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.U.S. Military HIV Research ProgramCollaboration for AIDS Vaccine Discover (OPP1032817)National Institutes of Health (U.S.) (3R01AI080289-02S1)National Institutes of Health (U.S.) (5R01AI080289-03)United States. Army Medical Research and Materiel Command (National Institute of Allergy and Infectious Diseases (U.S.) Interagency Agreement Y1-AI-2642-12)Henry M. Jackson Foundation for the Advancement of Military Medicine (U.S.) (United States. Dept. of Defense Cooperative Agreement W81XWH-07-2-0067

A SUPPORT VECTOR-BASED PREDICTIVE MODEL TO REVEAL THE RELATIONSHIPS AMONG ANTIBODY FEATURES AND THEIR EFFECTIVE FUNCTIONS AGAINST HIV

Despite 4 decades’ effort, an effective HIV-1 vaccine has not been produced owing to the inevitable antigenic diversity of the virus and millions of people around the world have lost their lives due to HIV. Increasing the knowledge of adaptive immune response to vaccination would ultimately lead to an effective HIV cure. Antibodies, which are responsible for protection and fighting against antigens, are vital parts of immune system response. In order to identify discriminative antibodies, which provide protection against HIV, and to disclose the associations between antibody features and their functional outcomes, computational methods, such as feature selection, regression and classification can be used to construct predictive models. Here we used our unsupervised K-Means Based Feature Selection (KBFS) method which is presented in our previous study, to identify functional antibodies that fight against HIV. The accuracy results for the proposed KBFS framework are compared with those presented in a recent study and are also compared with results from four different state-of-the-art unsupervised feature selection methods, namely MCFS, InFS, LapFS, and SPFS, along with the entire feature set. Then, support vector based systems are utilised to predict the associations between antibody features and their functional activities, namely gp120-specific antibody dependent cellular phagocytosis (ADCP), antibody dependent cellular cytotoxicity (ADCC) and cytokine release activities on RV144 vaccine recipients. Pearson Correlation Coefficient (PCC) metric is used to evaluate the prediction accuracy of the predictive models and to be consistent with the previous study. Our SVR based KBFS framework presented higher accuracy than the original study by improving prediction performance 16% for ADCP assay, 200% for the ADCC assay

A supervised feature selection framework in relation to prediction of antibody feature-function activity relationships in RV144 vaccines

Identification of functional characteristics of the virus-antibody interplay in individuals can provide insight to the development of effective vaccines against HIV virus. In order to reveal the functional interactions between human immune system and HIV virus, computational methods such as clustering, classification, feature selection and regression methods can be utilised to construct predictive models. The purpose of this study is to predict the associations between antibody features and effector function activities on RV144 vaccine recipients. The RV144 vaccine dataset contains 100 data samples in which 20 of them are the placebo samples and 80 of them are the vaccine injected samples. Each data sample has twenty antibody features that consist of features related to IgG subclass and antigen specificity. In this study, we proposed a novel supervised feature selection framework to identify the discriminating antibody features from RV144 vaccine dataset. Then, the Support Vector Regression is utilised to quantitatively predict the association between antibody features (IgGs) and effector function activities. Three different cell-mediated assays are utilised in this study to characterise effector function activities: antibody dependent cellular phagocytosis (ADCP), antibody dependent cellular cytotoxicity (ADCC), and natural killer cell cytokine release. Promising experimental results on these three cell-based assays have validated the effectiveness of our proposed framework. The prediction performance of proposed feature selection framework is compared to the previous studies which utilised the RV144 dataset for the same purpose

Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics

This book is a collection of original research articles in the field of computer-aided drug design. It reports the use of current and validated computational approaches applied to drug discovery as well as the development of new computational tools to identify new and more potent drugs

Evaluation of immune responses and viral fitness in HIV-1+ individuals displaying different rates of disease progression

Despite its great success, antiretroviral therapy (ART) fails to improve HIV-1-specific T-cell responses in treated individuals, although it does restore CMV-specific immunity. Furthermore, co-infection with CMV has been associated with increased risk of non-AIDS-related morbidities. This thesis comprehensively characterises HIV-1- and CMV-specific T-cell responses in HIV-1-infected subjects, in the context of both ART and different progression rates. Proliferative responses to CMV and HIV-1 were evaluated in HIV-1-infected and uninfected healthy controls (HC). ART led to a discordant increase in CMV-specific proliferation relative to that observed for HIV-1. High proliferative responses to both viruses in elite controllers contrasted the low CMV-specific proliferation in HC. Strong induction of the suppressive cytokine IL-10 was observed in response to CMV and HIV-1 Nef, but not to Gag, and this was higher in ART-naïve individuals compared to treated patients. Still, HC exhibited the highest levels of CMV-specific IL-10, pointing to a regulatory role for IL-10 in these responses, possibly explaining the low proliferative responses in HC. Analysis of T-cell activation, differentiation and exhaustion revealed similar frequencies of HIV-1-specific CD8 effector memory (TEM) cells between ART-naïve and treated individuals, but CMV- specific CD8 TEM cells were higher in treated individuals correlating with the increased anti-CMV T-cell function observed. Higher CD8 TEM and lower naïve CD8 T-cell frequencies were significantly associated with disease progression, whilst long-term non-progressors exhibited higher levels of CD8 terminally differentiated (TEMRA) cells. Moreover, CD8 T cells with an HLA-DR+CD38- phenotype were elevated in treated individuals compared to both HC and ART-naϊve patients, and linked with cytotoxic activity in a non-progressor. Detection of replication-competent virus from HIV-1 controllers at different outgrowth rates suggests reduced replicative fitness. Overall, these results detail the complex interplay between CMV and HIV-1 co-infection, the difference between ART-mediated and spontaneous immune control and point to potential targets for immunotherapeutic interventions.Open Acces

Legends of South African Science

Cite: Academy of Science of South Africa (ASSAf), (2017). Legends of South African Science. [Online] Available at: DOI http://dx.doi.org/10.17159/assaf.2016/0012Legends of South African Science introduces Academy Members who rank among the top achievers in the country. Legends profiles ASSAf Members who have received some of South Africa’s top awards, viz. the ASSAf Science-for-Society Gold Medal, National Orders of Mapungubwe and Baobab bestowed by the President, or the Harry Oppenheimer Fellowship. Among the Members featured in the book are a biologist and Nobel Laureate who helped decode DNA; an epidemiologist recognised for groundbreaking research on HIV prevention in women; a social scientist who nudged and cajoled into place the campaign to understand and contain HIV/AIDS in South Africa; a leading mathematics education proponent; a human geneticist whose work helped to clarify the origins of indigenous groups in Africa; one of the world's leading theorists in cosmology; and a leading immunologist and physician who pioneered higher education transformation in South Africa, in sometimes controversial ways.Department of Science and Technolog