Abstract
Obesity is associated with numerous comorbidities along with abnormalities of the endocrine system, more commonly manifesting as dysfunctions of the thyroid gland such as goiter. Changes in weight, especially an increase, could lead to an increase in the incidence of thyroid dysfunction; however, its pathophysiology remains to be elucidated. In the present study, we aimed to interrogate the changes in the protein distribution and abundance between the lean patients and patients with obesity thyroid tissue sections through utilizing this technique. The FFPE-fixed thyroid tissue blocks from the selected cases and controls were identified and targeted for matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) analysis. Patients in the 30 to 75 years age group and undergoing total thyroidectomy for benign thyroid disease were recruited. Patients with thyroid cancers, autoimmune disorders, and other inflammatory conditions were excluded from the study. The selected patients were divided into two groups according to their BMIs: lean (BMI < 25) and obese (BMI > 35). An initial trial set was used as a pilot study for the optimization of the MALDI IMS protocol that was next applied to the selected thyroid tissues. MALDI IMS data from all the samples were aligned and statistical analysis carried out by k-means and linear discriminant analysis (LDA) classification model using principle component analysis (PCA) results were evaluated between the two groups: controls (lean) and cases (obese). Receiver operator characteristic (ROC) curves were alternatively used to calculate the variability of the identified peptides. The discriminating peptides were also independently identified and related to their corresponding proteins by using liquid chromatography and tandem mass spectrometry (LC-MS/MS) analyses. Eight peptides mainly from thyroglobulin were found to be upregulated whereas 10 others were found to be downregulated in the lean compared to the obese group. Through this technique, we will be able to better understand the relationship between the disease entity and obesity.