A rapidly reversible mutation generates subclonal genetic diversity and unstable drug resistance.

Most genetic changes have negligible reversion rates. As most mutations that confer resistance to an adverse condition (e.g., drug treatment) also confer a growth defect in its absence, it is challenging for cells to genetically adapt to transient environmental changes. Here, we identify a set of rapidly reversible drug-resistance mutations in Schizosaccharomyces pombe that are caused by microhomology-mediated tandem duplication (MTD) and reversion back to the wild-type sequence. Using 10,000× coverage whole-genome sequencing, we identify nearly 6,000 subclonal MTDs in a single clonal population and determine, using machine learning, how MTD frequency is encoded in the genome. We find that sequences with the highest-predicted MTD rates tend to generate insertions that maintain the correct reading frame, suggesting that MTD formation has shaped the evolution of coding sequences. Our study reveals a common mechanism of reversible genetic variation that is beneficial for adaptation to environmental fluctuations and facilitates evolutionary divergence.

Synchronized replication of genes encoding the same protein complex in fast-proliferating cells.

DNA replication perturbs the dosage balance among genes; at mid-S phase, early-replicating genes have doubled their copies while late-replicating ones have not. Dosage imbalance among genes, especially within members of a protein complex, is toxic to cells. However, the molecular mechanisms that cells use to deal with such imbalance remain not fully understood. Here, we validate at the genomic scale that the dosage between early- and late-replicating genes is imbalanced in HeLa cells. We propose the synchronized replication hypothesis that genes sensitive to stoichiometric relationships will be replicated simultaneously to maintain stoichiometry. In support of this hypothesis, we observe that genes encoding the same protein complex have similar replication timing but mainly in fast-proliferating cells such as embryonic stem cells and cancer cells. We find that the synchronized replication observed in cancer cells, but not in slow-proliferating differentiated cells, is due to convergent evolution during tumorigenesis that restores synchronized replication timing within protein complexes. Taken together, our study reveals that the demand for dosage balance during S phase plays an important role in the optimization of the replication-timing program; this selection is relaxed during differentiation as the cell cycle prolongs and is restored during tumorigenesis as the cell cycle shortens.

A conserved expression signature predicts growth rate and reveals cell & lineage-specific differences.

Isogenic cells cultured together show heterogeneity in their proliferation rate. To determine the differences between fast and slow-proliferating cells, we developed a method to sort cells by proliferation rate, and performed RNA-seq on slow and fast proliferating subpopulations of pluripotent mouse embryonic stem cells (mESCs) and mouse fibroblasts. We found that slowly proliferating mESCs have a more naïve pluripotent character. We identified an evolutionarily conserved proliferation-correlated transcriptomic signature that is common to all eukaryotes: fast cells have higher expression of genes for protein synthesis and protein degradation. This signature accurately predicted growth rate in yeast and cancer cells, and identified lineage-specific proliferation dynamics during development, using C. elegans scRNA-seq data. In contrast, sorting by mitochondria membrane potential revealed a highly cell-type specific mitochondria-state related transcriptome. mESCs with hyperpolarized mitochondria are fast proliferating, while the opposite is true for fibroblasts. The mitochondrial electron transport chain inhibitor antimycin affected slow and fast subpopulations differently. While a major transcriptional-signature associated with cell-to-cell heterogeneity in proliferation is conserved, the metabolic and energetic dependency of cell proliferation is cell-type specific.

An experimental assay of the interactions of amino acids from orthologous sequences shaping a complex fitness landscape.

Characterizing the fitness landscape, a representation of fitness for a large set of genotypes, is key to understanding how genetic information is interpreted to create functional organisms. Here we determined the evolutionarily-relevant segment of the fitness landscape of His3, a gene coding for an enzyme in the histidine synthesis pathway, focusing on combinations of amino acid states found at orthologous sites of extant species. Just 15% of amino acids found in yeast His3 orthologues were always neutral while the impact on fitness of the remaining 85% depended on the genetic background. Furthermore, at 67% of sites, amino acid replacements were under sign epistasis, having both strongly positive and negative effect in different genetic backgrounds. 46% of sites were under reciprocal sign epistasis. The fitness impact of amino acid replacements was influenced by only a few genetic backgrounds but involved interaction of multiple sites, shaping a rugged fitness landscape in which many of the shortest paths between highly fit genotypes are inaccessible.

Promoter architecture determines cotranslational regulation of mRNA.

Information that regulates gene expression is encoded throughout each gene but if different regulatory regions can be understood in isolation, or if they interact, is unknown. Here we measure mRNA levels for 10,000 open reading frames (ORFs) transcribed from either an inducible or constitutive promoter. We find that the strength of cotranslational regulation on mRNA levels is determined by promoter architecture. By using a novel computational genetic screen of 6402 RNA-seq experiments, we identify the RNA helicase Dbp2 as the mechanism by which cotranslational regulation is reduced specifically for inducible promoters. Finally, we find that for constitutive genes, but not inducible genes, most of the information encoding regulation of mRNA levels in response to changes in growth rate is encoded in the ORF and not in the promoter. Thus, the ORF sequence is a major regulator of gene expression, and a nonlinear interaction between promoters and ORFs determines mRNA levels.

Large-scale mapping of gene regulatory logic reveals context-dependent repression by transcriptional activators.

Transcription factors (TFs) are key mediators that propagate extracellular and intracellular signals through to changes in gene expression profiles. However, the rules by which promoters decode the amount of active TF into target gene expression are not well understood. To determine the mapping between promoter DNA sequence, TF concentration, and gene expression output, we have conducted in budding yeast a large-scale measurement of the activity of thousands of designed promoters at six different levels of TF. We observe that maximum promoter activity is determined by TF concentration and not by the number of binding sites. Surprisingly, the addition of an activator site often reduces expression. A thermodynamic model that incorporates competition between neighboring binding sites for a local pool of TF molecules explains this behavior and accurately predicts both absolute expression and the amount by which addition of a site increases or reduces expression. Taken together, our findings support a model in which neighboring binding sites interact competitively when TF is limiting but otherwise act additively.

Recurrence-based information processing in gene regulatory networks.

Cellular information processing is generally attributed to the complex networks of genes and proteins that regulate cell behavior. It is still unclear, however, what are the main features of those networks that allow a cell to encode and interpret its ever changing environment. Here, we address this question by studying the computational capabilities of the transcriptional regulatory networks of five evolutionary distant organisms. We identify in all cases a cyclic recurrent structure, formed by a small core of genes, that is essential for dynamical encoding and information integration. The recent history of the cell is projected nonlinearly into this recurrent reservoir of nodes, where it is encoded by its transient dynamics, while the rest of the network forms a readout layer devoted to decode and interpret the high-dimensional dynamical state of the recurrent core. In that way, gene regulatory networks act as echo-state networks that perform optimally in standard memory-demanding tasks, with most of their memory residing in the recurrent reservoir. The biological significance of these results is analyzed in the particular case of the bacterium Escherichia coli. Our work thus suggests that recurrent nonlinear dynamics is a key element for the processing of complex time-dependent information by cells.

Promoter sequence determines the relationship between expression level and noise.

The ability of cells to accurately control gene expression levels in response to extracellular cues is limited by the inherently stochastic nature of transcriptional regulation. A change in transcription factor (TF) activity results in changes in the expression of its targets, but the way in which cell-to-cell variability in expression (noise) changes as a function of TF activity, and whether targets of the same TF behave similarly, is not known. Here, we measure expression and noise as a function of TF activity for 16 native targets of the transcription factor Zap1 that are regulated by it through diverse mechanisms. For most activated and repressed Zap1 targets, noise decreases as expression increases. Kinetic modeling suggests that this is due to two distinct Zap1-mediated mechanisms that both change the frequency of transcriptional bursts. Notably, we found that another mechanism of repression by Zap1, which is encoded in the promoter DNA, likely decreases the size of transcriptional bursts, producing a unique transcriptional state characterized by low expression and low noise. In addition, we find that further reduction in noise is achieved when a single TF both activates and represses a single target gene. Our results suggest a global principle whereby at low TF concentrations, the dominant source of differences in expression between promoters stems from differences in burst frequency, whereas at high TF concentrations differences in burst size dominate. Taken together, we show that the precise amount by which noise changes with expression is specific to the regulatory mechanism of transcription and translation that acts at each gene.

Huxley's revenge: cell-cycle entry, positive feedback, and the G1 cyclins.

In a recent issue of Nature, Skotheim et al. (2008) show that a transcriptional positive feedback loop plays a key role in the commitment to enter the yeast cell cycle.

Deciphering the rules by which 5'-UTR sequences affect protein expression in yeast.

The 5'-untranslated region (5'-UTR) of mRNAs contains elements that affect expression, yet the rules by which these regions exert their effect are poorly understood. Here, we studied the impact of 5'-UTR sequences on protein levels in yeast, by constructing a large-scale library of mutants that differ only in the 10 bp preceding the translational start site of a fluorescent reporter. Using a high-throughput sequencing strategy, we obtained highly accurate measurements of protein abundance for over 2,000 unique sequence variants. The resulting pool spanned an approximately sevenfold range of protein levels, demonstrating the powerful consequences of sequence manipulations of even 1-10 nucleotides immediately upstream of the start codon. We devised computational models that predicted over 70% of the measured expression variability in held-out sequence variants. Notably, a combined model of the most prominent features successfully explained protein abundance in an additional, independently constructed library, whose nucleotide composition differed greatly from the library used to parameterize the model. Our analysis reveals the dominant contribution of the start codon context at positions -3 to -1, mRNA secondary structure, and out-of-frame upstream AUGs (uAUGs) to phenotypic diversity, thereby advancing our understanding of how protein levels are modulated by 5'-UTR sequences, and paving the way toward predictably tuning protein expression through manipulations of 5'-UTRs.

Nonlinear fitness consequences of variation in expression level of a eukaryotic gene.

Levels of gene expression show considerable variation in eukaryotes, but no fine-scale maps have been made of the fitness consequences of such variation in controlled genetic backgrounds and environments. To address this, we assayed fitness at many levels of up- and down-regulated expression of a single essential gene, LCB2, involved in sphingolipid synthesis in budding yeast Saccharomyces cerevisiae. Reduced LCB2 expression rapidly decreases cellular fitness, yet increased expression has little effect. The wild-type expression level is therefore perched on the edge of a nonlinear fitness cliff. LCB2 is upregulated when cells are exposed to osmotic stress; consistent with this, the entire fitness curve is shifted upward to higher expression under osmotic stress, illustrating the selective force behind gene regulation. Expression levels of LCB2 are lower in wild yeast strains than in the experimental lab strain, suggesting that higher levels in the lab strain may be idiosyncratic. Reports indicate that the effect sizes of alleles contributing to variation in complex phenotypes differ among environments and genetic backgrounds; our results suggest that such differences may be explained as simple shifts in the position of nonlinear fitness curves.

Recruitment of Cln3 cyclin to promoters controls cell cycle entry via histone deacetylase and other targets.

In yeast, the G1 cyclin Cln3 promotes cell cycle entry by activating the transcription factor SBF. In mammals, there is a parallel system for cell cycle entry in which cyclin dependent kinase (CDK) activates transcription factor E2F/Dp. Here we show that Cln3 regulates SBF by at least two different pathways, one involving the repressive protein Whi5, and the second involving Stb1. The Rpd3 histone deacetylase complex is also involved. Cln3 binds to SBF at the CLN2 promoter, and removes previously bound Whi5 and histone deacetylase. Adding extra copies of the SBF binding site to the cell delays Start, possibly by titrating Cln3. Since Rpd3 is the yeast ortholog of mammalian HDAC1, there is now a virtually complete analogy between the proteins regulating cell cycle entry in yeast (SBF, Cln3, Whi5 and Stb1, Rpd3) and mammals (E2F, Cyclin D, Rb, HDAC1). The cell may titrate Cln3 molecules against the number of SBF binding sites, and this could be the underlying basis of the size-control mechanism for Start.

Receptor for advanced glycation end-products (RAGE) mediates phagocytosis in nonprofessional phagocytes.

In mammals, both professional phagocytes and nonprofessional phagocytes (NPPs) can perform phagocytosis. However, limited targets are phagocytosed by NPPs, and thus, the mechanism remains unclear. We find that spores of the yeast Saccharomyces cerevisiae are internalized efficiently by NPPs. Analyses of this phenomenon reveals that RNA fragments derived from cytosolic RNA species are attached to the spore wall, and these fragments serve as ligands to induce spore internalization. Furthermore, we show that a multiligand receptor, RAGE (receptor for advanced glycation end-products), mediates phagocytosis in NPPs. RAGE-mediated phagocytosis is not uniquely induced by spores but is an intrinsic mechanism by which NPPs internalize macromolecules containing RAGE ligands. In fact, artificial particles labeled with polynucleotides, HMGB1, or histone (but not bovine serum albumin) are internalized in NPPs. Our findings provide insight into the molecular basis of phagocytosis by NPPs, a process by which a variety of macromolecules are targeted for internalization.

Uncovering de novo gene birth in yeast using deep transcriptomics.

De novo gene origination has been recently established as an important mechanism for the formation of new genes. In organisms with a large genome, intergenic and intronic regions provide plenty of raw material for new transcriptional events to occur, but little is know about how de novo transcripts originate in more densely-packed genomes. Here, we identify 213 de novo originated transcripts in Saccharomyces cerevisiae using deep transcriptomics and genomic synteny information from multiple yeast species grown in two different conditions. We find that about half of the de novo transcripts are expressed from regions which already harbor other genes in the opposite orientation; these transcripts show similar expression changes in response to stress as their overlapping counterparts, and some appear to translate small proteins. Thus, a large fraction of de novo genes in yeast are likely to co-evolve with already existing genes.

Budding yeast complete DNA synthesis after chromosome segregation begins.

To faithfully transmit genetic information, cells must replicate their entire genome before division. This is thought to be ensured by the temporal separation of replication and chromosome segregation. Here we show that in 20-40% of unperturbed yeast cells, DNA synthesis continues during anaphase, late in mitosis. High cyclin-Cdk activity inhibits DNA synthesis in metaphase, and the decrease in cyclin-Cdk activity during mitotic exit allows DNA synthesis to finish at subtelomeric and some difficult-to-replicate regions. DNA synthesis during late mitosis correlates with elevated mutation rates at subtelomeric regions, including copy number variation. Thus, yeast cells temporally overlap DNA synthesis and chromosome segregation during normal growth, possibly allowing cells to maximize population-level growth rate while simultaneously exploring greater genetic space.

Impact of Chromosome Fusions on 3D Genome Organization and Gene Expression in Budding Yeast.

The three-dimensional (3D) organization of chromosomes can influence transcription. However, the frequency and magnitude of these effects remain debated. To determine how changes in chromosome positioning affect transcription across thousands of genes with minimal perturbation, we characterized nuclear organization and global gene expression in budding yeast containing chromosome fusions. We used computational modeling and single-cell imaging to determine chromosome positions, and integrated these data with genome-wide transcriptional profiles from RNA sequencing. We find that chromosome fusions dramatically alter 3D nuclear organization without leading to strong genome-wide changes in transcription. However, we observe a mild but significant and reproducible increase in the expression of genes displaced away from the periphery. The increase in transcription is inversely proportional to the propensity of a given locus to be at the nuclear periphery; for example, a 10% decrease in the propensity of a gene to reside at the nuclear envelope is accompanied by a 10% increase in gene expression. Modeling suggests that this is due to both deletion of telomeres and to displacement of genes relative to the nuclear periphery. These data suggest that basal transcriptional activity is sensitive to radial changes in gene position, and provide insight into the functional relevance of budding yeast chromosome-level 3D organization in gene expression.

Transcriptomics data of 11 species of yeast identically grown in rich media and oxidative stress conditions.

OBJECTIVE: The objective of this experiment was to identify transcripts in baker's yeast (Saccharomyces cerevisiae) that could have originated from previously non-coding genomic regions, or de novo. We generated this data to be able to compare the transcriptomes of different species of Ascomycota. DATA DESCRIPTION: We generated high-depth RNA sequencing data for 11 species of yeast: Saccharomyces cerevisiae, Saccharomyces paradoxus, Saccharomyces mikatae, Saccharomyces kudriavzevii, Saccharomyces bayanus, Naumovia castelii, Kluyveromyces lactis, Lachancea waltii, Lachancea thermotolerans, Lachancea kluyveri, and Schizosaccharomyces pombe. Using RNA-Seq from yeast grown in rich and oxidative conditions we created genome-guided de novo assemblies of the transcriptomes for each species. We included synthetic spike-in transcripts in each sample to determine the lower limit of detection of the sequencing platform as well as the reliability of our de novo transcriptome assembly pipeline. We subsequently compared the de novo transcripts assemblies to the reference gene annotations and generated assemblies that comprised both annotated and novel transcripts.

Single cell functional genomics reveals the importance of mitochondria in cell-to-cell phenotypic variation.

Mutations frequently have outcomes that differ across individuals, even when these individuals are genetically identical and share a common environment. Moreover, individual microbial and mammalian cells can vary substantially in their proliferation rates, stress tolerance, and drug resistance, with important implications for the treatment of infections and cancer. To investigate the causes of cell-to-cell variation in proliferation, we used a high-throughput automated microscopy assay to quantify the impact of deleting >1500 genes in yeast. Mutations affecting mitochondria were particularly variable in their outcome. In both mutant and wild-type cells mitochondrial membrane potential - but not amount - varied substantially across individual cells and predicted cell-to-cell variation in proliferation, mutation outcome, stress tolerance, and resistance to a clinically used anti-fungal drug. These results suggest an important role for cell-to-cell variation in the state of an organelle in single cell phenotypic variation.

Empirical mean-noise fitness landscapes reveal the fitness impact of gene expression noise.

The effects of cell-to-cell variation (noise) in gene expression have proven difficult to quantify because of the mechanistic coupling of noise to mean expression. To independently quantify the effects of changes in mean expression and noise we determine the fitness landscapes in mean-noise expression space for 33 genes in yeast. For most genes, short-lived (noise) deviations away from the expression optimum are nearly as detrimental as sustained (mean) deviations. Fitness landscapes can be classified by a combination of each gene's sensitivity to protein shortage or surplus. We use this classification to explore evolutionary scenarios for gene expression and find that certain landscape topologies can break the mechanistic coupling of mean and noise, thus promoting independent optimization of both properties. These results demonstrate that noise is detrimental for many genes and reveal non-trivial consequences of mean-noise-fitness topologies for the evolution of gene expression systems.