Yeast Transcriptome Research Articles

Transcriptomic balance and optimal growth are determined by cell size

Cell size and growth are intimately related across the evolutionary scale, but whether cell size is important to attain maximal growth or fitness is still an open question. We show that growth rate is a non-monotonic function of cell volume, with maximal values around the critical size of wild-type yeast cells. The transcriptome of yeast and mouse cells undergoes a relative inversion in response to cell size, which we associate theoretically and experimentally with the necessary genome-wide diversity in RNA polymerase II affinity for promoters. Although highly expressed genes impose strong negative effects on fitness when the DNA/mass ratio is reduced, transcriptomic alterations mimicking the relative inversion by cell size strongly restrain cell growth. In all, our data indicate that cells set the critical size to obtain a properly balanced transcriptome and, as a result, maximize growth and fitness during proliferation.

Deep mutational scanning quantifies DNA binding and predicts clinical outcomes of PAX6 variants.

Nonsense and missense mutations in the transcription factor PAX6 cause a wide range of eye development defects, including aniridia, microphthalmia and coloboma. To understand how changes of PAX6:DNA binding cause these phenotypes, we combined saturation mutagenesis of the paired domain of PAX6 with a yeast one-hybrid (Y1H) assay in which expression of a PAX6-GAL4 fusion gene drives antibiotic resistance. We quantified binding of more than 2700 single amino-acid variants to two DNA sequence elements. Mutations in DNA-facing residues of the N-terminal subdomain and linker region were most detrimental, as were mutations to prolines and to negatively charged residues. Many variants caused sequence-specific molecular gain-of-function effects, including variants in position 71 that increased binding to the LE9 enhancer but decreased binding to a SELEX-derived binding site. In the absence of antibiotic selection, variants that retained DNA binding slowed yeast growth, likely because such variants perturbed the yeast transcriptome. Benchmarking against known patient variants and applying ACMG/AMP guidelines to variant classification, we obtained supporting-to-moderate evidence that 977 variants are likely pathogenic and 1306 are likely benign. Our analysis shows that most pathogenic mutations in the paired domain of PAX6 can be explained simply by the effects of these mutations on PAX6:DNA association, and establishes Y1H as a generalisable assay for the interpretation of variant effects in transcription factors.

Extracellular Self-DNA Effects on Yeast Cell Cycle and Transcriptome during Batch Growth.

The cell cycle and the transcriptome dynamics of yeast exposed to extracellular self-DNA during an aerobic batch culture on glucose have been investigated using cytofluorimetric and RNA-seq analyses. In parallel, the same study was conducted on yeast cells growing in the presence of (heterologous) nonself-DNA. The self-DNA treatment determined a reduction in the growth rate and a major elongation of the diauxic lag phase, as well as a significant delay in the achievement of the stationary phase. This was associated with significant changes in the cell cycle dynamics, with slower exit from the G0 phase, followed by an increased level of cell percentage in the S phase, during the cultivation. Comparatively, the exposure to heterologous DNA did not affect the growth curve and the cell cycle dynamics. The transcriptomic analysis showed that self-DNA exposure produced a generalized downregulation of transmembrane transport and an upregulation of genes associated with sulfur compounds and the pentose phosphate pathway. Instead, in the case of the nonself treatment, a clear response to nutrient deprivation was detected. Overall, the presented findings represent further insights into the complex functional mechanisms of self-DNA inhibition.

The influence of flocculation upon global gene transcription in a yeast CYC8 mutant.

The transcriptome from a Saccharomyces cerevisiae tup1 deletion mutant was one of the first comprehensive yeast transcriptomes published. Subsequent transcriptomes from tup1 and cyc8 mutants firmly established the Tup1-Cyc8 complex as predominantly acting as a repressor of gene transcription. However, transcriptomes from tup1/cyc8 gene deletion or conditional mutants would all have been influenced by the striking flocculation phenotypes that these mutants display. In this study, we have separated the impact of flocculation from the transcriptome in a cyc8 conditional mutant to reveal those genes (i) subject solely to Cyc8p-dependent regulation, (ii) regulated by flocculation only and (iii) regulated by Cyc8p and further influenced by flocculation. We reveal a more accurate list of Cyc8p-regulated genes that includes newly identified Cyc8p-regulated genes that were masked by the flocculation phenotype and excludes genes which were indirectly influenced by flocculation and not regulated by Cyc8p. Furthermore, we show evidence that flocculation exerts a complex and potentially dynamic influence upon global gene transcription. These data should be of interest to future studies into the mechanism of action of the Tup1-Cyc8 complex and to studies involved in understanding the development of flocculation and its impact upon cell function.

An ultra high-throughput, massively multiplexable, single-cell RNA-seq platform in yeasts.

Yeasts are naturally diverse, genetically tractable, and easy to grow such that researchers can investigate any number of genotypes, environments, or interactions thereof. However, studies of yeast transcriptomes have been limited by the processing capabilities of traditional RNA sequencing techniques. Here we optimize a powerful, high-throughput single-cell RNA sequencing (scRNAseq) platform, SPLiT-seq (Split Pool Ligation-based Transcriptome sequencing), for yeasts and apply it to 43,388 cells of multiple species and ploidies. This platform utilizes a combinatorial barcoding strategy to enable massively parallel RNA sequencing of hundreds of yeast genotypes or growth conditions at once. This method can be applied to most species or strains of yeast for a fraction of the cost of traditional scRNAseq approaches. Thus, our technology permits researchers to leverage "the awesome power of yeast" by allowing us to survey the transcriptome of hundreds of strains and environments in a short period of time and with no specialized equipment. The key to this method is that sequential barcodes are probabilistically appended to cDNA copies of RNA while the molecules remain trapped inside of each cell. Thus, the transcriptome of each cell is labeled with a unique combination of barcodes. Since SPLiT-seq uses the cell membrane as a container for this reaction, many cells can be processed together without the need to physically isolate them from one another in separate wells or droplets. Further, the first barcode in the sequence can be chosen intentionally to identify samples from different environments or genetic backgrounds, enabling multiplexing of hundreds of unique perturbations in a single experiment. In addition to greater multiplexing capabilities, our method also facilitates a deeper investigation of biological heterogeneity, given its single-cell nature. For example, in the data presented here, we detect transcriptionally distinct cell states related to cell cycle, ploidy, metabolic strategies, and so forth, all within clonal yeast populations grown in the same environment. Hence, our technology has two obvious and impactful applications for yeast research: the first is the general study of transcriptional phenotypes across many strains and environments, and the second is investigating cell-to-cell heterogeneity across the entire transcriptome.

Post-transcriptional regulation shapes the transcriptome of quiescent budding yeast.

To facilitate long-term survival, cells must exit the cell cycle and enter quiescence, a reversible non-replicative state. Budding yeast cells reprogram their gene expression during quiescence entry to silence transcription, but how the nascent transcriptome changes in quiescence is unknown. By investigating the nascent transcriptome, we identified over a thousand noncoding RNAs in quiescent and G1 yeast cells, and found noncoding transcription represented a larger portion of the quiescent transcriptome than in G1. Additionally, both mRNA and ncRNA are subject to increased post-transcriptional regulation in quiescence compared to G1. We found that, in quiescence, the nuclear exosome-NNS pathway suppresses over one thousand mRNAs, in addition to canonical noncoding RNAs. RNA sequencing through quiescent entry revealed two distinct time points at which the nuclear exosome controls the abundance of mRNAs involved in protein production, cellular organization, and metabolism, thereby facilitating efficient quiescence entry. Our work identified a previously unknown key biological role for the nuclear exosome-NNS pathway in mRNA regulation and uncovered a novel layer of gene-expression control in quiescence.

M5U-GEPred: prediction of RNA 5-methyluridine sites based on sequence-derived and graph embedding features.

5-Methyluridine (m5U) is one of the most common post-transcriptional RNA modifications, which is involved in a variety of important biological processes and disease development. The precise identification of the m5U sites allows for a better understanding of the biological processes of RNA and contributes to the discovery of new RNA functional and therapeutic targets. Here, we present m5U-GEPred, a prediction framework, to combine sequence characteristics and graph embedding-based information for m5U identification. The graph embedding approach was introduced to extract the global information of training data that complemented the local information represented by conventional sequence features, thereby enhancing the prediction performance of m5U identification. m5U-GEPred outperformed the state-of-the-art m5U predictors built on two independent species, with an average AUROC of 0.984 and 0.985 tested on human and yeast transcriptomes, respectively. To further validate the performance of our newly proposed framework, the experimentally validated m5U sites identified from Oxford Nanopore Technology (ONT) were collected as independent testing data, and in this project, m5U-GEPred achieved reasonable prediction performance with ACC of 91.84%. We hope that m5U-GEPred should make a useful computational alternative for m5U identification.

Changing course: Glucose starvation drives nuclear accumulation of Hexokinase 2 in S. cerevisiae.

Glucose is the preferred carbon source for most eukaryotes, and the first step in its metabolism is phosphorylation to glucose-6-phosphate. This reaction is catalyzed by hexokinases or glucokinases. The yeast Saccharomyces cerevisiae encodes three such enzymes, Hxk1, Hxk2, and Glk1. In yeast and mammals, some isoforms of this enzyme are found in the nucleus, suggesting a possible moonlighting function beyond glucose phosphorylation. In contrast to mammalian hexokinases, yeast Hxk2 has been proposed to shuttle into the nucleus in glucose-replete conditions, where it reportedly moonlights as part of a glucose-repressive transcriptional complex. To achieve its role in glucose repression, Hxk2 reportedly binds the Mig1 transcriptional repressor, is dephosphorylated at serine 15 and requires an N-terminal nuclear localization sequence (NLS). We used high-resolution, quantitative, fluorescent microscopy of live cells to determine the conditions, residues, and regulatory proteins required for Hxk2 nuclear localization. Countering previous yeast studies, we find that Hxk2 is largely excluded from the nucleus under glucose-replete conditions but is retained in the nucleus under glucose-limiting conditions. We find that the Hxk2 N-terminus does not contain an NLS but instead is necessary for nuclear exclusion and regulating multimerization. Amino acid substitutions of the phosphorylated residue, serine 15, disrupt Hxk2 dimerization but have no effect on its glucose-regulated nuclear localization. Alanine substation at nearby lysine 13 affects dimerization and maintenance of nuclear exclusion in glucose-replete conditions. Modeling and simulation provide insight into the molecular mechanisms of this regulation. In contrast to earlier studies, we find that the transcriptional repressor Mig1 and the protein kinase Snf1 have little effect on Hxk2 localization. Instead, the protein kinase Tda1 regulates Hxk2 localization. RNAseq analyses of the yeast transcriptome dispels the idea that Hxk2 moonlights as a transcriptional regulator of glucose repression, demonstrating that Hxk2 has a negligible role in transcriptional regulation in both glucose-replete and limiting conditions. Our studies define a new model of cis- and trans-acting regulators of Hxk2 dimerization and nuclear localization. Based on our data, the nuclear translocation of Hxk2 in yeast occurs in glucose starvation conditions, which aligns well with the nuclear regulation of mammalian orthologs. Our results lay the foundation for future studies of Hxk2 nuclear activity.

Velvet Family Members Regulate Pigment Synthesis of the Fruiting Bodies of Auricularia cornea.

Color is a crucial feature to consider when breeding and improving strains of Auricularia cornea. To uncover the mechanism of white strain formation in A. cornea, this study selected parental strains that were homozygous for the color trait and analyzed the genetic laws of A. cornea color through genetic population construction, such as test-cross, back-cross, and self-cross populations, and the statistical analysis of color trait segregation. Moreover, the study developed SSR molecular markers to construct a genetic linkage map, perform the fine mapping the color-controlling genetic locus, and verify candidate genes using yeast two-hybrid, transcriptome analysis, and different light treatments. The results of the study indicated that the color trait of A. cornea is controlled by two pairs of alleles. When both pairs of loci are dominant, the fruiting body is purple, while when both pairs of loci are recessive or one pair of loci is recessive, the fruiting body is white. Based on the linkage map, the study finely mapped the color locus within Contig9_29,619bp-53,463bp in the A. cornea genome and successfully predicted the color-controlling locus gene A18078 (AcveA), which belongs to the Velvet factor family protein and has a conserved structure domain of the VeA protein. It can form a dimer with the VelB protein to inhibit pigment synthesis in filamentous fungi. Lastly, the study validated the interaction between AcVeA and VelB (AcVelB) in A. cornea at the gene, protein, and phenotype levels, revealing the mechanism of inhibition of pigment synthesis in A. cornea. Under dark conditions, dimerization occurs, allowing it to enter the nucleus and inhibit pigment synthesis, leading to a lighter fruiting body color. However, under light conditions, the dimer content is low and cannot enter the nucleus to inhibit pigment synthesis. In summary, this study clarified the mechanism of white strain formation in A. cornea, which could aid in improving white strains of A. cornea and studying the genetic basis of color in other fungi.

Transcriptional Response of Multi-Stress-Tolerant Saccharomyces cerevisiae to Sequential Stresses

During the fermentation process, yeast cells face different stresses, and their survival and fermentation efficiency depend on their adaptation to these challenging conditions. Yeast cells must tolerate not only a single stress but also multiple simultaneous and sequential stresses. However, the adaptation and cellular response when cells are sequentially stressed are not completely understood. To explore this, we exposed a multi-stress-tolerant strain (BT0510) to different consecutive stresses to globally explore a common response, focusing on the genes induced in both stresses. Gene Ontology, pathway analyses, and common transcription factor motifs identified many processes linked to this common response. A metabolic shift to the pentose phosphate pathway, peroxisome activity, and the oxidative stress response were some of the processes found. The SYM1, STF2, and HSP genes and the transcription factors Adr1 and Usv1 may play a role in this response. This study presents a global view of the transcriptome of a multi-resistance yeast and provides new insights into the response to sequential stresses. The identified response genes can indicate future directions for the genetic engineering of yeast strains, which could improve many fermentation processes, such as those used for bioethanol production and beverages.

A cap 0-dependent mRNA capture method to analyze the yeast transcriptome.

Analysis of the protein coding transcriptome by the RNA sequencing requires either enrichment of the desired fraction of coding transcripts or depletion of the abundant non-coding fraction consisting mainly of rRNA. We propose an alternative mRNA enrichment strategy based on the RNA-binding properties of the human IFIT1, an antiviral protein recognizing cap 0 RNA. Here, we compare for Saccharomyces cerevisiae an IFIT1-based mRNA pull-down with yeast targeted rRNA depletion by the RiboMinus method. IFIT1-based RNA capture depletes rRNA more effectively, producing high quality RNA-seq data with an excellent coverage of the protein coding transcriptome, while depleting cap-less transcripts such as mitochondrial or some non-coding RNAs. We propose IFIT1 as a cost effective and versatile tool to prepare mRNA libraries for a variety of organisms with cap 0 mRNA ends, including diverse plants, fungi and eukaryotic microbes.

P440 General protocol applied to the identification and production of new biomarkers with potential use for the diagnosis of histoplasmosis

Poster session 3, September 23, 2022, 12:30 PM - 1:30 PMObjective: To identify and produce novel biomarkers with potential use for the specific diagnosis of H. capsulatum infection.MethodsHere, we design a novel strategy to search and select new Candidate genes for biomarkers that integrates the use of a computational analysis model that includes the application of bioinformatic tools such as OrthoMCL, BLASTp, TargetP, and SignalP, applied on a local collection of proteome database obtained manually from GenBank-NCBI, and the analysis of previously published biological and experimental data sets, including a secreted proteome database obtained from pathogenic yeast-phase H. capsulatum culture filtrates, a Histoplasma yeast and mycelial transcriptomes database, and a urine-peptides database from Histoplasma-immunoassay-positive patients.For the synthesis of the Candidates, an internal protocol for the production of recombinant proteins in prokaryotic and eukaryotic systems was applied. Obtaining polyclonal antibodies (PAb) specific for each biomarker was carried out by adapting a rapid immunization protocol for BALB/c mice.Finally, the computational model was experimentally validated, evaluating the reactivity and specificity of PAb anti-Histoplasma with fungus culture extracts and samples from patients with histoplasmosis.ResultsUsing the computational analysis model, 2 Candidate genes for diagnostic biomarkers were identified. Subsequently, the construction of expression vector for each Candidate and the production of these genes were achieved using a standardized protocol for the production of recombinant proteins.Polyclonal antibodies (PAb) anti-histoplasma were obtained and shown to be reactive against purified H. capsulatum-antigens. Finally, we confirmed the presence of these antigens in yeast culture extracts of H. capsulatum and demonstrated the immunoreactivity of anti-Histoplasma PAb with urine samples from patients previously diagnosed with histoplasmosis.ConclusionThe generation of novel strategies that combine data analysis, computational tools, and transcriptomic and proteomic techniques could be very useful for the identification of new biomarker genes and the development of microbiological diagnostic tests for important pathogens.

Origins, evolution, and physiological implications of de novo genes in yeast.

De novo gene birth is the process by which new genes emerge in sequences that were previously noncoding. Over the past decade, researchers have taken advantage of the power of yeast as a model and a tool to study the evolutionary mechanisms and physiological implications of de novo gene birth. We summarize the mechanisms that have been proposed to explicate how noncoding sequences can become protein‐coding genes, highlighting the discovery of pervasive translation of the yeast transcriptome and its presumed impact on evolutionary innovation. We summarize current best practices for the identification and characterization of de novo genes. Crucially, we explain that the field is still in its nascency, with the physiological roles of most young yeast de novo genes identified thus far still utterly unknown. We hope this review inspires researchers to investigate the true contribution of de novo gene birth to cellular physiology and phenotypic diversity across yeast strains and species.

Role of ROX1, SKN7, and YAP6 Stress Transcription Factors in the Production of Secondary Metabolites in Xanthophyllomyces dendrorhous.

Xanthophyllomyces dendrorhous is a natural source of astaxanthin and mycosporines. This yeast has been isolated from high and cold mountainous regions around the world, and the production of these secondary metabolites may be a survival strategy against the stress conditions present in its environment. Biosynthesis of astaxanthin is regulated by catabolic repression through the interaction between MIG1 and corepressor CYC8–TUP1. To evaluate the role of the stress-associated transcription factors SKN7, ROX1, and YAP6, we employed an omic and phenotypic approach. Null mutants were constructed and grown in two fermentable carbon sources. The yeast proteome and transcriptome were quantified by iTRAQ and RNA-seq, respectively. The total carotenoid, sterol, and mycosporine contents were determined and compared to the wild-type strain. Each mutant strain showed significant metabolic changes compared to the wild type that were correlated to its phenotype. In a metabolic context, the principal pathways affected were glycolysis/gluconeogenesis, the pentose phosphate (PP) pathway, and the citrate (TCA) cycle. Additionally, fatty acid synthesis was affected. The absence of ROX1 generated a significant decline in carotenoid production. In contrast, a rise in mycosporine and sterol synthesis was shown in the absence of the transcription factors SKN7 and YAP6, respectively.

A myosin XI adaptor, TAPE, is essential for pollen tube elongation in rice.

Pollen tube (PT) elongation is important for double fertilization in angiosperms and affects the seed-setting rate and, therefore, crop productivity. Compared to Arabidopsis (Arabidopsis thaliana L.), information on PT elongation in rice (Oryza sativa L.) is limited by the difficulty in obtaining homozygous mutants. In a screen of T-DNA insertional mutants, we identified a mutant in the Tethering protein of actomyosin transport in pollen tube elongation (TAPE) gene with an unusual segregation ratio by genotyping analysis. A CRISPR/Cas9 knockout mutant of TAPE that produced a short PT was sterile, and TAPE was expressed specifically in pollen grains. TAPE is a homolog of a myosin XI adaptor in Arabidopsis with three tetratricopeptide repeat and Phox and Bem1 protein domains. TAPE showed latrunculin B-sensitive, actin-dependent localization to the endoplasmic reticulum. Yeast two-hybrid screening and transcriptome analysis revealed that TAPE interacted with pollen-specific LIM protein 2b and elongation factor 1-alpha. Loss of TAPE affected transcription of 1,259 genes, especially genes related to cell organization, which were downregulated. In summary, TAPE encodes a myosin XI adaptor essential for rice PT elongation.

Genome and transcriptome of a pathogenic yeast, Candida nivariensis.

We present a highly contiguous genome and transcriptome of the pathogenic yeast, Candida nivariensis. We sequenced both the DNA and RNA of this species using both the Oxford Nanopore Technologies and Illumina platforms. We assembled the genome into an 11.8 Mb draft composed of 16 contigs with an N50 of 886 Kb, including a circular mitochondrial sequence of 28 Kb. Using direct RNA nanopore sequencing and Illumina cDNA sequencing, we constructed an annotation of our new assembly, supplemented by lifting over genes from Saccharomyces cerevisiae and Candida glabrata.

RNA-seq-based transcriptomic comparison of Saccharomyces cerevisiae during spontaneous and inoculated fermentations of organic and conventional grapes

Viticulture practices influence yeast genetics and phenotypes during wine fermentation by affecting grape quality and yeast microflora attached to grape berries, but the influence is limited understood yet. The aim of this study was to evaluate the impact of organic viticulture on wine quality and yeast transcriptome-wide response, and their associated mechanisms. This work compared the yeast transcriptional response during the spontaneous and inoculated fermentation of grapes from organic and conventional agriculture systems, through genome-wide analysis of gene expression, by Illumina Hiseq sequencing platforms. Transcriptome profiling showed that 991 and 96 differentially expressed genes (DEGs) were detected in spontaneous and inoculated fermentations, respectively, indicating that organic viticulture was more likely to affect yeast transcriptome by influencing yeast microflora rather than grape/juice quality. Several pathways of Saccharomyces cerevisiae had more up-regulated DEGs than down-regulated ones in organic samples and in spontaneous fermentation group, including central carbon metabolism, translation, transport and catabolism, and energy metabolism. Besides, viticulture practices and fermentation modes had specific effects on wine quality. This study could enhance the understanding of yeast transcription changes during the fermentation of different grapes and with different strains, as well as elucidate the effect of viticulture practice on yeast transcriptome and wine quality.

Weighted Consensus Segmentations

The problem of segmenting linearly ordered data is frequently encountered in time-series analysis, computational biology, and natural language processing. Segmentations obtained independently from replicate data sets or from the same data with different methods or parameter settings pose the problem of computing an aggregate or consensus segmentation. This Segmentation Aggregation problem amounts to finding a segmentation that minimizes the sum of distances to the input segmentations. It is again a segmentation problem and can be solved by dynamic programming. The aim of this contribution is (1) to gain a better mathematical understanding of the Segmentation Aggregation problem and its solutions and (2) to demonstrate that consensus segmentations have useful applications. Extending previously known results we show that for a large class of distance functions only breakpoints present in at least one input segmentation appear in the consensus segmentation. Furthermore, we derive a bound on the size of consensus segments. As show-case applications, we investigate a yeast transcriptome and show that consensus segments provide a robust means of identifying transcriptomic units. This approach is particularly suited for dense transcriptomes with polycistronic transcripts, operons, or a lack of separation between transcripts. As a second application, we demonstrate that consensus segmentations can be used to robustly identify growth regimes from sets of replicate growth curves.

Detecting N6-methyladenosine sites from RNA transcriptomes using random forest

N6-methyladenosine (m6A) modifications are one the most frequently occurring RNA post transcriptional modifications. These modifications perform vital roles in different biological processes, including, localization and translation of proteins, X chromosome inactivation, cell stability, microRNA regulation, and reprogramming etc. Any abnormal change in m6A sites may lead to several abnormalities, including, cancer, brain-related disorders and many other life threatening diseases. Precise detection of m6A modifications is crucial for the diagnosis and treatment of these diseases. Existing methods suffer from the problem of inefficient detection of m6A sites, especially in yeast transcriptomes (due to varied structure) and inability of the computational techniques to capture the encoded information surrounding the m6A sites. In this work, we propose a novel method (called m6A-pred predictor) that utilizes a fusion of characteristics including, statistical, and chemical properties of the nucleotides, to precisely predict the presence of m6A sites in RNA sequences. The fusion of multiple types of features results in a high dimensional vector which is further optimized using an evolutionary algorithm. Finally, the random forest classifier is used to detect m6A sites by using the most discriminative features. The results, benchmarked on yeast transcriptomes, indicate that m6A-pred predictor outperforms all the previously reported predictors, notably, with an accuracy value of 78.58%, specificity value of 79.65% and Matthews correlation coefficient of 0.5717.

Degradation of Non-coding RNAs Promotes Recycling of Termination Factors at Sites of Transcription

A large share of the non-coding transcriptome in yeast is controlled by the Nrd1-Nab3-Sen1 (NNS) complex, which promotes transcription termination of non-coding RNA (ncRNA) genes, and by the nuclear exosome, which limits the steady-state levels of the transcripts produced. How unconstrained ncRNA levels affect RNA metabolism and gene expression are long-standing and important questions. Here, we show that degradation of ncRNAs by the exosome is required for freeing Nrd1 and Nab3 from the released transcript after termination. In exosome mutants, these factors are sequestered by ncRNAs and cannot be efficiently recycled to sites of transcription, inducing termination defects at NNS targets. ncRNA-dependent, genome-wide termination defects can be recapitulated by the expression of a degradation-resistant, circular RNA containing a natural NNS target in exosome-proficient cells. Our results have important implications for the mechanism of termination, the general impact of ncRNAs abundance, and the importance of nuclear ncRNA degradation.