Genetic Variability of Myxoma Virus Genomes.

Myxomatosis is a recurrent problem on rabbit farms throughout Europe despite the success of vaccines. To identify gene variations of field and vaccine strains that may be responsible for changes in virulence, immunomodulation, and immunoprotection, the genomes of 6 myxoma virus (MYXV) strains were sequenced: German field isolates Munich-1, FLI-H, 2604, and 3207; vaccine strain MAV; and challenge strain ZA. The analyzed genomes ranged from 147.6 kb (strain MAV) to 161.8 kb (strain 3207). All sequences were affected by several mutations, covering 24 to 93 open reading frames (ORFs) and resulted in amino acid substitutions, insertions, or deletions. Only strains Munich-1 and MAV revealed the deletion of 10 ORFs (M007L to M015L) and 11 ORFs (M007L to M008.1L and M149R to M008.1R), respectively. Major differences were observed in the 27 immunomodulatory proteins encoded by MYXV. Compared to the reference strain Lausanne, strains FLI-H, 2604, 3207, and ZA showed the highest amino acid identity (>98.4%). In strains Munich-1 and MAV, deletion of 5 and 10 ORFs, respectively, was observed, encoding immunomodulatory proteins with ankyrin repeats or members of the family of serine protease inhibitors. Furthermore, putative immunodominant surface proteins with homology to vaccinia virus (VACV) were investigated in the sequenced strains. Only strain MAV revealed above-average frequencies of amino acid substitutions and frameshift mutations. Finally, we performed recombination analysis and found signs of recombination in vaccine strain MAV. Phylogenetic analysis showed a close relationship of strain MAV and the MSW strain of Californian MYXV. However, in a challenge model, strain MAV provided full protection against lethal challenges with strain ZA. IMPORTANCE: Myxoma virus (MYXV) is pathogenic for European rabbits and two North American species. Due to sophisticated strategies in immune evasion and oncolysis, MYXV is an important model virus for immunological and pathological research. In its natural hosts, MYXV causes a benign infection, whereas in European rabbits, it causes the lethal disease myxomatosis. Since the introduction of MYXV into Australia and Europe for the biological control of European rabbits in the 1950s, a coevolution of host and pathogen has started, selecting for attenuated virus strains and increased resistance in rabbits. Evolution of viruses is a continuous process and influences the protective potential of vaccines. In our analyses, we sequenced 6 MYXV field, challenge, and vaccine strains. We focused on genes encoding proteins involved in virulence, host range, immunomodulation, and envelope composition. Genes affected most by mutations play a role in immunomodulation. However, attenuation cannot be linked to individual mutations or gene disruptions.

A model-based information sharing protocol for profile Hidden Markov Models used for HIV-1 recombination detection.

BACKGROUND: In many applications, a family of nucleotide or protein sequences classified into several subfamilies has to be modeled. Profile Hidden Markov Models (pHMMs) are widely used for this task, modeling each subfamily separately by one pHMM. However, a major drawback of this approach is the difficulty of dealing with subfamilies composed of very few sequences. One of the most crucial bioinformatical tasks affected by the problem of small-size subfamilies is the subtyping of human immunodeficiency virus type 1 (HIV-1) sequences, i.e., HIV-1 subtypes for which only a small number of sequences is known. RESULTS: To deal with small samples for particular subfamilies of HIV-1, we introduce a novel model-based information sharing protocol. It estimates the emission probabilities of the pHMM modeling a particular subfamily not only based on the nucleotide frequencies of the respective subfamily but also incorporating the nucleotide frequencies of all available subfamilies. To this end, the underlying probabilistic model mimics the pattern of commonality and variation between the subtypes with regards to the biological characteristics of HI viruses. In order to implement the proposed protocol, we make use of an existing HMM architecture and its associated inference engine. CONCLUSIONS: We apply the modified algorithm to classify HIV-1 sequence data in the form of partial HIV-1 sequences and semi-artificial recombinants. Thereby, we demonstrate that the performance of pHMMs can be significantly improved by the proposed technique. Moreover, we show that our algorithm performs significantly better than Simplot and Bootscanning.

jpHMM: recombination analysis in viruses with circular genomes such as the hepatitis B virus.

jpHMM is a very accurate and widely used tool for recombination detection in genomic sequences of HIV-1. Here, we present an extension of jpHMM to analyze recombinations in viruses with circular genomes such as the hepatitis B virus (HBV). Sequence analysis of circular genomes is usually performed on linearized sequences using linear models. Since linear models are unable to model dependencies between nucleotides at the 5'- and 3'-end of a sequence, this can result in inaccurate predictions of recombination breakpoints and thus in incorrect classification of viruses with circular genomes. The proposed circular jpHMM takes into account the circularity of the genome and is not biased against recombination breakpoints close to the 5'- or 3'-end of the linearized version of the circular genome. It can be applied automatically to any query sequence without assuming a specific origin for the sequence coordinates. We apply the method to genomic sequences of HBV and visualize its output in a circular form. jpHMM is available online at http://jphmm.gobics.de for download and as a web server for HIV-1 and HBV sequences.

Improving Hidden Markov Models for classification of human immunodeficiency virus-1 subtypes through linear classifier learning.

Profile Hidden Markov Models (pHMMs) are widely used to model nucleotide or protein sequence families. In many applications, a sequence family classified into several subfamilies is given and each subfamily is modeled separately by one pHMM. A major drawback of this approach is the difficulty of coping with subfamilies composed of very few sequences.Correct subtyping of human immunodeficiency virus-1 (HIV-1) sequences is one of the most crucial bioinformatic tasks affected by this problem of small subfamilies, i.e., HIV-1 subtypes with a small number of known sequences. To deal with small samples for particular subfamilies of HIV-1, we employ a machine learning approach. More precisely, we make use of an existing HMM architecture and its associated inference engine, while replacing the unsupervised estimation of emission probabilities by a supervised method. For that purpose, we use regularized linear discriminant learning together with a balancing scheme to account for the widely varying sample size. After training the multiclass linear discriminants, the corresponding weights are transformed to valid probabilities using a softmax function.We apply this modified algorithm to classify HIV-1 sequence data (in the form of partial-length HIV-1 sequences and semi-artificial recombinants) and show that the performance of pHMMs can be significantly improved by the proposed technique.

Detection of viral sequence fragments of HIV-1 subfamilies yet unknown.

BACKGROUND: Methods of determining whether or not any particular HIV-1 sequence stems - completely or in part - from some unknown HIV-1 subtype are important for the design of vaccines and molecular detection systems, as well as for epidemiological monitoring. Nevertheless, a single algorithm only, the Branching Index (BI), has been developed for this task so far. Moving along the genome of a query sequence in a sliding window, the BI computes a ratio quantifying how closely the query sequence clusters with a subtype clade. In its current version, however, the BI does not provide predicted boundaries of unknown fragments. RESULTS: We have developed Unknown Subtype Finder (USF), an algorithm based on a probabilistic model, which automatically determines which parts of an input sequence originate from a subtype yet unknown. The underlying model is based on a simple profile hidden Markov model (pHMM) for each known subtype and an additional pHMM for an unknown subtype. The emission probabilities of the latter are estimated using the emission frequencies of the known subtypes by means of a (position-wise) probabilistic model for the emergence of new subtypes. We have applied USF to SIV and HIV-1 sequences formerly classified as having emerged from an unknown subtype. Moreover, we have evaluated its performance on artificial HIV-1 recombinants and non-recombinant HIV-1 sequences. The results have been compared with the corresponding results of the BI. CONCLUSIONS: Our results demonstrate that USF is suitable for detecting segments in HIV-1 sequences stemming from yet unknown subtypes. Comparing USF with the BI shows that our algorithm performs as good as the BI or better.

HIV classification using the coalescent theory.

MOTIVATION: Existing coalescent models and phylogenetic tools based on them are not designed for studying the genealogy of sequences like those of HIV, since in HIV recombinants with multiple cross-over points between the parental strains frequently arise. Hence, ambiguous cases in the classification of HIV sequences into subtypes and circulating recombinant forms (CRFs) have been treated with ad hoc methods in lack of tools based on a comprehensive coalescent model accounting for complex recombination patterns. RESULTS: We developed the program ARGUS that scores classifications of sequences into subtypes and recombinant forms. It reconstructs ancestral recombination graphs (ARGs) that reflect the genealogy of the input sequences given a classification hypothesis. An ARG with maximal probability is approximated using a Markov chain Monte Carlo approach. ARGUS was able to distinguish the correct classification with a low error rate from plausible alternative classifications in simulation studies with realistic parameters. We applied our algorithm to decide between two recently debated alternatives in the classification of CRF02 of HIV-1 and find that CRF02 is indeed a recombinant of Subtypes A and G. AVAILABILITY: ARGUS is implemented in C++ and the source code is available at http://gobics.de/software.

jpHMM: improving the reliability of recombination prediction in HIV-1.

Previously, we developed jumping profile hidden Markov model (jpHMM), a new method to detect recombinations in HIV-1 genomes. The jpHMM predicts recombination breakpoints in a query sequence and assigns to each position of the sequence one of the major HIV-1 subtypes. Since incorrect subtype assignment or recombination prediction may lead to wrong conclusions in epidemiological or vaccine research, information about the reliability of the predicted parental subtypes and breakpoint positions is valuable. For this reason, we extended the output of jpHMM to include such information in terms of 'uncertainty' regions in the recombination prediction and an interval estimate of the breakpoint. Both types of information are computed based on the posterior probabilities of the subtypes at each query sequence position. Our results show that this extension strongly improves the reliability of the jpHMM recombination prediction. The jpHMM is available online at http://jphmm.gobics.de/.

The role of recombination in the emergence of a complex and dynamic HIV epidemic.

BACKGROUND: Inter-subtype recombinants dominate the HIV epidemics in three geographical regions. To better understand the role of HIV recombinants in shaping the current HIV epidemic, we here present the results of a large-scale subtyping analysis of 9435 HIV-1 sequences that involve subtypes A, B, C, G, F and the epidemiologically important recombinants derived from three continents. RESULTS: The circulating recombinant form CRF02_AG, common in West Central Africa, appears to result from recombination events that occurred early in the divergence between subtypes A and G, followed by additional recent recombination events that contribute to the breakpoint pattern defining the current recombinant lineage. This finding also corrects a recent claim that G is a recombinant and a descendant of CRF02, which was suggested to be a pure subtype. The BC and BF recombinants in China and South America, respectively, are derived from recent recombination between contemporary parental lineages. Shared breakpoints in South America BF recombinants indicate that the HIV-1 epidemics in Argentina and Brazil are not independent. Therefore, the contemporary HIV-1 epidemic has recombinant lineages of both ancient and more recent origins. CONCLUSIONS: Taken together, we show that these recombinant lineages, which are highly prevalent in the current HIV epidemic, are a mixture of ancient and recent recombination. The HIV pandemic is moving towards having increasing complexity and higher prevalence of recombinant forms, sometimes existing as "families" of related forms. We find that the classification of some CRF designations need to be revised as a consequence of (1) an estimated > 5% error in the original subtype assignments deposited in the Los Alamos sequence database; (2) an increasing number of CRFs are defined while they do not readily fit into groupings for molecular epidemiology and vaccine design; and (3) a dynamic HIV epidemic context.

jpHMM at GOBICS: a web server to detect genomic recombinations in HIV-1.

Detecting recombinations in the genome sequence of human immunodeficiency virus (HIV-1) is crucial for epidemiological studies and for vaccine development. Herein, we present a web server for subtyping and localization of phylogenetic breakpoints in HIV-1. Our software is based on a jumping profile Hidden Markov Model (jpHMM), a probabilistic generalization of the jumping-alignment approach proposed by Spang et al. The input data for our server is a partial or complete genome sequence from HIV-1; our tool assigns regions of the input sequence to known subtypes of HIV-1 and predicts phylogenetic breakpoints. jpHMM is available online at http://jphmm.gobics.de/.

A jumping profile Hidden Markov Model and applications to recombination sites in HIV and HCV genomes.

BACKGROUND: Jumping alignments have recently been proposed as a strategy to search a given multiple sequence alignment A against a database. Instead of comparing a database sequence S to the multiple alignment or profile as a whole, S is compared and aligned to individual sequences from A. Within this alignment, S can jump between different sequences from A, so different parts of S can be aligned to different sequences from the input multiple alignment. This approach is particularly useful for dealing with recombination events. RESULTS: We developed a jumping profile Hidden Markov Model (jpHMM), a probabilistic generalization of the jumping-alignment approach. Given a partition of the aligned input sequence family into known sequence subtypes, our model can jump between states corresponding to these different subtypes, depending on which subtype is locally most similar to a database sequence. Jumps between different subtypes are indicative of intersubtype recombinations. We applied our method to a large set of genome sequences from human immunodeficiency virus (HIV) and hepatitis C virus (HCV) as well as to simulated recombined genome sequences. CONCLUSION: Our results demonstrate that jumps in our jumping profile HMM often correspond to recombination breakpoints; our approach can therefore be used to detect recombinations in genomic sequences. The recombination breakpoints identified by jpHMM were found to be significantly more accurate than breakpoints defined by traditional methods based on comparing single representative sequences.