Investigation of SCCmec types using the real time PCR method in cefoxitin-resistant Staphylococcus aureus isolates.

BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) is an important pathogen that can cause many community and hospital-acquired infections. This study was conducted to investigate the SCCmec gene types responsible for methicillin resistance in MRSA isolates isolated from hospitalised patients. MATERIAL AND METHODS: MRSA isolates isolated from samples sent from various clinics to Gaziantep University Hospital Microbiology Laboratory between March 2021-January 2022 were included in the study. Bacteria were identified using by VITEK 2 automated system. Cefoxitin (FOX) resistance was determined by the disc diffusion method according to EUCAST standards. Cefoxitin resistance was confirmed by the Penicillin Binding Protein 2' latex agglutination test. Types of mecA, mecC, coa, nuc, Panton Valentin Leukocidin (PVL), ccrC2, class A mec, SCCmec types in isolates detected as MRSA were investigated by real-time PCR. RESULTS: In this study, 116 isolates meeting the study criteria were examined. By detecting the nuc and coa genes in all isolates by PCR, the phenotypic identification of S.aureus was confirmed. While the mecA gene was detected in all MRSA isolates, no mecC gene was detected in any isolates. Detected SCCmec types were as follows; SCCmec Type 1 (2.6%), Type II (28.4%), Type III (12.9%), Type IVa (11.2%), Type IVb (3.4%), Type IVc (3.4%), Type IVg (12.1%), Type V (0.9%), Type VII (4.3%), Type VIII (18.1%), Type IX (0.9%), Type XII (1.7%). On the other hand, SCCmec Type VI, X, XI and XIII were not found in any isolate. It was determined that four of the MRSA isolates (3.4%) carried the PVL gene that two (50%) of these were found in SCCmec Type VIII. CONCLUSION: Monitoring of FOX resistance is an effective and safe method for determination of MRSA isolates. The change in the mec gene causes resistance, which should be monitored regularly with molecular methods. Our study is the first study in Turkey.

Expression patterns of LncRNA-GAS5 and its target APOBEC3C gene through miR-103 in breast cancer patients.

Early diagnosis of breast cancer can increase the survivability of the patients and the patient's quality of life. There is growing evidence demonstrating the active role of LncRNA-GAS5 and miR-103 in cancer biology. APOBEC enzymes are important players in immunity and may contribute to carcinogenesis. Mutation and expression alteration in the APOBEC gene family was found to have a strong correlation with breast cancer risk. This study aimed to evaluate the expression level of lncRNA-GAS5 and its target APOBEC3C in women with breast cancer through expression evaluation of miR-103. Moreover, the interaction between lncRNA-GAS5 and miR-103 was studied. In the present study, forty paired tumor and normal samples classified based on breast cancer subtypes and clinical features of patients were analyzed using gene expression studies. Immunohistochemical analysis of the gene products was performed to classify tumors. The RNA samples were extracted from breast tissue. Real-time PCR was conducted for APOBEC3C and Lnc-RNA GAS5 expression. In addition, miR-103a miScript Primer Assay was utilized for the expression of miR-103-5p. It was revealed that the expression level of APOBEC3C and lncRNA-GAS5 were significantly down-regulated; however, the miRNA-103 expression level was significantly up-regulated. GAS5 expression was positively correlated with APOBEC3C expression and negatively correlated with miR-103 expression. In conclusion, we observed down-regulation of APOBEC3C and LncRNA-GAS5 and up-regulation of miRNA 103 in breast cancer patients. The expression of GAS5 may provide a new potential treatment target for breast cancer. To clarify the role of these molecules in the cellular signaling pathways, further studies are required.

Determination of the interaction between the receptor binding domain of 2019-nCoV spike protein, TMPRSS2, cathepsin B and cathepsin L, and glycosidic and aglycon forms of some flavonols.

The novel coronavirus (COVID-19, SARS-CoV-2) is a rapidly spreading disease with a high mortality. In this research, the interactions between specific flavonols and the 2019-nCoV receptor binding domain (RBD), transmembrane protease, serine 2 (TMPRSS2), and cathepsins (CatB and CatL) were analyzed. According to the relative binding capacity index (RBCI) calculated based on the free energy of binding and calculated inhibition constants, it was determined that robinin (ROB) and gossypetin (GOS) were the most effective flavonols on all targets. While the binding free energy of ROB with the spike glycoprotein RBD, TMPRSS2, CatB, and CatL were -5.02, -7.57, -10.10, and -6.11 kcal/mol, the values for GOS were -4.67, -5.24, -8.31, and -6.76, respectively. Furthermore, both compounds maintained their stability for at least 170 ns on respective targets in molecular dynamics simulations. The molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations also corroborated these data. Considering Lipinski's rule of five, ROB and GOS exhibited 3 (MW>500, N or O>10, NH or OH>5), and 1 (NH or OH>5) violations, respectively. Neither ROB nor GOS showed AMES toxicity or hepatotoxicity. The LD50 of these compounds in rats were 2.482 and 2.527 mol/kg, respectively. Therefore, we conclude that these compounds could be considered as alternative therapeutic agents in the treatment of COVID-19. However, the possible inhibitory effects of these compounds on cytochromes (CYPs) should be verified by in vitro or in vivo tests and their adverse effects on cellular energy metabolism should be minimized by performing molecular modifications if necessary.

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques.

To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group). A total of 10 methicillin-resistant S. aureus (MRSA), 3 methicillin-sensitive S. aureus (MSSA) and 6 L. pneumophila isolates were used. The obtained spectra indicated high reproducibility and repeatability with a high signal to noise ratio. Principal component analysis (PCA), hierarchical cluster analysis (HCA), and various supervised classification algorithms were used to discriminate both S. aureus strains and L. pneumophila. Although there were no noteworthy differences between MRSA and MSSA spectra when viewed with the naked eye, some peak intensity ratios such as 732/958, 732/1333, and 732/1450 proved that there could be a significant indicator showing the difference between them. The k-nearest neighbors (kNN) classification algorithm showed superior classification performance with 97.8% accuracy among the traditional classifiers including support vector machine (SVM), decision tree (DT), and naïve Bayes (NB). Our results indicate that SERS combined with machine learning can be used for the detection of antibiotic-resistant and susceptible bacteria and this technique is a very promising tool for clinical applications.

Antitumoral effect of a selective Rho-kinase inhibitor Y-27632 against Ehrlich ascites carcinoma in mice.

BACKGROUND: The Rho proteins and Rho-kinase (ROCK) enzymes are responsible for signal transduction, and cause cell permeability, contractility, differentiation, migration, proliferation or apoptosis depending on cell types. All of these functions are vital for cancer initiation and progression. In this study, the preventive and protective effects of a selective ROCK inhibitor Y-27632 against Ehrlich ascites carcinoma in Swiss albino mice were investigated. METHODS: Adult male albino mice were divided into five equal groups, and Y-27632 (0.1, 1, and 10 mg/kg) was given to groups as two steps; before (pre-carcinoma) and after inoculation of carcinoma cell suspensions (post-carcinoma). At the end of the experiments (at day 15), cardiac blood samples, the ascitic fluid, and intestinal specimens were collected for histopathology and biochemical investigation. RESULTS: Significant decreases in the body weight and immunostaining scores in small and large intestine for ROCK2, preservation of serum glutathione (GSH) levels, and an increase in tumor level of nitric oxide were recorded in groups pretreated with Y-27632. However, treatment with Y-27632 after tumor inoculation did not affect body weight and ROCK2 immunostaining scores, increased serum MDA levels, and decreased GSH levels. CONCLUSIONS: This is the first study on the effectiveness of Y-27632 in this experimental tumor model. Our findings provided direct evidence for ROCK involvement in tumor development. These data suggest that pretreatment with Y-27632 has a protective effect against tumor formation.

CDC 25A gene 263C/T, -350C/T, and -51C/G polymorphisms in breast carcinoma.

The family of cell division cycle 25 (CDC25) phosphatase is one of the important regulators of the cell cycle progression. In mammalian cells, three isoforms have been identified: CDC25A, CDC25B, and CDC25C. CDC25A is required to enter S time, and the overexpression of this phosphatase accelerates the entrance to S time. CDC25A overexpression could render tumor cells less sensitive to DNA replication checkpoints, thereby contributing to their genomic instability. We aimed to investigate, for the first time, the frequency of human CDC25A gene SNPs in metastatic and non-metastatic breast cancer. Total number of 281 eligible patients with histologically confirmed incident of breast cancer and 137 cancer-free controls were included. The detection of CDC25A gene polymorphisms was achieved with real-time polymerase chain reaction and restriction fragment length polymorphism techniques. We found that the 263C/T polymorphism was significantly associated with breast cancer and risk of metastasis. The -350C/T polymorphism in the promoter region of CDC25A gene was found to associate with neither breast cancer nor metastasis. The other promoter polymorphism -51C/G in the CDC25A gene associated with breast cancer but not associated with metastasis. These data suggest that 263C/T and -51C/G polymorphisms of CDC25A gene could be candidate markers for earlier diagnosis and targets for breast cancer therapy.