[Label-free quantitative peptidomics-based analysis on glycopeptides in deerhorn gelatin and deer-hide gelatin].

Nano-LC-MS/MS was used to analyze trypsin digested deer-horn gelatin( DCG) and deer-hide gelatin( DHG) samples.The glycopeptides in DCG and DHG were quantified by Label-free quantitative( LFQ) peptidomics,on the basis of which the glycopeptides with significant difference in DCG and DHG were determined. As a result,5 736 peptides were identified from DCG samples,including 213 galactosyl-hydroxylysine containing peptides( Gal-Hyl-peptides) and 102 glucosyl-galactosyl-hydroxylysine containing peptides( Glc-Gal-Hyl-peptides),while 6 836 peptides were identified from DHG samples,among which there were 250 Gal-Hyl-peptides and 98 Glc-Gal-Hyl-peptides. With over 3-fold peak area difference and highly significant intergroup difference( P < 0. 01) as the screening criteria,444 differential peptides were determined in DCG and DHG,including 16 Gal-Hyl-peptides and 5 Glc-Gal-Hyl-peptides. Then XIC peak shapes,standard deviation of peak area,and fold change were applied for further screening and 5 glycopeptides with significant differences in DCG and DHG were confirmed,which could serve as potential biomarkers for distinguishing DCG and DHG. The present study provided ideas and strategies for the in-depth investigation on the discrimination of DCG and DHG and is of good theoretical significance and application value for the further research on chemical constituents and quality control of gelatin derived Chinese medicinals.

[Analysis of hydroxylation and O-glycosylation on lysine sites in deer-hide gelatin].

Nano-LC MS/MS was used to analyze trypsin digested deer-hide gelatin(DHG) samples, hydroxylation and O-glycosylation on lysine sites of DHG were comprehensive identified by using PEAKS Studio software. The sites, sorts and amounts of hydroxylation and O-glycosylation on Type Ⅰ collagen α1 chain(COL1 A1) and α2 chain(COL1 A2) of DHG were revealed. As a result, 5 284 peptides were identified from DHG samples, which were mainly from COL1 A1 and COL1 A2. Among these peptides, there were 449 peptides with hydroxylysine, 442 with galactosyl-hydroxylysine, 449 with glucosyl-galactosyl-hydroxylysine. The major modified sites of hydroxylation and O-glycosylation in DHG were shown as follow: α1-9 N and α2-5 N in N-telopeptides, α1-87, α1-174, α1-930, α2-87, α2-174, α2-933 in triple helix domain, and α1-16 C in C-telopeptides. These hydroxylation and O-glycosylation were correlated with the formation and stability of collagen molecules and collagen fibrils. It is feasible for the collagens and peptides dissolving from deer skin collagen fibrils under high temperature and pressure decocting, high temperature and pressure also might destroy inter-molecular covalent cross-linking and help those glycol-peptides formations. The present study provided ideas and strategies for the in-depth investigation on DHG chemical constituents, and showed good theoretical significance and application value.

Collagen derived species-specific peptides for distinguishing donkey-hide gelatin (Asini Corii Colla).

Objective: As an important food therapy product with traditional Chinese medicine (TCM) applications, donkey-hide gelatin (Asini Corii Colla, ACC) has been used for thousands of years. However, till now few effective strategy had been proposed to distinguish ACC from other animal hide gelatins, especially closely related horse- and mule-hide gelatins, which was an embarrassment of ACC quality control. Methods: Combined mass spectrometry and bioinformatic methods have been applied to identify and verify two ACC-specific peptides (Pep-1 and Pep-2) capable of distinguishing ACC from other closely related animal gelatins with high selectivity. Results: It confirmed that these two peptides could be not only used for distinguishing ACC from highly homologous horse-hide and mule-hide gelatins as well as other animal hide gelatins. Conclusion: The present study provides a simple method for species-specific peptides discovery, which can be used for assessing the quality of animal gelatin products, and ensure they are authenticable and traceable.

Potential ceRNA networks involved in autophagy suppression of pancreatic cancer caused by chloroquine diphosphate: A study based on differentially­expressed circRNAs, lncRNAs, miRNAs and mRNAs.

Autophagy has been reported to be involved in the occurrence and development of pancreatic cancer. However, the mechanism of autophagy­associated non­coding RNAs (ncRNAs) in pancreatic cancer remains largely unknown. In the present study, microarrays were used to detect differential expression of mRNAs, microRNAs (miRNAs), long ncRNAs (lncRNAs) and circular RNAs (circRNAs) post autophagy suppression by chloroquine diphosphate in PANC­1 cells. Collectively, 3,966 mRNAs, 3,184 lncRNAs and 9,420 circRNAs were differentially expressed. Additionally, only two miRNAs (hsa­miR­663a­5p and hsa­miR­154­3p) were underexpressed in the PANC­1 cells in the autophagy­suppression group. Furthermore, miR­663a­5p with 9 circRNAs, 8 lncRNAs and 46 genes could form a prospective ceRNA network associated with autophagy in pancreatic cancer cells. In addition, another ceRNA network containing miR­154­3p, 5 circRNAs, 2 lncRNAs and 11 genes was also constructed. The potential multiple ceRNA, miRNA and mRNA associations may serve pivotal roles in the autophagy of pancreatic cancer cells, which lays the theoretical foundation for subsequent investigations on pancreatic cancer.

Comprehensive analysis of the long noncoding RNA HOXA11-AS gene interaction regulatory network in NSCLC cells.

BACKGROUND: Long noncoding RNAs (lncRNAs) are related to different biological processes in non-small cell lung cancer (NSCLC). However, the possible molecular mechanisms underlying the effects of the long noncoding RNA HOXA11-AS (HOXA11 antisense RNA) in NSCLC are unknown. METHODS: HOXA11-AS was knocked down in the NSCLC A549 cell line and a high throughput microarray assay was applied to detect changes in the gene profiles of the A549 cells. Bioinformatics analyses (gene ontology (GO), pathway, Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses) were performed to investigate the potential pathways and networks of the differentially expressed genes. The molecular signatures database (MSigDB) was used to display the expression profiles of these differentially expressed genes. Furthermore, the relationships between the HOXA11-AS, de-regulated genes and clinical NSCLC parameters were verified by using NSCLC patient information from The Cancer Genome Atlas (TCGA) database. In addition, the relationship between HOXA11-AS expression and clinical diagnostic value was analyzed by receiver operating characteristic (ROC) curve. RESULTS: Among the differentially expressed genes, 277 and 80 genes were upregulated and downregulated in NSCLC, respectively (fold change ≥2.0, P < 0.05 and false discovery rate (FDR) < 0.05). According to the degree of the fold change, six upregulated and three downregulated genes were selected for further investigation. Only four genes (RSPO3, ADAMTS8, DMBT1, and DOCK8) were reported to be related with the development or progression of NSCLC based on a PubMed search. Among all possible pathways, three pathways (the PI3K-Akt, TGF-beta and Hippo signaling pathways) were the most likely to be involved in NSCLC development and progression. Furthermore, we found that HOXA11-AS was highly expressed in both lung adenocarcinoma and squamous cell carcinoma based on TCGA database. The ROC curve showed that the area under curve (AUC) of HOXA11-AS was 0.727 (95% CI 0.663-0.790) for lung adenocarcinoma and 0.933 (95% CI 0.906-0.960) for squamous cell carcinoma patients. Additionally, the original data from TCGA verified that ADAMTS8, DMBT1 and DOCK8 were downregulated in both lung adenocarcinoma and squamous cell carcinoma, whereas RSPO3 expression was upregulated in lung adenocarcinoma and downregulated in lung squamous cell carcinoma. For the other five genes (STMN2, SPINK6, TUSC3, LOC100128054, and C8orf22), we found that STMN2, TUSC3 and C8orf22 were upregulated in squamous cell carcinoma and that STMN2 and USC3 were upregulated in lung adenocarcinoma. Furthermore, we compared the correlation between HOXA11-AS and de-regulated genes in NSCLC based on TCGA. The results showed that the HOXA11-AS expression was negatively correlated with DOCK8 in squamous cell carcinoma (r = -0.124, P = 0.048) and lung adenocarcinoma (r = -0.176, P = 0.005). In addition, RSPO3, ADAMTS8 and DOCK8 were related to overall survival and disease-free survival (all P < 0.05) of lung adenocarcinoma patients in TCGA. CONCLUSIONS: Our results showed that the gene profiles were significantly changed after HOXA11-AS knock-down in NSCLC cells. We speculated that HOXA11-AS may play an important role in NSCLC development and progression by regulating the expression of various pathways and genes, especially DOCK8 and TGF-beta pathway. However, the exact mechanism should be verified by functional experiments.