Identification of Circulating Bacterial Antigens by In Vivo Microbial Antigen Discovery

Detection of microbial antigens in clinical samples can lead to rapid diagnosis of an infection and administration of appropriate therapeutics. A major barrier in diagnostics development is determining which of the potentially hundreds or thousands of antigens produced by a microbe are actually present in patient samples in detectable amounts against a background of innumerable host proteins. In this report, we describe a strategy, termed in vivo microbial antigen discovery (InMAD), that we used to identify circulating bacterial antigens. This technique starts with “InMAD serum,” which is filtered serum that has been harvested from BALB/c mice infected with a bacterial pathogen. The InMAD serum, which is free of whole bacterial cells, is used to immunize syngeneic BALB/c mice. The resulting “InMAD immune serum” contains antibodies specific for the soluble microbial antigens present in sera from the infected mice. The InMAD immune serum is then used to probe blots of bacterial lysates or bacterial proteome arrays. Bacterial antigens that are reactive with the InMAD immune serum are precisely the antigens to target in an antigen immunoassay. By employing InMAD, we identified multiple circulating antigens that are secreted or shed during infection using Burkholderia pseudomallei and Francisella tularensis as model organisms. Potential diagnostic targets identified by the InMAD approach included bacterial proteins, capsular polysaccharide, and lipopolysaccharide. The InMAD technique makes no assumptions other than immunogenicity and has the potential to be a broad discovery platform to identify diagnostic targets from microbial pathogens

Identification of Circulating Bacterial Antigens by In Vivo Microbial Antigen Discovery

Detection of microbial antigens in clinical samples can lead to rapid diagnosis of an infection and administration of appropriate therapeutics. A major barrier in diagnostics development is determining which of the potentially hundreds or thousands of antigens produced by a microbe are actually present in patient samples in detectable amounts against a background of innumerable host proteins. In this report, we describe a strategy, termed in vivo microbial antigen discovery (InMAD), that we used to identify circulating bacterial antigens. This technique starts with "InMAD serum," which is filtered serum that has been harvested from BALB/c mice infected with a bacterial pathogen. The InMAD serum, which is free of whole bacterial cells, is used to immunize syngeneic BALB/c mice. The resulting " InMAD immune serum" contains antibodies specific for the soluble microbial antigens present in sera from the infected mice. The InMAD immune serum is then used to probe blots of bacterial lysates or bacterial proteome arrays. Bacterial antigens that are reactive with the InMAD immune serum are precisely the antigens to target in an antigen immunoassay. By employing InMAD, we identified multiple circulating antigens that are secreted or shed during infection using Burkholderia pseudomallei and Francisella tularensis as model organisms. Potential diagnostic targets identified by the InMAD approach included bacterial proteins, capsular polysaccharide, and lipopolysaccharide. The InMAD technique makes no assumptions other than immunogenicity and has the potential to be a broad discovery platform to identify diagnostic targets from microbial pathogens. IMPORTANCE Effective treatment of microbial infection is critically dependent on early diagnosis and identification of the etiological agent. One means for rapid diagnosis is immunoassay for antigens that are shed into body fluids during infection. Immunoassays can be inexpensive, rapid, and adaptable to a point-of-care format. A major impediment to immunoassay for diagnosis of infectious disease is identification of appropriate antigen targets. This report describes a strategy that can be used for identification of microbial antigens that are shed into serum during infection by the biothreats Burkholderia pseudomallei and Francisella tularensis. Termed InMAD (in vivo microbial antigen discovery), the strategy has the potential for application to a broad spectrum of microbial pathogens

SPR analysis of LPS binding of mAbs 1A4 IgG3, IgG1 and IgG2b using a biotinylated LPS-streptavidin capture platform.

<p>Biotinylated LPS was immobilized on a streptavidin (SA) sensor chip. Sensorgrams were generated by injecting different concentrations of mAbs 1A4 IgG3 (10.4–333 nM), 1A4 IgG1(1.7–8 μM) or 1A4 IgG2b (0.1–3.3 μM) over the chip surface. Data shown is representative of three independent experiments with similar results. <b>Panel A,</b> a diagram depicting the antigen-antibody complex (and a proposed antibody-antibody interaction) formed on the chip surface. <b>Panel B</b> presents the sensorgram profile of 1A4 IgG3 (<b>left</b>) and the corresponding steady-state affinity determination (<b>right</b>). The sensorgrams for 1A4 IgG1 and IgG2b are presented in <b>Panels C and D</b>, respectively.</p

Immunoglobulin G subclass switching impacts sensitivity of an immunoassay targeting <i>Francisella tularensis</i> lipopolysaccharide

<div><p>The CDC Tier 1 select agent <i>Francisella tularensis</i> is a small, Gram-negative bacterium and the causative agent of tularemia, a potentially life-threatening infection endemic in the United States, Europe and Asia. Currently, there is no licensed vaccine or rapid point-of-care diagnostic test for tularemia. The purpose of this research was to develop monoclonal antibodies (mAbs) specific to the <i>F</i>. <i>tularensis</i> surface-expressed lipopolysaccharide (LPS) for a potential use in a rapid diagnostic test. Our initial antigen capture ELISA was developed using murine IgG3 mAb 1A4. Due to the low sensitivity of the initial assay, IgG subclass switching, which is known to have an effect on the functional affinity of a mAb, was exploited for the purpose of enhancing assay sensitivity. The ELISA developed using the IgG1 or IgG2b mAbs from the subclass-switch family of 1A4 IgG3 yielded improved assay sensitivity. However, surface plasmon resonance (SPR) demonstrated that the functional affinity was decreased as a result of subclass switching. Further investigation using direct ELISA revealed the potential self-association of 1A4 IgG3, which could explain the higher functional affinity and higher assay background seen with this mAb. Additionally, the higher assay background was found to negatively affect assay sensitivity. Thus, enhancement of the assay sensitivity by subclass switching is likely due to the decrease in assay background, simply by avoiding the self-association of IgG3.</p></div

LPS binding affinity of each 1A4 subclass mAb analyzed by SPR using an antibody capture analysis approach.

<p>Anti-mouse antibody was covalently immobilized on a CM5 sensor chip. Each subclass of mAb 1A4 was injected individually over the chip surface, followed by injection of various concentrations of LPS (60–8,000 nM). Data shown is representative of three independent experiments with similar results. <b>Panel A</b> illustrates the complex formed on the chip surface. <b>Panel B</b> presents the sensorgrams (<b>top</b>) and steady-state binding analysis (<b>bottom</b>) of each 1A4 subclass variant.</p

Limit of detection of whole killed-cells <i>F</i>. <i>tularensis</i> (10⁴ CFU/mL) for each combination of 1A4 subclass variants as determined by antigen capture ELISA.

<p>Limit of detection of whole killed-cells <i>F</i>. <i>tularensis</i> (10⁴ CFU/mL) for each combination of 1A4 subclass variants as determined by antigen capture ELISA.</p

Competition ELISA for LPS binding of 1A4 subclass variants.

<p>Each 1A4 subclass mAb was assessed for its ability to compete with 1A4 IgG1 HRP conjugate for binding to LPS. Dose-response curve (%inhibition <i>vs</i> mAb concentration) of each 1A4 subclass variant was created by mixing HRP-labeled 1A4 IgG1 (fixed concentration) with various concentrations of unlabeled mAb (1A4 IgG3, IgG1 or IgG2b) before adding to microtiter plates pre-coated with LPS. Percent inhibition was calculated by % inhibition = [(OD₄₅₀ of 1A4 IgG1 HRP alone–OD₄₅₀ of 1A4 IgG1 HRP plus unlabeled mAb)/ OD₄₅₀ of 1A4 IgG1 HRP alone] x 100. The analysis was performed in quadruplicate and data shown are mean ± standard deviation. The IC₅₀ values were calculated from the sigmoidal dose-response curve model using SigmaPlot software.</p

Sequence alignments of mAb 1A4 IgG3, IgG1 and IgG2b variable regions.

<p>The nucleotide sequences of mAbs 1A4 IgG3, IgG1 and IgG2b variable regions were analyzed using IMGT/V-Quest (<a href="http://www.imgt.org" target="_blank">www.imgt.org</a>). The resulting amino acid sequences of heavy chain (<b>Panel A</b>) and light chain (<b>Panel B</b>) framework regions (FR) 1, 2, and 3 and complementarity-determining regions (CDR) 1, 2, and 3 are displayed. The grey highlights indicate the amino acid residues of CDRs.</p

Limit of detection of <i>F</i>. <i>tularensis</i> LPS (ng/mL) for each combination of 1A4 subclass variants as determined by antigen capture ELISA.

<p>Limit of detection of <i>F</i>. <i>tularensis</i> LPS (ng/mL) for each combination of 1A4 subclass variants as determined by antigen capture ELISA.</p

Antibody-antibody interaction as determined by direct ELISA.

<p><b>Panel A</b>, the binding interactions between 1A4 IgG3 and each mAb subclass are demonstrated. <b>Panel B</b>, self-association of each subclass of the mAb 1A4 are shown. The experiments were carried out in quadruplicate. Data shown are mean ± standard deviation.</p