Organ Mapping Antibody Panels: a community resource for standardized multiplexed tissue imaging.

Multiplexed antibody-based imaging enables the detailed characterization of molecular and cellular organization in tissues. Advances in the field now allow high-parameter data collection (>60 targets); however, considerable expertise and capital are needed to construct the antibody panels employed by these methods. Organ mapping antibody panels are community-validated resources that save time and money, increase reproducibility, accelerate discovery and support the construction of a Human Reference Atlas.

Diverse antiviral IgG effector activities are predicted by unique biophysical antibody features.

BACKGROUND: The critical role of antibody Fc-mediated effector functions in immune defense has been widely reported in various viral infections. These effector functions confer cellular responses through engagement with innate immune cells. The precise mechanism(s) by which immunoglobulin G (IgG) Fc domain and cognate receptors may afford protection are poorly understood, however, in the context of HIV/SHIV infections. Many different in vitro assays have been developed and utilized to measure effector functions, but the extent to which these assays capture distinct antibody activities has not been fully elucidated. RESULTS: In this study, six Fc-mediated effector function assays and two biophysical antibody profiling assays were performed on a common set of samples from HIV-1 infected and vaccinated subjects. Biophysical antibody profiles supported robust prediction of diverse IgG effector functions across distinct Fc-mediated effector function assays. While a number of assays showed correlated activities, supervised machine learning models indicated unique antibody features as primary contributing factors to the associated effector functions. Additional experiments established the mechanistic relevance of relationships discovered using this unbiased approach. CONCLUSIONS: In sum, this study provides better resolution on the diversity and complexity of effector function assays, offering a clearer perspective into this family of antibody mechanisms of action to inform future HIV-1 treatment and vaccination strategies.

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.

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.

Diversity of Antiviral IgG Effector Activities Observed in HIV-Infected and Vaccinated Subjects.

Diverse Ab effector functions mediated by the Fc domain have been commonly associated with reduced risk of infection in a growing number of nonhuman primate and human clinical studies. This study evaluated the anti-HIV Ab effector activities in polyclonal serum samples from HIV-infected donors, VAX004 vaccine recipients, and healthy HIV-negative subjects using a variety of primary and cell line-based assays, including Ab-dependent cellular cytotoxicity (ADCC), Ab-dependent cell-mediated viral inhibition, and Ab-dependent cellular phagocytosis. Additional assay characterization was performed with a panel of Fc-engineered variants of mAb b12. The goal of this study was to characterize different effector functions in the study samples and identify assays that might most comprehensively and dependably capture Fc-mediated Ab functions mediated by different effector cell types and against different viral targets. Deployment of such assays may facilitate assessment of functionally unique humoral responses and contribute to identification of correlates of protection with potential mechanistic significance in future HIV vaccine studies. Multivariate and correlative comparisons identified a set of Ab-dependent cell-mediated viral inhibition and phagocytosis assays that captured different Ab activities and were distinct from a group of ADCC assays that showed a more similar response profile across polyclonal serum samples. The activities of a panel of b12 monoclonal Fc variants further identified distinctions among the ADCC assays. These results reveal the natural diversity of Fc-mediated Ab effector responses among vaccine recipients in the VAX004 trial and in HIV-infected subjects, and they point to the potential importance of polyfunctional Ab responses.

Novel insights into embryonic stem cell self-renewal revealed through comparative human and mouse systems biology networks.

Embryonic stem cells (ESCs), characterized by their ability to both self-renew and differentiate into multiple cell lineages, are a powerful model for biomedical research and developmental biology. Human and mouse ESCs share many features, yet have distinctive aspects, including fundamental differences in the signaling pathways and cell cycle controls that support self-renewal. Here, we explore the molecular basis of human ESC self-renewal using Bayesian network machine learning to integrate cell-type-specific, high-throughput data for gene function discovery. We integrated high-throughput ESC data from 83 human studies (~1.8 million data points collected under 1,100 conditions) and 62 mouse studies (~2.4 million data points collected under 1,085 conditions) into separate human and mouse predictive networks focused on ESC self-renewal to analyze shared and distinct functional relationships among protein-coding gene orthologs. Computational evaluations show that these networks are highly accurate, literature validation confirms their biological relevance, and reverse transcriptase polymerase chain reaction (RT-PCR) validation supports our predictions. Our results reflect the importance of key regulatory genes known to be strongly associated with self-renewal and pluripotency in both species (e.g., POU5F1, SOX2, and NANOG), identify metabolic differences between species (e.g., threonine metabolism), clarify differences between human and mouse ESC developmental signaling pathways (e.g., leukemia inhibitory factor (LIF)-activated JAK/STAT in mouse; NODAL/ACTIVIN-A-activated fibroblast growth factor in human), and reveal many novel genes and pathways predicted to be functionally associated with self-renewal in each species. These interactive networks are available online at www.StemSight.org for stem cell researchers to develop new hypotheses, discover potential mechanisms involving sparsely annotated genes, and prioritize genes of interest for experimental validation.

A Framework for Optimizing High-Content Imaging of 3D Models for Drug Discovery.

Three-dimensional (3D) spheroid models are rapidly gaining favor for drug discovery applications due to their improved morphological characteristics, cellular complexity, long lifespan in culture, and higher physiological relevance relative to two-dimensional (2D) cell culture models. High-content imaging (HCI) of 3D spheroid models has the potential to provide valuable information to help researchers untangle disease pathophysiology and assess novel therapies more effectively. The transition from 2D monolayer models to dense 3D spheroids in HCI applications is not trivial, however, and requires 3D-optimized protocols, instrumentation, and resources. Here, we discuss considerations for moving from 2D to 3D models and present a framework for HCI and analysis of 3D spheroid models in a drug discovery setting. We combined scaffold-free, multicellular spheroid models with scalable, automation-compatible plate technology enabling image-based applications ranging from high-throughput screening to more complex, lower-throughput microphysiological systems of organ networks. We used this framework in three case studies: investigation of lipid droplet accumulation in a human liver nonalcoholic steatohepatitis (NASH) model, real-time immune cell interactions in a multicellular 3D lung cancer model, and a high-throughput screening application using a 3D co-culture model of gastric carcinoma to assess dose-dependent drug efficacy and specificity. The results of these proof-of-concept studies demonstrate the potential for high-resolution image-based analysis of 3D spheroid models for drug discovery applications, and confirm that cell-level and temporal-spatial analyses that fully exploit multicellular features of spheroid models are not only possible but soon will be routine practice in drug discovery workflows.

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.

Adjuvant-dependent innate and adaptive immune signatures of risk of SIVmac251 acquisition.

A recombinant vaccine containing Aventis Pasteur's canarypox vector (ALVAC)-HIV and gp120 alum decreased the risk of HIV acquisition in the RV144 vaccine trial. The substitution of alum with the more immunogenic MF59 adjuvant is under consideration for the next efficacy human trial. We found here that an ALVAC-simian immunodeficiency virus (SIV) and gp120 alum (ALVAC-SIV + gp120) equivalent vaccine, but not an ALVAC-SIV + gp120 MF59 vaccine, was efficacious in delaying the onset of SIVmac251 in rhesus macaques, despite the higher immunogenicity of the latter adjuvant. Vaccine efficacy was associated with alum-induced, but not with MF59-induced, envelope (Env)-dependent mucosal innate lymphoid cells (ILCs) that produce interleukin (IL)-17, as well as with mucosal IgG to the gp120 variable region 2 (V2) and the expression of 12 genes, ten of which are part of the RAS pathway. The association between RAS activation and vaccine efficacy was also observed in an independent efficacious SIV-vaccine approach. Whether RAS activation, mucosal ILCs and antibodies to V2 are also important hallmarks of HIV-vaccine efficacy in humans will require further studies.

Cell-type-specific predictive network yields novel insights into mouse embryonic stem cell self-renewal and cell fate.

Self-renewal, the ability of a stem cell to divide repeatedly while maintaining an undifferentiated state, is a defining characteristic of all stem cells. Here, we clarify the molecular foundations of mouse embryonic stem cell (mESC) self-renewal by applying a proven Bayesian network machine learning approach to integrate high-throughput data for protein function discovery. By focusing on a single stem-cell system, at a specific developmental stage, within the context of well-defined biological processes known to be active in that cell type, we produce a consensus predictive network that reflects biological reality more closely than those made by prior efforts using more generalized, context-independent methods. In addition, we show how machine learning efforts may be misled if the tissue specific role of mammalian proteins is not defined in the training set and circumscribed in the evidential data. For this study, we assembled an extensive compendium of mESC data: ∼2.2 million data points, collected from 60 different studies, under 992 conditions. We then integrated these data into a consensus mESC functional relationship network focused on biological processes associated with embryonic stem cell self-renewal and cell fate determination. Computational evaluations, literature validation, and analyses of predicted functional linkages show that our results are highly accurate and biologically relevant. Our mESC network predicts many novel players involved in self-renewal and serves as the foundation for future pluripotent stem cell studies. This network can be used by stem cell researchers (at http://StemSight.org) to explore hypotheses about gene function in the context of self-renewal and to prioritize genes of interest for experimental validation.

Text mining for the biocuration workflow.

Molecular biology has become heavily dependent on biological knowledge encoded in expert curated biological databases. As the volume of biological literature increases, biocurators need help in keeping up with the literature; (semi-) automated aids for biocuration would seem to be an ideal application for natural language processing and text mining. However, to date, there have been few documented successes for improving biocuration throughput using text mining. Our initial investigations took place for the workshop on 'Text Mining for the BioCuration Workflow' at the third International Biocuration Conference (Berlin, 2009). We interviewed biocurators to obtain workflows from eight biological databases. This initial study revealed high-level commonalities, including (i) selection of documents for curation; (ii) indexing of documents with biologically relevant entities (e.g. genes); and (iii) detailed curation of specific relations (e.g. interactions); however, the detailed workflows also showed many variabilities. Following the workshop, we conducted a survey of biocurators. The survey identified biocurator priorities, including the handling of full text indexed with biological entities and support for the identification and prioritization of documents for curation. It also indicated that two-thirds of the biocuration teams had experimented with text mining and almost half were using text mining at that time. Analysis of our interviews and survey provide a set of requirements for the integration of text mining into the biocuration workflow. These can guide the identification of common needs across curated databases and encourage joint experimentation involving biocurators, text mining developers and the larger biomedical research community.