Double immunofluorescence staining of whole-mount small intestinal mucosa samples as a tool for characterization of three-dimensional paratuberculosis granulomas.

Bovine paratuberculosis (PTB) is a chronic granulomatous enteritis, caused by Mycobacterium avium subsp. paratuberculosis (Map). The progression of PTB from subclinical to the clinical stage of the disease is determined locally at the level of the granuloma, a host defence hallmark against mycobacterial infection. Therefore, in-depth characterization of distinct cell populations controlling granuloma formation is critical to understanding PTB progression. Confocal laser scanning microscopy (CLSM) has been extensively used to visualize two or more proteins of interest concomitantly within a variety of cellular structures. As such, it is an invaluable tool for the correct identification and characterization of different cell populations. In this study, a novel approach, CLSM of whole-mount small intestinal mucosa samples, is used to characterize three-dimensional (3-D) paratuberculosis granulomas and epithelioid macrophages. Detailed optimized procedures to perform CLSM in whole mount small intestinal mucosa samples and also in formalin fixed paraffin embedded (FFPE) intestinal tissue sections of Holstein Friesian cows presenting different types of PTB-associated histological lesions are described.

Bovine Intelectin 2 Expression as a Biomarker of Paratuberculosis Disease Progression.

Paratuberculosis (PTB), a chronic granulomatous enteritis caused by Mycobacterium avium subsp. paratuberculosis (MAP), is responsible for important economic losses in the dairy industry. Our previous RNA-sequencing (RNA-Seq) analysis showed that bovine intelectin 2 (ITLN2) precursor gene was overexpressed in ileocecal valve (ICV) samples of animals with focal (log2 fold-change = 10.6) and diffuse (log2 fold-change = 6.8) PTB-associated lesions compared to animals without lesions. This study analyzes the potential use of ITLN2, a protein that has been described as fundamental in the innate immune response to infections, as a biomarker of MAP infection. The presence of ITLN2 was investigated by quantitative immunohistochemical analysis of ICV samples of 20 Holstein Friesian cows showing focal (n = 5), multifocal (n = 5), diffuse (n = 5) and no histological lesions (n = 5). Significant differences were observed in the mean number of ITLN2 immunostained goblet and Paneth cells between the three histopathological types and the control. The number of immunolabelled cells was higher in the focal histopathological type (116.9 ± 113.9) followed by the multifocal (108.7 ± 140.5), diffuse (76.5 ± 97.8) and control types (41.0 ± 81.3). These results validate ITLN2 as a post-mortem biomarker of disease progression.

Detection of latent forms of Mycobacterium avium subsp. paratuberculosis infection using host biomarker-based ELISAs greatly improves paratuberculosis diagnostic sensitivity.

Bovine paratuberculosis (PTB) is a chronic granulomatous enteritis, caused by Mycobacterium avium subsp. paratuberculosis (MAP), responsible for important economic losses in the dairy industry. Current diagnostic methods have low sensitivities for detection of latent forms of MAP infection, defined by focal granulomatous lesions and scarce humoral response or MAP presence. In contrast, patent infections correspond to multifocal and diffuse types of enteritis where there is increased antibody production, and substantial mycobacterial load. Our previous RNA-Seq analysis allowed the selection of five candidate biomarkers overexpressed in peripheral blood of MAP infected Holstein cows with focal (ABCA13 and MMP8) and diffuse (FAM84A, SPARC and DES) lesions vs. control animals with no detectable PTB-associated lesions in intestine and regional lymph nodes. The aim of the current study was to assess the PTB diagnostic potential of commercial ELISAs designed for the specific detection of these biomarkers. The ability of these ELISAs to identify animals with latent and/or patent forms of MAP infection was investigated using serum from naturally infected cattle (n = 88) and non-infected control animals (n = 67). ROC analysis revealed that the ABCA13-based ELISA showed the highest diagnostic accuracy for the detection of infected animals with focal lesions (AUC 0.837, sensitivity 79.25% and specificity 88.06%) and with any type of histological lesion (AUC 0.793, sensitivity 69.41% and specificity 86.57%) improving on the diagnostic performance of the popular IDEXX ELISA and other conventional diagnostic methods. SPARC and MMP8 showed the highest diagnostic accuracy for the detection of animals with multifocal (AUC 0.852) and diffuse lesions (AUC 0.831), respectively. In conclusion, our results suggest that quantification of ABCA13, SPARC and MMP8 by ELISA has the potential for implementation as a diagnostic tool to reliably identify MAP infection, greatly improving early detection of MAP latent infections when antibody responses and fecal shedding are undetectable using conventional diagnostic methods.

'Likelihood-ratio' and 'odds' applied to monitoring of patients as a supplement to 'reference change value' (RCV).

BACKGROUND: Interpretation of serial data in monitoring of patients is usually performed by use of the 'reference change value' (RCV). While this tool for interpretation of measured differences is simple and clear, there are a number of drawbacks attached to the uncritical use of this concept. It is a dichotomised interpretation of continuous data using a fixed probability without any counter hypothesis. Therefore, a tool for better understanding and interpretation of measured differences in monitoring is needed. THEORY: The concept of sensitivity, specificity, likelihood ratios and odds used for diagnostic test evaluations is applied to monitoring by substituting measured concentrations with measured differences. Thus, two frequency distributions of differences are assumed, one for a stable, steady-state, situation and one for a certain change. Values exceeding a measured difference will thus represent 'false change' for the stable and 'true change' for the change and the ratio between these will define the likelihood. By making the hypothesis of change variable and equal to the actual difference, the distribution corresponding to the true changes for the measured difference varies with this. Consequently, the likelihood ratio for change increases with growing measured difference and when used together with the pre-test odds or pre-test probability, the post-test odds and post-test probability, related to the clinical situation, can be calculated. RESULTS: One example is acute intermittent porphyria, where increasing excretion of porphobilinogen is characteristic for an attack. The within-subject biological variation is estimated to 25%, which for two measurements gives a variation of 35% for measured differences. Three pre-test probabilities are assumed and illustrate that post-test odds and probability depends on both the pre-test probability and the measured difference. A second example is monitoring women in a follow-up after treatment of breast cancer, using the tumour marker CA 15-3. The within-subject biological variation is estimated to 14.9% and for differences 21% (2(1/2) x 14.9 due to two measurements). Here, the monitoring is totally scheduled and the frequency of progression depends on the time after treatment. Thus, the pre-test probability varies with time so that a certain measured difference with a given likelihood ratio will result in varying post-test odds depending on time. CONCLUSIONS: The concept presented here expands the earlier concept of RCVs by making it possible to have an estimate of the post-test odds for a certain difference to occur based on likelihood ratios and pre-test odds.

Power function of the reference change value in relation to cut-off points, reference intervals and index of individuality.

The reference change value, defined as RCV = 1.96 x 2(1/2) x(s(I)(2) +s A(2))(1/2), where s(I) is within-subject biological variation and s A is analytical variation, has been used for many years to take clinical decisions in patient monitoring. Furthermore, the index of individuality was defined as II = (s(I)(2) +s(A)(2))(1/2)/s(G) , where s(G) is the between-subject biological variation. This index has been simplified by later authors to s(I)/s(G) and has been used in monitoring situations to determine the utility of population-based reference intervals. Harris stated that when the index of individuality is lower than 0.6, the specific reference interval of the individual - when available - is better than the population-based reference interval. However, if a change within a patient is equivalent to the RCV applied for the significant difference between two measurements, the probability of detecting this change is only 50% (the same probability of missing it). Therefore, to obtain a higher probability of detecting a change by the RCV (e.g., 90%) the interpretation of the index of individuality has to be reconsidered. This contribution compares the power of the RCV to the use of cut-off points and population-based reference intervals. The benefits of the RCV compared to the distance to cut-off point or reference limit are also described in relation to the index of individuality.