Protein modification in neurodegenerative diseases.

Posttranslational modifications play a crucial role in governing cellular functions and protein behavior. Researchers have implicated dysregulated posttranslational modifications in protein misfolding, which results in cytotoxicity, particularly in neurodegenerative diseases such as Alzheimer disease, Parkinson disease, and Huntington disease. These aberrant posttranslational modifications cause proteins to gather in certain parts of the brain that are linked to the development of the diseases. This leads to neuronal dysfunction and the start of neurodegenerative disease symptoms. Cognitive decline and neurological impairments commonly manifest in neurodegenerative disease patients, underscoring the urgency of comprehending the posttranslational modifications' impact on protein function for targeted therapeutic interventions. This review elucidates the critical link between neurodegenerative diseases and specific posttranslational modifications, focusing on Tau, APP, α-synuclein, Huntingtin protein, Parkin, DJ-1, and Drp1. By delineating the prominent aberrant posttranslational modifications within Alzheimer disease, Parkinson disease, and Huntington disease, the review underscores the significance of understanding the interplay among these modifications. Emphasizing 10 key abnormal posttranslational modifications, this study aims to provide a comprehensive framework for investigating neurodegenerative diseases holistically. The insights presented herein shed light on potential therapeutic avenues aimed at modulating posttranslational modifications to mitigate protein aggregation and retard neurodegenerative disease progression.

Analysis and review of techniques and tools based on machine learning and deep learning for prediction of lysine malonylation sites in protein sequences.

The post-translational modifications occur as crucial molecular regulatory mechanisms utilized to regulate diverse cellular processes. Malonylation of proteins, a reversible post-translational modification of lysine/k residues, is linked to a variety of biological functions, such as cellular regulation and pathogenesis. This modification plays a crucial role in metabolic pathways, mitochondrial functions, fatty acid oxidation and other life processes. However, accurately identifying malonylation sites is crucial to understand the molecular mechanism of malonylation, and the experimental identification can be a challenging and costly task. Recently, approaches based on machine learning (ML) have been suggested to address this issue. It has been demonstrated that these procedures improve accuracy while lowering costs and time constraints. However, these approaches also have specific shortcomings, including inappropriate feature extraction out of protein sequences, high-dimensional features and inefficient underlying classifiers. As a result, there is an urgent need for effective predictors and calculation methods. In this study, we provide a comprehensive analysis and review of existing prediction models, tools and benchmark datasets for predicting malonylation sites in protein sequences followed by a comparison study. The review consists of the specifications of benchmark datasets, explanation of features and encoding methods, descriptions of the predictions approaches and their embedding ML or deep learning models and the description and comparison of the existing tools in this domain. To evaluate and compare the prediction capability of the tools, a new bunch of data has been extracted based on the most updated database and the tools have been assessed based on the extracted data. Finally, a hybrid architecture consisting of several classifiers including classical ML models and a deep learning model has been proposed to ensemble the prediction results. This approach demonstrates the better performance in comparison with all prediction tools included in this study (the source codes of the models presented in this manuscript are available in https://github.com/Malonylation). Database URL: https://github.com/A-Golshan/Malonylation.

Machine learning-based approaches for ubiquitination site prediction in human proteins.

Protein ubiquitination is a critical post-translational modification (PTMs) involved in numerous cellular processes. Identifying ubiquitination sites (Ubi-sites) on proteins offers valuable insights into their function and regulatory mechanisms. Due to the cost- and time-consuming nature of traditional approaches for Ubi-site detection, there has been a growing interest in leveraging artificial intelligence for computer-aided Ubi-site prediction. In this study, we collected experimentally verified Ubi-sites of human proteins from the dbPTM database, then conducted comprehensive state-of-the art computational methods along with standard evaluation metrics and a proper validation strategy for Ubi-site prediction. We presented the effectiveness of our framework by comparing ten machine learning (ML) based approaches in three different categories: feature-based conventional ML methods, end-to-end sequence-based deep learning (DL) techniques, and hybrid feature-based DL models. Our results revealed that DL approaches outperformed the classical ML methods, achieving a 0.902 F1-score, 0.8198 accuracy, 0.8786 precision, and 0.9147 recall as the best performance for a DL model using both raw amino acid sequences and hand-crafted features. Interestingly, our experimental results disclosed that the performance of DL methods had a positive correlation with the length of amino acid fragments, suggesting that utilizing the entire sequence can lead to more accurate predictions in future research endeavors. Additionally, we developed a meticulously curated benchmark for Ubi-site prediction in human proteins. This benchmark serves as a valuable resource for future studies, enabling fair and accurate comparisons between different methods. Overall, our work highlights the potential of ML, particularly DL techniques, in predicting Ubi-sites and furthering our knowledge of protein regulation through ubiquitination in cells.

Epigenetic regulation in lung cancer.

Lung cancer is indeed a major cause of cancer-related deaths worldwide. The development of tumors involves a complex interplay of genetic, epigenetic, and environmental factors. Epigenetic mechanisms, including DNA methylation (DNAm), histone modifications, and microRNA expression, play a crucial role in this process. Changes in DNAm patterns can lead to the silencing of important genes involved in cellular functions, contributing to the development and progression of lung cancer. MicroRNAs and exosomes have also emerged as reliable biomarkers for lung cancer. They can provide valuable information about early diagnosis and treatment assessment. In particular, abnormal hypermethylation of gene promoters and its effects on tumorigenesis, as well as its roles in the Wnt signaling pathway, have been extensively studied. Epigenetic drugs have shown promise in the treatment of lung cancer. These drugs target the aberrant epigenetic modifications that are involved in the development and progression of the disease. Several factors have been identified as drug targets in non-small cell lung cancer. Recently, combination therapy has been discussed as a successful strategy for overcoming drug resistance. Overall, understanding the role of epigenetic mechanisms and their targeting through drugs is an important area of research in lung cancer treatment.

A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.

Post-translational modifications (PTMs) have key roles in extending the functional diversity of proteins and, as a result, regulating diverse cellular processes in prokaryotic and eukaryotic organisms. Phosphorylation modification is a vital PTM that occurs in most proteins and plays a significant role in many biological processes. Disorders in the phosphorylation process lead to multiple diseases, including neurological disorders and cancers. The purpose of this review is to organize this body of knowledge associated with phosphorylation site (p-site) prediction to facilitate future research in this field. At first, we comprehensively review all related databases and introduce all steps regarding dataset creation, data preprocessing, and method evaluation in p-site prediction. Next, we investigate p-site prediction methods, which are divided into two computational groups: algorithmic and machine learning (ML). Additionally, it is shown that there are basically two main approaches for p-site prediction by ML: conventional and end-to-end deep learning methods, both of which are given an overview. Moreover, this review introduces the most important feature extraction techniques, which have mostly been used in p-site prediction. Finally, we create three test sets from new proteins related to the released version of the database of protein post-translational modifications (dbPTM) in 2022 based on general and human species. Evaluating online p-site prediction tools on newly added proteins introduced in the dbPTM 2022 release, distinct from those in the dbPTM 2019 release, reveals their limitations. In other words, the actual performance of these online p-site prediction tools on unseen proteins is notably lower than the results reported in their respective research papers.

A computational study of the R120G mutation in human αB-crystallin: implications for structural stability and functionality.

The eye is a vital organ in the visual system, which is composed of transparent vascular tissue. αB-crystallin, a significant protein found in the lens, plays a crucial role in our understanding of lens diseases. Mutations in the αB-crystallin protein can cause lens diseases, such as cataracts and myopathy. However, the molecular mechanism underlying the R120G mutation is not fully understood. In this study, we utilized molecular dynamics simulations to illustrate, in atomic detail, how the R120G mutation leads to the aggregation of αB-crystallin and scattering of light in the lens. Our findings show that the R120G mutation alters the dynamic and structural properties of the αB-crystallin protein. Specifically, this mutation causes the angle of the hairpin at the C-terminal to increase from 80° to 150°, while reducing the distance between the hydrophobic patches around residues 10 and 44-55 from 1.5 nm to 1 nm. In addition, our results showed that the mutation could disrupt the IPI motif - ß4/ß8 interaction. The disruption of this interaction could affect the αB-crystallin oligomerization and the chaperone activity of αB-crystallin protein. The exposed hydrophobic area at the IPI motif - ß4/ß8 could become the primary site for interprotein interactions, which are responsible for large-scale aggregation. We have demonstrated that, in wild-type αB-crystallin protein, salt bridges R120 and D109, R107 and D80 are formed. However, in the case of the R120G mutation, the salt bridges R120 and R109 are disrupted, and a new salt bridge with a different pattern is formed. In our study, it has been found that all of the changes associated with the R120G mutation are located at the interface of chains A and B, which could impact the multimerization of the αB-crystallin. Previous research on the K92-E99 residue has shown that a salt bridge in the dimer I can reduce the chaperone activity of the protein. Furthermore, the salt bridges R120 and D109, as well as R107 and D80 in dimer II, induce changes in the hydrophobic envelope of ß-sheets in the α-crystallin domain (ACD). These changes could have an impact on the multimerization of the αB-crystallin, leading to disruption of the oligomer structure and aggregation. Moreover, the changes in the αB-crystallin resulting from the R120G mutation can lead to faulty interactions with other proteins, which can cause the aggregation of αB-crystallin with other proteins, such as desmin. These findings may provide new insights into the development of treatments for lens diseases.Communicated by Ramaswamy H. Sarma.

Synergistic cytotoxic effects of an extremely low-frequency electromagnetic field with doxorubicin on MCF-7 cell line.

Breast cancer is one of the leading causes of cancer deaths in women worldwide. Magnetic fields have shown anti-tumor effects in vitro and in vivo as a non-invasive therapy method that can affect cellular metabolism remotely. Doxorubicin (DOX) is one of the most commonly used drugs for treating breast cancer patients. It can be assumed that combining chemotherapy and magnetotherapy is one of the most effective treatments for breast cancer. This study aimed to investigate the potential cytotoxic effect of DOX at low concentrations in combination with extremely low-frequency electromagnetic fields (ELF-EMF; 50 Hz; 20 mT). The breast cancer cell line MCF-7 was examined for oxidative stress, cell cycle, and apoptosis. MCF-7 cells were treated with various concentrations of DOX as an apoptosis-inducing agent and ELF-EMF. Cytotoxicity was examined using the MTT colorimetric assay at 12, 24, and 48 h. Consequently, concentration- and time-dependent cytotoxicity was observed in MCF-7 cells for DOX within 24 h. The MTT assay results used showed that a 2 µM concentration of DOX reduced cell viability to 50% compared with control, and as well, the combination of ELF-EMF and DOX reduced cell viability to 50% compared with control at > 0.25 µM doses for 24 h. In MCF-7 cells, combining 0.25 µM DOX with ELF-EMF resulted in increased ROS levels and DOX-induced apoptosis. Flow cytometry analysis, on the other hand, revealed enhanced arrest of MCF-7 cells in the G0-G1 phase of the cell cycle, as well as inducing apoptotic cell death in MCF-7 cells, implying that the synergistic effects of 0.25 µM DOX and ELF-EMF may represent a novel and effective agent against breast cancer.

An Update of Epigenetic Drugs for the Treatment of Cancers and Brain Diseases: A Comprehensive Review.

Epigenetics has long been recognized as a significant field in biology and is defined as the investigation of any alteration in gene expression patterns that is not attributed to changes in the DNA sequences. Epigenetic marks, including histone modifications, non-coding RNAs, and DNA methylation, play crucial roles in gene regulation. Numerous studies in humans have been carried out on single-nucleotide resolution of DNA methylation, the CpG island, new histone modifications, and genome-wide nucleosome positioning. These studies indicate that epigenetic mutations and aberrant placement of these epigenetic marks play a critical role in causing the disease. Consequently, significant development has occurred in biomedical research in identifying epigenetic mechanisms, their interactions, and changes in health and disease conditions. The purpose of this review article is to provide comprehensive information about the different types of diseases caused by alterations in epigenetic factors such as DNA methylation and histone acetylation or methylation. Recent studies reported that epigenetics could influence the evolution of human cancer via aberrant methylation of gene promoter regions, which is associated with reduced gene function. Furthermore, DNA methyltransferases (DNMTs) in the DNA methylation process as well as histone acetyltransferases (HATs)/histone deacetylases (HDACs) and histone methyltransferases (HMTs)/demethylases (HDMs) in histone modifications play important roles both in the catalysis and inhibition of target gene transcription and in many other DNA processes such as repair, replication, and recombination. Dysfunction in these enzymes leads to epigenetic disorders and, as a result, various diseases such as cancers and brain diseases. Consequently, the knowledge of how to modify aberrant DNA methylation as well as aberrant histone acetylation or methylation via inhibitors by using epigenetic drugs can be a suitable therapeutic approach for a number of diseases. Using the synergistic effects of DNA methylation and histone modification inhibitors, it is hoped that many epigenetic defects will be treated in the future. Numerous studies have demonstrated a link between epigenetic marks and their effects on brain and cancer diseases. Designing appropriate drugs could provide novel strategies for the management of these diseases in the near future.

A review on antimicrobial peptides databases and the computational tools.

Antimicrobial Peptides (AMPs) have been considered as potential alternatives for infection therapeutics since antibiotic resistance has been raised as a global problem. The AMPs are a group of natural peptides that play a crucial role in the immune system in various organisms AMPs have features such as a short length and efficiency against microbes. Importantly, they have represented low toxicity in mammals which makes them potential candidates for peptide-based drugs. Nevertheless, the discovery of AMPs is accompanied by several issues which are associated with labour-intensive and time-consuming wet-lab experiments. During the last decades, numerous studies have been conducted on the investigation of AMPs, either natural or synthetic type, and relevant data are recently available in many databases. Through the advancement of computational methods, a great number of AMP data are obtained from publicly accessible databanks, which are valuable resources for mining patterns to design new models for AMP prediction. However, due to the current flaws in assessing computational methods, more interrogations are warranted for accurate evaluation/analysis. Considering the diversity of AMPs and newly reported ones, an improvement in Machine Learning algorithms are crucial. In this review, we aim to provide valuable information about different types of AMPs, their mechanism of action and a landscape of current databases and computational tools as resources to collect AMPs and beneficial tools for the prediction and design of a computational model for new active AMPs.

Assessment of BIV1-CovIran inactivated vaccine-elicited neutralizing antibody against the emerging SARS-CoV-2 variants of concern.

OBJECTIVES: The BIV1-CovIran vaccine is highly effective against COVID-19. The neutralizing potency of all SARS-CoV-2 vaccines seems to be decreased against variants of concern. We assessed the sensitivity of the Alpha (B.1.1.7), Beta (B.1.351), and Delta (B.1.617.2) variants to neutralizing antibodies (NAbs) present in sera from individuals who had received the BIV1-CovIran candidate vaccine compared with an original Wuhan-related strain. METHODS: The ability of vaccine serum to neutralize the variants was measured using the conventional virus neutralization test. The correlation of spike (S) protein antibody and anti-receptor binding domain with neutralizing activity was investigated. RESULTS: The current study demonstrated that 29 of 32 (90.6%; 95% CI: 75.0-98.0) of the vaccinees developed NAbs against a Wuhan-related strain. It is noteworthy that 28 (87.50%) and 24 of 32 (75%) of the recipients were able to produce NAbs against Alpha, Beta, and Delta variants, respectively. Serum virus-neutralizing titres for different SARS-CoV-2 strains were weakly correlated with anti-receptor binding domain antibodies (Spearman r = 36-42, p < 0.05), but not S-binding antibodies (p > 0.05). DISCUSSION: Although there was a reduction in neutralization titres against the Alpha, Beta, and Delta variants compared with the Wuhan strain, BIV1-CovIran still exhibited potent neutralizing activity against the SARS-CoV-2 variants of concern.

Predicting protein phosphorylation sites in soybean using interpretable deep tabular learning network.

Phosphorylation of proteins is one of the most significant post-translational modifications (PTMs) and plays a crucial role in plant functionality due to its impact on signaling, gene expression, enzyme kinetics, protein stability and interactions. Accurate prediction of plant phosphorylation sites (p-sites) is vital as abnormal regulation of phosphorylation usually leads to plant diseases. However, current experimental methods for PTM prediction suffers from high-computational cost and are error-prone. The present study develops machine learning-based prediction techniques, including a high-performance interpretable deep tabular learning network (TabNet) to improve the prediction of protein p-sites in soybean. Moreover, we use a hybrid feature set of sequential-based features, physicochemical properties and position-specific scoring matrices to predict serine (Ser/S), threonine (Thr/T) and tyrosine (Tyr/Y) p-sites in soybean for the first time. The experimentally verified p-sites data of soybean proteins are collected from the eukaryotic phosphorylation sites database and database post-translational modification. We then remove the redundant set of positive and negative samples by dropping protein sequences with >40% similarity. It is found that the developed techniques perform >70% in terms of accuracy. The results demonstrate that the TabNet model is the best performing classifier using hybrid features and with window size of 13, resulted in 78.96 and 77.24% sensitivity and specificity, respectively. The results indicate that the TabNet method has advantages in terms of high-performance and interpretability. The proposed technique can automatically analyze the data without any measurement errors and any human intervention. Furthermore, it can be used to predict putative protein p-sites in plants effectively. The collected dataset and source code are publicly deposited at https://github.com/Elham-khalili/Soybean-P-sites-Prediction.

Thrombosis in COVID-19 infection: Role of platelet activation-mediated immunity.

BACKGROUND: Thrombosis plays an important role in the Coronavrus Disease 2019 (COVID-19) infection-related complications such as acute respiratory distress syndrome and myocardial infarction. Multiple factors such as oxygen demand injuries, endothelial cells injury related to infection, and plaque formation. MAIN BODY: Platelets obtained from the patients may have severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA, showing that the increased activation potential recommends platelet can be hyper-activated in severely ill SARS-CoV-2 cases. Platelets contain multiple receptors that interact with specific ligands. Pathogen's receptors such as Toll-like receptors (TLRs), NOD-like receptor, C-type lectin receptor family, glycoprotein (GP) such as GPαIIbß3 and GPIbα which allow pathogens to interact with platelets. Platelet TLRs and NOD2 are involved in platelet activation and thrombosis. Accordingly, TLRs are critical receptors that could recognize various endogenous damage-associated molecular patterns and exogenous pathogen-associated molecular patterns (PAMPs). TLRs are considered as important components in the activation of innate immunity response against pathogenic and non-pathogenic components like damaged tissues. TLRs-1,-2,-4,-6,-7 expression on or within platelets has been reported previously. Various PAMPs were indicated to be capable of binding to platelet-TLRs and inducing both the activation and promotion of downstream proinflammatory signaling cascade. CONCLUSION: It is possible that the increased TLRs expression and TLR-mediated platelets activation during COVID-19 may enhance vascular and coronary thrombosis. It may be hypothesized using TLRs antagonist and monoclonal antibody against P-selectin, as the marker of leukocyte recruitment and platelet activation, besides viral therapy provide therapeutic advances in fighting against the thrombosis related complications in COVID-19.

Posttranslational modifications in proteins: resources, tools and prediction methods.

Posttranslational modifications (PTMs) refer to amino acid side chain modification in some proteins after their biosynthesis. There are more than 400 different types of PTMs affecting many aspects of protein functions. Such modifications happen as crucial molecular regulatory mechanisms to regulate diverse cellular processes. These processes have a significant impact on the structure and function of proteins. Disruption in PTMs can lead to the dysfunction of vital biological processes and hence to various diseases. High-throughput experimental methods for discovery of PTMs are very laborious and time-consuming. Therefore, there is an urgent need for computational methods and powerful tools to predict PTMs. There are vast amounts of PTMs data, which are publicly accessible through many online databases. In this survey, we comprehensively reviewed the major online databases and related tools. The current challenges of computational methods were reviewed in detail as well.

Protein C Promotor Haplotypes Associated with Large-Artery Atherosclerosis Stroke in Iranian Population.

Ischemic stroke (IS) is a complex disease regarding its risk factors; among those factors, genetics has an important role. Protein C (PC) is an important antithrombotic enzyme which its genetic variations disrupt the normal cascade of blood coagulation, resulting in thrombosis and increases the chance of stroke. Therefore, we aimed to investigate three single-nucleotide polymorphisms (SNPs) located in the core promoter of PC in order to find their role in this condition in the Iranian population. Blood samples from IS patients (n = 249) and healthy volunteers (n = 203) were collected. Biochemical analysis was performed. Genotyping was conducted on the extracted DNA from blood samples via the HRM technique. Bioinformatic investigations were used to assess how these SNPs may be involved in the IS. Smoking, hypertension, low-density lipoprotein cholesterol, and fasting blood glucose were significantly different between healthy and IS groups. rs1799809 and rs1799810 SNPs were significantly more frequent among IS patients. Also, among four identified haplotypes, CGT was found associated with IS (p = 0.001). It was also found that these SNPs may interfere with the binding of transcription factors to alter the expression of PC. Our data predict that SNPs at the core promoter of PC can affect the binding affinity of transcription factors which in turn reduces the expression of PC and increases the risk of IS.

Study and Characterization of Long Non-coding RUNX1-IT1 among Large Artery Atherosclerosis Stroke Patients Based on the ceRNA Hypothesis.

Recent studies have shed light on the involvement of long non-coding RNAs (lncRNAs) in the initiation and development of stroke. However, the regulatory function of many lncRNAs in large artery atherosclerosis (LAA) has not been fully elucidated. Based on the competing endogenous RNA (ceRNA) hypothesis recently proposed by Pandolfi, the present study was conducted using experimental techniques and bioinformatics to investigate the expression and regulatory function of a lncRNA involved in the development of LAA. The lncRNAs differentially expressed in stroke were obtained using meta-analysis, and one lncRNA was selected for experimental studies on patients with LAA (n = 100) and healthy controls (n = 100) using quantitative real-time polymerase chain reaction (qRT-PCR). The patients were also evaluated through meta-analysis to identify the function of the selected lncRNA, miRNAs, and mRNAs with altered expression in stroke. Finally, the experimental results and meta-analysis findings were integrated, and different functional groups were assigned. The results indicated that the level of lncRNA-RUNX1-IT1 was significantly lower in the patients with LAA compared to the healthy control subjects (p > 0.05). Logistic regression analyses revealed that the expression of lncRNA-RUNX1-IT1 was inversely correlated with LAA (P = 009, OR = 0.871, 95% CI: 0.786-0.965). In addition, a network of differentially expressed genes (DE genes) was created for miRNAs and mRNAs based on their association with lncRNA-RUNX1-IT1. Functional analysis showed that the DE genes in the network are involved in the apoptosis and alternative splicing of RNAs. The findings of the present study suggest that the downregulation of lncRNA-RUNX1-IT1 is associated with LAA development by interrupting the regulatory network of cells. The results of network analysis demonstrated that the lncRNA-RUNX1-IT1 could influence the expression of mRNAs and miRNAs involved in the apoptosis and alternative splicing of RNAs.

Machine Learning Techniques for Soybean Charcoal Rot Disease Prediction.

Early prediction of pathogen infestation is a key factor to reduce the disease spread in plants. Macrophomina phaseolina (Tassi) Goid, as one of the main causes of charcoal rot disease, suppresses the plant productivity significantly. Charcoal rot disease is one of the most severe threats to soybean productivity. Prediction of this disease in soybeans is very tedious and non-practical using traditional approaches. Machine learning (ML) techniques have recently gained substantial traction across numerous domains. ML methods can be applied to detect plant diseases, prior to the full appearance of symptoms. In this paper, several ML techniques were developed and examined for prediction of charcoal rot disease in soybean for a cohort of 2,000 healthy and infected plants. A hybrid set of physiological and morphological features were suggested as inputs to the ML models. All developed ML models were performed better than 90% in terms of accuracy. Gradient Tree Boosting (GBT) was the best performing classifier which obtained 96.25% and 97.33% in terms of sensitivity and specificity. Our findings supported the applicability of ML especially GBT for charcoal rot disease prediction in a real environment. Moreover, our analysis demonstrated the importance of including physiological featured in the learning. The collected dataset and source code can be found in https://github.com/Elham-khalili/Soybean-Charcoal-Rot-Disease-Prediction-Dataset-code.

Evaluation of post-translational modifications in histone proteins: A review on histone modification defects in developmental and neurological disorders.

Post-translational modification (PTM) in histone proteins is a covalent modification which mainly consists of methylation, phosphorylation, acetylation, ubiquitylation, SUMOylation, glycosylation, and ADP-ribosylation. PTMs have fundamental roles in chromatin structure and function. Histone modifications have also been known as epigenetic markers. The PTMs that have taken place in histone proteins can affect gene expression by altering chromatin structure. Histone modifications act in varied biological processes such as transcriptional activation/inactivation, chromosome packaging, mitosis, meiosis, apoptosis, and DNA damage/repair. Defects in the PTMs pathway have been associated with the occurrence and progression of various human diseases, such as cancer, heart failure, autoimmune diseases, and neurodegenerative disorders such as Parkinson's disease, Alzheimer's disease, and Huntington's disease. Histone modifications are reversible and used as potential targets for cancer therapy and prevention. Recent different histone PTMs have key roles in cancer cells since it has been shown that histone PTMs markers in cancers are acetylation, methylation, phosphorylation, and ubiquitylation. In this review, we have summarized the six most studied histone modifications and have examined the role of these modifications in the development of cancer.

Association of lymphotoxin-alpha gene polymorphisms (rs909253, rs1800683 and rs2229094) and risk of large-artery atherosclerosis stroke in Iranian population.

BACKGROUND: Lymphotoxin-alpha (LTA), a proinflammatory cytokine, is significantly associated with the progression of atherosclerosis as an independent hazard factor for stroke. According to new genetic studies, polymorphisms in the LTA gene that influence its expression or biological function may play a role in the progress of stroke; thus, the present case-control study investigated LTA gene polymorphisms (rs909253, rs1800683 and rs2229094) and the risk of large artery atherosclerosis stroke (LAA) in an Iranian population. METHODS: For 211 large artery atherosclerosis patients and 186 ischemic stroke-free controls, genotypes were determined using the tetra-primer amplification-refractory mutation system polymerase chain reaction method. Linkage disequilibrium and estimated haplotypes were analyzed using SNP Analyzer 2 software. The strength of the link between LTA gene polymorphisms (rs1800683, rs909253, and rs2229094) and the risk of stroke was determined using conditional logistic regression. RESULTS: Analysis revealed that the patterns of the rs1800683, rs909253 and rs2229094 genotypes showed no significant difference between the LAA and control group, although the distribution of the GAT (rs1800683G, rs909253A and rs2229094T) haplotype was significantly higher in the control group (odds ratio = 0.707, 95% confidence interval = 0.53-0.942, p = 0.0355). CONCLUSIONS: Our results indicate that the GAT haplotype in LTA gene is associated with a decreased risk of LAA incidence in a northeastern Iranian population.

Thromboxane A synthase 1 gene expression and promotor haplotypes are associated with risk of large artery-atherosclerosis stroke in Iranian population.

Large artery atherosclerosis (LAA) is known as an important cause of ischemic stroke (IS), which is a multifactorial disorder. Many candidate genes have been proposed for IS like (TBXAS1) that plays a significant role in LAA stroke pathogenesis. This is the first study on the evaluation of the association of the five single-nucleotide polymorphisms (SNPs) in TBXAS1 promoter region and the level of TBXAS1 transcript with large-artery atherosclerosis stroke. Five SNPs in TBXAS1 genes were investigated in 248 patients with large-artery atherosclerosis stroke and 199 healthy controls in Iranian population in this case-control study through using the high-resolution melting assay. In addition, the relationships between the selected SNPs with alteration of TBXAS1 gene expressions were investigated in terms of blood platelets through the reverse transcription-quantitative polymerase chain reaction. Multivariate logistic analysis with adjustments indicated that rs10256282CC, rs10237429CC, and rs4590360GG genotypes were associated with large-artery atherosclerosis stroke (adjusted odds ratio = 2.804, 2.872, and 2.432, respectively; P < 0.05, q < 0.05). Furthermore, the frequency of CACCG haplotype in the patients was greatly higher than that in the controls (OR = 1.424, 95% CI: 1.071-1.893, P = 0.014738). In addition, TBXAS1 expression was higher in patients compared to the controls (P = 0.021), and individuals with the homozygous mutated genotypes of these SNPs showed a higher expression level compared to other genotype (P < 0.05). In total, our findings indicate a significant association of TBXAS1 gene rs10256282CC, rs10237429CC, and rs4590360GG polymorphisms with large-artery atherosclerosis stroke susceptibility and the level of TBXAS1 expression, which was not previously reported in any population.

Aflatoxin M1 Contamination Levels in Cheeses Sold in Isfahan Province, Iran.

OBJECTIVES: Aflatoxin M1 (AFM1)-contaminated dairy products pose serious human health risks, causing liver and renal failure if consumed. They are also related to decreased milk and egg production in infected animals. This study investigated the AFM1 contamination levels in cheeses sold in Isfahan province, Iran, by enzyme-linked immunosorbent assay (ELISA). METHODS: A total of 100 white cheese samples were randomly collected from supermarkets in Isfahan province and after extraction using dichloromethane were prepared for the ELISA. RESULTS: Of the 100 samples, 52 (52%) were contaminated by AFM1, at levels ranging from 50.2 to 424.4 ng/kg. The remaining 48% of the samples had undetectable AFM1 levels (< 50 ng/kg). Based on the standard limit set by the European Commission and Iran, 8% (8/100) of the AFM1-positive samples (with concentrations between 250.2 and 424.4 ng/kg) had levels higher than the permissible value of 250 ng/kg. CONCLUSION: Although the percentage of cheese samples in Isfahan province with AFM1 levels exceeding the national permissible limit was low, the examination of cheeses and the milk used for their production is nevertheless important for ensuring public health. Furthermore, optimum storage conditions of animal feed should be ensured, and livestock nutrition must be monitored for the presence of AFM1 and other aflatoxins.