Defining the humoral immune response to infectious agents using high-density protein microarrays

Abstract

A major component of the adaptive immune response to infection is the generation of protective and long-lasting humoral immunity. Traditional approaches to understanding the host's humoral immune response are unable to provide an integrated understanding of the antibody repertoire generated in response to infection. By studying multiple antigenic responses in parallel, we can learn more about the breadth and dynamics of the antibody response to infection. Measurement of antibody production following vaccination is also a gauge for efficacy, as generation of antibodies can protect from future infections and limit disease. Protein microarrays are well suited to identify, quantify and compare individual antigenic responses following exposure to infectious agents. This technology can be applied to the development of improved serodiagnostic tests, discovery of subunit vaccine antigen candidates, epidemiologic research and vaccine development, as well as providing novel insights into infectious disease and the immune system. In this review, we will discuss the use of protein microarrays as a powerful tool to define the humoral immune response to bacteria and viruses.

Figure 1. Representative protein microarray image and population comparative analysis

(A) A representative microarray image of sera from a melioidosis-positive patient screened for reactivity to a small collection of Burkholderia pseudomallei antigens. Seroreactivity is detected using a fluorescently labeled anti-human IgG antibody. The arrays were read in a laser confocal scanner and the signal intensity of each antigen is represented by rainbow palette of blue, green, red and white by increasing signal intensity. A representative microarray containing 214 B. pseudomallei proteins, positive and negative control spots is depicted in (A). Each array contains positive control spots printed from four serial dilutions of human IgG. Each array also contained six ‘No DNA’ negative control spots. There are also four serially diluted EBNA1 protein control spots which are reactive to varying degrees in different subjects, as expected, and provide a methodological control. The remaining spots on the array are in vitro transcription/translation reactions expressing 183 different B. pseudomallei proteins. (B) Seroreactivity of individual melioidosis-positive patients and healthy controls can be depicted in a heatmap. The patient samples are in columns and sorted left to right by increasing average intensity to serodiagnostic antigens. The antigens are in rows and are grouped according to differential reactivity. Differentially reactive antigens are considered serodiagnostic and similarly reactive antigens are considered cross-reactive. The normalized intensity is shown according to the colorized scale with red strongest, bright green weakest and black in between. EBNA: Epstein–Barr virus nuclear antigen; HMIg: Human Ig.

Figure 2. Comparison of cross-reactivity to Burkholderia pseudomallei and Coxiella burnetti

(A) The mean seroreactivity was compared between melioidosis positive, melioidosis negative, healthy naives from a nonendemic region, and other infections. The 31 most reactive serodiagnostic and 31 of the most reactive cross-reactive antigens are shown. Differentially reactive antigens are shown in the top graph and have a p-value of less than 0.05. Cross-reactive antigens are similarly reactive in all groups and have an analysis of variance p-value of 0.08. (B) The mean seroreactivity to C. burnetii was compared with Q-fever-positive patients and naive controls. Naive controls display low reactivity to many antigens and many serodiagnostic antigens are distinctly different in patients with Q-fever.