Deconvolution analysis for classifying gastric adenocarcinoma patients based on differential scanning calorimetry serum thermograms.
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ABSTRACT:
SUBMITTER: Vega S
PROVIDER: S-EPMC4303881 | biostudies-other | 2015
REPOSITORIES: biostudies-other
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Project description:Differential Scanning Calorimetry (DSC) has been used in the past to study the thermal unfolding of many different viruses. Here we present the first DSC analysis of rabies virus. We show that non-inactivated, purified rabies virus unfolds cooperatively in two events centered at approximately 62 and 73 °C. Beta-propiolactone (BPL) treatment does not alter significantly viral unfolding behavior, indicating that viral inactivation does not alter protein structure significantly. The first unfolding event was absent in bromelain treated samples, causing an elimination of the G-protein ectodomain, suggesting that this event corresponds to G-protein unfolding. This hypothesis was confirmed by the observation that this first event was shifted to higher temperatures in the presence of three monoclonal, G-protein specific antibodies. We show that dithiothreitol treatment of the virus abolishes the first unfolding event, indicating that the reduction of G-protein disulfide bonds causes dramatic alterations to protein structure. Inactivated virus samples heated up to 70 °C also showed abolished recognition of conformational G-protein specific antibodies by Surface Plasmon Resonance analysis. The sharpness of unfolding transitions and the low standard deviations of the Tm values as derived from multiple analysis offers the possibility of using this analytical tool for efficient monitoring of the vaccine production process and lot to lot consistency.
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Project description:DSC is used to determine thermally-induced conformational changes of biomolecules within a blood plasma sample. Recent research has indicated that DSC curves (or thermograms) may have different characteristics based on disease status and, thus, may be useful as a monitoring and diagnostic tool for some diseases. Since thermograms are curves measured over a range of temperature values, they are considered functional data. In this paper we apply functional data analysis techniques to analyze differential scanning calorimetry (DSC) data from individuals from the Lupus Family Registry and Repository (LFRR). The aim was to assess the effect of lupus disease status as well as additional covariates on the thermogram profiles, and use FD analysis methods to create models for classifying lupus vs. control patients on the basis of the thermogram curves.Thermograms were collected for 300 lupus patients and 300 controls without lupus who were matched with diseased individuals based on sex, race, and age. First, functional regression with a functional response (DSC) and categorical predictor (disease status) was used to determine how thermogram curve structure varied according to disease status and other covariates including sex, race, and year of birth. Next, functional logistic regression with disease status as the response and functional principal component analysis (FPCA) scores as the predictors was used to model the effect of thermogram structure on disease status prediction. The prediction accuracy for patients with Osteoarthritis and Rheumatoid Arthritis but without Lupus was also calculated to determine the ability of the classifier to differentiate between Lupus and other diseases. Data were divided 1000 times into separate 2/3 training and 1/3 test data for evaluation of predictions. Finally, derivatives of thermogram curves were included in the models to determine whether they aided in prediction of disease status.Functional regression with thermogram as a functional response and disease status as predictor showed a clear separation in thermogram curve structure between cases and controls. The logistic regression model with FPCA scores as the predictors gave the most accurate results with a mean 79.22% correct classification rate with a mean sensitivity = 79.70%, and specificity = 81.48%. The model correctly classified OA and RA patients without Lupus as controls at a rate of 75.92% on average with a mean sensitivity = 79.70% and specificity = 77.6%. Regression models including FPCA scores for derivative curves did not perform as well, nor did regression models including covariates.Changes in thermograms observed in the disease state likely reflect covalent modifications of plasma proteins or changes in large protein-protein interacting networks resulting in the stabilization of plasma proteins towards thermal denaturation. By relating functional principal components from thermograms to disease status, our Functional Principal Component Analysis model provides results that are more easily interpretable compared to prior studies. Further, the model could also potentially be coupled with other biomarkers to improve diagnostic classification for lupus.
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Project description:Differential scanning calorimetry (DSC) has been used widely to study various biomarkers from blood, less is known about the protein profiles from saliva. The aim of the study was to investigate the use DSC in order to detect saliva thermal profiles and determine the most appropriate sampling procedure to collect and process saliva. Saliva was collected from 25 healthy young individuals and processed using different protocols based on centrifugation and filtering. The most effective protocol was centrifugation at 5000g for 10 min at 4°C followed by filtration through Millex 0.45 μm filter. Prepared samples were transferred to 3 mL calorimetric ampoules and then loaded into TAM48 calibrated to 30°C until analysis. DSC scans were recorded from 30°C to 90°C at a scan rate of 1°C/h with a pre-conditioning the samples to starting temperature for 1 h. The results show that the peak distribution of protein melting points was clearly bimodal, and the majority of peaks appeared between 40-50°C. Another set of peaks is visible between 65°C- 75°C. Additionally, the peak amplitude and area under the peak are less affected by the concentration of protein in the sample than by the individual differences between people. In conclusion, the study shows that with right preparation of the samples, there is a possibility to have thermograms of salivary proteins that show peaks in similar temperature regions between different healthy volunteers.
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