Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast

Ccp1 modulates epigenetic stability at centromeres and affects heterochromatin distribution in Schizosaccharomyces pombe

Distinct chromatin organization features, such as centromeres and heterochromatin domains, are inherited epigenetically. However, the mechanisms that modulate the accuracy of epigenetic inheritance, especially at the individual nucleosome level, are not well-understood. Here, using ChIP and next-generation sequencing (ChIP-Seq), we characterized Ccp1, a homolog of the histone chaperone Vps75 in budding yeast that functions in centromere chromatin duplication and heterochromatin maintenance in fission yeast (Schizosaccharomyces pombe). We show that Ccp1 is enriched at the central core regions of the centromeres. Of note, among all histone chaperones characterized, deletion of the ccp1 gene uniquely reduced the rate of epigenetic switching, manifested as position effect variegation within the centromeric core region (CEN-PEV). In contrast, gene deletion of other histone chaperones either elevated the PEV switching rates or did not affect centromeric PEV. Ccp1 and the kinetochore components Mis6 and Sim4 were mutually dependent for centromere or kinetochore association at the proper levels. Moreover, Ccp1 influenced heterochromatin distribution at multiple loci in the genome, including the subtelomeric and the pericentromeric regions. We also found that Gar2, a protein predominantly enriched in the nucleolus, functions similarly to Ccp1 in modulating the epigenetic stability of centromeric regions, although its mechanism remained unclear. Together, our results identify Ccp1 as an important player in modulating epigenetic stability and maintaining proper organization of multiple chromatin domains throughout the fission yeast genome.

Integrating genetic and protein-protein interaction networks maps a functional wiring diagram of a cell

Systematic experimental approaches have led to construction of comprehensive genetic and protein-protein interaction networks for the budding yeast, Saccharomyces cerevisiae. Genetic interactions capture functional relationships between genes using phenotypic readouts, while protein-protein interactions identify physical connections between gene products. These complementary, and largely non-overlapping, networks provide a global view of the functional architecture of a cell, revealing general organizing principles, many of which appear to be evolutionarily conserved. Here, we focus on insights derived from the integration of large-scale genetic and protein-protein interaction networks, highlighting principles that apply to both unicellular and more complex systems, including human cells. Network integration reveals fundamental connections involving key functional modules of eukaryotic cells, defining a core network of cellular function, which could be elaborated to explore cell-type specificity in metazoans.

SAHA and cisplatin sensitize gastric cancer cells to doxorubicin by induction of DNA damage, apoptosis and perturbation of AMPK-mTOR signalling

Chemotherapy remains the most prescribed anti-cancer therapy, despite patients suffering severe side effects and frequently developing chemoresistance. These complications can be partially overcome by combining different chemotherapeutic agents that target multiple biological pathways. However, selecting efficacious drug combinations remains challenging. We previously used fission yeast Schizosaccharomycespombe as a surrogate model to predict drug combinations, and showed that suberoylanilide hydroxamic acid (SAHA) and cisplatin can sensitise gastric adenocarcinoma cells toward the cytotoxic effects of doxorubicin. Yet, how this combination undermines cell viability is unknown. Here, we show that SAHA and doxorubicin markedly enhance the cleavage of two apoptosis markers, caspase 3 and poly-ADP ribose polymerase (PARP-1), and increase the phosphorylation of γH2AX, a marker of DNA damage. Further, we found a prominent reduction in Ser485 phosphorylation of AMP-dependent protein kinase (AMPK), and reductions in its target mTOR and downstream ribosomal protein S6 phosphorylation. We show that SAHA contributes most of the effect, as confirmed using another histone deacetylase inhibitor, trichostatin A. Overall, our results show that the combination of SAHA and doxorubicin can induce apoptosis in gastric adenocarcinoma in a synthetically lethal manner, and that fission yeast offers an efficient tool for identifying potent drug combinations against human cancer cells.

Testing the Domino Theory of Gene Loss in Buchnera aphidicola: The Relevance of Epistatic Interactions

The domino theory of gene loss states that when some particular gene loses its function and cripples a cellular function, selection will relax in all functionally related genes, which may allow for the non-functionalization and loss of these genes. Here we study the role of epistasis in determining the pattern of gene losses in a set of genes participating in cell envelope biogenesis in the endosymbiotic bacteria Buchnera aphidicola. We provide statistical evidence indicating pairs of genes in B. aphidicola showing correlated gene loss tend to have orthologs in Escherichia coli known to have alleviating epistasis. In contrast, pairs of genes in B. aphidicola not showing correlated gene loss tend to have orthologs in E. coli known to have aggravating epistasis. These results suggest that during the process of genome reduction in B. aphidicola by gene loss, positive or alleviating epistasis facilitates correlated gene losses while negative or aggravating epistasis impairs correlated gene losses. We interpret this as evidence that the reduced proteome of B. aphidicola contains less pathway redundancy and more compensatory interactions, mimicking the situation of E. coli when grown under environmental constrains.

Whole-Genome Sequencing of Suppressor DNA Mixtures Identifies Pathways That Compensate for Chromosome Segregation Defects in Schizosaccharomyces pombe

Suppressor screening is a powerful method to identify genes that, when mutated, rescue the temperature sensitivity of the original mutation. Previously, however, identification of suppressor mutations has been technically difficult. Due to the small genome size of Schizosaccharomyces pombe, we developed a spontaneous suppressor screening technique, followed by a cost-effective sequencing method. Genomic DNAs of 10 revertants that survived at the restrictive temperature of the original temperature sensitive (ts) mutant were mixed together as one sample before constructing a library for sequencing. Responsible suppressor mutations were identified bioinformatically based on allele frequency. Then, we isolated a large number of spontaneous extragenic suppressors for three ts mutants that exhibited defects in chromosome segregation at their restrictive temperature. Screening provided new insight into mechanisms of chromosome segregation: loss of Ufd2 E4 multi-ubiquitination activity suppresses defects of an AAA ATPase, Cdc48. Loss of Wpl1, a releaser of cohesin, compensates for the Eso1 mutation, which may destabilize sister chromatid cohesion. The segregation defect of a ts histone H2B mutant is rescued if it fails to be deubiquitinated by the SAGA complex, because H2B is stabilized by monoubiquitination.

High-Throughput Quantitative Genetic Interaction Mapping in the Fission Yeast Schizosaccharomyces pombe

Epistasis mapping, in which the phenotype that emerges from combining pairs of mutations is measured quantitatively, is a powerful tool for unbiased study of gene function. When performed at a large scale, this approach has been used to assign function to previously uncharacterized genes, define functional modules and pathways, and study their cross talk. These experiments rely heavily on methods for rapid sampling of binary combinations of mutant alleles by systematic generation of a series of double mutants. Epistasis mapping technologies now exist in various model systems. Here we provide an overview of different epistasis mapping technologies, including the pombe epistasis mapper (PEM) system designed for the collection of quantitative genetic interaction data in fission yeast Schizosaccharomyces pombe Comprising a series of high-throughput selection steps for generation and characterization of double mutants, the PEM system has provided insight into a wide range of biological processes as well as facilitated evolutionary analysis of genetic interactomes across different species.

Unique molecular mechanisms for maintenance and alteration of genetic information in the budding yeast Saccharomyces cerevisiae

The high-fidelity transmission of genetic information is crucial for the survival of organisms, the cells of which have the ability to protect DNA against endogenous and environmental agents, including reactive oxygen species (ROS), ionizing radiation, and various chemical compounds. The basis of protection mechanisms has been evolutionarily conserved from yeast to humans; however, each organism often has a specialized mode of regulation that uses different sets of machineries, particularly in lower eukaryotes. The divergence of molecular mechanisms among related organisms has provided insights into the evolution of cellular machineries to a higher architecture. Uncommon characteristics of machineries may also contribute to the development of new applications such as drugs with novel mechanisms of action. In contrast to the cellular properties for maintaining genetic information, living organisms, particularly microbes, inevitably undergo genetic alterations in order to adapt to environmental conditions. The maintenance and alteration of genetic information may be inextricably linked to each other. In this review, we describe recent findings on the unconventional molecular mechanisms of DNA damage response and DNA double-strand break (DSB) repair in the budding yeast Saccharomyces cerevisiae. We also introduce our previous research on genetic and phenotypic instabilities observed in a clonal population of clinically-derived S. cerevisiae. The molecular mechanisms of this case were associated with mutations to generate tyrosine-inserting tRNA-Tyr ochre suppressors and the position effects of mutation frequencies among eight tRNA-Tyr loci dispersed in the genome. Phenotypic variations among different strain backgrounds have also been observed by another type of nonsense suppressor, the aberrant form of the translation termination factor. Nonsense suppressors are considered to be responsible for the genome-wide translational readthrough of termination codons, including natural nonsense codons. The nonsense suppressor-mediated acquisition of phenotypic variations may be advantageous for adaptation to environmental conditions and survival during evolution.

Systematic Genetic Screens Reveal the Dynamic Global Functional Organization of the Bacterial Translation Machinery

Bacterial protein synthesis is an essential, conserved, and environmentally responsive process. Yet, many of its components and dependencies remain unidentified. To address this gap, we used quantitative synthetic genetic arrays to map functional relationships among >48,000 gene pairs in Escherichia coli under four culture conditions differing in temperature and nutrient availability. The resulting data provide global functional insights into the roles and associations of genes, pathways, and processes important for efficient translation, growth, and environmental adaptation. We predict and independently verify the requirement of unannotated genes for normal translation, including a previously unappreciated role of YhbY in 30S biogenesis. Dynamic changes in the patterns of genetic dependencies across the four growth conditions and data projections onto other species reveal overarching functional and evolutionary pressures impacting the translation system and bacterial fitness, underscoring the utility of systematic screens for investigating protein synthesis, adaptation, and evolution.

Application of Distributive Conjugal DNA Transfer in Mycobacterium smegmatis To Establish a Genome-Wide Synthetic Genetic Array

Genetic redundancy can obscure phenotypic effects of single-gene mutations. Two individual mutations may be viable separately but are lethal when combined, thus synthetically linking the two gene products in an essential process. Synthetic genetic arrays (SGAs), in which defined mutations are combined, provide a powerful approach to identify novel genetic interactions and redundant pathways. A genome-scale SGA can offer an initial assignment of function to hypothetical genes by uncovering interactions with known genes or pathways. Here, we take advantage of the chromosomal conjugation system of Mycobacterium smegmatis to combine individual donor and recipient mutations on a genome-wide scale. We demonstrated the feasibility of a high-throughput mycobacterial SGA (mSGA) screen by using mutants of esx3, fxbA, and recA as query genes, which were combined with an arrayed library of transposon mutants by conjugation. The mSGA identified interacting genes that we had predicted and, most importantly, identified novel interacting genes-encoding both proteins and a noncoding RNA (ncRNA). In combination with other molecular genetic approaches, the mSGA has great potential to both reduce the high number of conserved hypothetical protein annotations in mycobacterial genomes and further define mycobacterial pathways and gene interactions.IMPORTANCE Mycobacterium smegmatis is the model organism of choice for the study of mycobacterial pathogens, because it is a fast-growing nonpathogenic species harboring many genes that are conserved throughout mycobacteria. In this work, we describe a synthetic genetic array (mSGA) approach for M. smegmatis, which combines mutations on a genome-wide scale with high efficiency. Analysis of the double mutant strains enables the identification of interacting genes and pathways that are normally hidden by redundant biological pathways. The mSGA is a powerful genetic tool that enables functions to be assigned to the many conserved hypothetical genes found in all mycobacterial species.

Lingering single-strand breaks trigger Rad51-independent homology-directed repair of collapsed replication forks in the polynucleotide kinase/phosphatase mutant of fission yeast

The DNA repair enzyme polynucleotide kinase/phosphatase (PNKP) protects genome integrity by restoring ligatable 5'-phosphate and 3'-hydroxyl termini at single-strand breaks (SSBs). In humans, PNKP mutations underlie the neurological disease known as MCSZ, but these individuals are not predisposed for cancer, implying effective alternative repair pathways in dividing cells. Homology-directed repair (HDR) of collapsed replication forks was proposed to repair SSBs in PNKP-deficient cells, but the critical HDR protein Rad51 is not required in PNKP-null (pnk1Δ) cells of Schizosaccharomyces pombe. Here, we report that pnk1Δ cells have enhanced requirements for Rad3 (ATR/Mec1) and Chk1 checkpoint kinases, and the multi-BRCT domain protein Brc1 that binds phospho-histone H2A (γH2A) at damaged replication forks. The viability of pnk1Δ cells depends on Mre11 and Ctp1 (CtIP/Sae2) double-strand break (DSB) resection proteins, Rad52 DNA strand annealing protein, Mus81-Eme1 Holliday junction resolvase, and Rqh1 (BLM/WRN/Sgs1) DNA helicase. Coupled with increased sister chromatid recombination and Rad52 repair foci in pnk1Δ cells, these findings indicate that lingering SSBs in pnk1Δ cells trigger Rad51-independent homology-directed repair of collapsed replication forks. From these data, we propose models for HDR-mediated tolerance of persistent SSBs with 3' phosphate in pnk1Δ cells.

New roles for Dicer in the nucleolus and its relevance to cancer

The nucleolus is a distinct compartment of the nucleus responsible for ribosome biogenesis. Mis-regulation of nucleolar functions and of the cellular translation machinery has been associated with disease, in particular with many types of cancer. Indeed, many tumor suppressors (p53, Rb, PTEN, PICT1, BRCA1) and proto-oncogenes (MYC, NPM) play a direct role in the nucleolus, and interact with the RNA polymerase I transcription machinery and the nucleolar stress response. We have identified Dicer and the RNA interference pathway as having an essential role in the nucleolus of quiescent Schizosaccharomyces pombe cells, distinct from pericentromeric silencing, by controlling RNA polymerase I release. We propose that this novel function is evolutionarily conserved and may contribute to the tumorigenic pre-disposition of DICER1 mutations in mammals.

Genetic Analysis of Schizosaccharomyces pombe

In this introduction we discuss some basic genetic tools and techniques that are used with the fission yeast Schizosaccharomyces pombe Genes commonly used for selection or as reporters are discussed, with an emphasis on genes that permit counterselection, intragenic complementation, or colony-color assays. S. pombe is most stable as a haploid organism. We describe its mating-type system, how to perform genetic crosses and methods for selecting and propagating diploids. We discuss the relative merits of tetrad dissection and random spore preparation in strain construction and genetic analyses. Finally, we present several types of mutant screens, with an evaluation of their respective strengths and limitations in the light of emerging technologies such as next-generation sequencing.

Systems level analysis of the Chlamydomonas reinhardtii metabolic network reveals variability in evolutionary co-conservation

Metabolic networks, which are mathematical representations of organismal metabolism, are reconstructed to provide computational platforms to guide metabolic engineering experiments and explore fundamental questions on metabolism. Systems level analyses, such as interrogation of phylogenetic relationships within the network, can provide further guidance on the modification of metabolic circuitries. Chlamydomonas reinhardtii, a biofuel relevant green alga that has retained key genes with plant, animal, and protist affinities, serves as an ideal model organism to investigate the interplay between gene function and phylogenetic affinities at multiple organizational levels. Here, using detailed topological and functional analyses, coupled with transcriptomics studies on a metabolic network that we have reconstructed for C. reinhardtii, we show that network connectivity has a significant concordance with the co-conservation of genes; however, a distinction between topological and functional relationships is observable within the network. Dynamic and static modes of co-conservation were defined and observed in a subset of gene-pairs across the network topologically. In contrast, genes with predicted synthetic interactions, or genes involved in coupled reactions, show significant enrichment for both shorter and longer phylogenetic distances. Based on our results, we propose that the metabolic network of C. reinhardtii is assembled with an architecture to minimize phylogenetic profile distances topologically, while it includes an expansion of such distances for functionally interacting genes. This arrangement may increase the robustness of C. reinhardtii's network in dealing with varied environmental challenges that the species may face. The defined evolutionary constraints within the network, which identify important pairings of genes in metabolism, may offer guidance on synthetic biology approaches to optimize the production of desirable metabolites.

Synthetic lethality and cancer

A synthetic lethal interaction occurs between two genes when the perturbation of either gene alone is viable but the perturbation of both genes simultaneously results in the loss of viability. Key to exploiting synthetic lethality in cancer treatment are the identification and the mechanistic characterization of robust synthetic lethal genetic interactions. Advances in next-generation sequencing technologies are enabling the identification of hundreds of tumour-specific mutations and alterations in gene expression that could be targeted by a synthetic lethality approach. The translation of synthetic lethality to therapy will be assisted by the synthesis of genetic interaction data from model organisms, tumour genomes and human cell lines.

Genetic interaction mapping in mammalian cells using CRISPR interference

We describe a combinatorial CRISPR interference (CRISPRi) screening platform for mapping genetic interactions in mammalian cells. We targeted 107 chromatin-regulation factors in human cells with pools of either single or double single guide RNAs (sgRNAs) to downregulate individual genes or gene pairs, respectively. Relative enrichment analysis of individual sgRNAs or sgRNA pairs allowed for quantitative characterization of genetic interactions, and comparison with protein-protein-interaction data revealed a functional map of chromatin regulation.

A strategy to apply quantitative epistasis analysis on developmental traits

BACKGROUND

Genetic interactions are keys to understand complex traits and evolution. Epistasis analysis is an effective method to map genetic interactions. Large-scale quantitative epistasis analysis has been well established for single cells. However, there is a substantial lack of such studies in multicellular organisms and their complex phenotypes such as development. Here we present a method to extend quantitative epistasis analysis to developmental traits.

METHODS

In the nematode Caenorhabditis elegans, we applied RNA interference on mutants to inactivate two genes, used an imaging system to quantitatively measure phenotypes, and developed a set of statistical methods to extract genetic interactions from phenotypic measurement.

RESULTS

Using two different C. elegans developmental phenotypes, body length and sex ratio, as examples, we showed that this method could accommodate various metazoan phenotypes with performances comparable to those methods in single cell growth studies. Comparing with qualitative observations, this method of quantitative epistasis enabled detection of new interactions involving subtle phenotypes. For example, several sex-ratio genes were found to interact with brc-1 and brd-1, the orthologs of the human breast cancer genes BRCA1 and BARD1, respectively. We confirmed the brc-1 interactions with the following genes in DNA damage response: C34F6.1, him-3 (ortholog of HORMAD1, HORMAD2), sdc-1, and set-2 (ortholog of SETD1A, SETD1B, KMT2C, KMT2D), validating the effectiveness of our method in detecting genetic interactions.

CONCLUSIONS

We developed a reliable, high-throughput method for quantitative epistasis analysis of developmental phenotypes.

High-content behavioral profiling reveals neuronal genetic network modulating Drosophila larval locomotor program

Background

Two key questions in understanding the genetic control of behaviors are: what genes are involved and how these genes interact. To answer these questions at a systems level, we conducted high-content profiling of Drosophila larval locomotor behaviors for over 100 genotypes.

Results

We studied 69 genes whose C. elegans orthologs were neuronal signalling genes with significant locomotor phenotypes, and conducted RNAi with ubiquitous, pan-neuronal, or motor-neuronal Gal4 drivers. Inactivation of 42 genes, including the nicotinic acetylcholine receptors nAChRα1 and nAChRα3, in the neurons caused significant movement defects. Bioinformatic analysis suggested 81 interactions among these genes based on phenotypic pattern similarities. Comparing the worm and fly data sets, we found that these genes were highly conserved in having neuronal expressions and locomotor phenotypes. However, the genetic interactions were not conserved for ubiquitous profiles, and may be mildly conserved for the neuronal profiles. Unexpectedly, our data also revealed a possible motor-neuronal control of body size, because inactivation of Rdl and Gαo in the motor neurons reduced the larval body size. Overall, these data established a framework for further exploring the genetic control of Drosophila larval locomotion.

Conclusions

High content, quantitative phenotyping of larval locomotor behaviours provides a framework for system-level understanding of the gene networks underlying such behaviours.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-017-0513-7) contains supplementary material, which is available to authorized users.

A systematic screen for morphological abnormalities during fission yeast sexual reproduction identifies a mechanism of actin aster formation for cell fusion

In non-motile fungi, sexual reproduction relies on strong morphogenetic changes in response to pheromone signaling. We report here on a systematic screen for morphological abnormalities of the mating process in fission yeast Schizosaccharomyces pombe. We derived a homothallic (self-fertile) collection of viable deletions, which, upon visual screening, revealed a plethora of phenotypes affecting all stages of the mating process, including cell polarization, cell fusion and sporulation. Cell fusion relies on the formation of the fusion focus, an aster-like F-actin structure that is marked by strong local accumulation of the myosin V Myo52, which concentrates secretion at the fusion site. A secondary screen for fusion-defective mutants identified the myosin V Myo51-associated coiled-coil proteins Rng8 and Rng9 as critical for the coalescence of the fusion focus. Indeed, rng8Δ and rng9Δ mutant cells exhibit multiple stable dots at the cell-cell contact site, instead of the single focus observed in wildtype. Rng8 and Rng9 accumulate on the fusion focus, dependent on Myo51 and tropomyosin Cdc8. A tropomyosin mutant allele, which compromises Rng8/9 localization but not actin binding, similarly leads to multiple stable dots instead of a single focus. By contrast, myo51 deletion does not strongly affect fusion focus coalescence. We propose that focusing of the actin filaments in the fusion aster primarily relies on Rng8/9-dependent cross-linking of tropomyosin-actin filaments.

Marked for death: targeting epigenetic changes in cancer

In the past few years, it has become clear that mutations in epigenetic regulatory genes are common in human cancers. Therapeutic strategies are now being developed to target cancers with mutations in these genes using specific chemical inhibitors. In addition, a complementary approach based on the concept of synthetic lethality, which allows exploitation of loss-of-function mutations in cancers that are not targetable by conventional methods, has gained traction. Both of these approaches are now being tested in several clinical trials. In this Review, we present recent advances in epigenetic drug discovery and development, and suggest possible future avenues of investigation to drive progress in this area.

A global genetic interaction network maps a wiring diagram of cellular function

We generated a global genetic interaction network for Saccharomyces cerevisiae, constructing more than 23 million double mutants, identifying about 550,000 negative and about 350,000 positive genetic interactions. This comprehensive network maps genetic interactions for essential gene pairs, highlighting essential genes as densely connected hubs. Genetic interaction profiles enabled assembly of a hierarchical model of cell function, including modules corresponding to protein complexes and pathways, biological processes, and cellular compartments. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections among gene pairs, rather than shared functionality. The global network illustrates how coherent sets of genetic interactions connect protein complex and pathway modules to map a functional wiring diagram of the cell.

Evolution of protein-protein interaction networks in yeast

Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.

Centromere Stability: The Replication Connection

The fission yeast centromere, which is similar to metazoan centromeres, contains highly repetitive pericentromere sequences that are assembled into heterochromatin. This is required for the recruitment of cohesin and proper chromosome segregation. Surprisingly, the pericentromere replicates early in the S phase. Loss of heterochromatin causes this domain to become very sensitive to replication fork defects, leading to gross chromosome rearrangements. This review examines the interplay between components of DNA replication, heterochromatin assembly, and cohesin dynamics that ensures maintenance of genome stability and proper chromosome segregation.

iSeq: A New Double-Barcode Method for Detecting Dynamic Genetic Interactions in Yeast

Systematic screens for genetic interactions are a cornerstone of both network and systems biology. However, most screens have been limited to characterizing interaction networks in a single environment. Moving beyond this static view of the cell requires a major technological advance to increase the throughput and ease of replication in these assays. Here, we introduce iSeq-a platform to build large double barcode libraries and rapidly assay genetic interactions across environments. We use iSeq in yeast to measure fitness in three conditions of nearly 400 clonal strains, representing 45 possible single or double gene deletions, including multiple replicate strains per genotype. We show that iSeq fitness and interaction scores are highly reproducible for the same clonal strain across replicate cultures. However, consistent with previous work, we find that replicates with the same putative genotype have highly variable genetic interaction scores. By whole-genome sequencing 102 of our strains, we find that segregating variation and de novo mutations, including aneuploidy, occur frequently during strain construction, and can have large effects on genetic interaction scores. Additionally, we uncover several new environment-dependent genetic interactions, suggesting that barcode-based genetic interaction assays have the potential to significantly expand our knowledge of genetic interaction networks.

Multiple Transcriptional and Post-transcriptional Pathways Collaborate to Control Sense and Antisense RNAs of Tf2 Retroelements in Fission Yeast

Retrotransposons are mobile genetic elements that colonize eukaryotic genomes by replicating through an RNA intermediate. As retrotransposons can move within the host genome, defense mechanisms have evolved to repress their potential mutagenic activities. In the fission yeast Schizosaccharomyces pombe, the mRNA of Tf2 long terminal repeat retrotransposons is targeted for degradation by the 3'-5' exonucleolytic activity of the exosome-associated protein Rrp6. Here, we show that the nuclear poly(A)-binding protein Pab2 functions with Rrp6 to negatively control Tf2 mRNA accumulation. Furthermore, we found that Pab2/Rrp6-dependent RNA elimination functions redundantly to the transcriptional silencing mediated by the CENP-B homolog, Abp1, in the suppression of antisense Tf2 RNA accumulation. Interestingly, the absence of Pab2 attenuated the derepression of Tf2 transcription and the increased frequency of Tf2 mobilization caused by the deletion of abp1 Our data also reveal that the expression of antisense Tf2 transcripts is developmentally regulated and correlates with decreased levels of Tf2 mRNA. Our findings suggest that transcriptional and post-transcriptional pathways cooperate to control sense and antisense RNAs expressed from Tf2 retroelements.

Pan- and core- network analysis of co-expression genes in a model plant

Genome-wide gene expression experiments have been performed using the model plant Arabidopsis during the last decade. Some studies involved construction of coexpression networks, a popular technique used to identify groups of co-regulated genes, to infer unknown gene functions. One approach is to construct a single coexpression network by combining multiple expression datasets generated in different labs. We advocate a complementary approach in which we construct a large collection of 134 coexpression networks based on expression datasets reported in individual publications. To this end we reanalyzed public expression data. To describe this collection of networks we introduced concepts of 'pan-network' and 'core-network' representing union and intersection between a sizeable fractions of individual networks, respectively. We showed that these two types of networks are different both in terms of their topology and biological function of interacting genes. For example, the modules of the pan-network are enriched in regulatory and signaling functions, while the modules of the core-network tend to include components of large macromolecular complexes such as ribosomes and photosynthetic machinery. Our analysis is aimed to help the plant research community to better explore the information contained within the existing vast collection of gene expression data in Arabidopsis.

Big data mining powers fungal research: recent advances in fission yeast systems biology approaches

Biology research has entered into big data era. Systems biology approaches therefore become the powerful tools to obtain the whole landscape of how cell separate, grow, and resist the stresses. Fission yeast Schizosaccharomyces pombe is wonderful unicellular eukaryote model, especially studying its division and metabolism can facilitate to understanding the molecular mechanism of cancer and discovering anticancer agents. In this perspective, we discuss the recent advanced fission yeast systems biology tools, mainly focus on metabolomics profiling and metabolic modeling, protein-protein interactome and genetic interaction network, DNA sequencing and applications, and high-throughput phenotypic screening. We therefore hope this review can be useful for interested fungal researchers as well as bioformaticians.

Network biology concepts in complex disease comorbidities

The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.

Evolution of a G protein-coupled receptor response by mutations in regulatory network interactions

All cellular functions depend on the concerted action of multiple proteins organized in complex networks. To understand how selection acts on protein networks, we used the yeast mating receptor Ste2, a pheromone-activated G protein-coupled receptor, as a model system. In Saccharomyces cerevisiae, Ste2 is a hub in a network of interactions controlling both signal transduction and signal suppression. Through laboratory evolution, we obtained 21 mutant receptors sensitive to the pheromone of a related yeast species and investigated the molecular mechanisms behind this newfound sensitivity. While some mutants show enhanced binding affinity to the foreign pheromone, others only display weakened interactions with the network's negative regulators. Importantly, the latter changes have a limited impact on overall pathway regulation, despite their considerable effect on sensitivity. Our results demonstrate that a new receptor–ligand pair can evolve through network-altering mutations independently of receptor–ligand binding, and suggest a potential role for such mutations in disease.

Co-evolution of a new receptor-ligand pair will affect the downstream signal transduction network. Here, the authors use experimental evolution of yeast mating receptor Ste2 to show the effect of enhanced binding affinity and weakened interactions with the network's negative regulators on protein evolution.

A Network of Conserved Synthetic Lethal Interactions for Exploration of Precision Cancer Therapy

An emerging therapeutic strategy for cancer is to induce selective lethality in a tumor by exploiting interactions between its driving mutations and specific drug targets. Here we use a multi-species approach to develop a resource of synthetic lethal interactions relevant to cancer therapy. First, we screen in yeast ∼169,000 potential interactions among orthologs of human tumor suppressor genes (TSG) and genes encoding drug targets across multiple genotoxic environments. Guided by the strongest signal, we evaluate thousands of TSG-drug combinations in HeLa cells, resulting in networks of conserved synthetic lethal interactions. Analysis of these networks reveals that interaction stability across environments and shared gene function increase the likelihood of observing an interaction in human cancer cells. Using these rules, we prioritize ∼10(5) human TSG-drug combinations for future follow-up. We validate interactions based on cell and/or patient survival, including topoisomerases with RAD17 and checkpoint kinases with BLM.

A Proteome-wide Fission Yeast Interactome Reveals Network Evolution Principles from Yeasts to Human

Here, we present FissionNet, a proteome-wide binary protein interactome for S. pombe, comprising 2,278 high-quality interactions, of which ∼ 50% were previously not reported in any species. FissionNet unravels previously unreported interactions implicated in processes such as gene silencing and pre-mRNA splicing. We developed a rigorous network comparison framework that accounts for assay sensitivity and specificity, revealing extensive species-specific network rewiring between fission yeast, budding yeast, and human. Surprisingly, although genes are better conserved between the yeasts, S. pombe interactions are significantly better conserved in human than in S. cerevisiae. Our framework also reveals that different modes of gene duplication influence the extent to which paralogous proteins are functionally repurposed. Finally, cross-species interactome mapping demonstrates that coevolution of interacting proteins is remarkably prevalent, a result with important implications for studying human disease in model organisms. Overall, FissionNet is a valuable resource for understanding protein functions and their evolution.

Perturbation Detection Through Modeling of Gene Expression on a Latent Biological Pathway Network: A Bayesian hierarchical approach

Cellular response to a perturbation is the result of a dynamic system of biological variables linked in a complex network. A major challenge in drug and disease studies is identifying the key factors of a biological network that are essential in determining the cell's fate. Here our goal is the identification of perturbed pathways from high-throughput gene expression data. We develop a three-level hierarchical model, where (i) the first level captures the relationship between gene expression and biological pathways using confirmatory factor analysis, (ii) the second level models the behavior within an underlying network of pathways induced by an unknown perturbation using a conditional autoregressive model, and (iii) the third level is a spike-and-slab prior on the perturbations. We then identify perturbations through posterior-based variable selection. We illustrate our approach using gene transcription drug perturbation profiles from the DREAM7 drug sensitivity predication challenge data set. Our proposed method identified regulatory pathways that are known to play a causative role and that were not readily resolved using gene set enrichment analysis or exploratory factor models. Simulation results are presented assessing the performance of this model relative to a network-free variant and its robustness to inaccuracies in biological databases.

Association genetics and transcriptome analysis reveal a gibberellin-responsive pathway involved in regulating photosynthesis

Gibberellins (GAs) regulate a wide range of important processes in plant growth and development, including photosynthesis. However, the mechanism by which GAs regulate photosynthesis remains to be understood. Here, we used multi-gene association to investigate the effect of genes in the GA-responsive pathway, as constructed by RNA sequencing, on photosynthesis, growth, and wood property traits, in a population of 435 Populus tomentosa By analyzing changes in the transcriptome following GA treatment, we identified many key photosynthetic genes, in agreement with the observed increase in measurements of photosynthesis. Regulatory motif enrichment analysis revealed that 37 differentially expressed genes related to photosynthesis shared two essential GA-related cis-regulatory elements, the GA response element and the pyrimidine box. Thus, we constructed a GA-responsive pathway consisting of 47 genes involved in regulating photosynthesis, including GID1, RGA, GID2, MYBGa, and 37 photosynthetic differentially expressed genes. Single nucleotide polymorphism (SNP)-based association analysis showed that 142 SNPs, representing 40 candidate genes in this pathway, were significantly associated with photosynthesis, growth, and wood property traits. Epistasis analysis uncovered interactions between 310 SNP-SNP pairs from 37 genes in this pathway, revealing possible genetic interactions. Moreover, a structural gene-gene matrix based on a time-course of transcript abundances provided a better understanding of the multi-gene pathway affecting photosynthesis. The results imply a functional role for these genes in mediating photosynthesis, growth, and wood properties, demonstrating the potential of combining transcriptome-based regulatory pathway construction and genetic association approaches to detect the complex genetic networks underlying quantitative traits.

Differential network analysis reveals the genome-wide landscape of estrogen receptor modulation in hormonal cancers

Several mutual information (MI)-based algorithms have been developed to identify dynamic gene-gene and function-function interactions governed by key modulators (genes, proteins, etc.). Due to intensive computation, however, these methods rely heavily on prior knowledge and are limited in genome-wide analysis. We present the modulated gene/gene set interaction (MAGIC) analysis to systematically identify genome-wide modulation of interaction networks. Based on a novel statistical test employing conjugate Fisher transformations of correlation coefficients, MAGIC features fast computation and adaption to variations of clinical cohorts. In simulated datasets MAGIC achieved greatly improved computation efficiency and overall superior performance than the MI-based method. We applied MAGIC to construct the estrogen receptor (ER) modulated gene and gene set (representing biological function) interaction networks in breast cancer. Several novel interaction hubs and functional interactions were discovered. ER+ dependent interaction between TGFβ and NFκB was further shown to be associated with patient survival. The findings were verified in independent datasets. Using MAGIC, we also assessed the essential roles of ER modulation in another hormonal cancer, ovarian cancer. Overall, MAGIC is a systematic framework for comprehensively identifying and constructing the modulated interaction networks in a whole-genome landscape. MATLAB implementation of MAGIC is available for academic uses at https://github.com/chiuyc/MAGIC.

A genetic interaction map of cell cycle regulators

A combination of genome-scale RNA interference screening and genetic interaction analysis using process-directed phenotypes is used to assign components to specific pathways and complexes for modulators of mitosis and cytokinesis in Drosophila S2 cells.

Cell-based RNA interference (RNAi) is a powerful approach to screen for modulators of many cellular processes. However, resulting candidate gene lists from cell-based assays comprise diverse effectors, both direct and indirect, and further dissecting their functions can be challenging. Here we screened a genome-wide RNAi library for modulators of mitosis and cytokinesis in Drosophila S2 cells. The screen identified many previously known genes as well as modulators that have previously not been connected to cell cycle control. We then characterized ∼300 candidate modifiers further by genetic interaction analysis using double RNAi and a multiparametric, imaging-based assay. We found that analyzing cell cycle–relevant phenotypes increased the sensitivity for associating novel gene function. Genetic interaction maps based on mitotic index and nuclear size grouped candidates into known regulatory complexes of mitosis or cytokinesis, respectively, and predicted previously uncharacterized components of known processes. For example, we confirmed a role for the Drosophila CCR4 mRNA processing complex component l(2)NC136 during the mitotic exit. Our results show that the combination of genome-scale RNAi screening and genetic interaction analysis using process-directed phenotypes provides a powerful two-step approach to assigning components to specific pathways and complexes.

A functional genome-wide genetic screening identifies new pathways controlling the G1/S transcriptional wave

The Schizosaccharomyces pombe MBF complex activates the transcription of genes required for DNA synthesis and S phase. The MBF complex contains several proteins, including the core components Cdc10, Res1 and Res2, the co-repressor proteins Yox1 and Nrm1 and the co-activator Rep2. It has recently been shown how MBF is regulated when either the DNA damage or the DNA synthesis checkpoints are activated. However, how MBF is regulated in a normal unperturbed cell cycle is still not well understood. We have set up a genome-wide genomic screen searching for global regulators of MBF. We have crossed our knock-out collection library with a reporter strain that allows the measurement of MBF activity in live cells by flow cytometry. We confirm previously known regulators of MBF and show that COP9/signalosome and tRNA methyltransferases also regulate MBF activity.

Structural and Functional Characterization of a Caenorhabditis elegans Genetic Interaction Network within Pathways

A genetic interaction (GI) is defined when the mutation of one gene modifies the phenotypic expression associated with the mutation of a second gene. Genome-wide efforts to map GIs in yeast revealed structural and functional properties of a GI network. This provided insights into the mechanisms underlying the robustness of yeast to genetic and environmental insults, and also into the link existing between genotype and phenotype. While a significant conservation of GIs and GI network structure has been reported between distant yeast species, such a conservation is not clear between unicellular and multicellular organisms. Structural and functional characterization of a GI network in these latter organisms is consequently of high interest. In this study, we present an in-depth characterization of ~1.5K GIs in the nematode Caenorhabditis elegans. We identify and characterize six distinct classes of GIs by examining a wide-range of structural and functional properties of genes and network, including co-expression, phenotypical manifestations, relationship with protein-protein interaction dense subnetworks (PDS) and pathways, molecular and biological functions, gene essentiality and pleiotropy. Our study shows that GI classes link genes within pathways and display distinctive properties, specifically towards PDS. It suggests a model in which pathways are composed of PDS-centric and PDS-independent GIs coordinating molecular machines through two specific classes of GIs involving pleiotropic and non-pleiotropic connectors. Our study provides the first in-depth characterization of a GI network within pathways of a multicellular organism. It also suggests a model to understand better how GIs control system robustness and evolution.

Network biology has focused for years on protein-protein interaction (PPI) networks, identifying nodes with central structural functions and modules associated to bioprocesses, phenotypes and diseases. Network biology field moved to a higher level of abstraction, and started characterizing a less intuitive kind of interactions, called genetic interactions (GIs) or epistasis. Mostly due to technical challenges associated to the genome-wide mapping of GIs, these studies primarily focused on unicellular organisms. They uncovered modules embedded within the structure of these networks and started characterizing their relationship with PPI-network and biological functions. We provide here the first in-depth characterization of a network composed of ~600 GIs within signaling and metabolic pathways of a multicellular organism, the nematode Caenorhabditis elegans. We characterize the structure of this network, and the function of GI classes found in this network. We also discuss how these GI classes contribute to the genomic robustness and the adaptive evolution of multicellular organisms.

Association Studies in Populus tomentosa Reveal the Genetic Interactions of Pto-MIR156c and Its Targets in Wood Formation

MicroRNAs (miRNAs) regulate gene expression in many biological processes, but the significance of the interaction between a miRNA and its targets in perennial trees remains largely unknown. Here, we employed transcript profiling and association studies in Populus tomentosa (Pto) to decipher the effect of genetic variation and interactions between Pto-miR156c and its potential targets (Pto-SPL15, Pto-SPL20, and Pto-SPL25) in 435 unrelated individuals from a natural population of P. tomentosa. Single-SNP (single-nucleotide polymorphism) based association studies with analysis of the underlying additive and dominant effects identified 69 significant associations (P < 0.01), representing 51 common SNPs (minor allele frequency > 0.05) from Pto-MIR156c and its three potential targets, with six wood and growth traits, revealing their common roles in wood formation. Epistasis analysis uncovered 129 significant SNP-SNP associations with ten traits, indicating the potential genetic interactions of Pto-MIR156c and its three putative targets. Interestingly, expression analysis in stem (phloem, cambium, and xylem) revealed that Pto-miR156c expression showed strong negative correlations with Pto-SPL20 (r = -0.90, P < 0.01) and Pto-SPL25 (r = -0.65, P < 0.01), and a positive correlation with Pto-SPL15 (r = 0.40, P < 0.01), which also indicated the putative interactions of Pto-miR156c and its potential targets and their common roles in wood formation. Thus, our study provided an alternative approach to decipher the interaction between miRNAs and their targets and to dissect the genetic architecture of complex traits in trees.

An ultra-dense library resource for rapid deconvolution of mutations that cause phenotypes in Escherichia coli

With the wide availability of whole-genome sequencing (WGS), genetic mapping has become the rate-limiting step, inhibiting unbiased forward genetics in even the most tractable model organisms. We introduce a rapid deconvolution resource and method for untagged causative mutations after mutagenesis, screens, and WGS in Escherichia coli. We created Deconvoluter—ordered libraries with selectable insertions every 50 kb in the E. coli genome. The Deconvoluter method uses these for replacement of untagged mutations in the genome using a phage-P1-based gene-replacement strategy. We validate the Deconvoluter resource by deconvolution of 17 of 17 phenotype-altering mutations from a screen of N-ethyl-N-nitrosourea-induced mutants. The Deconvoluter resource permits rapid unbiased screens and gene/function identification and will enable exploration of functions of essential genes and undiscovered genes/sites/alleles not represented in existing deletion collections. This resource for unbiased forward-genetic screens with mapping-by-sequencing (‘forward genomics’) demonstrates a strategy that could similarly enable rapid screens in many other microbes.

Cell-based screens and phenomics with fission yeast

Next-generation sequencing approaches have considerably advanced our understanding of genome function and regulation. However, the knowledge of gene function and complex cellular processes remains a challenge and bottleneck in biological research. Phenomics is a rapidly emerging area, which seeks to rigorously characterize all phenotypes associated with genes or gene variants. Such high-throughput phenotyping under different conditions can be a potent approach toward gene function. The fission yeast Schizosaccharomyces pombe (S. pombe) is a proven eukaryotic model organism that is increasingly used for genomewide screens and phenomic assays. In this review, we highlight current large-scale, cell-based approaches used with S. pombe, including computational colony-growth measurements, genetic interaction screens, parallel profiling using barcodes, microscopy-based cell profiling, metabolomic methods and transposon mutagenesis. These diverse methods are starting to offer rich insights into the relationship between genotypes and phenotypes.

Natural Variation of Plant Metabolism: Genetic Mechanisms, Interpretive Caveats, and Evolutionary and Mechanistic Insights

Combining quantitative genetics studies with metabolomics/metabolic profiling platforms, genomics, and transcriptomics is creating significant progress in identifying the causal genes controlling natural variation in metabolite accumulations and profiles. In this review, we discuss key mechanistic and evolutionary insights that are arising from these studies. This includes the potential role of transport and other processes in leading to a separation of the site of mechanistic causation and metabolic consequence. A reilluminated observation is the potential for genomic variation in the organelle to alter phenotypic variation alone and in epistatic interaction with the nuclear genetic variation. These studies are also highlighting new aspects of metabolic pleiotropy both in terms of the breadth of loci altering metabolic variation as well as the potential for broader effects on plant defense regulation of the metabolic variation than has previously been predicted. We also illustrate caveats that can be overlooked when translating quantitative genetics descriptors such as heritability and per-locus r(2) to mechanistic or evolutionary interpretations.

Escape from Mitotic Arrest: An Unexpected Connection Between Microtubule Dynamics and Epigenetic Regulation of Centromeric Chromatin in Schizosaccharomyces pombe

Accurate chromosome segregation is necessary to ensure genomic integrity. Segregation depends on the proper functioning of the centromere, kinetochore, and mitotic spindle microtubules and is monitored by the spindle assembly checkpoint (SAC). In the fission yeast Schizosaccharomyces pombe, defects in Dis1, a microtubule-associated protein that influences microtubule dynamics, lead to mitotic arrest as a result of an active SAC and consequent failure to grow at low temperature. In a mutant dis1 background (dis1-288), loss of function of Msc1, a fission yeast homolog of the KDM5 family of proteins, suppresses the growth defect and promotes normal mitosis. Genetic analysis implicates a histone deacetylase (HDAC)-linked pathway in suppression because HDAC mutants clr6-1, clr3∆, and sir2∆, though not hos2∆, also promote normal mitosis in the dis1-288 mutant. Suppression of the dis phenotype through loss of msc1 function requires the spindle checkpoint protein Mad2 and is limited by the presence of the heterochromatin-associated HP1 protein homolog Swi6. We speculate that alterations in histone acetylation promote a centromeric chromatin environment that compensates for compromised dis1 function by allowing for successful kinetochore-microtubule interactions that can satisfy the SAC. In cells arrested in mitosis by mutation of dis1, loss of function of epigenetic determinants such as Msc1 or specific HDACs can promote cell survival. Because the KDM5 family of proteins has been implicated in human cancers, an appreciation of the potential role of this family of proteins in chromosome segregation is warranted.

Evolutionary rescue by compensatory mutations is constrained by genomic and environmental backgrounds

Since deleterious mutations may be rescued by secondary mutations during evolution, compensatory evolution could identify genetic solutions leading to therapeutic targets. Here, we tested this hypothesis and examined whether these solutions would be universal or would need to be adapted to one's genetic and environmental makeups. We performed experimental evolutionary rescue in a yeast disease model for the Wiskott–Aldrich syndrome in two genetic backgrounds and carbon sources. We found that multiple aspects of the evolutionary rescue outcome depend on the genotype, the environment, or a combination thereof. Specifically, the compensatory mutation rate and type, the molecular rescue mechanism, the genetic target, and the associated fitness cost varied across contexts. The course of compensatory evolution is therefore highly contingent on the initial conditions in which the deleterious mutation occurs. In addition, these results reveal biologically favored therapeutic targets for the Wiskott–Aldrich syndrome, including the target of an unrelated clinically approved drug. Our results experimentally illustrate the importance of epistasis and environmental evolutionary constraints that shape the adaptive landscape and evolutionary rate of molecular networks.

Yeast: a microbe with macro-implications to antimicrobial drug discovery

Paramount to any rational discovery of new antibiotics displaying novel mechanisms of action is a deep knowledge of the genetic basis of microbial growth, division and virulence. The bakers' yeast,Saccharomyces cerevisiae, illustrates the highest understanding of the genetic underpinnings of microbial life, and from this framework, a systems biology paradigm has evolved, begging to be emulated in antibacterial discovery. Here, we review landmark events in the history of yeast genomics that provide this new foundation for antibacterial drug discovery.

Condition-specific genetic interaction maps reveal crosstalk between the cAMP/PKA and the HOG MAPK pathways in the activation of the general stress response

Cells must quickly respond and efficiently adapt to environmental changes. The yeast Saccharomyces cerevisiae has multiple pathways that respond to specific environmental insults, as well as a generic stress response program. The later is regulated by two transcription factors, Msn2 and Msn4, that integrate information from upstream pathways to produce fast, tunable, and robust response to different environmental changes. To understand this integration, we employed a systematic approach to genetically dissect the contribution of various cellular pathways to Msn2/4 regulation under a range of stress and growth conditions. We established a high-throughput liquid handling and automated flow cytometry system and measured GFP levels in 68 single-knockout and 1,566 double-knockout strains that carry an HSP12-GFP allele as a reporter for Msn2/4 activity. Based on the expression of this Msn2/4 reporter in five different conditions, we identified numerous genetic and epistatic interactions between different components in the network upstream to Msn2/4. Our analysis gains new insights into the functional specialization of the RAS paralogs in the repression of stress response and identifies a three-way crosstalk between the Mediator complex, the HOG MAPK pathway, and the cAMP/PKA pathway.

Low-Rank and Sparse Matrix Decomposition for Genetic Interaction Data

Background. Epistatic miniarray profile (EMAP) studies have enabled the mapping of large-scale genetic interaction networks and generated large amounts of data in model organisms. One approach to analyze EMAP data is to identify gene modules with densely interacting genes. In addition, genetic interaction score (S score) reflects the degree of synergizing or mitigating effect of two mutants, which is also informative. Statistical approaches that exploit both modularity and the pairwise interactions may provide more insight into the underlying biology. However, the high missing rate in EMAP data hinders the development of such approaches. To address the above problem, we adopted the matrix decomposition methodology “low-rank and sparse decomposition” (LRSDec) to decompose EMAP data matrix into low-rank part and sparse part. Results. LRSDec has been demonstrated as an effective technique for analyzing EMAP data. We applied a synthetic dataset and an EMAP dataset studying RNA-related processes in Saccharomyces cerevisiae. Global views of the genetic cross talk between different RNA-related protein complexes and processes have been structured, and novel functions of genes have been predicted.

The cancer cell map initiative: defining the hallmark networks of cancer

Progress in DNA sequencing has revealed the startling complexity of cancer genomes, which typically carry thousands of somatic mutations. However, it remains unclear which are the key driver mutations or dependencies in a given cancer and how these influence pathogenesis and response to therapy. Although tumors of similar types and clinical outcomes can have patterns of mutations that are strikingly different, it is becoming apparent that these mutations recurrently hijack the same hallmark molecular pathways and networks. For this reason, it is likely that successful interpretation of cancer genomes will require comprehensive knowledge of the molecular networks under selective pressure in oncogenesis. Here we announce the creation of a new effort, The Cancer Cell Map Initiative (CCMI), aimed at systematically detailing these complex interactions among cancer genes and how they differ between diseased and healthy states. We discuss recent progress that enables creation of these cancer cell maps across a range of tumor types and how they can be used to target networks disrupted in individual patients, significantly accelerating the development of precision medicine.

Cancer type-dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types

Cancers, like many diseases, are normally caused by combinations of genetic alterations rather than by changes affecting single genes. It is well established that the genetic alterations that drive cancer often interact epistatically, having greater or weaker consequences in combination than expected from their individual effects. In a stringent statistical analysis of data from > 3,000 tumors, we find that the co-occurrence and mutual exclusivity relationships between cancer driver alterations change quite extensively in different types of cancer. This cannot be accounted for by variation in tumor heterogeneity or unrecognized cancer subtypes. Rather, it suggests that how genomic alterations interact cooperatively or partially redundantly to driver cancer changes in different types of cancers. This re-wiring of epistasis across cell types is likely to be a basic feature of genetic architecture, with important implications for understanding the evolution of multicellularity and human genetic diseases. In addition, if this plasticity of epistasis across cell types is also true for synthetic lethal interactions, a synthetic lethal strategy to kill cancer cells may frequently work in one type of cancer but prove ineffective in another.

Quantitative Fitness Analysis Identifies exo1∆ and Other Suppressors or Enhancers of Telomere Defects in Schizosaccharomyces pombe

Synthetic genetic array (SGA) has been successfully used to identify genetic interactions in S. cerevisiae and S. pombe. In S. pombe, SGA methods use either cycloheximide (C) or heat shock (HS) to select double mutants before measuring colony size as a surrogate for fitness. Quantitative Fitness Analysis (QFA) is a different method for determining fitness of microbial strains. In QFA, liquid cultures are spotted onto solid agar and growth curves determined for each spot by photography and model fitting. Here, we compared the two S. pombe SGA methods and found that the HS method was more reproducible for us. We also developed a QFA procedure for S. pombe. We used QFA to identify genetic interactions affecting two temperature sensitive, telomere associated query mutations (taz1Δ and pot1-1). We identify exo1∆ and other gene deletions as suppressors or enhancers of S. pombe telomere defects. Our study identifies known and novel gene deletions affecting the fitness of strains with telomere defects. The interactions we identify may be relevant in human cells.