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

The applications of machine learning in HIV neutralizing antibodies research-A systematic review

Machine learning algorithms play an essential role in bioinformatics and allow exploring the vast and noisy biological data in unrivaled ways. This paper is a systematic review of the applications of machine learning in the study of HIV neutralizing antibodies. This significant and vast research domain can pave the way to novel treatments and to a vaccine. We selected the relevant papers by investigating the available literature from the Web of Science and PubMed databases in the last decade. The computational methods are applied in neutralization potency prediction, neutralization span prediction against multiple viral strains, antibody-virus binding sites detection, enhanced antibodies design, and the study of the antibody-induced immune response. These methods are viewed from multiple angles spanning data processing, model description, feature selection, evaluation, and sometimes paper comparisons. The algorithms are diverse and include supervised, unsupervised, and generative types. Both classical machine learning and modern deep learning were taken into account. The review ends with our ideas regarding future research directions and challenges.

Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning

MOTIVATION

Knowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivity can be used to find potent antibody combinations that broadly neutralize multiple and diverse HIV strains. Sensitivity prediction can be combined with other methods such as generative algorithms to design novel antibodies in silico or with feature selection to uncover the sites of interest in the sequence. However, these tools are limited in the absence of in silico accurate prediction methods.

RESULTS

Our method leverages the CATNAP dataset, probably the most comprehensive collection of HIV-antibodies assays, and predicts the antibody-virus sensitivity in the form of binary classification. The methods proposed by others focus primarily on analyzing the virus sequences. However, our article demonstrates the advantages gained by modeling the antibody-virus sensitivity as a function of both virus and antibody sequences. The input is formed by the virus envelope and the antibody variable region aminoacid sequences. No structural features are required, which makes our system very practical, given that sequence data is more common than structures. We compare with two other state-of-the-art methods that leverage the same dataset and use sequence data only. Our approach, based on neuronal networks and transfer learning, measures increased predictive performance as measured on a set of 31 specific broadly neutralizing antibodies.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vlad-danaila/deep_hiv_ab_pred/tree/fc-att-fix.

A systems approach to elucidate personalized mechanistic complexities of antibody-Fc receptor activation post-vaccination

Immunoglobulin G (IgG) antibodies that activate Fc-mediated immune functions have been correlated with vaccine efficacy, but it is difficult to unravel the relative roles of multiple IgG and Fc receptor (FcR) features that have the capacity to influence IgG-FcR complex formation but vary on a personalized basis. Here, we develop an ordinary differential-equation model to determine how personalized variability in IgG subclass concentrations and binding affinities influence IgG-FcγRIIIa complex formation and validate it with samples from the HIV RV144 vaccine trial. The model identifies individuals who are sensitive, insensitive, or negatively affected by increases in HIV-specific IgG1, which is validated with the addition of HIV-specific IgG1 monoclonal antibodies to vaccine samples. IgG1 affinity to FcγRIIIa is also prioritized as the most influential parameter for dictating activation broadly across a population. Overall, this work presents a quantitative tool for evaluating personalized differences underlying FcR activation, which is relevant to ongoing efforts to improve vaccine efficacy.

Fc-mediated immune functions have been correlated with protection in HIV vaccine trials

A model reveals personalized mechanisms that drive variation in FcγR activation

The model predicts individuals who are sensitive to changes in IgG1 concentration

IgG1 affinity to FcγR best dictates activation across a heterogeneous population

Tensor-structured decomposition improves systems serology analysis

Systems serology provides a broad view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease-relevant antigen targets, alongside additional measurements made for single antigens or in an antigen-generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Here, we report that coupled matrix-tensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV- and SARS-CoV-2-infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen-binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data exploration in systems serology.

Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic

The world has never been prepared for global pandemics like the COVID-19, currently posing an immense threat to the public and consistent pressure on the global healthcare systems to navigate optimized tools, equipments, medicines, and techno-driven approaches to retard the infection spread. The synergized outcome of artificial intelligence paradigms and human-driven control measures elicit a significant impact on screening, analysis, prediction, and tracking the currently infected individuals, and likely the future patients, with precision and accuracy, generating regular international and national data on confirmed, recovered, and death cases, as the current status of 3,820,869 infected patients worldwide. Artificial intelligence is a frontline concept, with time-saving, cost-effective, and productive access to disease management, rendering positive results in physician assistance in high workload conditions, radiology imaging, computational tomography, and database formulations, to facilitate availability of information accessible to researchers all over the globe. The review tends to elaborate the role of industry 4.0 technology, fast diagnostic procedures, and convolutional neural networks, as artificial intelligence aspects, in potentiating the COVID-19 management criteria and differentiating infection in SARS-CoV-2 positive and negative groups. Therefore, the review successfully supplements the processes of vaccine development, disease management, diagnosis, patient records, transmission inhibition, social distancing, and future pandemic predictions, with artificial intelligence revolution and smart techno processes to ensure that the human race wins this battle with COVID-19 and many more combats in the future.

Patterns of autism symptoms: hidden structure in the ADOS and ADI-R instruments

We simultaneously revisited the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule (ADOS) with a comprehensive data-analytics strategy. Here, the combination of pattern-analysis algorithms and extensive data resources (n = 266 patients aged 7-49 years) allowed identifying coherent clinical constellations in and across ADI-R and ADOS assessments widespread in clinical practice. Our clustering approach revealed low- and high-severity patient groups, as well as a group scoring high only in the ADI-R domains, providing quantitative contours for the widely assumed autism subtypes. Sparse regression approaches uncovered the most clinically predictive questionnaire domains. The social and communication domains of the ADI-R showed convincing performance to predict the patients' symptom severity. Finally, we explored the relative importance of each of the ADI-R and ADOS domains conditioning on age, sex, and fluid IQ in our sample. The collective results suggest that (i) identifying autism subtypes and severity for a given individual may be most manifested in the ADI-R social and communication domains, (ii) the ADI-R might be a more appropriate tool to accurately capture symptom severity, and (iii) the ADOS domains were more relevant than the ADI-R domains to capture sex differences.

Topical Tenofovir Pre-exposure Prophylaxis and Mucosal HIV-Specific Fc-Mediated Antibody Activities in Women

The RV144 HIV-vaccine trial highlighted the importance of envelope-specific non-neutralizing antibody (nNAb) Fc-mediated functions as immune correlates of reduced risk of infection. Since pre-exposure prophylaxis (PrEP) and HIV-vaccines are being used as a combination prevention strategy in at risk populations, the effects of PrEP on nNAb functions both mucosally and systemically remain undefined. Previous animal and human studies demonstrated reduced HIV-specific antibody binding avidity post-HIV seroconversion with PrEP, which in turn may affect antibody functionality. In seroconverters from the CAPRISA 004 tenofovir gel trial, we previously reported significantly higher detection and titres of HIV-specific binding antibodies in the plasma and genital tract (GT) that distinguished the tenofovir from the placebo arm. We hypothesized that higher HIV-specific antibody titres and detection reflected corresponding increased antibody-dependent neutrophil-mediated phagocytosis (ADNP) and NK-cell-activated antibody-dependent cellular cytotoxic (ADCC) activities. HIV-specific V1V2-gp70, gp120, gp41, p66, and p24 antibodies in GT and plasma samples of 48 seroconverters from the CAPRISA 004 tenofovir gel trial were tested for ADCP and ADCC at 3, 6- and 12-months post-HIV-infection. GT gp41- and p24-specific ADNP were significantly higher in the tenofovir than the placebo arm at 6 and 12 months respectively (p < 0.05). Plasma gp120-, gp41-, and p66-specific ADNP, and GT gp41-specific ADCC increased significantly over time (p < 0.05) in the tenofovir arm. In the tenofovir arm only, significant inverse correlations were observed between gp120-specific ADCC and gp120-antibody titres (r = −0.54; p = 0.009), and gp41-specific ADNP and gp41-specific antibody titres at 6 months post-infection (r = −0.50; p = 0.015). In addition, in the tenofovir arm, gp41-specific ADCC showed significant direct correlations between the compartments (r = 0.53; p = 0.045). Certain HIV-specific nNAb activities not only dominate specific immunological compartments but can also exhibit diverse functions within the same compartment. Our previous findings of increased HIV specific antibody detection and titres in women who used tenofovir gel, and the limited differences in nNAb activities between the arms, suggest that prior PrEP did not modulate these nNAb functions post-HIV seroconversion. Together these data provide insight into envelope-specific-nNAb Fc-mediated functions at the site of exposure which may inform on ensuing immunity during combination HIV prevention strategies including PrEP and HIV vaccines.

Antibody-Dependent Cellular Phagocytosis of HIV-1-Infected Cells Is Efficiently Triggered by IgA Targeting HIV-1 Envelope Subunit gp41

Antibodies mediate a broad array of non-neutralizing Fc-mediated functions against HIV-1 including antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP). Accordingly, ADCC and ADCP induced by anti-HIV envelope gp120 IgG have been correlated to the limited success of the HIV-1 phase III vaccine trial RV144. It remains elusive whether ADCP can also be triggered by IgA, the isotype predominant at mucosal surfaces through which HIV-1 is mainly transmitted. Yet, we have previously shown that the HIV envelope subunit gp41-specific broadly neutralizing antibody 2F5 under the IgA isotype (2F5-IgA) triggers ADCC and cooperates with 2F5-IgG to increase HIV-1-infected cell lysis. Here, we now demonstrate that 2F5-IgA, more efficiently than 2F5-IgG, induces ADCP not only of gp41-coated beads but also of primary HIV-1-infected cells in a FcαRI-dependent manner. Both primary monocytes and neutrophils can act as effector cells of 2F5-IgA-mediated ADCP, although with different kinetics with faster neutrophil phagocytosis. However, unlike for ADCC, 2F5-IgA and 2F5-IgG do not cooperate to increase ADCP. Altogether, our results reveal that gp41-specific IgA mediate the efficient phagocytosis of HIV-1-infected cells. Inducing such ADCC and ADCP-prone IgA response by vaccination in addition to anti-HIV envelope IgG, might increase the protection against HIV acquisition at mucosal level.

Systems serology for decoding infection and vaccine-induced antibody responses to HIV-1

PURPOSE OF REVIEW

Experimental and analytical advances have enabled systematic, high-resolution studies of humoral immune responses, and are beginning to define mechanisms of immunity to HIV.

RECENT FINDINGS

High-throughput, information-rich experimental and analytical methods, whether genomic, proteomic, or transcriptomic, have firmly established their value across a diversity of fields. Consideration of these tools as trawlers in 'fishing expeditions' has faded as 'data-driven discovery' has come to be valued as an irreplaceable means to develop fundamental understanding of biological systems. Collectively, studies of HIV-1 infection and vaccination including functional, biophysical, and biochemical humoral profiling approaches have provided insights into the phenotypic characteristics of individual and pools of antibodies. Relating these measures to clinical status, protection/efficacy outcomes, and cellular profiling data using machine learning has offered the possibility of identifying unanticipated mechanisms of action and gaining insights into fundamental immunological processes that might otherwise be difficult to decipher.

SUMMARY

Recent evidence establishes that systematic data collection and application of machine learning approaches can identify humoral immune correlates that are generalizable across distinct HIV-1 immunogens and vaccine regimens and translatable between model organisms and the clinic. These outcomes provide a strong rationale supporting the utility and further expansion of these approaches both in support of vaccine development and more broadly in defining mechanisms of immunity.

Use of artificial intelligence in infectious diseases

Infectious diseases are caused by microorganisms belonging to the class of bacteria, viruses, fungi, or parasites. These pathogens are transmitted, directly or indirectly, and can lead to epidemics or even pandemics. The resulting infection may lead to mild-to-severe symptoms such as life-threatening fever or diarrhea. Infectious diseases may be asymptomatic in some individuals but may lead to disastrous effects in others. Despite the advances in medicine, infectious diseases are a leading cause of death worldwide, especially in low-income countries. With the advent of mathematical tools, scientists are now able to better predict epidemics, understand the specificity of each pathogen, and identify potential targets for drug development. Artificial intelligence and its components have been widely publicized for their ability to better diagnose certain types of cancer from imaging data. This chapter aims at identifying potential applications of machine learning in the field of infectious diseases. We are deliberately focusing on key aspects of infection: diagnosis, transmission, response to treatment, and resistance. We are proposing the use of extreme values as an avenue of interest for future developments in the field of infectious diseases. This chapter covers a series of applications selectively chosen to showcase how artificial intelligence is moving the field of infectious disease further and how it helps institutions to better tackles them, especially in low-income countries.

Antibody Fab-Fc properties outperform titer in predictive models of SIV vaccine-induced protection

Characterizing the antigen-binding and innate immune-recruiting properties of the humoral response offers the chance to obtain deeper insights into mechanisms of protection than revealed by measuring only overall antibody titer. Here, a high-throughput, multiplexed Fab-Fc Array was employed to profile rhesus macaques vaccinated with a gp120-CD4 fusion protein in combination with different genetically encoded adjuvants, and subsequently subjected to multiple heterologous simian immunodeficiency virus (SIV) challenges. Systems analyses modeling protection and adjuvant differences using Fab-Fc Array measurements revealed a set of correlates yielding strong and robust predictive performance, while models based on measurements of response magnitude alone exhibited significantly inferior performance. At the same time, rendering Fab-Fc measurements mathematically independent of titer had relatively little impact on predictive performance. Similar analyses for a distinct SIV vaccine study also showed that Fab-Fc measurements performed significantly better than titer. These results suggest that predictive modeling with measurements of antibody properties can provide detailed correlates with robust predictive power, suggest directions for vaccine improvement, and potentially enable discovery of mechanistic associations.

Differential contribution of education through KIR2DL1, KIR2DL3, and KIR3DL1 to antibody-dependent (AD) NK cell activation and ADCC

The engagement of activating NK receptors (aNKR) stimulates NK cell activity, provided that interactions between inhibitory NK receptors (iNKR) with their HLA ligands do not override them. Abs bound to target cells can also activate NK cells by engaging the CD16 aNKR. NK cell education status is an important factor for Ab‐dependent NK cell activation (ADNKA) of some NK cell subsets. However, whether NK cell education also influences Ab‐dependent cellular cytotoxicity (ADCC) levels is not fully known. ADCC‐GranToxiLux (GTL) assays measured ADCC activity as the frequency of granzyme B positive (%GzB⁺) target cells. Target cells were anti‐HIV Immunoglobulin G (HIVIG)‐opsonized CEM‐NKr.CCR5 (CEM) cells. Lymphocytes and sorted single positive (SP) NKG2A⁺, KIR2DL1⁺, KIR2DL3⁺, and KIR3DL1⁺ NK cells, to self‐ and nonself HLA, were used as effectors in ADCC‐GTL assays to examine how education status influenced ADCC activity. ADNKA activity was assessed by stimulating lymphocytes with HIVIG‐opsonized CEMs and measuring the frequency of NK cell populations defined by their expression of iNKRs, along with IFN‐γ, CCL4, and CD107a functions. ADCC: the %GzB⁺ CEM cells generated by self‐ versus nonself HLA‐specific SPiNKR did not differ. ADNKA: More NK cells educated through KIR2DL1 and KIR3DL1, but not KIR2DL3, responded to ADNKA than their uneducated counterparts. CD16 engagement induced ADCC and ADNKA activity. With the proviso that groups’ sizes were small, our results support the notion that NK cell education does not influence ADCC levels but does contribute to ADNKA activity.

Identification of Immune Signatures of Novel Adjuvant Formulations Using Machine Learning

Adjuvants have long been critical components of vaccines, but the exact mechanisms of their action and precisely how they alter or enhance vaccine-induced immune responses are often unclear. In this study, we used broad immunoprofiling of antibody, cellular, and cytokine responses, combined with data integration and machine learning to gain insight into the impact of different adjuvant formulations on vaccine-induced immune responses. A Self-Assembling Protein Nanoparticles (SAPN) presenting the malarial circumsporozoite protein (CSP) was used as a model vaccine, adjuvanted with three different liposomal formulations: liposome plus Alum (ALFA), liposome plus QS21 (ALFQ), and both (ALFQA). Using a computational approach to integrate the immunoprofiling data, we identified distinct vaccine-induced immune responses and developed a multivariate model that could predict the adjuvant condition from immune response data alone with 92% accuracy (p = 0.003). The data integration also revealed that commonly used readouts (i.e. serology, frequency of T cells producing IFN-γ, IL2, TNFα) missed important differences between adjuvants. In summary, broad immune-profiling in combination with machine learning methods enabled the reliable and clear definition of immune signatures for different adjuvant formulations, providing a means for quantitatively characterizing the complex roles that adjuvants can play in vaccine-induced immunity. The approach described here provides a powerful tool for identifying potential immune correlates of protection, a prerequisite for the rational pairing of vaccines candidates and adjuvants.

High-Dimensional Immunology for Schizophrenia Research: A Short Perspective

There is evidence that many diseases are accompanied by immunological perturbations and even when the perturbations are not directly pathogenic, they can provide correlative signatures of pathology that can be useful diagnostically. For example, the neuromuscular disease, multiple sclerosis, has a pathophysiology that is immunologically mediated, evinced by the use of increasingly sophisticated immunosuppression therapy and by animal studies in which many of the symptoms can be reproduced by breaking immunological tolerance to myelin basic protein. By contrast, immunological correlates exist for other diseases, such as schizophrenia, but it is not clear which, if any, are causative. The problem is compounded in that genome-wide association studies have shown strong genetic correlation between schizophrenia and bipolar disorder, moderate correlation with schizophrenia and major depressive disease, and low correlation with autism spectrum disorders, yet schizophrenia and autism spectrum disorders share immunological signatures. This example illustrates the problem of ferreting out specific, and hopefully causal, immunological correlates with schizophrenia that differentiate it from genetically or immunologically related psychiatric disorders. Fortunately, recent advances in systems immunology provide potent tools to tackle this problem. This review will illustrate these tools by recent examples and sketch out possible pathways to use them for identification of schizophrenia-specific immunological correlates.

HIV Vaccination: A Roadmap among Advancements and Concerns

Since the identification of the Human Immunodeficiency Virus type 1 (HIV-1) as the etiologic agent of AIDS (Acquired Immunodeficiency Syndrome), many efforts have been made to stop the AIDS pandemic. A major success of medical research has been the development of the highly active antiretroviral therapy and its availability to an increasing number of people worldwide, with a considerable effect on survival. However, a safe and effective vaccine able to prevent and eradicate the HIV pandemic is still lacking. Clinical trials and preclinical proof-of-concept studies in nonhuman primate (NHP) models have provided insights into potential correlates of protection against the HIV-1 infection, which include broadly neutralizing antibodies (bnAbs), non-neutralizing antibodies targeting the variable loops 1 and 2 (V1V2) regions of the HIV-1 envelope (Env), polyfunctional antibody, and Env-specific T-cell responses. In this review, we provide a brief overview of different HIV-1 vaccine approaches and discuss the current understanding of the cellular and humoral correlates of HIV-1 immunity.

HIV-specific Fc effector function early in infection predicts the development of broadly neutralizing antibodies

While the induction of broadly neutralizing antibodies (bNAbs) is a major goal of HIV vaccination strategies, there is mounting evidence to suggest that antibodies with Fc effector function also contribute to protection against HIV infection. Here we investigated Fc effector functionality of HIV-specific IgG plasma antibodies over 3 years of infection in 23 individuals, 13 of whom developed bNAbs. Antibody-dependent cellular phagocytosis (ADCP), complement deposition (ADCD), cellular cytotoxicity (ADCC) and cellular trogocytosis (ADCT) were detected in almost all individuals with levels of activity increasing over time. At 6 months post-infection, individuals with bNAbs had significantly higher levels of ADCD and ADCT that correlated with antibody binding to C1q and FcγRIIa respectively. In addition, antibodies from individuals with bNAbs showed more IgG subclass diversity to multiple HIV antigens which also correlated with Fc polyfunctionality. Germinal center activity represented by CXCL13 levels and expression of activation-induced cytidine deaminase (AID) was found to be associated with neutralization breadth, Fc polyfunctionality and IgG subclass diversity. Overall, multivariate analysis by random forest classification was able to group bNAb individuals with 85% sensitivity and 80% specificity based on the properties of their antibody Fc early in HIV infection. Thus, the Fc effector function profile predicted the development of neutralization breadth in this cohort, suggesting that intrinsic immune factors within the germinal center provide a mechanistic link between the Fc and Fab of HIV-specific antibodies.

Some HIV-infected individuals develop antibodies that are capable of neutralizing the majority of HIV strains, a highly desirable function mediated by the antibody Fab portion. While antibodies elicited by current vaccines have failed to recreate this activity, the partial protection seen in the RV144 vaccine trial has been attributed to antibody Fc-mediated effector functions such as cell killing. In this study, we found that HIV-infected individuals who show a diversified and potent Fc response early in infection were more likely to develop broadly neutralizing antibodies later on. Examination of B cell functions associated with good germinal center activity, provided evidence for a common mechanistic link between the regulation of the Fc and Fab mediated activities in these individuals. Our finding of an Fc effector function profile that arises early and predicts neutralization breadth could be used in the evaluation of vaccine candidates designed to generate neutralizing antibodies. Common immune determinants associated with both Fab and Fc function could furthermore be exploited for vaccine design to harness the full potential of HIV-specific antibodies.

High-resolution definition of humoral immune response correlates of effective immunity against HIV

Defining correlates of immunity by comprehensively interrogating the extensive biological diversity in naturally or experimentally protected subjects may provide insights critical for guiding the development of effective vaccines and antibody-based therapies. We report advances in a humoral immunoprofiling approach and its application to elucidate hallmarks of effective HIV-1 viral control. Systematic serological analysis for a cohort of HIV-infected subjects with varying viral control was conducted using both a high-resolution, high-throughput biophysical antibody profiling approach, providing unbiased dissection of the humoral response, along with functional antibody assays, characterizing antibody-directed effector functions such as complement fixation and phagocytosis that are central to protective immunity. Profiles of subjects with varying viral control were computationally analyzed and modeled in order to deconvolute relationships among IgG Fab properties, Fc characteristics, and effector functions and to identify humoral correlates of potent antiviral antibody-directed effector activity and effective viral suppression. The resulting models reveal multifaceted and coordinated contributions of polyclonal antibodies to diverse antiviral responses, and suggest key biophysical features predictive of viral control.

Systems serology: profiling vaccine induced humoral immunity against HIV

The results of the RV144 HIV vaccine, in combination with several recent non-human primate vaccine studies continue to highlight the potentially protective role of non-neutralizing Fc functional antibodies in HIV vaccine design. For many currently licensed vaccines, assays that detect antigen-specific antibody titers or neutralization levels have been used as a correlate of protection. However, antibodies can confer protection through multiple other mechanisms beyond neutralization, or mechanisms which are not dependent on total antibody titers. Alternative strategies that allow us to further understand the precise mechanisms by which antibodies confer protection against HIV and other infectious pathogens is vitally important for the development of future vaccines. Systems serology aims to comprehensively survey a diverse array of antibody features and functions, in order to simultaneously examine the mechanisms behind and distinguish the most important antibody features required for protection, thus identifying key targets for future experimental vaccine testing. This review will focus on the technical aspects required for the application of Systems serology and summarizes the recent advances provided by application of this systemic analytical approach.

Prospects from systems serology research

Antibodies are highly functional glycoproteins capable of providing immune protection through multiple mechanisms, including direct pathogen neutralization and the engagement of their Fc portions with surrounding effector immune cells that induce anti-pathogenic responses. Small modifications to multiple antibody biophysical features induced by vaccines can significantly alter functional immune outcomes, though it is difficult to predict which combinations confer protective immunity. In order to give insight into the highly complex and dynamic processes that drive an effective humoral immune response, here we discuss recent applications of 'Systems Serology', a new approach that uses data-driven (also called 'machine learning') computational analysis and high-throughput experimental data to infer networks of important antibody features associated with protective humoral immunity and/or Fc functional activity. This approach offers the ability to understand humoral immunity beyond single correlates of protection, assessing the relative importance of multiple biophysical modifications to antibody features with multivariate computational approaches. Systems Serology has the exciting potential to help identify novel correlates of protection from infection and may generate a more comprehensive understanding of the mechanisms behind protection, including key relationships between specific Fc functions and antibody biophysical features (e.g. antigen recognition, isotype, subclass and/or glycosylation events). Reviewed here are some of the experimental and computational technologies available for Systems Serology research and evidence that the application has broad relevance to multiple different infectious diseases including viruses, bacteria, fungi and parasites.

A survey of machine learning applications in HIV clinical research and care

A wealth of genetic, demographic, clinical and biomarker data is collected from routine clinical care of HIV patients and exists in the form of medical records available among the medical care and research communities. Machine learning (ML) methods have the ability to identify and discover patterns in complex datasets and predict future outcomes of HIV treatment. We survey published studies that make use of ML techniques in HIV clinical research and care. An advanced search relevant to the use of ML in HIV research was conducted in the PubMed biomedical database. The survey outcomes of interest include data sources, ML techniques, ML tasks and ML application paradigms. A growing trend in application of ML in HIV research was observed. The application paradigm has diversified to include practical clinical application, but statistical analysis remains the most dominant application. There is an increase in the use of genomic sources of data and high performance non-parametric ML methods with a focus on combating resistance to antiretroviral therapy (ART). There is need for improvement in collection of health records data and increased training in ML so as to translate ML research into clinical application in HIV management.

Panels of HIV-1 Subtype C Env Reference Strains for Standardized Neutralization Assessments

In the search for effective immunologic interventions to prevent and treat HIV-1 infection, standardized reference reagents are a cost-effective way to maintain robustness and reproducibility among immunological assays. To support planned and ongoing studies where clade C predominates, here we describe three virus panels, chosen from 200 well-characterized clade C envelope (Env)-pseudotyped viruses from early infection. All 200 Envs were expressed as a single round of replication pseudoviruses and were tested to quantify neutralization titers by 16 broadly neutralizing antibodies (bnAbs) and sera from 30 subjects with chronic clade C infections. We selected large panels of 50 and 100 Envs either to characterize cross-reactive breadth for sera identified as having potent neutralization activity based on initial screening or to evaluate neutralization magnitude-breadth distributions of newly isolated antibodies. We identified these panels by downselection after hierarchical clustering of bnAb neutralization titers. The resulting panels represent the diversity of neutralization profiles throughout the range of virus sensitivities identified in the original panel of 200 viruses. A small 12-Env panel was chosen to screen sera from vaccine trials or natural-infection studies for neutralization responses. We considered panels selected by previously described methods but favored a computationally informed method that enabled selection of viruses representing diverse neutralization sensitivity patterns, given that we do not a priori know what the neutralization-response profile of vaccine sera will be relative to that of sera from infected individuals. The resulting 12-Env panel complements existing panels. Use of standardized panels enables direct comparisons of data from different trials and study sites testing HIV-1 clade C-specific products.

IMPORTANCE HIV-1 group M includes nine clades and many recombinants. Clade C is the most common lineage, responsible for roughly half of current HIV-1 infections, and is a focus for vaccine design and testing. Standard reference reagents, particularly virus panels to study neutralization by antibodies, are crucial for developing cost-effective and yet rigorous and reproducible assays against diverse examples of this variable virus. We developed clade C-specific panels for use as standardized reagents to monitor complex polyclonal sera for neutralization activity and to characterize the potency and breadth of cross-reactive neutralization by monoclonal antibodies, whether engineered or isolated from infected individuals. We chose from 200 southern African, clade C envelope-pseudotyped viruses with neutralization titers against 16 broadly neutralizing antibodies and 30 sera from chronic clade C infections. We selected panels to represent the diversity of bnAb neutralization profiles and Env neutralization sensitivities. Use of standard virus panels can facilitate comparison of results across studies and sites.

Systems serology for evaluation of HIV vaccine trials

The scale and scope of the global epidemic, coupled to challenges with traditional vaccine development approaches, point toward a need for novel methodologies for HIV vaccine research. While the development of vaccines able to induce broadly neutralizing antibodies remains the ultimate goal, to date, vaccines continue to fail to induce these rare humoral immune responses. Conversely, growing evidence across vaccine platforms in both non-human primates and humans points to a role for polyclonal vaccine-induced antibody responses in protection from infection. These candidate vaccines, despite employing disparate viral vectors and immunization strategies, consistently identify a role for functional or non-traditional antibody activities as correlates of immunity. However, the precise mechanism(s) of action of these "binding" antibodies, their specific characteristics, and their ability to be selectively induced and/or potentiated to result in complete protection merits parallel investigation to neutralizing antibody-based vaccine design approaches. Ultimately, while neutralizing and functional antibody-based vaccine strategies need not be mutually exclusive, defining the specific characteristics of "protective" functional antibodies may provide a target immune profile to potentially induce more robust immunity against HIV. Specifically, one approach to guide the development of functional antibody-based vaccine strategies, termed "systems serology", offers an unbiased and comprehensive approach to systematically survey humoral immune responses, capturing the array of functions and humoral response characteristics that may be induced following vaccination with high resolution. Coupled to machine learning tools, large datasets that explore the "antibody-ome" offer a means to step back from anticipated correlates and mechanisms of protection and toward a more fundamental understanding of coordinated aspects of humoral immune responses, to more globally differentiate among vaccine candidates, and most critically, to identify the features of humoral immunity that distinguish protective from non-protective responses. Overall, the systematic serological approach described here aimed at broadly capturing the enormous biodiversity in antibody profiles that may emerge following vaccination, complements the existing cutting edge tools in the cellular immunology space that survey vaccine-induced polyfunctional cellular activity by flow cytometry, transcriptional profiling, epigenetic, and metabolomic analysis to offer a means to develop both a more nuanced and a more complete understanding of correlates of protection to support the design of functional vaccine strategies.

Unsupervised selection of RV144 HIV vaccine-induced antibody features correlated to natural killer cell-mediated cytotoxic reactions

HIV-1 vaccine injection has been shown less effective due to the diversity of antigens. Increasing the knowledge of the associations between immune system and virus would ultimately result in producing effective vaccines against HIV-1 virus. To increase the understanding of immunological information, computational models can be utilised to construct predictive models. The aim of this study is, therefore, to predict the effect of antibody features (IgGs) and primary Natural Killing (NK) cells' cytotoxic activities on RV144 vaccine recipients and to disclose the functional relationship between immune system and HIV virus. The RV144 vaccine data set 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 paper, five different unsupervised feature selection methods (USFSMs) are utilised in order to identify the discriminating antibody features as USFSMs are regarded as unbiased approach. Then, the support vector based methods are utilised to assess association between cellular cytotoxicity by Natural Killer (NK) cells and cells that release glycoprotein (gp)120 antibody. The results yield high correlation coefficient as much as 0.48 and 0.65 for classificationthe support vector regression (SVR) and classification (SVM) predictive models, respectively.

Multiplexed Fc array for evaluation of antigen-specific antibody effector profiles

Antibodies are widely considered to be a frequent primary and often mechanistic correlate of protection of approved vaccines; thus evaluating the antibody response is of critical importance in attempting to understand and predict the efficacy of novel vaccine candidates. Historically, antibody responses have been analyzed by determining the titer of the humoral response using measurements such as an ELISA, neutralization, or agglutination assays. In the simplest case, sufficiently high titers of antibody against vaccine antigen(s) are sufficient to predict protection. However, antibody titer provides only a partial measure of antibody function, which is dependent on both the variable region (Fv) to bind the antigen target, and the constant region (Fc) to elicit an effector response from the innate arm of the immune system. In the case of some diseases, such as HIV, for which an effective vaccine has proven elusive, antibody effector function has been shown to be an important driver of monoclonal antibody therapy outcomes, of viral control in infected patients, and of vaccine-mediated protection in preclinical and clinical studies. We sought to establish a platform for the evaluation of the Fc domain characteristics of antigen-specific antibodies present in polyclonal samples in order to better develop insights into Fc receptor-mediated antibody effector activity, more fully understand how antibody responses may differ in association with disease progression and between subject groups, and differentiate protective from non-protective responses. To this end we have developed a high throughput biophysical platform capable of simultaneously evaluating many dimensions of the antibody effector response.

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High-throughput array-based characterization platform for polyclonal antibodies.

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Development of a biophysical proxy for antibody effector function.

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Antigen and Fc receptor recognition characteristics are captured.

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Enables systematic serologic studies of NHP and human antibody samples.

Computer-Aided Antibody Design: An Overview

The use of monoclonal antibody as the next generation protein therapeutics with remarkable success has surged the development of antibody engineering to design molecules for optimizing affinity, better efficacy, greater safety and therapeutic function. Therefore, computational methods have become increasingly important to generate hypotheses, interpret and guide experimental works. In this chapter, we discussed the overall antibody design by computational approches.

Biophysical and Functional Characterization of Rhesus Macaque IgG Subclasses

Antibodies raised in Indian rhesus macaques [Macaca mulatta (MM)] in many preclinical vaccine studies are often evaluated in vitro for titer, antigen-recognition breadth, neutralization potency, and/or effector function, and in vivo for potential associations with protection. However, despite reliance on this key animal model in translation of promising candidate vaccines for evaluation in first in man studies, little is known about the properties of MM immunoglobulin G (IgG) subclasses and how they may compare to human IgG subclasses. Here, we evaluate the binding of MM IgG1, IgG2, IgG3, and IgG4 to human Fc gamma receptors (FcγR) and their ability to elicit the effector functions of human FcγR-bearing cells, and unlike in humans, find a notable absence of subclasses with dramatically silent Fc regions. Biophysical, in vitro, and in vivo characterization revealed MM IgG1 exhibited the greatest effector function activity followed by IgG2 and then IgG3/4. These findings in rhesus are in contrast with the canonical understanding that IgG1 and IgG3 dominate effector function in humans, indicating that subclass-switching profiles observed in rhesus studies may not strictly recapitulate those observed in human vaccine studies.

The role of Fc receptors in HIV prevention and therapy

Over the past decade, a wealth of experimental evidence has accumulated supporting the importance of Fc receptor (FcR) ligation in antibody-mediated pathology and protection in many disease states. Here we present the diverse evidence base that has accumulated as to the importance of antibody effector functions in the setting of HIV prevention and therapy, including clinical correlates, genetic associations, viral evasion strategies, and a rapidly growing number of compelling animal model experiments. Collectively, this work identifies antibody interactions with FcR as important to both therapeutic and prophylactic strategies involving both passive and active immunity. These findings mirror those in other fields as investigators continue to work toward identifying the right antibodies and the right effectors to be present at the right sites at the right time.

Polyfunctional HIV-Specific Antibody Responses Are Associated with Spontaneous HIV Control

Elite controllers (ECs) represent a unique model of a functional cure for HIV-1 infection as these individuals develop HIV-specific immunity able to persistently suppress viremia. Because accumulating evidence suggests that HIV controllers generate antibodies with enhanced capacity to drive antibody-dependent cellular cytotoxicity (ADCC) that may contribute to viral containment, we profiled an array of extra-neutralizing antibody effector functions across HIV-infected populations with varying degrees of viral control to define the characteristics of antibodies associated with spontaneous control. While neither the overall magnitude of antibody titer nor individual effector functions were increased in ECs, a more functionally coordinated innate immune-recruiting response was observed. Specifically, ECs demonstrated polyfunctional humoral immune responses able to coordinately recruit ADCC, other NK functions, monocyte and neutrophil phagocytosis, and complement. This functionally coordinated response was associated with qualitatively superior IgG3/IgG1 responses, whereas HIV-specific IgG2/IgG4 responses, prevalent among viremic subjects, were associated with poorer overall antibody activity. Rather than linking viral control to any single activity, this study highlights the critical nature of functionally coordinated antibodies in HIV control and associates this polyfunctionality with preferential induction of potent antibody subclasses, supporting coordinated antibody activity as a goal in strategies directed at an HIV-1 functional cure.