In vitro and in silico perspectives to explain anticancer activity of a novel syringic acid analog ((4-(1H-1, 3-benzodiazol-2-yl)-2, 6-dimethoxy phenol)) through apoptosis activation and NFkB inhibition in K562 leukemia cells.

Syringic acid (SA) is an active carcinogenesis inhibitor; however, the low bioavailability and unstable functional groups hinder its activity. Here, a chemically synthesized novel SA analog (SA10) is evaluated for its anticancer activity using in-vitro and in-silico studies. K562 cell line study revealed that SA10 had shown a higher rate of inhibition (IC50 = 50.40 µg/mL) than its parental compound, SA (IC50 = 96.92 µg/mL), at 50 µM concentration. The inhibition ratio was also been evaluated by checking the expression level of NFkB and Bcl-2 and showing that SA10 has two-fold increase in the inhibitory mechanism than SA. This result demonstrates that SA10 acts as an NFkB inhibitor and an apoptosis inducer. Further, molecular docking and simulation have been performed to get insights into the possible inhibitory mechanism of SA and SA10 on NFkB at the atomistic level. The molecular docking results exemplify that both SA and SA10 bind to the active site of NFkB, thereby interfering with the association between DNA and NFkB. SA10 exhibits a more robust binding affinity than SA and is firmly docked well into the interior of the NFkB, as confirmed by MM-PBSA calculations. In a nutshell, the Benzimidazole scaffold containing SA10 has shown more NFkB inhibitory activity in K562 cells than SA, which could be helpful as an ideal therapeutic NFkB inhibitor for treating cancers.

Eco-friendly synthesis of silver nanoparticles from the whole plant of Cleome viscosa and evaluation of their characterization, antibacterial, antioxidant and antidiabetic properties.

The current research is to develop an easy and eco-friendly method for the synthesis of three different concentrations of silver nanoparticles (1mMCvAgNPs, 2mMCvAgNPs and 3mMCvAgNPs) using aqueous whole plant extract of Cleome viscosa and to evaluate their antibacterial, antioxidant and antidiabetic properties. CvAgNPs were characterized by Using UV-vis spectrophotometer, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), scanning electron microscope (SEM) and transmission electron microscope (TEM). The formation of CvAgNPs was confirmed by the observation of band between 250 nm to 600 nm UV-vis spectrum. The crystalline structure of CvAgNPs with a face-centered cubic (FCC) was confirmed by XRD. The responsible phytochemicals for the reduction and capping material of CvAgNPs were observed with FT-IR. The SEM analysis confirmed the size and shapes of CvAgNPs. The CvAgNPs have shown the rich content of total phenolic and total flavonoid components. The CvAgNPs have shown significant antibacterial activity on multi drug resistance Gram-negative and Gram-positive bacteria and also have shown significant strong antioxidant activities (DPPH, ABTS, H2O2 scavenging, Phosphomolybdenum assay and reducing power). The inhibitory action of CvAgNPs on α-glucosidase and α-amylase was stronger than the inhibitory action of acarbose. To best of our knowledge, this is the first attempt on the synthesis of AgNPs using C. viscosa whole plant aqueous extract. The synthesized CvAgNPs exhibited good antimicrobial, antioxidant and antidiabetic properties. Hence, to validate our results, the in vivo studies at the molecular level are needed to develop Cleome viscosa as an antibacterial, antioxidant and anti-diabetic agent.

Effect of abiotic and biotic stress factors analysis using machine learning methods in zebrafish.

In order to understand the mechanisms underlying stress responses, meta-analysis of transcriptome is made to identify differentially expressed genes (DEGs) and their biological, molecular and cellular mechanisms in response to stressors. The present study is aimed at identifying the effect of abiotic and biotic stress factors, and it is found that several stress responsive genes are common for both abiotic and biotic stress factors in zebrafish. The meta-analysis of micro-array studies revealed that almost 4.7% i.e., 108 common DEGs are differentially regulated between abiotic and biotic stresses. This shows that there is a global coordination and fine-tuning of gene regulation in response to these two types of challenges. We also performed dimension reduction methods, principal component analysis, and partial least squares discriminant analysis which are able to segregate abiotic and biotic stresses into separate entities. The supervised machine learning model, recursive-support vector machine, could classify abiotic and biotic stresses with 100% accuracy using a subset of DEGs. Beside these methods, the random forests decision tree model classified five out of 8 stress conditions with high accuracy. Finally, Functional enrichment analysis revealed the different gene ontology terms, transcription factors and miRNAs factors in the regulation of stress responses.