Immunohistochemical and morphometric analysis of intestinal full-thickness biopsy samples from cats with lymphoplasmacytic inflammatory bowel disease

Fecal Calprotectin Concentrations in Cats with Chronic Enteropathies

Diagnosis of feline chronic inflammatory enteropathies (CIE) and the differentiation from small cell intestinal lymphoma (SCL) can be challenging. Intestinally expressed calprotectin (S100A8/A9 protein complex) appears to be part of the complex pathogenesis of feline chronic enteropathies (FCE). Fecal calprotectin is a non-invasive biomarker for intestinal inflammation in humans and dogs but has not yet been evaluated in cats. We hypothesized that fecal calprotectin (fCal) concentrations are increased in FCE, correlate with clinical and/or histologic disease severity, and distinguish cases of CIE from SCL. This case-control study included fecal samples and patient data from cats with CIE (n = 34), SCL (n = 17), other gastrointestinal (GI) diseases (n = 16), and cats with no clinical signs of GI disease (n = 32). fCal concentrations were measured using the immunoturbidimetric fCal turbo assay (Bühlmann Laboratories). Compared to healthy cats, fCal concentrations were significantly increased in CIE, SCL, and other diseases (all p < 0.0001), but were not different between these three groups (all p > 0.05), or between cats with extra-GI diseases and healthy controls. These findings suggest that fCal may have utility as a clinical biomarker for FCE but not for intestinal disease differentiation. It further supports the role of calprotectin in the pathogenesis of the spectrum of FCE, which includes CIE and SCL.

ACVIM consensus statement guidelines on diagnosing and distinguishing low-grade neoplastic from inflammatory lymphocytic chronic enteropathies in cats

BACKGROUND

Lymphoplasmacytic enteritis (LPE) and low-grade intestinal T cell lymphoma (LGITL) are common diseases in older cats, but their diagnosis and differentiation remain challenging.

OBJECTIVES

To summarize the current literature on etiopathogenesis and diagnosis of LPE and LGITL in cats and provide guidance on the differentiation between LPE and LGITL in cats. To provide statements established using evidence-based approaches or where such evidence is lacking, statements based on consensus of experts in the field.

ANIMALS

None.

METHODS

A panel of 6 experts in the field (2 internists, 1 radiologist, 1 anatomic pathologist, 1 clonality expert, 1 oncologist) with the support of a human medical immunologist, was formed to assess and summarize evidence in the peer-reviewed literature and complement it with consensus recommendations.

RESULTS

Despite increasing interest on the topic for clinicians and pathologists, few prospective studies were available, and interpretation of the pertinent literature often was challenging because of the heterogeneity of the cases. Most recommendations by the panel were supported by a moderate or low level of evidence. Several understudied areas were identified, including cellular markers using immunohistochemistry, genomics, and transcriptomic studies.

CONCLUSIONS AND CLINICAL IMPORTANCE

To date, no single diagnostic criterion or known biomarker reliably differentiates inflammatory lesions from neoplastic lymphoproliferations in the intestinal tract of cats and a diagnosis currently is established by integrating all available clinical and diagnostic data. Histopathology remains the mainstay to better differentiate LPE from LGITL in cats with chronic enteropathy.

An ancient haplotype containing antimicrobial peptide gene variants is associated with severe fungal skin disease in Persian cats

Dermatophytosis, also known as ringworm, is a contagious fungal skin disease affecting humans and animals worldwide. Persian cats exhibit severe forms of the disease more commonly than other breeds of cat, including other long-haired breeds. Certain types of severe dermatophytosis in humans are reportedly caused by monogenic inborn errors of immunity. The goal of this study was to identify genetic variants in Persian cats contributing to the phenotype of severe dermatophytosis. Whole-genome sequencing of case and control Persian cats followed by a genome-wide association study identified a highly divergent, disease-associated haplotype on chromosome F1 containing the S100 family of genes. S100 calcium binding protein A9 (S100A9), which encodes a subunit of the antimicrobial heterodimer known as calprotectin, contained 13 nonsynonymous variants between cases and controls. Evolutionary analysis of S100A9 haplotypes comparing cases, controls, and wild felids suggested the divergent disease-associated haplotype was likely introgressed into the domestic cat lineage and maintained via balancing selection. We demonstrated marked upregulation of calprotectin expression in the feline epidermis during dermatophytosis, suggesting involvement in disease pathogenesis. Given this divergent allele has been maintained in domestic cat and wildcat populations, this haplotype may have beneficial effects against other pathogens. The pathogen specificity of this altered protein should be investigated before attempting to reduce the allele frequency in the Persian cat breed. Further work is needed to clarify if severe Persian dermatophytosis is a monogenic disease or if hidden disease-susceptibility loci remain to be discovered. Consideration should be given to engineering antimicrobial peptides such as calprotectin for topical treatment of dermatophytosis in humans and animals.

Fungal skin infections known as ringworm or dermatophytosis affect billions of humans and animals worldwide. Normally the disease is self-limiting in affected individuals. The Persian cat breed is a popular breed known for its long hair coat and short nose as well as its propensity to develop severe, chronic dermatophytosis. By examining the genomes of Persian cats, we discovered that a specific region of DNA is highly altered between cats with and without severe dermatophytosis. The DNA sequence in this region is particularly divergent within a cluster of genes involved in immune defense against pathogens. Notably, alterations to the DNA sequence cause several changes in the antimicrobial protein known as calprotectin, which defends against pathogens in the skin of cats. Persian cats with severe dermatophytosis have a version of calprotectin similar to a version maintained by certain desert-dwelling wild felids such as sand cats and Asiatic wildcats. Therefore, we think this version of the protein is beneficial in some environments or against certain pathogens but not against the fungus that causes ringworm in cats. Our findings suggest changes to calprotectin may affect pathogen specificity and engineered calprotectin could be considered as a novel therapy for dermatophytosis in humans and animals.

Evaluation of supervised machine-learning algorithms to distinguish between inflammatory bowel disease and alimentary lymphoma in cats

Inflammatory bowel disease (IBD) and alimentary lymphoma (ALA) are common gastrointestinal diseases in cats. The very similar clinical signs and histopathologic features of these diseases make the distinction between them diagnostically challenging. We tested the use of supervised machine-learning algorithms to differentiate between the 2 diseases using data generated from noninvasive diagnostic tests. Three prediction models were developed using 3 machine-learning algorithms: naive Bayes, decision trees, and artificial neural networks. The models were trained and tested on data from complete blood count (CBC) and serum chemistry (SC) results for the following 3 groups of client-owned cats: normal, inflammatory bowel disease (IBD), or alimentary lymphoma (ALA). Naive Bayes and artificial neural networks achieved higher classification accuracy (sensitivities of 70.8% and 69.2%, respectively) than the decision tree algorithm (63%, p < 0.0001). The areas under the receiver-operating characteristic curve for classifying cases into the 3 categories was 83% by naive Bayes, 79% by decision tree, and 82% by artificial neural networks. Prediction models using machine learning provided a method for distinguishing between ALA–IBD, ALA–normal, and IBD–normal. The naive Bayes and artificial neural networks classifiers used 10 and 4 of the CBC and SC variables, respectively, to outperform the C4.5 decision tree, which used 5 CBC and SC variables in classifying cats into the 3 classes. These models can provide another noninvasive diagnostic tool to assist clinicians with differentiating between IBD and ALA, and between diseased and nondiseased cats.