Prospects from systems serology research

Abstract

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.

Keywords: Fab; Fc; Fc receptors; antibody; vaccine.

Figure 1

Dynamic complexity of the humoral immune response. (a) The functional capacity of the humoral immune response is determined by complex biophysical antibody features including (i) the pathogen being targeted and the ability of the antibody's Fab to recognize different antigens, (ii) an antibody's Fc region's diversity, which in turn can modulate the antibodies capacity to engage with (iii) Fc receptor/immune molecules and (iv) availability of the Fc receptors on different effector cells/immune molecules in the surrounding environment. (b). The combination of the pathogen targeted (e.g. infected cell versus small infectious particles) and binding by an antibody's Fab determines opsonization, neutralization and immune complex formation. The composition of the Fc‐regions of these antibodies can in turn modulate the functional immune response by surrounding effector cells/immune molecules potentially inducing a range of functions including but not limited to ADCC, antibody‐mediated secretion of cytokines, antibody‐mediated enzyme release/NET (neutrophil extracellular trap) formation, antibody‐dependent phagocytosis, antibody‐mediated complement activity, mucus trapping etc., dependent on the cellular Fc receptor expression or immune components available.

Figure 2

Systems serology data‐driven modeling approaches. Systems Serology involves running high‐throughput experimental assays that measure antibody biophysical and functional data (X) in parallel with functional or clinical outcomes (Y). Upon collation, the data sets can be interrogated by unsupervised and supervised machine learning computational techniques, including principal component analysis (PCA), correlation networks, partial least square discriminant analysis and regression (PLSDA and PLSR), and decision trees. The correlation network figure was kindly contributed by Manu Kumar and Doug Lauffenburger (MIT).