145 research outputs found

    Linker flexibility of IVS3-S4 loops modulates voltage-dependent activation of L-type Ca<sup>2+</sup> channels

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    <p>Extracellular S3-S4 linkers of domain IV (IVS3-S4) of L-type Ca<sup>2+</sup> channels (Ca<sub>V</sub>1) are subject to alternative splicing, resulting into distinct gating profiles serving for diverse physiological roles. However, it has remained elusive what would be the determining factor of IVS3-S4 effects on Ca<sub>V</sub>1 channels. In this study, we systematically compared IVS3-S4 variants from Ca<sub>V</sub>1.1-1.4, and discover that the flexibility of the linker plays a prominent role in gating characteristics. Chimeric analysis and mutagenesis demonstrated that changes in half activation voltage (V<sub>1/2</sub>) or activation time constant (τ) are positively correlated with the numbers of flexible glycine residues within the linker. Moreover, antibodies that reduce IVS3-S4 flexibility negatively shifted V<sub>1/2</sub>, emerging as a new category of Ca<sub>V</sub>1 enhancers. In summary, our results suggest that the flexibility or rigidity of IVS3-S4 linker underlies its modulations on Ca<sub>V</sub>1 activation (V<sub>1/2</sub> and τ), paving the way to dissect the core mechanisms and to develop innovative perturbations pertaining to voltage-sensing S4 and its vicinities.</p

    PCA and RDA analysis data sheet

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    The data were collected in the field. Excel was used to create the data. Gram+-gram positive bacteria, Gram--gram negative bacteria, AMF- arbuscular mycorrhizal fungi, B/F-bacteria to fungi ratio, SOC- soil organic carbon, TN- soil total nitrogen, C/N-soil organic carbon to total nitrogen ratio. Treatment: C- control,DU-dung and urine return; M-mowing; T-trampling; DU+M-mowing combined with the addition of dung and urine; M+T-mowing combined with trampling; DU+T-trampling combined with the addition of dung and urine; DU+M+T-mowing combined with trampling and the addition of dung and urin

    Soil microbes' parameters

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    The data were collected in the field. Excel was used to create the data. SE-standard error, Gram+-gram positive bacteria, Gram--gram negative bacteria, AMF- arbuscular mycorrhizal fungi, B/F-bacteria to fungi ratio. Treatment: C- control,DU-dung and urine return; M-mowing; T-trampling; DU+M-mowing combined with the addition of dung and urine; M+T-mowing combined with trampling; DU+T-trampling combined with the addition of dung and urine; DU+M+T-mowing combined with trampling and the addition of dung and urin

    Soil parameters

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    The data were collected in the field. Excel was used to create the data. SE-standard error, MBC-soil microbial biomass carbon, MBN- soil microbial biomass nitrogen. Treatment: C- control,DU-dung and urine return; M-mowing; T-trampling; DU+M-mowing combined with the addition of dung and urine; M+T-mowing combined with trampling; DU+T-trampling combined with the addition of dung and urine; DU+M+T-mowing combined with trampling and the addition of dung and urin

    Additional file 1 of Identifying quantitatively differential chromosomal compartmentalization changes and their biological significance from Hi-C data using DARIC

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    Additional file 1: Figure 1. Introduction of the DARIC framework. A Scatter plot showing the high correlation between PIS and PC1 values from the H1ESC Hi-C data. B MA plot showing the systematic differences between H1ESC and K562 cells. Each dot represents a 50kb bin. The Red dashed line represents the fitted line from the M and A values. C MA plot after normalization showing the elimination of the systematic differences between the two cell types. D-E The emission matrix (D) and state coverage matrix (E) for the 5-state HMM model. F Confusion matrix showing the overlap between the states of 5-state model and those of the 4-state model. Numbers represent 50kb bins. Figure 2. Functional association between gene regulation and differential compartments revealed by DARIC. A-B Heatmap showing the enrichment of cell type-specific genes (A) and superenhancers. (B) in the four states identified by DARIC. Values show the log2(observed/expected) enrichment. C Bar plots showing the expression of SOX2 and MYB genes in H1ESC and K562 cells. Figure 3. Comparison between DARIC and existing methods. A Venn diagram presenting the overlap between the ‘Strong-’ state revealed by DARIC and the ‘AB’ state in conventional analyses. The numbers in the plot represent the numbers of 50kb bins. B Violin plot showing the PIS differences for the three types of domains defined in (A). C-D Violin plots showing the comparisons of Lamina1-DamID signal changes (C), and gene expression fold changes (D) in the three types of domains defined in (A). E Venn diagram showing the overlap of genomic bins identified with decreased PIS/PC1 values in K562 by DARIC and dcHiC. Numbers of 50kb bins were shown in the diagram. F Enrichment of H1ESC-specific genes for the three types of genomic regions defined in (E). G An exemplary region showing DARIC and dcHiC output with decreased PIS in K562 cells. H-K Performance comparison between DARIC and HOMER using H1ESC versus K562 as an example. (H) Venn diagram showing the overlap of genomic bins identified with increased PIS values in K562 by DARIC and HOMER. Numbers of 50kb bins were shown in the diagram. (I) Enrichment of K562-specific genes for the three types of genomic regions defined in (H). (J) Venn diagram showing the overlap of genomic bins identified with decreased PIS values in K562 by DARIC and HOMER. Numbers of 50kb bins were shown in the diagram. (K) Enrichment of H1ESC-specific genes for the three types of genomic regions defined in (J). Figure 4. DARIC is robust to technical variations in Hi-C data, such as choices of restriction enzymes and sequencing depth. A Snapshot of chromosome 6 showing the comparison in scaling differences in PIS from three different restriction enzymes before and after the normalization step performed by DARIC. B Snapshot of chromosome 6 showing the high similarity of PIS from Hi-C data at different sequencing depths. Figure 5. Applying DARIC to delineating compartment changes during cardiomyocyte differentiation. A Emission matrix resulting from the HMM model trained in the cardiomyocyte system. B Cardiomyocyte-specific genes associated with significant PIS increases during the differentiation tend to be involved in longer loops than those without PIS increases. C GO enrichment analysis for two sets of cardiomyocyte-specific genes classified by whether associated with significant PIS changes. Figure 6. Applying DARIC to a compendium of Hi-C datasets across many cell types. A Distribution of TSA-seq signals in the five variability states in the three cell lines. B Distribution of DamID signals in K562 cells. C Stacked bar plots showing the composition percentages of the five sub-compartments in the five variability states. D PIS variability comparison for the five sub-compartments

    CAR-T Therapy Targets Extra Domain B of Fibronectin Positive Solid Tumor Cells

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    CAR-T cell immunotherapy has achieved remarkable success in malignant B-cell malignancies, but progress in solid tumors is slow, and one of the key reasons is the lack of ideal targets. Cancer-specific extra domain B of fibronectin (EDB-FN) is widely upregulated in solid tumors and expressed at low levels in normal tissues. Many imaging and targeted cancer therapies based on EDB-FN targets have been developed and tested in clinical trials, making EDB-FN an ideal target for immunotherapy. We constructed two EDB-FN-targeted CAR-Ts based on the peptide APT0 and the single-chain antibody CGS2 in a lentiviral infection manner for the first time. Luciferase cytotoxicity assay to assess CAR-T killing of tumor cells. An enzyme-linked immunosorbent assay was used to detect the release of the cytokine IFN-γ. Fluorescence imaging to evaluate the dynamics of CAR-T cell and tumor cell coculture. Knockdown assays were used to validate the target specificity of CAR-T cells. In this research, two CAR-Ts targeting EDB-FN, APT0 CAR-T, and CGS2 CAR-T, were constructed. In vitro, both CAR-T cells produced broad-spectrum killing of multiple EDB-FN-positive solid tumor cell lines and were accompanied by cytokine IFN-γ release. Regarding safety, the two CAR-T cells did not affect T cells’ normal growth and proliferation and were not toxic to HEK-293T human embryonic kidney epithelial cells. APT0 CAR-T and CGS2 CAR-T cells are two new CAR-Ts targeting EDB-FN. Both CAR-T cells can successfully identify and specifically kill various EDB-FN-positive solid tumor cells with potential clinical applications.</p

    The “Gate Keeper” Role of Trp222 Determines the Enantiopreference of Diketoreductase toward 2-Chloro-1-Phenylethanone

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    <div><p>Trp222 of diketoreductase (DKR), an enzyme responsible for reducing a variety of ketones to chiral alcohols, is located at the hydrophobic dimeric interface of the C-terminus. Single substitutions at DKR Trp222 with either canonical (Val, Leu, Met, Phe and Tyr) or unnatural amino acids (UAAs) (4-cyano-L-phenylalanine, 4-methoxy-L-phenylalanine, 4-phenyl-L-phenyalanine, <i>O</i>-<i>tert</i>-butyl-L-tyrosine) inverts the enantiotope preference of the enzyme toward 2-chloro-1-phenylethanone with close side chain correlation. Analyses of enzyme activity, substrate affinity and ternary structure of the mutants revealed that substitution at Trp222 causes a notable change in the overall enzyme structure, and specifically in the entrance tunnel to the active center. The size of residue 222 in DKR is vital to its enantiotope preference. Trp222 serves as a “gate keeper” to control the direction of substrate entry into the active center. Consequently, opposite substrate-binding orientations produce respective alcohol enantiomers.</p></div

    Location of the study areas in China.

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    <p>Location of the study areas in China.</p

    Details of soil samples from alfalfa grasslands growing for different numbers of years in the three regions.

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    <p>Details of soil samples from alfalfa grasslands growing for different numbers of years in the three regions.</p
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