Epistatic relationships reveal the functional organization of yeast transcription factors

The Role of Sfp1 in Candida albicans Cell Wall Maintenance

The cell wall is the first interface for Candida albicans interaction with the surrounding environment and the host cells. Therefore, maintenance of cell wall integrity (CWI) is crucial for C. albicans survival and host-pathogen interaction. In response to environmental stresses, C. albicans undergoes cell wall remodeling controlled by multiple signaling pathways and transcription regulators. Here, we explored the role of the transcription factor Sfp1 in CWI. A deletion of the SFP1 gene not only caused changes in cell wall properties, cell wall composition and structure but also modulated expression of cell wall biosynthesis and remodeling genes. In addition, Cas5 is a known transcription regulator for C. albicans CWI and cell wall stress response. Interestingly, our results indicated that Sfp1 negatively controls the CAS5 gene expression by binding to its promoter element. Together, this study provides new insights into the regulation of C. albicans CWI and stress response.

Genetic-interaction screens uncover novel biological roles and regulators of transcription factors in fission yeast

In Schizosaccharomyces pombe, systematic analyses of single transcription factor deletion or overexpression strains have made substantial advances in determining the biological roles and target genes of transcription factors, yet these characteristics are still relatively unknown for over a quarter of them. Moreover, the comprehensive list of proteins that regulate transcription factors remains incomplete. To further characterize Schizosaccharomyces pombe transcription factors, we performed synthetic sick/lethality and synthetic dosage lethality screens by synthetic genetic array. Examination of 2,672 transcription factor double deletion strains revealed a sick/lethality interaction frequency of 1.72%. Phenotypic analysis of these sick/lethality strains revealed potential cell cycle roles for several poorly characterized transcription factors, including SPBC56F2.05, SPCC320.03, and SPAC3C7.04. In addition, we examined synthetic dosage lethality interactions between 14 transcription factors and a miniarray of 279 deletion strains, observing a synthetic dosage lethality frequency of 4.99%, which consisted of known and novel transcription factor regulators. The miniarray contained deletions of genes that encode primarily posttranslational-modifying enzymes to identify putative upstream regulators of the transcription factor query strains. We discovered that ubiquitin ligase Ubr1 and its E2/E3-interacting protein, Mub1, degrade the glucose-responsive transcriptional repressor Scr1. Loss of ubr1⁺ or mub1⁺ increased Scr1 protein expression, which resulted in enhanced repression of flocculation through Scr1. The synthetic dosage lethality screen also captured interactions between Scr1 and 2 of its known repressors, Sds23 and Amk2, each affecting flocculation through Scr1 by influencing its nuclear localization. Our study demonstrates that sick/lethality and synthetic dosage lethality screens can be effective in uncovering novel functions and regulators of Schizosaccharomyces pombe transcription factors.

Partitioned usage of chromatin remodelers by nucleosome-displacing factors

Nucleosome-displacing-factors (NDFs) in yeast, similar to pioneer factors in higher eukaryotes, can open closed chromatin and generate nucleosome-depleted regions (NDRs). NDRs in yeast are also affected by ATP-dependent chromatin remodelers (CRs). However, how NDFs and CRs coordinate in nucleosome invasion and NDR formation is still unclear. Here, we design a high-throughput method to systematically study the interplay between NDFs and CRs. By combining an integrated synthetic oligonucleotide library with DNA methyltransferase-based, single-molecule nucleosome mapping, we measure the impact of CRs on NDRs generated by individual NDFs. We find that CRs are dispensable for nucleosome invasion by NDFs, and they function downstream of NDF binding to modulate the NDR length. A few CRs show high specificity toward certain NDFs; however, in most cases, CRs are recruited in a factor-nonspecific and NDR length-dependent manner. Overall, our study provides a framework to investigate how NDFs and CRs cooperate to regulate chromatin opening.

Chromatin accessibility in yeast is regulated by nucleosome-displacing-factors (NDFs) and chromatin remodelers (CRs). Chen et al. show that NDFs first invade into nucleosomes and then recruit CRs to modulate the NDR length. NDF-specific and NDR length-dependent recruitment of CRs allow partitioned usage of CRs by NDFs.

Auxotrophic and prototrophic conditional genetic networks reveal the rewiring of transcription factors in Escherichia coli

Bacterial transcription factors (TFs) are widely studied in Escherichia coli. Yet it remains unclear how individual genes in the underlying pathways of TF machinery operate together during environmental challenge. Here, we address this by applying an unbiased, quantitative synthetic genetic interaction (GI) approach to measure pairwise GIs among all TF genes in E. coli under auxotrophic (rich medium) and prototrophic (minimal medium) static growth conditions. The resulting static and differential GI networks reveal condition-dependent GIs, widespread changes among TF genes in metabolism, and new roles for uncharacterized TFs (yjdC, yneJ, ydiP) as regulators of cell division, putrescine utilization pathway, and cold shock adaptation. Pan-bacterial conservation suggests TF genes with GIs are co-conserved in evolution. Together, our results illuminate the global organization of E. coli TFs, and remodeling of genetic backup systems for TFs under environmental change, which is essential for controlling the bacterial transcriptional regulatory circuits.

The bacterium E. coli has around 300 transcriptional factors, but the functions of many of them, and the interactions between their respective regulatory networks, are unclear. Here, the authors study genetic interactions among all transcription factor genes in E. coli, revealing condition-dependent interactions and roles for uncharacterized transcription factors.

Functional interaction between the RNA exosome and the sirtuin deacetylase Hst3 maintains transcriptional homeostasis

In this study, Bryll et al. found that inactivation of the RNA exosome leads to global reduction of nascent mRNA transcripts, and that this defect is accentuated by loss of deposition of histone variant H2A.Z. They identify the mRNA for the sirtuin deacetylase Hst3 as a key target for the RNA exosome that mediates communication between RNA degradation and transcription machineries.

Eukaryotic cells maintain an optimal level of mRNAs through unknown mechanisms that balance RNA synthesis and degradation. We found that inactivation of the RNA exosome leads to global reduction of nascent mRNA transcripts, and that this defect is accentuated by loss of deposition of histone variant H2A.Z. We identify the mRNA for the sirtuin deacetylase Hst3 as a key target for the RNA exosome that mediates communication between RNA degradation and transcription machineries. These findings reveal how the RNA exosome and H2A.Z function together to control a deacetylase, ensuring proper levels of transcription in response to changes in RNA degradation.

Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast

Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in "transport", which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.

Integration of transcription coregulator complexes with sequence-specific DNA-binding factor interactomes

The domain of transcription regulation has been notoriously difficult to annotate in the Gene Ontology, partly because of the intricacies of gene regulation which involve molecular interactions with DNA as well as amongst protein complexes. The molecular function 'transcription coregulator activity' is a part of the biological process 'regulation of transcription, DNA-templated' that occurs in the cellular component 'chromatin'. It can mechanistically link sequence-specific DNA-binding transcription factor (dbTF) regulatory DNA target sites to coactivator and corepressor target sites through the molecular function 'cis-regulatory region sequence-specific DNA binding'. Many questions arise about transcription coregulators (coTF). Here, we asked how many unannotated, putative coregulators can be identified in protein complexes? Therefore, we mined the CORUM and hu.MAP protein complex databases with known and strongly presumed human transcription coregulators. In addition, we trawled the BioGRID and IntAct molecular interaction databases for interactors of the known 1457 human dbTFs annotated by the GREEKC and GO consortia. This yielded 1093 putative transcription factor coregulator complex subunits, of which 954 interact directly with a dbTF. This substantially expands the set of coTFs that could be annotated to 'transcription coregulator activity' and sets the stage for renewed annotation and wet-lab research efforts. To this end, we devised a prioritisation score based on existing GO annotations of already curated transcription coregulators as well as interactome representation. Since all the proteins that we mined are parts of protein complexes, we propose to concomitantly engage in annotation of the putative transcription coregulator-containing complexes in the Complex Portal database.

The lipid composition of yeast cells modulates the response to iron deficiency

Iron is a vital micronutrient for all eukaryotes because it participates as a redox cofactor in multiple metabolic pathways, including lipid biosynthesis. In response to iron deficiency, the Saccharomyces cerevisiae iron-responsive transcription factor Aft1 accumulates in the nucleus and activates a set of genes that promote iron acquisition at the cell surface. In this study, we report that yeast cells lacking the transcription factor Mga2, which promotes the expression of the iron-dependent Δ9-fatty acid desaturase Ole1, display a defect in the activation of the iron regulon during the adaptation to iron limitation. Supplementation with exogenous unsaturated fatty acids (UFAs) or OLE1 expression rescues the iron regulon activation defect of mga2Δ cells. These observations and fatty acid measurements suggest that the mga2Δ defect in iron regulon expression is due to low UFA levels. Subcellular localization studies reveal that low UFAs cause a mislocalization of Aft1 protein to the vacuole upon iron deprivation that prevents its nuclear accumulation. These results indicate that Mga2 and Ole1 are essential to maintain the UFA levels required for Aft1-dependent activation of the iron regulon in response to iron deficiency, and directly connect the biosynthesis of fatty acids to the response to iron depletion.

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several hypothetical molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in baker's yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.

Slx5/Slx8-dependent ubiquitin hotspots on chromatin contribute to stress tolerance

Chromatin is a highly regulated environment, and protein association with chromatin is often controlled by post‐translational modifications and the corresponding enzymatic machinery. Specifically, SUMO‐targeted ubiquitin ligases (STUbLs) have emerged as key players in nuclear quality control, genome maintenance, and transcription. However, how STUbLs select specific substrates among myriads of SUMOylated proteins on chromatin remains unclear. Here, we reveal a remarkable co‐localization of the budding yeast STUbL Slx5/Slx8 and ubiquitin at seven genomic loci that we term “ubiquitin hotspots”. Ubiquitylation at these sites depends on Slx5/Slx8 and protein turnover on the Cdc48 segregase. We identify the transcription factor‐like Ymr111c/Euc1 to associate with these sites and to be a critical determinant of ubiquitylation. Euc1 specifically targets Slx5/Slx8 to ubiquitin hotspots via bipartite binding of Slx5 that involves the Slx5 SUMO‐interacting motifs and an additional, novel substrate recognition domain. Interestingly, the Euc1‐ubiquitin hotspot pathway acts redundantly with chromatin modifiers of the H2A.Z and Rpd3L pathways in specific stress responses. Thus, our data suggest that STUbL‐dependent ubiquitin hotspots shape chromatin during stress adaptation.

Integrated Functions of Membrane Property Sensors and a Hidden Side of the Unfolded Protein Response

Eukaryotic cells face the challenge of maintaining the complex composition of several coexisting organelles. The molecular mechanisms underlying the homeostasis of subcellular membranes and their adaptation during stress are only now starting to emerge. Here, we discuss three membrane property sensors of the endoplasmic reticulum (ER), namely OPI1, MGA2, and IRE1, each controlling a large cellular program impacting the lipid metabolic network. OPI1 coordinates the production of membrane and storage lipids, MGA2 regulates the production of unsaturated fatty acids required for membrane biogenesis, and IRE1 controls the unfolded protein response (UPR) to adjust ER size, protein folding, and the secretory capacity of the cell. Although these proteins use remarkably distinct sensing mechanisms, they are functionally connected via the ER membrane and cooperate to maintain membrane homeostasis. As a rationalization of the recently described mechanism of UPR activation by lipid bilayer stress, we propose that IRE1 can sense the protein-to-lipid ratio in the ER membrane to ensure a balanced production of membrane proteins and lipids.

Layers of Cryptic Genetic Variation Underlie a Yeast Complex Trait

To better understand cryptic genetic variation, Lee et al. comprehensively map the genetic basis of a trait that is typically suppressed in a yeast cross. By determining how three different genetic perturbations give rise...

Cryptic genetic variation may be an important contributor to heritable traits, but its extent and regulation are not fully understood. Here, we investigate the cryptic genetic variation underlying a Saccharomyces cerevisiae colony phenotype that is typically suppressed in a cross of the laboratory strain BY4716 (BY) and a derivative of the clinical isolate 322134S (3S). To do this, we comprehensively dissect the trait’s genetic basis in the BYx3S cross in the presence of three different genetic perturbations that enable its expression. This allows us to detect and compare the specific loci that interact with each perturbation to produce the trait. In total, we identify 21 loci, all but one of which interact with just a subset of the perturbations. Beyond impacting which loci contribute to the trait, the genetic perturbations also alter the extent of additivity, epistasis, and genotype–environment interaction among the detected loci. Additionally, we show that the single locus interacting with all three perturbations corresponds to the coding region of the cell surface gene FLO11. While nearly all of the other remaining loci influence FLO11 transcription in cis or trans, the perturbations tend to interact with loci in different pathways and subpathways. Our work shows how layers of cryptic genetic variation can influence complex traits. Here, these layers mainly represent different regulatory inputs into the transcription of a single key gene.

Genomewide mechanisms of chronological longevity by dietary restriction in budding yeast

Dietary restriction is arguably the most promising nonpharmacological intervention to extend human life and health span. Yet, only few genetic regulators mediating the cellular response to dietary restriction are known, and the question remains which other regulatory factors are involved. Here, we measured at the genomewide level the chronological lifespan of Saccharomyces cerevisiae gene deletion strains under two nitrogen source regimens, glutamine (nonrestricted) and γ‐aminobutyric acid (restricted). We identified 473 mutants with diminished or enhanced extension of lifespan. Functional analysis of such dietary restriction genes revealed novel processes underlying longevity by the nitrogen source quality, which also allowed us to generate a prioritized catalogue of transcription factors orchestrating the dietary restriction response. Importantly, deletions of transcription factors Msn2, Msn4, Snf6, Tec1, and Ste12 resulted in diminished lifespan extension and defects in cell cycle arrest upon nutrient starvation, suggesting that regulation of the cell cycle is a major mechanism of chronological longevity. We further show that STE12 overexpression is enough to extend lifespan, linking the pheromone/invasive growth pathway with cell survivorship. Our global picture of the genetic players of longevity by dietary restriction highlights intricate regulatory cross‐talks in aging cells.

The ribosome assembly gene network is controlled by the feedback regulation of transcription elongation

Ribosome assembly requires the concerted expression of hundreds of genes, which are transcribed by all three nuclear RNA polymerases. Transcription elongation involves dynamic interactions between RNA polymerases and chromatin. We performed a synthetic lethal screening in Saccharomyces cerevisiae with a conditional allele of SPT6, which encodes one of the factors that facilitates this process. Some of these synthetic mutants corresponded to factors that facilitate pre-rRNA processing and ribosome biogenesis. We found that the in vivo depletion of one of these factors, Arb1, activated transcription elongation in the set of genes involved directly in ribosome assembly. Under these depletion conditions, Spt6 was physically targeted to the up-regulated genes, where it helped maintain their chromatin integrity and the synthesis of properly stable mRNAs. The mRNA profiles of a large set of ribosome biogenesis mutants confirmed the existence of a feedback regulatory network among ribosome assembly genes. The transcriptional response in this network depended on both the specific malfunction and the role of the regulated gene. In accordance with our screening, Spt6 positively contributed to the optimal operation of this global network. On the whole, this work uncovers a feedback control of ribosome biogenesis by fine-tuning transcription elongation in ribosome assembly factor-coding genes.

The m6A methyltransferase Ime4 epitranscriptionally regulates triacylglycerol metabolism and vacuolar morphology in haploid yeast cells

N⁶-Methyladenosine (m⁶A) is among the most common modifications in eukaryotic mRNA. The role of yeast m⁶A methyltransferase, Ime4, in meiosis and sporulation in diploid strains is very well studied, but its role in haploid strains has remained unknown. Here, with the help of an immunoblotting strategy and Ime4-GFP protein localization studies, we establish the physiological role of Ime4 in haploid cells. Our data showed that Ime4 epitranscriptionally regulates triacylglycerol metabolism and vacuolar morphology through the long-chain fatty acyl-CoA synthetase Faa1, independently of the RNA methylation complex (MIS complex). The MIS complex consists of the Ime4, Mum2, and Slz1 proteins. Our affinity enrichment strategy (methylated RNA immunoprecipitation assays) using m⁶A polyclonal antibodies coupled with mRNA isolation, quantitative real-time PCR, and standard PCR analyses confirmed the presence of m⁶A-modified FAA1 transcripts in haploid yeast cells. The term "epitranscriptional regulation" encompasses the RNA modification-mediated regulation of genes. Moreover, we demonstrate that the Aft2 transcription factor up-regulates FAA1 expression. Because the m⁶A methylation machinery is fundamentally conserved throughout eukaryotes, our findings will help advance the rapidly emerging field of RNA epitranscriptomics. The metabolic link identified here between m⁶A methylation and triacylglycerol metabolism via the Ime4 protein provides new insights into lipid metabolism and the pathophysiology of lipid-related metabolic disorders, such as obesity. Because the yeast vacuole is an analogue of the mammalian lysosome, our findings pave the way to better understand the role of m⁶A methylation in lysosome-related functions and diseases.

Synthetic Genetic Arrays: Automation of Yeast Genetics

Genome-sequencing efforts have led to great strides in the annotation of protein-coding genes and other genomic elements. The current challenge is to understand the functional role of each gene and how genes work together to modulate cellular processes. Genetic interactions define phenotypic relationships between genes and reveal the functional organization of a cell. Synthetic genetic array (SGA) methodology automates yeast genetics and enables large-scale and systematic mapping of genetic interaction networks in the budding yeast,Saccharomyces cerevisiae SGA facilitates construction of an output array of double mutants from an input array of single mutants through a series of replica pinning steps. Subsequent analysis of genetic interactions from SGA-derived mutants relies on accurate quantification of colony size, which serves as a proxy for fitness. Since its development, SGA has given rise to a variety of other experimental approaches for functional profiling of the yeast genome and has been applied in a multitude of other contexts, such as genome-wide screens for synthetic dosage lethality and integration with high-content screening for systematic assessment of morphology defects. SGA-like strategies can also be implemented similarly in a number of other cell types and organisms, includingSchizosaccharomyces pombe,Escherichia coli, Caenorhabditis elegans, and human cancer cell lines. The genetic networks emerging from these studies not only generate functional wiring diagrams but may also play a key role in our understanding of the complex relationship between genotype and phenotype.

A high-resolution gene expression atlas of epistasis between gene-specific transcription factors exposes potential mechanisms for genetic interactions

BACKGROUND

Genetic interactions, or non-additive effects between genes, play a crucial role in many cellular processes and disease. Which mechanisms underlie these genetic interactions has hardly been characterized. Understanding the molecular basis of genetic interactions is crucial in deciphering pathway organization and understanding the relationship between genotype, phenotype and disease.

RESULTS

To investigate the nature of genetic interactions between gene-specific transcription factors (GSTFs) in Saccharomyces cerevisiae, we systematically analyzed 72 GSTF pairs by gene expression profiling double and single deletion mutants. These pairs were selected through previously published growth-based genetic interactions as well as through similarity in DNA binding properties. The result is a high-resolution atlas of gene expression-based genetic interactions that provides systems-level insight into GSTF epistasis. The atlas confirms known genetic interactions and exposes new ones. Importantly, the data can be used to investigate mechanisms that underlie individual genetic interactions. Two molecular mechanisms are proposed, "buffering by induced dependency" and "alleviation by derepression".

CONCLUSIONS

These mechanisms indicate how negative genetic interactions can occur between seemingly unrelated parallel pathways and how positive genetic interactions can indirectly expose parallel rather than same-pathway relationships. The focus on GSTFs is important for understanding the transcription regulatory network of yeast as it uncovers details behind many redundancy relationships, some of which are completely new. In addition, the study provides general insight into the complex nature of epistasis and proposes mechanistic models for genetic interactions, the majority of which do not fall into easily recognizable within- or between-pathway relationships.

Structures and Functions of the Multiple KOW Domains of Transcription Elongation Factor Spt5

The eukaryotic Spt4-Spt5 heterodimer forms a higher-order complex with RNA polymerase II (and I) to regulate transcription elongation. Extensive genetic and functional data have revealed diverse roles of Spt4-Spt5 in coupling elongation with chromatin modification and RNA-processing pathways. A mechanistic understanding of the diverse functions of Spt4-Spt5 is hampered by challenges in resolving the distribution of functions among its structural domains, including the five KOW domains in Spt5, and a lack of their high-resolution structures. We present high-resolution crystallographic results demonstrating that distinct structures are formed by the first through third KOW domains (KOW1-Linker1 [K1L1] and KOW2-KOW3) of Saccharomyces cerevisiae Spt5. The structure reveals that K1L1 displays a positively charged patch (PCP) on its surface, which binds nucleic acids in vitro, as shown in biochemical assays, and is important for in vivo function, as shown in growth assays. Furthermore, assays in yeast have shown that the PCP has a function that partially overlaps that of Spt4. Synthesis of our results with previous evidence suggests a model in which Spt4 and the K1L1 domain of Spt5 form functionally overlapping interactions with nucleic acids upstream of the transcription bubble, and this mechanism may confer robustness on processes associated with transcription elongation.

SUB1 Plays a Negative Role during Starvation Induced Sporulation Program in Saccharomyces cerevisiae

Saccharomyces cerevisiae Sub1 is involved in several cellular processes such as, transcription initiation, elongation, mRNA processing and DNA repair. It has also been reported to provide cellular resistance during conditions of oxidative DNA damage and osmotic stress. Here, we report a novel role of SUB1 during starvation stress-induced sporulation, which leads to meiosis and spore formation in diploid yeast cells. Deletion of SUB1 gene significantly increased sporulation efficiency as compared to the wild-type cells in S288c genetic background. Whereas, the sporulation functions of the sub1(Y66A) missense mutant were similar to Sub1. SUB1 transcript and protein levels are downregulated during sporulation, in highly synchronized and sporulation proficient wild-type SK1 cells. The changes in Sub1 levels during sporulation cascade correlate with the induction of middle sporulation gene expression. Deletion of SUB1 increased middle sporulation gene transcript levels with no effect on their induction kinetics. In wild-type cells, Sub1 associates with chromatin at these loci in a temporal pattern that correlates with their enhanced gene expression seen in sub1Δ cells. We show that SUB1 genetically interacts with HOS2, which led us to speculate that Sub1 might function with Set3 repressor complex during sporulation. Positive Cofactor 4, human homolog of Sub1, complemented the sub1Δ sporulation phenotype, suggesting conservation of function. Taken together, our results suggest that SUB1 acts as a negative regulator of sporulation.

Data Imputation in Epistatic MAPs by Network-Guided Matrix Completion

Epistatic miniarray profile (E-MAP) is a popular large-scale genetic interaction discovery platform. E-MAPs benefit from quantitative output, which makes it possible to detect subtle interactions with greater precision. However, due to the limits of biotechnology, E-MAP studies fail to measure genetic interactions for up to 40% of gene pairs in an assay. Missing measurements can be recovered by computational techniques for data imputation, in this way completing the interaction profiles and enabling downstream analysis algorithms that could otherwise be sensitive to missing data values. We introduce a new interaction data imputation method called network-guided matrix completion (NG-MC). The core part of NG-MC is low-rank probabilistic matrix completion that incorporates prior knowledge presented as a collection of gene networks. NG-MC assumes that interactions are transitive, such that latent gene interaction profiles inferred by NG-MC depend on the profiles of their direct neighbors in gene networks. As the NG-MC inference algorithm progresses, it propagates latent interaction profiles through each of the networks and updates gene network weights toward improved prediction. In a study with four different E-MAP data assays and considered protein-protein interaction and gene ontology similarity networks, NG-MC significantly surpassed existing alternative techniques. Inclusion of information from gene networks also allowed NG-MC to predict interactions for genes that were not included in original E-MAP assays, a task that could not be considered by current imputation approaches.

Sub1 and Maf1, two effectors of RNA polymerase III, are involved in the yeast quiescence cycle

Sub1 and Maf1 exert an opposite effect on RNA polymerase III transcription interfering with different steps of the transcription cycle. In this study, we present evidence that Sub1 and Maf1 also exhibit an opposite role on yeast chronological life span. First, cells lacking Sub1 need more time than wild type to exit from resting and this lag in re-proliferation is correlated with a delay in transcriptional reactivation. Second, our data show that the capacity of the cells to properly establish a quiescent state is impaired in the absence of Sub1 resulting in a premature death that is dependent on the Ras/PKA and Tor1/Sch9 signalling pathways. On the other hand, we show that maf1Δ cells are long-lived mutant suggesting a connection between Pol III transcription and yeast longevity.

Gene coexpression measures in large heterogeneous samples using count statistics

With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to process these data and distill insights into systems biology has been an important part of the "big data" challenge. Gene coexpression is one of the earliest techniques developed that is still widely in use for functional annotation, pathway analysis, and, most importantly, the reconstruction of gene regulatory networks, based on gene expression data. However, most coexpression measures do not specifically account for local features in expression profiles. For example, it is very likely that the patterns of gene association may change or only exist in a subset of the samples, especially when the samples are pooled from a range of experiments. We propose two new gene coexpression statistics based on counting local patterns of gene expression ranks to take into account the potentially diverse nature of gene interactions. In particular, one of our statistics is designed for time-course data with local dependence structures, such as time series coupled over a subregion of the time domain. We provide asymptotic analysis of their distributions and power, and evaluate their performance against a wide range of existing coexpression measures on simulated and real data. Our new statistics are fast to compute, robust against outliers, and show comparable and often better general performance.

Upregulation of the Saccharomyces cerevisiae efflux pump Tpo1 rescues an Imp2 transcription factor-deficient mutant from bleomycin toxicity

Yeast mutants lacking the transcriptional co-activator Imp2 are hypersensitive to the anticancer drug bleomycin, although the gene targets involved in this process remain elusive. A search for multicopy suppressors that rescue the imp2Δ mutant from bleomycin toxicity revealed the transcriptional activator Yap1, which can turn on many target genes such as transporters involved in regulating drug resistance. We show that YAP1 overexpression stimulated the expression of the TPO1 gene encoding a polyamine efflux pump, and that Yap1 failed to rescue the imp2Δ mutant from bleomycin toxicity in the absence of the TPO1 gene. Moreover, TPO1 overexpression, and not the related transporter gene QDR3, conferred upon the tpo1Δ imp2Δ double mutant parental resistance to bleomycin. We conclude that YAP1 overexpression rescues the imp2Δ mutant from bleomycin toxicity by triggering Tpo1 expression to expel the drug. Our data provide the first evidence that bleomycin could be a substrate for the Tpo1 efflux pump.

Mapping yeast transcriptional networks

The term “transcriptional network” refers to the mechanism(s) that underlies coordinated expression of genes, typically involving transcription factors (TFs) binding to the promoters of multiple genes, and individual genes controlled by multiple TFs. A multitude of studies in the last two decades have aimed to map and characterize transcriptional networks in the yeast Saccharomyces cerevisiae. We review the methodologies and accomplishments of these studies, as well as challenges we now face. For most yeast TFs, data have been collected on their sequence preferences, in vivo promoter occupancy, and gene expression profiles in deletion mutants. These systematic studies have led to the identification of new regulators of numerous cellular functions and shed light on the overall organization of yeast gene regulation. However, many yeast TFs appear to be inactive under standard laboratory growth conditions, and many of the available data were collected using techniques that have since been improved. Perhaps as a consequence, comprehensive and accurate mapping among TF sequence preferences, promoter binding, and gene expression remains an open challenge. We propose that the time is ripe for renewed systematic efforts toward a complete mapping of yeast transcriptional regulatory mechanisms.

Genome evolution predicts genetic interactions in protein complexes and reveals cancer drug targets

Genetic interactions reveal insights into cellular function and can be used to identify drug targets. Here we construct a new model to predict negative genetic interactions in protein complexes by exploiting the evolutionary history of genes in parallel converging pathways in metabolism. We evaluate our model with protein complexes of Saccharomyces cerevisiae and show that the predicted protein pairs more frequently have a negative genetic interaction than random proteins from the same complex. Furthermore, we apply our model to human protein complexes to predict novel cancer drug targets, and identify 20 candidate targets with empirical support and 10 novel targets amenable to further experimental validation. Our study illustrates that negative genetic interactions can be predicted by systematically exploring genome evolution, and that this is useful to identify novel anti-cancer drug targets.

Genetic interactions can reveal insights into cellular functions. Here, Lu et al. show that negative genetic interactions in protein complexes can be predicted by systematically exploring the evolutionary history of genes, which may be useful for the identification of novel targets for anti-cancer drugs.

Predicting complex phenotype-genotype interactions to enable yeast engineering: Saccharomyces cerevisiae as a model organism and a cell factory

There is an increasing use of systems biology approaches in both "red" and "white" biotechnology in order to enable medical, medicinal, and industrial applications. The intricate links between genotype and phenotype may be explained through the use of the tools developed in systems biology, synthetic biology, and evolutionary engineering. Biomedical and biotechnological research are among the fields that could benefit most from the elucidation of this complex relationship. Researchers have studied fitness extensively to explain the phenotypic impacts of genetic variations. This elaborate network of dependencies and relationships so revealed are further complicated by the influence of environmental effects that present major challenges to our achieving an understanding of the cellular mechanisms leading to healthy or diseased phenotypes or optimized production yields. An improved comprehension of complex genotype-phenotype interactions and their accurate prediction should enable us to more effectively engineer yeast as a cell factory and to use it as a living model of human or pathogen cells in intelligent screens for new drugs. This review presents different methods and approaches undertaken toward improving our understanding and prediction of the growth phenotype of the yeast Saccharomyces cerevisiae as both a model and a production organism.

Functional characterization of fission yeast transcription factors by overexpression analysis

In Schizosaccharomyces pombe, over 90% of transcription factor genes are nonessential. Moreover, the majority do not exhibit significant growth defects under optimal conditions when deleted, complicating their functional characterization and target gene identification. Here, we systematically overexpressed 99 transcription factor genes with the nmt1 promoter and found that 64 transcription factor genes exhibited reduced fitness when ectopically expressed. Cell cycle defects were also often observed. We further investigated three uncharacterized transcription factor genes (toe1(+)-toe3(+)) that displayed cell elongation when overexpressed. Ectopic expression of toe1(+) resulted in a G1 delay while toe2(+) and toe3(+) overexpression produced an accumulation of septated cells with abnormalities in septum formation and nuclear segregation, respectively. Transcriptome profiling and ChIP-chip analysis of the transcription factor overexpression strains indicated that Toe1 activates target genes of the pyrimidine-salvage pathway, while Toe3 regulates target genes involved in polyamine synthesis. We also found that ectopic expression of the putative target genes SPBC3H7.05c, and dad5(+) and SPAC11D3.06 could recapitulate the cell cycle phenotypes of toe2(+) and toe3(+) overexpression, respectively. Furthermore, single deletions of the putative target genes urg2(+) and SPAC1399.04c, and SPBC3H7.05c, SPACUNK4.15, and rds1(+), could suppress the phenotypes of toe1(+) and toe2(+) overexpression, respectively. This study implicates new transcription factors and metabolism genes in cell cycle regulation and demonstrates the potential of systematic overexpression analysis to elucidate the function and target genes of transcription factors in S. pombe.

The 26S proteasome drives trinucleotide repeat expansions

Trinucleotide repeat (TNR) expansion is the causative mutation for at least 17 inherited neurological diseases. An important question in the field is which proteins drive the expansion process. This study reports that the multi-functional protein Sem1 is a novel driver of TNR expansions in budding yeast. Mutants of SEM1 suppress up to 90% of expansions. Subsequent analysis showed that Sem1 facilitates expansions via its function in the 26S proteasome, a highly conserved multi-subunit complex with both proteolytic and non-proteolytic functions. The proteolytic function of the 26S proteasome is relevant to expansions, as mutation of additional proteasome components or treatment of yeast with a proteasome inhibitor suppressed CTG•CAG expansions. The 26S proteasome also drives expansions in human cells. In a human astrocytic cell line, siRNA-mediated knockdown of 26S proteasome subunits PSMC5 or PSMB3 reduced expansions. This expansion phenotype, both in yeast and human cells, is dependent on the proteolytic activity of the proteasome rather than a stress response owing to depletion of free ubiquitin. Thus, the 26S proteasome is a novel factor that drives expansions in both yeast and human cells by a mechanism involving protein degradation.

Statistical analysis reveals co-expression patterns of many pairs of genes in yeast are jointly regulated by interacting loci

Expression quantitative trait loci (eQTL) studies have generated large amounts of data in different organisms. The analyses of these data have led to many novel findings and biological insights on expression regulations. However, the role of epistasis in the joint regulation of multiple genes has not been explored. This is largely due to the computational complexity involved when multiple traits are simultaneously considered against multiple markers if an exhaustive search strategy is adopted. In this article, we propose a computationally feasible approach to identify pairs of chromosomal regions that interact to regulate co-expression patterns of pairs of genes. Our approach is built on a bivariate model whose covariance matrix depends on the joint genotypes at the candidate loci. We also propose a filtering process to reduce the computational burden. When we applied our method to a yeast eQTL dataset profiled under both the glucose and ethanol conditions, we identified a total of 225 and 224 modules, with each module consisting of two genes and two eQTLs where the two eQTLs epistatically regulate the co-expression patterns of the two genes. We found that many of these modules have biological interpretations. Under the glucose condition, ribosome biogenesis was co-regulated with the signaling and carbohydrate catabolic processes, whereas silencing and aging related genes were co-regulated under the ethanol condition with the eQTLs containing genes involved in oxidative stress response process.

eQTL studies collect both gene expression and genotype data, and they are highly informative as to how genes regulate expressions. Although much progress has been made in the analysis of such data, most studies have considered one marker at a time. As a result, those markers with weak marginal yet strong interactive effects may not be inferred from these single-marker-based analyses. In this article, using joint expression patterns between two genes (versus one gene) as the primary phenotype, we propose a novel statistical method to conduct an exhaustive search for joint marker analysis. When our method is applied to a well-studied dataset, we were able to identify many novel features that were overlooked by existing methods. Our general strategy has general applicability to other scientific problems.

The prefoldin bud27 mediates the assembly of the eukaryotic RNA polymerases in an rpb5-dependent manner

The unconventional prefoldin URI/RMP, in humans, and its orthologue in yeast, Bud27, have been proposed to participate in the biogenesis of the RNA polymerases. However, this role of Bud27 has not been confirmed and is poorly elucidated. Our data help clarify the mechanisms governing biogenesis of the three eukaryotic RNA pols. We show evidence that Bud27 is the first example of a protein that participates in the biogenesis of the three eukaryotic RNA polymerases and the first example of a protein modulating their assembly instead of their nuclear transport. In addition we demonstrate that the role of Bud27 in RNA pols biogenesis depends on Rpb5. In fact, lack of BUD27 affects growth and leads to a substantial accumulation of the three RNA polymerases in the cytoplasm, defects offset by the overexpression of RPB5. Supporting this, our data demonstrate that the lack of Bud27 affects the correct assembly of Rpb5 and Rpb6 to the three RNA polymerases, suggesting that this process occurs in the cytoplasm and is a required step prior to nuclear import. Also, our data support the view that Rpb5 and Rpb6 assemble somewhat later than the rest of the complexes. Furthermore, Bud27 Rpb5-binding but not PFD-binding domain is necessary for RNA polymerases biogenesis. In agreement, we also demonstrate genetic interactions between BUD27, RPB5, and RPB6. Bud27 shuttles between the nucleus and the cytoplasm in an Xpo1-independent manner, and also independently of microtubule polarization and possibly independently of its association with the RNA pols. Our data also suggest that the role of Bud27 in RNA pols biogenesis is independent of the chaperone prefoldin (PFD) complex and of Iwr1. Finally, the role of URI seems to be conserved in humans, suggesting conserved mechanisms in RNA pols biogenesis.

The mechanisms governing the assembly and the transport of the three eukaryotic RNA polymerases to the nucleus are in discussion. Interesting papers have demonstrated the participation of some proteins in the assembly of the nuclear RNA polymerases and in their transport to the nucleus, but the mechanisms involved are poorly understood. Our data help clarify the mechanisms governing biogenesis of the three eukaryotic RNA pols and demonstrate that the prefoldin Bud27 of Saccharomyces cerevisiae mediates the correct assembly of the three complexes prior to their translocation to the nucleus, in a process which is dependent on Rpb5. In addition, our data support the view that, during the assembly of the RNA pols, Rpb5 and Rpb6 assemble rather late compared to the rest of the complexes. Furthermore, this role of Bud27 seems to be specific, as it is not extended to other prefoldin members. Finally, the role of Bud27 seems to be conserved in humans, suggesting conserved mechanisms in RNA pols biogenesis.

Differential analysis of high-throughput quantitative genetic interaction data

Synthetic genetic arrays have been very effective at measuring genetic interactions in yeast in a high-throughput manner and recently have been expanded to measure quantitative changes in interaction, termed 'differential interactions', across multiple conditions. Here, we present a strategy that leverages statistical information from the experimental design to produce a novel, quantitative differential interaction score, which performs favorably compared to previous differential scores. We also discuss the added utility of differential genetic-similarity in differential network analysis. Our approach is preferred for differential network analysis, and our implementation, written in MATLAB, can be found at http://chianti.ucsd.edu/~gbean/compute_differential_scores.m.

The zinc cluster protein Sut1 contributes to filamentation in Saccharomyces cerevisiae

Sut1 is a transcriptional regulator of the Zn(II)(2)Cys(6) family in the budding yeast Saccharomyces cerevisiae. The only function that has been attributed to Sut1 is sterol uptake under anaerobic conditions. Here, we show that Sut1 is also expressed in the presence of oxygen, and we identify a novel function for Sut1. SUT1 overexpression blocks filamentous growth, a response to nutrient limitation, in both haploid and diploid cells. This inhibition by Sut1 is independent of its function in sterol uptake. Sut1 downregulates the expression of GAT2, HAP4, MGA1, MSN4, NCE102, PRR2, RHO3, and RHO5. Several of these Sut1 targets (GAT2, HAP4, MGA1, RHO3, and RHO5) are essential for filamentation in haploids and/or diploids. Furthermore, the expression of the Sut1 target genes, with the exception of MGA1, is induced during filamentous growth. We also show that SUT1 expression is autoregulated and inhibited by Ste12, a key transcriptional regulator of filamentation. We propose that Sut1 partially represses the expression of GAT2, HAP4, MGA1, MSN4, NCE102, PRR2, RHO3, and RHO5 when nutrients are plentiful. Filamentation-inducing conditions relieve this repression by Sut1, and the increased expression of Sut1 targets triggers filamentous growth.

Hierarchical modularity and the evolution of genetic interactomes across species

To date, cross-species comparisons of genetic interactomes have been restricted to small or functionally related gene sets, limiting our ability to infer evolutionary trends. To facilitate a more comprehensive analysis, we constructed a genome-scale epistasis map (E-MAP) for the fission yeast Schizosaccharomyces pombe, providing phenotypic signatures for ~60% of the nonessential genome. Using these signatures, we generated a catalog of 297 functional modules, and we assigned function to 144 previously uncharacterized genes, including mRNA splicing and DNA damage checkpoint factors. Comparison with an integrated genetic interactome from the budding yeast Saccharomyces cerevisiae revealed a hierarchical model for the evolution of genetic interactions, with conservation highest within protein complexes, lower within biological processes, and lowest between distinct biological processes. Despite the large evolutionary distance and extensive rewiring of individual interactions, both networks retain conserved features and display similar levels of functional crosstalk between biological processes, suggesting general design principles of genetic interactomes.

Transcription factors: from enhancer binding to developmental control

Developmental progression is driven by specific spatiotemporal domains of gene expression, which give rise to stereotypically patterned embryos even in the presence of environmental and genetic variation. Views of how transcription factors regulate gene expression are changing owing to recent genome-wide studies of transcription factor binding and RNA expression. Such studies reveal patterns that, at first glance, seem to contrast with the robustness of the developmental processes they encode. Here, we review our current knowledge of transcription factor function from genomic and genetic studies and discuss how different strategies, including extensive cooperative regulation (both direct and indirect), progressive priming of regulatory elements, and the integration of activities from multiple enhancers, confer specificity and robustness to transcriptional regulation during development.

Making sense of transcribing chromatin

Eukaryotic cells package their genomes into a nucleoprotein form called chromatin. The basic unit of chromatin is the nucleosome, formed by the wrapping of ∼147bp of DNA around an octameric complex of core histones. Advances in genomic technologies have enabled the locations of nucleosomes to be mapped across genomes. This has revealed a striking organisation with respect to transcribed genes in a diverse range of eukaryotes. This consists of a nucleosome depleted region upstream of promoters, with an array of well spaced nucleosomes extending into coding regions. This observation reinforces the links between chromatin organisation and transcription. Central to this is the paradox that while chromatin is required by eukaryotes to restrict inappropriate access to DNA, this must be overcome in order for genetic information to be expressed. This conundrum is at its most flagrant when considering the need for nucleic acid polymerase's to transit 1000's of based pairs of DNA wrapped as arrays of nucleosomes.

A mitochondrial-focused genetic interaction map reveals a scaffold-like complex required for inner membrane organization in mitochondria

Statement MITO-MAP, a high-density genetic interaction map in budding yeast, identifies a mitochondrial inner membrane–associated complex that promotes normal mitochondrial membrane organization and morphology.

To broadly explore mitochondrial structure and function as well as the communication of mitochondria with other cellular pathways, we constructed a quantitative, high-density genetic interaction map (the MITO-MAP) in Saccharomyces cerevisiae. The MITO-MAP provides a comprehensive view of mitochondrial function including insights into the activity of uncharacterized mitochondrial proteins and the functional connection between mitochondria and the ER. The MITO-MAP also reveals a large inner membrane–associated complex, which we term MitOS for mitochondrial organizing structure, comprised of Fcj1/Mitofilin, a conserved inner membrane protein, and five additional components. MitOS physically and functionally interacts with both outer and inner membrane components and localizes to extended structures that wrap around the inner membrane. We show that MitOS acts in concert with ATP synthase dimers to organize the inner membrane and promote normal mitochondrial morphology. We propose that MitOS acts as a conserved mitochondrial skeletal structure that differentiates regions of the inner membrane to establish the normal internal architecture of mitochondria.

Drosophila RNAi screening in a postgenomic world

Drosophila melanogaster has a long history as a model organism with several unique features that make it an ideal research tool for the study of the relationship between genotype and phenotype. Importantly fundamental genetic principles as well as key human disease genes have been uncovered through the use of Drosophila. The contribution of the fruit fly to science and medicine continues in the postgenomic era as cell-based Drosophila RNAi screens are a cost-effective and scalable enabling technology that can be used to quantify the contribution of different genes to diverse cellular processes. Drosophila high-throughput screens can also be used as integral part of systems-level approaches to describe the architecture and dynamics of cellular networks.

Integrated genome-scale prediction of detrimental mutations in transcription networks

A central challenge in genetics is to understand when and why mutations alter the phenotype of an organism. The consequences of gene inhibition have been systematically studied and can be predicted reasonably well across a genome. However, many sequence variants important for disease and evolution may alter gene regulation rather than gene function. The consequences of altering a regulatory interaction (or “edge”) rather than a gene (or “node”) in a network have not been as extensively studied. Here we use an integrative analysis and evolutionary conservation to identify features that predict when the loss of a regulatory interaction is detrimental in the extensively mapped transcription network of budding yeast. Properties such as the strength of an interaction, location and context in a promoter, regulator and target gene importance, and the potential for compensation (redundancy) associate to some extent with interaction importance. Combined, however, these features predict quite well whether the loss of a regulatory interaction is detrimental across many promoters and for many different transcription factors. Thus, despite the potential for regulatory diversity, common principles can be used to understand and predict when changes in regulation are most harmful to an organism.

The genomes of individuals differ in sequence at thousands of base pairs. Some of these polymorphisms affect the sequence of proteins, but many are likely to alter how genes are regulated. When are changes in gene regulation detrimental to an organism? We have used an integrative analysis of transcription factor binding site conservation in budding yeast to address the extent to which different features predict when potential changes in gene regulation are detrimental. We found that, despite the diversity of transcription factors and regulatory regions in a genome, a few simple properties can be used to predict and understand when changes in regulation are most harmful.