Amyloid Formation of Stefin B Protein Studied by Infrared Spectroscopy.

BACKGROUND: Stefin B, an established model protein for studying the stability and mechanism of protein folding, was used for monitoring protein aggregation and formation of amyloid structure by infrared spectroscopy. METHODS: The analyses of the integral intensities of the low frequency part of the Amide I band, which is directly connected to the appearance of the cross-ß structure reveals the temperature but not pH dependence of stefin B structure. RESULTS: We show that pH value has significant role in the monomer stability of stefin B. Protein is less stable in acidic environment and becomes more stable in neutral or basic conditions. While in the case of the Amide I band area analysis we apply only spectral regions characteristic for only part of the protein in cross-ß structure, the temperature study using multivariate curve resolution (MCR) analysis contains also information about the protein conformation states which do not correspond to native protein nor protein in cross-ß structure. CONCLUSIONS: These facts results in the slightly different shapes of fitted sigmoid functions fitted to the weighted amount of the second basic spectrum (sc2), which is the closed approximation of the protein spectra with cross-ß structure. Nevertheless, the applied method detects the initial change of the protein structure. Upon the analysis of infrared data a model for stefin B aggregation is proposed.

Exploration of macromolecular phenotype of human skeletal muscle in diabetes using infrared spectroscopy.

Introduction: The global burden of diabetes mellitus is escalating, and more efficient investigative strategies are needed for a deeper understanding of underlying pathophysiological mechanisms. The crucial role of skeletal muscle in carbohydrate and lipid metabolism makes it one of the most susceptible tissues to diabetes-related metabolic disorders. In tissue studies, conventional histochemical methods have several technical limitations and have been shown to inadequately characterise the biomolecular phenotype of skeletal muscle to provide a holistic view of the pathologically altered proportions of macromolecular constituents. Materials and methods: In this pilot study, we examined the composition of five different human skeletal muscles from male donors diagnosed with type 2 diabetes and non-diabetic controls. We analysed the lipid, glycogen, and collagen content in the muscles in a traditional manner with histochemical assays using different staining techniques. This served as a reference for comparison with the unconventional analysis of tissue composition using Fourier-transform infrared spectroscopy as an alternative methodological approach. Results: A thorough chemometric post-processing of the infrared spectra using a multi-stage spectral decomposition allowed the simultaneous identification of various compositional details from a vibrational spectrum measured in a single experiment. We obtained multifaceted information about the proportions of the different macromolecular constituents of skeletal muscle, which even allowed us to distinguish protein constituents with different structural properties. The most important methodological steps for a comprehensive insight into muscle composition have thus been set and parameters identified that can be used for the comparison between healthy and diabetic muscles. Conclusion: We have established a methodological framework based on vibrational spectroscopy for the detailed macromolecular analysis of human skeletal muscle that can effectively complement or may even serve as an alternative to histochemical assays. As this is a pilot study with relatively small sample sets, we remain cautious at this stage in drawing definitive conclusions about diabetes-related changes in skeletal muscle composition. However, the main focus and contribution of our work has been to provide an alternative, simple and efficient approach for this purpose. We are confident that we have achieved this goal and have brought our methodology to a level from which it can be successfully transferred to a large-scale study that allows the effects of diabetes on skeletal muscle composition and the interrelationships between the macromolecular tissue alterations due to diabetes to be investigated.

Application of FTIR Spectroscopy to Detect Changes in Skeletal Muscle Composition Due to Obesity with Insulin Resistance and STZ-Induced Diabetes.

Age, obesity, and diabetes mellitus are pathophysiologically interconnected factors that significantly contribute to the global burden of non-communicable diseases. These metabolic conditions are associated with impaired insulin function, which disrupts the metabolism of carbohydrates, lipids, and proteins and can lead to structural and functional changes in skeletal muscle. Therefore, the alterations in the macromolecular composition of skeletal muscle may provide an indication of the underlying mechanisms of insulin-related disorders. The aim of this study was to investigate the potential of Fourier transform infrared (FTIR) spectroscopy to reveal the changes in macromolecular composition in weight-bearing and non-weight-bearing muscles of old, obese, insulin-resistant, and young streptozotocin (STZ)-induced diabetic mice. The efficiency of FTIR spectroscopy was evaluated by comparison with the results of gold-standard histochemical techniques. The differences in biomolecular phenotypes and the alterations in muscle composition in relation to their functional properties observed from FTIR spectra suggest that FTIR spectroscopy can detect most of the changes observed in muscle tissue by histochemical analyses and more. Therefore, it could be used as an effective alternative because it allows for the complete characterization of macromolecular composition in a single, relatively simple experiment, avoiding some obvious drawbacks of histochemical methods.

Application of the time-strain superposition - Part II: Prediction of the frequency-dependent behaviour of brain tissue.

Knowing the real material properties of brain tissue is of great importance when it comes to the precise prediction of its mechanical response. The efficiency of these procedures depends on the adequacy of experimental data and the analytical and numerical tools utilized. In this study, we combine existing approaches within the theory of viscoelasticity in order to predict the frequency-dependent behaviour of the porcine brain from the known stress relaxation data. Time-strain superposition is applied to the brain shear relaxation segments for the construction of the long-term master curve in the linear viscoelastic range. A widely-used and well-established numerical procedure is then utilized for the prediction of the frequency-dependent modulus based on the constructed master curve. The demonstrated methodology is evaluated using the porcine brain experimental data available from the literature. The results show reasonably good agreement between the predicted and the previously measured and published storage modulus data in the whole frequency range investigated. On the other hand, prediction of the loss modulus is only possible within certain frequency ranges related to the time frame of experimentally known relaxation behaviour. Nevertheless, the outcomes of the paper speak in favour of the validity of the linear viscoelastic interconversion relations between the time- and frequency-dependent material functions of the porcine brain tissue exposed to strain up to the tissue's linear viscoelastic limit.

Application of the time-strain superposition - Part I: Prediction of the nonlinear constant shear rate response of brain tissue.

Modern surgical training, better understanding of the biomechanics of traumatic brain injury, and precise quantification of the difference between mechanical response of healthy and disease-modified brain tissue, require reliable experimental data and efficient mathematical/computational models. In this paper, a new methodology is proposed for prediction of the nonlinear viscoelastic behaviour of porcine brain. Time-strain superposition is applied to the brain stress relaxation data for construction of the overall master curve. The nonlinear internal-clock viscoelastic model, which is based on the free volume concept, is utilized to predict the constant shear rate (CSR) response, based on the known stress relaxation master curve. Demonstrated theoretical procedure is evaluated on the porcine brain experimental data available from the literature. RESULTS: show good agreement between the predicted CSR response and the previously published CSR measurements. We may justifiably speculate that the proposed approach serves well for prediction of the nonlinear CSR behaviour of the porcine brain tissue. Since the methodology is strongly supported by the physical background, it exhibits the potential to be utilized for prediction of nonlinear behaviour in other loading modes, as well as of other tissues or viscoelastic materials.