Constrained evolution drives limited influenza diversity

H3N2 influenza A viruses have been widely circulating in human populations since the pandemic of 1968. A striking feature of the evolutionary development of this strain has been its 'canalized' nature, with narrow evolutionary trees dominated by long trunks with few branching, or bifurcation events and a consequent lack of standing diversity at any single point. This is puzzling, as one might expect that the strong human immune response against the virus would create an environment encouraging more diversity, not less. Previous models have used various assumptions in order to account for this finding. A new analysis published in BMC Biology suggests that this processive evolution down a single path can be recapitulated by a relatively simple model incorporating only two primary parameters - the mutation rate of the virus, and the immunological distance created by each mutation - so long as these parameters are within a particular narrow but biologically plausible range

PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions

BACKGROUND: Many different aspects of cellular signalling, trafficking and targeting mechanisms are mediated by interactions between proteins and peptides. Representative examples are MHC-peptide complexes in the immune system. Developing computational methods for protein-peptide binding prediction is therefore an important task with applications to vaccine and drug design. METHODS: Previous learning approaches address the binding prediction problem using traditional margin based binary classifiers. In this paper we propose PepDist: a novel approach for predicting binding affinity. Our approach is based on learning peptide-peptide distance functions. Moreover, we suggest to learn a single peptide-peptide distance function over an entire family of proteins (e.g. MHC class I). This distance function can be used to compute the affinity of a novel peptide to any of the proteins in the given family. In order to learn these peptide-peptide distance functions, we formalize the problem as a semi-supervised learning problem with partial information in the form of equivalence constraints. Specifically, we propose to use DistBoost [1,2], which is a semi-supervised distance learning algorithm. RESULTS: We compare our method to various state-of-the-art binding prediction algorithms on MHC class I and MHC class II datasets. In almost all cases, our method outperforms all of its competitors. One of the major advantages of our novel approach is that it can also learn an affinity function over proteins for which only small amounts of labeled peptides exist. In these cases, our method's performance gain, when compared to other computational methods, is even more pronounced. We have recently uploaded the PepDist webserver which provides binding prediction of peptides to 35 different MHC class I alleles. The webserver which can be found at is powered by a prediction engine which was trained using the framework presented in this paper. CONCLUSION: The results obtained suggest that learning a single distance function over an entire family of proteins achieves higher prediction accuracy than learning a set of binary classifiers for each of the proteins separately. We also show the importance of obtaining information on experimentally determined non-binders. Learning with real non-binders generalizes better than learning with randomly generated peptides that are assumed to be non-binders. This suggests that information about non-binding peptides should also be published and made publicly available

MRKAd5 HIV-1 Gag/Pol/Nef Vaccine-Induced T-Cell Responses Inadequately Predict Distance of Breakthrough HIV-1 Sequences to the Vaccine or Viral Load

Background: The sieve analysis for the Step trial found evidence that breakthrough HIV-1 sequences for MRKAd5/HIV-1 Gag/Pol/Nef vaccine recipients were more divergent from the vaccine insert than placebo sequences in regions with predicted epitopes. We linked the viral sequence data with immune response and acute viral load data to explore mechanisms for and consequences of the observed sieve effect. Methods: Ninety-one male participants (37 placebo and 54 vaccine recipients) were included; viral sequences were obtained at the time of HIV-1 diagnosis. T-cell responses were measured 4 weeks post-second vaccination and at the first or second week post-diagnosis. Acute viral load was obtained at RNA-positive and antibody-negative visits. Findings: Vaccine recipients had a greater magnitude of post-infection CD8+ T cell response than placebo recipients (median 1.68% vs 1.18%; p = 0.04) and greater breadth of post-infection response (median 4.5 vs 2; p = 0.06). Viral sequences for vaccine recipients were marginally more divergent from the insert than placebo sequences in regions of Nef targeted by pre-infection immune responses (p = 0.04; Pol p = 0.13; Gag p = 0.89). Magnitude and breadth of pre-infection responses did not correlate with distance of the viral sequence to the insert (p. 0.50). Acute log viral load trended lower in vaccine versus placebo recipients (estimated mean 4.7 vs 5.1) but the difference was not significant (p = 0.27). Neither was acute viral load associated with distance of the viral sequence to the insert (p>0.30). Interpretation: Despite evidence of anamnestic responses, the sieve effect was not well explained by available measures of T-cell immunogenicity. Sequence divergence from the vaccine was not significantly associated with acute viral load. While point estimates suggested weak vaccine suppression of viral load, the result was not significant and more viral load data would be needed to detect suppression.National Institute of Allergy and Infectious Diseases [R37AI054165-08, UM1AI068635, UM1AI068618]National Institute of Allergy and Infectious Disease

Study of Healthcare Personnel with Influenza and other Respiratory Viruses in Israel (SHIRI): study protocol

Abstract Background The Study of Healthcare Personnel with Influenza and other Respiratory Viruses in Israel (SHIRI) prospectively follows a cohort of healthcare personnel (HCP) in two hospitals in Israel. SHIRI will describe the frequency of influenza virus infections among HCP, identify predictors of vaccine acceptance, examine how repeated influenza vaccination may modify immunogenicity, and evaluate influenza vaccine effectiveness in preventing influenza illness and missed work. Methods Cohort enrollment began in October, 2016; a second year of the study and a second wave of cohort enrollment began in June 2017. The study will run for at least 3 years and will follow approximately 2000 HCP (who are both employees and members of Clalit Health Services [CHS]) with routine direct patient contact. Eligible HCP are recruited using a stratified sampling strategy. After informed consent, participants complete a brief enrollment survey with questions about occupational responsibilities and knowledge, attitudes, and practices about influenza vaccines. Blood samples are collected at enrollment and at the end of influenza season; HCP who choose to be vaccinated contribute additional blood one month after vaccination. During the influenza season, participants receive twice-weekly short message service (SMS) messages asking them if they have acute respiratory illness or febrile illness (ARFI) symptoms. Ill participants receive follow-up SMS messages to confirm illness symptoms and duration and are asked to self-collect a nasal swab. Information on socio-economic characteristics, current and past medical conditions, medical care utilization and vaccination history is extracted from the CHS database. Information about missed work due to illness is obtained by self-report and from employee records. Respiratory specimens from self-collected nasal swabs are tested for influenza A and B viruses, respiratory syncytial virus, human metapneumovirus, and coronaviruses using validated multiplex quantitative real-time reverse transcription polymerase chain reaction assays. The hemagglutination inhibition assay will be used to detect the presence of neutralizing influenza antibodies in serum. Discussion SHIRI will expand our knowledge of the burden of respiratory viral infections among HCP and the effectiveness of current and repeated annual influenza vaccination in preventing influenza illness, medical utilization, and missed workdays among HCP who are in direct contact with patients. Trial registration NCT03331991 . Registered on November 6, 2017.https://deepblue.lib.umich.edu/bitstream/2027.42/146186/1/12879_2018_Article_3444.pd

Constrained evolution drives limited influenza diversity

Abstract H3N2 influenza A viruses have been widely circulating in human populations since the pandemic of 1968. A striking feature of the evolutionary development of this strain has been its 'canalized' nature, with narrow evolutionary trees dominated by long trunks with few branching, or bifurcation events and a consequent lack of standing diversity at any single point. This is puzzling, as one might expect that the strong human immune response against the virus would create an environment encouraging more diversity, not less. Previous models have used various assumptions in order to account for this finding. A new analysis published in BMC Biology suggests that this processive evolution down a single path can be recapitulated by a relatively simple model incorporating only two primary parameters - the mutation rate of the virus, and the immunological distance created by each mutation - so long as these parameters are within a particular narrow but biologically plausible range. See research article: http://www.biomedcentral.com/1741-7007/10/38</p

Learning distance functions for image retrieval

Image retrieval critically relies on the distance function used to compare a query image to images in the database. We suggest to learn such distance functions by training binary classifiers with margins, where the classifiers are defined over the product space of pairs of images. The classifiers are trained to distinguish between pairs in which the images are from the same class and pairs which contain images from different classes. The signed margin is used as a distance function. We explore several variants of this idea, based on using SVM and Boosting algorithms as product space classifiers. Our main contribution is a distance learning method which combines boosting hypotheses over the product space with a weak learner based on partitioning the original feature space. The weak learner used is a Gaussian mixture model computed using a constrained EM algorithm, where the constraints are equivalence constraints on pairs of data points. This approach allows us to incorporate unlabeled data into the training process. Using some benchmark databases from the UCI repository, we show that our margin based methods significantly outperform existing metric learning methods, which are based on learning a Mahalanobis distance. We then show comparative results of image retrieval in a distributed learning paradigm, using two databases: a large database of facial images (YaleB), and a database of natural images taken from a commercial CD. In both cases our GMM based boosting method outperforms all other methods, and its generalization to unseen classes is superior. 1