nmda receptor The label free quantitative proteomic

The label-free quantitative proteomic analysis was performed using Normalized Spectral Index (SI) based on the previous description [3,4]. This label-free quantitative value, SI, consists of multiple MS abundance features for a given protein hit: peptide count, spectral count for all assigned peptides, total nmda receptor intensities of all matched MS/MS spectra, and protein length (number of amino acids). Firstly, the abundance of a protein is calculated as the cumulative ion intensities of all assigned MS/MS spectra for a protein hit. Secondly, the intensity of the protein is normalized by dividing its intensity by the total intensities of all proteins identified in the dataset. Thirdly, the normalized intensity of this protein is divided by the protein length to obtain the relative molar concentration of this protein in the sample. In this study, we grouped the identified proteins according to their gene names, then filtered out the duplicate spectra occurring in the same group, and finally calculated the SI value for each protein group using the following formula: SI calculation was performed using a home-made Excel VBA script. Mascot search results of detailed peptide/protein identification in each sample, e.g. peptide-spectrum matches, protein hits, and ion intensities of MS/MS spectral queries, etc. are shown in Supplementary Table 1. In this table, the proteomic data necessary for SI calculation are highlighted in blue. The SI values of identified proteins among three samples in each group and the results of statistical analysis are shown in Supplementary Table 2. The SI values of each protein in the three samples of the groups i-GS and Nor were summed, compared and plotted in Fig. 2. The data suggest that complement components (C4, C5, C6, C7, C8, C9) and their regulators (CFH, CFHR1, vitronectin, clusterin) are at least twice increased in i-GS than Nor. More importantly, the over-expression of complement pathway indicates a general up-going tendency from i-GS to GS (see Ref. [1], Figs. 3 and 4). Additionally, diverse extracellular matrix proteins are found predominantly accumulated in sclerotic glomeruli whereas various podocyte proteins important to slit diaphragm and cytoskeletal integrity are dramatically reduced or lost (Fig. 3).
Funding sources This work was supported by Grant-in-Aid for Young Scientists B (15K19448) to Y.Z. from Japan Society for the Promotion of Science, Grant-in-Aid for Publication of Scientific Research Results (228071) to T.Y. from Ministry of Education, Culture, Sports, Science and Technology in Japan and Grant-in-aid for Diabetic Nephropathy and Nephrosclerosis Research to T.Y. from the Ministry of Health, Labor and Welfare of Japan.
Acknowledgments
Specifications Direct link to deposited data http://www.ncbi.nlm.nih.gov.eleen.top/geo/query/acc.cgi?acc=GSE64132.
Experimental design, materials and methods
Transcriptome profiling
Discussion In the WT, 1597 genes were expressed (75th percentile) consistently in both replicates [1]. Number of M. smegmatis genes that were significantly differentially expressed (4-fold change) during treatment and drug-resistant conditions The common up-regulated genes are enriched [5,6] in the functional; categories of response to stress, oxidation–reduction processes, lipid metabolism, ion transport, response to stimulus, molybdate transport and carbohydrate metabolism. The common down-regulated genes were enriched in processes such as amino sugar metabolism, mannose metabolism, reactive oxygen species metabolism, DNA-dependent transcription and detection of chemical stimulus. The transcriptome analysis revealed that there are many variations in the gene expression patterns in the drug-resistant strains. This indicates that drug resistance is associated with differential regulation of several genes in addition to the few genome sequence variations [4], leading to global variations.
Conflict of interest
Specifications Table