Facilitated diffusion buffers noise in gene expression.

Transcription factors perform facilitated diffusion [three-dimensional (3D) diffusion in the cytosol and 1D diffusion on the DNA] when binding to their target sites to regulate gene expression. Here, we investigated the influence of this binding mechanism on the noise in gene expression. Our results showed that, for biologically relevant parameters, the binding process can be represented by a two-state Markov model and that the accelerated target finding due to facilitated diffusion leads to a reduction in both the mRNA and the protein noise.The following article has been published by Physical Review E. It can be found at: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.90.032701. Copyright 2014 American Physical Society

Functionally informed fine-mapping and polygenic localization of complex trait heritability

Fine-mapping aims to identify causal variants impacting complex traits. We propose PolyFun, a computationally scalable framework to improve fine-mapping accuracy by leveraging functional annotations across the entire genome-not just genome-wide-significant loci-to specify prior probabilities for fine-mapping methods such as SuSiE or FINEMAP. In simulations, PolyFun + SuSiE and PolyFun + FINEMAP were well calibrated and identified >20% more variants with a posterior causal probability >0.95 than identified in their nonfunctionally informed counterparts. In analyses of 49 UK Biobank traits (average n = 318,000), PolyFun + SuSiE identified 3,025 fine-mapped variant-trait pairs with posterior causal probability >0.95, a >32% improvement versus SuSiE. We used posterior mean per-SNP heritabilities from PolyFun + SuSiE to perform polygenic localization, constructing minimal sets of common SNPs causally explaining 50% of common SNP heritability; these sets ranged in size from 28 (hair color) to 3,400 (height) to 2 million (number of children). In conclusion, PolyFun prioritizes variants for functional follow-up and provides insights into complex trait architectures. PolyFun is a computationally scalable framework for functionally informed fine-mapping that makes full use of genome-wide data. It prioritizes more variants than previous methods when applied to 49 complex traits from UK Biobank.Peer reviewe

Autoinducer concentration tracks the shift from genetically-mixed to clonal groups.

<p>A) Snapshots of our simulations show how <b>Q1</b> (red) outompetes <b>N</b> (green) when cells are sorted by genotype (high relatedness from the start). Here, <b>Q1</b> (and even <b>C</b>, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004848#pcbi.1004848.s003" target="_blank">S3 Fig</a>) can outcompete <b>N</b>. <b>Q1</b> initiates public good production early while <b>Q4</b> delays secretion for longer due to its higher quorum sensing threshold (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004848#pcbi.1004848.s009" target="_blank">S1 Table</a>). B) and C) show results from the same simulation setup but where cells were positioned randomly at the start (as in simulations in Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004848#pcbi.1004848.g002" target="_blank">2</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004848#pcbi.1004848.g003" target="_blank">3</a>). Now, <b>Q1</b> (red) cannot outcompete non-secretors (green) in local competition (B), whereas in C) <b>Q4</b> (red) outcompetes non-secretors (green). D-F) Left y-axis and red/green lines: Biomass over time of non-secretors <b>N</b> and secretor genotypes <b>Q1</b> and <b>Q4</b> in the corresponding simulations shown in A-C). Right y-axis and black lines: peak concentration of public good (maximum concentration in anywhere within the colony) in the biofilm over time. G-I) Segregation index which is a measure of relatedness in the simulations over time. Key to a successful strategy is initiating public good secretion only at a sufficiently high segregation index: when cells are sorted from the beginning, secretion can start early (A, G), but if cells are initially well-mixed, only delayed public good secretion leads to success (C, I).</p

Quorum Sensing is beneficial in competitive, spatially structured environments.

<p>A) Relative fitness of different secretor genotypes (<b>C</b>: constitutive, <b>Q1–Q4</b>: quorum sensing secretors with increasing quorum sensing thresholds, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004848#pcbi.1004848.s003" target="_blank">S3 Fig</a>, <b>N</b>: non-secretor control) in competition with non-secretor genotypes. Higher genotypic diversity is reflected in lower initial proportions of the secretor genotype. We show results of 100 independent simulations each and plot the resulting mean relative fitness in black. <b>Q</b> genotypes have a higher relative fitness than <b>C</b> because they outgrow <b>N</b> more frequently (more runs fall above the 0 line in the bimodal distribution). This—rather than a higher benefit from the public good—is what allows <b>Q</b> to succeed, and is a result of <b>Q</b> genotypes competing better than <b>C</b> during the early stages of colony growth. B) Direct competition between <b>C</b>, <b>Q4</b> and <b>N</b>. 80 cells of <b>C</b> and <b>Q4</b> respectively, and 640 cells of <b>N</b> were seeded simultaneously. Relative fitness from 100 independent simulations was calculated for <b>Q4</b> and <b>C</b> by considering their fitness against the average competitor fitness (<b>N</b>+<b>Q4</b> and <b>N</b>+<b>C</b> respectively, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004848#sec010" target="_blank">Methods</a>), and the mean values are indicated by bars. C) Snapshots of an individual simulation shown in panel B where <b>Q4</b> succeeds.</p

Canonical Quorum Sensing.

<p>A) Growth of biomass over time in simulations of well-mixed cultures of a constitutively public good secreting genotype (<b>C</b>, blue), a quorum sensing genotype that only begins secreting public goods after a threshold concentration of the autoinducer has been reached (<b>Q</b>, red) and a control genotype that does not produce any public goods (<b>N</b>, green). The arrows on the time axis indicate the onset of public good secretion by the two secretor genotypes, circles indicate the first time point at which the public good has reached above-threshold concentration in the simulation and cells begin to benefit from the public good. B) Relative fitness of <b>C</b> and <b>Q</b> (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004848#sec010" target="_blank">Materials and Methods</a>) compared with competitors of genotype <b>N</b> when growing alone (filled circles) or in direct competition in well-mixed culture (open squares). The horizontal line represents equal fitness of <b>N</b> and its competitor (either <b>C</b> or <b>Q</b>). Secretion (<b>C</b> and <b>Q</b>) is only favoured over non secretion when genotypes grow alone. C) Comparison of biomass growth for the three genotypes in spatially structured clonal colonies. Time axis labels as in A). On the right: snapshots of the simulated colonies at t = 7 h marked with asterisks in the plot.</p

Constitutive secretors lose when surrounded by non-secretor cells.

<p>A) Relative fitness of a constitutive secretor genotype <b>C</b> in competition with non-secretor genotypes <b>N</b> in spatially structured simulations. Constitutive secretors can outcompete non-secretors when competing with few other genotypes (higher proportion of <b>C</b> cells) and fewer cells (lower initial cell number). At high evolutionary competition where many non-secretor genotypes compete with the secretor genotype (1:4), constitutive secretors will be outcompeted by non-secretors. The black line connects mean relative fitness values (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004848#sec010" target="_blank">Materials and Methods</a>), while the dotted line indicates equal fitness of <b>N</b> and <b>C</b>. B) Constitutive secretion can succeed (relative fitness >0) when genotypes segregate from each other in space. This occurs when nutrients are limited (strong ecological competition). Here, 400 cells of genotype <b>C</b> were competed with 400 cells of <b>N</b> and nutrient concentrations were varied. Inset: The segregation index at the end of simulations in pure colonies of <b>N</b> decreases with nutrient concentrations and the colony remains more mixed. Non-secretors were used for this analysis, because the positive feedback of successful public good cooperation would also increase the segregation index (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004848#sec010" target="_blank">Materials and Methods</a>).</p

The benefit of quorum sensing under strong competition.

<p>Cartoon showing how quorum sensing genotypes can outperform constitutive secretors and non-secretors via its function as a timing mechansim. While constitutive secretors get overgrown by non-secretors, the delayed secretion of quorum sensing genotypes allows those cells to prevent being overgrown, by competing well during early stages, and as a result surrounding themselves with clonemates. Only then do they invest into a costly secretion.</p

Leveraging Polygenic Functional Enrichment to Improve GWAS Power.

Functional genomics data has the potential to increase GWAS power by identifying SNPs that have a higher prior probability of association. Here, we introduce a method that leverages polygenic functional enrichment to incorporate coding, conserved, regulatory, and LD-related genomic annotations into association analyses. We show via simulations with real genotypes that the method, functionally informed novel discovery of risk loci (FINDOR), correctly controls the false-positive rate at null loci and attains a 9%-38% increase in the number of independent associations detected at causal loci, depending on trait polygenicity and sample size. We applied FINDOR to 27 independent complex traits and diseases from the interim UK Biobank release (average N = 130K). Averaged across traits, we attained a 13% increase in genome-wide significant loci detected (including a 20% increase for disease traits) compared to unweighted raw p values that do not use functional data. We replicated the additional loci in independent UK Biobank and non-UK Biobank data, yielding a highly statistically significant replication slope (0.66-0.69) in each case. Finally, we applied FINDOR to the full UK Biobank release (average N&nbsp;= 416K), attaining smaller relative improvements (consistent with simulations) but larger absolute improvements, detecting an additional 583 GWAS loci. In conclusion, leveraging functional enrichment using our method robustly increases GWAS power