SAXSMoW 3.0: New advances in the determination of the molecular weight of proteins in dilute solutions from SAXS intensity data on a relative scale

Human gingival mesenchymal stem cells-lyosecretome attenuates adverse effect of hydrogen peroxide-induced oxidative stress on osteoblast cells

Objective

To determine total protein content, antioxidant activity, and protective ability of lyophilized human gingival mesenchymal stem cells (hGMSCs)-secretome in hydrogen peroxide (H2O2) induced oxidative stress model.

Methods

Human GMSCs were cultured to obtain a conditioned medium (secretome), then lyophilized to produce lyosecretome. Total protein was determined by bicinchoninic acid assay (BCA) and SDS-PAGE to improve protein measurements. Antioxidant concentration was measured by ABTS assay, while the protective ability of lyosecretome against oxidative stress was determined by the metabolic activity of osteoblast cells. The study group was divided into a control group (culture medium) and a lyosecretome treatment group (0.0; 0.157, 0.313, 0.625, 1.25, 2.5, 5, and 10 mg/mL + H2O2).

Results

Lyosecretome had a protein concentration of 2086.00 ± 0.20 μg/ml, with a molecular weight of 174, 74, 61, 55, and 26 kDa, which are thought to facilitate cell migration, as well as bind cytokines and growth factors. Lyosecretome also provided the highest antioxidant activity of 93.51% at a concentration of 4.8 mg/ml, with an IC50 value of 2.08 mg/ml. The highest cell metabolic activity (79.53 ± 2.41%) was shown in the 1.25 mg/ml lyosecretome treatment group. All concentrations of hGMSC-lyosecretome attenuate the adverse effect of H2O2-induced oxidative stress.

Conclusion

Lyosecretome obtained from hGMSCs can maintain metabolic activity in osteoblast cells as protection against H2O2 oxidative stress.

In Vivo Incorporation of Photoproteins into GroEL Chaperonin Retaining Major Structural and Functional Properties

The incorporation of photoproteins into proteins of interest allows the study of either their localization or intermolecular interactions in the cell. Here we demonstrate the possibility of in vivo incorporating the photoprotein Aequorea victoria enhanced green fluorescent protein (EGFP) or Gaussia princeps luciferase (GLuc) into the tetradecameric quaternary structure of GroEL chaperonin and describe some physicochemical properties of the labeled chaperonin. Using size-exclusion and affinity chromatography, electrophoresis, fluorescent and electron transmission microscopy (ETM), small-angle X-ray scattering (SAXS), and bioluminescence resonance energy transfer (BRET), we show the following: (i) The GroEL₁₄-EGFP is evenly distributed within normally divided E. coli cells, while gigantic undivided cells are characterized by the uneven distribution of the labeled GroEL₁₄ which is mainly localized close to the cellular periplasm; (ii) EGFP and likely GLuc are located within the inner cavity of one of the two GroEL chaperonin rings and do not essentially influence the protein oligomeric structure; (iii) GroEL₁₄ containing either EGFP or GLuc is capable of interacting with non-native proteins and the cochaperonin GroES.

From dilute to concentrated solutions of intrinsically disordered proteins: Interpretation and analysis of collected data

Intrinsically disordered proteins (IDPs) have a broad energy landscape and consequently sample many different conformations in solution. The innate flexibility of IDPs is exploited in their biological function, and in many instances allows a single IDP to regulate a range of processes in vivo. Due to their highly flexible nature, characterizing the structural properties of IDPs is not straightforward. Often solution-based methods such as Nuclear Magnetic Resonance (NMR), Förster Resonance Energy Transfer (FRET), and Small-Angle X-ray Scattering (SAXS) are used. SAXS is indeed a powerful technique to study the structural and conformational properties of IDPs in solution, and from the obtained SAXS spectra, information about the average size, shape, and extent of oligomerization can be determined. In this chapter, we will introduce model-free methods that can be used to interpret SAXS data and introduce methods that can be used to interpret SAXS data beyond analytical models, for example, by using atomistic and different levels of coarse-grained models in combination with molecular dynamics (MD) and Monte Carlo simulations.

Data analysis and modeling of small-angle neutron scattering data with contrast variation from bio-macromolecular complexes

Small-angle neutron scattering (SANS) with contrast variation (CV) is a valuable technique in the structural biology toolchest. Accurate structural parameters-e.g., radii of gyration, volumes, dimensions, and distance distribution(s)-can be derived from the SANS-CV data to yield the shape and disposition of the individual components within stable complexes. Contrast variation is achieved through the substitution of hydrogen isotopes (¹H for ²H) in molecules and solvents to alter the neutron scattering properties of each component of a complex. While SANS-CV can be used a stand-alone technique for interrogating the overall structure of biomacromolecules in solution, it also complements other methods such as small-angle X-ray scattering, crystallography, nuclear magnetic resonance, and cryo-electron microscopy. Undertaking a SANS-CV experiment is challenging, due in part to the preparation of significant quantities of monodisperse samples that may require deuterium (²H) labeling. Nevertheless, SANS-CV can be used to study a diverse range biomacromolecular complexes including protein-protein and protein-nucleic acid systems, membrane proteins, and flexible systems resistant to crystallization. This chapter describes how to approach the data analysis and modeling of SANS data, including: (1) Analysis of the forward scattering (I(0)) and calculation of theoretical estimates of contrast; (2) Analysis of the contrast dependence of the radius of gyration using the Stuhrmann plot and parallel axis theorem; (3) Calculation of composite scattering functions to evaluate the size, shape, and dispositions of individual components within a complex, and; (4) Development of real-space models to fit the SANS-CV data using volume-element bead modeling or atomistic rigid body modeling.