Vicent Pelechano

Extensive transcriptional heterogeneity revealed by isoform profiling

Transcript function is determined by sequence elements arranged on an individual RNA molecule. Variation in transcripts can affect messenger RNA stability, localization and translation, or produce truncated proteins that differ in localization or function. Given the existence of overlapping, variable transcript isoforms, determining the functional impact of the transcriptome requires identification of full-length transcripts, rather than just the genomic regions that are transcribed. Here, by jointly determining both transcript ends for millions of RNA molecules, we reveal an extensive layer of isoform diversity previously hidden among overlapping RNA molecules. Variation in transcript boundaries seems to be the rule rather than the exception, even within a single population of yeast cells. Over 26 major transcript isoforms per protein-coding gene were expressed in yeast. Hundreds of short coding RNAs and truncated versions of proteins are concomitantly encoded by alternative transcript isoforms, increasing protein diversity. In addition, approximately 70% of genes express alternative isoforms that vary in post-transcriptional regulatory elements, and tandem genes frequently produce overlapping or even bicistronic transcripts. This extensive transcript diversity is generated by a relatively simple eukaryotic genome with limited splicing, and within a genetically homogeneous population of cells. Our findings have implications for genome compaction, evolution and phenotypic diversity between single cells. These data also indicate that isoform diversity as well as RNA abundance should be considered when assessing the functional repertoire of genomes.

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

INTRODUCTION Genetic interactions occur when mutations in two or more genes combine to generate an unexpected phenotype. An extreme negative or synthetic lethal genetic interaction occurs when two mutations, neither lethal individually, combine to cause cell death. Conversely, positive genetic interactions occur when two mutations produce a phenotype that is less severe than expected. Genetic interactions identify functional relationships between genes and can be harnessed for biological discovery and therapeutic target identification. They may also explain a considerable component of the undiscovered genetics associated with human diseases. Here, we describe construction and analysis of a comprehensive genetic interaction network for a eukaryotic cell. RATIONALE Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to predict inherited phenotypes remains limited, in large part due to the extensive buffering of genomes, making most individual eukaryotic genes dispensable for life. To explore the extent to which genetic interactions reveal cellular function and contribute to complex phenotypes, and to discover the general principles of genetic networks, we used automated yeast genetics to construct a global genetic interaction network. RESULTS We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including ~550,000 negative and ~350,000 positive interactions, spanning ~90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell. CONCLUSION A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function. This network emphasizes the prevalence of genetic interactions and their potential to compound phenotypes associated with single mutations. Negative genetic interactions tend to connect functionally related genes and thus may be predicted using alternative functional information. Although less functionally informative, positive interactions may provide insights into general mechanisms of genetic suppression or resiliency. We anticipate that the ordered topology of the global genetic network, in which genetic interactions connect coherently within and between protein complexes and pathways, may be exploited to decipher genotype-to-phenotype relationships. A global network of genetic interaction profile similarities. (Left) Genes with similar genetic interaction profiles are connected in a global network, such that genes exhibiting more similar profiles are located closer to each other, whereas genes with less similar profiles are positioned farther apart. (Right) Spatial analysis of functional enrichment was used to identify and color network regions enriched for similar Gene Ontology bioprocess terms. 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.

Polyadenylation site–induced decay of upstream transcripts enforces promoter directionality

Active human promoters produce promoter-upstream transcripts (PROMPTs). Why these RNAs are coupled to decay, whereas their neighboring promoter-downstream mRNAs are not, is unknown. Here high-throughput sequencing demonstrates that PROMPTs generally initiate in the antisense direction closely upstream of the transcription start sites (TSSs) of their associated genes. PROMPT TSSs share features with mRNA-producing TSSs, including stalled RNA polymerase II (RNAPII) and the production of small TSS-associated RNAs. Notably, motif analyses around PROMPT 3′ ends reveal polyadenylation (pA)-like signals. Mutagenesis studies demonstrate that PROMPT pA signals are functional but linked to RNA degradation. Moreover, pA signals are under-represented in promoter-downstream versus promoter-upstream regions, thus allowing for more efficient RNAPII progress in the sense direction from gene promoters. We conclude that asymmetric sequence distribution around human gene promoters serves to provide a directional RNA output from an otherwise bidirectional transcription process.

Functional consequences of bidirectional promoters.

Several studies have shown that promoters of protein-coding genes are origins of pervasive non-coding RNA transcription and can initiate transcription in both directions. However, only recently have researchers begun to elucidate the functional implications of this bidirectionality and non-coding RNA production. Increasing evidence indicates that non-coding transcription at promoters influences the expression of protein-coding genes, revealing a new layer of transcriptional regulation. This regulation acts at multiple levels, from modifying local chromatin to enabling regional signal spreading and more distal regulation. Moreover, the bidirectional activity of a promoter is regulated at multiple points during transcription, giving rise to diverse types of transcripts.

A Complete Set of Nascent Transcription Rates for Yeast Genes

The amount of mRNA in a cell is the result of two opposite reactions: transcription and mRNA degradation. These reactions are governed by kinetics laws, and the most regulated step for many genes is the transcription rate. The transcription rate, which is assumed to be exercised mainly at the RNA polymerase recruitment level, can be calculated using the RNA polymerase densities determined either by run-on or immunoprecipitation using specific antibodies. The yeast Saccharomyces cerevisiae is the ideal model organism to generate a complete set of nascent transcription rates that will prove useful for many gene regulation studies. By combining genomic data from both the GRO (Genomic Run-on) and the RNA pol ChIP-on-chip methods we generated a new, more accurate nascent transcription rate dataset. By comparing this dataset with the indirect ones obtained from the mRNA stabilities and mRNA amount datasets, we are able to obtain biological information about posttranscriptional regulation processes and a genomic snapshot of the location of the active transcriptional machinery. We have obtained nascent transcription rates for 4,670 yeast genes. The median RNA polymerase II density in the genes is 0.078 molecules/kb, which corresponds to an average of 0.096 molecules/gene. Most genes have transcription rates of between 2 and 30 mRNAs/hour and less than 1% of yeast genes have >1 RNA polymerase molecule/gene. Histone and ribosomal protein genes are the highest transcribed groups of genes and other than these exceptions the transcription of genes is an infrequent phenomenon in a yeast cell.

An efficient method for genome-wide polyadenylation site mapping and RNA quantification

The use of alternative poly(A) sites is common and affects the post-transcriptional fate of mRNA, including its stability, subcellular localization and translation. Here, we present a method to identify poly(A) sites in a genome-wide and strand-specific manner. This method, termed 3′T-fill, initially fills in the poly(A) stretch with unlabeled dTTPs, allowing sequencing to start directly after the poly(A) tail into the 3′-untranslated regions (UTR). Our comparative analysis demonstrates that it outperforms existing protocols in quality and throughput and accurately quantifies RNA levels as only one read is produced from each transcript. We use this method to characterize the diversity of polyadenylation in Saccharomyces cerevisiae, showing that alternative RNA molecules are present even in a genetically identical cell population. Finally, we observe that overlap of convergent 3′-UTRs is frequent but sharply limited by coding regions, suggesting factors that restrict compression of the yeast genome.

Rrp6p Controls mRNA Poly(A) Tail Length and Its Decoration with Poly(A) Binding Proteins

Poly(A) (pA) tail binding proteins (PABPs) control mRNA polyadenylation, stability, and translation. In a purified system, S. cerevisiae PABPs, Pab1p and Nab2p, are individually sufficient to provide normal pA tail length. However, it is unknown how this occurs in more complex environments. Here we find that the nuclear exosome subunit Rrp6p counteracts the in vitro and in vivo extension of mature pA tails by the noncanonical pA polymerase Trf4p. Moreover, PABP loading onto nascent pA tails is controlled by Rrp6p; while Pab1p is the major PABP, Nab2p only associates in the absence of Rrp6p. This is because Rrp6p can interact with Nab2p and displace it from pA tails, potentially leading to RNA turnover, as evidenced for certain pre-mRNAs. We suggest that a nuclear mRNP surveillance step involves targeting of Rrp6p by Nab2p-bound pA-tailed RNPs and that pre-mRNA abundance is regulated at this level.

Association of condensin with chromosomes depends on DNA binding by its HEAT-repeat subunits

Condensin complexes have central roles in the three-dimensional organization of chromosomes during cell divisions, but how they interact with chromatin to promote chromosome segregation is largely unknown. Previous work has suggested that condensin, in addition to encircling chromatin fibers topologically within the ring-shaped structure formed by its SMC and kleisin subunits, contacts DNA directly. Here we describe the discovery of a binding domain for double-stranded DNA formed by the two HEAT-repeat subunits of the Saccharomyces cerevisiae condensin complex. From detailed mapping data of the interfaces between the HEAT-repeat and kleisin subunits, we generated condensin complexes that lack one of the HEAT-repeat subunits and consequently fail to associate with chromosomes in yeast and human cells. The finding that DNA binding by condensins HEAT-repeat subunits stimulates the SMC ATPase activity suggests a multistep mechanism for the loading of condensin onto chromosomes.

Regulon-specific control of transcription elongation across the yeast genome.

Transcription elongation by RNA polymerase II was often considered an invariant non-regulated process. However, genome-wide studies have shown that transcriptional pausing during elongation is a frequent phenomenon in tightly-regulated metazoan genes. Using a combination of ChIP-on-chip and genomic run-on approaches, we found that the proportion of transcriptionally active RNA polymerase II (active versus total) present throughout the yeast genome is characteristic of some functional gene classes, like those related to ribosomes and mitochondria. This proportion also responds to regulatory stimuli mediated by protein kinase A and, in relation to cytosolic ribosomal-protein genes, it is mediated by the silencing domain of Rap1. We found that this inactive form of RNA polymerase II, which accumulates along the full length of ribosomal protein genes, is phosphorylated in the Ser5 residue of the CTD, but is hypophosphorylated in Ser2. Using the same experimental approach, we show that the in vivo–depletion of FACT, a chromatin-related elongation factor, also produces a regulon-specific effect on the expression of the yeast genome. This work demonstrates that the regulation of transcription elongation is a widespread, gene class–dependent phenomenon that also affects housekeeping genes.

Alternative polyadenylation diversifies post‐transcriptional regulation by selective RNA–protein interactions

Recent research has uncovered extensive variability in the boundaries of transcript isoforms, yet the functional consequences of this variation remain largely unexplored. Here, we systematically discriminate between the molecular phenotypes of overlapping coding and non‐coding transcriptional events from each genic locus using a novel genome‐wide, nucleotide‐resolution technique to quantify the half‐lives of 3′ transcript isoforms in yeast. Our results reveal widespread differences in stability among isoforms for hundreds of genes in a single condition, and that variation of even a single nucleotide in the 3′ untranslated region (UTR) can affect transcript stability. While previous instances of negative associations between 3′ UTR length and transcript stability have been reported, here, we find that shorter isoforms are not necessarily more stable. We demonstrate the role of RNA‐protein interactions in conditioning isoform‐specific stability, showing that PUF3 binds and destabilizes specific polyadenylation isoforms. Our findings indicate that although the functional elements of a gene are encoded in DNA sequence, the selective incorporation of these elements into RNA through transcript boundary variation allows a single gene to have diverse functional consequences.