06-04-2016, 11:04 AM
My name is Asha K,i need the compelete details regarding the protein profiling technical seminar topic.Sir can u please help me by giving details of this topic.
06-04-2016, 11:04 AM
My name is Asha K,i need the compelete details regarding the protein profiling technical seminar topic.Sir can u please help me by giving details of this topic.
09-04-2016, 11:51 AM
To get information about the topic “protein profiling” full report ppt and related topic refer the link below
http://keck.med.yale.edu/proteomics/tech...profiling/
20-07-2016, 11:06 AM
Protein Profiling The MS & Proteomics Protein Profiling unit has seven different complementary state-of-the-art protein expression analysis approaches in place for discovery of protein biomarkers that underlie the disease or treatment of interest. These protein profiling approaches include differential (fluorescence) gel electrophoresis (DIGE), Multiplexed Isobaric Tagging Technology for Relative Quantitation (iTRAQ), Isotope-coded affinity-tag-based protein profiling (ICAT), Stable Isotopic Labeling by Amino Acids in Cell Culture or SILAC, ProteomeLab PF2D (2D LC), Multi-dimensional Protein Identification Technology (MudPIT) and Label Free Quantitation. Several of these protein expression approaches can also be utilized in combination with locating post translational modifications (PTMs) (e.g. phosphopeptide quantitation using SILAC) in order to further understand the differential changes of PTMs and their impact on disease. Introduction Human ageing is associated with a number of changes in how the body and its organs function1. Among visible signs of ageing are greying of hair, changes in posture and loss of skin elasticity2,3. Less noticeable signs include hearing loss, increase in blood pressure or sarcopenia4. On the molecular level, ageing is associated with numerous processes, such as telomere length reduction, changes in metabolic and gene-transcription profiles and an altered DNA-methylation pattern5,6,7,8,9,10. In addition to chronological time, lifestyle factors such as smoking or stress can affect both the pattern of DNA-methylation11 and telomere length12 and thereby the aging of an individual. Ageing and lifestyle are the strongest known risk factors for many common non-communicable diseases, hence, lifestyle factors or molecular markers have been used as 5-year mortality predictors13,14. Additionally, specific food-items have been associated with lowered all cause mortality15. Various predictor models have been developed using measures of facial morphology16, physical fitness and physiology12,17, telomere length18 and methylation pattern6 to predict ones chronological age. Remarkably, some models are able to predict chronological age with correlation coefficients (R2) to actual age up to 0.75, and even above 0.90, when based on DNA-methylation status over 353 or 71 CpG-sites6,19. Comparisons of the actual chronological age with the predicted age, sometimes denoted the biological age, can be used as an indicator of health status, monitor the effect of lifestyle changes and even aid in the decision on treatment strategies for cancer patients16,20. To date, no current models have explored the potential of using the plasma protein profile for age prediction. Furthermore, while lifestyle factors such as stress have been shown to affect the rate of cellular ageing12, to the best of our knowledge, no studies have examined the effect of a wide range of lifestyle factors, including smoking or dietary habits, on the predicted age. We have previously characterized abundance levels of 144 circulating plasma proteins using the proximity extension assay (PEA) and have found over 40% of investigated proteins to be significantly correlated with one or more of the following factors, age, weight, length and hip circumference10,21. We therefore reasoned that the plasma protein profile might also be predictive of these traits. Here we demonstrate for the first time that the profile of circulating plasma proteins can be used to accurately predict chronological age, as well as anthropometrical measures such as height, weight and hip circumference. Moreover, we used the plasma protein-based model to identify lifestyle choices that accelerate or decelerate the predicted age. The protein analysis method used has previously been applied to dried blood spot material22. Interestingly, the ability to accurately predict anthropometrical characteristics from a dried blood spot sample could potentially be applicable in forensic investigations. Results Phenotype prediction from plasma protein profiles We have previously quantified abundance levels of circulating plasma proteins from cardiovascular and cancer biomarker panels using the highly sensitive protein extension assay (PEA)10,21 in 976 individuals from the Northern Swedish Population Health Study (NSPHS). Seventy-seven of these protein measurements were used to build models to predict chronological age, weight, height and hip circumference. Prediction models were built using generalized linear models with penalized maximum likelihoods as implemented by the glmnet-package23 in R24 and models were optimized using a 10-fold cross-validation scheme on 75% of the observation and subsequently evaluated using the remaining 25% (see Methods for details). We repeated the process 500 times and recorded which proteins were selected in the model. As expected, individual variation in protein abundance values and the distribution of phenotypes, gave rise to some variation in the proteins selected to be part of the final model. On average 68 of the 77 proteins were included in the model predicting age (Fig. 1A, Table 1). In total, all 77 proteins were included at least once in any of the age predicting models and a core set of 29 proteins was present in all models. The models for age, height, weight and hip circumference performed well on the test and training sets (Table 1) and summary statistics (including protein inclusion statistics) for all models and traits are reported in Supplementary tables 2–5. The models predicted chronological age with an R2 = 0.83, while predicting weight (R2 = 0.48), height (R2 = 0.34) and hip circumference (R2 = 0.60) with somewhat lower correlation coefficients. An example of the correlation between chronological and predicted age for one model is shown in Fig. 1B, and the distribution of prediction errors for 500 age models in Fig. 1C. In the test sets, 95% of the average errors for each of the models were within +/− 1.23 years and there was no statistically significant difference (p = 0.52, Wilcoxon Ranked Sum test) between the distribution of errors in the training and test sets, indicating that the models were not over-fitted to the training data. In terms of accuracy, the plasma protein profile predicted chronological age within 5.0 years, weight within 6.8 kg, height within 4.7 cm and hip circumference within 5.1 cm, for 50% of the observations. Additional performance measurements for the models are shown in Supplementary Figures 1–3. We also evaluated the performance of the models when restricted to a core set of proteins that were included in all models for each trait (Table 1). Interestingly, the models based on the core set of proteins showed similar performance statistics as the models using the full set of proteins, suggesting that a smaller set of proteins can capture most of the phenotype variation. This observation was also confirmed by an analysis of the fraction of variance of the traits that can be explained by individual and combined proteins included in the prediction models (Supplementary Figure 4, Supplementary Tables 2–5). An analysis of the overlap between the proteins that were present in the four core-models showed that only 4 proteins (Fig. 2) were common between all models. These were Tissue plasminogen activator (tPA), Tumor necrosis factor receptor 1 (TNFR1), the Receptor tyrosine-protein kinase ErbB-3 (ErbB3) and Endothelial cell-specific molecule 1 (ESM-1). None of the genes coding for these proteins have been implicated in a recent GWAS for variation in human adult height25. In our material, out of the four proteins common to all models, ESM-1 explains the largest proportion of the variance seen in height (9.8%, Supplementary Table 4). ESM-1 is mainly expressed in endothelial cells in lung and kidney tissue but circulates in the bloodstream26. We have found no evidence relating ESM-1 to height in the literature but speculate that circulating levels of ESM-1 could be a reflection of lung volume, which is correlated to height27. reference; http://www.naturearticles/srep17282 |
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