Structural, Stability, Dynamic and Binding Properties of the ALS-Causing T46I Mutant of the hVAPB MSP Domain as Revealed by NMR and MD Simulations

T46I is the second mutation on the hVAPB MSP domain which was recently identified from non-Brazilian kindred to cause a familial amyotrophic lateral sclerosis (ALS). Here using CD, NMR and molecular dynamics (MD) simulations, we characterized the structure, stability, dynamics and binding capacity of the T46I-MSP domain. The results reveal: 1) unlike P56S which we previously showed to completely eliminate the native MSP structure, T46I leads to no significant disruption of the native secondary and tertiary structures, as evidenced from its far-UV CD spectrum, as well as Cα and Cβ NMR chemical shifts. 2) Nevertheless, T46I does result in a reduced thermodynamic stability and loss of the cooperative urea-unfolding transition. As such, the T46I-MSP domain is more prone to aggregation than WT at high protein concentrations and temperatures in vitro, which may become more severe in the crowded cellular environments. 3) T46I only causes a 3-fold affinity reduction to the Nir2 peptide, but a significant elimination of its binding to EphA4. 4) EphA4 and Nir2 peptide appear to have overlapped binding interfaces on the MSP domain, which strongly implies that two signaling networks may have a functional interplay in vivo. 5) As explored by both H/D exchange and MD simulations, the MSP domain is very dynamic, with most loop residues and many residues on secondary structures highly fluctuated or/and exposed to bulk solvent. Although T46I does not alter overall dynamics, it does trigger increased dynamics of several local regions of the MSP domain which are implicated in binding to EphA4 and Nir2 peptide. Our study provides the structural and dynamic understanding of the T46I-causing ALS; and strongly highlights the possibility that the interplay of two signaling networks mediated by the FFAT-containing proteins and Eph receptors may play a key role in ALS pathogenesis

Dynamically-Driven Inactivation of the Catalytic Machinery of the SARS 3C-Like Protease by the N214A Mutation on the Extra Domain

Despite utilizing the same chymotrypsin fold to host the catalytic machinery, coronavirus 3C-like proteases (3CLpro) noticeably differ from picornavirus 3C proteases in acquiring an extra helical domain in evolution. Previously, the extra domain was demonstrated to regulate the catalysis of the SARS-CoV 3CLpro by controlling its dimerization. Here, we studied N214A, another mutant with only a doubled dissociation constant but significantly abolished activity. Unexpectedly, N214A still adopts the dimeric structure almost identical to that of the wild-type (WT) enzyme. Thus, we conducted 30-ns molecular dynamics (MD) simulations for N214A, WT, and R298A which we previously characterized to be a monomer with the collapsed catalytic machinery. Remarkably, three proteases display distinctive dynamical behaviors. While in WT, the catalytic machinery stably retains in the activated state; in R298A it remains largely collapsed in the inactivated state, thus implying that two states are not only structurally very distinguishable but also dynamically well separated. Surprisingly, in N214A the catalytic dyad becomes dynamically unstable and many residues constituting the catalytic machinery jump to sample the conformations highly resembling those of R298A. Therefore, the N214A mutation appears to trigger the dramatic change of the enzyme dynamics in the context of the dimeric form which ultimately inactivates the catalytic machinery. The present MD simulations represent the longest reported so far for the SARS-CoV 3CLpro, unveiling that its catalysis is critically dependent on the dynamics, which can be amazingly modulated by the extra domain. Consequently, mediating the dynamics may offer a potential avenue to inhibit the SARS-CoV 3CLpro

Combined analysis and validation for DNA methylation and gene expression profiles associated with prostate cancer

Abstract Background Prostate cancer (PCa) is a malignancy cause of cancer deaths and frequently diagnosed in male. This study aimed to identify tumor suppressor genes, hub genes and their pathways by combined bioinformatics analysis. Methods A combined analysis method was used for two types of microarray datasets (DNA methylation and gene expression profiles) from the Gene Expression Omnibus (GEO). Differentially methylated genes (DMGs) were identified by the R package minfi and differentially expressed genes (DEGs) were screened out via the R package limma. A total of 4451 DMGs and 1509 DEGs, identified with nine overlaps between DMGs, DEGs and tumor suppressor genes, were screened for candidate tumor suppressor genes. All these nine candidate tumor suppressor genes were validated by TCGA (The Cancer Genome Atlas) database and Oncomine database. And then, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed by DAVID (Database for Annotation, Visualization and Integrated Discovery) database. Protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. At last, Kaplan–Meier analysis was performed to validate these genes. Results The candidate tumor suppressor genes were IKZF1, PPM1A, FBP1, SMCHD1, ALPL, CASP5, PYHIN1, DAPK1 and CASP8. By validation in TCGA database, PPM1A, DAPK1, FBP1, PYHIN1, ALPL and SMCHD1 were significant. The hub genes were FGFR1, FGF13 and CCND1. These hub genes were identified from the PPI network, and sub-networks revealed by these genes were involved in significant pathways. Conclusion In summary, the study indicated that the combined analysis for identifying target genes with PCa by bioinformatics tools promote our understanding of the molecular mechanisms and underlying the development of PCa. And the hub genes might serve as molecular targets and diagnostic biomarkers for precise diagnosis and treatment of PCa

A multiscale analysis of the relationship between urbanization and CO2 emissions using geo-weighted regression model

Abstract It is of great practical significance to explore the relationship between urbanization and CO2 emissions for the low-carbon development of cities. However, the multiscale assessment of spatial relationship between population, land and economic urbanization and CO2 emissions is lacked. In this study, we first adopted the spatial statistical methods to evaluate the spatial pattern of China’s CO2 emissions in 2019. Then, we spatially quantified China’s urbanization of land, population and economy based on statistical data. Finally, we used the geo-weighted regression model to explore the spatial relationship between urbanization and CO2 emissions at the national-economic zone-province scale. The results displayed that there is obvious spatial heterogeneity in the relationship between China’s urbanization and CO2 emissions. The significant positive correlation between urbanization and CO2 emissions were mainly located in the northeastern, eastern and southwestern regions, consistent with the characteristics of the Heihe–Tengchong Line. The uneven development of land, population and economic urbanization would lead to more CO2 emissions. We suggest that China should attend the balanced development of urban land, population and economy, and avoid the additional carbon emissions caused by incongruence, to further the development of low-carbon cities

Impacts of Energy Price on Agricultural Production, Energy Consumption, and Carbon Emission in China: A Price Endogenous Partial Equilibrium Model Analysis

Energy market volatility will have systemic effects on agricultural production, energy consumption and carbon emissions. This paper aims to evaluate the impacts of energy price on agricultural production, energy consumption, and carbon emission in China. To achieve the objective, this paper, firstly, constructed a price endogenous partial equilibrium model, and then designed four scenarios of energy price fluctuations, finally evaluating the impacts of energy price fluctuations on agricultural production and its energy consumption and carbon emission. The results revealed that: (1) The impacts on agricultural production are very limited, but higher energy price will result in producers’ welfare loss by 0.6% to 1.4%, under different scenarios. (2) Energy price drives negative impacts on agricultural energy consumption and carbon emission, 1.6%/3.2% and 1.3%/2.6%, respectively, in low/high amplitude scenarios. (3) Heterogeneous impacts are confirmed in the regional analysis; South China is simulated to be the most sensitive area. To mitigate the impacts from energy price and reduce carbon emission in agriculture, several policy implications have recently been proposed, including strengthening supervision of the energy market, constructing an energy saving price-setting mechanism, launching policy instruments to improve energy efficiencies and facilitate cleaner farming techniques, and formulating specific measurements of energy saving and emission reduction for different regions

Improved Algal Sludge Methane Production and Dewaterability by Zerovalent Iron-Assisted Fermentation

This study investigated the methane production improvement of algal sludge by zerovalent iron (ZVI)-assisted anaerobic digestion. The zerovalent iron added were 0.5, 2, 5, 10, and 20 g center dot ZVI/g center dot TS (total solid). The results indicated that the addition of ZVI at 2, 5, 10, and 20 g center dot ZVI/g center dot TS has improved the methane production 1.07, 1.24, 1.41, and 1.46 times as compared with no ZVI added. The dewaterability of treated algal sludge has improved 1.06, 1.08, 1.08, and 1.11 times as compared with no ZVI addition. The biochemical methane production test results fitted to both one-substrate and two-substrate models. The one-substrate model indicated that the hydrolysis rate k has increased 8.21, 7.07, 9.39, 3.50, and 5.07 times as compared with R1 where no ZVI was added. The two-substrate model implied that the rapid hydrolysis rate k(rapid) values were 5.23, 4.5, 5.98, 2.23, and 3.23 times as compared with RI. The one-substrate model predicted that the value of methane production was in high correlation with the actual value (R-2 &gt; 0.98). The addition of ZVI in algal sludge for methane production without an extra pretreatment process has improved the hydrolysis rate and methane production. This has the potential to be developed as an effective and economic technology in resource recovery from algal sludge.</p

Pore Scale Thermal Hydraulics Investigations of Molten Salt Cooled Pebble Bed High Temperature Reactor with BCC and FCC Configurations

The present paper systematically investigated pore scale thermal hydraulics characteristics of molten salt cooled high temperature pebble bed reactor. By using computational fluid dynamics (CFD) methods and employing simplified body center cubic (BCC) and face center cubic (FCC) model, pressure drop and local mean Nusselt number are calculated. The simulation result shows that the high Prandtl number molten salt in packed bed has unique fluid-dynamics and thermodynamic properties. There are divergences between CFD results and empirical correlations’ predictions of pressure drop and local Nusselt numbers. Local pebble surface temperature distributions in several default conditions are investigated. Thermal removal capacities of molten salt are confirmed in the case of nominal condition; the pebble surface temperature under the condition of local power distortion shows the tolerance of pebble in extreme neutron dose exposure. The numerical experiments of local pebble insufficient cooling indicate that in the molten salt cooled pebble bed reactor, the pebble surface temperature is not very sensitive to loss of partial coolant. The methods and results of this paper would be useful for optimum designs and safety analysis of molten salt cooled pebble bed reactors