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Amyloid fibril is an important pharmaceutical target for diagnostic and therapeutic treatment of neurodegenerative diseases. However, rational design of chemical compounds that interact with amyloid fibrils is unachievable due to the lack of mechanistic understanding of the ligand–fibril interaction. Here we used cryoelectron microscopy to survey the amyloid fibril-binding mechanism of a series of compounds including classic dyes, (pre)clinical imaging tracers and newly identified binders from high-throughput screening. We obtained clear densities of several compounds in complex with an α-synuclein fibril. These structures unveil the basic mechanism of the ligand–fibril interaction, which exhibits remarkable difference from the canonical ligand–protein interaction. In addition, we discovered a druggable pocket that is also conserved in the ex vivo α-synuclein fibrils from multiple system atrophy. Collectively, these findings expand our knowledge of protein–ligand interaction in the amyloid fibril state, which will enable rational design of amyloid binders in a medicinally beneficial way. Tao et al. reported a series of cryo-EM structures of α-synuclein fibrils in complex with amyloid dyes and imaging tracers, and identified druggable pockets in the fibrils of multiple system atrophy.

Protein arginine methylation plays an important role in regulating protein functions in different cellular processes, and its dysregulation may lead to a variety of human diseases. Recently, arginine methylation was found to be involved in modulating protein liquid—liquid phase separation (LLPS), which drives the formation of different membraneless organelles (MLOs). Here, we developed a steric effect—based chemicalenrichment method (SECEM) coupled with liquid chromatography—tandem mass spectrometry to analyze arginine dimethylation (DMA) at the proteome level. We revealed by SECEM that, in mammalian cells, the DMA sites occurring in the RG/RGG motifs are preferentially enriched within the proteins identified in different MLOs, especially stress granules (SGs). Notably, global decrease of protein arginine methylation severely impairs the dynamic assembly and disassembly of SGs. By further profiling the dynamic change of DMA upon SG formation by SECEM, we identified that the most dramatic change of DMA occurs at multiple sites of RG/RGG—rich regions from several key SG-contained proteins, including G3BP1, FUS, hnRNPA1, and KHDRBS1. Moreover, both in vitro arginine methylation and mutation of the identified DMA sites significantly impair LLPS capability of the four different RG/RGG—rich regions. Overall, we provide a global profiling of the dynamic changes of protein DMA in the mammalian cells under different stress conditions by SECEM and reveal the important role of DMA in regulating protein LLPS and SG dynamics.

α‐Syn fibrils, a key pathological hallmark of Parkinson's disease, is closely associated with disease initiation and progression. Several small molecules are found to bind or dissolve α‐syn fibrils, offering potential therapeutic applications. Here, an innovative optical tweezers‐based, fluorescence‐combined approach is developed to probe the mechanical characteristics of α‐syn fibrils at the single‐molecule level. When subjected to axial stretching, local deformation within α‐syn fibrils appeared at forces above 50 pN. These structural alternations occurred stepwise and are irreversible, suggesting unfolding of individual α‐syn molecules or subdomains. Additionally, α‐syn fibrils exhibits high heterogeneity in lateral disruption, with rupture force ranging from 50 to 500 pN. The impact of different compounds on the structure and mechanical features of α‐syn fibrils is further examined. Notably, epigallocatechin gallate (EGCG) generally attenuates the rupture force of fibrils by wedging into the N‐terminal polar groove and induces fibril dissociation. Conversely, copper chlorophyllin A (CCA) attaches to four different sites wrapping around the fibril core, reinforcing the stability of the fibril against rupture forces. The work offers an effective method for characterizing single‐fibril properties and bridges compound‐induced structural alternations with mechanical response. These insights are valuable for understanding amyloid fibril mechanics and their regulation by small molecules. This work developed an innovative method combining optical tweezers and fluorescence microscopy to study the mechanics of individual α‐synuclein fibrils, a hallmark of Parkinson's disease. At the single‐fibril level, α‐syn fibrils exhibit rigidity and heterogeneity in disruption and deformation. By integrating cryo‐EM, compound‐induced structural changes are linked to mechanical responses, offering insights into fibril mechanics and their regulation by small molecules.

Background Seed amplification assays (SAA) enable the amplification of pathological misfolded proteins, including α-synuclein (αSyn), in both tissue homogenates and body fluids of Parkinson’s disease (PD) patients. SAA involves repeated cycles of shaking or sonication coupled with incubation periods. However, this amplification scheme has limitations in tracking protein propagation due to repeated fragmentation. Methods We introduced a modified form of SAA, known as Quiescent SAA (QSAA), and evaluated biopsy and autopsy samples from individuals clinically diagnosed with PD and those without synucleinopathies (control group). Brain biopsy samples were obtained from 14 PD patients and 6 controls without synucleinopathies. Additionally, skin samples were collected from 214 PD patients and 208 control subjects. Data were analyzed from April 2019 to May 2023. Results QSAA successfully amplified αSyn aggregates in brain tissue sections from mice inoculated with pre-formed fibrils. In the skin samples from 214 PD cases and 208 non-PD cases, QSAA demonstrated high sensitivity (90.2%) and specificity (91.4%) in differentiating between PD and non-PD cases. Notably, more αSyn aggregates were detected by QSAA compared to immunofluorescence with the pS129-αSyn antibody in consecutive slices of both brain and skin samples. Conclusion We introduced the new QSAA method tailored for in situ amplification of αSyn aggregates in brain and skin samples while maintaining tissue integrity, providing a streamlined approach to diagnosing PD with individual variability. The integration of seeding activities with the location of deposition of αSyn seeds advances our understanding of the mechanism underlying αSyn misfolding in PD.

Amyloid fibrils represent a pathological state of protein polymer that is closely associated with various neurodegenerative diseases. Polysaccharides have a prominent role in recognizing amyloid fibrils and mediating their pathogenicity. However, the mechanism underlying the amyloid–polysaccharide interaction remains elusive. We also do not know its impact on the structure and pathology of formed fibrils. Here, we used cryo-electron microscopy to analyze the atomic structures of mature α-synuclein (α-syn) fibrils upon binding with polymeric heparin and heparin-like oligosaccharides. The fibril structure, including the helical twist and conformation of α-syn, changed over time upon the binding of heparin but not oligosaccharides. The sulfation pattern and numbers of saccharide units are important for the binding. Similarly, negatively charged biopolymers typically interact with amyloid fibrils, including tau and various α-syn polymorphs, leading to alterations in their conformation. Moreover, we show that heparin-like oligosaccharides can not only block neuronal uptake and propagation of formed α-syn fibrils but also inhibit α-syn fibrillation. This work demonstrates a distinctive activity of heparin and biopolymers in remodeling amyloid fibrils and suggests the pharmaceutical potential of heparin-like oligosaccharides. Here, the authors reveal progressive conformational changes of α-synuclein fibrils upon binding with polysaccharide heparin, suggesting that biopolymers have a crucial role in remodeling the structures and pathological activities of amyloid fibrils.

Lung cancer is the leading cause of cancer-related mortality worldwide, with high incidence and low survival rate. Over the past decades, innovative treatment modalities like targeted drug therapy and immunotherapy have emerged, offering potential improvements in safety and effectiveness. Immunotherapy allows for a sustained therapeutic response by enhancing the immune response of the body to cancer cells, providing long-term survival benefits with a manageable safety profile. However, the experiences of patients receiving immunotherapy and their family caregivers remain insufficiently explored. This study aimed to explore the requirements of healthcare professionals in immunotherapy of lung cancer patients by examining and synthesizing qualitative data about the experiences of lung cancer patients receiving immunotherapy and their family caregivers, focusing on the psychological, information, and support needs to improve the quality of life of patients. Seven articles reporting on five studies were included, comprising 216 participants (96 patients, 70 healthcare professionals, and 50 family members). Four major themes emerged: (1) The quality of life of patients could be significantly influenced by their psychological state. (2) Anxiety and stress are common in patients and consequently cause their uncertainty about the treatment. (3) Although physical side effects may occur, they are generally less severe and have less impact on overall health and daily functioning compared with those associated with chemotherapy. (4) Family caregivers faced substantial stress due to financial burdens and the impact on family relations.

The nucleocapsid (N) protein of SARS-CoV-2 has been reported to have a high ability of liquid-liquid phase separation, which enables its incorporation into stress granules (SGs) of host cells. However, whether SG invasion by N protein occurs in the scenario of SARS-CoV-2 infection is unknow, neither do we know its consequence. Here, we used SARS-CoV-2 to infect mammalian cells and observed the incorporation of N protein into SGs, which resulted in markedly impaired self-disassembly but stimulated cell cellular clearance of SGs. NMR experiments further showed that N protein binds to the SG-related amyloid proteins via non-specific transient interactions, which not only expedites the phase transition of these proteins to aberrant amyloid aggregation in vitro, but also promotes the aggregation of FUS with ALS-associated P525L mutation in cells. In addition, we found that ACE2 is not necessary for the infection of SARS-CoV-2 to mammalian cells. Our work indicates that SARS-CoV-2 infection can impair the disassembly of host SGs and promote the aggregation of SG-related amyloid proteins, which may lead to an increased risk of neurodegeneration.

The Xiaoxinganling Mountains–Sanjiang Plain region represents a crucial ecological security barrier for the Northeast China Plain and serves as a vital region for national grain production. Over the past two decades, the region has undergone numerous ecological restoration projects. Nevertheless, the combined impact of enhanced vegetation greening and global climate change on the regional hydrological cycle remains inadequately understood. This study employed the distributed hydrological model ESSI-3, reanalysis datasets, and multi-source satellite remote sensing data to quantitatively evaluate the influences of climate change and vegetation dynamics on regional hydrological processes. The study period spans from 2000 to 2020, during which there were significant increases in regional precipitation and leaf area index (p < 0.05). The hydrological simulation results exhibited strong agreement with observed river discharge, evapotranspiration, and terrestrial water storage anomalies, thereby affirming the ESSI-3 model’s reliability in hydrological change assessment. By employing both a constant scenario that solely considered climate change and a dynamic scenario that integrated vegetation dynamics, the findings reveal that: (1) Regionally, climate change driven by increased precipitation significantly augmented runoff fluxes (0.4 mm/year) and water storage components (2.57 mm/year), while evapotranspiration trends downward, attributed primarily to reductions in solar radiation and wind speed; (2) Vegetation greening reversed the decreasing trend in evapotranspiration to an increasing trend, thus exerting a negative impact on runoff and water storage. However, long-term simulations demonstrated that regional runoff fluxes (0.38 mm/year) and water storage components (2.21 mm/year) continue to increase, mainly due to precipitation increments surpassing those of evapotranspiration; (3) Spatially, vegetation greening altered the surface soil moisture content trend in the eastern forested areas from an increase to a decrease. These findings suggested that sub-regional ecological restoration initiatives, such as afforestation, significantly influence the hydrological cycle, especially in areas with higher vegetation greening. Nevertheless, persistent increases in precipitation could effectively mitigate the moisture deficits induced by vegetation greening. The study’s outcomes provide a basis for alleviating concerns regarding potential water consumption risks associated with future ecological restoration and extensive vegetation greening projects, thereby offering scientific guidance for sustainable water resource management.

Accurate landslide susceptibility mapping is vital for disaster forecasting and risk management. To address the problem of limited accuracy of individual classifiers and lack of model interpretability in machine learning-based models, a coupled multi-model framework for landslide susceptibility mapping is proposed. Using Jiuzhaigou County, Sichuan Province, as a case study, we developed an evaluation index system incorporating 14 factors. We employed three base models—logistic regression, support vector machine, and Gaussian Naive Bayes—assessed through four ensemble methods: Stacking, Voting, Bagging, and Boosting. The decision mechanisms of these models were explained via a SHAP (SHapley Additive exPlanations) analysis. Results demonstrate that integrating machine learning with ensemble learning and SHAP yields more reliable landslide susceptibility mapping and enhances model interpretability. This approach effectively addresses the challenges of unreliable landslide susceptibility mapping in complex environments.