The cohesin ring concatenates sister DNA molecules

Sister chromatid cohesion, which is essential for mitosis, is mediated by a multi-subunit protein complex called cohesin whose Scc1, Smc1, and Smc3 subunits form a tripartite ring structure. It has been proposed that cohesin holds sister DNAs together by trapping them inside its ring. To test this, we used site-specific cross-linking to create chemical connections at the three interfaces between the ring’s three constituent polypeptides, thereby creating covalently closed cohesin rings. As predicted by the ring entrapment model, this procedure produces dimeric DNA/cohesin structures that are resistant to protein denaturation. We conclude that cohesin rings concatenate individual sister minichromosome DNAs

RNA-controlled nucleocytoplasmic shuttling of mRNA decay factors regulates mRNA synthesis and a novel mRNA decay pathway

mRNA level is controlled by factors that mediate both mRNA synthesis and decay, including the 5' to 3' exonuclease Xrn1. Here we show that nucleocytoplasmic shuttling of several yeast mRNA decay factors plays a key role in determining both mRNA synthesis and decay. Shuttling is regulated by RNAcontrolled binding of the karyopherin Kap120 to two nuclear localization sequences (NLSs) in Xrn1, location of one ofwhich is conserved fromyeast to human. The decaying RNA binds and masks NLS1, establishing a link between mRNA decay and Xrn1 shuttling. Preventing Xrn1 import, either by deleting KAP120 or mutating the two Xrn1 NLSs, compromises transcription and, unexpectedly, also cytoplasmic decay, uncovering a cytoplasmic decay pathway that initiates in the nucleus.MostmRNAs are degraded by both pathways - the ratio between them represents a full spectrum. Importantly, Xrn1 shuttling is required for proper responses to environmental changes, e.g., fluctuating temperatures, involving proper changes in mRNA abundance and in cell proliferation rate

Genome-Wide Association Data Reveal a Global Map of Genetic Interactions among Protein Complexes

This work demonstrates how gene association studies can be analyzed to map a global landscape of genetic interactions among protein complexes and pathways. Despite the immense potential of gene association studies, they have been challenging to analyze because most traits are complex, involving the combined effect of mutations at many different genes. Due to lack of statistical power, only the strongest single markers are typically identified. Here, we present an integrative approach that greatly increases power through marker clustering and projection of marker interactions within and across protein complexes. Applied to a recent gene association study in yeast, this approach identifies 2,023 genetic interactions which map to 208 functional interactions among protein complexes. We show that such interactions are analogous to interactions derived through reverse genetic screens and that they provide coverage in areas not yet tested by reverse genetic analysis. This work has the potential to transform gene association studies, by elevating the analysis from the level of individual markers to global maps of genetic interactions. As proof of principle, we use synthetic genetic screens to confirm numerous novel genetic interactions for the INO80 chromatin remodeling complex

Human embryonic stem cells from aneuploid blastocysts identified by pre-implantation genetic screening

Human embryonic stem cells are derived from the inner cell mass of pre-implantation embryos. The cells have unlimited proliferation potential and capacity to differentiate into the cells of the three germ layers. Human embryonic stem cells are used to study human embryogenesis and disease modeling and may in the future serve as cells for cell therapy and drug screening. Human embryonic stem cells are usually isolated from surplus normal frozen embryos and were suggested to be isolated from diseased embryos detected by pre-implantation genetic diagnosis. Here we report the isolation of 12 human embryonic stem cell lines and their thorough characterization. The lines were derived from embryos detected to have aneuploidy by pre-implantation genetic screening. Karyotype analysis of these cell lines showed that they are euploid, having 46 chromosomes. Our interpretation is that the euploid cells originated from mosaic embryos, and in vitro selection favored the euploid cells. The undifferentiated cells exhibited long-term proliferation and expressed markers typical for embryonic stem cells such as OCT4, NANOG, and TRA-1-60. The cells manifested pluripotent differentiation both in vivo and in vitro. To further characterize the different lines, we have analyzed their ethnic origin and the family relatedness among them. The above results led us to conclude that the aneuploid mosaic embryos that are destined to be discarded can serve as source for normal euploid human embryonic stem cell lines. These lines represent various ethnic groups; more lines are needed to represent all populations

Mapping Genetically Compensatory Pathways from Synthetic Lethal Interactions in Yeast

Background: Synthetic lethal genetic interaction analysis has been successfully applied to predicting the functions of genes and their pathway identities. In the context of synthetic lethal interaction data alone, the global similarity of synthetic lethal interaction patterns between two genes is used to predict gene function. With physical interaction data, such as proteinprotein interactions, the enrichment of physical interactions within subsets of genes and the enrichment of synthetic lethal interactions between those subsets of genes are used as an indication of compensatory pathways. Result: In this paper, we propose a method of mapping genetically compensatory pathways from synthetic lethal interactions. Our method is designed to discover pairs of gene-sets in which synthetic lethal interactions are depleted among the genes in an individual set and where such gene-set pairs are connected by many synthetic lethal interactions. By its nature, our method could select compensatory pathway pairs that buffer the deleterious effect of the failure of either one, without the need of physical interaction data. By focusing on compensatory pathway pairs where genes in each individual pathway have a highly homogenous cellular function, we show that many cellular functions have genetically compensatory properties. Conclusion: We conclude that synthetic lethal interaction data are a powerful source to map genetically compensatory pathways, especially in systems lacking physical interaction information, and that the cellular function network contain

Identification of seipin-linked factors that act as determinants of a lipid droplet subpopulation

Functional heterogeneity within the lipid droplet (LD) pool of a single cell has been observed, yet the underlying mechanisms remain enigmatic. Here, we report on identification of a specialized LD subpopulation characterized by a unique proteome and a defined geographical location at the nucleus-vacuole junction contact site. In search for factors determining identity of these LDs, we screened ∼6,000 yeast mutants for loss of targeting of the subpopulation marker Pdr16 and identified Ldo45 (LD organization protein of 45 kD) as a crucial targeting determinant. Ldo45 is the product of a splicing event connecting two adjacent genes (YMR147W and YMR148W/OSW5/LDO16). We show that Ldo proteins cooperate with the LD biogenesis component seipin and establish LD identity by defining positioning and surface-protein composition. Our studies suggest a mechanism to establish functional differentiation of organelles, opening the door to better understanding of metabolic decisions in cells

Maximal Extraction of Biological Information from Genetic Interaction Data

Targeted genetic perturbation is a powerful tool for inferring gene function in model organisms. Functional relationships between genes can be inferred by observing the effects of multiple genetic perturbations in a single strain. The study of these relationships, generally referred to as genetic interactions, is a classic technique for ordering genes in pathways, thereby revealing genetic organization and gene-to-gene information flow. Genetic interaction screens are now being carried out in high-throughput experiments involving tens or hundreds of genes. These data sets have the potential to reveal genetic organization on a large scale, and require computational techniques that best reveal this organization. In this paper, we use a complexity metric based in information theory to determine the maximally informative network given a set of genetic interaction data. We find that networks with high complexity scores yield the most biological information in terms of (i) specific associations between genes and biological functions, and (ii) mapping modules of co-functional genes. This information-based approach is an automated, unsupervised classification of the biological rules underlying observed genetic interactions. It might have particular potential in genetic studies in which interactions are complex and prior gene annotation data are sparse

Epistasis in a Model of Molecular Signal Transduction

Biological functions typically involve complex interacting molecular networks, with numerous feedback and regulation loops. How the properties of the system are affected when one, or several of its parts are modified is a question of fundamental interest, with numerous implications for the way we study and understand biological processes and treat diseases. This question can be rephrased in terms of relating genotypes to phenotypes: to what extent does the effect of a genetic variation at one locus depend on genetic variation at all other loci? Systematic quantitative measurements of epistasis – the deviation from additivity in the effect of alleles at different loci – on a given quantitative trait remain a major challenge. Here, we take a complementary approach of studying theoretically the effect of varying multiple parameters in a validated model of molecular signal transduction. To connect with the genotype/phenotype mapping we interpret parameters of the model as different loci with discrete choices of these parameters as alleles, which allows us to systematically examine the dependence of the signaling output – a quantitative trait – on the set of possible allelic combinations. We show quite generally that quantitative traits behave approximately additively (weak epistasis) when alleles correspond to small changes of parameters; epistasis appears as a result of large differences between alleles. When epistasis is relatively strong, it is concentrated in a sparse subset of loci and in low order (e.g. pair-wise) interactions. We find that focusing on interaction between loci that exhibit strong additive effects is an efficient way of identifying most of the epistasis. Our model study defines a theoretical framework for interpretation of experimental data and provides statistical predictions for the structure of genetic interaction expected for moderately complex biological circuits

Predicting Quantitative Genetic Interactions by Means of Sequential Matrix Approximation

Despite the emerging experimental techniques for perturbing multiple genes and measuring their quantitative phenotypic effects, genetic interactions have remained extremely difficult to predict on a large scale. Using a recent high-resolution screen of genetic interactions in yeast as a case study, we investigated whether the extraction of pertinent information encoded in the quantitative phenotypic measurements could be improved by computational means. By taking advantage of the observation that most gene pairs in the genetic interaction screens have no significant interactions with each other, we developed a sequential approximation procedure which ranks the mutation pairs in order of evidence for a genetic interaction. The sequential approximations can efficiently remove background variation in the double-mutation screens and give increasingly accurate estimates of the single-mutant fitness measurements. Interestingly, these estimates not only provide predictions for genetic interactions which are consistent with those obtained using the measured fitness, but they can even significantly improve the accuracy with which one can distinguish functionally-related gene pairs from the non-interacting pairs. The computational approach, in general, enables an efficient exploration and classification of genetic interactions in other studies and systems as well

A Directed RNAi Screen Based on Larval Growth Arrest Reveals New Modifiers of C. elegans Insulin Signaling

Genes regulating Caenorhabditis elegans insulin/IGF signaling (IIS) have largely been identified on the basis of their involvement in dauer development or longevity. A third IIS phenotype is the first larval stage (L1) diapause, which is also influenced by asna-1, a regulator of DAF-28/insulin secretion. We reasoned that new regulators of IIS strength might be identified in screens based on the L1 diapause and the asna-1 phenotype. Eighty- six genes were selected for analysis by virtue of their predicted interaction with ASNA-1 and screened for asna-1-like larval arrest. ykt-6, mrps-2, mrps-10 and mrpl-43 were identified as genes which, when inactivated, caused larval arrest without any associated feeding defects. Several tests indicated that IIS strength was weaker and that insulin secretion was defective in these animals. This study highlights the role of the Golgi network and the mitochondria in insulin secretion and provides a new list of genes that modulate IIS in C. elegans