Endocytosis, the process whereby the plasma membrane invaginates to form vesicles, is essential for bringing many substances into the cell and for membrane turnover. The mechanism driving clathrin-mediated endocytosis (CME) involves > 50 different protein components assembling at a single location on the plasma membrane in a temporally ordered and hierarchal pathway. These proteins perform precisely choreographed steps that promote receptor recognition and clustering, membrane remodeling, and force-generating actin-filament assembly and turnover to drive membrane invagination and vesicle scission. Many critical aspects of the CME mechanism are conserved from yeast to mammals and were first elucidated in yeast, demonstrating that it is a powerful system for studying endocytosis. In this review, we describe our current mechanistic understanding of each step in the process of yeast CME, and the essential roles played by actin polymerization at these sites, while providing a historical perspective of how the landscape has changed since the preceding version of the YeastBook was published 17 years ago (1997). Finally, we discuss the key unresolved issues and where future studies might be headed.
S. cerevisiae; Arp2/3 complex; clathrin; membrane; endocytosis
SUMMARY Achieving a thorough understanding of the events and ramifications of meiosis is a common learning objective for undergraduate introductory biology, genetics, and cell biology courses. Meiosis is also one of the most challenging cellular processes for students to conceptualize. Connecting textbook descriptions of meiosis to current research in the field of genetics in a problem-based learning format may aid students’ understanding of this important biological concept. This primer seeks to assist students and instructors by providing an introductory framework upon which to integrate discussions of current meiosis research into traditional genetics or cell biology curriculum.
Related article in GENETICS: Collins, K. et al., 2014 Corolla Is a Novel Protein That Contributes to the Architecture of the Synaptonemal Complex of Drosophila. Genetics 198:
education; meiosis; Drosophila; oocyte
The loss of genome stability is an early event that drives the development and progression of virtually all tumor types. Recent studies have revealed that certain histone post-translational modifications exhibit dynamic and global increases in abundance that coincide with mitosis and exhibit essential roles in maintaining genomic stability. Histone H2B ubiquitination at lysine 120 (H2Bub1) is regulated by RNF20, an E3 ubiquitin ligase that is altered in many tumor types. Through an evolutionarily conserved trans-histone pathway, H2Bub1 is an essential prerequisite for subsequent downstream dimethylation events at lysines 4 (H3K4me2) and 79 (H3K79me2) of histone H3. Although the role that RNF20 plays in tumorigenesis has garnered much attention, the downstream components of the trans-histone pathway, H3K4me2 and H3K79me2, and their potential contributions to genome stability remain largely overlooked. In this study, we employ single-cell imaging and biochemical approaches to investigate the spatial and temporal patterning of RNF20, H2Bub1, H3K4me2, and H3K79me2 throughout the cell cycle, with a particular focus on mitosis. We show that H2Bub1, H3K4me2, and H3K79me2 exhibit distinct temporal progression patterns throughout the cell cycle. Most notably, we demonstrate that H3K79me2 is a highly dynamic histone post-translational modification that reaches maximal abundance during mitosis in an H2Bub1-independent manner. Using RNAi and chemical genetic approaches, we identify DOT1L as a histone methyltransferase required for the mitotic-associated increases in H3K79me2. We also demonstrate that the loss of mitotic H3K79me2 levels correlates with increases in chromosome numbers and increases in mitotic defects. Collectively, these data suggest that H3K79me2 dynamics during mitosis are normally required to maintain genome stability and further implicate the loss of H3K79me2 during mitosis as a pathogenic event that contributes to the development and progression of tumors.
chromosomal instability; H2Bub1; H3K79me2; mitotic increase; RNF20; DOT1L
Estimation of epidemiological and population parameters from molecular sequence data has become central to the understanding of infectious disease dynamics. Various models have been proposed to infer details of the dynamics that describe epidemic progression. These include inference approaches derived from Kingman’s coalescent theory. Here, we use recently described coalescent theory for epidemic dynamics to develop stochastic and deterministic coalescent susceptible–infected–removed (SIR) tree priors. We implement these in a Bayesian phylogenetic inference framework to permit joint estimation of SIR epidemic parameters and the sample genealogy. We assess the performance of the two coalescent models and also juxtapose results obtained with a recently published birth–death-sampling model for epidemic inference. Comparisons are made by analyzing sets of genealogies simulated under precisely known epidemiological parameters. Additionally, we analyze influenza A (H1N1) sequence data sampled in the Canterbury region of New Zealand and HIV-1 sequence data obtained from known United Kingdom infection clusters. We show that both coalescent SIR models are effective at estimating epidemiological parameters from data with large fundamental reproductive number R0 and large population size S0. Furthermore, we find that the stochastic variant generally outperforms its deterministic counterpart in terms of error, bias, and highest posterior density coverage, particularly for smaller R0 and S0. However, each of these inference models is shown to have undesirable properties in certain circumstances, especially for epidemic outbreaks with R0 close to one or with small effective susceptible populations.
Bayesian inference; phylodynamics; coalescent; epidemic; stochastic
Every data visualization can be improved with some level of interactivity. Interactive graphics hold particular promise for the exploration of high-dimensional data. R/qtlcharts is an R package to create interactive graphics for experiments to map quantitative trait loci (QTL) (genetic loci that influence quantitative traits). R/qtlcharts serves as a companion to the R/qtl package, providing interactive versions of R/qtl’s static graphs, as well as additional interactive graphs for the exploration of high-dimensional genotype and phenotype data.
QTL; data visualization; software; D3
As in other organisms, CRISPR/Cas9 methods provide a powerful approach for genome editing in the nematode Caenorhabditis elegans. Oligonucleotides are excellent repair templates for introducing substitutions and short insertions, as they are cost effective, require no cloning, and appear in other organisms to target changes by homologous recombination at DNA double-strand breaks (DSBs). Here, I describe a methodology in C. elegans to efficiently knock in epitope tags in 8–9 days, using a temperature-sensitive lethal mutation in the pha-1 gene as a co-conversion marker. I demonstrate that 60mer oligos with 29 bp of homology drive efficient knock-in of point mutations, and that disabling nonhomologous end joining by RNAi inactivation of the cku-80 gene significantly improves knock-in efficiency. Homology arms of 35–80 bp are sufficient for efficient editing and DSBs up to 54 bp away from the insertion site produced knock-ins. These findings will likely be applicable for a range of genome editing approaches in C. elegans, which will improve editing efficiency and minimize screening efforts.
oligonucleotide-mediated homologous recombination; CRISPR/Cas9; nonhomologous end joining; pha-1; co-conversion
Two alternative hypotheses attribute different benefits to codon-anticodon adaptation. The first assumes that protein production is rate limited by both initiation and elongation and that codon-anticodon adaptation would result in higher elongation efficiency and more efficient and accurate protein production, especially for highly expressed genes. The second claims that protein production is rate limited only by initiation efficiency but that improved codon adaptation and, consequently, increased elongation efficiency have the benefit of increasing ribosomal availability for global translation. To test these hypotheses, a recent study engineered a synthetic library of 154 genes, all encoding the same protein but differing in degrees of codon adaptation, to quantify the effect of differential codon adaptation on protein production in Escherichia coli. The surprising conclusion that “codon bias did not correlate with gene expression” and that “translation initiation, not elongation, is rate-limiting for gene expression” contradicts the conclusion reached by many other empirical studies. In this paper, I resolve the contradiction by reanalyzing the data from the 154 sequences. I demonstrate that translation elongation accounts for about 17% of total variation in protein production and that the previous conclusion is due to the use of a codon adaptation index (CAI) that does not account for the mutation bias in characterizing codon adaptation. The effect of translation elongation becomes undetectable only when translation initiation is unrealistically slow. A new index of translation elongation ITE is formulated to facilitate studies on the efficiency and evolution of the translation machinery.
codon usage bias; codon-anticodon adaptation; translation elongation; translation efficiency; index of translation elongation
The segregation of homologous chromosomes during the Meiosis I division requires an obligate crossover per homolog pair (crossover assurance). In Saccharomyces cerevisiae and mammals, Msh4 and Msh5 proteins stabilize Holliday junctions and its progenitors to facilitate crossing over. S. cerevisiae msh4/5 hypomorphs that reduce crossover levels up to twofold at specific loci on chromosomes VII, VIII, and XV without affecting homolog segregation were identified recently. We use the msh4–R676W hypomorph to ask if the obligate crossover is insulated from variation in crossover frequencies, using a S. cerevisiae S288c/YJM789 hybrid to map recombination genome-wide. The msh4–R676W hypomorph made on average 64 crossovers per meiosis compared to 94 made in wild type and 49 in the msh4Δ mutant confirming the defect seen at individual loci on a genome-wide scale. Crossover reductions in msh4–R676W and msh4Δ were significant across chromosomes regardless of size, unlike previous observations made at specific loci. The msh4–R676W hypomorph showed reduced crossover interference. Although crossover reduction in msh4–R676W is modest, 42% of the four viable spore tetrads showed nonexchange chromosomes. These results, along with modeling of crossover distribution, suggest the significant reduction in crossovers across chromosomes and the loss of interference compromises the obligate crossover in the msh4 hypomorph. The high spore viability of the msh4 hypomorph is maintained by efficient segregation of the natural nonexchange chromosomes. Our results suggest that variation in crossover frequencies can compromise the obligate crossover and also support a mechanistic role for interference in obligate crossover formation.
crossover assurance; obligate crossover; genome-wide recombination map; whole-genome sequencing; nonexchange chromosomes
Fitness is a central quantity in evolutionary models of viruses. However, it remains difficult to determine viral fitness experimentally, and existing in vitro assays can be poor predictors of in vivo fitness of viral populations within their hosts. Next-generation sequencing can nowadays provide snapshots of evolving virus populations, and these data offer new opportunities for inferring viral fitness. Using the equilibrium distribution of the quasispecies model, an established model of intrahost viral evolution, we linked fitness parameters to the composition of the virus population, which can be estimated by next-generation sequencing. For inference, we developed a Bayesian Markov chain Monte Carlo method to sample from the posterior distribution of fitness values. The sampler can overcome situations where no maximum-likelihood estimator exists, and it can adaptively learn the posterior distribution of highly correlated fitness landscapes without prior knowledge of their shape. We tested our approach on simulated data and applied it to clinical human immunodeficiency virus 1 samples to estimate their fitness landscapes in vivo. The posterior fitness distributions allowed for differentiating viral haplotypes from each other, for determining neutral haplotype networks, in which no haplotype is more or less credibly fit than any other, and for detecting epistasis in fitness landscapes. Our implemented approach, called QuasiFit, is available at http://www.cbg.ethz.ch/software/quasifit.
quasi-species; fitness landscapes; epistasis; Bayesian inference; next-generation sequencing
Clinically relevant features of monogenic diseases, including severity of symptoms and age of onset, can vary widely in response to environmental differences as well as to the presence of genetic modifiers affecting the trait’s penetrance and expressivity. While a better understanding of modifier loci could lead to treatments for Mendelian diseases, the rarity of individuals harboring both a disease-causing allele and a modifying genotype hinders their study in human populations. We examined the genetic architecture of monogenic trait modifiers using a well-characterized yeast model of the human Mendelian disease classic galactosemia. Yeast strains with loss-of-function mutations in the yeast ortholog (GAL7) of the human disease gene (GALT) fail to grow in the presence of even small amounts of galactose due to accumulation of the same toxic intermediates that poison human cells. To isolate and individually genotype large numbers of the very rare (∼0.1%) galactose-tolerant recombinant progeny from a cross between two gal7Δ parents, we developed a new method, called “FACS-QTL.” FACS-QTL improves upon the currently used approaches of bulk segregant analysis and extreme QTL mapping by requiring less genome engineering and strain manipulation as well as maintaining individual genotype information. Our results identified multiple distinct solutions by which the monogenic trait could be suppressed, including genetic and nongenetic mechanisms as well as frequent aneuploidy. Taken together, our results imply that the modifiers of monogenic traits are likely to be genetically complex and heterogeneous.
galactose; galactosemia, aneuploidy; genetic modifier; QTL mapping
Homology-dependent exchange of genetic information between DNA molecules has a profound impact on the maintenance of genome integrity by facilitating error-free DNA repair, replication, and chromosome segregation during cell division as well as programmed cell developmental events. This chapter will focus on homologous mitotic recombination in budding yeast Saccharomyces cerevisiae. However, there is an important link between mitotic and meiotic recombination (covered in the forthcoming chapter by Hunter et al. 2015) and many of the functions are evolutionarily conserved. Here we will discuss several models that have been proposed to explain the mechanism of mitotic recombination, the genes and proteins involved in various pathways, the genetic and physical assays used to discover and study these genes, and the roles of many of these proteins inside the cell.
recombination; repair; budding yeast
Heritability is a population parameter of importance in evolution, plant and animal breeding, and human medical genetics. It can be estimated using pedigree designs and, more recently, using relationships estimated from markers. We derive the sampling variance of the estimate of heritability for a wide range of experimental designs, assuming that estimation is by maximum likelihood and that the resemblance between relatives is solely due to additive genetic variation. We show that well-known results for balanced designs are special cases of a more general unified framework. For pedigree designs, the sampling variance is inversely proportional to the variance of relationship in the pedigree and it is proportional to 1/N, whereas for population samples it is approximately proportional to 1/N2, where N is the sample size. Variation in relatedness is a key parameter in the quantification of the sampling variance of heritability. Consequently, the sampling variance is high for populations with large recent effective population size (e.g., humans) because this causes low variation in relationship. However, even using human population samples, low sampling variance is possible with high N.
experimental design; genomic relationship; heritability; maximum likelihood; sampling variance
Transposable elements are a common source of genetic variation that may play a substantial role in contributing to gene expression variation. However, the contribution of transposable elements to expression variation thus far consists of a handful of examples. We used previously published gene expression data from 37 inbred Drosophila melanogaster lines from the Drosophila Genetic Reference Panel to perform a genome-wide assessment of the effects of transposable elements on gene expression. We found thousands of transcripts with transposable element insertions in or near the transcript and that the presence of a transposable element in or near a transcript is significantly associated with reductions in expression. We estimate that within this example population, ∼2.2% of transcripts have a transposable element insertion, which significantly reduces expression in the line containing the transposable element. We also find that transcripts with insertions within 500 bp of the transcript show on average a 0.67 standard deviation decrease in expression level. These large decreases in expression level are most pronounced for transposable element insertions close to transcripts and the effect diminishes for more distant insertions. This work represents the first genome-wide analysis of gene expression variation due to transposable elements and suggests that transposable elements are an important class of mutation underlying expression variation in Drosophila and likely in other systems, given the ubiquity of these mobile elements in eukaryotic genomes.
transposable elements; gene expression; rare alleles of large effect; DGRP
Genetic association studies have explained only a small proportion of the estimated heritability of complex traits, leaving the remaining heritability “missing.” Genetic interactions have been proposed as an explanation for this, because they lead to overestimates of the heritability and are hard to detect. Whether this explanation is true depends on the proportion of variance attributable to genetic interactions, which is difficult to measure in outbred populations. Founder populations exhibit a greater range of kinship than outbred populations, which helps in fitting the epistatic variance. We extend classic theory to founder populations, giving the covariance between individuals due to epistasis of any order. We recover the classic theory as a limit, and we derive a recently proposed estimator of the narrow sense heritability as a corollary. We extend the variance decomposition to include dominance. We show in simulations that it would be possible to estimate the variance from pairwise interactions with samples of a few thousand from strongly bottlenecked human founder populations, and we provide an analytical approximation of the standard error. Applying these methods to 46 traits measured in a yeast (Saccharomyces cerevisiae) cross, we estimate that pairwise interactions explain 10% of the phenotypic variance on average and that third- and higher-order interactions explain 14% of the phenotypic variance on average. We search for third-order interactions, discovering an interaction that is shared between two traits. Our methods will be relevant to future studies of epistatic variance in founder populations and crosses.
cross; epistasis; founder; heritability; interaction
Population structure is a confounding factor in genome-wide association studies, increasing the rate of false positive associations. To correct for it, several model-based algorithms such as ADMIXTURE and STRUCTURE have been proposed. These tend to suffer from the fact that they have a considerable computational burden, limiting their applicability when used with large datasets, such as those produced by next generation sequencing techniques. To address this, nonmodel based approaches such as sparse nonnegative matrix factorization (sNMF) and EIGENSTRAT have been proposed, which scale better with larger data. Here we present a novel nonmodel-based approach, population structure inference using kernel-PCA and optimization (PSIKO), which is based on a unique combination of linear kernel-PCA and least-squares optimization and allows for the inference of admixture coefficients, principal components, and number of founder populations of a dataset. PSIKO has been compared against existing leading methods on a variety of simulation scenarios, as well as on real biological data. We found that in addition to producing results of the same quality as other tested methods, PSIKO scales extremely well with dataset size, being considerably (up to 30 times) faster for longer sequences than even state-of-the-art methods such as sNMF. PSIKO and accompanying manual are freely available at https://www.uea.ac.uk/computing/psiko.
admixture inference; kernel-PCA; population structure; genome-wide association studies; Q-matrix
Whole-genome sequencing of pathogens has recently been used to investigate disease outbreaks and is likely to play a growing role in real-time epidemiological studies. Methods to analyze high-resolution genomic data in this context are still lacking, and inferring transmission dynamics from such data typically requires many assumptions. While recent studies have proposed methods to infer who infected whom based on genetic distance between isolates from different individuals, the link between epidemiological relationship and genetic distance is still not well understood. In this study, we investigated the distribution of pairwise genetic distances between samples taken from infected hosts during an outbreak. We proposed an analytically tractable approximation to this distribution, which provides a framework to evaluate the likelihood of particular transmission routes. Our method accounts for the transmission of a genetically diverse inoculum, a possibility overlooked in most analyses. We demonstrated that our approximation can provide a robust estimation of the posterior probability of transmission routes in an outbreak and may be used to rule out transmission events at a particular probability threshold. We applied our method to data collected during an outbreak of methicillin-resistant Staphylococcus aureus, ruling out several potential transmission links. Our study sheds light on the accumulation of mutations in a pathogen during an epidemic and provides tools to investigate transmission dynamics, avoiding the intensive computation necessary in many existing methods.
infectious diseases; epidemics; genetic distance; transmission routes
Evolutionary theory predicts that genetic constraints should be widespread, but empirical support for their existence is surprisingly rare. Commonly applied univariate and bivariate approaches to detecting genetic constraints can underestimate their prevalence, with important aspects potentially tractable only within a multivariate framework. However, multivariate genetic analyses of data from natural populations are challenging because of modest sample sizes, incomplete pedigrees, and missing data. Here we present results from a study of a comprehensive set of life history traits (juvenile survival, age at first breeding, annual fecundity, and longevity) for both males and females in a wild, pedigreed, population of red deer (Cervus elaphus). We use factor analytic modeling of the genetic variance–covariance matrix (G) to reduce the dimensionality of the problem and take a multivariate approach to estimating genetic constraints. We consider a range of metrics designed to assess the effect of G on the deflection of a predicted response to selection away from the direction of fastest adaptation and on the evolvability of the traits. We found limited support for genetic constraint through genetic covariances between traits, both within sex and between sexes. We discuss these results with respect to other recent findings and to the problems of estimating these parameters for natural populations.
genetic correlations; life history trade-off; heritability; sexual antagonism; selection
THE Genetics Society of America Medal is awarded to an individual for outstanding contributions to the field of genetics in the past 15 years. Recipients of the GSA Medal are recognized for elegant and highly meaningful contributions to modern genetics. The 2014 recipient, Angelika B. Amon, has uncovered key principles governing the cell cycle and was the first to demonstrate a connection between the physical completion of anaphase and the initiation of mitotic exit. More recently, her research has focused on the consequences of aneuploidy. GENETICS spoke with Dr. Amon about her approach to science and what is next on the horizon.
THE Genetics Society of America’s George W. Beadle Award honors individuals who have made outstanding contributions to the community of genetics researchers and who exemplify the qualities of its namesake as a respected academic, administrator, and public servant. The 2014 recipient, Hugo Bellen, has made seminal contributions to the fields of genetics, developmental biology, and neuroscience. In parallel with his landmark science, he has worked to expand the toolbox available to Drosophila geneticists. He has helped develop technologies now used by the majority of Drosophila labs, advancing almost all fields of biology.
THE Genetics Society of America’s Elizabeth W. Jones Award for Excellence in Education recognizes significant and sustained impact on genetics education. Consistent with her philosophy of linking research and education, the 2014 Awardee Robin Wright includes undergraduate students in all of her research. She seeks to teach how to think like and to actually be a biologist, working in teams and looking at real-world problems. She emphasizes a learner-centered model of classroom work that promotes and enhances lifelong skills, and has transformed biology education at the University of Minnesota through several efforts including developing the interactive, stimulating Foundations of Biology course sequence, encouraging active learning and open-ended research; supporting the construction of Active Learning Classrooms; and establishing Student Learning Outcomes, standards that measure biology education. She serves as founding editor-in-chief of CourseSource, focusing national effort to collect learner-centered, outcomes-based teaching resources in undergraduate biology.
THE Genetics Society of America’s Thomas Hunt Morgan Medal is awarded to an individual GSA member for lifetime achievement in the field of genetics. The 2014 recipient is Frederick Ausubel, whose 40-year career has centered on host–microbe interactions and host innate immunity. He is widely recognized as a key scientist responsible for establishing the modern postrecombinant DNA field of host–microbe interactions using simple nonvertebrate hosts. He has used genetic approaches to conduct pioneering work that spawned six related areas of research: the evolution and regulation of Rhizobium genes involved in symbiotic nitrogen fixation; the regulation of Rhizobium genes by two-component regulatory systems involving histidine kinases; the establishment of Arabidopsis thaliana as a worldwide model system; the identification of a large family of plant disease resistance genes; the identification of so-called multi-host bacterial pathogens; and the demonstration that Caenorhabditis elegans has an evolutionarily conserved innate immune system that shares features of both plant and mammalian immunity.
THE Genetics Society of America’s Edward Novitski Prize recognizes an extraordinary level of creativity and intellectual ingenuity in the solution of significant problems in genetics research. The 2014 recipient, Charles Boone, has risen to the top of the emergent discipline of postgenome systems biology by focusing on the global mapping of genetic interaction networks. Boone invented the synthetic genetic array (SGA) technology, which provides an automated method to cross thousands of strains carrying precise mutations and map large-scale yeast genetic interactions. These network maps offer researchers a functional wiring diagram of the cell, which clusters genes into specific pathways and reveals functional connections.
CRISPR/Cas9 system of RNA-guided genome editing is revolutionizing genetics research in a wide spectrum of organisms. Even for the laboratory mouse, a model that has thrived under the benefits of embryonic stem (ES) cell knockout capabilities for nearly three decades, CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)/Cas9 technology enables one to manipulate the genome with unprecedented simplicity and speed. It allows generation of null, conditional, precisely mutated, reporter, or tagged alleles in mice. Moreover, it holds promise for other applications beyond genome editing. The crux of this system is the efficient and targeted introduction of DNA breaks that are repaired by any of several pathways in a predictable but not entirely controllable manner. Thus, further optimizations and improvements are being developed. Here, we summarize current applications and provide a practical guide to use the CRISPR/Cas9 system for mouse mutagenesis, based on published reports and our own experiences. We discuss critical points and suggest technical improvements to increase efficiency of RNA-guided genome editing in mouse embryos and address practical problems such as mosaicism in founders, which complicates genotyping and phenotyping. We describe a next-generation sequencing strategy for simultaneous characterization of on- and off-target editing in mice derived from multiple CRISPR experiments. Additionally, we report evidence that elevated frequency of precise, homology-directed editing can be achieved by transient inhibition of the Ligase IV-dependent nonhomologous end-joining pathway in one-celled mouse embryos.
CRISPR; genome editing; nonhomologous end joining; mouse knockouts