Insight into the impact of environments on structure of chimera C3 of human β-defensins 2 and 3 from molecular dynamics simulations

<div><p>C3 is a chimera from human β-defensins 2 and 3 and possesses higher antimicrobial activity compared with its parental molecules, so it is an attractive candidate for clinical application of antimicrobial peptides. In continuation with the previous studies, molecular dynamics (MD) simulations were carried out for further investigating the effect of ambient environments (temperature and bacterial membrane) on C3 dynamics. Our results reveal that C3 has higher flexibility, larger intensity of motion, and more relevant secondary structural changes at 363 K to adapt the high temperature and maintain its antimicrobial activity, comparison with it at 293 K; when C3 molecule associates with the bacterial membrane, it slightly fluctuates and undergoes local conformational changes; in summary, C3 molecule demonstrates stable conformations under these environments. Furthermore, MD results analysis show that the hydrophobic contacts, the hydrogen bonds, and disulfide bonds in the peptide are responsible for maintaining its stable conformation. In addition, our simulation shows that C3 peptides can make anionic lipids clustered in the bacterial membrane; it means that positive charges and pronounced regional cationic charge density of C3 are most key factors for its antimicrobial activity.</p></div

CCAST: A Model-Based Gating Strategy to Isolate Homogeneous Subpopulations in a Heterogeneous Population of Single Cells

<div><p>A model-based gating strategy is developed for sorting cells and analyzing populations of single cells. The strategy, named CCAST, for Clustering, Classification and Sorting Tree, identifies a gating strategy for isolating homogeneous subpopulations from a heterogeneous population of single cells using a data-derived decision tree representation that can be applied to cell sorting. Because CCAST does not rely on expert knowledge, it removes human bias and variability when determining the gating strategy. It combines any clustering algorithm with silhouette measures to identify underlying homogeneous subpopulations, then applies recursive partitioning techniques to generate a decision tree that defines the gating strategy. CCAST produces an optimal strategy for cell sorting by automating the selection of gating markers, the corresponding gating thresholds and gating sequence; all of these parameters are typically manually defined. Even though CCAST is optimized for cell sorting, it can be applied for the identification and analysis of homogeneous subpopulations among heterogeneous single cell data. We apply CCAST on single cell data from both breast cancer cell lines and normal human bone marrow. On the SUM159 breast cancer cell line data, CCAST indicates at least five distinct cell states based on two surface markers (CD24 and EPCAM) and provides a gating sorting strategy that produces more homogeneous subpopulations than previously reported. When applied to normal bone marrow data, CCAST reveals an efficient strategy for gating T-cells without prior knowledge of the major T-cell subtypes and the markers that best define them. On the normal bone marrow data, CCAST also reveals two major mature B-cell subtypes, namely CD123+ and CD123- cells, which were not revealed by manual gating but show distinct intracellular signaling responses. More generally, the CCAST framework could be used on other biological and non-biological high dimensional data types that are mixtures of unknown homogeneous subpopulations.</p></div

Elucidating proton-mediated conformational changes in an acid-sensing ion channel 1a through molecular dynamics simulation

Elucidating proton-mediated conformational changes in an acid-sensing ion channel 1a through molecular dynamics simulatio

CCAST applied to single cell analysis of B-cells.

<p><b>A</b> Silhouette plot showing evidence of 5 B-cell types. <b>B</b> CCAST gating strategy for B-cell types based on CD45, CD34, CD38, and CD123 markers using 3 levels of gating. The estimated ranges for the split point variables are provided at each node. Note Celltype 3 is distributed across three gated populations. <b>C</b> Cross classification heatmap of manually gated and CCAST predicted B-cell types indicates strong evidence that the most abundant Mature CD38low B-cells comprise a mixture of other subtypes (Celltype 2 and 4). <b>D</b> Heatmaps show evidence of the two derived distinct mature B-cell states corresponding to Celltypes 2 and 4 based mainly on CD123 (label highlighted in red).</p

Inhibition mechanism understanding from molecular dynamics simulation of the interactions between several flavonoids and proton-dependent glucose transporter

Proton-dependent glucose transporters as important drug targets can have different protonation states and adjust their conformational state under different pHs. So based on this character, research on its inhibition mechanism is a significant work. In this article, to study its inhibitory mechanism, we performed the molecular dynamics of several classical flavonoid molecules (Three inhibitors Phloretin, Naringenin, Resveratrol. Two non-inhibitors Isoliquiritigenin, Butein) with glucose transporters under two distinct environmental pHs. The results show inhibitors occupy glucose binding sites (GLN137, ILE255, ASN256) and have strong hydrophobic interactions with proteins through core moiety (C6-Cn-C6). In addition, inhibitors had better inhibitory effects in protonation state. In contrast, non-inhibitors can not occupy glucose binding sites (GLN137, ILE255, ASN256), thus they do not have intense interactions with the protein. It is suggested that favorable inhibitors should effectively take up the glucose-binding site (GLN137, ILE255, ASN256) and limit the protein conformational changes. Communicated by Ramaswamy H. Sarma</p

CCAST analysis on SUM159 breast cancer results.

<p><b>A</b> Results for the estimation process for all the split point statistics in all the inner nodes in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003664#pcbi-1003664-g006" target="_blank">Figure 6</a>. The root node corresponding to EPCAM shows one local maxima and one global maximum. Gating the data from this global maximum results in 9 distinct subpopulations. Nodes 3, 4, 8, 9, 13 and 14 have clear natural maxima indicating optimal splits for the data into these 9 homogenous subpopulations (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003664#pcbi-1003664-g006" target="_blank">Figure 6</a>) corresponding to the 9 bar plots in <b>B</b>. <b>B</b> Bar plots of the 9 homogenous subpopulations from <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003664#pcbi-1003664-g006" target="_blank">Figure 6</a> across all 3 markers with standard deviation intervals for each marker. The values on the bars on the left side of each plot correspond to the minimum value for all 3 bar heights. Each side bar gives a sense of the relative difference between bar heights. The main title for each plot shows the corresponding leaf node bin on the tree in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003664#pcbi-1003664-g006" target="_blank">Figure 6</a>. Predicted Celltypes 3 and 1 correspond to P3, P4, P7 and P5, P6, P8 respectively indicating more homogeneous sub populations than expected. The bar plots show evidence of at least 5 distinct sub populations i.e. P1, P2, P5, P7 and P9. <b>C</b> Gupta <i>et al.... </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003664#pcbi.1003664-Gupta1" target="_blank">[3]</a> gating strategy isolated 3 cell states (Basal, stem, and luminal) using EPCAM as the major marker. They further use CD24 to sort out these 3 states. We also automatically identify EPCAM as the major marker but use a combination of multiple splits from CD24 and EPCAM to produce 9 homogeneous bins. <b>D</b> Comparison of predicted breast cancer subpopulations comparing the CCAST versus Gupta <i>et al.... </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003664#pcbi.1003664-Gupta1" target="_blank">[3]</a> gating strategy shows potential evidence of contamination after sorting. This analysis indicated the CCAST subpopulation P9 is clearly a mixture of basal, stem, and luminal subpopulations from Gutpa <i>et al.... </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003664#pcbi.1003664-Gupta1" target="_blank">[3]</a>. Unique CCAST subpopulations P1 and P2 were not even identified by Gupta <i>et al.... </i><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003664#pcbi.1003664-Gupta1" target="_blank">[3]</a>.</p

CCAST gating strategy on SUM159 breast cancer cell line.

<p>CCAST gating strategy for SUM159 breast cancer cell lines isolates 5 pure cell states (across 9 bins) based on CD24 and EPCAM. Visualization of these 5 subpopulations is clearly not apparent from the biaxial side scatter (SSC) vs. biomarker plots. Split point estimates (dotted red lines) go through density contour plot (orange) on the distributed data providing visual evidence for suitable cut-offs through bimodal contours. Note the split point lines for nodes 3 and 4 concentrate on the zero point mass; this indicates there are several cells with zero expression values for EPCAM or CD24 staining but with higher expression values with respect to CD44.</p

Sevoflurane preconditioning increased the expression of HIF-1α, HIF-2α, VEGF, p-Akt/Akt in MSCs against hypoxia and serum deprivation.

<p>(A) Representative western blot of Akt, p-Akt, HIF-1α, HIF-2α and VEGF. (B) Quantitative protein analysis of Akt, p-Akt, HIF-1α, HIF-2α and VEGF. Levels are expressed as ratios to control. Data are presented as mean ± SEM of six independent experiments. *<i>P</i><0.05 versus control group. #<i>P</i><0.05 versus H/SD group.</p

Sevoflurane preconditioning did not promote the proliferation of MSCs against hypoxia and serum deprivation.

<p>(A) Flow cytometry of MSCs cell cycle. (B) Quantification of cell cycle. Data are presented as mean ± SEM of six independent experiments. *<i>P</i><0.05 versus control group.</p

CCAST applied to single cell analysis of T-cells.

<p><b>A</b> The CCAST gating strategy based on the unlabeled T-cell data in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003664#pcbi-1003664-g002" target="_blank">Figure 2</a>, post filtering, showing that 4 cell types can be derived using only Marker 5 and Marker 2 with Marker 5 as the root node. Split points along with the minimum-maximum range for each split point are provided at each node. <b>B</b> Histogram plots for sample split point for each node is obtained via bootstrapping. The multi modal nature of the distributions makes it difficult to calculate a true confidence intervals on the split point estimates. <b>C</b> CCAST result without filtering represented as a 2D scatter plot of the 4 cell types, which each cell type color coded; note that gating the yellow-colored cells will likely result in contamination of green-colored cells. <b>D</b> CCAST result with filtering represented as a 2D scatter plot of the 4 pure cell types, with each cell type color coded. Note all contaminating cells mixed with various clusters have been removed. For manual gating purposes, comparing the two schemes <b>C</b> and <b>D</b> provides a visual evaluation of the expected contamination levels from sorting subpopulations. <b>E</b> CCAST gating strategy for all Tcell types with labels reveals that the key gating markers are CD4 and CD45RA markers. <b>F</b> 2D scatter plot for the four, labeled T-cell types based on CD4 and CD45RA.</p