Adhesion to Vitronectin and Collagen I Promotes Osteogenic Differentiation of Human Mesenchymal Stem Cells

The mechanisms controlling human mesenchymal stem cells (hMSC) differentiation are not entirely understood. We hypothesized that the contact with extracellular matrix (ECM) proteins normally found in bone marrow would promote osteogenic differentiation of hMSC in vitro. To test this hypothesis, we cultured hMSC on purified ECM proteins in the presence or absence of soluble osteogenic supplements, and assayed for the presence of well-established differentiation markers (production of mineralized matrix, osteopontin, osteocalcin, collagen I, and alkaline phosphatase expression) over a 16-day time course. We found that hMSC adhere to ECM proteins with varying affinity ([Formula: see text]) and through distinct integrin receptors. Importantly, the greatest osteogenic differentiation occurred in cells plated on vitronectin and collagen I and almost no differentiation took place on fibronectin or uncoated plates. We conclude that the contact with vitronectin and collagen I promotes the osteogenic differentiation of hMSC, and that ECM contact alone may be sufficient to induce differentiation in these cells

Epigenome-wide association study of kidney function identifies trans-ethnic and ethnic-specific loci

BACKGROUND: DNA methylation (DNAm) is associated with gene regulation and estimated glomerular filtration rate (eGFR), a measure of kidney function. Decreased eGFR is more common among US Hispanics and African Americans. The causes for this are poorly understood. We aimed to identify trans-ethnic and ethnic-specific differentially methylated positions (DMPs) associated with eGFR using an agnostic, genome-wide approach. METHODS: The study included up to 5428 participants from multi-ethnic studies for discovery and 8109 participants for replication. We tested the associations between whole blood DNAm and eGFR using beta values from Illumina 450K or EPIC arrays. Ethnicity-stratified analyses were performed using linear mixed models adjusting for age, sex, smoking, and study-specific and technical variables. Summary results were meta-analyzed within and across ethnicities. Findings were assessed using integrative epigenomics methods and pathway analyses. RESULTS: We identified 93 DMPs associated with eGFR at an FDR of 0.05 and replicated 13 and 1 DMPs across independent samples in trans-ethnic and African American meta-analyses, respectively. The study also validated 6 previously published DMPs. Identified DMPs showed significant overlap enrichment with DNase I hypersensitive sites in kidney tissue, sites associated with the expression of proximal genes, and transcription factor motifs and pathways associated with kidney tissue and kidney development. CONCLUSIONS: We uncovered trans-ethnic and ethnic-specific DMPs associated with eGFR, including DMPs enriched in regulatory elements in kidney tissue and pathways related to kidney development. These findings shed light on epigenetic mechanisms associated with kidney function, bridging the gap between population-specific eGFR-associated DNAm and tissue-specific regulatory context

Limits to the Rate of Adaptive Substitution in Sexual Populations

In large populations, many beneficial mutations may be simultaneously available and may compete with one another, slowing adaptation. By finding the probability of fixation of a favorable allele in a simple model of a haploid sexual population, we find limits to the rate of adaptive substitution, , that depend on simple parameter combinations. When variance in fitness is low and linkage is loose, the baseline rate of substitution is , where is the population size, is the rate of beneficial mutations per genome, and is their mean selective advantage. Heritable variance in log fitness due to unlinked loci reduces by under polygamy and under monogamy. With a linear genetic map of length Morgans, interference is yet stronger. We use a scaling argument to show that the density of adaptive substitutions depends on , , , and only through the baseline density: . Under the approximation that the interference due to different sweeps adds up, we show that , implying that interference prevents the rate of adaptive substitution from exceeding one per centimorgan per 200 generations. Simulations and numerical calculations confirm the scaling argument and confirm the additive approximation for ; for higher , the rate of adaptation grows above , but only very slowly. We also consider the effect of sweeps on neutral diversity and show that, while even occasional sweeps can greatly reduce neutral diversity, this effect saturates as sweeps become more common—diversity can be maintained even in populations experiencing very strong interference. Our results indicate that for some organisms the rate of adaptive substitution may be primarily recombination-limited, depending only weakly on the mutation supply and the strength of selection

Epigenome-wide association study of kidney function identifies trans-ethnic and ethnic-specific loci

Background DNA methylation (DNAm) is associated with gene regulation and estimated glomerular filtration rate (eGFR), a measure of kidney function. Decreased eGFR is more common among US Hispanics and African Americans. The causes for this are poorly understood. We aimed to identify trans-ethnic and ethnic-specific differentially methylated positions (DMPs) associated with eGFR using an agnostic, genome-wide approach. Methods The study included up to 5428 participants from multi-ethnic studies for discovery and 8109 participants for replication. We tested the associations between whole blood DNAm and eGFR using beta values from Illumina 450K or EPIC arrays. Ethnicity-stratified analyses were performed using linear mixed models adjusting for age, sex, smoking, and study-specific and technical variables. Summary results were meta-analyzed within and across ethnicities. Findings were assessed using integrative epigenomics methods and pathway analyses. Results We identified 93 DMPs associated with eGFR at an FDR of 0.05 and replicated 13 and 1 DMPs across independent samples in trans-ethnic and African American meta-analyses, respectively. The study also validated 6 previously published DMPs. Identified DMPs showed significant overlap enrichment with DNase I hypersensitive sites in kidney tissue, sites associated with the expression of proximal genes, and transcription factor motifs and pathways associated with kidney tissue and kidney development. Conclusions We uncovered trans-ethnic and ethnic-specific DMPs associated with eGFR, including DMPs enriched in regulatory elements in kidney tissue and pathways related to kidney development. These findings shed light on epigenetic mechanisms associated with kidney function, bridging the gap between population-specific eGFR-associated DNAm and tissue-specific regulatory context

Fitness loss and recognition losses of escape mutations are predicted to correlate positively, with a slope that deceases in time.

<p>Sites are randomly sampled from the simulation described in the caption to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g002" target="_blank">Figure 2</a> in order to simulate the effect of acute samples (high ranking sites), late chronic samples (low ranking sites) or patient samples from random times (random ranks). (A) No CTL decay. The slope of the correlation from acute (blue) or late chronic (red) sampled escape mutations is the same, however, it is lower for escape mutations sampled at random times (green). (B) CTL decay causes the slope of the correlation to decrease in time, due to decrease of CTL selection pressure. CTL decay is introduced as described in the caption to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g002" target="_blank">Figure 2</a> and <i>Model</i>.</p

The pattern of emergence of escape variants in a single epitope contains information about the fraction of recognition and fitness lost by single-site mutations in the epitope.

<p>Using simulation of the model (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g001" target="_blank">Figure 1A</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi.1003878.e114" target="_blank">Equations 6</a> to <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi.1003878.e116" target="_blank">8</a>) with two sites per epitope, , the pattern of escape is calculated for a range of recognition and fitness losses. The pattern that is obtained is plotted as a function of the parameters of recognition loss at the first and second site ( and , respectively). In each panel, certain parameters are fixed in order to focus on the effect of recognition loss. Fixed parameters are: the escape rate of the first haplotype () and the number of targeted epitopes (), values which correspond to escape mutations that occur in acute infection (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi.1003878.s003" target="_blank">Figure S3</a> for parameters that correspond to later in infection). Fitness costs are chosen such that the second site is less costly than the first: equal to 3 (A) or much less costly than the first, (B). Other parameters given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-t001" target="_blank">Table 1</a>. Mostowy: 2012iv Equations S6 (red line) and S9 (blue line) determine the region where the leapfrog pattern can be observed. Regions that require are not allowed by definition (magenta line). The shaded regions between these three lines correspond to regions of parameter space where both sites escape. The corresponding patterns are: “leapfrog” (, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g004" target="_blank">Figure 4C</a>), “nested” (, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g004" target="_blank">Figure 4E</a>), “nested leapfrog” (). Observation of the leapfrog pattern in an epitope tightly constrains the fraction of CTL recognition loss conferred by sites in an epitope. The inset shows the length of time during which haplotype 01 is dominant in the escaping epitope.</p

The escape trajectory in the cost-benefit plane bends over time due to CTL decay.

<p>Fitness costs and recognition losses are randomly generated for 100 sites (10 epitopes with 10 sites per epitope) in order to study the sequence of escaped sites (black line) in the whole genome without CTL decay (A,B) or with CTL decay (C,D) for 1000 simulation runs. (A) For each site that escapes, the fractional fitness cost, , multiplied by the number of epitopes, , and fractional recognition loss, (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi.1003878.e044" target="_blank">Equation 1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-t001" target="_blank">Table 1</a>) is shown. Colors show the predicted rank of escape mutations, from early escape mutations (blue) to late escape mutations (red). The average trajectory over all runs (black) moves from high recognition loss, low fitness cost to low recognition loss, high fitness cost. Inset: The best-fit slope for each escape rank. A positive correlation is observed between the fitness and recognition losses for all epitopes that escape at a given rank. (B) The maximum escape rate of any epitope site for all 10 epitopes for a representative simulation run. (C–D) As in (A–B), except including CTL decay. CTL decay is simulated by reducing recognition losses for all epitope sites in epitopes that have partially escaped according to , summing over all <i>i</i> sites in the epitope that have escaped with per escape. When CTLs decay in response to an escape in an epitope, the immune pressure on all other sites in that epitope is decreased. The result is that the average trajectory in the cost-benefit plane bends towards the horizontal axis.</p

Model parameters.

<p>Model parameters for the model of escape from multiple CTL shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003878#pcbi-1003878-g001" target="_blank">Figure 1</a>.</p><p>Model parameters.</p