Accurate inference of stochastic gene expression from nascent transcript heterogeneity

Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy, but in experiments cells may have two gene copies as cells replicate their genome during the cell cycle. It is thus unclear how accurately the inferred parameters reflect transcription. To address these issues, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle stage. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle stage and compare the results to live-cell transcription measurements of the same gene. We conclude that: (i) not accounting for cell cycle dynamics in nascent mRNA data overestimates the magnitude of promoter switching rates and the initiation rate, and underestimates the fraction of time spent in the active state and the burst size. (ii) use of mature mRNA data, instead of nascent data, significantly increases the errors in parameter estimation and can mistakenly classify a gene as non-bursting. Furthermore, we show how to correctly adjust for measurement noise in smFISH at low nascent transcript numbers. Simulations with parameters estimated from nascent smFISH data corrected for cell cycle phases and measurement noise leads to autocorrelation functions that agree with those obtained from live-cell imaging. Therefore, our novel data curation method yields a quantitatively accurate picture of gene expression.

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Investigating the Central Dogma of Molecular Biology in the Context of Nuclear Architecture and Cell Cycle

10.1101/810499 ◽

2019 ◽

Author(s):

Shivnarayan Dhuppar ◽

Aprotim Mazumder

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Single Molecule ◽

Mammalian Cells ◽

Nuclear Architecture ◽

Nuclear Lamina ◽

Gene Copy ◽

A Cell ◽

Cycle Dependent

AbstractNuclear architecture is the organization of the genome within a cell nucleus with respect to different nuclear landmarks such as nuclear lamina, matrix or nucleoli. Lately it has emerged as a major regulator of gene expression in mammalian cells. The studies connecting nuclear architecture with gene expression are largely population-averaged and do not report on the heterogeneity in genome organization or in gene expression within a population. In this report we present a method for combining 3D DNA Fluorescence in situ Hybridization (FISH) with single molecule RNA FISH (smFISH) and immunofluorescence to study nuclear architecture-dependent gene regulation on a cell-by-cell basis. We further combine it with an imaging-based cell cycle staging to correlate nuclear architecture with gene expression across the cell cycle. We present this in the context of Cyclin A2 (CCNA2) gene for its known cell cycle-dependent expression. We show that, across the cell cycle, the expression of a CCNA2 gene copy is stochastic and depends neither on its sub-nuclear position—which usually lies close to nuclear lamina—nor on the expression from the other copies.

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Low Level Amplification (Duplication) of 1q21 in Myeloma and Prognosis; the Role of CKS1B.

Blood ◽

10.1182/blood.v106.11.624.624 ◽

2005 ◽

Vol 106 (11) ◽

pp. 624-624

Author(s):

Rafael Fonseca ◽

Scott Van Wier ◽

Wee-Joo Chng ◽

Rhett Ketterling ◽

Martha Lacy ◽

...

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Gene Duplication ◽

Plasma Cell ◽

Plasma Cells ◽

Prognostic Significance ◽

Positive Association ◽

Gene Copy ◽

High Dose ◽

Control Probe

Abstract Introduction: Molecular cytogenetic studies have revealed that, to a great extent, the heterogeneity of myeloma is largely dictated by the underlying genetic and cytogenetic aberrations present in the clonal plasma cells. Most recently the group from the University of Arkansas identified strong prognostic associations with an increased level of gene expression of a cell cycle associated gene, CKS1B. CKS1B favors cell cycle progression by promoting degradation of p27 with release of the cyclin dependent kinases and entry into mitosis. Patients and methods: To further test this hypothesis we studied: a) via FISH for CKS1B amplification in a cohort of patients treated at the Mayo Clinic with high dose chemotherapy and stem cell support, as well as a group of patients with cytogenetically defined hypodiploidy, and b) a cohort of myeloma patients that were studied by gene expression profiling. Gene expression analysis was performed on CD138-enriched plasma-cell RNA using Affymetrix U133A chips (Affymetrix, Santa Clara, CA). Results: Of 159 patients studied 46 exhibited FISH abnormalities consistent with increase number of signals for CKS1B (30%): amplification was marginal or low in 44 cases, and in only two cases the ratio between CKS1B and control probe was greater than 2.0. Therefore, the predominant pattern is one of gene duplication rather than amplification where gene copy usually exceeds 10 and ratio with control probe exceeds 5 (e.g. HER-2/neu amplification). Patients with CKS1B duplication had a higher prevalence of chromosome 13 deletion (72%), and t(4;14) (29%). The presence of CKS1B duplication was more frequent (53%) among 19 patients with hypodiploidy. Using gene expression data for CKS1B, we found a positive association with t(14;16)(q32;q23). The level of CKS1B gene expression positively correlated with the PCLI (p<0.0001) but there was heterogeneity in this relationship (r2 = 0.34). We found no correlation between the expression level of CKS1B and p27 (r2 = 0.008, p = 0.37). While CKS1B gene duplication had a negative effect on survival this effect was weak (29.9 versus 38 months, p = 0.124. Median follow-up is 55 months) and disappeared when the variable was entered into the multivariate model including PCLI, B2-microglobulin and all the major genetic abnormalities by FISH. Likewise the level of expression of CKS1B determined by gene expression only carried prognostic significance when extreme levels of expression were utilized as a prognostic variable. Discussion: In this study we show that increase copy number of CKS1B is present in one third of patients with MM (majority of these being gene duplication) and seem to be associated with a shorter overall survival, but the net effect of this is rather weak in our series. Further study is needed to understand its potential role as a progression factor in the plasma cell neoplasms.

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Intracellular Diffusion in Fission Yeast Cells Depends on Cell Cycle Stage

Biophysical Journal ◽

10.1016/j.bpj.2009.12.1989 ◽

2010 ◽

Vol 98 (3) ◽

pp. 369a

Author(s):

Christine Selhuber-Unkel ◽

Pernille Yde ◽

Kirstine Berg-Sørensen ◽

Lene Oddershede

Keyword(s):

Cell Cycle ◽

Fission Yeast ◽

Yeast Cells ◽

Cell Cycle Stage ◽

Intracellular Diffusion

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Analytical distributions for detailed models of stochastic gene expression in eukaryotic cells

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1910888117 ◽

2020 ◽

Vol 117 (9) ◽

pp. 4682-4692 ◽

Cited By ~ 20

Author(s):

Zhixing Cao ◽

Ramon Grima

Keyword(s):

Gene Expression ◽

Messenger Rna ◽

Negative Binomial ◽

Gene Activation ◽

Embryonic Stem ◽

Time Dependent ◽

Eukaryotic Cells ◽

Mature Mrna ◽

Eukaryotic Genes ◽

Nascent Mrna

The stochasticity of gene expression presents significant challenges to the modeling of genetic networks. A two-state model describing promoter switching, transcription, and messenger RNA (mRNA) decay is the standard model of stochastic mRNA dynamics in eukaryotic cells. Here, we extend this model to include mRNA maturation, cell division, gene replication, dosage compensation, and growth-dependent transcription. We derive expressions for the time-dependent distributions of nascent mRNA and mature mRNA numbers, provided two assumptions hold: 1) nascent mRNA dynamics are much faster than those of mature mRNA; and 2) gene-inactivation events occur far more frequently than gene-activation events. We confirm that thousands of eukaryotic genes satisfy these assumptions by using data from yeast, mouse, and human cells. We use the expressions to perform a sensitivity analysis of the coefficient of variation of mRNA fluctuations averaged over the cell cycle, for a large number of genes in mouse embryonic stem cells, identifying degradation and gene-activation rates as the most sensitive parameters. Furthermore, it is shown that, despite the model’s complexity, the time-dependent distributions predicted by our model are generally well approximated by the negative binomial distribution. Finally, we extend our model to include translation, protein decay, and auto-regulatory feedback, and derive expressions for the approximate time-dependent protein-number distributions, assuming slow protein decay. Our expressions enable us to study how complex biological processes contribute to the fluctuations of gene products in eukaryotic cells, as well as allowing a detailed quantitative comparison with experimental data via maximum-likelihood methods.

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Xbp1-Mediated Repression of CLB Gene Expression Contributes to the Modifications of Yeast Cell Morphology and Cell Cycle Seen during Nitrogen-Limited Growth

Molecular and Cellular Biology ◽

10.1128/mcb.21.11.3714-3724.2001 ◽

2001 ◽

Vol 21 (11) ◽

pp. 3714-3724 ◽

Cited By ~ 20

Author(s):

Chaouki Miled ◽

Carl Mann ◽

Gérard Faye

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Cell Morphology ◽

Transcriptional Activation ◽

Nitrogen Limitation ◽

Culture Conditions ◽

Yeast Cells ◽

Cell Cycle Regulators ◽

Chemostat Cultures ◽

Agar Media

ABSTRACT Yeast cells undergo morphological transformations in response to diverse environmental signals. One such event, called pseudohyphal differentiation, occurs when diploid yeast cells are partially starved for nitrogen on a solid agar medium. The nitrogen-starved cells elongate, and a small fraction form filaments that penetrate the agar surface. The molecular basis for the changes in cell morphology and cell cycle in response to nitrogen limitation are poorly defined, in part because the heterogeneous growth states of partially starved cells on agar media are not amenable to biochemical analysis. In this work, we used chemostat cultures to study the role of cell cycle regulators with respect to yeast differentiation in response to nitrogen limitation under controlled, homogeneous culture conditions. We found that Clb1, Clb2, and Clb5 cyclin levels are reduced in nitrogen-limited chemostat cultures compared to levels in rich-medium cultures, whereas the Xbp1 transcriptional repressor is highly induced under these conditions. Furthermore, the deletion of XBP1 prevents the drop in Clb2 levels and inhibits cellular elongation in nitrogen-limited chemostat cultures as well as inhibiting pseudohyphal growth on nitrogen-limited agar media. Deletion of the CLB2gene restores an elongated morphology and filamentation to thexbp1Δ mutant in response to nitrogen limitation. Transcriptional activation of the XBP1 gene and the subsequent repression of CLB gene expression are thus key responses of yeast cells to nitrogen limitation.

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Mutational analysis of a DNA sequence involved in linking gene expression to the cell cycle

Biochemistry and Cell Biology ◽

10.1139/o92-152 ◽

1992 ◽

Vol 70 (10-11) ◽

pp. 1073-1080 ◽

Cited By ~ 14

Author(s):

Brenda J. Andrews ◽

Lynda Moore

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Target Genes ◽

Mutational Analysis ◽

Consensus Sequence ◽

Yeast Cells ◽

Regulatory Sequences ◽

Sequence Element ◽

Dependent Cell

Entry of budding yeast cells into the mitotic cell cycle requires the activity of a conserved regulatory kinase encoded by the CDC28 gene. The kinase is thought to trigger entry into the cell cycle or START, through association with a number of regulatory subunits known as G1cyclins. A number of genes whose transcription is dependent on CDC28 and thus linked to START are controlled by two transcription factors, SWI4 and SWI6. The genes controlled by SWI4 and SWI6 include two known G1 cyclins (CLN1 and CLN2), a putative new G1 cyclin (HCS26), and the HO gene whose product initiates cell type switching. SWI4 and SWI6 act through a repeated sequence element, SCB (SWI4,6-dependent cell cycle box), found 2–10 times in the upstream regulatory sequences of target genes. We have constructed a library of mutants in the SCB using doped oligonucleotide mutagenesis. All single base pair changes examined compromised the ability of the SCB to activate transcription in vivo. Analysis of the behaviour of the mutant SCBs in an in vitro DNA binding assay shows that the inability to activate transcription can be explained by reduced binding of SWI4 and SWI6 to the mutant SCBs. This analysis, together with a consideration of the SCBs found upstream of known SWI4,6-dependent genes, leads to the proposal of a revised consensus sequence for this important regulatory element.Key words: cell cycle control, gene expression, SWI4,6-dependent cell cycle box, SWI4, SWI6.

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Size-dependent increase in RNA Polymerase II initiation rates mediates gene expression scaling with cell size

10.1101/754788 ◽

2019 ◽

Cited By ~ 1

Author(s):

Xi-Ming Sun ◽

Anthony Bowman ◽

Miles Priestman ◽

Francois Bertaux ◽

Amalia Martinez-Segura ◽

...

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Fission Yeast ◽

Rna Polymerase Ii ◽

Cell Size ◽

Transcription Initiation ◽

Molecular Mechanisms ◽

Single Cells ◽

Yeast Cells ◽

Limiting Factor

ABSTRACTCell size varies during the cell cycle and in response to external stimuli. This requires the tight coordination, or “scaling”, of mRNA and protein quantities with the cell volume in order to maintain biomolecules concentrations and cell density. Evidence in cell populations and single cells indicates that scaling relies on the coordination of mRNA transcription rates with cell size. Here we use a combination of single-molecule fluorescence in situ hybridisation (smFISH), time-lapse microscopy and mathematical modelling in single fission yeast cells to uncover the precise molecular mechanisms that control transcription rates scaling with cell size. Linear scaling of mRNA quantities is apparent in single fission yeast cells during a normal cell cycle. Transcription rates of both constitutive and regulated genes scale with cell size without evidence for transcriptional bursting. Modelling and experimental data indicate that scaling relies on the coordination of RNAPII transcription initiation rates with cell size and that RNAPII is a limiting factor. We show using real-time quantitative imaging that size increase is accompanied by a rapid concentration independent recruitment of RNAPII onto chromatin. Finally, we find that in multinucleated cells, scaling is set at the level of single nuclei and not the entire cell, making the nucleus the transcriptional scaling unit. Integrating our observations in a mechanistic model of RNAPII mediated transcription, we propose that scaling of gene expression with cell size is the consequence of competition between genes for limiting RNAPII.

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EBP2 Is a Member of the Yeast RRB Regulon, a Transcriptionally Coregulated Set of Genes That Are Required for Ribosome and rRNA Biosynthesis

Molecular and Cellular Biology ◽

10.1128/mcb.21.24.8638-8650.2001 ◽

2001 ◽

Vol 21 (24) ◽

pp. 8638-8650 ◽

Cited By ~ 50

Author(s):

Christopher Wade ◽

Kathleen A. Shea ◽

Roderick V. Jensen ◽

Michael A. McAlear

Keyword(s):

Gene Expression ◽

Cell Cycle ◽

Expression Profiles ◽

Yeast Cells ◽

Diauxic Shift ◽

Rrna Processing ◽

Data Set ◽

Consensus Sequences ◽

Alpha Factor ◽

Yeast Genes

ABSTRACT In an effort to identify sets of yeast genes that are coregulated across various cellular transitions, gene expression data sets derived from yeast cells progressing through the cell cycle, sporulation, and diauxic shift were analyzed. A partitioning algorithm was used to divide each data set into 24 clusters of similar expression profiles, and the membership of the clusters was compared across the three experiments. A single cluster of 189 genes from the cell cycle experiment was found to share 65 genes with a cluster of 159 genes from the sporulation data set. Many of these genes were found to be clustered in the diauxic-shift experiment as well. The overlapping set was enriched for genes required for rRNA biosynthesis and included genes encoding RNA helicases, subunits of RNA polymerases I and III, and rRNA processing factors. A subset of the 65 genes was tested for expression by a quantitative-relative reverse transcriptase PCR technique, and they were found to be coregulated after release from alpha factor arrest, heat shock, and tunicamycin treatment. Promoter scanning analysis revealed that the 65 genes within this ribosome and rRNA biosynthesis (RRB) regulon were enriched for two motifs: the 13-base GCGATGAGATGAG and the 11-base TGAAAAATTTT consensus sequences. Both motifs were found to be important for promoting gene expression after release from alpha factor arrest in a test rRNA processing gene (EBP2), which suggests that these consensus sequences may function broadly in the regulation of a set of genes required for ribosome and rRNA biosynthesis.

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