(-)-Epigallocatechin-3-gallate induced apoptosis by dissociation of c-FLIP/Ku70 complex in gastric cancer cells

Anti-cancer properties of (-)-epigallocatechin-3-gallate (EGCG) are mediated via apoptosis induction, as well as inhibition of cell proliferation and histone deacetylase. Accumulation of stabilized cellular FLICE-inhibitory protein (c-FLIP)/Ku70 complex in the cytoplasm inhibits apoptosis through interruption of extrinsic apoptosis pathway. In this study, we evaluated the anti-cancer role of EGCG in gastric cancer (GC) cells through dissociation of c-FLIP/Ku70 complex. MKN-45 cells were treated with EGCG or its antagonist MG149 for 24 h. Apoptosis was evaluated by flow cytometry and quantitative RT-PCR. Protein expression of c-FLIP and Ku70 was analysed using western blot and immunofluorescence. Dissociation of c-FLIP/Ku70 complex as well as Ku70 translocation were studied by sub-cellular fractionation and co-immunoprecipitation. EGCG induced apoptosis in MKN-45 cells with substantial up-regulation of P53 and P21, down-regulation of c-Myc and Cyclin D1 as well as cell cycle arrest in S and G2/M check points. Moreover, EGCG treatment suppressed the expression of c-FLIP and Ku70, decreased their interaction while increasing the Ku70 nuclear content. By dissociating the c-FLIP/Ku70 complex, EGCG could be an alternative component to the conventional HDAC inhibitors in order to induce apoptosis in GC cells. Thus, its combination with other cancer therapy protocols could result in a better therapeutic outcome.     

Wound dressing based on PVA nanofiber containing silk fibroin modified with GO/ZnO nanoparticles for superficial wound healing: In vitro and in vivo evaluations

Silk fibroin (SF), extracted from Bombyx mori, has unique physicochemical properties to achieve an efficient wound dressing. In this study, reduced graphene oxide (RGO)/ZnO NPs/silk fibroin nanocomposite was made, and an innovative nanofiber of SF/polyvinyl alcohol (PVA)/RGO/ZnO NPs was ready with the electrospinning technique and successfully characterized. The results of MIC and OD analyses were used to investigate the synthesized materials' antibacterial effects and displayed that the synthesized materials could inhibit growth against Staphylococcus aureus and Escherichia coli bacteria. However, both in vitro cytotoxicity (MTT) and scratch wound studies have shown that RGO/ZnO NPs and SF/PVA/RGO/ZnO NPs are not only non-toxic to NIH 3T3 fibroblasts, but also can cause cell viability, cell proliferation, and cell migration. Furthermore, improving the synthesized nanofiber's structural properties in the presence of RGO and ZnO NPs has been confirmed by performing tensile strength, contact angle, and biodegradation analyses. Also, in a cell attachment analysis, fibroblast cells had migrated and expanded well in the nanofibrous structures. Moreover, in vivo assay, SF/PVA/RGO/ZnO NPs nanofiber treated rats and has been shown significant healing activity and tissue regeneration compared with other treated groups. Therefore, this study suggests that SF/PVA/RGO/ZnO NPs nanofiber is a hopeful wound dressing for preventing bacteria growth and improving superficial wound repair.     

Determining the Most Important Physiological and Agronomic Traits Contributing to Maize Grain Yield through Machine Learning Algorithms: A New Avenue in Intelligent Agriculture

Prediction is an attempt to accurately forecast the outcome of a specific situation while using input information obtained from a set of variables that potentially describe the situation. They can be used to project physiological and agronomic processes; regarding this fact, agronomic traits such as yield can be affected by a large number of variables. In this study, we analyzed a large number of physiological and agronomic traits by screening, clustering, and decision tree models to select the most relevant factors for the prospect of accurately increasing maize grain yield. Decision tree models (with nearly the same performance evaluation) were the most useful tools in understanding the underlying relationships in physiological and agronomic features for selecting the most important and relevant traits (sowing date-location, kernel number per ear, maximum water content, kernel weight, and season duration) corresponding to the maize grain yield. In particular, decision tree generated by C&RT algorithm was the best model for yield prediction based on physiological and agronomical traits which can be extensively employed in future breeding programs. No significant differences in the decision tree models were found when feature selection filtering on data were used, but positive feature selection effect observed in clustering models. Finally, the results showed that the proposed model techniques are useful tools for crop physiologists to search through large datasets seeking patterns for the physiological and agronomic factors, and may assist the selection of the most important traits for the individual site and field. In particular, decision tree models are method of choice with the capability of illustrating different pathways of yield increase in breeding programs, governed by their hierarchy structure of feature ranking as well as pattern discovery via various combinations of features.     

miRNAs; a novel strategy for the treatment of COVID-19

Coronavirus disease 2019 (COVID-19) is the seventh member of the bat severe acute respiratory syndrome family. COVID-19 can fuse their envelopes with the host cell membranes and deliver their genetic material. COVID-19 attacks the respiratory system and stimulates the host inflammatory responses, enhances the recruitment of immune cells, and promotes angiotensin-converting enzyme 2 activities. Patients with confirmed COVID-19 may have experienced fever, dry cough, headache, dyspnea, acute kidney injury, acute respiratory distress syndrome, and acute heart injury. Several strategies such as oxygen therapy, ventilation, antibiotic or antiviral therapy, and renal replacement therapy are commonly used to decrease COVID-19-associated mortality. However, these approaches may not be good treatment options. Therefore, the search for an alternative-novel therapy is urgently important to prevent the disease progression. Recently, microRNAs (miRNAs) have emerged as a promising strategy for COVID-19. The design of oligonucleotide against the genetic material of COVID-19 might suppress virus RNA translation. Several previous studies have shown that host miRNAs play an antiviral role and improve the treatment of patients with COVID-19. miRNAs by binding to the 3′-untranslated region (UTR) or 5′-UTR of viral RNA play an important role in COVID-19-host interplay and viral replication. miRNAs interact with multiple pathways and reduce inflammatory biomarkers, thrombi formation, and tissue damage to accelerate the patient outcome. The information in this review provides a summary of the current clinical application of miRNAs for the treatments of patients with COVID-19.     

Using Chou’s Five-steps Rule to Classify and Predict Glutathione S-transferases with Different Machine Learning Algorithms and Pseudo Amino Acid Composition

International Journal of Peptide Research and Therapeutics - The Glutathione S-Transferases (GSTs) are detoxification enzymes which exist in variety of living organisms such as bacteria, fungi,...     

Cloning,expression, and immunogenicity of novel fusion protein of Mycobacterium tuberculosis based on ESAT-6 and truncated C-terminal fragment of HSP70

Tuberculosis (TB) remains as a major public health problem worldwide. Identification and selection of immunodominant antigens of Mycobacterium tuberculosis (MTB), capable of efficiently inducing a protective immune response is the ultimate goal of TB vaccine development studies.Accordingly, this study was designed to produce a novel M. tuberculosis fusion protein consisted of MTB ESAT-6 (early secreted antigenic target-6 kDa), as a potent immunogenic protein, fused to C-terminus of MTB HSP70 (HSP70_359–610), as an appropriate carrier and adjuvant.The constructed gene was inserted into a prokaryotic expression vector (pQE30); consequently, the recombinant fusion protein with a 6xHis-tag was successfully over expressed in Escherichia coli M15. Inclusion bodies from bacterial cell lysates were solubilized and the recombinant fusion protein was easily purified by Ni-NTA affinity chromatography under denaturing conditions followed by urea gradient dialysis. The purified and refolded protein was then applied for immunization of mice that resulted in the detection of high titers of specific antibodies, high level of IFN-γ and cell proliferation.The results of our study could confirm the capability of E6H70C fusion protein, as a potential tuberculosis vaccine candidate, for the efficient induction of specific immune responses in a mouse model. However, further investigation need to evaluate the protectivity of this recombinant protein in host model.     

Prediction of thermostability from amino acid attributes by combination of clustering with attribute weighting: a new vista in engineering enzymes

The engineering of thermostable enzymes is receiving increased attention. The paper, detergent, and biofuel industries, in particular, seek to use environmentally friendly enzymes instead of toxic chlorine chemicals. Enzymes typically function at temperatures below 60°C and denature if exposed to higher temperatures. In contrast, a small portion of enzymes can withstand higher temperatures as a result of various structural adaptations. Understanding the protein attributes that are involved in this adaptation is the first step toward engineering thermostable enzymes. We employed various supervised and unsupervised machine learning algorithms as well as attribute weighting approaches to find amino acid composition attributes that contribute to enzyme thermostability. Specifically, we compared two groups of enzymes: mesostable and thermostable enzymes. Furthermore, a combination of attribute weighting with supervised and unsupervised clustering algorithms was used for prediction and modelling of protein thermostability from amino acid composition properties. Mining a large number of protein sequences (2090) through a variety of machine learning algorithms, which were based on the analysis of more than 800 amino acid attributes, increased the accuracy of this study. Moreover, these models were successful in predicting thermostability from the primary structure of proteins. The results showed that expectation maximization clustering in combination with uncertainly and correlation attribute weighting algorithms can effectively (100%) classify thermostable and mesostable proteins. Seventy per cent of the weighting methods selected Gln content and frequency of hydrophilic residues as the most important protein attributes. On the dipeptide level, the frequency of Asn-Glu was the key factor in distinguishing mesostable from thermostable enzymes. This study demonstrates the feasibility of predicting thermostability irrespective of sequence similarity and will serve as a basis for engineering thermostable enzymes in the laboratory.