Apolipoprotein A4 Defines the Villus-Crypt Border in Duodenal Specimens for Celiac Disease Morphometry

Biopsy Proteome Scoring to Determine Mucosal Remodeling in Celiac Disease

BACKGROUND & AIMS

Histologic evaluation of gut biopsies is a cornerstone for diagnosis and management of celiac disease (CeD). Despite its wide use, the method depends on proper biopsy orientation, and it suffers from interobserver variability. Biopsy proteome measurement reporting on the tissue state can be obtained by mass spectrometry analysis of formalin-fixed paraffin-embedded tissue. Here we aimed to transform biopsy proteome data into numerical scores that give observer-independent measures of mucosal remodeling in CeD.

METHODS

A pipeline using glass-mounted formalin-fixed paraffin-embedded sections for mass spectrometry-based proteome analysis was established. Proteome data were converted to numerical scores using 2 complementary approaches: a rank-based enrichment score and a score based on machine learning using logistic regression. The 2 scoring approaches were compared with each other and with histology analyzing 18 patients with CeD with biopsies collected before and after treatment with a gluten-free diet as well as biopsies from patients with CeD with varying degree of remission (n = 22). Biopsies from individuals without CeD (n = 32) were also analyzed.

RESULTS

The method yielded reliable proteome scoring of both unstained and H&E-stained glass-mounted sections. The scores of the 2 approaches were highly correlated, reflecting that both approaches pick up proteome changes in the same biological pathways. The proteome scores correlated with villus height-to-crypt depth ratio. Thus, the method is able to score biopsies with poor orientation.

CONCLUSIONS

Biopsy proteome scores give reliable observer and orientation-independent measures of mucosal remodeling in CeD. The proteomic method can readily be implemented by nonexpert laboratories in parallel to histology assessment and easily scaled for clinical trial settings.

Opportunities for Improving Biopsy and Non-Biopsy-Based Diagnosis of Celiac Disease

The accumulating data regarding a non-biopsy diagnosis of celiac disease has led to its adoption in certain scenarios, although debate on whether and when to use non-biopsy criteria in clinical practice is ongoing. Despite the growing popularity and evidence basis for a biopsy-free approach to diagnosis in the context of highly elevated serologies, there will continue to be a role for a biopsy in some groups. This review summarizes the current evidence supporting a non-biopsy approach and arguments supporting continued reliance on biopsy, and focuses on opportunities to improve both approaches.

Serological Investigation of Persistent Villous Atrophy in Celiac Disease

INTRODUCTION

Persistent villous atrophy (VA) is not uncommon in celiac disease (CeD) while patients take a gluten-free diet (GFD).

METHODS

We conducted a retrospective study with 122 serum samples collected from controls and patients with CeD either at the initial diagnosis or at the follow-up during endoscopy. These samples were assigned to 3 groups: nonceliac control, non-VA CeD (Marsh score 0-2), and VA CeD (Marsh score 3a-3c). We established an in-house multiplex assay to identify potential serological biomarkers for VA. We assessed autoantibodies reported to affect the small intestine, including IgA and IgG antibodies against tissue transglutaminase (tTG), interferons, villin, actin, autoimmune enteropathy-related 75 kDa antigen (AIE-75), and tryptophan hydroxylase (TPH)-1, as well as 27 cytokines. The apolipoproteins quantified included apo A1, apo B-100, and apo A4, which were produced predominantly by the intestinal epithelium or expressed specifically in villi.

RESULTS

Autoantibody levels were high only for tTG antibodies, which performed well in initial CeD diagnosis, but suboptimally for VA prediction during follow-up, because 14.6% of the follow-up patients with VA had low tTG-IgA. Increasing dilution improved tTG-IgA quantification, particularly when the antibody levels were extremely high but did not significantly improve VA detection. Among those with low tTG-IgA and persistent VA, high proinflammatory cytokines were observed in 2 patients. Median low-density lipoprotein cholesterol levels were significantly lower in the VA CeD group ( P = 0.03). Apolipoprotein levels were similar in patients with and without VA but diverged between those on a GFD or not.

DISCUSSION

tTG-IgA as a biomarker is suboptimal for VA prediction while on a GFD. Persistent VA is associated with low low-density lipoprotein cholesterol levels and partially related to persistent high proinflammatory cytokines.

Pathologist-trained machine learning classifiers developed to quantitate celiac disease features differentiate endoscopic biopsies according to modified marsh score and dietary intervention response

BACKGROUND

Histologic evaluation of the mucosal changes associated with celiac disease is important for establishing an accurate diagnosis and monitoring the impact of investigational therapies. While the Marsh-Oberhuber classification has been used to categorize the histologic findings into discrete stages (i.e., Type 0-3c), significant variability has been documented between observers using this ordinal scoring system. Therefore, we evaluated whether pathologist-trained machine learning classifiers can be developed to objectively quantitate the pathological changes of villus blunting, intraepithelial lymphocytosis, and crypt hyperplasia in small intestine endoscopic biopsies.

METHODS

A convolutional neural network (CNN) was trained and combined with a secondary algorithm to quantitate intraepithelial lymphocytes (IEL) with 5 classes on CD3 immunohistochemistry whole slide images (WSI) and used to correlate feature outputs with ground truth modified Marsh scores in a total of 116 small intestine biopsies.

RESULTS

Across all samples, median %CD3 counts (positive cells/enterocytes) from villous epithelium (VE) increased with higher Marsh scores (Type 0%CD3 VE = 13.4; Type 1-3%CD3 VE = 41.9, p < 0.0001). Indicators of villus blunting and crypt hyperplasia were also observed (Type 0-2 villous epithelium/lamina propria area ratio = 0.81; Type 3a-3c villous epithelium/lamina propria area ratio = 0.29, p < 0.0001), and Type 0-1 crypt/villous epithelial area ratio = 0.59; Type 2-3 crypt/villous epithelial area ratio = 1.64, p < 0.0001). Using these individual features, a combined feature machine learning score (MLS) was created to evaluate a set of 28 matched pre- and post-intervention biopsies captured before and after dietary gluten restriction. The disposition of the continuous MLS paired biopsy result aligned with the Marsh score in 96.4% (27/28) of the cohort.

CONCLUSIONS

Machine learning classifiers can be developed to objectively quantify histologic features and capture additional data not achievable with manual scoring. Such approaches should be further investigated to improve biopsy evaluation, especially for clinical trials.

Histologic evaluation in the diagnosis and management of celiac disease: practical challenges, current best practice recommendations and beyond

Celiac disease (CD) is an immunoallergic enteropathy affecting genetically susceptible individuals upon dietary exposure to gluten. In current clinical practice, the diagnosis of CD is based on a combination of clinical, serologic, and histologic factors with the possible exception of pediatric patients. Histopathologic evaluation of small intestinal tissue plays a critical role in the disease diagnosis and management, despite many practical challenges. Recently published best practice guidelines help to standardize biopsy sample procurement, tissue preparation, histology interpretation, and reporting, to optimize patient care. In addition, an increasing demand for monitoring the disease course, particularly demonstrating the efficacy of dietary and nondietary interventions for disease management, calls for the use of quantitative histology. With the advent of a gradual transition toward digital pathology in routine diagnostic practice, quantitative histopathologic evaluation in CD shows a promising future.

Apolipoprotein A-IV Has Bi-Functional Actions in Alcoholic Hepatitis by Regulating Hepatocyte Injury and Immune Cell Infiltration

Alcohol abuse can lead to alcoholic hepatitis (AH), a worldwide public health issue with high morbidity and mortality. Here, we identified apolipoprotein A-IV (APOA4) as a biomarker and potential therapeutic target for AH. APOA4 expression was detected by Gene Expression Omnibus (GEO) databases, Immunohistochemistry, and qRT-PCR in AH. Bioinformatics Methods (protein-protein interaction (PPI) network, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Set Enrichment Analysis (GSEA) were used to show down-stream gene and pathways of APOA4 in AH. AML-12 cells were used to evaluate the biological function of APOA4 using an ELISA kit (AST, ALT, and IL-1β) and flow cytometry (ROS activity). Both in vivo and in vitro, APOA4 expression was significantly elevated in the AH model induced by alcohol (ETOH). AML-12 cell damage was specifically repaired by APOA4 deficiency, while AST, ALT, and IL-1β activity that was increased by ETOH (200 µmol, 12 h) were suppressed. APOA4 inhibition increased intracellular ROS induced by ETOH, which was detected by flow cytometry. Functional and PPI network analyses showed Fcgamma receptor (FCGR) and platelet activation signaling were potential downstream pathways. We identified CIDEC as a downstream gene of APOA4. The CIDEC AUC values for the ROC curves were 0.861. At the same time, APOA4 silencing downregulated the expression of CIDEC, whereas the knockdown of CIDEC did not influence the expression of APOA4 in AML-12 cells. Collectively, APOA4 regulates CIDEC expression and immune cell infiltration and may hold great potential as a biomarker and therapeutic target for AH.