27 research outputs found
The correlations between the geographic distance and the genetic distance (a), and gene flow (b).
<p>The correlations between the geographic distance and the genetic distance (a), and gene flow (b).</p
The geographic distance the genetic distance and the gene flow among the three examined geographic populations of the Greater long-tailed hamsters.
<p>The geographic distance the genetic distance and the gene flow among the three examined geographic populations of the Greater long-tailed hamsters.</p
Geographic Distance Affects Dispersal of the Patchy Distributed Greater Long-Tailed Hamster (<i>Tscherskia triton</i>)
<div><p>Dispersal is a fundamental process in ecology influencing the genetic structure and the viability of populations. Understanding how variable factors influence the dispersal of the population is becoming an important question in animal ecology. To date, geographic distance and geographic barriers are often considered as main factors impacting dispersal, but their effects are variable depending on different conditions. In general, geographic barriers affect more significantly than geographic distance on dispersal. In rapidly expanding populations, however, geographic barriers have less effect on dispersal than geographic distance. The effects of both geographic distance and geographic barriers in low-density populations with patchy distributions are poorly understood. By using a panel of 10 microsatellite loci we investigated the genetic structure of three patchy-distributed populations of the Greater long-tailed hamster (<i>Tscherskia triton</i>) from Raoyang, Guan and Shunyi counties of the North China Plain. The results showed that (i) high genetic diversity and differentiation exist in three geographic populations with patchy distributions; (ii) gene flow occurs among these three populations with physical barriers of Beijing city and Hutuo River, which potentially restricted the dispersal of the animal; (iii) the gene flow is negatively correlated with the geographic distance, while the genetic distance shows the positive correlation. Our results suggest that the effect of the physical barriers is conditional-dependent, including barrier capacity or individual potentially dispersal ability. Geographic distance also acts as an important factor affecting dispersal for the patchy distributed geographic populations. So, gene flow is effective, even at relatively long distances, in balancing the effect of geographic barrier in this study.</p></div
Diversity indices calculated from microsatellites of three Greater long-tailed hamster (<i>Tscherskia triton</i>) populations in North China Plain.
<p><i>N</i>, Sample size;</p><p><i>A</i>, average number of alleles/locus;</p><p><i>A<sub>R</sub></i>, allelic richness;</p><p><i>I</i>, Shannon's Information index;</p><p><i>He</i>, expected heterozygosity;</p><p><i>Ho</i>, observed heterozygosity;</p><p><i>P<sub>HW</sub></i>, result of Hardy–Weinberg probability test for deviation from expected Hardy–Weinberg proportions.</p
Acid-Sensitive Peptide-Conjugated Doxorubicin Mediates the Lysosomal Pathway of Apoptosis and Reverses Drug Resistance in Breast Cancer
The extended use of doxorubicin (DOX)
could be limited because
of the emergence of drug resistance associated with its treatment.
To reverse the drug resistance, two thiol-modified peptide sequences
HAIYPRHGGC and THRPPMWÂSPVWPGGC were, respectively, conjugated
to DOXO-EMCH, forming a maleimide bridge in this study (i.e., T10-DOX
and T15-DOX). The structures and properties of peptide–DOX
conjugates were characterized using <sup>1</sup>H NMR, <sup>13</sup>C NMR, mass spectrometry, and high-performance liquid chromatography.
Their stability was also evaluated. By using MCF-7/ADR cells as an <i>in vitro</i> model system and nude mice bearing MCF-7/ADR xenografts
as an <i>in vivo</i> model, the ability of these novel peptide–DOX
conjugates to reverse drug resistance was accessed as compared with
free DOX. As a result, the IC<sub>50</sub> values for T10-DOX and
T15-DOX significantly decreased (31.6 ± 1.6 μM and 27.2
± 0.8 μM), whereas the percentage of apoptotic cell population
increased (35.4% and 39.3%). The <i>in vivo</i> extent of
inhibition was more evident in the mice groups treated with peptide–DOX
conjugates (59.6 ± 8.99% and 46.4 ± 6.63%), which had DOX
primarily accumulated in tumor. These conjugates also showed a longer
half-life in plasma and cleared much more slowly from the body. Furthermore,
T10-DOX may be more effective than T15-DOX with a higher efficacy
and a lower side effect. Most importantly, evidence was provided to
support the enhanced intracellular drug accumulation and the induction
of lysosomal pathway of apoptosis underlying the drug resistance.
As an endosomal/lysosomal marker, cathepsin D permealized the destabilized
organelle membrane and was detected in the cytoplasm, leading to the
activation of the effector caspase-3 in cell apoptosis. This report
is among the first to demonstrate that peptide–DOX-like conjugates
promote apoptosis through the initiation of the lysosomal pathway
Chromatic median analysis of chromosome interactions in the cell cycle of WI38 and 10A.
<p>The chromatic median algorithm determines correspondence between homologs across nuclei based upon their interactions with other CT. This algorithm determined a median matrix for CT interactions in WI38 (<b>A–B</b>) and 10A (<b>C–D</b>) in G1 (<b>A, C</b>) and S phase (<b>B,D</b>). Each cell in the matrix represents the percent of input nuclei that have an interaction between those homologs. The values are color coded on a colorscale from low (red) to high (green).</p
Alterations in interaction profiles from G1 to S phase.
<p>Calculation of the ratio between pairwise CT (S∶G1) reveals different alterations of CT interactions from G1 to S. Some alterations demonstrate an overall increase (type 1) in interaction while others have an overall decrease in interaction (type 2). Others switch from having greater levels with a singular interaction to those with more nuclei with multiple interactions (type 3) or vice versa (type 4). Others do not change from G1 to S (type 5). Values are color coded with green increasing the most from G1 to S and red decreasing the most. The types of alterations in interaction are displayed for WI38 and 10A in the center rows.</p
Interaction profiles of CT are altered across the cell cycle and cell types.
<p>Since each CT has two homologs there are four possible interactions in each nucleus. (<b>A–D</b>) Differences across the cell cycle- The percent of cells with only 1 interaction (<b>A</b>) and the percent of cells that have 2 or more interactions (<b>B</b>) in WI38 are shown for each of the 21 pairwise combinations of CT (G1, n = 46; S, n = 47). The percentage with only 1 (<b>C</b>) and 2 or more (<b>D</b>) in 10A are shown (G1, n = 56; S, n = 54); blue bars are G1 and red are S. (<b>E–H</b>) differences between cell types- The percent of cells with only 1 interaction (<b>E</b>) and the percent of cells that have 2 or more interactions (<b>F</b>) in G1 of WI38 and 10A are shown. The percent with only 1 (<b>G</b>) and 2 or more (<b>H</b>) in S of WI38 and 10A are shown, green bars are WI38 and black are 10A. error bars = SEM. Black asterisks indicate chi-square test, p<0.05, while red indicate p<0.10. Full p values are presented in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003857#pcbi.1003857.s012" target="_blank">Tables S3</a>,<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003857#pcbi.1003857.s015" target="_blank">S6</a>.</p
Patterns of multiple interactions of CT pairs.
<p>(<b>A</b>) Categories of multiple interactions of CT pairs for 2, 3 and 4 interactions are illustrated; (<b>B</b>) the percent of multiple interactions that are type 2a, 2b, 3 interactions were calculated and are shown for WI38 and 10A. Green depicts the highest levels of interaction between CT pairs and red the lowest. Chi-square test indicates many significant differences between 2A–2C between CT pairs across the cell cycle and between cell types.</p
Preferred probabilistic network models of chromosome interactions in the cell cycle of WI38 and 10A.
<p>Thresholding of the WI38 (32%) and 10A (31%) matrices above a level where there are no connections in random simulations or randomizations of input matrices or random simulations level interactions (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003857#pcbi.1003857.s009" target="_blank">Fig. S9</a>) reveals 14–18 CT interactions in G1 of WI38 (<b>A, E</b>), S of WI38 (<b>B, F</b>), <b>G1 of</b> 10A (<b>C, G</b>), and S of 10A (<b>D, H</b>). Red lines are unique connections in G1 or S phase (<b>A–D</b>), blue lines indicate unique connections between cell types in each stage of the cell cycle (<b>E–H</b>) and black lines represent common connections. Comparison of the models generated from this analysis indicate more differences between G1 and S phases in 10A compared to WI38 cells and virtually completely different networks between cell types across the cell cycle (<b>I</b>). Thick connections are within the top third of percent pairwise interactions within the model. Connections of medium thickness are in the middle third and thin lines are within the bottom third. Sorensons analysis <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003857#pcbi.1003857-Sorenson1" target="_blank">[42]</a> determined that a given nucleus will contain an average 39–42% of the connections within these models.</p