Journal of Epigenetics
Volume:2 Issue: 2, Autumn 2021

Taxol facilitates nerve regeneration but it has unwanted side-effects via the tumor suppressive function of miR-34a which induces XIST expression and affects paclitaxel administration. We aimed to examine the neuroprotective effect of Paclitaxel-loaded niosomes (PLNs) to enhance fertility by combining with the Belpharis Persica (B.P) extract in male Wistar rat model of spinal cord injury (SCI).

PLNs were prepared using the thin-film hydration method. After laminectomy at thoracic level (T9/T10) SCI was induced using the weight drop method. Rat treated with either PLNs (2.5mg/kg), PLNs (2.5mg/kg)+B.P extract (300mg/kg-14 days), B.P extract (300mg/kg-14 days), Normal, SCI (normal saline), and sham (laminectomy).

The transmission electron microscopy TEM and laser diffraction methods were confirmed the existence of spherical vesicles with feature sizes about 200 nm. The various behavioral, pathological and molecular parameters (CATSPER gene expression) were distinguished. SCI resulted in a significant decrease (P<0.05) in sperm motility, behavioral skills, and CATSPER genes expression were all regenerated by treatment with combination therapy (PLNs+B.P extract) in comparison to the others. Besides, the increased in sperm motility, and CATSPER genes expression were significantly (P<0.05) demonstrated in PLNs and B.P treated groups.

Taken together, the results indicated that the PLNs and B.P groups showed a significant increase in sperm parameters, but combination therapy caused the greater recovery of both gene expression and motor functions to decrease the neurological deficits no serious adverse reactions and enhanced fertility.

Machine learning methods have been used in genetic studies to build models capable of predicting missing genotypes for both human and animal genetic variations. Genotype imputation is an important process of predicting unknown genotypes. The objective of this study was to investigate the idea of using machine learning as imputation to compare the family-based methods and tried to offer improving the imputation performance in different scenarios. Also, the accuracies of different methods i.e. Support vector Machine; SVM, Random forest; RF are compared. The final population were simulated in the form of different family structures. Therefore, 100 families including one sire with different number of genotyped progenies (2, 3, 4, 5 or 7) were simulated. The number of markers was set to 5000 for whole genome. The sires in families and other scenarios such as, BothParents, sire/dam and one progeny, sire and maternal grandsire were defined to investigate the ability of learning machine algorithm for imputation. The imputation accuracy ranged from 0.78 to 0.99 in different scenarios. Also, least amount of imputation accuracy were achieved for sire and maternal grand sire scenario with both methods. Increasing in number of progenies from 2 to 3 was considerably increased in imputation accuracy (SVM and RF). The imputation of non-genotyped individuals based on parent-offspring trios and close relatives paired is possible. But, the use of child- one parent genotyped, BothParents genotyped and sire and maternal grandsire genotyped, average imputation accuracy would not exceed 85%. While genotyped progenies are the best source of predicted genotypes for ungenotyped individuals and if the number of progeny is more than 4, the imputation accuracy is increased more than 95%. These results confirmed, that the performance of machine learning methods in family of trios has a good accuracy and computational speed, which can be used in estimated breeding value.

Staphylococcus aureus is a bacterial pathogen that is responsible for a broad range of human infections. Infections caused by drug-resistant S. aureus are a global health threat. Biofilm formation capability of S. aureus, which is an important factor in infection establishment and drug resistance and thus therapeutic failure, is mainly mediated by ica operon, which is responsible for the production of polysaccharide intercellular adhesions. Inhibition of bacterial biofilm as a promising antibacterial approach has gained attention recently. In addition, several natural compounds, especially of plant origin, have displayed anti-biofilm potential against bacteria. Allicin is the major bioactive compound found in garlic (Allium sativum) and is responsible for its antimicrobial activity. Thus, this study aimed to evaluate the effects of garlic extract in combination with ciprofloxacin on the biofilm formation of pathogenic S. aureus. Antimicrobial effects and minimum inhibitory concentration (MIC) of either the agent alone or in combination were determined, and the bacterial biofilm formation and expression of the icaA gene were investigated. The results showed that garlic extract in combination with ciprofloxacin had a strong biofilm inhibitory potential and reduced biofilm formation by 54-61% compared to the control. The reduction of bacterial biofilm can be attributed to the reduction of the icaA gene, which was reduced by 59-65%. Thus, this study showed that garlic extract can be used as a promising anti-biofilm agent in combination with antibiotics and can be a good candidate to combat bacterial infections.

Today the disease of COVID-19 comprises the most serious problem against human health worldwide with a high rate of virulence and mortality. The disease is caused by the SARS-CoV-2 virus from the beta coronavirus family. The virus makes use of its surface glycoprotein named S protein or spike to enter the human cells. The virus is attached to its receptor of angiotensin-converting enzyme 2 on the cell surface via its receptor-binding domain and fused after cleavage at S2' site that is carried out by surface protease. Vaccines or drugs interfering with S protein binding or blocking the cleavage sites of S protein could be considered as a treat to get rid of the infection. In the current work and through molecular docking and molecular dynamics experiments, 14 drugs were selected based on their molecular weights among 100 drugs to study their shielding potency toward S protein binding sites. The obtained results indicate that fidaxomicin, niclosamide, and flubendazole bind specifically to the S2' cleavage site; while ivermectin, rapamycin, heparin, azithromycin, clarithromycin, and erythromycin bind both receptor-binding domain and S2' and can prevent virus attachment to its receptor and may be useful as a prophylactic candidate for COVID-19 management after clinical approval.

One of the most important topics in Colorectal Cancer (CRC) is identifying the genes that play a crucial role in cancer progression. Therefore, recognizing the important genes during the CRC progression is important as a biomarker for prognosis. Noncoding RNAs (ncRNAs), the major transcribed RNA molecules in animal cells, do not encode proteins and are mainly involved with the regulation of gene expression. The Colon Cancer Associated Transcript 2 (CCAT2) gene is transcribed to a LncRNA molecule and seems to be associated with several cancers.

A total number of 20 colon cancer specimens and their corresponding healthy specimens from adjacent tissues were studied to investigate the expression of CCAT2. Total RNA was extracted, cDNA was synthesized, and finally, the expression of CCAT2 was determined in CRC and healthy tissues using Real-time Polymerase Chain Reaction (RT-PCR) assay.

The expression of CCAT2 in colorectal tumors and their adjacent healthy tissues was investigated. According to the results, the expression of CCAT2 in colorectal tumors and healthy tissues was significantly different. In other words, the expression of CCAT2 in cancer tissues was higher than the healthy tissues by 4.5 folds.

The increased expression of LncRNA/CCAT2 is a common characteristic of colorectal cancer, and could be a good prognostic marker for this cancer.

Colorectal cancer (CRC) is the third cause of cancer death globally. New evidence suggests that colorectal microbiome dysbiosis may be involved in the cause and development of CRC. This study aimed to investigate the differences in bacterial composition and diversity between CRC samples and healthy individuals (HC) based on age through high-throughput 16S rRNA sequences. Biopsy samples were obtained from 17 CRC patients and 13 healthy controls (HC). We analyzed the colon microbiome composition and diversity by alpha and beta diversity. The results showed that colon microbial diversity was significantly higher in the CRC-32-50 and CRC-50-75 groups than in the healthy controls. Still, on the other hand, the diversity of group HC-32-50 was lower than all other groups. Prevotella, Faecalibacterium, Fusobacterium, and Akkermansia were overrepresented in the CRC-32-50, while Bacteroides were in the HC-32-50 group. Our results showed that the diversity and composition of the two groups, HC-32-50 and CRC-32-50, were significantly different. These findings suggest that dysbiosis is more common in CRC patients under the age of 50 than in those over 50. Further studies on the colon microbiome are needed to determine the diversity and composition of the colon microbiome in age-related colorectal cancer to complete our understanding of the impact of the microbiome on the progression of colon cancer.