Biological Mechanism on SIRT1/NLRP3/IL-18 Signaling Pathway of Acupuncture for Treatment of Ischemic Stroke with Center Poststroke Pain.

Objective: To evaluate the effect of acupuncture on an animal model of ischemic stroke with central poststroke pain (CPSP) through Sirtuin 1 (SIRT1)/NOD-like receptor thermal protein domain associated protein 3 (NLRP3)/interleukin-18 (IL-18) signaling pathway. Methods: Data mining was performed with R package "edgeR," "limma," "pathview," etc., from NCBI Gene Expression Omnibus (GEO) database. Sprague Dawley (SD) rats were divided into 4 groups: sham operation group (Sham group, n = 5), poststroke central pain group (CPSP group, n = 5), poststroke central pain + acupuncture group (AP group, n = 5), central pain after stroke + acupuncture + SIRT1 inhibitor EX527 group (EX527 group, n = 5). Pain behavior testing was performed to determine the mechanical withdrawal threshold (MWT). Quantitative real-time PCR (qRT-PCR) was performed to verify the data mining results from the GEO database. Results: The KEGG key pathway map was created using the R package "pathview" package, demonstrating that the expression levels of NLRP3's downstream inflammatory factors IL-18 were downregulated in both of siSIRT1 group compared to the control group and the NLRP3 reconstituted group compared to NLRP3 KO group. QRT-PCR results on animal models of CPSP ischemic stroke showed that the expression levels of SIRT1 were downregulated, the activation of the NLRP3 inflammasome was upregulated, and the expression levels of IL-18 were upregulated in the brain tissues of the surrounding area of the injury. As the pain threshold of CPSP rats was increased, the expression level of S1RT1 was upregulated, and the activation of NLRP3 inflammasome was downregulated. The expression level of IL-18 was downregulated after acupuncture treatment. Conclusion: Acupuncture may inhibit CPSP in an animal model of ischemic stroke by upregulating SIRT1 expression levels, inhibition of the activation of the inflammasome, and downregulating IL-18 expression levels.

[Amino acid composition and classification of acidic and alkaline enzymes].

It is of theoretical and practical significance to understand the mechanism of enzyme adaptation to acidic and alkaline environments and classification of them based on sequence information. In present work, the amino acid composition of 105 acidic and 111 alkaline enzyme sequences was systematically analyzed. Acidic enzymes contained significantly more Trp, Tyr, Thr and Ser, whereas less Glu, Lys, Met and Arg. On the other hand, alkaline enzymes have slightly more Trp, Ala and Cys, whereas less Lys, Arg and Glu. The amount of Ala, Glu, Leu, Asn, Arg, Ser and Thr in acidic and alkaline enzymes varied largely. Hence, a weighted amino acid composition method was developed for the discrimination of acidic and alkaline enzymes. Using the back-check and the 5-fold cross validation methods, the overall accuracy could reach 86.1% and 83.3%, respectively. A new method to classify acidic and alkaline enzymes based on their sequences was established.

[Influence of amino acid and dipeptide composition on protein stability of piezophilic microbes].

OBJECTIVE: To compare the amino acid and dipeptide composition of proteins from piezophilic and non-piezophilic microbes is of great significance for understanding the stability of piezophilic protein and directed mutation of them. METHODS: The amino acids of different secondary structure and the dipeptides of 639 orthologs proteins were counted and the deviation of them were calculated. RESULTS: The amino acid composition based on secondary structure and the dipeptide composition reveals some common trends. The amino acids vary widely in beta sheet and coil. In beta sheet, piezophilic proteins contain more amino acids such as Val, Ile and Leu, whereas less Arg, Lys and Asp; in coil, piezophilic proteins contained more amino acids such as Val and Ile, whereas less Gly and Pro. On the other hand, piezophilic proteins contain more dipeptides such as YM, MN, KD, QC, CI, MW, MM, CY, WQ, HC, RC and QH, whereas less dipeptides such as TW, MS, VD, DH, YE, CT, MW, CF, CK, CM, MY, QI, TH, MQ, QQ and MC. CONCLUSION: Dipeptide contains more structure and sequence information than amino acid, and it will be more helpful for understanding the mechanism of piezophilic adaptation and guiding the engineering of proteins.

[Discriminating acidic, neutral and alkaline enzymes with secondary structure amino acid composition].

In this work, we systematically analyzed the secondary structure amino acid compositions of acidic and alkaline enzymes and compared them with neutral ones. We found that the propensity of the individual residues to participate in secondary structures and the consistently higher composition of neutral and tiny residues might be the general stability mechanisms for their adaptation to pH extremes. Based on this, we presented a secondary structure amino acid composition method for extracting useful features from sequence. The overall prediction accuracy evaluated by the 10-fold cross-validation reached 80.3%. Comparing our method with other feature extraction methods, the improvement of the overall prediction accuracy ranged from 9.4% to 18.7%. The random forests algorithm also outperformed other machine learning techniques with an improvement ranging from 2.7% to 21.8%.

Predicting lipase types by improved Chou's pseudo-amino acid composition.

By proposing a improved Chou's pseudo amino acid composition approach to extract the features of the sequences, a powerful predictor based on k-nearest neighbor was introduced to identify the types of lipases according to their sequences. To avoid redundancy and bias, demonstrations were performed on a dataset where none of the proteins has > or =25% sequence identity to any other. The overall success rate thus obtained by the 10-fold cross-validation test was over 90%, indicating that the improved Chou's pseudo amino acid composition might be a useful tool for extracting the features of protein sequences, or at lease can play a complementary role to many of the other existing approaches.

[Prediction of lipases types by different scale pseudo-amino acid composition].

Lipases are widely used enzymes in biotechnology. Although they catalyze the same reaction, their sequences vary. Therefore, it is highly desired to develop a fast and reliable method to identify the types of lipases according to their sequences, or even just to confirm whether they are lipases or not. By proposing two scales based pseudo amino acid composition approaches to extract the features of the sequences, a powerful predictor based on k-nearest neighbor was introduced to address the problems. The overall success rates thus obtained by the 10-fold cross-validation test were shown as below: for predicting lipases and nonlipase, the success rates were 92.8%, 91.4% and 91.3%, respectively. For lipase types, the success rates were 92.3%, 90.3% and 89.7%, respectively. Among them, the Z scales based pseudo amino acid composition was the best, T scales was the second. They outperformed significantly than 6 other frequently used sequence feature extraction methods. The high success rates yielded for such a stringent dataset indicate predicting the types of lipases is feasible and the different scales pseudo amino acid composition might be a useful tool for extracting the features of protein sequences, or at lease can play a complementary role to many of the other existing approaches.