Yeast Growth Plasticity Is Regulated by Environment-Specific Multi-QTL Interactions

For a unicellular, non-motile organism like Saccharomyces cerevisiae, carbon sources act both as nutrients and as signaling molecules and consequently affect various fitness parameters including growth. It is therefore advantageous for yeast strains to adapt their growth to carbon source variation. The ability of a given genotype to manifest different phenotypes in varying environments is known as phenotypic plasticity. To identify quantitative trait loci (QTL) that drive plasticity in growth, two growth parameters (growth rate and biomass) were measured in a published dataset from meiotic recombinants of two genetically divergent yeast strains grown in different carbon sources. To identify QTL contributing to plasticity across pairs of environments, gene-environment interaction mapping was performed, which identified several QTL that have a differential effect across environments, some of which act antagonistically across pairs of environments. Multi-QTL analysis identified loci interacting with previously known growth affecting QTL as well as novel two-QTL interactions that affect growth. A QTL that had no significant independent effect was found to alter growth rate and biomass for several carbon sources through two-QTL interactions. Our study demonstrates that environment-specific epistatic interactions contribute to the growth plasticity in yeast. We propose that a targeted scan for epistatic interactions, such as the one described here, can help unravel mechanisms regulating phenotypic plasticity

Yeast Growth Plasticity Is Regulated by Environment-Specific Multi-QTL Interactions

For a unicellular, non-motile organism like Saccharomyces cerevisiae, carbon sources act both as nutrients and as signaling molecules and consequently affect various fitness parameters including growth. It is therefore advantageous for yeast strains to adapt their growth to carbon source variation. The ability of a given genotype to manifest different phenotypes in varying environments is known as phenotypic plasticity. To identify quantitative trait loci (QTL) that drive plasticity in growth, two growth parameters (growth rate and biomass) were measured in a published dataset from meiotic recombinants of two genetically divergent yeast strains grown in different carbon sources. To identify QTL contributing to plasticity across pairs of environments, gene-environment interaction mapping was performed, which identified several QTL that have a differential effect across environments, some of which act antagonistically across pairs of environments. Multi-QTL analysis identified loci interacting with previously known growth affecting QTL as well as novel two-QTL interactions that affect growth. A QTL that had no significant independent effect was found to alter growth rate and biomass for several carbon sources through two-QTL interactions. Our study demonstrates that environment-specific epistatic interactions contribute to the growth plasticity in yeast. We propose that a targeted scan for epistatic interactions, such as the one described here, can help unravel mechanisms regulating phenotypic plasticity

Bird to human transmission biases and vaccine escape mutants in H5N1 infections.

BACKGROUND:The avian influenza A H5N1 virus occasionally infects humans, with high mortality rates. Although all current human infections are from avian-to-human transmission, it has been shown that H5N1 can be evolved to transmit between mammals, and is therefore a pandemic threat. For H5N1 surveillance, it is of interest to identify the avian isolates most likely to infect humans. In this study, we develop a method to identify mutations significantly associated with avian to human transmission. METHOD:Using protein sequences for the surface glycoprotein hemagglutinin from avian and human H5N1 isolates in China, Egypt, and Indonesia from the years 1996-2011, we used Principle Component Analysis and a Maximum Likelihood Multinomial method to identify mutations associated with avian to human transmission. In each geographic region, transmission bias residues were identified using two signatures: a) significantly different amino-acid frequencies in human isolates compared to avian isolates from the same year, and b) significantly low probability of neutral evolution of the human isolates from the avian viral pool of the previous year. RESULTS:In each geographic region, we find specific transmission bias mutations associated with human infections. These mutations are located in antigenic regions and receptor binding, glycosylation and polybasic cleavage sites of HA. We show that human isolates derive from a limited, subset of the avian pool characterized by geography specific mutations. In Egypt, two of three PCA clusters have very few human isolates but are highly enriched in mutations associated with a vaccine escape mutant H5N1 avian sub-clade that is known to be resistant to the Mexican H5N2 vaccine Furthermore, at these transmission bias associated residues, the mutations characteristic of these two clusters are distinct from those associated with the cluster enriched in human isolates, suggesting that vaccine resistant avian strains are unable to infect humans. Our results are relevant for surveillance and vaccination strategies for human H5N1 infections

Transmission bias mutations that are conserved or at high frequency in closely clustering human and avian isolates.

<p>Conserved residues and residues with high-frequency amino acids in closely clustering human isolates which have significantly low probability to neutrally evolve from and significant differences in amino-acid frequencies from the entire avian viral pool of each geographical region and year. Frequencies for other significant mutations are in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100754#pone.0100754.s006" target="_blank">Table S3</a>.</p>a<p>Information on the function of residues is taken from Duvvuri et al. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100754#pone.0100754-Li1" target="_blank">[11]</a>.</p>b<p>The numbers in the rows next to each country's names indicate the number of samples in each class.</p>c<p>The years of first report in each country as per the information available in the NCBI Influenza Virus Resource database.</p>d<p>“∼” indicates adjacent residue.</p>e<p>Epitope.</p>f<p>Frequency in percent.</p>g<p>Receptor-binding site.</p>h<p>Glycosylation site.</p>i<p>Polybasic cleavage site.</p>j<p>The first human isolate of H5N1 reported was in 2003, but there was contiguous reporting of human isolates only since 2005.</p>k<p>3 residues upstream.</p

Methods to detect transmission Bias of H5N1 strains from birds to humans.

<p>A hypothetical scenario of a mutation under positive selection for human infections, but selectively neutral in avian infections, will result in a transmission bias of H5N1 strains from birds to humans. Such a residue would show: a) Significant increase in the frequency of residues in human isolates compared to their frequency in the avian viral pool; b) Low probability that the current human isolates are derived from neutral evolution of the avian viral pool of the previous year. We use these two tests to identify transmission-bias in human infections.</p

Average annual frequency of significant HA mutations responsible for geography specific transmission bias of H5N1.

<p>Average annual frequencies of the major amino-acid at significant residues (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100754#pone.0100754.s004" target="_blank">Table S1</a>) for human isolates (red), avian isolates which cluster with human isolates (teal), and other avian isolates (black) from Egypt (A), China (B) and Indonesia (C). The grey bars represent two standard deviation variation in the observed annual frequency.</p

Transmission bias mutations compared to vaccine evasion mutations in Egypt.

<p>PCA plots distinguish the frequencies of transmission bias loci (A) and vaccine evasion mutations (B). In (A) transmission bias mutations P-74, D-97, H-110, S-123, S-141, F-144, N-165 and M-226 (total = 8) have a high frequency in human isolates and closely clustering avian isolates but not in other avian isolates. In (B), vaccine-evasion mutations S-74, G-140, P-141, Y-144 and K-162 (total = 5) from Cattoli et al <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0100754#pone.0100754-Cattoli1" target="_blank">[12]</a>, which are responsible for resistance to the Mexican H5N2 vaccine strain commonly used in commercial poultry farms in Egypt are overrepresented in a different subcluster compared to the transmission bias mutations (A).</p

Population substructure and transmission bias in H5N1 strains.

<p>The figure shows the first two principal components from PCA of HA amino acid sequences from avian and human isolates from China (A), Egypt (B) and Indonesia (C). Closely clustering human and avian isolates, in each region, were identified using an algorithm which clusters strains by using a distance cutoff in principal component space (Methods).</p

Sporulation Genes Associated with Sporulation Efficiency in Natural Isolates of Yeast

<div><p>Yeast sporulation efficiency is a quantitative trait and is known to vary among experimental populations and natural isolates. Some studies have uncovered the genetic basis of this variation and have identified the role of sporulation genes (<i>IME1</i>, <i>RME1</i>) and sporulation-associated genes (<i>FKH2</i>, <i>PMS1</i>, <i>RAS2</i>, <i>RSF1</i>, <i>SWS2</i>), as well as non-sporulation pathway genes (<i>MKT1</i>, <i>TAO3</i>) in maintaining this variation. However, these studies have been done mostly in experimental populations. Sporulation is a response to nutrient deprivation. Unlike laboratory strains, natural isolates have likely undergone multiple selections for quick adaptation to varying nutrient conditions. As a result, sporulation efficiency in natural isolates may have different genetic factors contributing to phenotypic variation. Using <i>Saccharomyces cerevisiae</i> strains in the genetically and environmentally diverse SGRP collection, we have identified genetic loci associated with sporulation efficiency variation in a set of sporulation and sporulation-associated genes. Using two independent methods for association mapping and correcting for population structure biases, our analysis identified two linked clusters containing 4 non-synonymous mutations in genes – <i>HOS4</i>, <i>MCK1</i>, <i>SET3</i>, and <i>SPO74</i>. Five regulatory polymorphisms in five genes such as <i>MLS1</i> and <i>CDC10</i> were also identified as putative candidates. Our results provide candidate genes contributing to phenotypic variation in the sporulation efficiency of natural isolates of yeast.</p></div