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.

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.

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.

A computational method for the identification of Dengue, Zika and Chikungunya virus species and genotypes.

In recent years, an increasing number of outbreaks of Dengue, Chikungunya and Zika viruses have been reported in Asia and the Americas. Monitoring virus genotype diversity is crucial to understand the emergence and spread of outbreaks, both aspects that are vital to develop effective prevention and treatment strategies. Hence, we developed an efficient method to classify virus sequences with respect to their species and sub-species (i.e. serotype and/or genotype). This tool provides an easy-to-use software implementation of this new method and was validated on a large dataset assessing the classification performance with respect to whole-genome sequences and partial-genome sequences. Available online: http://krisp.org.za/tools.php.

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.

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: 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.

UNLABELLED: 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.

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.

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.

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.

A public HTLV-1 molecular epidemiology database for sequence management and data mining.

BACKGROUND: It is estimated that 15 to 20 million people are infected with the human T-cell lymphotropic virus type 1 (HTLV-1). At present, there are more than 2,000 unique HTLV-1 isolate sequences published. A central database to aggregate sequence information from a range of epidemiological aspects including HTLV-1 infections, pathogenesis, origins, and evolutionary dynamics would be useful to scientists and physicians worldwide. Described here, we have developed a database that collects and annotates sequence data and can be accessed through a user-friendly search interface. The HTLV-1 Molecular Epidemiology Database website is available at http://htlv1db.bahia.fiocruz.br/. METHODOLOGY/PRINCIPAL FINDINGS: All data was obtained from publications available at GenBank or through contact with the authors. The database was developed using Apache Webserver 2.1.6 and SGBD MySQL. The webpage interfaces were developed in HTML and sever-side scripting written in PHP. The HTLV-1 Molecular Epidemiology Database is hosted on the Gonçalo Moniz/FIOCRUZ Research Center server. There are currently 2,457 registered sequences with 2,024 (82.37%) of those sequences representing unique isolates. Of these sequences, 803 (39.67%) contain information about clinical status (TSP/HAM, 17.19%; ATL, 7.41%; asymptomatic, 12.89%; other diseases, 2.17%; and no information, 60.32%). Further, 7.26% of sequences contain information on patient gender while 5.23% of sequences provide the age of the patient. CONCLUSIONS/SIGNIFICANCE: The HTLV-1 Molecular Epidemiology Database retrieves and stores annotated HTLV-1 proviral sequences from clinical, epidemiological, and geographical studies. The collected sequences and related information are now accessible on a publically available and user-friendly website. This open-access database will support clinical research and vaccine development related to viral genotype.

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.

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.

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.