ABSTRACT
Objective To analyze the specific peptide of cystatin C (CysC) and its characteristics by bioinformatics technology,and verify the predicted results by mass spectrometry.Methods Online software was applied to analyze the physicochemical properties and homology of CysC peptides hydrolyzed by trypsin and predict the associated parameters of ionized fragmentation of specific peptide by mass spectrometry.Precursor ion scan and product ion scan were conducted on the samples of synthetic specific peptide.The recombinant human CysC and serum samples were analyzed by mass spectrometry after trypsin digestion.The results of analysis were compared with the outcomes predicted by bioinformatics.Results T3 (ALDFAVGEYNK) was considered as the specific peptide of CysC by software analysis.When selecting[M + 2H] 2 + for product ion scan,almost all the y and b ions of fragmentation were observed using tandem mass spectrometry (MS/MS),showing consistency with Skyline predictions.Moreover,both the peptides from the human recombinant CysC and serum sample following the trypsin digestion were eluted at the same time with the isotope-labeled T3 * under the fixed conditions.Conclusion Bioinformatics technology could be available for picking out the specific peptides of target protein quickly and efficiently and predicting the ionized fragmentation precisely by mass spectrometry scanning.
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Objective To establish the methodology of uncertainty evaluation with reference measurement procedure by Monte Carlo method (MCM).Methods According to JCGM 101:2008,we established the methodology of uncertainty evaluation by MCM in the example of GGT reference measurement procedure.We could calculate an estimate of GGT concentration,the associated standard uncertainty and a coverage interval with a specified probability by MATLAB software,setting the uncertainty evaluation by GUM method as a control.Results When the uncertainty was evaluated by GUM method,the results of sample A and Sample B were (95.8 ±2.4) U/L (k =2) with coverage interval [93.4 U/L,98.2 U/L] and (180.0 ± 3.9) U/L (k =2) with coverage interval [176.1 U/L,183.9 U/L] respectively,while using MCM method,the uncertainty evaluation result of sample A and Sample B were (95.8 ± 2.4) U/L (k =2) with coverage interval [93.4 U/L,98.2 U/L] and (180.0 ± 3.9) U/L with symmetrical 95％ coverage interval [176.2 U/L,183.8 U/L].The output quantity simulated by MCM was normal distributed.Conclusions When the distribution of the output quantity is normal,the measurement uncertainty evaluated by both MCM and GUM method is nearly the same.When the distribution of the output quantity is unknown,MCM can be used as a verification of GUM method.
ABSTRACT
Objective The effects of nemo-like kinase(NLK)in inflammation of central nervous system were explored. Methods An animal model with central nervous system inflammation disease induced by intracerebroventricular infusion of lipopolysaccharide was constructed. A model of neuronal apoptosis induced by glutamate in PC12 cells was constructed. A pcdna 3.1-NLK over-expression plasmid and a pSilencer 4. 1-CMV-NLK small interfering plasmid were also constructed. The expression and location of NLK were detected using western blot analysis, double immunofluorescent staining, and cell counting kit-8 assay. Results The protein level of NLK was reduced after induction of inflammation in the brain(t =2. 718-3. 106, all P ＜0. 01), and NLK was only expressed in both cortical and hippocampal neurons. At the cellular level, NLK expression was gradually reduced along with neuronal apoptosis induced by glutamate in PC12(t =4. 032, P ＜0. 01). Over-expression of NLK would reduce the apoptosis of neurons induced by glutamate(t =3. 930, P ＜ 0. 01). Conversely, interfering the expression of NLK would enhance neuronal apoptosis(t = 2. 845, P ＜ 0. 01). Conclusion NLK can protect neurons by inhibiting apoptosis in the process of central nervous system inflammation.