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Background Obesity-related diseases are major contributors to morbidity and mortality in the developed world. Molecular diagnostics and targets of therapies to combat nutritional imbalance are urgently needed in the clinic. Invertebrate animals have been a cornerstone of basic research efforts to dissect the genetics of metabolism and nutrient response. We set out to use fruit flies reared on restricted and nutrient-rich diets to identify genes associated with starvation resistance, body mass and composition, in a survey of genetic variation across the Drosophila Genetic Reference Panel (DGRP). Results We measured starvation resistance, body weight and composition in DGRP lines on each of two diets and used several association mapping strategies to harness this panel of phenotypes for molecular insights. We tested DNA sequence variants for a relationship with single metabolic traits and with multiple traits at once, using a scheme for cross-phenotype association mapping; we focused our association tests on homologs of human disease genes and common polymorphisms; and we tested for gene-by-diet interactions. The results revealed gene and gene-by-diet associations between 17 variants and body mass, whole-body triglyceride and glucose content, or starvation resistance. Focused molecular experiments validated the role in body mass of an uncharacterized gene, CG43921 (which we rename heavyweight), and previously unknown functions for the diacylglycerol kinase rdgA, the huntingtin homolog htt, and the ceramide synthase schlank in nutrient-dependent body mass, starvation resistance, and lifespan. Conclusions Our findings implicate a wealth of gene candidates in fly metabolism and nutrient response, and ascribe novel functions to htt, rdgA, hwt and schlank. Electronic supplementary material The online version of this article (doi:10.1186/s12864-016-3137-9) contains supplementary material, which is available to authorized users.

Organismal fitness depends on the ability of gene networks to function robustly in the face of environmental and genetic perturbations. Understanding the mechanisms of this stability is one of the key aims of modern systems biology. Dissecting the basis of robustness to mutation has proven a particular challenge, with most experimental models relying on artificial DNA sequence variants engineered in the laboratory. In this work, we hypothesized that negative regulatory feedback could stabilize gene expression against the disruptions that arise from natural genetic variation. We screened yeast transcription factors for feedback and used the results to establish ROX1 ( R epressor of hyp OX ia) as a model system for the study of feedback in circuit behaviors and its impact across genetically heterogeneous populations. Mutagenesis experiments revealed the mechanism of Rox1 as a direct transcriptional repressor at its own gene, enabling a regulatory program of rapid induction during environmental change that reached a plateau of moderate steady-state expression. Additionally, in a given environmental condition, Rox1 levels varied widely across genetically distinct strains; the ROX1 feedback loop regulated this variation, in that the range of expression levels across genetic backgrounds showed greater spread in ROX1 feedback mutants than among strains with the ROX1 feedback loop intact. Our findings indicate that the ROX1 feedback circuit is tuned to respond to perturbations arising from natural genetic variation in addition to its role in induction behavior. We suggest that regulatory feedback may be an important element of the network architectures that confer mutational robustness across biology.

Expression variation is widespread between species. The ability to distinguish regulatory change driven by natural selection from the consequences of neutral drift remains a major challenge in comparative genomics. In this work, we used observations of mRNA expression and promoter sequence to analyze signatures of selection on groups of functionally related genes in Saccharomycete yeasts. In a survey of gene regulons with expression divergence between Saccharomyces cerevisiae and S. paradoxus, we found that most were subject to variation in trans-regulatory factors that provided no evidence against a neutral model. However, we identified one regulon of membrane protein genes controlled by unlinked cis- and trans-acting determinants with coherent effects on gene expression, consistent with a history of directional, nonneutral evolution. For this membrane protein group, S. paradoxus alleles at regulatory loci were associated with elevated expression and altered stress responsiveness relative to other yeasts. In a phylogenetic comparison of promoter sequences of the membrane protein genes between species, the S. paradoxus lineage was distinguished by a short branch length, indicative of strong selective constraint. Likewise, sequence variants within the S. paradoxus population, but not across strains of other yeasts, were skewed toward low frequencies in promoters of genes in the membrane protein regulon, again reflecting strong purifying selection. Our results support a model in which a distinct expression program for the membrane protein genes in S. paradoxus has been preferentially maintained by negative selection as the result of an increased importance to organismal fitness. These findings illustrate the power of integrating expression- and sequence-based tests of natural selection in the study of evolutionary forces that underlie regulatory change.

Morphological differences among individuals in a species represent one of the most striking aspects of biology, and a primary aim of modern genetics is to uncover the molecular basis of morphological variation. In a survey of meiosis phenotypes among environmental isolates of Saccharomyces cerevisiae, we observed an unusual arrangement of meiotic spores within the spore sac in a strain from Ivory Coast, West Africa. We mined population genomic data to identify CDC28 as the major genetic determinant of meiotic and budding cell shape behaviors in this strain. Molecular genetic methods confirmed the role of the Ivory Coast variant of CDC28 in the arrangement of spores after meiosis, in the shape of budding cells in rich medium and in the morphology of filamentous growth during nitrogen limitation. Our results shed new light on the role of CDC28 in yeast cell division, and our work suggests that with the growing availability of genomic data sets in many systems, a priori prediction of functional variants will become an increasingly powerful strategy in molecular genetics.

Elucidating the connection between genotype, phenotype, and adaptation in wild populations is fundamental to the study of evolutionary biology, yet it remains an elusive goal, particularly for microscopic taxa, which comprise the majority of life. Even for microbes that can be reliably found in the wild, defining the boundaries of their populations and discovering ecologically relevant phenotypes has proved extremely difficult. Here, we have circumvented these issues in the microbial eukaryote Neurospora crassa by using a “reverse-ecology” population genomic approach that is free of a priori assumptions about candidate adaptive alleles. We performed Illumina whole-transcriptome sequencing of 48 individuals to identify single nucleotide polymorphisms. From these data, we discovered two cryptic and recently diverged populations, one in the tropical Caribbean basin and the other endemic to subtropical Louisiana. We conducted high-resolution scans for chromosomal regions of extreme divergence between these populations and found two such genomic “islands.” Through growth-rate assays, we found that the subtropical Louisiana population has a higher fitness at low temperature (10 °C) and that several of the genes within these distinct regions have functions related to the response to cold temperature. These results suggest the divergence islands may be the result of local adaptation to the 9 °C difference in average yearly minimum temperature between these two populations. Remarkably, another of the genes identified using this unbiased, whole-genome approach is the well-known circadian oscillator frequency , suggesting that the 2.4°–10.6° difference in latitude between the populations may be another important environmental parameter.

SummaryMany genes that affect replicative lifespan (RLS) in the budding yeast Saccharomyces cerevisiae also affect aging in other organisms such as C. elegans and M. musculus. We performed a systematic analysis of yeast RLS in a set of 4,698 viable single-gene deletion strains. Multiple functional gene clusters were identified, and full genome-to-genome comparison demonstrated a significant conservation in longevity pathways between yeast and C. elegans. Among the mechanisms of aging identified, deletion of tRNA exporter LOS1 robustly extended lifespan. Dietary restriction (DR) and inhibition of mechanistic Target of Rapamycin (mTOR) exclude Los1 from the nucleus in a Rad53-dependent manner. Moreover, lifespan extension from deletion of LOS1 is nonadditive with DR or mTOR inhibition, and results in Gcn4 transcription factor activation. Thus, the DNA damage response and mTOR converge on Los1-mediated nuclear tRNA export to regulate Gcn4 activity and aging.

The evolution of novel traits can involve many mutations scattered throughout the genome1,2. Detecting and validating such a suite of alleles, particularly if they arose long ago, remains a key challenge in evolutionary genetics1-3. Here we dissect an evolutionary tradeoff of unprecedented genetic complexity between long-diverged species. When cultured in 1% glucose medium supplemented with galactose, Saccharomyces cerevisiae, but not S. bayanus or other Saccharomyces species, delayed commitment to galactose metabolism until glucose was exhausted. Promoters of seven galactose (GAL) metabolic genes from S. cerevisiae, when introduced together into S. bayanus, largely recapitulated the delay phenotype in 1% glucose-galactose medium, and most had partial effects when tested in isolation. Variation in GAL coding regions also contributed to the delay when tested individually in 1% glucose-galactose medium. When combined, S. cerevisiae GAL coding regions gave rise to profound growth defects in the S. bayanus background. In medium containing 2.5% glucose supplemented with galactose, wild-type S. cerevisiae repressed GAL gene expression and had a robust growth advantage relative to S. bayanus; transgenesis of S. cerevisiae GAL promoter alleles or GAL coding regions was sufficient for partial reconstruction of these phenotypes. S. cerevisiae GAL genes thus encode a regulatory program of slow induction and avid repression, and a fitness detriment during the glucose-galactose transition but a benefit when glucose is in excess. Together, these results make clear that genetic mapping of complex phenotypes is within reach, even in deeply diverged species.

Many studies have identified quantitative trait loci (QTLs) that contribute to continuous variation in heritable traits of interest. However, general principles regarding the distribution of QTL numbers, effect sizes, and combined effects of multiple QTLs remain to be elucidated. Here, we characterize complex genetics underlying inheritance of thousands of transcript levels in a cross between two strains of Saccharomyces cerevisiae . Most detected QTLs have weak effects, with a median variance explained of 27% for highly heritable transcripts. Despite the high statistical power of the study, no QTLs were detected for 40% of highly heritable transcripts, indicating extensive genetic complexity. Modeling of QTL detection showed that only 3% of highly heritable transcripts are consistent with single-locus inheritance, 17–18% are consistent with control by one or two loci, and half require more than five loci under additive models. Strikingly, analysis of parent and progeny trait distributions showed that a majority of transcripts exhibit transgressive segregation. Sixteen percent of highly heritable transcripts exhibit evidence of interacting loci. Our results will aid design of future QTL mapping studies and may shed light on the evolution of quantitative traits.

Natural genetic variation can cause significant differences in gene expression, but little is known about the polymorphisms that affect gene regulation. We analyzed regulatory variation in a cross between laboratory and wild strains of Saccharomyces cerevisiae. Clustering and linkage analysis defined groups of coregulated genes and the loci involved in their regulation. Most expression differences mapped to trans-acting loci. Positional cloning and functional assays showed that polymorphisms in GPA1 and AMN1 affect expression of genes involved in pheromone response and daughter cell separation, respectively. We also asked whether particular classes of genes were more likely to contain trans-regulatory polymorphisms. Notably, transcription factors showed no enrichment, and trans-regulatory variation seems to be broadly dispersed across classes of genes with different molecular functions.

Vitamin D has multiple roles including the regulation of bone and calcium homeostasis. Deficiency of 25-hydroxyvitaminD, the major circulating form of vitamin D, is associated with an increased risk of age-related chronic diseases including Alzheimer’s disease, Parkinson’s disease, cognitive impairment, and cancer. In this study, we utilized Caenorhabditis elegans to examine the mechanism by which vitamin D influences aging. We found that Vitamin D3-induced lifespan extension requires the stress response pathway genes SKN-1, IRE-1, and XBP-1. Vitamin D3 (D3) induced expression of SKN-1 target genes, but not canonical targets of IRE-1/XBP-1. D3 suppressed an important molecular pathology of aging, that of widespread protein insolubility, and prevented toxicity caused by human β-amyloid. Our observation that D3 improves protein homeostasis and slows aging highlights the importance of maintaining appropriate vitamin D serum levels, and may explain why such a wide variety of human age-related diseases are associated with vitamin D deficiency.

Aging is a root cause of many chronic diseases and associated morbidity and mortality. A number of interventions have been suggested to improve overall lifespan and health, with one of the most successful being dietary restriction. Despite the generally well-demonstrated health and longevity benefits of dietary restriction, cases remain in model organisms where some genotypes are not affected or are negatively impacted. With the use of the Drosophila Genetic Reference Panel, we have identified a new set of genes that regulate health or longevity either diet-dependently or diet-independently. Associated with longevity are the genes CG34351 and Fdxh. We have also revealed CG33690, a previously uncharacterized gene, as being a regulator of health in response to diet. Through alteration of the expression of these genes via RNAi and gene disruption, we have validated their roles in impacting on lifespan or climbing ability independently. In addition to ascribing novel functions to these genes, our results demonstrate that lifespan and healthspan depend on a complex set of inputs including genetic polymorphisms and diet.

Host specialization by pathogens requires a repertoire of virulence factors as well as fine-tuned regulation of gene expression. The fungal wheat pathogen Zymoseptoria tritici (synonym Mycosphaerella graminicola) is a powerful model system for the discovery of genetic elements that underlie virulence and host specialization. We transcriptionally profiled the early stages of Z. tritici infection of a compatible host (wheat) and a noncompatible host (Brachypodium distachyon). The results revealed infection regulatory programs common to both hosts and genes with striking wheat-specific expression, with many of the latter showing sequence signatures of positive selection along the Z. tritici lineage. Genes specifically regulated during infection of wheat populated two large clusters of coregulated genes that may represent candidate pathogenicity islands. On evolutionarily labile, repeat-rich accessory chromosomes (ACs), we identified hundreds of highly expressed genes with signatures of evolutionary constraint and putative biological function. Phylogenetic analyses suggested that gene duplication events on these ACs were rare and largely preceded the diversification of Zymoseptoria species. Together, our data highlight the likely relevance for fungal growth and virulence of hundreds of Z. tritici genes, deepening the annotation and functional inference of the genes of this model pathogen.

There is a large literature that reports oil−water partition coefficients of small molecule compounds. A goal of much of this work is to obtain hydrophobicity parameters for models of folding, docking, binding, conformational changes, partitioning, and flow, mainly of biomolecules and drugs. The quantity obtained from experiments is the difference between a solute's oil- and water-phase chemical potentials Δμ ⋮ μwat − μoil, but the quantity needed for models is a contact free energy per unit area. Usually, solvation modelers find this quantity in two steps: (i) subtract off some assumed “irrelevant” solute entropies from the experimental Δμ's and then (ii) divide the result by the solute area. Here we show that such a procedure can lead to large errors. We use a two -dimensional lattice model of hydrocarbon liquids in NVT Monte Carlo simulations, where the underlying contact energy is rigorously known in advance, to test strategies for converting partitioning data to contact energies. We find that rather...

A popular approach to the computational modeling of ligand/receptor interactions is to use an empirical free energy like model with adjustable parameters. Parameters are learned from one set of complexes, then used to predict another set. To improve these empirical methods requires an independent way to study their inherent errors. We introduce a toy model of ligand/receptor binding as a workbench for testing such errors. We study the errors incurred from the two state binding assumption--the assumption that a ligand is either bound in one orientation, or unbound. We find that the two state assumption can cause large errors in free energy predictions, but it does not affect rank order predictions significantly. We show that fitting parameters using data from high affinity ligands can reduce two state errors; so can using more physical models that do not use the two state assumption. We also find that when using two state models to predict free energies, errors are more severe on high affinity ligands than low affinity ligands. And we show that two state errors can be diagnosed by systematically adding new binding modes when predicting free energies: if predictions worsen as the modes are added, then the two state assumption in the fitting step may be at fault.

Comparative genomic studies have reported widespread variation in levels of gene expression within and between species. Using these data to infer organism-level trait divergence has proven to be a key challenge in the field. We have used a wild Malaysian population of S. cerevisiae as a test bed in the search to predict and validate trait differences based on observations of regulatory variation. Malaysian yeast, when cultured in standard medium, activated regulatory programs that protect cells from the toxic effects of high iron. Malaysian yeast also showed a hyperactive regulatory response during culture in the presence of excess iron and had a unique growth defect in conditions of high iron. Molecular validation experiments pinpointed the iron metabolism factors AFT1, CCC1, and YAP5 as contributors to these molecular and cellular phenotypes; in genome-scale sequence analyses, a suite of iron toxicity response genes showed evidence for rapid protein evolution in Malaysian yeast. Our findings support a model in which iron metabolism has diverged in Malaysian yeast as a consequence of a change in selective pressure, with Malaysian alleles shifting the dynamic range of iron response to low-iron concentrations and weakening resistance to extreme iron toxicity. By dissecting the iron scarcity specialist behavior of Malaysian yeast, our work highlights the power of expression divergence as a signpost for biologically and evolutionarily relevant variation at the organismal level. Interpreting the phenotypic relevance of gene expression variation is one of the primary challenges of modern genomics.

Understanding the molecular basis of common traits is a primary challenge of modern genetics. One model holds that rare mutations in many genetic backgrounds may often phenocopy one another, together explaining the prevalence of the resulting trait in the population. For the vast majority of phenotypes, the role of rare variants and the evolutionary forces that underlie them are unknown. In this work, we use a population of Saccharomyces paradoxus yeast as a model system for the study of common trait variation. We observed an unusual, flocculation and invasive-growth phenotype in one-third of S. paradoxus strains, which were otherwise unrelated. In crosses with each strain in turn, these morphologies segregated as a recessive Mendelian phenotype, mapping either to IRA1 or to IRA2, yeast homologs of the hypermutable human neurofibromatosis gene NF1. The causal IRA1 and IRA2 haplotypes were of distinct evolutionary origin and, in addition to their morphological effects, associated with hundreds of stress-resistance and growth traits, both beneficial and disadvantageous, across S. paradoxus. Single-gene molecular genetic analyses confirmed variant IRA1 and IRA2 haplotypes as causal for these growth characteristics, many of which were independent of morphology. Our data make clear that common growth and morphology traits in yeast result from a suite of variants in master regulators, which function as a mutation-driven switch between phenotypic states.

One of the outstanding challenges in comparative genomics is to interpret the evolutionary importance of regulatory variation between species. Rigorous molecular evolution-based methods to infer evidence for natural selection from expression data are at a premium in the field, and to date, phylogenetic approaches have not been well-suited to address the question in the small sets of taxa profiled in standard surveys of gene expression. We have developed a strategy to infer evolutionary histories from expression profiles by analyzing suites of genes of common function. In a manner conceptually similar to molecular evolution models in which the evolutionary rates of DNA sequence at multiple loci follow a gamma distribution, we modeled expression of the genes of an \emph{a priori}-defined pathway with rates drawn from an inverse gamma distribution. We then developed a fitting strategy to infer the parameters of this distribution from expression measurements, and to identify gene groups whose expression patterns were consistent with evolutionary constraint or rapid evolution in particular species. Simulations confirmed the power and accuracy of our inference method. As an experimental testbed for our approach, we generated and analyzed transcriptional profiles of four \emph{Saccharomyces} yeasts. The results revealed pathways with signatures of constrained and accelerated regulatory evolution in individual yeasts and across the phylogeny, highlighting the prevalence of pathway-level expression change during the divergence of yeast species. We anticipate that our pathway-based phylogenetic approach will be of broad utility in the search to understand the evolutionary relevance of regulatory change., 30 pages, 12 figures, 2 tables, contact authors for supplementary tables

Understanding how genomes encode complex cellular and organismal behaviors has become the outstanding challenge of modern genetics. Unlike classical screening methods, analysis of genetic variation that occurs naturally in wild populations can enable rapid, genome-scale mapping of genotype to phenotype with a medium-throughput experimental design. Here we describe the results of the first genome-wide association study (GWAS) used to identify novel loci underlying trait variation in a microbial eukaryote, harnessing wild isolates of the filamentous fungus Neurospora crassa. We genotyped each of a population of wild Louisiana strains at 1 million genetic loci genome-wide, and we used these genotypes to map genetic determinants of microbial communication. In N. crassa, germinated asexual spores (germlings) sense the presence of other germlings, grow toward them in a coordinated fashion, and fuse. We evaluated germlings of each strain for their ability to chemically sense, chemotropically seek, and undergo cell fusion, and we subjected these trait measurements to GWAS. This analysis identified one gene, NCU04379 (cse-1, encoding a homolog of a neuronal calcium sensor), at which inheritance was strongly associated with the efficiency of germling communication. Deletion of cse-1 significantly impaired germling communication and fusion, and two genes encoding predicted interaction partners of CSE1 were also required for the communication trait. Additionally, mining our association results for signaling and secretion genes with a potential role in germling communication, we validated six more previously unknown molecular players, including a secreted protease and two other genes whose deletion conferred a novel phenotype of increased communication and multi-germling fusion. Our results establish protein secretion as a linchpin of germling communication in N. crassa and shed light on the regulation of communication molecules in this fungus. Our study demonstrates the power of population-genetic analyses for the rapid identification of genes contributing to complex traits in microbial species., PLoS Genetics, 9 (8), ISSN:1553-7390, ISSN:1553-7404

DNA variation can be used as a systematic source of perturbation in segregating populations as a way to infer regulatory networks via the integration of large-scale, high-dimensional molecular profiling data., Cells employ multiple levels of regulation, including transcriptional and translational regulation, that drive core biological processes and enable cells to respond to genetic and environmental changes. Small-molecule metabolites are one category of critical cellular intermediates that can influence as well as be a target of cellular regulations. Because metabolites represent the direct output of protein-mediated cellular processes, endogenous metabolite concentrations can closely reflect cellular physiological states, especially when integrated with other molecular-profiling data. Here we develop and apply a network reconstruction approach that simultaneously integrates six different types of data: endogenous metabolite concentration, RNA expression, DNA variation, DNA–protein binding, protein–metabolite interaction, and protein–protein interaction data, to construct probabilistic causal networks that elucidate the complexity of cell regulation in a segregating yeast population. Because many of the metabolites are found to be under strong genetic control, we were able to employ a causal regulator detection algorithm to identify causal regulators of the resulting network that elucidated the mechanisms by which variations in their sequence affect gene expression and metabolite concentrations. We examined all four expression quantitative trait loci (eQTL) hot spots with colocalized metabolite QTLs, two of which recapitulated known biological processes, while the other two elucidated novel putative biological mechanisms for the eQTL hot spots., Author Summary It is now possible to score variations in DNA across whole genomes, RNA levels and alternative isoforms, metabolite levels, protein levels and protein state information, protein–protein interactions, and protein–DNA interactions, in a comprehensive fashion in populations of individuals. Interactions among these molecular entities define the complex web of biological processes that give rise to all higher order phenotypes, including disease. The development of analytical approaches that simultaneously integrate different dimensions of data is essential if we are to extract the meaning from large-scale data to elucidate the complexity of living systems. Here, we use a novel Bayesian network reconstruction algorithm that simultaneously integrates DNA variation, RNA levels, metabolite levels, protein–protein interaction data, protein–DNA binding data, and protein–small-molecule interaction data to construct molecular networks in yeast. We demonstrate that these networks can be used to infer causal relationships among genes, enabling the identification of novel genes that modulate cellular regulation. We show that our network predictions either recapitulate known biology or can be prospectively validated, demonstrating a high degree of accuracy in the predicted network.

Surveys of transcription in many organisms have observed widespread expression of RNAs with no known function, encoded within and between canonical coding genes. The search to distinguish functional RNAs from transcriptional noise represents one of the great challenges in genomic biology. Here we report a next-generation sequencing technique designed to facilitate the inference of function of uncharacterized transcript forms by improving their coverage in sequencing libraries, in parallel with the detection of canonical mRNAs. We piloted this protocol, which is based on the capture of 3′ ends of polyadenylated RNAs, in budding yeast. Analysis of transcript ends in coding regions uncovered hundreds of alternative-length coding forms, which harbored a unique sequence motif and showed signatures of regulatory function in particular gene categories; independent single-gene measurements confirmed the differential regulation of short coding forms during heat shock. In addition, our 3′-end RNA-seq method applied to wild-type strains detected putative noncoding transcripts previously reported only in RNA surveillance mutants, and many such transcripts showed differential expression in yeast cultures grown under chemical stress. Our results underscore the power of the 3′-end protocol to improve detection of noncanonical transcript forms in a sequencing experiment of standard depth, and our findings strongly suggest that many unannotated, polyadenylated RNAs may have as yet uncharacterized regulatory functions.

The search to understand how genomes innovate in response to selection dominates the field of evolutionary biology. Powerful molecular evolution approaches have been developed to test individual loci for signatures of selection. In many cases, however, an organism's response to changes in selective pressure may be mediated by multiple genes, whose products function together in a cellular process or pathway. Here we assess the prevalence of polygenic evolution in pathways in the yeasts Saccharomyces cerevisiae and S. bayanus . We first established short-read sequencing methods to detect cis -regulatory variation in a diploid hybrid between the species. We then tested for the scenario in which selective pressure in one species to increase or decrease the activity of a pathway has driven the accumulation of cis -regulatory variants that act in the same direction on gene expression. Application of this test revealed a variety of yeast pathways with evidence for directional regulatory evolution. In parallel, we also used population genomic sequencing data to compare protein and cis -regulatory variation within and between species. We identified pathways with evidence for divergence within S. cerevisiae , and we detected signatures of positive selection between S. cerevisiae and S. bayanus. Our results point to polygenic, pathway-level change as a common evolutionary mechanism among yeasts. We suggest that pathway analyses, including our test for directional regulatory evolution, will prove to be a relevant and powerful strategy in many evolutionary genomic applications.

The mitochondrial genome (mtDNA) is required for normal cellular function; inherited and somatic mutations in mtDNA lead to a variety of diseases. Saccharomyces cerevisiae has served as a model to study mtDNA integrity, in part because it can survive without mtDNA. A measure of defective mtDNA in S. cerevisiae is the formation of petite colonies. The frequency at which spontaneous petite colonies arise varies by ∼100-fold between laboratory and natural isolate strains. To determine the genetic basis of this difference, we applied quantitative trait locus (QTL) mapping to two strains at the opposite extremes of the phenotypic spectrum: the widely studied laboratory strain S288C and the vineyard isolate RM11-1a. Four main genetic determinants explained the phenotypic difference. Alleles of SAL1, CAT5, and MIP1 contributed to the high petite frequency of S288C and its derivatives by increasing the formation of petite colonies. By contrast, the S288C allele of MKT1 reduced the formation of petite colonies and compromised the growth of petite cells. The former three alleles were found in the EM93 strain, the founder that contributed ∼88% of the S288C genome. Nearly all of the phenotypic difference between S288C and RM11-1a was reconstituted by introducing the common alleles of these four genes into the S288C background. In addition to the nuclear gene contribution, the source of the mtDNA influenced its stability. These results demonstrate that a few rare genetic variants with individually small effects can have a profound phenotypic effect in combination. Moreover, the polymorphisms identified in this study open new lines of investigation into mtDNA maintenance.

A key goal of biology is to construct networks that predict complex system behavior. We combine multiple types of molecular data, including genotypic, expression, transcription factor binding site (TFBS), and protein-protein interaction (PPI) data previously generated from a number of yeast experiments, in order to reconstruct causal gene networks. Networks based on different types of data are compared using metrics devised to assess the predictive power of a network. We show that a network reconstructed by integrating genotypic, TFBS and PPI data is the most predictive. This network is used to predict causal regulators responsible for hot spots of gene expression activity in a segregating yeast population. We also show that the network can elucidate the mechanisms by which causal regulators give rise to larger-scale changes in gene expression activity. We then prospectively validate predictions, providing direct experimental evidence that predictive networks can be constructed by integrating multiple, appropriate data types.

As wild organisms adapt to the laboratory environment, they become less relevant as biological models. It has been suggested that a commonly used S. cerevisiae strain has rapidly accumulated mutations in the lab. We report a low-to-intermediate rate of protein evolution in this strain relative to wild isolates.

Interactions between polymorphisms at different quantitative trait loci (QTLs) are thought to contribute to the genetics of many traits, and can markedly affect the power of genetic studies to detect QTLs1. Interacting loci have been identified in many organisms1,2,3,4,5. However, the prevalence of interactions6,7,8, and the nucleotide changes underlying them9,10, are largely unknown. Here we search for naturally occurring genetic interactions in a large set of quantitative phenotypes—the levels of all transcripts in a cross between two strains of Saccharomyces cerevisiae7. For each transcript, we searched for secondary loci interacting with primary QTLs detected by their individual effects. Such locus pairs were estimated to be involved in the inheritance of 57% of transcripts; statistically significant pairs were identified for 225 transcripts. Among these, 67% of secondary loci had individual effects too small to be significant in a genome-wide scan. Engineered polymorphisms in isogenic strains confirmed an interaction between the mating-type locus MAT and the pheromone response gene GPA1. Our results indicate that genetic interactions are widespread in the genetics of transcript levels, and that many QTLs will be missed by single-locus tests but can be detected by two-stage tests that allow for interactions.

To begin to understand the genetic architecture of natural variation in gene expression, we carried out genetic linkage analysis of genomewide expression patterns in a cross between a laboratory strain and a wild strain of Saccharomyces cerevisiae . Over 1500 genes were differentially expressed between the parent strains. Expression levels of 570 genes were linked to one or more different loci, with most expression levels showing complex inheritance patterns. The loci detected by linkage fell largely into two categories: cis-acting modulators of single genes and trans-acting modulators of many genes. We found eight such trans-acting loci, each affecting the expression of a group of 7 to 94 genes of related function.

Strain selection and strain improvement are the first, and arguably most important, steps in the industrial production of biological compounds by microorganisms. While traditional methods of mutagenesis and selection have been effective in improving production of compounds at a commercial scale, the genetic changes underpinning the altered phenotypes have remained largely unclear. We utilized high-throughput Illumina short read sequencing of a wild Penicillium chrysogenum strain in order to make whole genome comparisons to a sequenced improved strain (WIS 54-1255). We developed an assembly-free method of identifying chromosomal rearrangements and validated the in silico predictions with a PCR-based assay and Sanger sequencing. Despite many rounds of mutagen treatment and artificial selection, WIS 54-1255 differs from its wild progenitor at only one of the identified rearrangements. We suggest that natural variants predisposed for high penicillin production were instrumental in the success of WIS 54-1255 as an industrial strain. In addition to finding a previously published inversion in the penicillin biosynthesis cluster, we located several genes related to penicillin production associated with these rearrangements. By comparing the configuration of rearrangement events among several historically important strains known to be high penicillin producers to a collection of recently isolated wild strains, we suggest that wild strains with rearrangements similar to those in known high penicillin producers may be viable candidates for further improvement efforts.

We have determined structures of binary and ternary complexes of five Asn229 variants of thymidylate synthase (TS) and related their structures to the kinetic constants measured previously. Asn229 forms two hydrogen bonds to the pyrimidine ring of the substrate 2′-deoxyuridine-5′-monophosphate (dUMP). These hydrogen bonds constrain the orientation of dUMP in binary complexes with dUMP, and in ternary complexes with dUMP and the TS cofactor, 5,10-methylene-5,6,7,8-tetrahydrofolate. In N229 mutants, where these hydrogen bonds cannot be made, dUMP binds in a misoriented or more disordered fashion. Most N229 mutants exhibit no activity for the dehalogenation of 5-bromo-dUMP, which requires correct orientation of dUMP against Cys198. Since bound dUMP forms the binding surface against which the pterin ring of cofactor binds, misorientation of dUMP results in higher Km values for cofactor. At the same time, binding of the cofactor aids in ordering and positioning dUMP for catalysis. Hydrophobic mutants, such as N229I, favor an arrangement of solvent molecules and side-chains around the ligands similar to that in a proposed transition state for ternary complex formation in wild-type TS, and kcat values are similar to the wild-type value. Smaller, more hydrophilic mutants favor arrangements of the solvent and side-chains surrounding the ligands that do not resemble the proposed transition state. These changes correspond to decreases in kcat of up to 2000-fold, with only modest increases in Km or Kd. These results are consistent with the proposal that the hydrogen-bonding network between water, dUMP and side-chains in the active-site cavity contributes to catalysis in TS. Asn229 has the unique ability to maintain this critical network, without sterically interfering with dUMP binding.

We present two methods for designing amino acid sequences of proteins that will fold to have good hydrophobic cores. Given the coordinates of the desired target protein or polymer structure, the methods generate sequences of hydrophobic (H) and polar (P) monomers that are intended to fold to these structures. One method designs hydrophobic inside, polar outside; the other minimizes an energy function in a sequence evolution process. The sequences generated by these methods agree at the level of 60-80% of the sequence positions in 20 proteins in the Protein Data Bank. A major challenge in protein design is to create sequences that can fold uniquely, i.e. to a single conformation rather than to many. While an earlier lattice-based sequence evolution method was shown not to design unique folders, our method generates unique folders in lattice model tests. These methods may also be useful in designing other types of foldable polymer not based on amino acids.

Gene expression varies widely between individuals of a population, and regulatory change can underlie phenotypes of evolutionary and biomedical relevance. A key question in the field is how DNA sequence variants impact gene expression, with most mechanistic studies to date focused on the effects of genetic change on regulatory regions upstream of protein-coding sequence. By contrast, the role of RNA 3′-end processing in regulatory variation remains largely unknown, owing in part to the challenge of identifying functional elements in 3′ untranslated regions. In this work, we conducted a genomic survey of transcript ends in lymphoblastoid cells from genetically distinct human individuals. Our analysis mapped the cis-regulatory architecture of 3′ gene ends, finding that transcript end positions did not fall randomly in untranslated regions, but rather preferentially flanked the locations of 3′ regulatory elements, including miRNA sites. The usage of these transcript length forms and motifs varied across human individuals, and polymorphisms in polyadenylation signals and other 3′ motifs were significant predictors of expression levels of the genes in which they lay. Independent single-gene experiments confirmed the effects of polyadenylation variants on steady-state expression of their respective genes, and validated the regulatory function of 3′ cis-regulatory sequence elements that mediated expression of these distinct RNA length forms. Focusing on the immune regulator IRF5, we established the effect of natural variation in RNA 3′-end processing on regulatory response to antigen stimulation. Our results underscore the importance of two mechanisms at play in the genetics of 3′-end variation: the usage of distinct 3′-end processing signals and the effects of 3′ sequence elements that determine transcript fate. Our findings suggest that the strategy of integrating observed 3′-end positions with inferred 3′ regulatory motifs will prove to be a critical tool in continued efforts to interpret human genome variation., Author Summary Messenger RNAs carry the instructions necessary to synthesize proteins that do work for the cell. Extending beyond the protein-coding sequence of a given mRNA is an additional stretch of sequence, harboring signals that govern how much protein is made and how long the mRNA remains in the cell before it is broken down. The incorporation of this end region into mature mRNA is itself subject to change; for the vast majority of human genes, how and why cells use different mRNA ends remains largely unknown. In this work, we surveyed mRNA ends from ∼10,000 genes in immune cells from genetically distinct human individuals. We found that mRNA end positions were not randomly distributed, but rather preferentially flanked the locations of regulatory signals that govern mRNA fate. The usage of these mRNA length forms and regulatory elements varied across individuals and could be dissected molecularly. Our results uncover key mechanisms and regulatory effects of transcript end processing, particularly as these are perturbed by genetic differences between humans.

Identifying DNA polymorphisms that affect molecular processes like transcription, splicing, or translation typically requires genotyping and experimentally characterizing tissue from large numbers of individuals, which remains expensive and time consuming. Here we introduce an alternative strategy: a “synthetic association study” in which we computationally predict molecular phenotypes on artificial genomes containing randomly sampled combinations of polymorphic alleles, and perform a classical association study to identify genotypes underlying variation in these computationally predicted annotations. We applied this method to characterize the effects on gene structure of 32,792 single-nucleotide polymorphisms between two strains of the antibiotic producing fungus Penicilium chrysogenum. Although these SNPs represent only 0.1 percent of the nucleotides in the genome, they collectively altered 1.8 percent of predicted gene models between these strains. To determine which SNPs or combinations of SNPs were responsible for this variation, we predicted protein-coding genes in 500 intermediate genomes, each identical except for randomly chosen alleles at each SNP position. Of 30,468 gene models in the genome, 557 varied across these 500 genomes. 226 of these polymorphic gene models (40%) were perfectly correlated with individual SNPs, all of which were within or immediately proximal to the affected gene. The genetic architectures of the other 321 were more complex, with several examples of SNP epistasis that would have been difficult to predict a priori. We expect that many of the SNPs that affect computational gene structure reflect a biologically unrealistic sensitivity of the gene prediction algorithm to sequence changes, and we propose that genome annotation algorithms could be improved by minimizing their sensitivity to natural polymorphisms. However, many of the SNPs we identified are likely to affect transcript structure in vivo, and the synthetic association study approach can be easily generalized to any computed genome annotation to uncover relationships between genotype and important molecular phenotypes.

SummaryChronic itch is a prevalent and debilitating condition for which few effective therapies are available. We harnessed the natural variation across genetically distinct mouse strains to identify transcripts co-regulated with itch behavior. This survey led to the discovery of the serotonin receptor HTR7 as a key mediator of serotonergic itch. Activation of HTR7 promoted opening of the ion channel TRPA1, which in turn triggered itch behaviors. In addition, acute itch triggered by serotonin or a selective serotonin reuptake inhibitor required both HTR7 and TRPA1. Aberrant serotonin signaling has long been linked to a variety of human chronic itch conditions, including atopic dermatitis. In a mouse model of atopic dermatitis, mice lacking HTR7 or TRPA1 displayed reduced scratching and skin lesion severity. These data highlight a role for HTR7 in acute and chronic itch and suggest that HTR7 antagonists may be useful for treating a variety of pathological itch conditions.