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Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules.

Choubey S, Kondev J, Sanchez A - PLoS Comput. Biol. (2015)

Bottom Line: To demonstrate the usefulness of this approach, we test our theory against published nascent RNA data for twelve constitutively expressed yeast genes.Rather than transcription being initiated through a single rate limiting step, as it had been previously proposed, our single-cell analysis reveals the presence of at least two rate limiting steps.Surprisingly, half of the genes analyzed have nearly identical rates of transcription initiation, suggesting a common mechanism.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Brandeis University, Waltham, Massachusetts, United States of America.

ABSTRACT
Deciphering how the regulatory DNA sequence of a gene dictates its expression in response to intra and extracellular cues is one of the leading challenges in modern genomics. The development of novel single-cell sequencing and imaging techniques, as well as a better exploitation of currently available single-molecule imaging techniques, provides an avenue to interrogate the process of transcription and its dynamics in cells by quantifying the number of RNA polymerases engaged in the transcription of a gene (or equivalently the number of nascent RNAs) at a given moment in time. In this paper, we propose that measurements of the cell-to-cell variability in the number of nascent RNAs provide a mostly unexplored method for deciphering mechanisms of transcription initiation in cells. We propose a simple kinetic model of transcription initiation and elongation from which we calculate nascent RNA copy-number fluctuations. To demonstrate the usefulness of this approach, we test our theory against published nascent RNA data for twelve constitutively expressed yeast genes. Rather than transcription being initiated through a single rate limiting step, as it had been previously proposed, our single-cell analysis reveals the presence of at least two rate limiting steps. Surprisingly, half of the genes analyzed have nearly identical rates of transcription initiation, suggesting a common mechanism. Our analytical framework can be used to extract quantitative information about dynamics of transcription from single-cell sequencing data, as well as from single-molecule imaging and electron micrographs of fixed cells, and provides the mathematical means to exploit the quantitative power of these technologies.

No MeSH data available.


Related in: MedlinePlus

Comparison of predicted and measured Fano factors for cytoplasmic mRNA distributions.Fano factors for the cytoplasmic mRNA distributions, as predicted by the one-step (RPB1), two-step (KAP104, TAF5, TAF6, TAF12, RPB2, RPB3, MDN1) and three-step (PUP1, PRE3, PRE7, PRP8) mechanisms of initiation, are shown as blue bars. These are compared with the measured cytoplasmic mRNA distributions, shown in green bars, as reported in ref [25]. In cases when the measured distributions have higher Fano factors than predicted, this is indicative of significant sources of noise downstream to transcription initiation and elongation that affect the cell-to-cell variability of cytoplasmic mRNA.
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pcbi.1004345.g004: Comparison of predicted and measured Fano factors for cytoplasmic mRNA distributions.Fano factors for the cytoplasmic mRNA distributions, as predicted by the one-step (RPB1), two-step (KAP104, TAF5, TAF6, TAF12, RPB2, RPB3, MDN1) and three-step (PUP1, PRE3, PRE7, PRP8) mechanisms of initiation, are shown as blue bars. These are compared with the measured cytoplasmic mRNA distributions, shown in green bars, as reported in ref [25]. In cases when the measured distributions have higher Fano factors than predicted, this is indicative of significant sources of noise downstream to transcription initiation and elongation that affect the cell-to-cell variability of cytoplasmic mRNA.

Mentions: In order to demonstrate that the distribution of mRNAs can be affected by stochastic processes that occur downstream of transcription, thereby obscuring the signature of transcription initiation dynamics, we compare the nascent RNA and cytoplasmic mRNA distributions for the twelve yeast genes analyzed in Fig 4. First, we compute the Fano factor of the cytoplasmic mRNA distribution predicted by the initiation mechanism inferred from the measured nascent RNA distribution for all twelve genes studied (23). (See the S1 Text for details of the calculation.) Then we compare the results of our calculations with the experimentally determined distributions obtained by counting cytoplasmic mRNA. We find that for all of the yeast genes examined the predicted Fano factors for the cytoplasmic mRNA distributions are less than the measured ones, as shown in Fig 4. In other words the signature of two-step initiation observed in the nascent RNA distribution is washed out at the cytoplasmic mRNA level due to other sources of noise. It remains unclear what processes are responsible for these differences. In a recent study of transcription in fly embryos, it was also found that the variability of nascent and cytoplasmic mRNA could differ more than six fold [9]. In this case, the reason for this difference is spatial and temporal averaging of mRNA by diffusion and accumulation of mRNA transcripts during nuclear cycles. The yeast and fly examples demonstrate that the relationship between nascent and cytoplasmic RNA distributions is complex and context dependent.


Deciphering Transcriptional Dynamics In Vivo by Counting Nascent RNA Molecules.

Choubey S, Kondev J, Sanchez A - PLoS Comput. Biol. (2015)

Comparison of predicted and measured Fano factors for cytoplasmic mRNA distributions.Fano factors for the cytoplasmic mRNA distributions, as predicted by the one-step (RPB1), two-step (KAP104, TAF5, TAF6, TAF12, RPB2, RPB3, MDN1) and three-step (PUP1, PRE3, PRE7, PRP8) mechanisms of initiation, are shown as blue bars. These are compared with the measured cytoplasmic mRNA distributions, shown in green bars, as reported in ref [25]. In cases when the measured distributions have higher Fano factors than predicted, this is indicative of significant sources of noise downstream to transcription initiation and elongation that affect the cell-to-cell variability of cytoplasmic mRNA.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4636183&req=5

pcbi.1004345.g004: Comparison of predicted and measured Fano factors for cytoplasmic mRNA distributions.Fano factors for the cytoplasmic mRNA distributions, as predicted by the one-step (RPB1), two-step (KAP104, TAF5, TAF6, TAF12, RPB2, RPB3, MDN1) and three-step (PUP1, PRE3, PRE7, PRP8) mechanisms of initiation, are shown as blue bars. These are compared with the measured cytoplasmic mRNA distributions, shown in green bars, as reported in ref [25]. In cases when the measured distributions have higher Fano factors than predicted, this is indicative of significant sources of noise downstream to transcription initiation and elongation that affect the cell-to-cell variability of cytoplasmic mRNA.
Mentions: In order to demonstrate that the distribution of mRNAs can be affected by stochastic processes that occur downstream of transcription, thereby obscuring the signature of transcription initiation dynamics, we compare the nascent RNA and cytoplasmic mRNA distributions for the twelve yeast genes analyzed in Fig 4. First, we compute the Fano factor of the cytoplasmic mRNA distribution predicted by the initiation mechanism inferred from the measured nascent RNA distribution for all twelve genes studied (23). (See the S1 Text for details of the calculation.) Then we compare the results of our calculations with the experimentally determined distributions obtained by counting cytoplasmic mRNA. We find that for all of the yeast genes examined the predicted Fano factors for the cytoplasmic mRNA distributions are less than the measured ones, as shown in Fig 4. In other words the signature of two-step initiation observed in the nascent RNA distribution is washed out at the cytoplasmic mRNA level due to other sources of noise. It remains unclear what processes are responsible for these differences. In a recent study of transcription in fly embryos, it was also found that the variability of nascent and cytoplasmic mRNA could differ more than six fold [9]. In this case, the reason for this difference is spatial and temporal averaging of mRNA by diffusion and accumulation of mRNA transcripts during nuclear cycles. The yeast and fly examples demonstrate that the relationship between nascent and cytoplasmic RNA distributions is complex and context dependent.

Bottom Line: To demonstrate the usefulness of this approach, we test our theory against published nascent RNA data for twelve constitutively expressed yeast genes.Rather than transcription being initiated through a single rate limiting step, as it had been previously proposed, our single-cell analysis reveals the presence of at least two rate limiting steps.Surprisingly, half of the genes analyzed have nearly identical rates of transcription initiation, suggesting a common mechanism.

View Article: PubMed Central - PubMed

Affiliation: Department of Physics, Brandeis University, Waltham, Massachusetts, United States of America.

ABSTRACT
Deciphering how the regulatory DNA sequence of a gene dictates its expression in response to intra and extracellular cues is one of the leading challenges in modern genomics. The development of novel single-cell sequencing and imaging techniques, as well as a better exploitation of currently available single-molecule imaging techniques, provides an avenue to interrogate the process of transcription and its dynamics in cells by quantifying the number of RNA polymerases engaged in the transcription of a gene (or equivalently the number of nascent RNAs) at a given moment in time. In this paper, we propose that measurements of the cell-to-cell variability in the number of nascent RNAs provide a mostly unexplored method for deciphering mechanisms of transcription initiation in cells. We propose a simple kinetic model of transcription initiation and elongation from which we calculate nascent RNA copy-number fluctuations. To demonstrate the usefulness of this approach, we test our theory against published nascent RNA data for twelve constitutively expressed yeast genes. Rather than transcription being initiated through a single rate limiting step, as it had been previously proposed, our single-cell analysis reveals the presence of at least two rate limiting steps. Surprisingly, half of the genes analyzed have nearly identical rates of transcription initiation, suggesting a common mechanism. Our analytical framework can be used to extract quantitative information about dynamics of transcription from single-cell sequencing data, as well as from single-molecule imaging and electron micrographs of fixed cells, and provides the mathematical means to exploit the quantitative power of these technologies.

No MeSH data available.


Related in: MedlinePlus