Recurrent signature patterns in HIV-1 B clade envelope glycoproteins associated with either early or chronic infections

Here we have identified HIV-1 B clade Envelope (Env) amino acid signatures from early in infection that may be favored at transmission, as well as patterns of recurrent mutation in chronic infection that may reflect common pathways of immune evasion. To accomplish this, we compared thousands of sequences derived by single genome amplification from several hundred individuals that were sampled either early in infection or were chronically infected. Samples were divided at the outset into hypothesis-forming and validation sets, and we used phylogenetically corrected statistical strategies to identify signatures, systematically scanning all of Env. Signatures included single amino acids, glycosylation motifs, and multi-site patterns based on functional or structural groupings of amino acids. We identified signatures near the CCR5 co-receptor-binding region, near the CD4 binding site, and in the signal peptide and cytoplasmic domain, which may influence Env expression and processing. Two signatures patterns associated with transmission were particularly interesting. The first was the most statistically robust signature, located in position 12 in the signal peptide. The second was the loss of an N-linked glycosylation site at positions 413-415; the presence of this site has been recently found to be associated with escape from potent and broad neutralizing antibodies, consistent with enabling a common pathway for immune escape during chronic infection. Its recurrent loss in early infection suggests it may impact fitness at the time of transmission or during early viral expansion. The signature patterns we identified implicate Env expression levels in selection at viral transmission or in early expansion, and suggest that immune evasion patterns that recur in many individuals during chronic infection when antibodies are present can be selected against when the infection is being established prior to the adaptive immune response.

Genetic signatures in the envelope glycoproteins of HIV-1 that associate with broadly neutralizing antibodies

A steady increase in knowledge of the molecular and antigenic structure of the gp120 and gp41 HIV-1 envelope glycoproteins (Env) is yielding important new insights for vaccine design, but it has been difficult to translate this information to an immunogen that elicits broadly neutralizing antibodies. To help bridge this gap, we used phylogenetically corrected statistical methods to identify amino acid signature patterns in Envs derived from people who have made potently neutralizing antibodies, with the hypothesis that these Envs may share common features that would be useful for incorporation in a vaccine immunogen. Before attempting this, essentially as a control, we explored the utility of our computational methods for defining signatures of complex neutralization phenotypes by analyzing Env sequences from 251 clonal viruses that were differentially sensitive to neutralization by the well-characterized gp120-specific monoclonal antibody, b12. We identified ten b12-neutralization signatures, including seven either in the b12-binding surface of gp120 or in the V2 region of gp120 that have been previously shown to impact b12 sensitivity. A simple algorithm based on the b12 signature pattern was predictive of b12 sensitivity/resistance in an additional blinded panel of 57 viruses. Upon obtaining these reassuring outcomes, we went on to apply these same computational methods to define signature patterns in Env from HIV-1 infected individuals who had potent, broadly neutralizing responses. We analyzed a checkerboard-style neutralization dataset with sera from 69 HIV-1-infected individuals tested against a panel of 25 different Envs. Distinct clusters of sera with high and low neutralization potencies were identified. Six signature positions in Env sequences obtained from the 69 samples were found to be strongly associated with either the high or low potency responses. Five sites were in the CD4-induced coreceptor binding site of gp120, suggesting an important role for this region in the elicitation of broadly neutralizing antibody responses against HIV-1.

Influenza vaccine strain selection and recent studies on the global migration of seasonal influenza viruses

Annual influenza epidemics in humans affect 5-15% of the population, causing an estimated half million deaths worldwide per year [Stohr K. Influenza-WHO cares. Lancet Infectious Diseases 2002;2(9):517]. The virus can infect this proportion of people year after year because the virus has an extensive capacity to evolve and thus evade the immune response. For example, since the influenza A(H3N2) subtype entered the human population in 1968 the A(H3N2) component of the influenza vaccine has had to be updated almost 30 times to track the evolution of the viruses and remain effective. The World Health Organization Global Influenza Surveillance Network (WHO GISN) tracks and analyzes the evolution and epidemiology of influenza viruses for the primary purpose of vaccine strain selection and to improve the strain selection process through studies aimed at better understanding virus evolution and epidemiology. Here we give an overview of the strain selection process and outline recent investigations into the global migration of seasonal influenza viruses.

Antigenic and genetic evolution of swine influenza A (H3N2) viruses in Europe

In the early 1970s, a human influenza A/Port Chalmers/1/73 (H3N2)-like virus colonized the European swine population. Analyses of swine influenza A (H3N2) viruses isolated in The Netherlands and Belgium revealed that in the early 1990s, antigenic drift had occurred, away from A/Port Chalmers/1/73, the strain commonly used in influenza vaccines for pigs. Here we show that Italian swine influenza A (H3N2) viruses displayed antigenic and genetic changes similar to those observed in Northern European viruses in the same period. We used antigenic cartography methods for quantitative analyses of the antigenic evolution of European swine H3N2 viruses and observed a clustered virus evolution as seen for human viruses. Although the antigenic drift of swine and human H3N2 viruses has followed distinct evolutionary paths, potential cluster-differentiating amino acid substitutions in the influenza virus surface protein hemagglutinin (HA) were in part the same. The antigenic evolution of swine viruses occurred at a rate approximately six times slower than the rate in human viruses, even though the rates of genetic evolution of the HA at the nucleotide and amino acid level were similar for human and swine H3N2 viruses. Continuous monitoring of antigenic changes is recommended to give a first indication as to whether vaccine strains may need updating. Our data suggest that humoral immunity in the population plays a smaller role in the evolutionary selection processes of swine H3N2 viruses than in human H3N2 viruses.

Mutations, drift, and the influenza archipelago

Extract: Annual influenza (flu) epidemics in humans affect 5-15% of the population, causing an estimated half million deaths worldwide per year. Antibodies against the viral surface glycoprotein hemagglutinin (HA) provide protective immunity to influenza virus infection and this protein is therefore the primary component of influenza vaccines. However, the antigenic structure of HA has changed significantly over time, a process known as antigenic drift. In as many years, antigenic drift necessitates an update of the influenza vaccine to ensure sufficient efficacy against newly emerging virus variants. Antigenic drift is therefore both the root cause of the enormous public health burden of influenza epidemics, and a primary reason why the virus is such a fascinating pathogen from a scientific perspective. Thousands of influenza viruses are isolated and analyzed each year by the national and international laboratories that form the World Health Organization (WHO) global influenza surveillance network. This worldwide surveillance effort produces the data for the twice-yearly vaccine strain selection meetings, and has resulted in the establishment of a remarkable historical record of the global evolution of this important pathogen. The degree to which immunity induced by one strain is effective against another is mostly dependent on the extent of the antigenic difference between the strains. The analysis of antigenic differences between strains is therefore critical for surveillance and vaccine strain selection, and is also a cornerstone of basic and applied research in virology.

Mapping the Antigenic and Genetic Evolution of Influenza Virus

The antigenic evolution of influenza A (H3N2) virus was quantified and visualized from its introduction into humans in 1968 to 2003. Although there was remarkable correspondence between antigenic and genetic evolution, significant differences were observed: Antigenic evolution was more punctuated than genetic evolution, and genetic change sometimes had a disproportionately large antigenic effect. The method readily allows monitoring of antigenic differences among vaccine and circulating strains and thus estimation of the effects of vaccination. Further, this approach offers a route to predicting the relative success of emerging strains, which could be achieved by quantifying the combined effects of population level immune escape and viral fitness on strain evolution.

Recognition of homo- and heterosubtypic variants of influenza A viruses by human CD8+ T lymphocytes

In the present study, the recognition of epitope variants of influenza A viruses by human CTL was investigated. To this end, human CD8(+) CTL clones, specific for natural variants of the HLA-B*3501-restricted epitope in the nucleoprotein (NP(418-426)), were generated. As determined in (51)Cr release assays and by flow cytometry with HLA-B*3501-peptide tetrameric complexes, CTL clones were found to be specific for epitopes within one subtype or cross-reactive with heterosubtypic variants of the epitope. Using eight natural variants of the epitope, positions in the 9-mer important for T cell recognition and involved in escape from CTL immunity were identified and visualized using multidimensional scaling. It was shown that positions 4 and 5 in the 9-mer epitope were important determinants of T cell specificity. The in vivo existence of CD8(+) cells cross-reactive with homo- and heterosubtypic variants of the epitope was further confirmed using polyclonal T cell populations obtained after stimulation of PBMC with different influenza A viruses. Based on the observed recognition patterns of the clonal and polyclonal T cell populations and serology, it is hypothesized that consecutive infections with influenza viruses containing different variants of the epitope select for cross-reactive T cells in vivo.

Probabilistic code for DNA recognition by proteins of the EGR family

A recognition code for protein-DNA interactions would allow for the prediction of binding sites based on protein sequence, and the identification of binding proteins for specific DNA targets. Crystallographic studies of protein-DNA complexes showed that a simple, deterministic recognition code does not exist. Here, we present a probabilistic recognition code (P-code) that assigns energies to all possible base-pair-amino acid interactions for the early growth response factor (EGR) family of zinc-finger transcription factors. The specific energy values are determined by a maximum likelihood method using examples from in vitro randomisation experiments (namely, SELEX and phage display) reported in the literature. The accuracy of the model is tested in several ways, including the ability to predict in vivo binding sites of EGR proteins and other non-EGR zinc-finger proteins, and the correlation between predicted and measured binding affinities of various EGR proteins to several different DNA sites. We also show that this model improves significantly upon the prediction capabilities of previous qualitative and quantitative models. The probabilistic code we develop uses information about the interacting positions between the protein and DNA, but we show that such information is not necessary, although it reduces the number of parameters to be determined. We also employ the assumption that the total binding energy is the sum of the energies of the individual contacts, but we describe how that assumption can be relaxed at the cost of additional parameters.

Is there a code for protein-DNA recognition? Probab(ilistical)ly

Transcriptional regulation of all genes is initiated by the specific binding of regulatory proteins called transcription factors to specific sites on DNA called promoter regions. Transcription factors employ a variety of mechanisms to recognise their DNA target sites. In the last few decades, attempts have been made to describe these mechanisms by general sets of rules and associated models. We give an overview of these models, starting with a historical review of the somewhat controversial issue of a "recognition code" governing protein-DNA interaction. We then present a probabilistic framework in which advantages and disadvantages of various models can be discussed. Finally, we conclude that simplifying assumptions about additivity of interactions are sufficiently justified in many situations (and can be suitably extended in other situations) to allow a unifying concept of a "probabilistic code" for protein-DNA recognition to be defined.

Complex systems analysis: a tool for shock research

For the past century, students of shock have focused research efforts to illuminate specific mechanisms that cause, or fail as a consequence of, circulatory collapse. Although clinical strategies aimed at supporting or restoring individual organ systems have proven effective, many patients succumb to more generalized multiple organ system failure. We suggest that general biological systems failure cannot be interpreted through reliance on reductionist science. We propose that complex systems analysis is an essential tool for shock research and we evaluate its application to genomic technologies.

Exploring protein sequence space using knowledge-based potentials

Knowledge-based potentials can be used to decide whether an amino acid sequence is likely to fold into a prescribed native protein structure. We use this idea to survey the sequence-structure relations in protein space. In particular, we test the following two propositions which were found to be important for efficient evolution: the sequences folding into a particular native fold form extensive neutral networks that percolate through sequence space. The neutral networks of any two native folds approach each other to within a few point mutations. Computer simulations using two very different potential functions, M. Sippl's PROSA pair potential and a neural network based potential, are used to verify these claims.

SAMIE: statistical algorithm for modeling interaction energies

We are investigating the rules that govern protein-DNA interactions, using a statistical mechanics based formalism that is related to the Boltzmann Machine of the neural net literature. Our approach is data-driven, in which probabilistic algorithms are used to model protein-DNA interactions, given SELEX and/or phage data as input. In the current report, we trained the network using SELEX data, under the "one-to-one" model of interactions (i.e. one amino acid contacts one base). The trained network was able to successfully identify the wild-type binding sites of EGR and MIG protein families. The predictions using our method are the same or better than that of methods existing in the literature. However our methodology offers the potential to capitalise in quantitative detail, as well as to be used to explore more general model of interactions, given availability of data.

Genetic correlations in mutation processes

We study the role of phylogenetic trees on correlations in mutation processes. Generally, correlations decay exponentially with the generation number. We find that two distinct regimes of behavior exist. For mutation rates smaller than a critical rate, the underlying tree morphology is almost irrelevant, while mutation rates higher than this critical rate lead to strong tree-dependent correlations. We show analytically that identical critical behavior underlies all multiple point correlations. This behavior generally characterizes branching processes undergoing mutation.

Superadditive correlation

The fact that correlation does not imply causation is well known. Correlation between variables at two sites does not imply that the two sites directly interact, because, e.g., correlation between distant sites may be induced by chaining of correlation between a set of intervening, directly interacting sites. Such "noncausal correlation" is well understood in statistical physics: an example is long-range order in spin systems, where spins which have only short-range direct interactions, e.g., the Ising model, display correlation at a distance. It is less well recognized that such long-range "noncausal" correlations can in fact be stronger than the magnitude of any causal correlation induced by direct interactions. We call this phenomenon superadditive correlation (SAC). We demonstrate this counterintuitive phenomenon by explicit examples in (i) a model spin system and (ii) a model continuous variable system, where both models are such that two variables have multiple intervening pathways of indirect interaction. We apply the technique known as decimation to explain SAC as an additive, constructive interference phenomenon between the multiple pathways of indirect interaction. We also explain the effect using a definition of the collective mode describing the intervening spin variables. Finally, we show that the SAC effect is mirrored in information theory, and is true for mutual information measures in addition to correlation measures. Generic complex systems typically exhibit multiple pathways of indirect interaction, making SAC a potentially widespread phenomenon. This affects, e.g., attempts to deduce interactions by examination of correlations, as well as, e.g., hierarchical approximation methods for multivariate probability distributions, which introduce parameters based on successive orders of correlation.

Covariation of mutations in the V3 loop of human immunodeficiency virus type 1 envelope protein: an information theoretic analysis

The V3 loop of the human immunodeficiency virus type 1 (HIV-1) envelope protein is a highly variable region that is both functionally and immunologically important. Using available amino acid sequences from the V3 region, we have used an information theoretic quantity called mutual information, a measure of covariation, to quantify dependence between mutations in the loop. Certain pairs of sites, including non-contiguous sites along the sequence, do not have independent mutations but display considerable, statistically significant, covarying mutations as measured by mutual information. For the pairs of sites with the highest mutual information, specific amino acids were identified that were highly predictive of amino acids in the linked site. The observed interdependence between variable sites may have implications for structural or functional relationships; separate experimental evidence indicates functional linkage between some of the pairs of sites with high mutual information. Further specific mutational studies of the V3 loop's role in determining viral phenotype are suggested by our analyses. Also, the implications of our results may be important to consider for V3 peptide vaccine design. The methods used here are generally applicable to the study of variable proteins.