Heterogeneity and molecular landscape of melanoma: implications for targeted therapy.

Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".

Spectroscopic investigation and structural simulation in human serum albumin with hydroxychloroquine/Silybum marianum and a possible potential COVID-19 drug candidate.

In this study, the interaction between human serum albumin (HSA) and the hydroxychloroquine/Silybum marianum (HCQ/SM) mixture was investigated using various techniques. The observed high binding constant (Kb) and Stern-Volmer quenching constant (KSV) indicate a strong binding affinity between the HCQ/SM mixture and HSA. The circular dichroism (CD) analysis revealed that HCQ/SM induced conformational changes in the secondary structure of HSA, leading to a decrease in the α-helical content. UV-Vis analysis exhibited a slight redshift, indicating that the HCQ/SM mixture could adapt to the flexible structure of HSA. The experimental results demonstrated the significant conformational changes in HSA upon binding with HCQ/SM. Theoretical studies were carried out using molecular dynamics simulation via the Gromacs simulation package to explore insights into the drug interaction with HSA-binding sites. Furthermore, molecular docking studies demonstrated that HCQ/SM-HSA exhibited favorable docking scores with the receptor (5FUZ), suggesting a potential therapeutic relevance in combating COVID-19 with a value of -6.24 kcal mol-1. HCQ/SM exhibited stronger interaction with both SARS-CoV-2 virus main proteases compared to favipiravir. Ultimately, the experimental data and molecular docking analysis presented in this research offer valuable insights into the pharmaceutical and biological properties of HCQ/SM mixtures when interacting with serum albumin.

Deciphering the impact of microbial interactions on COPD exacerbation: An in-depth analysis of the lung microbiome.

In microbiome studies, the diversity and types of microbes have been extensively explored; however, the significance of microbial ecology is equally paramount. The comprehension of metabolic interactions among the wide array of microorganisms in the lung microbiota is indispensable for understanding chronic pulmonary disease and for the development of potent treatments. In this investigation, metabolic networks were simulated, and ecological theory was employed to assess the diagnosis of COPD, subsequently suggesting innovative treatment strategies for COPD exacerbation. Lung sputum 16S rRNA paired-end data from 112 COPD patients were utilized, and a supervised machine-learning algorithm was applied to identify taxa associated with sex and mortality. Subsequently, an OTU table with Greengenes 99 % dataset was generated. Finally, the interactions between bacterial species were analyzed using a simulated metabolic network. A total of 1781 OTUs and 1740 bacteria at the genus level were identified. We employed an additional dataset to validate our analyses. Notably, among the more abundant genera, Pseudomonas was detected in females, while Lactobacillus was detected in males. Additionally, a decrease in bacterial diversity was observed during COPD exacerbation, and mortality was associated with the high abundance of the Staphylococcus and Pseudomonas genera. Moreover, an increase in Proteobacteria abundance was observed during COPD exacerbations. In contrast, COPD patients exhibited decreased levels of Firmicutes and Bacteroidetes. Significant connections between microbial ecology and bacterial diversity in COPD patients were discovered, highlighting the critical role of microbial ecology in the understanding of COPD. Through the simulation of metabolic interactions among bacteria, the observed dysbiosis in COPD was elucidated. Furthermore, the prominence of anaerobic bacteria in COPD patients was revealed to be influenced by parasitic relationships. These findings have the potential to contribute to improved clinical management strategies for COPD patients.

Restoring the epigenetic landscape of lung microbiome: potential therapeutic approach for chronic respiratory diseases.

BACKGROUND: Chronic respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and bronchiectasis, present significant threats to global health. Recent studies have revealed the crucial role of the lung microbiome in the development of these diseases. Pathogens have evolved complex strategies to evade the immune response, with the manipulation of host cellular epigenetic mechanisms playing a pivotal role. There is existing evidence regarding the effects of Pseudomonas on epigenetic modifications and their association with pulmonary diseases. Therefore, this study aims to directly assess the connection between Pseudomonas abundance and chronic respiratory diseases. We hope that our findings will shed light on the molecular mechanisms behind lung pathogen infections. METHODS: We analyzed data from 366 participants, including individuals with COPD, acute exacerbations of COPD (AECOPD), bronchiectasis, and healthy individuals. Previous studies have given limited attention to the impact of Pseudomonas on these groups and their comparison with healthy individuals. Two independent datasets from different ethnic backgrounds were used for external validation. Each dataset separately analyzed bacteria at the genus level. RESULTS: The study reveals that Pseudomonas, a bacterium, was consistently found in high concentrations in all chronic lung disease datasets but it was present in very low abundance in the healthy datasets. This suggests that Pseudomonas may influence cellular mechanisms through epigenetics, contributing to the development and progression of chronic respiratory diseases. CONCLUSIONS: This study emphasizes the importance of understanding the relationship between the lung microbiome, epigenetics, and the onset of chronic pulmonary disease. Enhanced recognition of molecular mechanisms and the impact of the microbiome on cellular functions, along with a better understanding of these concepts, can lead to improved diagnosis and treatment.

A deep learning-based framework for predicting survival-associated groups in colon cancer by integrating multi-omics and clinical data.

Precise prognostic classification of patients and identifying survival subgroups and their associated genes can be important clinical references when designing treatment strategies for cancer patients. Multi-omics and data integration techniques are powerful tools to achieve this goal. This study aimed to introduce a machine learning method to integrate three types of biological data, and investigate the performance of two other methods, in identifying the survival dependency of patients. The data included TCGA RNA-seq gene expression, DNA methylation, and clinical data from 368 patients with colon cancer also we use an independent external validation data set, containing 232 samples. Three methods including, hyper-parameter optimized autoencoders (HPOAE), normal autoencoder, and penalized principal component analysis (PPCA) were used for simultaneous data integration and estimation under a COX hazards model. The HPOAE was thought to outperform other methods. The HPOAE had the Log Rank Mantel-Cox value of 14.27 ± 2, and a Breslow-Generalized Wilcoxon value of 13.13 ± 1. Ten miRNA, 11 methylated genes, and 28 mRNA all by (importance of marginal cutoff > 0.95) were identified. The study demonstrated that hsa-miR-485-5p targets both ZMYM1 and tp53, the latter of which has been previously associated with cancer in numerous studies. Furthermore, compared to other methods, the HPOAE exhibited a greater capacity for identifying survival subgroups and the genes associated with them in patients with colon cancer. However, all of the results were obtained by computational methods, and clinical and experimental studies are needed to validate these results.

Cohort profile update: Tehran cardiometabolic genetic study.

The Tehran cardiometabolic genetic study (TCGS) is a large population-based cohort study that conducts periodic follow-ups. TCGS has created a comprehensive database comprising 20,367 participants born between 1911 and 2015 selected from four main ongoing studies in a family-based longitudinal framework. The study's primary goal is to identify the potential targets for prevention and intervention for non-communicable diseases that may develop in mid-life and late life. TCGS cohort focuses on cardiovascular, endocrine, metabolic abnormalities, cancers, and some inherited diseases. Since 2017, the TCGS cohort has augmented by encoding all health-related complications, including hospitalization outcomes and self-reports according to ICD11 coding, and verifying consanguineous marriage using genetic markers. This research provides an update on the rationale and design of the study, summarizes its findings, and outlines the objectives for precision medicine.

Parallel molecular alteration between Alzheimer's disease and major depressive disorder in the human brain dorsolateral prefrontal cortex: an insight from gene expression and methylation profile analyses.

Alzheimer's disease (AD) and major depressive disorder (MDD) are comorbid neuropsychiatric disorders that are among the leading causes of long-term disability worldwide. Recent research has indicated the existence of parallel molecular mechanisms between AD and MDD in the dorsolateral prefrontal cortex (DLPFC). However, the premorbid history and molecular mechanisms have not yet been well characterized. In this study, differentially expressed gene (DEG), differentially co-expressed gene and protein-protein interaction (PPI) network propagation analyses were applied to gene expression data of postmortem DLPFC samples from human individuals diagnosed with and without AD or MDD (AD: cases = 310, control = 157; MDD: cases = 75, control = 161) to identify the main genes in the two disorders' specific and shared biological pathways. Subsequently, the results were evaluated using another four assessment datasets (n1 = 230, n2 = 65, n3 = 58, n4 = 48). Moreover, the postmortem DLPFC methylation status of human subjects with AD or MDD was compared using 68 and 608 samples for AD and MDD, respectively. Eight genes (XIST, RPS4Y1, DDX3Y, USP9Y, DDX3X, TMSB4Y, ZFY and E1FAY) were common DEGs in DLPFC of subjects with AD or MDD. These genes play important roles in the nervous system and the innate immune system. Furthermore, we found HSPG2, DAB2IP, ARHGAP22, TXNRD1, MYO10, SDK1 and KRT82 as common differentially methylated genes in the DLPFC of cases with AD or MDD. Finally, as evidence of shared molecular mechanisms behind this comorbidity, we propose some genes as candidate biomarkers for both AD and MDD. However, more research is required to clarify the molecular mechanisms underlying the co-existence of these two important neuropsychiatric disorders.

A knowledge-based protein-protein interaction inhibition (KPI) pipeline: an insight from drug repositioning for COVID-19 inhibition.

The inhibition of protein-protein interactions (PPIs) by small molecules is an exciting drug discovery strategy. Here, we aimed to develop a pipeline to identify candidate small molecules to inhibit PPIs. Therefore, KPI, a Knowledge-based Protein-Protein Interaction Inhibition pipeline, was introduced to improve the discovery of PPI inhibitors. Then, phytochemicals from a collection of known Middle Eastern antiviral herbs were screened to identify potential inhibitors of key PPIs involved in COVID-19. Here, the following investigations were sequenced: 1) Finding the binding partner and the interface of the proteins in PPIs, 2) Performing the blind ligand-protein inhibition (LPI) simulations, 3) Performing the local LPI simulations, 4) Simulating the interactions of the proteins and their binding partner in the presence and absence of the ligands, and 5) Performing the molecular dynamics simulations. The pharmacophore groups involved in the LPI were also characterized. Aloin, Genistein, Neoglucobrassicin, and Rutin are our new pipeline candidates for inhibiting PPIs involved in COVID-19. We also propose KPI for drug repositioning studies.Communicated by Ramaswamy H. Sarma.

Impact of 5HydroxyMethylCytosine (5hmC) on reverse/direct association of cell-cycle, apoptosis, and extracellular matrix pathways in gastrointestinal cancers.

BACKGROUND: Aberrant levels of 5-hydroxymethylcytosine (5-hmC) can lead to cancer progression. Identification of 5-hmC-related biological pathways in cancer studies can produce better understanding of gastrointestinal (GI) cancers. We conducted a network-based analysis on 5-hmC levels extracted from circulating free DNAs (cfDNA) in GI cancers including colon, gastric, and pancreatic cancers, and from healthy donors. The co-5-hmC network was reconstructed using the weighted-gene co-expression network method. The cancer-related modules/subnetworks were detected. Preservation of three detected 5-hmC-related modules was assessed in an external dataset. The 5-hmC-related modules were functionally enriched, and biological pathways were identified. The relationship between modules was assessed using the Pearson correlation coefficient (p-value < 0.05). An elastic network classifier was used to assess the potential of the 5-hmC modules in distinguishing cancer patients from healthy individuals. To assess the efficiency of the model, the Area Under the Curve (AUC) was computed using five-fold cross-validation in an external dataset. RESULTS: The main biological pathways were the cell cycle, apoptosis, and extracellular matrix (ECM) organization. Direct association between the cell cycle and apoptosis, inverse association between apoptosis and ECM organization, and inverse association between the cell cycle and ECM organization were detected for the 5-hmC modules in GI cancers. An AUC of 92% (0.73-1.00) was observed for the predictive model including 11 genes. CONCLUSION: The intricate association between biological pathways of identified modules may reveal the hidden significance of 5-hmC in GI cancers. The identified predictive model and new biomarkers may be beneficial in cancer detection and precision medicine using liquid biopsy in the early stages.

Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug-gene interaction networks analysis.

Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF-TG (transcription factors-target genes) network was drawn. Afterward, a list of drugs targeting TF-TG genes was obtained from the DGIdb V4.0 database, and two drug-gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF-TG, and drug-gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF-TG, and drug-gene interaction networks.

The AGT epistasis pattern proposed a novel role for ZBED9 in regulating blood pressure: Tehran Cardiometabolic genetic study (TCGS).

INTRODUCTION: High blood pressure is widely regarded as the most important risk factor for cardiovascular diseases. Epistasis analysis may provide additional insight into the genetic basis of hypertension. METHODS: A nested case-control design was used on 4214 unrelated Tehran Cardiometabolic Genetic Study (TCGS) adults to evaluate 65 SNPs of previously associated genes, including ZBED9, AGT, and TNXB. The integrated effect of each gene was determined using the Sequence-based Kernel Association Test (SKAT). We used model-based multifactor dimension reduction (Mb-MDR) and entropy-based gene-gene interaction (IGENT) methods to determine interaction and epistasis patterns. RESULTS: The integrated effect of each gene has a statistically significant association with blood pressure traits (P-value < 0.05). The single-locus analysis identified two missense variants in ZBED9 (rs450630) and AGT (rs4762) associated with hypertension. In the ZBED9 gene, significant local interactions were discovered. The G allele in rs450630 showed an antagonistic effect on hypertension, but interestingly, IGENT analysis revealed significant epistasis effects for different combinations of ZBED9, AGT, and TNXB loci. CONCLUSION: We discovered a novel interaction effect between a significant variant in an essential gene for hypertension (AGT) and a missense variant in ZBED9, which has shifted our focus to ZBED9's role in blood pressure regulation.

Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study.

BACKGROUND: Metabolic syndrome (MetS) is a prevalent multifactorial disorder that can increase the risk of developing diabetes, cardiovascular diseases, and cancer. We aimed to compare different machine learning classification methods in predicting metabolic syndrome status as well as identifying influential genetic or environmental risk factors. METHODS: This candidate gene study was conducted on 4756 eligible participants from the Tehran Cardio-metabolic Genetic study (TCGS). We compared predictive models using logistic regression (LR), Random Forest (RF), decision tree (DT), support vector machines (SVM), and discriminant analyses. Demographic and clinical features, as well as variables regarding common GCKR gene polymorphisms, were included in the models. We used a 10-repeated tenfold cross-validation to evaluate model performance. RESULTS: 50.6% of participants had MetS. MetS was significantly associated with age, gender, schooling years, BMI, physical activity, rs780094, and rs780093 (P < 0.05) as indicated by LR. RF showed the best performance overall (AUC-ROC = 0.804, AUC-PR = 0.776, and Accuracy = 0.743) and indicated BMI, physical activity, and age to be the most influential model features. According to the DT, a person with BMI < 24 and physical activity < 8.8 possesses a 4% chance for MetS. In contrast, a person with BMI ≥ 25, physical activity < 2.7, and age ≥ 33, has 77% probability of suffering from MetS. CONCLUSION: Our findings indicated that, on average, machine learning models outperformed conventional statistical approaches for patient classification. These well-performing models may be used to develop future support systems that use a variety of data sources to identify persons at high risk of getting MetS.

Sex, age, and ethnic dependency of lipoprotein variants as the risk factors of ischemic heart disease: a detailed study on the different age-classes and genders in Tehran Cardiometabolic Genetic Study (TCGS).

Biological processes involving environmental and genetic factors drive the interplay between age- and sex-regulating lipid profile. The relation between variations in the LPA gene with increasing the risk of coronary heart disease is dependent on population differences, sex, and age. The present study tried to do a gene candidate association analysis in people with myocardial infarction (MI) in a 22 year cohort family-based longitudinal cohort study, Tehran Cardiometabolic Genetic Study (TCGS). After adjusting p value by the FDR method, only the association of rs6415084 with the MI probability and the age-of-CHD-onset was significant in males in their middle age (p < 0.005). Surprisingly, a lack of association was observed for the rest of the markers (16 SNPs). These results revealed the moderator effects of age and sex on the association between the genetic variants (SNPs) of LPA and heart disease risk. Our observations may provide new insights into the biology that underlies lipid profile with age or the sexual dimorphism of Lp(a) metabolism. Finally, Lp(a) appears to be an independent risk factor; however, the role of sex and ethnicity is important.

Computer Gaming and Physiological Changes in the Brain: An Insight from QEEG Complexity Analysis.

To compare the pattern of brain waves in video game addicts and normal individuals, a case-control study was carried out on both. Thirty participants were recruited from 14 to 20 years old males from two gaming centers. Twenty healthy participants were gathered from different schools in Tehran using the available sampling method. The QEEG data collection was performed in three states: closed-eye and open-eye states, and during a working memory task. As expected, the power ratios did not show a significant difference between the two groups. Regarding our interest in the complexity of signals, we used the Higuchi algorithm as the feature extractor to provide the input materials for the multilayer perceptron classifier. The results showed that the model had at least a 95% precision rate in classifying the addicts and healthy controls in all three types of tasks. Moreover, significant differences in the Higuchi Fractal Dimension of a few EEG channels have been observed. This study confirms the importance of brain wave complexity in QEEG data analysis and assesses the correlation between EEG-complexity and gaming disorder. Moreover, feature extraction by Higuchi algorithm can render support vector machine classification of the brain waves of addicts and healthy controls more accurate.

High-throughput analysis of the interactions between viral proteins and host cell RNAs.

RNA-protein interactions of a virus play a major role in the replication of RNA viruses. The replication and transcription of these viruses take place in the cytoplasm of the host cell; hence, there is a probability for the host RNA-viral protein and viral RNA-host protein interactions. The current study applies a high-throughput computational approach, including feature extraction and machine learning methods, to predict the affinity of protein sequences of ten viruses to three categories of RNA sequences. These categories include RNAs involved in the protein-RNA complexes stored in the RCSB database, the human miRNAs deposited at the mirBase database, and the lncRNA deposited in the LNCipedia database. The results show that evolution not only tries to conserve key viral proteins involved in the replication and transcription but also prunes their interaction capability. These proteins with specific interactions do not perturb the host cell through undesired interactions. On the other hand, the hypermutation rate of NSP3 is related to its affinity to host cell RNAs. The Gene Ontology (GO) analysis of the miRNA with affiliation to NSP3 suggests that these miRNAs show strongly significantly enriched GO terms related to the known symptoms of COVID-19. Docking and MD simulation study of the obtained miRNA through high-throughput analysis suggest a non-coding RNA (an RNA antitoxin, ToxI) as a natural aptamer drug candidate for NSP5 inhibition. Finally, a significant interplay of the host RNA-viral protein in the host cell can disrupt the host cell's system by influencing the RNA-dependent processes of the host cells, such as a differential expression in RNA. Furthermore, our results are useful to identify the side effects of mRNA-based vaccines, many of which are caused by the off-label interactions with the human lncRNAs.

Revealing transcriptional and post-transcriptional regulatory mechanisms of γ-glutamyl transferase and keratin isoforms as novel cooperative biomarkers in low-grade glioma and glioblastoma multiforme.

Gamma-glutamyltransferase (GGT) and keratins (KRT) are key factors in regulating tumor progression rely on emerging evidence. However, the prognostic values of GGT and KRT isoforms and their regulation patterns at transcriptional and post-transcriptional levels have been rarely studied. In this study, we aimed to identify cooperative prognostic biomarker signature conducted by GGT and KRT genes for overall survival prediction and discrimination in patients with low-grade glioma (LGG) and glioblastoma multiforme (GBM). To this end, we employed a differential expression network analysis on LGG-NORMAL, GBM-NORMAL, and LGG-GBM datasets. Then, all the differentially expressed genes related to a GO term "GGT activity" were excluded. After that, for obtained potential biomarkers genes, differentially expressed lncRNAs were used to detect cis-regulatory elements (CREs) and trans-regulatory elements (TREs). To scrutinize the regulation on the cytoplasm, potential interactions between these biomarker genes and DElncRNAs were predicted. Our analysis, for the first time, revealed that GGT6, KRT33B, and KRT75 in LGG, GGT2, and KRT75 in GBM and KRT75 for LGG to GBM transformation tumors can be novel cooperative prognostic biomarkers that may be applicable for early detection of LGG, GBM, and LGG to GBM transformation tumors. Consequently, KRT75 was the most important gene being regulated at both transcriptional and post-transcriptional levels significantly. Furthermore, CREs and their relative genes were coordinative up-regulated or down-regulated suggesting CREs as regulation points of these genes. In the end, up-regulation of most DElncRNAs that had physical interaction with target genes pints out that the transcripted genes may have obstacles for translation process.

Interplay between SARS-CoV-2 and human long non-coding RNAs.

The long non-coding RNAs (lncRNAs) play a critical regulatory role in the host response to the viral infection. However, little is understood about the transcriptome architecture, especially lncRNAs pattern during the SARS-CoV-2 infection. In the present study, using publicly available RNA sequencing data of bronchoalveolar lavage fluid (BALF) and peripheral blood mononuclear cells (PBMC) samples from COVID-19 patients and healthy individuals, three interesting findings highlighted: (a) More than half of the interactions between lncRNAs-PCGs of BALF samples established by three trans-acting lncRNAs (HOTAIRM1, PVT1 and AL392172.1), which also exhibited the high affinity for binding to the SARS-CoV-2 genome, suggesting the major regulatory role of these lncRNAs during the SARS-CoV-2 infection. (b) lncRNAs of MALAT1 and NEAT1 are possibly contributed to the inflammation development in the SARS-CoV-2 infected cells. (c) In contrast to the 3' part of the SARS-CoV-2 genome, the 5' part can interact with many human lncRNAs. Therefore, the mRNA-based vaccines will not show any side effects because of the off-label interactions with the human lncRNAs. Overall, the putative functionalities of lncRNAs can be promising to design the non-coding RNA-based drugs and to inspect the efficiency of vaccines to overcome the current pandemic.

In silico design of novel aptamers utilizing a hybrid method of machine learning and genetic algorithm.

Aptamers can be regarded as efficient substitutes for monoclonal antibodies in many diagnostic and therapeutic applications. Due to the tedious and prohibitive nature of SELEX (systematic evolution of ligands by exponential enrichment), the in silico methods have been developed to improve the enrichment processes rate. However, the majority of these methods did not show any effort in designing novel aptamers. Moreover, some target proteins may have not any binding RNA candidates in nature and a reductive mechanism is needed to generate novel aptamer pools among enormous possible combinations of nucleotide acids to be examined in vitro. We have applied a genetic algorithm (GA) with an embedded binding predictor fitness function to in silico design of RNA aptamers. As a case study of this research, all steps were accomplished to generate an aptamer pool against aminopeptidase N (CD13) biomarker. First, the model was developed based on sequential and structural features of known RNA-protein complexes. Then, utilizing RNA sequences involved in complexes with positive prediction results, as the first-generation, novel aptamers were designed and top-ranked sequences were selected. A 76-mer aptamer was identified with the highest fitness value with a 3 to 6 time higher score than parent oligonucleotides. The reliability of obtained sequences was confirmed utilizing docking and molecular dynamic simulation. The proposed method provides an important simplified contribution to the oligonucleotide-aptamer design process. Also, it can be an underlying ground to design novel aptamers against a wide range of biomarkers.

Low HDL concentration in rs2048327-G carriers can predispose men to develop coronary heart disease: Tehran Cardiometabolic genetic study (TCGS).

Recent genome-wide association studies (GWAS) highlighted the importance of genetic variations on SLC22A3 and MIA3 genes in developing coronary heart disease (CHD) among different ethnicities. However, the influence of these variations is not recognized within the Iranian population. Hence, in the present study, we aim to investigate two key single nucleotide polymorphisms (SNPs) on CHD incidence in this population. For this purpose, from Tehran Cardiometabolic Genetic Study (TCGS), 453 individuals with CHD were selected as a case and 453 individuals as a control that matched their age and gender. After quality control of two selected SNPs, rs2048327 (SLC22A3) and rs17465637 (MIA3), we used genotyps resulted from chip-typing technology and conducted the logistic regression analysis adjusted for non-genetic risk factors to detect the possible association of these SNPs with the CHD development. Our findings demonstrated the rs2048327-G and rs17465637-C can significantly increase the risk of CHD development about two times in only males and females, respectively. Interestingly, in the male carriers of the risk allele (G) of rs2048327, the low high-density lipoprotein (HDL) level can significantly predispose them to develop coronary heart disease in the future. According to our results, paying more attention to gender and genetic markers can help more efficient coronary heart disease screening and diagnosis.

SARS-CoV-2 infection susceptibility influenced by ACE2 genetic polymorphisms: insights from Tehran Cardio-Metabolic Genetic Study.

The genetic variations among individuals are one of the notable factors determining disease severity and drug response. Nowadays, COVID-19 pandemic has been adversely affecting many aspects of human life. We used the Tehran Cardio-Metabolic Genetic Study (TCGS) data that is an ongoing genetic study including the whole-genome sequencing of 1200 individuals and chip genotyping of more than 15,000 participants. Here, the effect of ACE2 variations by focusing on the receptor-binding site of SARS-CoV-2 and ACE2 cleavage by TMPRSS2 protease were investigated through simulations study. After analyzing TCGS data, 570 genetic variations on the ACE2 gene, including single nucleotide polymorphisms (SNP) and insertion/deletion (INDEL) were detected. Interestingly, two observed missense variants, K26R and S331F, which only the first one was previously reported, can reduce the receptor affinity for the viral Spike protein. Moreover, our bioinformatics simulation of 3D structures and docking of proteins explains important details of ACE2-Spike and ACE2-TMPRSS2 interactions, especially the critical role of Arg652 of ACE2 for protease function of TMPRSS2 was uncovered. As our results show that the genetic variation of ACE2 can at least influence the affinity of this receptor to its partners, we need to consider the genetic variations on ACE2 as well as other genes in the pathways that contribute to the pathogenesis of COVID-19 for designing efficient drugs and vaccines.