Cancer is one of the major causes of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has attracted increased attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction. Though some ACP prediction tools have been developed recently, their performances are not well enough and most of them do not offer a function to distinguish ACPs from antimicrobial peptides (AMPs). Considering the fact that a growing number of studies have shown that some AMPs exhibit anticancer function, this work tries to build a model for distinguishing AMPs from ACPs in addition to a model that predicts ACPs from whole peptides.This study chooses amino acid composition, N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzes them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) for distinguishing ACPs from whole peptides. Another model (model 1) that distinguishes ACPs from AMPs is also developed. Comparing to previous models, models developed in this research show better performance (accuracy: 85.5% for model 1 and 95.2% for model 2).This work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-optimized models show better performance than non-optimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as a feature, performs better than all other existing tools.
Abstract The hetero-chitooligosaccharide (HTCOS) is a naturally occurring biopolymer in the exoskeleton of crustaceans and insects. Although some studies have been carried out on HTCOS in inducing plant resistance and promoting growth, the molecular mechanism of HTCOS in plants is not clear. In this study, an integrated analysis of metabolomics and transcriptomics was performed to analyze the response of Brassica napus to hetero-chitooligosaccharides treatment. The levels of 26 metabolites in B. napus were significantly changed under the HTCOS treatment. Amongst these metabolites, 9 metabolites were significantly up-regulated, including pentonic acid, indole-3-acetate, and γ-aminobutyric acid. Transcriptome data showed that there were 817 significantly up-regulated genes and 1064 significantly down-regulated genes in B. napus under the HTCOS treatment. Interestingly, the indole-3-acetate (IAA) content under the HTCOS treatment was about five times higher than that under the control condition. Moreover, four genes related to plant hormone signal transduction, three AUX/IAA genes, and one ARF gene, were significantly up-regulated under the HTCOS treatment. Furthermore, the plant height, branching number, and biomass of B. napus under the HTCOS treatment were significantly increased compared to that in the control condition. This evidence indicated that the HTCOS treatment contributed to accumulating the content of plant hormone IAA in the B. napus , up-regulating the expression of key genes in the signaling pathway of plant growth and improving the agronomic traits of B. napus .
Different from the hot debates on holocaust novels and films,holocaust poetry seems seldom to enter into the sights of the readers and researchers.In fact,American Jewish poets' Post-Auschwitz Holocaust Poetic writings have not only become an essential part of American Jewish poetry,but in some senses shaped American Jewish poetics.In face of the holocaust which denied the humanity,American Jewish poets tend to be trapped in the dilemmas of writing or not writing,telling or not telling,forgetting or not forgetting,and the secret to walk out of this predicament is the combination of Jewish poets'poetic philosophies and their ethical choices.It is reasonable to say that Post-Auschwitz Holocaust Poetic writing is a realm constructed by poetics and ethics.
In this work, an egg yolk protein hydrolysate (EYPH) with a high iron-chelating ability (87.32%) was prepared. The fractionation using 60% (v/v) ethanol concentration (E3 fraction) led to the efficiently accumulating the iron-chelating peptides in EYPH. The characterization results showed that iron mainly chelated with carboxyl, amino and phosphate groups of peptides. From E3 fraction, six iron-chelating peptides with MW ranging from 1372.36 to 2937.04 Da were identified and a hypothesized molecular model of DDSSSpSpSpSpSpSVLSK-Fe was simulated. In vitro stability determination showed that E3-Fe chelate owned a good heat, alkalinity and digestion tolerance, but a relatively bad acid tolerance. Finally, iron transport analysis showed that iron in the E3-Fe would be absorbed in caco-2 cell membrane more effectively than that of iron salts, indicating that it was possible to apply the E3-Fe complex as iron supplements.
The advancement of high-throughput RNA sequencing has uncovered the profound truth in biology, ranging from the study of differential expressed genes to the identification of different genomic phenotype across multiple conditions. However, lack of biological replicates and low expressed data are still obstacles to measuring differentially expressed genes effectively. We present an algorithm based on differential entropy-like function (DEF) to test for the differential expression across time-course data or multi-sample data with few biological replicates. Compared with limma, edgeR, DESeq2, and baySeq, DEF maintains equivalent or better performance on the real data of two conditions. Moreover, DEF is well suited for predicting the genes that show the greatest differences across multiple conditions such as time-course data and identifies various biologically relevant genes.
Polysaccharide from Gynostemma pentaphyllum Makino (GPP) could prevent H22 tumour growth and have immunostimulatory in mice, but the mechanism is largely unknown. In this study, we further investigated whether GPP prevents tumour growth by inducing cytotoxicity to the tumour cells or enhancing immunity in H22 ascites tumour-bearing mice. The results showed that GPP prevented the H22 ascites tumour growth in vivo, but showed little effect on cell viability and cycles to H22 cells in vitro. GPP improved the proportion and mitochondrial level of T cells, and promoted the secretion of IL-2 and IFN-γ in the ascites of the mice. Furthermore, GPP in vitro enhanced the cell viability and promoted activation (in particular T cells), proliferation, and the secretion of IL-2 and IFN-γ of lymphocyte. Taken together, our results demonstrated that GPP prevents H22 ascites tumour growth by enhancing immunity rather than cytotoxicity in mice.
Abstract Purpose Circular RNAs (circRNAs) appear to exert critical functions in breast cancer (BC). The objective of this study is to explore the usefulness of circRNAs as potential diagnostic and prognostic biomarkers of BC. Methods The Gene Expression Omnibus database was referenced to identify differentially expressed circRNAs in BC. We found that circ_0001756 was associated with the malignant potential of BC. Also, the expression levels of circ_0001756 in BC tissues and cell lines were determined by real-time quantitative polymerase chain reaction analysis. The functions of circ_0001756 were investigated both in vitro and in vivo . The luciferase reporter and rescue assays were used to clarify the molecular mechanisms of circ_0001756. Additionally, the clinical value of circ_0001756 as a serum biomarker and potential correlations with the clinicopathological characteristics of BC patients were investigated. Results Circ_0001756 expression was upregulated in BC tissues and substantially correlated with tumor size and tumor-node-metastasis (TNM) stage. Knockdown of circ_0001756 markedly inhibited the malignant potential of BC both in vitro and in vivo . Mechanistically, circ_0001756 acted as a miR-584-5p sponge to regulate TRAF6 in BC cells. Serum levels of circ_0001756 were significantly higher in pre-operative BC patients than in healthy controls, fibroadenoma patients, and post-operative BC patients. Also, serum circ_0001756 was remarkably correlated with tumor size, patient age, metastasis state, and TNM stage. The combination of the traditional tumor markers carcinoembryonic antigen and cancer antigen 15 − 3 with circ_0001756 significantly improved the diagnostic accuracy of BC. Conclusion Circ_0001756 promotes the malignancy of BC through the miR-584-5p/TRAF6 signaling axis. Additionally, serum circ_0001756 is a promising biomarker for screening and diagnosis of BC.
Glioblastoma (GBM) is the most aggressive brain tumor. Reportedly, circular RNAs (circRNAs) participate in regulation of the development and progression of diverse cancers, including GBM.Dysregulated circRNAs in GBM tissues were screened out from GEO database. The expression of candidate circRNAs in GBM cells was measured by qRT-PCR. Loss-of function assays, including colony formation assay, EdU assay, TUNEL assay, and flow cytometry analysis were conducted to determine the effects of circ-AHCY knockdown on GBM cell proliferation and apoptosis. Animal study was further used to prove the inhibitory effect of circ-AHCY silencing on GMB cell growth. Mechanistic experiments like luciferase reporter, RNA pull-down and RNA-binding protein immunoprecipitation (RIP) assays were performed to unveil the downstream molecular mechanism of circ-AHCY. Nanosight Nanoparticle Tracking Analysis (NTA) and PKH67 staining were applied to identify the existence of exosomes.Circ-AHCY was confirmed to be highly expressed in GBM cells. Circ-AHCY silencing suppressed GBM cell proliferation both in vitro and in vivo. Mechanistically, circ-AHCY activates Wnt/β-catenin signaling pathway by sequestering miR-1294 to up-regulate MYC which activated CTNNB1 transcription. It was also found that circ-AHCY recruited EIF4A3 to stabilize TCF4 mRNA. Enhanced levels of TCF4 and β-catenin contributed to the stability of TCF4/β-catenin complex. In turn, TCF4/β-catenin complex strengthened the transcriptional activity of circ-AHCY. Exosomal circ-AHCY derived from GBM cells induced abnormal proliferation of normal human astrocytes (NHAs).Exosomal circ-AHCY forms a positive feedback loop with Wnt/β-catenin signaling pathway to promote GBM cell growth.
Abstract BackgroundCancer is a major cause of death worldwide. To treat cancer, the use of anticancer peptides (ACPs) has received increasing attention in recent years. ACPs are a unique group of small molecules that can target and kill cancer cells fast and directly. However, identifying ACPs by wet-lab experiments is time-consuming and labor-intensive. Therefore, it is significant to develop computational tools for ACPs prediction.ResultsThis study chose amino acid composition (AAC), N5C5, k-space, position-specific scoring matrix (PSSM) as features, and analyzed them by machine learning methods, including support vector machine (SVM) and sequential minimal optimization (SMO) to build a model (model 2) distinguishing ACPs from non-ACPs. Since a growing number of studies have shown that some antimicrobial peptides (AMPs) exhibit anticancer function, a model (model 1) to distinguish ACPs from AMPs is also been developed. Comparing to previous models, models developed in this research show better performance (accuracy: 82.5% for model 1 and 93.5% for model 2).ConclusionsThis work utilizes a new feature, PSSM, which contributes to better performance than other features. In addition to SVM, SMO is used in this research for optimizing SVM and the SMO-models show better performance than unoptimized models. Last but not least, this work provides two different functions, including distinguishing ACPs from AMPs and distinguishing ACPs from all peptides. The second SMO-optimized model, which utilizes PSSM as feature, performs better than all other existing tools.
Petri net is used widely to model and analyze various systems formally. Recently many Petri nets mania devote their efforts to enhancing and extending the expressive power of Petri nets. One such effort is to extend Petri nets with object-oriented concepts. This paper informally introduces colored object-oriented Petri nets (COOPN) with the application of the AUV system. According to the characteristic of the AUV running environment, this paper uses object-oriented method not only to disport system modules but also to construct refined running model of AUV system. Then it uses the COOPN method to establish hierarchically detailed model in order to get the performance analyzing information of the system
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