Dynamic antibody responses to the Mycobacterium tuberculosis proteome

Abstract

Considerable effort has been directed toward controlling tuberculosis, which kills almost two million people yearly. High on the research agenda is the discovery of biomarkers of active tuberculosis (TB) for diagnosis and for monitoring treatment outcome. Rational biomarker discovery requires understanding host-pathogen interactions leading to biomarker expression. Here we report a systems immunology approach integrating clinical data and bacterial metabolic and regulatory information with high-throughput detection in human serum of antibodies to the entire Mycobacterium tuberculosis proteome. Sera from worldwide TB suspects recognized approximately 10% of the bacterial proteome. This result defines the M. tuberculosis immunoproteome, which is rich in membrane-associated and extracellular proteins. Additional analyses revealed that during active tuberculosis (i) antibody responses focused on an approximately 0.5% of the proteome enriched for extracellular proteins, (ii) relative target preference varied among patients, and (iii) responses correlated with bacillary burden. These results indicate that the B cell response tracks the evolution of infection and the pathogen burden and replicative state and suggest functions associated with B cell-rich foci seen in tuberculous lung granulomas. Our integrated proteome-scale approach is applicable to other chronic infections characterized by diverse antibody target recognition.

Fig. 1.

Estimation of significant serum reactivity. The M. tuberculosis proteome arrays were probed with sera from 561 TB suspects [TB (n = 254) and non-TB disease (NTBD; n = 307)]. (A) Examples of proteins showing a second, high-tail distribution in the active TB sera. The distributions of protein spot intensities obtained with each protein are shown as violin plots. One plot represents one protein. In these plots, the y axis represents the log-transformed measurements of microarray spot intensity; the x axis represents the number of observations; the width of the plot is determined by a kernel density estimation (www.itl.nist.gov); the median of each distribution is shown as a horizontal black bar. All violin plot areas are equal. (B) The method used to define a reactivity call, i.e., the assignment of significant reactivity of one serum to one protein, is depicted using a hypothetical violin plot. I = Log-transformed intensity measurements; I_z = Z-scores (i.e., the distance from the mean of the reference intensity distribution, in units of SD); P (I_z > x) = P value associated with Z-score. As described in the text, P values were corrected for multiple testing by calculating false discovery rates (fdr) for each serum.

Fig. 2.

The immunoproteome of M. tuberculosis. Each of the 484 reactive proteins is mapped onto a circular plot with the corresponding Rv (ID) numbers increasing clockwise, starting at position 1. One bar represents one protein. The bar location on the circular plot closely approximates that of the corresponding gene on the genome. (A) Number of reactive TB suspects’ sera per protein; (B) The same as A but stratified as percent of active TB sera (red) and NTBD sera (blue) reacting with each protein.

Fig. 3.

Reactivity of the immunoproteome. Each bar represents one of the 4,000 proteins of M. tuberculosis. The bar height and blue color gradient reflect the number of reactive sera to each protein. The lightest, outer portion of the proteome (flat white bars) represents non-reactive proteins (approximately 90% of the proteome). The raised bars of any hue of blue represent almost 500 reactive proteins, which define the immunoproteome (approximately 10% of the proteome). The flat, light-blue bars located in between represent an arbitrary number of rarely reactive proteins that may have been missed in the screen. Within the immunoproteome, the dark blue, tallest bars are the proteins showing statistical association with active TB status (approximately 0.5% of the proteome).

Fig. 4.

Patterns of serum reactivity to 13 proteins in active TB. The heat map shows reactivity of sera from active TB patients to each of 13 TB-associated proteins. Shown are only active TB sera reacting to at least one of the 13 proteins (n = 118). Each column represents one serum and each row represents one TB-associated protein. Normalized signal intensities (Z-scores), are visualized as a color spectrum, as indicated.

Fig. 5.

Protein classes associated with serum reactivity in active TB. Results of protein class analysis are shown. False discovery rate correction (fdr) (y axis) is plotted as a function of CERNO P values (x axis) for each protein class tested. Protein classes are shown as per the BioCyc ontology (red symbols), Sanger Institute ontology (blue symbols), and the MtbNet regulon classification (green symbols). Annotations are shown only for protein classes having fdr < 0.1. Numbers in parentheses indicate the number of proteins in each class (listed in SI Appendix, Appendix 1).