quasi-species with insertions/deletions of a different length.

An automated genotyping system for analysis of HIV-1 and other microbial sequences

MOTIVATION

Genetic analysis of HIV-1 is important not only for vaccine development, but also to guide treatment strategies, track the emergence of new viral variants and ensure that diagnostic assays are contemporary and fully optimized. However, most genotyping methods are laborious and complex, and involve the use of multiple software applications. Here, we describe the development of an automated genotyping system that can be easily applied to HIV-1 and other rapidly evolving viral pathogens.

RESULTS

The new REGA subtyping tool, developed using Java programming and PERL scripts, combines phylogenetic analyses with boot-scanning methods for the genetic subtyping of full-length and subgenomic fragments of HIV-1. When used to investigate the subtype of previously published reference datasets that were analysed using manual phylogenetic methods, the automated method correctly identified 97.5-100% of non-recombinant and circulating recombinant forms of HIV-1, including 108 full-length, 108 gag and 221 env sequences downloaded from the Los Alamos database.

Analysis of complex HIV-1 intersubtype recombinants using a Bayesian scanning method

The increased complexity of HIV-1 genetic heterogeneity raises the issue for reliable classification and analysis of these sequences. Until now, bootscanning analysis has been the main method used for the analysis of potential HIV-1 intersubtype recombinants. We show evidence that in some cases of complex recombinants, where three or more segments with discordant phylogenetic signal may exist in protease (PR) and partial reverse transcriptase (RT) region, Bayesian scanning provides a clearer picture than bootscanning plots about the boundaries of potential recombination. Thus, a recently developed Bayesian scanning tool can facilitate the analysis and classification of HIV-1 mosaic sequences.

SlidingBayes: exploring recombination using a sliding window approach based on Bayesian phylogenetic inference

We developed a software tool (SlidingBayes) for recombination analysis based on Bayesian phylogenetic inference. Sliding-Bayes provides a powerful approach for detecting potential recombination, especially between highly divergent sequences and complex HIV-1 recombinants for which simpler methods like neighbor joining (NJ) may be less powerful. SlidingBayes guides Markov Chain Monte Carlo (MCMC) sampling performed by MrBayes in a sliding window across the alignment (Bayesian scanning). The tool can be used for nucleotide and amino acid sequences and combines all the modeling possibilities of MrBayes with the ability to plot the posterior probability support for clustering of various combinations of taxa.