High-density lipoproteins (HDL) are crucial for cardiovascular health. HDL dysfunction is strongly linked to atherosclerosis and other diseases. Although HDL is often treated as a single entity, it comprises a spectrum of subfractions differing in size, structure, and composition. Understanding the ultrastructure and function of these subfractions is essential for uncovering the molecular mechanisms behind HDL dysfunction and improving disease risk prediction. We employed Small-Angle X-ray Scattering (SAXS) and cryogenic-electron tomography (cryo-ET) to analyze the structural features of total HDL and its subfractions: HDL2b, HDL2a, and HDL3. We present updated, detailed structural models showing that the structure of HDL changes dramatically from the smallest to the largest subfraction, with HDL3 partially exposing the core to the particle's surface. Moreover, total HDL reflects the weighted sum of its subfractions, emphasizing that analysis of total HDL alone may be misleading. HDL subfractions were also studied in serum samples from 16 individuals classified as low- or high-risk for atherosclerosis. Even though biochemical differences appeared mainly in HDL2, structural differences were most pronounced in HDL3 addressing that particle composition alone cannot fully distinguish HDL dysfunction. Multimodal analysis integrating structural and biochemical data was able to separate risk groups and revealed correlations between structural parameters and cardiovascular risk status. Our findings support the importance of analyzing HDL subfractions to uncover molecular drivers of disease risk and suggest candidate biomarkers for atherosclerosis upon validation in larger cohorts.

Background: Understanding the structure of plant seeds cultivated for human consumption and food manufacturing is vital to provide sustainable products as well as to investigate early growth stages. This includes structural variation between different plant species, varieties and cultivars depending on genetic setup, as well as structural modifications upon germination, aging and storing or seed treatment during processing. For plant seeds as multi-component biological materials, structural characterization must extend across multiple length scales, from molecular organization to cellular architecture. Results: We apply scanning Small- and Wide-Angle X-ray Scattering (SWAXS) and X-ray Fluorescence (XRF) on yellow pea seeds to combine local structural information on the molecular scale with imaging of cellular structures on the micrometer scale, enabling a comprehensive analysis of hierarchical organization. To identify and characterize heterogeneous regions within the pea seeds, we implement a fitting-free, data-driven segmentation and analysis workflow based on machine learning tools. This approach allows for classification of structurally distinct domains and enables quantitative comparison across samples without relying on predefined models. Furthermore, we incorporate multi-modal analysis by combining structural imaging with complementary elemental information obtained from XRF. The integration of compositional and structural data provides deeper insight into structure-composition relationships. Conclusions: This multi-scale, multi-modal approach opens new possibilities for investigating hierarchical structures and their development under diverse conditions and enables systematic comparison between different species or seeds at different developmental stages or exposed to different processing steps. The approach is broadly applicable to various kinds of samples and other hierarchically organized biological materials, which makes it a valuable technique for plant science as well as plant-based food science.

Hypothesis: Digestion pathways of plant proteins are of high relevance to optimize bioavailability and allergenicity profiles of sustainable and vegan food products. Understanding the structural breakdown of solid food presents a particular challenge, due to the complexity to realize a good model system suitable for comprehensive multi-technique characterization under realistic conditions. Experiments: We used a microfluidic chip to study the structural evolution during in-vitro digestion of solid gels from pea proteins. The gel structure was probed in-situ combining confocal microscopy, small-angle neutron and X-ray scattering using the same experimental platform. SDS-PAGE analysis was performed on related solution and gel samples subjected to different digestion times. Findings: Combining multiple techniques we reach a multi-scale picture of gel digestion, revealing the breakdown of a more homogeneous gel into more open connected domains with hierarchical internal structures. SDS-PAGE outlines effects of processing on resulting digestion pathways. As central result, we observe a clear two-step digestion process across techniques, switching at about 8-10 min from the initial response to long-term digestion. Overall, the presented methodology holds promise for detailed structural information in future studies aimed at developing new foods with optimized mechanical, nutritional value, and reduced allergenicity.

Introduction: Dental implants are standard prosthetics used to replace defective teeth. Despite their widespread use, over 5% of implants fail, for reasons that are not yet fully understood. To address this issue, it is crucial to gain a deeper understanding of the bone formation mechanisms at the bone tissue–implant interface. Factors contributing to implant failures may include locally elevated stresses around the implant, caused either by the surgical protocol or by chewing forces.In this study, we analyzed the effects of loading—through simulated chewing forces—and the surgical protocol on bone quality. Our investigation focused on the mechanosensitive osteocyte lacunar-canalicular network, bone formation, and the bone mineral density distribution in the jawbone surrounding titanium implants.

Methods: To investigate the effects of predrilling protocols (a standard dental practice) and loading on the success of dental implants, sheep were implanted with dental prosthetics following three different protocols. The control group underwent non-undersized predrilling and did not experience loading after implantation. In contrast, the NU-L group received non-undersized predrilling, and the implants were cyclically loaded with 50 N after implantation. The U-L group underwent undersized predrilling, followed by the same loading protocol (50 N).Quantitative backscattered electron microscopy images were acquired and segmented into two regions of interest: 0–100 µm (close) and 100 µm to the maximum of the image (far). We analyzed the following parameters: bone mass, mineralization, and osteocyte lacunar density using the software XamFlow. The data were then subjected to statistical analysis, including one-way and two-way ANOVA.

Results: Analysis of the mean calcium content, osteocyte lacunar density, and bone area fraction revealed higher mineralization both close to and far from the implant in the U-L group compared to the NU-L group. Additionally, there was a trend toward more bone being present close to the implant (0.06<p<0.07). The osteocyte lacunar density was significantly lower in the U-L group compared to the control group (p=0.047).

Discussion: We observed that most differences were localized within the 0–100 μm region, which is directly influenced by the mechanical forces induced by the implant on the bone tissue. The elevated bone mass and mean calcium content in the loaded group with undersized holes (U-L) indicates good adaptation of the bone to the implant. However, the lower osteocyte lacunae density suggests a potentially impaired osteocyte differentiation during bone formation.

Allostery constitutes the major mechanism of intra- and interprotein signaling. Detecting these signaling channels, their underlying structural rearrangements and especially transition dynamics is challenging, as it requires a detailed analysis down to atomic scales and timescales that cover several orders of magnitude. We here present an integrative research approach to detect and follow allosteric information transfer across multiple time- and length scales. We employ molecular dynamics simulations on a multi-μs scale combined with nanosecond fluorescence correlation spectroscopy and neutron scattering experiments, including neutron spin echo experiments, to verify the predicted dynamics and structural changes. Using the heat shock protein 90 (Hsp90) as the test system, we reveal molecule-spanning and highly diffusive dynamics on the order of 100 ns that may serve as the dynamic basis for allosteric changes. Furthermore, we can follow protein dynamics upon hydrolysis of ATP to ADP from Angstroms and nanoseconds (bond ruptures and local rearrangements) to nanometers and microseconds (protein domain reorientations). Intriguingly, a single amino acid is crucial for transferring structural information from the nucleotide binding site to the full protein dimer.

Allostery constitutes the major mechanism of intraprotein and interprotein signaling. Detecting these signaling channels, their underlying structural rearrangements and especially transition dynamics is challenging as it requires a detailed analysis down to atomic scales and timescales that cover several orders of magnitude. We here present an integrative research approach to detect and follow allosteric information transfer across multiple time and length scales. We employ molecular dynamics simulations on a multi-μs scale combined with nanosecond fluorescence correlation spectroscopy and neutron scattering experiments, including neutron spin echo experiments, to verify the predicted dynamics and structural changes. Using the heat shock protein 90 (Hsp90) as test system, we reveal molecule-spanning and highly diffusive dynamics on the order of 100 ns that may serve as the dynamical basis for allosteric changes. Furthermore, we can follow protein dynamics upon hydrolysis of ATP to ADP from Angstroms and nanoseconds (bond ruptures and local rearrangements) to nanometers and microseconds (protein domain reorientations). Intriguingly, a single amino acid is crucial for transferring structural information from the nucleotide-binding site to the full protein dimer.

Neutron spectroscopy uniquely and non-destructively accesses diffusive dynamics in soft and biological matter, including for instance proteins in hydrated powders or in solution, and more generally dynamic properties of condensed matter on the molecular level. Given the limited neutron flux resulting in long counting times, it is important to optimize data acquisition for the specific question, in particular for time-resolved (kinetic) studies. The required acquisition time was recently significantly reduced by measurements of discrete energy transfers rather than quasi-continuous neutron scattering spectra on neutron backscattering spectrometers. Besides this reduction in acquisition times, smaller amounts of samples can be measured with better statistics, and most importantly, kinetically changing samples, such as aggregating or crystallizing samples, can be followed. However, given the small number of discrete energy transfers probed in this mode, established analysis frameworks for full spectra can break down. Presented here are new approaches to analyze measurements of diffusive dynamics recorded within fixed windows in energy transfer, and these are compared with the analysis of full spectra. The new approaches are tested by both modeled scattering functions and a comparative analysis of fixed energy window data and full spectra on well understood reference samples. This new approach can be employed successfully for kinetic studies of the dynamics focusing on the short-time apparent center-of-mass diffusion.

Protein interactions in solution and the question of cluster formation are of paramount importance to a wide range of research fields and industrial applications. Protein clustering can arise due to a delicate interplay between the short-range attraction and the long-range repulsion in the interaction potential. This study employs small angle X-ray scattering (SAXS) to examine the clustering behavior of three protein systems in solution: bovine serum albumin (BSA), bovine beta-lactoglobulin (BLG) and lysozyme (LYZ) under varying protein concentrations, ionic strengths and temperatures. The goal is to elucidate the influence of the attractive potential's nature on clustering behavior. BSA serves as a reference system, where electrostatic repulsions dominate the effective interactions, resulting in a mean protein-protein distance as a function of protein concentration which follows the 1/3 power law and precludes protein cluster formation. In contrast, BLG and LYZ solutions exhibit exponents less than 1/3, indicating concentration-dependent cluster formation. The effective interactions are well described using a two-Yukawa type potential comprising a long-ranged repulsion and a short-ranged attraction. While increasing ionic strength by adding NaCl reduced repulsion and correlation in all three systems, the response of protein solutions to ionic strength differed for clustering systems of BLG and LYZ. BLG solutions exhibited a reduced attraction with increasing ionic strength, leading to cluster dissolution, while LYZ solutions experienced enhanced attraction, favoring larger cluster formation. Notably, BLG solutions showed minimal temperature dependence, while LYZ solutions exhibited increased attraction with decreasing temperature, further promoting cluster formation. These results demonstrate the crucial role of short-ranged attractions of specific nature in determining protein clustering behavior. A thorough understanding of protein interactions is essential for predicting protein clustering and phase behavior.

High-density lipoproteins (HDLs) are responsible for removing cholesterol from arterial walls, through a process known as reverse cholesterol transport. The main protein in HDL, apolipoprotein A-I (ApoA-I), is essential to this process, and changes in its sequence significantly alter HDL structure and functions. ApoA-I amyloidogenic variants, associated with a particular hereditary degenerative disease, are particularly effective at facilitating cholesterol removal, thus protecting carriers from cardiovascular disease. Thus, it is conceivable that reconstituted HDL (rHDL) formulations containing ApoA-I proteins with functional/structural features similar to those of amyloidogenic variants hold potential as a promising therapeutic approach. Here we explored the effect of protein cargo and lipid composition on the function of rHDL containing one of the ApoA-I amyloidogenic variants G26R or L174S by Fourier transformed infrared spectroscopy and neutron reflectometry. Moreover, small-angle x-ray scattering uncovered the structural and functional differences between rHDL particles, which could help to comprehend higher cholesterol efflux activity and apparent lower phospholipid (PL) affinity. Our findings indicate distinct trends in lipid exchange (removal vs. deposition) capacities of various rHDL particles, with the rHDL containing the ApoA-I amyloidogenic variants showing a markedly lower ability to remove lipids from artificial membranes compared to the rHDL containing the native protein. This effect strongly depends on the level of PL unsaturation and on the particles' ultrastructure. The study highlights the importance of the protein cargo, along with lipid composition, in shaping rHDL structure, contributing to our understanding of lipid-protein interactions and their behavior.