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Benabdelkamel H, Rafiullah M, Masood A, Alsaif A, Musambil M, Alfadda AA. Proteomic profiling of thyroid tissue in patients with obesity and benign diffuse goiter. Front Endocrinol (Lausanne) 2022; 13:923465. [PMID: 35966064 PMCID: PMC9365950 DOI: 10.3389/fendo.2022.923465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/04/2022] [Indexed: 11/13/2022] Open
Abstract
Goiter is a term to describe the enlargement of the thyroid gland. The pathophysiology and molecular changes behind development of diffuse benign goiter remains unclear. The present study targeted to identify and describe the alterations in the thyroid tissue proteome from patients (obese euthyroid) with benign diffuse goiter (BDG) using proteomics approach. Thyroid tissue samples, from 7 age and sex matched, patients with BDG and 7 controls were obtained at the time of surgery. An untargeted proteomic analysis of the thyroid tissue was performed out utilizing two-dimensional difference (2D-DIGE) in gel electrophoresis followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for identification of the proteins. Progenesis software was used to identify changes in expression of tissue proteins and found statistically significant differences in abundance in a total of 90 proteins, 46 up and 44 down (1.5-fold change, ANOVA, p ≤ 0.05) in BDG compared to the control group. Bioinformatic analysis using Ingenuity Pathway Analysis (IPA) identified dysregulation of signalling pathways linked to ERK1/2, Glutathione peroxidase and NADPH oxidase associated to organismal injury and abnormalities, endocrine system disorders and cancer. The thyroid tissue proteome in patients with BDG revealed a significant decrease in thyroglobulin along with dysregulation of glycolysis and an increase in prooxidant peroxidase enzymes. Dysregulation of metabolic pathways related to glycolysis, redox proteins, and the proteins associated with maintaining the cytoskeletal structure of the thyrocytes was also identified.
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Affiliation(s)
- Hicham Benabdelkamel
- Proteomics Resource Unit, Obesity Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Mohamed Rafiullah
- Strategic Center for Diabetes Research, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Afshan Masood
- Proteomics Resource Unit, Obesity Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Abdulaziz Alsaif
- Division of Endocrine and Breast Surgery, Department of Surgery, College of Medicine and King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
| | - Mohthash Musambil
- Proteomics Resource Unit, Obesity Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
| | - Assim A. Alfadda
- Proteomics Resource Unit, Obesity Research Center, College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Strategic Center for Diabetes Research, College of Medicine, King Saud University, Riyadh, Saudi Arabia
- Department of Medicine, College of Medicine and King Saud University Medical City, King Saud University, Riyadh, Saudi Arabia
- *Correspondence: Assim A. Alfadda,
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Coupling Machine Learning and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease. A General Overview. Biomolecules 2021; 11:biom11030473. [PMID: 33810079 PMCID: PMC8004861 DOI: 10.3390/biom11030473] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Revised: 03/08/2021] [Accepted: 03/18/2021] [Indexed: 12/15/2022] Open
Abstract
Hepatic biopsy is the gold standard for staging nonalcoholic fatty liver disease (NAFLD). Unfortunately, accessing the liver is invasive, requires a multidisciplinary team and is too expensive to be conducted on large segments of the population. NAFLD starts quietly and can progress until liver damage is irreversible. Given this complex situation, the search for noninvasive alternatives is clinically important. A hallmark of NAFLD progression is the dysregulation in lipid metabolism. In this context, recent advances in the area of machine learning have increased the interest in evaluating whether multi-omics data analysis performed on peripheral blood can enhance human interpretation. In the present review, we show how the use of machine learning can identify sets of lipids as predictive biomarkers of NAFLD progression. This approach could potentially help clinicians to improve the diagnosis accuracy and predict the future risk of the disease. While NAFLD has no effective treatment yet, the key to slowing the progression of the disease may lie in predictive robust biomarkers. Hence, to detect this disease as soon as possible, the use of computational science can help us to make a more accurate and reliable diagnosis. We aimed to provide a general overview for all readers interested in implementing these methods.
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