Microcalorimetry of blood serum proteome: a modified interaction network in the multiple myeloma case

Deconvolution Analysis of the Non-Ionic Iomeprol, Iobitridol and Iodixanol Contrast Media-Treated Human Whole Blood Thermograms: A Comparative Study

To study the effect of non-ionic contrast media on anticoagulated and non-anticoagulated human whole blood samples, calorimetric measurements were performed. The anticoagulated plasma showed the greatest fall in the total ΔH after Iodixanol treatment. The plasma-free erythrocytes revealed a pronounced shift in the Tmax and a decrease in the ΔH of hemoglobin and transferrin. The total ΔH of Iodixanol treatment showed the highest decline, while Iomeprol and Iobitridol had fewer adverse effects. Similarly, the non-anticoagulated samples revealed a decrease both in the Tmax and the ΔH of albumin and immunoglobulin-specific transitions. The total ΔH showed that Iodixanol had more influence on the serum. The serum-free erythrocyte samples resulted in a significant drop in the Tmax of erythrocyte and transferrin (~5-6 °C). The ΔH of deconvolved hemoglobin and transferrin decreased considerably; however, the ΔH of albumin increased. Surprisingly, compared to Iomeprol and Iobitridol treatments, the total ΔH of Iodixanol was less pronounced in the non-anticoagulated erythrocyte samples. In sum, each non-ionic contrast medium affected the thermal stability of anticoagulated and non-anticoagulated erythrocyte proteins. Interestingly, Iodixanol treatment caused more significant effects. These findings suggest that conformational changes in blood components can occur, which can potentially lead to the increased prevalence of cardiovascular dysfunctions and blood clotting.

Plasma Thermogram Parameters Differentiate Status and Overall Survival of Melanoma Patients

Melanoma is the fifth most common cancer in the United States and the deadliest of all skin cancers. Even with recent advancements in treatment, there is still a 13% two-year recurrence rate, with approximately 30% of recurrences being distant metastases. Identifying patients at high risk for recurrence or advanced disease is critical for optimal clinical decision-making. Currently, there is substantial variability in the selection of screening tests and imaging, with most modalities characterized by relatively low accuracy. In the current study, we built upon a preliminary examination of differential scanning calorimetry (DSC) in the melanoma setting to examine its utility for diagnostic and prognostic assessment. Using regression analysis, we found that selected DSC profile (thermogram) parameters were useful for differentiation between melanoma patients and healthy controls, with more complex models distinguishing melanoma patients with no evidence of disease from patients with active disease. Thermogram features contributing to the third principal component (PC3) were useful for differentiation between controls and melanoma patients, and Cox proportional hazards regression analysis indicated that PC3 was useful for predicting the overall survival of active melanoma patients. With the further development and optimization of the classification method, DSC could complement current diagnostic strategies to improve screening, diagnosis, and prognosis of melanoma patients.

Thermodynamic Signatures of Blood Plasma Proteome in Neurodegenerative Pathologies

Discovery of diagnostic biomarkers for age-related neurodegenerative pathologies (NDDs) is essential for accurate diagnosis, following disease progression and drug development. Blood plasma and blood cells are important peripheral sources for NDDs' biomarkers that, although present in lower concentrations than in cerebrospinal fluid, would allow noninvasive diagnostics. To identify new biomarkers for Parkinson's disease (PD) and amyotrophic lateral sclerosis (ALS), in this work we have evaluated the modifications in the thermodynamic behavior of blood plasma proteome exploring differential scanning calorimetry. The plasma thermodynamics reflected the complexity and heterogeneity of the two pathologies. The unfolding temperature of the most abundant plasma protein albumin and the weighted average center of the calorimetric profile appeared as the two thermodynamic signatures that reflected modifications of the plasma proteome, i.e., strong thermal stabilization of albumin and plasma proteins' interaction network, related to both pathologies. Based on those two signatures, both PD and ALS patients were stratified in two sets, except several cases with thermodynamic parameters that strongly differed from those of the calorimetric sets. Along with modifications of the plasma thermodynamic behavior, we found altered globulin levels in all PD and ALS patients' plasma (higher level of α- and β-globulin fractions and lower level of γ-globulin fraction than the respective reference values) employing capillary electrophoresis. The presented results reveal the potential of calorimetry to indirectly identify NDDs' biomarkers in blood plasma.

Thermodynamic Sensitivity of Blood Plasma Components in Patients Afflicted with Skin, Breast and Pancreatic Forms of Cancer

According to the World Health Organization's 2018 Global Cancer Survey, cancer is the second leading cause of death. From this survey, the third most common is breast cancer, the fifth is melanoma malignum and pancreatic adenocarcinoma ranks twentieth. Undoubtedly, the early diagnosis and monitoring of these tumors and related research is important for aspects of patient care. The aim of our present review was to explain an impressive methodology that is deemed suitable in reference to studying blood sample deviations in the case of solid tumors. Essentially, we compared the heat denaturation responses of blood plasma components through differential scanning calorimetry (DSC). In the control, between five and seven separable components can be detected, in which the primary component was albumin, while in the case of tumorous patients, the peaks of immunoglobulins were dominant. Moreover, the shape of the plasma DSC curves changed with a shift in the higher temperature ranges; thus, their pattern can be used as a suitable marker of direct immunological responses. The further development of the analysis of DSC curves raises the possibility of the early diagnosis of a potential tumor, the monitoring of diseases, or testing the efficacy of the therapy from a single drop of blood.

Calorimetric Markers for Detection and Monitoring of Multiple Myeloma

Simple Summary

This review highlights the potential of differential scanning calorimetry for multiple myeloma diagnosis and monitoring of the treatment outcome. The thermodynamic signatures of blood sera from patients with multiple myeloma are strongly dependent on the concentration and isotype of the secreted monoclonal immunoglobulins. Mathematical methods developed to analyze the biocalorimetry data and distinguish “diseased” from “healthy” thermogram to stratify plasma calorimetric profiles and determine specific interrelations between calorimetric and biochemical/clinical data are discussed.

Abstract

This review summarizes data obtained thus far on the application of differential scanning calorimetry (DSC) for the analysis of blood sera from patients diagnosed with multiple myeloma (MM) with the secretion of the most common isotypes of monoclonal proteins (M-proteins), free light chains (FLC) and non-secretory MM, as well as Waldenström macroglobulinemia and the premalignant state monoclonal gammopathy of undetermined significance. The heterogeneous nature of MM is reflected in the thermal stability profiles of the blood serum proteome of MM patients found to depend on both the level and the isotype of the secreted M-proteins or FLC. Common calorimetric markers feature the vast majority of the different myeloma types, i.e., stabilization of the major serum proteins and decrease in the albumin/globulin heat capacity ratio. A unique calorimetric fingerprint of FLC molecules forming amorphous aggregates is the low-temperature transition centered at 57 °C for a calorimetric set of FLC MM and at 46–47 °C for a single FLC MM case for which larger aggregates were formed. The calorimetric assay proved particularly advantageous for non-secretory MM and is thus a suitable tool for monitoring such patients during treatment courses. Thus, DSC provides a promising blood-based approach as a complementary tool for MM detection and monitoring.

Sample Processing Considerations for Protein Stability Studies of Low Concentration Biofluid Samples using Differential Scanning Calorimetry

Background:

The analysis of biofluid samples with low protein content (e.g., urine or saliva) can be challenging for downstream analysis methods with limited sensitivity. To circumvent this problem, sample processing methods are employed to increase the protein concentration in analyzed samples. However, for some techniques, like differential scanning calorimetry (DSC) that characterizes thermally-induced unfolding of biomolecules, sample processing must not affect native protein structure and stability.

Methods:

We evaluated centrifugal concentration and stirred cell ultrafiltration, two common methods of sample concentration characterized by a low risk of protein denaturation, with the goal of establishing a protocol for DSC analysis of low concentration biospecimens.

Results:

Our studies indicate that both methods can affect protein stability assessed by DSC and, even after optimization of several parameters, the obtained DSC profile (thermogram) suggested that sample processing affects the structure or intermolecular interactions of component proteins contributing to altered thermal stability detectable by DSC. We also found a relationship between changes in thermograms and low protein concentration, indicating that diluting biospecimens to concentrations below 0.1 mg/mL can perturb the intermolecular environment and affect the structure of proteins present in the solution.

Conclusions:

Dilution of samples below 0.1 mg/mL, as well as concentration of samples with low protein content, resulted in affected thermogram shapes suggesting changes in protein stability. This should be taken into account when concentrating dilute samples or employing techniques that lower the protein concentration (e.g., fractionation), when downstream applications include techniques, such as DSC, that require the preservation of native protein forms.

A Possible Way to Relate the Effects of SARS-CoV-2-Induced Changes in Transferrin to Severe COVID-19-Associated Diseases

SARS-CoV-2 infections are responsible for the COVID-19 pandemic. Transferrin has been found to explain the link between diseases associated with impaired iron transport and COVID-19 infection. The effect of SARS-CoV-2 on human whole blood was studied by differential scanning calorimetry. The analysis of the thermal transition curves showed that the melting temperature of the transferrin-related peak decreased in the presence of SARS-CoV-2. The ratio of the under-curve area of the two main peaks was greatly affected, while the total enthalpy of the heat denaturation remained nearly unchanged in the presence of the virus. These results indicate that SARS-CoV-2, through binding to transferrin, may influence its Fe3+ uptake by inducing thermodynamic changes. Therefore, transferrin may remain in an iron-free apo-conformational state, which depends on the SARS-CoV-2 concentration. SARS-CoV-2 can induce disturbance in erythropoiesis due to toxicity generated by free iron overload.

The Utility of Differential Scanning Calorimetry Curves of Blood Plasma for Diagnosis, Subtype Differentiation and Predicted Survival in Lung Cancer

Early detection of lung cancer (LC) significantly increases the likelihood of successful treatment and improves LC survival rates. Currently, screening (mainly low-dose CT scans) is recommended for individuals at high risk. However, the recent increase in the number of LC cases unrelated to the well-known risk factors, and the high false-positive rate of low-dose CT, indicate a need to develop new, non-invasive methods for LC detection. Therefore, we evaluated the use of differential scanning calorimetry (DSC) for LC patients' diagnosis and predicted survival. Additionally, by applying mass spectrometry, we investigated whether changes in O- and N-glycosylation of plasma proteins could be an underlying mechanism responsible for observed differences in DSC curves of LC and control subjects. Our results indicate selected DSC curve features could be useful for differentiation of LC patients from controls with some capable of distinction between subtypes and stages of LC. DSC curve features also correlate with LC patients' overall/progression free survival. Moreover, the development of classification models combining patients' DSC curves with selected plasma protein glycosylation levels that changed in the presence of LC could improve the sensitivity and specificity of the detection of LC. With further optimization and development of the classification method, DSC could provide an accurate, non-invasive, radiation-free strategy for LC screening and diagnosis.

Blood serum denaturation profile examined by differential scanning calorimetry reflects the effort put into ultramarathon by amateur long-distance runners

The impact of participation in the ultramarathon on the health and mental and physical condition is very complex. Undoubtedly, exercise brings many benefits but also involves health risks. Especially such an extreme effort as the one associated with finishing the ultramarathon run, can be dangerous to the health of the runner. With the variety of possible biomarkers of excessive fatigue that threaten health and life, a question arises which of them are the best and which should be considered in amateur long-distance runners showing particularly high individual variability. In this study differential scanning calorimetry (DSC) has been applied to show the overall effect of the 12-h run on blood sera of participants. Serum samples were obtained from the blood of ten male amateur long-distance runners, collected before and immediately after the run. Distinct changes in the shape of DSC curves have been observed for serum after finishing the run relative to pre-race serum. Statistically significant differences between stages "before" and "after" ultramarathon running have been found for parameters of the endothermic transition associated with denaturation of serum proteins. An increase in the temperature (from 70.9 ± 0.9 to 75.8 ± 2.9 °C) and excess heat capacity (from 0.859 ± 0.201 to 1.102 ± 0.226 Jg-1 °C-1) at peak maximum, the enthalpy of serum denaturation (from 18.55 ± 6.52 to 22.08 ± 5.61 Jg^-1) and the first moment of the thermal transition with respect to the temperature (from the value of 67.0 ± 2.1 to 72.6 ± 2.1 °C) has been observed. These results show a clear impact of running an ultramarathon on the participant's blood serum.

Differences in cryostimulation and sauna effects on post-exercise changes in blood serum of athletes

OBJECTIVES

There is a growing body of evidence supporting the role of whole-body cryostimulation (WBC) and sauna - bathing as treatments for relaxation, mental well-being and several health problems. Despite their polar opposite temperatures, both of these treatments come with a dose of similar health benefits. This study is designed to compare effects of WBC and sauna application on the athletes' response to exercise.

DESIGN

The blood samples were collected from 10 professional cross-country skiers at four stages: before exercise, after exercise, at 1-h recovery and after 24 h of rest in sessions before and after 10 thermal treatments. Differential scanning calorimetry (DSC) was used to examine the process of serum denaturation. The parameters of endothermic transition were compared at various stages of each exercise session.

RESULTS

Post-exercise changes in DSC profiles of athlete's blood serum are similar in character but clearly stronger in the session held after sauna treatments and slightly weaker after WBC than those in the session not preceded by treatments. These changes can be, at least in part, explained by the exercise induced increase in the concentration of oxidized albumin. A return of serum denaturation transition to pre-exercise shape has been observed within a few hours of rest. It suggests relatively quick restoration of a fraction of non-oxidized albumin molecules during the recovery period.

CONCLUSIONS

An exercise performed by athletes after a series of sauna treatments leads to temporary greater modification of the blood serum proteome than the similar exercise during the session preceded by WBC treatments.

Multi-group diagnostic classification of high-dimensional data using differential scanning calorimetry plasma thermograms

The thermoanalytical technique differential scanning calorimetry (DSC) has been applied to characterize protein denaturation patterns (thermograms) in blood plasma samples and relate these to a subject’s health status. The analysis and classification of thermograms is challenging because of the high-dimensionality of the dataset. There are various methods for group classification using high-dimensional data sets; however, the impact of using high-dimensional data sets for cancer classification has been poorly understood. In the present article, we proposed a statistical approach for data reduction and a parametric method (PM) for modeling of high-dimensional data sets for two- and three- group classification using DSC and demographic data. We compared the PM to the non-parametric classification method K-nearest neighbors (KNN) and the semi-parametric classification method KNN with dynamic time warping (DTW). We evaluated the performance of these methods for multiple two-group classifications: (i) normal versus cervical cancer, (ii) normal versus lung cancer, (iii) normal versus cancer (cervical + lung), (iv) lung cancer versus cervical cancer as well as for three-group classification: normal versus cervical cancer versus lung cancer. In general, performance for two-group classification was high whereas three-group classification was more challenging, with all three methods predicting normal samples more accurately than cancer samples. Moreover, specificity of the PM method was mostly higher or the same as KNN and DTW-KNN with lower sensitivity. The performance of KNN and DTW-KNN decreased with the inclusion of demographic data, whereas similar performance was observed for the PM which could be explained by the fact that the PM uses fewer parameters as compared to KNN and DTW-KNN methods and is thus less susceptible to the risk of overfitting. More importantly the accuracy of the PM can be increased by using a greater number of quantile data points and by the inclusion of additional demographic and clinical data, providing a substantial advantage over KNN and DTW-KNN methods.

Plasmatic Signature of Disease by Differential Scanning Calorimetry (DSC)

Differential scanning calorimetry (DSC) has been used for several decades to characterize thermal stability of macromolecules such as proteins and DNA. It allows to determine the denaturation temperature and enthalpy of individual domains of proteins, thus giving new insights into their domain organization and ligand interaction. Over the past decade, it has been shown that this technique can also be used to study biofluids such as plasma or cerebrospinal fluid to obtain denaturation profiles. An increasing number of studies demonstrated that such profiles obtained from patients were significantly different from profiles obtained using biofluids of healthy individuals. This opens interesting perspectives for new diagnostics and monitoring tools for a large number of diseases. Nevertheless, the extensive studies of plasma samples from patients with different pathologies as well as the development of standardized methods of data analysis are necessary to reach the promising diagnostic potential of this methodology. Using plasma samples from healthy individuals and glioblastoma patients, we outline the steps necessary to obtain a plasmatic calorimetric profile with VP-DSC instrument and describe a cluster analysis of obtained data.

MALDI-TOF analysis of blood serum proteome can predict the presence of monoclonal gammopathy of undetermined significance

Monoclonal gammopathy of undetermined significance (MGUS) is a plasma cell dyscrasia that can progress to malignant multiple myeloma (MM). Specific molecular biomarkers to classify the MGUS status and discriminate the initial asymptomatic phase of MM have not been identified. We examined the serum peptidome profile of MGUS patients and healthy volunteers using MALDI-TOF mass spectrometry and developed a predictive model for classifying serum samples. The predictive model was built using a support vector machine (SVM) supervised learning method tuned by applying a 20-fold cross-validation scheme. Predicting class labels in a blinded test set containing randomly selected MGUS and healthy control serum samples validated the model. The generalization performance of the predictive model was evaluated by a double cross-validation method that showed 88% average model accuracy, 89% average sensitivity and 86% average specificity. Our model, which classifies unknown serum samples as belonging to either MGUS patients or healthy individuals, can be applied to clinical diagnosis.

Differential scanning calorimetry of plasma in glioblastoma: toward a new prognostic / monitoring tool

Glioblastoma is the most frequent and aggressive primary brain tumor in adults. Recently, a growing number of studies have shown that denaturation profile of plasma samples obtained by differential scanning calorimetry (DSC) can represent a signature of a disease. In this study, we analyzed for the first time the DSC denaturation profiles of the plasma from patients with recurrent glioblastoma (n=17). Comparison to the one of healthy individuals (n=10) and to already described profiles in others cancer showed clear differences suggesting that this DSC profile may constitute a signature of glioblastoma. Parameters extracted from these profiles were used for cluster analysis which revealed the existence of glioblastoma profile subgroups which correlated with prognostic factors. Moreover, we showed that the presence of circulating bevacizumab and carmustine did not alter this calorimetric signature of the disease, indicating that an evolution of the profile could be followed without being masked by ongoing systemic treatment. Thus, our results constitute a very promising proof of principle that a specific calorimetric profile could be detected in the plasma of glioblastoma patients. Moreover, we believe that our findings point to a potential easy-to-use non-invasive monitoring tool for glioblastoma patients.

Calorimetric markers for monitoring of multiple myeloma and Waldenström's macroglobulinemia patients

The blood proteome has been studied extensively for identification of novel reliable disease biomarkers. In recent years, differential scanning calorimetry has emerged as a new tool for characterization of the thermodynamic properties of the major serum/plasma proteins and for the establishment of calorimetric markers for a variety of diseases. Here we applied calorimetry to monitor the effect of treatment of patients diagnosed with multiple myeloma and Waldenström's macroglobulinemia on the calorimetric profiles of patients' blood sera. The parameters derived from the calorimetric profiles were compared with the primary serum biomarkers, monoclonal immunoglobulin (M protein) concentration, and κ/λ free light chain ratio. For the secretory cases, the calorimetric parameters thermogram's shape similarity and weighted average center strongly depended on the M protein level but had lower sensitivity and specificity. By contrast, for non-secretory cases, the calorimetric parameters did not depend on the κ/λ free light chains ratio and exhibited significantly higher sensitivity and specificity than M protein levels. A combination of the immunological and calorimetric tests was found to greatly improve the sensitivity and specificity of the clinical status evaluation. The pronounced differences in blood sera thermograms before and during monitoring reflected the individual patients' response to treatment received and showed maintenance of heterogeneity during the disease course.

Blood Serum Calorimetry Indicates the Chemotherapeutic Efficacy in Lung Cancer Treatment

Chemotherapy is a primary treatment for the metastatic lung cancer patients. To select the most effective combination of drugs, we need an efficient way of assessing tumor response. Here, we showed that differential scanning calorimetry (DSC) analysis of blood serum proteins could reveal the patient response to the treatment. If chemotherapy is effective, serum proteins DSC curve of non-small cellular lung cancer (NSCLC) case is similar to the those of the healthy individuals. If treatment fails, notable changes occur in the DSC profile of NSCLC patient's blood serum. Our preliminary work illustrates how thermal analysis of changes in the heat capacity of blood serum proteins can provide an insight into patient response to chemotherapy - the essential information for any successive lung cancer treatment.

Characterization and classification of lupus patients based on plasma thermograms

Objective

Plasma thermograms (thermal stability profiles of blood plasma) are being utilized as a new diagnostic approach for clinical assessment. In this study, we investigated the ability of plasma thermograms to classify systemic lupus erythematosus (SLE) patients versus non SLE controls using a sample of 300 SLE and 300 control subjects from the Lupus Family Registry and Repository. Additionally, we evaluated the heterogeneity of thermograms along age, sex, ethnicity, concurrent health conditions and SLE diagnostic criteria.

Methods

Thermograms were visualized graphically for important differences between covariates and summarized using various measures. A modified linear discriminant analysis was used to segregate SLE versus control subjects on the basis of the thermograms. Classification accuracy was measured based on multiple training/test splits of the data and compared to classification based on SLE serological markers.

Results

Median sensitivity, specificity, and overall accuracy based on classification using plasma thermograms was 86%, 83%, and 84% compared to 78%, 95%, and 86% based on a combination of five antibody tests. Combining thermogram and serology information together improved sensitivity from 78% to 86% and overall accuracy from 86% to 89% relative to serology alone. Predictive accuracy of thermograms for distinguishing SLE and osteoarthritis / rheumatoid arthritis patients was comparable. Both gender and anemia significantly interacted with disease status for plasma thermograms (p<0.001), with greater separation between SLE and control thermograms for females relative to males and for patients with anemia relative to patients without anemia.

Conclusion

Plasma thermograms constitute an additional biomarker which may help improve diagnosis of SLE patients, particularly when coupled with standard diagnostic testing. Differences in thermograms according to patient sex, ethnicity, clinical and environmental factors are important considerations for application of thermograms in a clinical setting.

Application and interpretation of functional data analysis techniques to differential scanning calorimetry data from lupus patients

Background

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.

Methods

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.

Results

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.

Conclusion

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.

A novel DSC approach for evaluating protectant drugs efficacy against dementia

Differential scanning calorimetry was applied to evaluate the efficacy of preventive treatments with biologically active compounds of plant origin against neurodegenerative disorder in mice. As we reported recently, large differences exist between the heat capacity profiles of water-soluble brain proteome fractions from healthy animals and from animals with scopolamine-induced dementia: the profiles for healthy animals displayed well expressed exothermic event peaking at 40-45°C, by few degrees above body temperature, but still preceding in temperature the proteome endothermic denaturational transitions; the low-temperature exotherm was completely abolished by the scopolamine treatment. Here we explored this signature difference in the heat capacity profiles to assess the efficacy of preventive treatments with protectant drugs anticipated to slow down or block progression of dementia (myrtenal, ellagic acid, lipoic acid and their combinations, including also ascorbic acid). We found that these neuroprotectants counteract the scopolamine effect and partially or completely preserve the 'healthy' thermogram, and specifically the low-temperature exotherm. These results well correlate with the changes in the cognitive functions of the animals assessed using the Step Through Test for learning and memory. The exothermic event is deemed to be associated with a reversible process of fibrillization and/or aggregation of specific water-soluble brain protein fractions preceding their denaturation. Most importantly, the results demonstrate that the effect of scopolamine and its prevention by protectant substances are clearly displayed in the heat capacity profiles of the brain proteome, thus identifying DSC as a powerful method in drug testing and discovery.

Intercriteria analysis of calorimetric data of blood serum proteome

BACKGROUND

Biological microcalorimetry has entered into a phase where its potential for disease diagnostics is readily recognized. A wide variety of oncological and immunological disorders have been characterized by differential scanning calorimetry (DSC) and characteristic thermodynamic profiles were reported. Now the challenge before DSC is not the experimental data collection but the development of analysis protocols for reliable data stratification/classification and discrimination of disease specific features (calorimetric markers).

METHODS

In this work we apply InterCriteria Analysis (ICA) approach combined with Pearson's and Spearman's correlation analysis to a large dataset of calorimetric and biochemical parameters derived for the serum proteome of patients diagnosed with multiple myeloma (MM).

RESULTS

We have identified intercriteria dependences that are general for the various types of MM and thus can be regarded as a characteristic of this largely heterogeneous disease: strong contribution of the monoclonal (M) protein concentration to the excess heat capacity of the immunoglobulins-assigned thermal transition; shift of the albumin assigned calorimetric transition to allocation where it overlaps with the globulins assigned transition and strong shift of the globulins assigned transition temperature attributable to M proteins conformational changes.

CONCLUSIONS

Our data justify the applicability of ICA for deciphering of the complex thermodynamic behavior of the MM blood serum proteome.

GENERAL SIGNIFICANCE

The applied approach is suitable for more general application in the analysis of biocalorimetric data since it can help identify the biological relevance of the distinguished thermodynamic features observed for variety of diseases.

Investigation of early and advanced stages in ovarian cancer using human plasma by differential scanning calorimetry and mass spectrometry

Ovarian cancer is recognized with high mortality due to asymptomatic nature of the disease and difficulties in diagnosing early stage of the cancer. The present study evaluates the use of differential scanning calorimetry (DSC) in differentiating the severity of ovarian cancer from healthy women. 47 diseased women were subdivided into four stages with respect to clinical relevance and severity. Stages I-II were regarded as early stages and stages III-IV were regarded as advanced stages. The two average transition temperatures (T m ) increased with disease severity from 64.84 and 70.32 °C (healthy) to 68.46 and 75.24 °C (stage IV), respectively. T m were increased depending on clinical groups. In addition, the change in heat capacity was also dependent on the disease severity. To further support and investigate the nature of the proposed interactions, matrix assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) analysis is employed. The results suggest the differences in peptide expression between early and advanced stage of ovarian cancer, affected abundant proteins in plasma. The combined DSC and MS approach was supportive in identifying a unique signature of ovarian cancer stages, and demonstrates the potential of DSC as a complementary diagnostic tool in the evaluation of early stage ovarian cancer.

Differential scanning calorimetry as a complementary diagnostic tool for the evaluation of biological samples

BACKGROUND

Differential scanning calorimetry (DSC) is a tool for measuring the thermal stability profiles of complex molecular interactions in biological fluids. DSC profiles (thermograms) of biofluids provide specific signatures which are being utilized as a new diagnostic approach for characterizing disease but the development of these approaches is still in its infancy.

METHODS

This article evaluates several approaches for the analysis of thermograms which could increase the utility of DSC for clinical application. Thermograms were analyzed using localized thermogram features and principal components (PCs). The performance of these methods was evaluated alongside six models for the classification of a data set comprised of 300 systemic lupus erythematosus (SLE) patients and 300 control subjects obtained from the Lupus Family Registry and Repository (LFRR).

RESULTS

Classification performance was substantially higher using the penalized algorithms relative to localized features/PCs alone. The models were grouped into two sets, the first having smoother solution vectors but lower classification accuracies than the second with seemingly noisier solution vectors.

CONCLUSIONS

Coupling thermogram technology with modern classification algorithms provides a powerful diagnostic approach for analysis of biological samples. The solution vectors from the models may reflect important information from the thermogram profiles for discriminating between clinical groups.

GENERAL SIGNIFICANCE

DSC thermograms show sensitivity to changes in the bulk plasma proteome that correlate with clinical status. To move this technology towards clinical application the development of new approaches is needed to extract discriminatory parameters from DSC profiles for the comparison and diagnostic classification of patients.

Characterization of monoclonal gammopathy of undetermined significance by calorimetric analysis of blood serum proteome

Monoclonal gammopathy of undetermined significance (MGUS) is a premalignant proliferative disorder that may progress to multiple myeloma, a malignant plasma cell neoplasia. We evaluated differential scanning calorimetry (DSC) as an experimental tool for differentiating serum samples of MGUS patients from healthy individuals. DSC thermograms can be used for monitoring changes in the serum proteome associated with MGUS. MGUS patients showed great variability in serum thermogram characteristics, which depended on the IgG, IgA or IgM isotypes and/or the κ or λ light chains. Thermogram feature parameters distinguished patients with MGUS from healthy people. Serum samples, named as non-MGUS, were also collected from patients with subjacent immunological pathologies who were discarded of having MGUS through serum immunofixation. They were used to verify the sensitivity of DSC for discriminating MGUS from related blood dyscrasias. Only some DSC thermogram feature parameters differentiated, to a lesser extent, between MGUS and non-MGUS individuals. We contemplate DSC as a tool for early diagnosis and monitoring of MGUS.

Deconvolution analysis for classifying gastric adenocarcinoma patients based on differential scanning calorimetry serum thermograms

Recently, differential scanning calorimetry (DSC) has been acknowledged as a novel tool for diagnosing and monitoring several diseases. This highly sensitive technique has been traditionally used to study thermally induced protein folding/unfolding transitions. In previous research papers, DSC profiles from blood samples of patients were analyzed and they exhibited marked differences in the thermal denaturation profile. Thus, we investigated the use of this novel technology in blood serum samples from 25 healthy subjects and 30 patients with gastric adenocarcinoma (GAC) at different stages of tumor development with a new multiparametric approach. The analysis of the calorimetric profiles of blood serum from GAC patients allowed us to discriminate three stages of cancer development (I to III) from those of healthy individuals. After a multiparametric analysis, a classification of blood serum DSC parameters from patients with GAC is proposed. Certain parameters exhibited significant differences (P < 0.05) and allowed the discrimination of healthy subjects/patients from patients at different tumor stages. The results of this work validate DSC as a novel technique for GAC patient classification and staging, and offer new graphical tools and value ranges for the acquired parameters in order to discriminate healthy from diseased subjects with increased disease burden.

Calorimetric markers of Bence Jones and nonsecretory multiple myeloma serum proteome

The present work provides a thermodynamic description of blood serum from patients diagnosed with Bence Jones myeloma (BJMM) and nonsecretory myeloma (NSMM) by means of differential scanning calorimetry (DSC), serum protein electrophoresis, and free light chain assay. Specific alterations in the thermodynamic behavior of both BJMM and NSMM proteome have been revealed. On the basis of the transition temperature of the main transition in the calorimetric profiles and the shape similarity criterion, we defined BJMM and NSMM sets/subsets of thermograms with very similar thermodynamic features. We show that some of the BJMM and NSMM subsets correlate with previously defined secretory myeloma subsets (Todinova et al. Anal. Chem. 2011, 83, 7992). The established analogies strongly suggest that common molecular markers contribute to the calorimetric profiles of the different, secretory and nonsecretory, myeloma types; our data show robust evidence that these are ligands stabilizing the major serum proteins. We demonstrate that the DSC approach might be highly beneficial, especially for NSMM patients, since the characteristic modifications in the DSC profiles might serve as calorimetric markers when no monoclonal proteins can be detected in the bloodstream and the diagnosis heavily relies on invasive methods.

Clinical application of plasma thermograms. Utility, practical approaches and considerations

Differential scanning calorimetry (DSC) studies of blood plasma are part of an emerging area of the clinical application of DSC to biofluid analysis. DSC analysis of plasma from healthy individuals and patients with various diseases has revealed changes in the thermal profiles of the major plasma proteins associated with the clinical status of the patient. The sensitivity of DSC to the concentration of proteins, their interactions with other proteins or ligands, or their covalent modification underlies the potential utility of DSC analysis. A growing body of literature has demonstrated the versatility and performance of clinical DSC analysis across a range of biofluids and in a number of disease settings. The principles, practice and challenges of DSC analysis of plasma are described in this article.

Spectrum-effect relationships between UPLC fingerprints and bioactivities of crude secondary roots of Aconitum carmichaelii Debeaux (Fuzi) and its three processed products on mitochondrial growth coupled with canonical correlation analysis

ETHNOPHARMACOLOGICAL RELEVANCE

The crude secondary roots of Aconitum carmichaelii Debeaux (Fuzi), together with its processed products, including Yanfuzi, Heishunpian and Paofupian, are commonly applied in clinic using for thousands of years, such as collapse, syncope, rheumatic fever, painful joints and various tumors.

AIM OF THE STUDY

To explore the different effects of Fuzi and its processed products on energy metabolism, with mitochondria as the model with the aim of guiding the clinical use of Fuzi and its products. fingerprints of Fuzi, Yanfuzi, Heishunpian and Paofupian were established by Ultra-high Performance Liquid Chromatography (UPLC) and effects of Fuzi and its processed products on rat's liver׳s mitochondrial metabolism were studied by microcalorimetry. Spectrum-effect relationships between UPLC fingerprints and energy metabolism of mitochondria were investigated using canonical correlation analysis (CCA).

RESULTS

Because of their inherent differences in chemical compositions, the main activities of energy metabolism of mitochondria were different among Fuzi and its processed products. The potential bioactivity sequence of the tested products was Fuzi>Heishunpian>Paofupian>Yanfuzi. RESULTS of CCA showed that compounds mesaconitine, benzoylaconitine, and benzoylhypacoitine might be the principal active components.

CONCLUSION

Altogether, this work provides a general model of combination of UPLC and microcalorimetry to study the spectrum-effect relationships of Fuzi and its processed products which can offer some references for detecting principal components of traditional Chinese medicine on bioactivity to mitochondrial growth.

Detection of cervical cancer biomarker patterns in blood plasma and urine by differential scanning calorimetry and mass spectrometry

Improved methods for the accurate identification of both the presence and severity of cervical intraepithelial neoplasia (CIN) and extent of spread of invasive carcinomas of the cervix (IC) are needed. Differential scanning calorimetry (DSC) has recently been shown to detect specific changes in the thermal behavior of blood plasma proteins in several diseases. This methodology is being explored to provide a complementary approach for screening of cervical disease. The present study evaluated the utility of DSC in differentiating between healthy controls, increasing severity of CIN and early and advanced IC. Significant discrimination was apparent relative to the extent of disease with no clear effect of demographic factors such as age, ethnicity, smoking status and parity. Of most clinical relevance, there was strong differentiation of CIN from healthy controls and IC, and amongst patients with IC between FIGO Stage I and advanced cancer. The observed disease-specific changes in DSC profiles (thermograms) were hypothesized to reflect differential expression of disease biomarkers that subsequently bound to and affected the thermal behavior of the most abundant plasma proteins. The effect of interacting biomarkers can be inferred from the modulation of thermograms but cannot be directly identified by DSC. To investigate the nature of the proposed interactions, mass spectrometry (MS) analyses were employed. Quantitative assessment of the low molecular weight protein fragments of plasma and urine samples revealed a small list of peptides whose abundance was correlated with the extent of cervical disease, with the most striking plasma peptidome data supporting the interactome theory of peptide portioning to abundant plasma proteins. The combined DSC and MS approach in this study was successful in identifying unique biomarker signatures for cervical cancer and demonstrated the utility of DSC plasma profiles as a complementary diagnostic tool to evaluate cervical cancer health.

Calorimetric analysis of the plasma proteome: identification of type 1 diabetes patients with early renal function decline

BACKGROUND

Microalbuminuria (MA) has been questioned as a predictor of progressive renal dysfunction in patients with type 1 diabetes (T1D). Consequently, new clinical end points are needed that identify or predict patients that are at risk for early renal function decline (ERFD). The potential clinical utility of differential scanning calorimetry (DSC) analysis of blood plasma and other biofluids has recently been reported. This method provides an alternate physical basis with which to study disease-associated changes in the bulk plasma proteome.

METHODS

DSC analysis of blood plasma was applied to identify unique signatures of ERFD in subjects enrolled in the 1st Joslin Study of the Natural History of Microalbuminuria in Type 1 Diabetes, a prospective cohort study of T1D patients. Recent data suggests that differences in the plasma peptidome of these patients correlate with longitudinal measures of renal function. Differences in DSC profile (thermogram) features were evaluated between T1D MA individuals exhibiting ERFD (n=15) and matched control subjects (n=14).

RESULTS

The average control group thermogram resembled a previously defined healthy thermogram. Differences were evident between ERFD and control individuals. Heat capacity values of the main two transitions were found to be significant discriminators of patient status.

CONCLUSIONS

Results from this pilot study suggest the potential utility of DSC proteome analysis to prognostic indicators of renal disease in T1D.

GENERAL SIGNIFICANCE

DSC shows sensitivity to changes in the bulk plasma proteome that correlate with clinical status in T1D providing additional support for the utility of DSC profiling in clinical diagnostics.