Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

Divergent antibody subclass and specificity profiles but not protective HLA-B alleles are associated with variable antibody effector function among HIV-1 controllers.

UNLABELLED: Understanding the coordination between humoral and cellular immune responses may be the key to developing protective vaccines, and because genetic studies of long-term HIV-1 nonprogressors have associated specific HLA-B alleles with spontaneous control of viral replication, this subject group presents an opportunity to investigate relationships between arms of the adaptive immune system. Given evidence suggesting that cellular immunity may play a role in viral suppression, we sought to determine whether and how the humoral immune response might vary among controllers. Significantly, Fc-mediated antibody effector functions have likewise been associated with durable viral control. In this study, we compared the effector function and biophysical features of HIV-specific antibodies in a cohort of controllers with and without protective HLA-B alleles in order to investigate whether there was evidence for multiple paths to HIV-1 control, or whether cellular and humoral arms of immunity might exhibit coordinated profiles. However, with the exception of IgG2 antibodies to gp41, HLA status was not associated with divergent humoral responses. This finding did not result from uniform antibody responses across subjects, as controllers could be regrouped according to strong differences in their HIV-specific antibody subclass specificity profiles. These divergent antibody profiles were further associated with significant differences in nonneutralizing antibody effector function, with levels of HIV-specific IgG1 acting as the major distinguishing factor. Thus, while HLA background among controllers was associated with minimal differences in humoral function, antibody subclass and specificity profiles were associated with divergent effector function, suggesting that these features could be used to make functional predictions. Because these nonneutralizing antibody activities have been associated with spontaneous viral control, reduced viral load, and nonprogression in infected subjects and protection in vaccinated subjects, understanding the specific features of IgGs with potentiated effector function may be critical to vaccine and therapeutic antibody development. IMPORTANCE: In this study, we investigated whether the humoral and cellular arms of adaptive immunity exhibit coordinated or compensatory activity by studying the antibody response among HIV-1 controllers with different genetic backgrounds.

New insight into gut microbiota-derived metabolites to enhance liver regeneration via network pharmacology study.

We intended to identify favourable metabolite(s) and pharmacological mechanism(s) of gut microbiota (GM) for liver regeneration (LR) through network pharmacology. We utilized the gutMGene database to obtain metabolites of GM, and targets associated with metabolites as well as LR-related targets were identified using public databases. Furthermore, we performed a molecular docking assay on the active metabolite(s) and target(s) to verify the network pharmacological concept. We mined a total of 208 metabolites in the gutMGene database and selected 668 targets from the SEA (1,256 targets) and STP (947 targets) databases. Finally, 13 targets were identified between 61 targets and the gutMGene database (243 targets). Protein-protein interaction network analysis showed that AKT1 is a hub target correlated with 12 additional targets. In this study, we describe the potential microbe from the microbiota (E. coli), chemokine signalling pathway, AKT1 and myricetin that accelerate LR, providing scientific evidence for further clinical trials.

An empirical approach to model selection through validation for censored survival data.

Medical prognostic models can be designed to predict the future course or outcome of disease progression after diagnosis or treatment. The existing variable selection methods may be precluded by full model advocates when we build a prediction model owing to their estimation bias and selection bias in right-censored time-to-event data. If our objective is to optimize predictive performance by some criterion, we can often achieve a reduced model that has a little bias with low variance, but whose overall performance is enhanced. To accomplish this goal, we propose a new variable selection approach that combines Stepwise Tuning in the Maximum Concordance Index (STMC) with Forward Nested Subset Selection (FNSS) in two stages. In the first stage, the proposed variable selection is employed to identify the best subset of risk factors optimized with the concordance index using inner cross-validation for optimism correction in the outer loop of cross-validation, yielding potentially different final models for each of the folds. We then feed the intermediate results of the prior stage into another selection method in the second stage to resolve the overfitting problem and to select a final model from the variation of predictors in the selected models. Two case studies on relatively different sized survival data sets as well as a simulation study demonstrate that the proposed approach is able to select an improved and reduced average model under a sufficient sample and event size compared with other selection methods such as stepwise selection using the likelihood ratio test, Akaike Information Criterion (AIC), and lasso. Finally, we achieve better final models in each dataset than their full models by most measures. These results of the model selection models and the final models are assessed in a systematic scheme through validation for the independent performance.

Polyfunctional Fc-effector profiles mediated by IgG subclass selection distinguish RV144 and VAX003 vaccines.

The human phase 2B RV144 ALVAC-HIV vCP1521/AIDSVAX B/E vaccine trial, held in Thailand, resulted in an estimated 31.2% efficacy against HIV infection. By contrast, vaccination with VAX003 (consisting of only AIDSVAX B/E) was not protective. Because protection within RV144 was observed in the absence of neutralizing antibody activity or cytotoxic T cell responses, we speculated that the specificity or qualitative differences in Fc-effector profiles of nonneutralizing antibodies may have accounted for the efficacy differences observed between the two trials. We show that the RV144 regimen elicited nonneutralizing antibodies with highly coordinated Fc-mediated effector responses through the selective induction of highly functional immunoglobulin G3 (IgG3). By contrast, VAX003 elicited monofunctional antibody responses influenced by IgG4 selection, which was promoted by repeated AIDSVAX B/E protein boosts. Moreover, only RV144 induced IgG1 and IgG3 antibodies targeting the crown of the HIV envelope V2 loop, albeit with limited coverage of breakthrough viral sequences. These data suggest that subclass selection differences associated with coordinated humoral functional responses targeting strain-specific protective V2 loop epitopes may underlie differences in vaccine efficacy observed between these two vaccine trials.

Natural variation in Fc glycosylation of HIV-specific antibodies impacts antiviral activity.

While the induction of a neutralizing antibody response against HIV remains a daunting goal, data from both natural infection and vaccine-induced immune responses suggest that it may be possible to induce antibodies with enhanced Fc effector activity and improved antiviral control via vaccination. However, the specific features of naturally induced HIV-specific antibodies that allow for the potent recruitment of antiviral activity and the means by which these functions are regulated are poorly defined. Because antibody effector functions are critically dependent on antibody Fc domain glycosylation, we aimed to define the natural glycoforms associated with robust Fc-mediated antiviral activity. We demonstrate that spontaneous control of HIV and improved antiviral activity are associated with a dramatic shift in the global antibody-glycosylation profile toward agalactosylated glycoforms. HIV-specific antibodies exhibited an even greater frequency of agalactosylated, afucosylated, and asialylated glycans. These glycoforms were associated with enhanced Fc-mediated reduction of viral replication and enhanced Fc receptor binding and were consistent with transcriptional profiling of glycosyltransferases in peripheral B cells. These data suggest that B cell programs tune antibody glycosylation actively in an antigen-specific manner, potentially contributing to antiviral control during HIV infection.

A hybrid approach to survival model building using integration of clinical and molecular information in censored data.

In medical society, the prognostic models, which use clinicopathologic features and predict prognosis after a certain treatment, have been externally validated and used in practice. In recent years, most research has focused on high dimensional genomic data and small sample sizes. Since clinically similar but molecularly heterogeneous tumors may produce different clinical outcomes, the combination of clinical and genomic information, which may be complementary, is crucial to improve the quality of prognostic predictions. However, there is a lack of an integrating scheme for clinic-genomic models due to the P ≥ N problem, in particular, for a parsimonious model. We propose a methodology to build a reduced yet accurate integrative model using a hybrid approach based on the Cox regression model, which uses several dimension reduction techniques, L2 penalized maximum likelihood estimation (PMLE), and resampling methods to tackle the problem. The predictive accuracy of the modeling approach is assessed by several metrics via an independent and thorough scheme to compare competing methods. In breast cancer data studies on a metastasis and death event, we show that the proposed methodology can improve prediction accuracy and build a final model with a hybrid signature that is parsimonious when integrating both types of variables.

High-throughput, multiplexed IgG subclassing of antigen-specific antibodies from clinical samples.

In vivo, the activity of antibodies relies critically on properties of both the variable domain, responsible for antigen recognition, and the constant domain, responsible for innate immune recognition. Here, we describe a flexible, microsphere-based array format for capturing information about both functional ends of disease-specific antibodies from complex, polyclonal clinical serum samples. Using minimal serum, we demonstrate IgG subclass profiling of multiple antibody specificities. We further capture and determine the subclass of epitope-specific antibodies. The data generated in this array provides a profile of the humoral immune response with multi-dimensional metrics regarding properties of both variable and constant IgG domains. Significantly, these properties are assessed simultaneously, and therefore information about the relationship between variable and constant domain characteristics is captured, and can be used to predict functions such as antibody effector activity.