Counting Mycobacteria in Infected Human Cells and Mouse Tissue: A Comparison between qPCR and CFU

Due to the slow growth rate and pathogenicity of mycobacteria, enumeration by traditional reference methods like colony counting is notoriously time-consuming, inconvenient and biohazardous. Thus, novel methods that rapidly and reliably quantify mycobacteria are warranted in experimental models to facilitate basic research, development of vaccines and anti-mycobacterial drugs. In this study we have developed quantitative polymerase chain reaction (qPCR) assays for simultaneous quantification of mycobacterial and host DNA in infected human macrophage cultures and in mouse tissues. The qPCR method cannot discriminate live from dead bacteria and found a 10- to 100-fold excess of mycobacterial genomes, relative to colony formation. However, good linear correlations were observed between viable colony counts and qPCR results from infected macrophage cultures (Pearson correlation coefficient [r] for M. tuberculosis = 0.82; M. a. avium = 0.95; M. a. paratuberculosis = 0.91). Regression models that predict colony counts from qPCR data in infected macrophages were validated empirically and showed a high degree of agreement with observed counts. Similar correlation results were also obtained in liver and spleen homogenates of M. a. avium infected mice, although the correlations were distinct for the early phase (<day 9 post-infection) and later phase (≥day 20 post-infection) liver r = 0.94 and r = 0.91; spleen r = 0.91 and r = 0.87, respectively. Interestingly, in the mouse model the number of live bacteria as determined by colony counts constituted a much higher proportion of the total genomic qPCR count in the early phase (geometric mean ratio of 0.37 and 0.34 in spleen and liver, respectively), as compared to later phase of infection (geometric mean ratio of 0.01 in both spleen and liver). Overall, qPCR methods offer advantages in biosafety, time-saving, assay range and reproducibility compared to colony counting. Additionally, the duplex format allows enumeration of bacteria per host cell, an advantage in experiments where variable cell death can give misleading colony counts

A Sugar Rush for Innate Immunity

Early detection of microbial patterns is a hallmark of innate immunity and essential for clearance of invading pathogens. A recent Nature publication by Zhou et al. (2018) has uncovered ALPK1 as a pattern recognition receptor for Gram-negative bacteria triggering NF-kappaB activation and identified the bacterial sugar ADP-Hep as its ligand

Photochemical Internalization of Peptide Antigens Provides a Novel Strategy to Realize Therapeutic Cancer Vaccination

Effective priming and activation of tumor-specific CD8+ cytotoxic T lymphocytes (CTLs) is crucial for realizing the potential of therapeutic cancer vaccination. This requires cytosolic antigens that feed into the MHC class I presentation pathway, which is not efficiently achieved with most current vaccination technologies. Photochemical internalization (PCI) provides an emerging technology to route endocytosed material to the cytosol of cells, based on light-induced disruption of endosomal membranes using a photosensitizing compound. Here, we investigated the potential of PCI as a novel, minimally invasive, and well-tolerated vaccination technology to induce priming of cancer-specific CTL responses to peptide antigens. We show that PCI effectively promotes delivery of peptide antigens to the cytosol of antigen-presenting cells (APCs) in vitro. This resulted in a 30-fold increase in MHC class I/peptide complex formation and surface presentation, and a subsequent 30- to 100-fold more efficient activation of antigen-specific CTLs compared to using the peptide alone. The effect was found to be highly dependent on the dose of the PCI treatment, where optimal doses promoted maturation of immature dendritic cells, thus also providing an adjuvant effect. The effect of PCI was confirmed in vivo by the successful induction of antigen-specific CTL responses to cancer antigens in C57BL/6 mice following intradermal peptide vaccination using PCI technology. We thus show new and strong evidence that PCI technology holds great potential as a novel strategy for improving the outcome of peptide vaccines aimed at triggering cancer-specific CD8+ CTL responses

Regression models for predicting log(CFU) from log(qPCR) in macrophage cell cultures.

<p>Regression models for predicting log(CFU) from log(qPCR) for <i>M. tuberculosis</i>, <i>M. a. avium</i> and <i>M. a. paratuberculosis</i> in <i>in vitro</i> infected macrophage cell cultures derived from the data in respective training subset for each mycobacteria. Regression line in the middle with 95% prediction limits for an <u>individual log(CFU)</u> on each side. As it is customary to do multiple replicate CFU measurements from the same biological sample, note that the 95% prediction limits for an individual log(CFU) are wider apart than the corresponding 95% prediction limits for the predicted mean log(CFU) (not shown) for multiple measurements. Hence, the regressed point estimate for the predicted log(CFU) will be the same, but using the models as presented will tend to give wider and in fact more conservative estimates of the confidence intervals if used to predict the mean log(CFU) of multiple measurements as compared to the individual log(CFU).</p

Standard Curves for the Mycobacterial, Human and Mouse targets used.

<p>Typical standard curves for the qPCR assays generated from serial dilutions of genomic <i>M. tuberculosis H37Rv</i> with slope −3,255 (A.), human genomic DNA with slope −3,141(B.) and mouse genomic DNA with slope −3,225(C.). The PCR reactions display similar efficiency (E) of near 100% as given by the equation E = 10^(−1/slope)−1.</p

Experimentally determined regression equations for prediction of log(CFU) from log(qPCR) in infected macrophage cultures.

<p>Experimentally determined regression equations for prediction of log(CFU) from log(qPCR) in infected macrophage cultures.</p

Key Characteristics of the Mycobacterial <i>GroEL2 gene</i> qPCR Assay.

<p>Key Characteristics of the Mycobacterial <i>GroEL2 gene</i> qPCR Assay.</p

Comparison of Duplex qPCR with corresponding Singleplex qPCR in three simulated situations.

<p>C_T = Treshold cycle.</p