Recent developments and application of metabolomics in cancer diseases

      Metabolomics studies provide useful information about health and disease status. Metabolite based investigations on various cancers is a powerful approach to diagnosis, prognosis and therapy of cancer diseases. Recently by using advanced analytical techniques such as NMR and MS and its hyphenation methods, global metabolic profiling of diseases has been possible. It is predictable that international contributions and software developments in the future will lead to accurate instrumental analysis based on  a large number of  human samples that finally will improve validation methods and reach this field from the research phase to the clinical phase. In this review, we also discussed the latest developments in analytical methods, application of data analysis, investigation of useful databases and the curent application of metabolomics in cancer diseases that have led to the identification of related biomarkers. In continuation, we listed biomarkers involved in cancer diseases that have been published during recent years.

Effect of ghrelin on serum metabolites in Alzheimer’s disease model rats; a metabolomics studies based on 1H-NMR technique

Objective(s): Alzheimer’s disease (AD) is dysfunction of the central nervous system and as a neurodegenerative disease. The objective of this work is to investigate metabolic profiling in the serum of animal models of AD compared to healthy controls and then to peruse the role of ghrelin as a therapeutic approach for the AD.Materials and Methods: Nuclear magnetic resonance (NMR) technique was used for identification of metabolites that are differentially expressed in the serum of a rat model of the AD with or without ghrelin treatment. Using multivariate statistical analysis, models were built and indicated.Results: There were significant differences and high predictive power between AD and ghrelin-treated groups. The area under curve (AUC) of receiver operating characteristic (ROC) curve and Q2 were 0.870 and 0.759, respectively. A biomarker panel consisting of 14 metabolites was identified to discriminate the AD from the control group. Another panel of 12 serum metabolites was used to differentiate AD models from treated models. Conclusion: Both panels had good agreements with clinical diagnosis. Analysis of the results displayed that ghrelin improved memory and cognitive abilities. Affected pathways by ghrelin included oxidative stress, and osteoporosis pathways and vascular risk factors

Investigation of metabonomics technique by analyze of NMR data, which method is better? Mean center or auto scale?

The factors such as disease can disrupt homeostasis, resulting in perturbations of endogenous biochemicals that are involved in key metabolic profiles. Metabonomics is useful technique to quantitative description of endogenous metabolites present in a biological sample such as urine, plasma and tissue. High resolution 1H nuclear magnetic resonance (NMR)-based metabonomics is a technique used to analyze and interpret multivariate metabolic data that correlate with changes of physiological conditions. Before any explanation for metabolite data, preprocessing the spectroscopic data is essential. In this paper, we show scaling effects in metabonomics investigation of patients diagnosed with Crohn's and Celiac disease. two techniques of scaling were applied as follows: mean centering and auto scaling. Results reveal that the mean centering is more useful to segregate patients from healthy subjects in the data set of Crohn's and Celiac disease

In-silico study MM/GBSA binding free energy and molecular dynamics simulation of some designed remdesivir derivatives as the inhibitory potential of SARS-CoV-2 main protease

Background and purpose: Coronavirus disease (COVID-19) is one ofthe greatest challenges ofthe twentieth century. Recently, in silico tools help to predict new inhibitors of SARS-CoV-2. In this study, the new compounds based on the remdesivir structure (12 compounds) were designed. Experimental approach: The main interactions of remdesivir and designed compounds were investigated in the 3CLpro active site. The binding free energy of compounds by the MM-GBSA method was calculated and the best compound (compound 12 with the value of -88.173 kcal/mol) was introduced to the molecular dynamics simulation study. Findings/Results: The simulation results were compared with the results of protein simulation without the presence of an inhibitor and in the presence of remdesivir. Additionally, the RMSD results for the protein backbone showed that compound 12 in the second 50 nanoseconds has less fluctuation than the protein alone and in the presence of remdesivir, which indicates the stability of the compound in the active site of the Mpro protein. Furthermore, protein compactness was investigated in the absence of compounds and the presence of compound 12 and remdesivir. The Rg diagram shows a fluctuation of approximately 0.05 A, which indicates the compressibility of the protein in the presence and absence of compounds. The results of the RMSF plot also show the stability of essential amino acids during protein binding. Conclusion and implications: Supported by the theoretical results, compound 12 could have the potential to inhibit the 3CLpro enzyme, which requires further in vitro studies and enzyme inhibition must also be confirmed at protein levels

suppl._Table_1 – Supplemental material for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study

<p>Supplemental material, suppl._Table_1 for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study by Reyhaneh Farrokhi Yekta, Afsaneh Arefi Oskouie, Mostafa Rezaei Tavirani, Mohammad R. Mohajeri-Tehrani and Ahmad R. Soroush in The International Journal of Biological Markers</p

suppl._Figure_2 – Supplemental material for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study

<p>Supplemental material, suppl._Figure_2 for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study by Reyhaneh Farrokhi Yekta, Afsaneh Arefi Oskouie, Mostafa Rezaei Tavirani, Mohammad R. Mohajeri-Tehrani and Ahmad R. Soroush in The International Journal of Biological Markers</p

suppl._Figure_1 – Supplemental material for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study

<p>Supplemental material, suppl._Figure_1 for Decreased apolipoprotein A4 and increased complement component 3 as potential markers for papillary thyroid carcinoma: A proteomic study by Reyhaneh Farrokhi Yekta, Afsaneh Arefi Oskouie, Mostafa Rezaei Tavirani, Mohammad R. Mohajeri-Tehrani and Ahmad R. Soroush in The International Journal of Biological Markers</p

Serum Proteomic Profiling of Obsessive-Compulsive Disorder, Washing Subtype: A Preliminary Study

Introduction: Obsessive-Compulsive Disorder (OCD) is a disabling mental condition that its proteomic profiling is not yet investigated. Proteomics is a valuable tool to discover biomarker approaches. It can be helpful to detect protein expression changes in complex disorders such as OCD. Methods: Here, by the application of 2D gel electrophoresis (2DE), a pilot study of serum proteome profile of females with washing subtype of OCD was performed. Serum samples were obtained from females with washing subtype of OCD. Following the protein extraction from the serum with acetone perception, the samples were subjected to 2DE for separation based on pI and molecular weight (MW) with triple replications. Finally, the protein spots were visualized using Coomassie blue staining method and analyzed by Progenesis SameSpots software. Furthermore, protein-protein interaction (PPI) network analysis was handled by the application of Cytoscape software.&nbsp; Results: The results suggested that 41 matched spots demonstrated significant expression alterations among which 5 proteins including immunoglobulin heavy constant alpha-1 (IGHA1), apolipoprotein A-4 (APOA4), haptoglobin (HP), protein &alpha;-1-antitrypsin (SERPINA1), and component 3 (C3) were identified by database query. Additionally, PPI network analysis indicated the central role of SERPINA1 and C3 in the network integrity. However, albumin (ALB), amyloid precursor protein (APP), and protein &alpha;-1-antitrypsin (APOA1) proteins were important in OCD PPI network as well. The identified proteins were related to 3 processes: acute-phase response, hydrogen peroxide catabolic process, and regulation of triglyceride metabolic process. Conclusion: It was concluded that these proteins may have a fundamental role in OCD pathogenesis. Moreover, the dysregulation of inflammatory and antioxidant systems in OCD risk was suggested by the current study. However, evaluation of bigger sample sizes and application of mass spectrometry are essential requirements to confirm this preliminary evaluation

Introducing Transthyretin as a Differentially Expressed Protein in Washing Subtype of Obsessive-Compulsive Disorder

Introduction: Obsessive-Compulsive Disorder (OCD) as one of the important mental problems is valuable topic for proteomic research studies to better understand the underlying mechanisms of this disorder. Methods: In this paper, gel-based proteomic was used to investigate the proteome profile of 16 female patients with OCD, washing subtype before and after treatment with fluoxetine and comparing them with 20 healthy female controls. Results: One of the abnormally expressed protein spots in this study was introduced and examined for protein-protein interaction network analysis via Cytoscape and its plug-ins. Transthyretin (TTR) protein showed significant expression changes (fold change=1.7, P<0.05). While the expression level of TTR is significantly decreased in OCD patients before any treatments, the trend is partially normalized after treatment with fluoxetine in positive responders. Furthermore, TTR interaction profile shows that the proteins interacting with this protein may get affected as this protein expression trend changes in OCD patients. Conclusion: TTR can be considered for further studies to be validated as a potential biomarker for OCD