Proteome-scale antibody responses and outcome of Mycobacterium tuberculosis infection in nonhuman primates and in tuberculosis patients

Abstract

Background: Biomarkers of progression from latent Mycobacterium tuberculosis infection to active tuberculosis are needed. We assessed correlations between infection outcome and antibody responses in macaques and humans by high-throughput, proteome-scale serological studies.

Methods: Mycobacterium tuberculosis proteome microarrays were probed with serial sera from macaques representing various infection outcomes and with single-point human sera from tuberculosis suspects. Fluorescence intensity data were analyzed by calculating Z scores and associated P values. Temporal changes in macaque antibody responses were analyzed by polynomial regression. Correlations between human responses and sputum bacillary burden were assessed by quantile and hurdle regression.

Results: Macaque outcome groups exhibited distinct antibody profiles: early, transient responses in latent infection and stable antibody increase in active and reactivation disease. In humans, antibody levels and reactive protein numbers increased with bacillary burden. Responses to a subset of 10 proteins were more tightly associated with disease state than reactivity to the broader reactive proteome.

Conclusions: Integration of macaque and human data reveals dynamic properties of antibody responses in relation to outcome and leads to actionable findings for translational research. These include the potential of antibody responses to detect acute infection and preclinical tuberculosis and to identify serodiagnostic proteins for the spectrum of bacillary burden in tuberculosis.

Figure 1.

Antibody responses in cynomolgus macaques. Fourteen cynomolgus macaques were infected intratracheally with approximately 25 colony-forming units of Mycobacterium tuberculosis strain Erdman per animal. Preinfection sera and approximately 10 postinfection sera per animal were tested with proteome microarrays. The number of sera per macaque varied depending on the duration of follow-up, which was shortest for the animals in the active class. Based on Z statistics, 101 proteins were identified as significantly reactive to postinfection sera, compared with pre-infection sera. A, Maximal antibody levels obtained at any time point to the 101 reactive proteins (columns) are shown. The data were normalized to preinfection sera signal intensities by Z score estimation; the red color indicates Z score = 2, and the black color indicates Z score = 0. A, active class; L, latent class; R, reactivation class. B, Maximal antibody levels over time. Each circle represents maximal antibody level to any of the 101 reactive proteins in a particular macaque. Outcome groups are color-coded: blue, active; green, latent; red, reactivation. Locally estimated scatterpoint smoothing (LOESS) regression curves are shown. C, Numbers of reactive proteins over time. Each circle represents the total number of reactive proteins in a particular macaque. As in B, outcome groups are color-coded, and LOESS regression curves are shown.

Figure 2.

Antibody levels in tuberculosis suspects. The Mycobacterium tuberculosis proteome arrays were probed with sera from 169 active tuberculosis and 228 nontuberculosis disease (NTBD) patients. Reactive proteins (n = 356) were identified by Z statistics, and proteins associated with active tuberculosis (n = 10) were identified by odds ratio calculation. A, Heat map of reactivity (Z scores) to 10 tuberculosis-associated proteins (rows) is shown for reactive sera from tuberculosis patients (columns); the red color indicates Z score = 3 and the black color indicates Z score = 0. B, Distribution of maximal antibody levels to any of the 356 reactive proteins by diagnostic class. The box plots show the median of the distribution (horizontal line in the box), the interquartile range (box), the range excluding outliers (whiskers), and the outliers (circles). C, Distribution of maximal antibody levels to any of the 10 tuberculosis-associated proteins by diagnostic class. Data are presented as box plots, as in B.