ABSTRACT
AIMS: We evaluated the potential of Parkinson's disease (PD) fecal microbiota transplantation to initiate or exacerbate PD pathologies and investigated the underlying mechanisms. METHODS: We transplanted the fecal microbiota from PD patients into mice by oral gavage and assessed the motor and intestinal functions, as well as the inflammatory and pathological changes in the colon and brain. Furthermore, 16S rRNA gene sequencing combined with metabolomics analysis was conducted to assess the impacts of fecal delivery on the fecal microbiota and metabolism in recipient mice. RESULTS: The fecal microbiota from PD patients increased intestinal inflammation, deteriorated intestinal barrier function, intensified microglia and astrocyte activation, abnormal deposition of α-Synuclein, and dopaminergic neuronal loss in the brains of A53T mice. A mechanistic study revealed that the fecal microbiota of PD patients stimulated the TLR4/NF-κB/NLRP3 pathway in both the brain and colon. Additionally, multiomics analysis found that transplantation of fecal microbiota from PD patients not only altered the composition of the gut microbiota but also influenced the fecal metabolic profile of the recipient mice. CONCLUSION: The fecal microbiota from PD patients intensifies inflammation and neurodegeneration in A53T mice. Our findings demonstrate that imbalance and dysfunction in the gut microbiome play significant roles in the development and advancement of PD.
Subject(s)
Fecal Microbiota Transplantation , Gastrointestinal Microbiome , Parkinson Disease , Animals , Mice , Parkinson Disease/microbiology , Parkinson Disease/metabolism , Humans , Gastrointestinal Microbiome/physiology , Male , Inflammation/metabolism , Inflammation/microbiology , Feces/microbiology , Mice, Transgenic , Mice, Inbred C57BL , Female , alpha-Synuclein/metabolism , Brain/metabolism , Brain/pathologyABSTRACT
Heavy metals present a substantial threat to both the environment and human health. Considering the delicate ecological equilibrium of the Tibetan Plateau (TP) and its heightened susceptibility to anthropogenic impacts, scholarly attention has progressively turned toward the examination of heavy metal pollution within the plateau's environment. In this study, we conducted a comprehensive analysis of various heavy metals (As, Cr, Co, Ni, Cu, Mo, Cd, Pb, and Sb), utilizing topsoil samples collected from the TP during the period of 2018-2021. Additionally, snow and cryoconite samples obtained from TP glaciers during the same timeframe were also subjected to analysis. The results indicate elevated concentrations of total heavy metals in the eastern and western TP (328.7 µg/g), as opposed to the central and southern TP (145.7 µg/g). Most heavy metals exhibit a consistent spatial distribution pattern. High Enrichment Factors (EFs) and Geoaccumulation Index (Igeo) values for As and Cd suggest their enrichment in TP topsoil. Receptor modeling identified three primary sources of heavy metals within the topsoil: industrial sources (42.3%), inherent natural sources within the surface soil (20.6%), and vehicular emissions (14.2%). Substantial differences in heavy metal concentrations and spatial distribution were observed between the topsoil and the glacial snow-cryoconite matrix. The prominent presence of Sb in the snow-cryoconite matrix, in contrast to its low abundance in the topsoil, indicates distinct source influences of long-range transported materials between the two environments. Our inference suggests that the influence of heavy metals from distant pollutants undergo mixing and dilution in the topsoil due to the presence of local indigenous heavy metals, although such influence is notably observed on the glacier surface of the TP. Consequently, this underscores the significant impact of long-range transported sources on heavy metals, surpassing the influence of local TP soils, to the alpine glaciers and even other atmospheric sediments in Tibetan Plateau.
Subject(s)
Metals, Heavy , Soil Pollutants , Humans , Tibet , Ice Cover , Cadmium/analysis , Environmental Monitoring/methods , Metals, Heavy/analysis , Soil , Soil Pollutants/analysis , China , Risk AssessmentABSTRACT
Low-dimensional ferroelectric tunnel junctions are appealing for the realization of nanoscale nonvolatile memory devices due to their inherent advantages of device miniaturization. Those based on current mechanisms have limitations, including low tunneling electroresistance (TER) effects and complex heterostructures. Here, we introduce an entirely new TER mechanism to construct a nanotube ferroelectric tunnel junction with ferroelectric nanotubes as the tunneling region. When rolling a ferroelectric monolayer into a nanotube, due to the coexistence of its intrinsic ferroelectric polarization with the flexoelectric polarization induced by bending, a metal-insulator transition occurs depending on the radiative polarization states. For the pristine monolayer, its out-of-plane polarization is tunable by an in-plane electric field, and the conducting states of the ferroelectric nanotube can thus be tuned between metallic and insulating states via axial electric means. Using α-In2Se3 as an example, our first-principles density functional theory calculations and nonequilibrium Green's function formalism confirm the feasibility of the TER mechanism and indicate an ultrahigh TER ratio that exceeds 9.9 × 1010% of the proposed nanotube ferroelectric tunnel junctions. Our findings provide a promising approach based on simple homogeneous structures for high density ferroelectric microelectric devices with excellent ON/OFF performance.
ABSTRACT
JOURNAL/nrgr/04.03/01300535-202408000-00039/figure1/v/2023-12-16T180322Z/r/image-tiff Biomarkers are required for the early detection, prognosis prediction, and monitoring of amyotrophic lateral sclerosis, a progressive disease. Proteomics is an unbiased and quantitative method that can be used to detect neurochemical signatures to aid in the identification of candidate biomarkers. In this study, we used a label-free quantitative proteomics approach to screen for substantially differentially regulated proteins in ten patients with sporadic amyotrophic lateral sclerosis compared with five healthy controls. Substantial upregulation of serum proteins related to multiple functional clusters was observed in patients with sporadic amyotrophic lateral sclerosis. Potential biomarkers were selected based on functionality and expression specificity. To validate the proteomics profiles, blood samples from an additional cohort comprising 100 patients with sporadic amyotrophic lateral sclerosis and 100 healthy controls were subjected to enzyme-linked immunosorbent assay. Eight substantially upregulated serum proteins in patients with sporadic amyotrophic lateral sclerosis were selected, of which the cathelicidin-related antimicrobial peptide demonstrated the best discriminative ability between patients with sporadic amyotrophic lateral sclerosis and healthy controls (area under the curve [AUC] = 0.713, P < 0.0001). To further enhance diagnostic accuracy, a multi-protein combined discriminant algorithm was developed incorporating five proteins (hemoglobin beta, cathelicidin-related antimicrobial peptide, talin-1, zyxin, and translationally-controlled tumor protein). The algorithm achieved an AUC of 0.811 and a P-value of < 0.0001, resulting in 79% sensitivity and 71% specificity for the diagnosis of sporadic amyotrophic lateral sclerosis. Subsequently, the ability of candidate biomarkers to discriminate between early-stage amyotrophic lateral sclerosis patients and controls, as well as patients with different disease severities, was examined. A two-protein panel comprising talin-1 and translationally-controlled tumor protein effectively distinguished early-stage amyotrophic lateral sclerosis patients from controls (AUC = 0.766, P < 0.0001). Moreover, the expression of three proteins (FK506 binding protein 1A, cathelicidin-related antimicrobial peptide, and hemoglobin beta-1) was found to increase with disease progression. The proteomic signatures developed in this study may help facilitate early diagnosis and monitor the progression of sporadic amyotrophic lateral sclerosis when used in combination with current clinical-based parameters.