Metabolomic signature of amino acids in plasma of patients with non-segmental Vitiligo.

INTRODUCTION: Vitiligo pathogenesis is complicated, and several possibilities were suggested. However, it is well-known that the metabolism of pigments plays a significant role in the pathogenicity of the disease. OBJECTIVES: We explored the role of amino acids in vitiligo using targeted metabolomics. METHODS: The amino acid profile was studied in plasma using liquid chromatography. First, 22 amino acids were derivatized and precisely determined. Next, the concentrations of the amino acids and the molar ratios were calculated in 31 patients and 34 healthy individuals. RESULTS: The differential concentrations of amino acids were analyzed and eight amino acids, i.e., cysteine, arginine, lysine, ornithine, proline, glutamic acid, histidine, and glycine were observed differentially. The ratios of cysteine, glutamic acid, and proline increased significantly in Vitiligo patients, whereas arginine, lysine, ornithine, glycine, and histidine decreased significantly compared to healthy individuals. Considering the percentage of skin area, we also showed that glutamic acid significantly has a higher amount in patients with less than 25% involvement compared to others. Finally, cysteine and lysine are considered promising candidates for diagnosing and developing the disorder with high accuracy (0.96). CONCLUSION: The findings are consistent with the previously illustrated mechanism of Vitiligo, such as production deficiency in melanin and an increase in immune activity and oxidative stress. Furthermore, new evidence was provided by using amino acids profile toward the pathogenicity of the disorder.

Hippocampal tandem mass tag (TMT) proteomics analysis during kindling epileptogenesis in rat.

Epilepsy is a neurological disorder that remains difficult to treat due to the lack of a clear molecular mechanism and incomplete understanding of involved proteins. To identify potential therapeutic targets, it is important to gain insight into changes in protein expression patterns related to epileptogenesis. One promising approach is to analyze proteomic data, which can provide valuable information about these changes. In this study, to evaluate the changes in gene expression during epileptogenesis, LC-MC2 analysis was carried out on hippocampus during stages of electrical kindling in rat models. Subsequently, progressive changes in the expression of proteins were detected as a result of epileptogenesis development. In line with behavioral kindled seizure stages and according to the proteomics data, we described epileptogenesis phases by comparing Stage3 versus Control (S3/C0), Stage5 versus Stage3 (S5/S3), and Stage5 versus Control group (S5/C0). Gene ontology analysis on differentially expressed proteins (DEPs) showed significant changes of proteins involved in immune responses like Csf1R, Aif1 and Stat1 during S3/C0, regulation of synaptic plasticity like Bdnf, Rac1, CaMK, Cdc42 and P38 during S5/S3, and nervous system development throughout S5/C0 like Bdnd, Kcc2 and Slc1a3.There were also proteins like Cox2, which were altered commonly among all three phases. The pathway enrichment analysis of DEPs was also done to discover molecular connections between phases and we have found that the targets like Csf1R, Bdnf and Cox2 were analyzed throughout all three phases were highly involved in the PPI network analysis as hub nodes. Additionally, these same targets underwent changes which were confirmed through Western blotting. Our results have identified proteomic patterns that could shed light on the molecular mechanisms underlying epileptogenesis which may allow for novel targeted therapeutic strategies.

Extracting Potential New Targets for Treatment of Adenoid Cystic Carcinoma using Bioinformatic Methods.

Background: Adenoid cystic carcinoma is a slow-growing malignancy that most often occurs in the salivary glands. Currently, no FDA-approved therapeutic target or diagnostic biomarker has been identified for this cancer. The aim of this study was to find new therapeutic and diagnostic targets using bioinformatics methods. Methods: We extracted the gene expression information from two GEO datasets (including GSE59701 and GSE88804). Different expression genes between adenoid cystic carcinoma (ACC) and normal samples were extracted using R software. The biochemical pathways involved in ACC were obtained by using the Enrichr database. PPI network was drawn by STRING, and important genes were extracted by Cytoscape. Real-time PCR and immunohistochemistry were used for biomarker verification. Results: After analyzing the PPI network, 20 hub genes were introduced to have potential as diagnostic and therapeutic targets. Among these genes, PLCG1 was presented as new biomarker in ACC. Furthermore, by studying the function of the hub genes in the enriched biochemical pathways, we found that insulin-like growth factor type 1 receptor and PPARG pathways most likely play a critical role in tumorigenesis and drug resistance in ACC and have a high potential for selection as therapeutic targets in future studies. Conclusion: In this study, we achieved the recognition of the pathways involving in ACC pathogenesis and also found potential targets for treatment and diagnosis of ACC. Further experimental studies are required to confirm the results of this study.

The Amino Acid Profile in Seminal Plasma of Normozoospermic Men: A Correlation Analysis with Spermiogram Parameters and Total Antioxidant Capacity.

Background: Male infertility is usually determined by the manual evaluation of the semen, namely the standard semen analysis. It is currently impossible to predict sperm fertilizing ability based on the semen analysis alone. Therefore, a more sensitive and selective diagnosis tool is required. Methods: Twelve fresh semen samples were collected from fertile volunteers attending the Avicenna Fertility Center (Tehran, Iran). The seminal plasma (SP) was prepared and subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS), and the total antioxidant capacity (TAC) was analysis. Thirty-four amino acids including essential amino acids (EAA), non-essential amino acids (NEAA), and non-proteinogenic amino acids (NPAA) relative concentration were determined, and the correlation between their concentration with spermiogram parameters and TAC of the SP was analyzed. Results: Significant positive correlations have been found between selected amino acids with the motility (Met and Gln, rs=0.92; Cys, rs=0.72; and Asn, rs=0.82), normal sperm morphology (Met, rs=0.92; Cys, rs=0.72; Glu, rs=0.92; and Asn, rs=0.82), and sperm concentration (Trp, Phe, and Ala). In contrast, several AAs, including Gly, Ser, and Ile showed negative correlations with sperm concentration (rs=-0.93, r=-0.92, and r=-0.89, respectively). Furthermore, TAC showed a positive association only with Tyr (rs=0.79). Conclusion: The strong positive/negative correlations between the seminal metabolic signature and spermiogram demonstrate the significance of determining metabolite levels under normal conditions for normal sperm functions. Combining the metabolome with the clinical characteristics of semen would enable clinicians to look beyond biomarkers toward the clinical interpretation of seminal parameters to explain the biological basis of sperm pathology.