Make Them, Break Them, and Catch Them: Studying Rare Ubiquitin Chains

Uckelmann, Michael; Sixma, Titia K.

doi:10.1016/j.molcel.2015.03.024

Cited by 14 publications

(13 citation statements)

References 8 publications

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“…When proteins are actively degraded, this noise cancelling mechanism would be less efficient. We note that a similar, but likely active, balancing between synthesis and dilution was observed in mammalian cells where transcription rate is adjusted to cell size [ 44 , 45 ]. The homeostatic mechanism we observed does not necessarily act on noise from other sources, such as fluctuations in RNA polymerase availability [ 18 ] or transcription factors [ 2 ], if they are not synchronized with exponential volume growth.…”

Section: Discussionmentioning

confidence: 67%

Generation and filtering of gene expression noise by the bacterial cell cycle

2016

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BackgroundGene expression within cells is known to fluctuate stochastically in time. However, the origins of gene expression noise remain incompletely understood. The bacterial cell cycle has been suggested as one source, involving chromosome replication, exponential volume growth, and various other changes in cellular composition. Elucidating how these factors give rise to expression variations is important to models of cellular homeostasis, fidelity of signal transmission, and cell-fate decisions.ResultsUsing single-cell time-lapse microscopy, we measured cellular growth as well as fluctuations in the expression rate of a fluorescent protein and its concentration. We found that, within the population, the mean expression rate doubles throughout the cell cycle with a characteristic cell cycle phase dependent shape which is different for slow and fast growth rates. At low growth rate, we find the mean expression rate was initially flat, and then rose approximately linearly by a factor two until the end of the cell cycle. The mean concentration fluctuated at low amplitude with sinusoidal-like dependence on cell cycle phase. Traces of individual cells were consistent with a sudden two-fold increase in expression rate, together with other non-cell cycle noise. A model was used to relate the findings and to explain the cell cycle-induced variations for different chromosomal positions.ConclusionsWe found that the bacterial cell cycle contribution to expression noise consists of two parts: a deterministic oscillation in synchrony with the cell cycle and a stochastic component caused by variable timing of gene replication. Together, they cause half of the expression rate noise. Concentration fluctuations are partially suppressed by a noise cancelling mechanism that involves the exponential growth of cellular volume. A model explains how the functional form of the concentration oscillations depends on chromosome position.Electronic supplementary materialThe online version of this article (doi:10.1186/s12915-016-0231-z) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 67%

Generation and filtering of gene expression noise by the bacterial cell cycle

2016

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“…This observation is in agreement with several previous reports ( Guccione et al, 2006 ; Guo et al, 2014 ; Lin et al, 2012 ; Nie et al, 2012 ). Recent work has identified WDR5, a WD40-repeat-containing protein, which is a part of the MLL/SET methyltransferases that methylate H3K4 and the MOF/NSL histone acetyltransferases that acetylate histone H4, as a direct interaction partner of MYC ( Thomas et al, 2015 ). MYC binds to WDR5 with a K D of 9.3 µM via MYC BoxIII ( Thomas et al, 2015 ), a domain that is not part of the DNA-binding domain, suggesting that binding of MYC to WDR5 occurs independently of binding to DNA.…”

Section: Resultsmentioning

confidence: 99%

“…Recent work has identified WDR5, a WD40-repeat-containing protein, which is a part of the MLL/SET methyltransferases that methylate H3K4 and the MOF/NSL histone acetyltransferases that acetylate histone H4, as a direct interaction partner of MYC ( Thomas et al, 2015 ). MYC binds to WDR5 with a K D of 9.3 µM via MYC BoxIII ( Thomas et al, 2015 ), a domain that is not part of the DNA-binding domain, suggesting that binding of MYC to WDR5 occurs independently of binding to DNA. A modified model (model 2; Figure 3F ; see also Appendix 1) that assumes (i) that WDR5 is constantly bound to its target sites (ii) that MYC and WDR5 are free to bind to each other when both are bound to chromatin in close proximity predicts an EC 50 value of 0.014 µM for MYC occupancy of an E-box in the presence of WDR5 ( Figure 3G ).…”

Section: Resultsmentioning

confidence: 99%

Different promoter affinities account for specificity in MYC-dependent gene regulation

Lorenzin

Benary

Baluapuri

et al. 2016

eLife

150

201

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Enhanced expression of the MYC transcription factor is observed in the majority of tumors. Two seemingly conflicting models have been proposed for its function: one proposes that MYC enhances expression of all genes, while the other model suggests gene-specific regulation. Here, we have explored the hypothesis that specific gene expression profiles arise since promoters differ in affinity for MYC and high-affinity promoters are fully occupied by physiological levels of MYC. We determined cellular MYC levels and used RNA- and ChIP-sequencing to correlate promoter occupancy with gene expression at different concentrations of MYC. Mathematical modeling showed that binding affinities for interactions of MYC with DNA and with core promoter-bound factors, such as WDR5, are sufficient to explain promoter occupancies observed in vivo. Importantly, promoter affinity stratifies different biological processes that are regulated by MYC, explaining why tumor-specific MYC levels induce specific gene expression programs and alter defined biological properties of cells.DOI: http://dx.doi.org/10.7554/eLife.15161.001

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“…Recent crystal structures show that this jaw-downstream duplex interaction (centered at +15) is also present in initiation complexes [26]. Partial deletion of the jaw (β’△1149-1190; Figure 2 ) reduces the lifetime of the λP R stable OC to ~3% of the WT value [7, 9, 27].…”

Section: Resultsmentioning

confidence: 99%

E. coli RNA Polymerase Determinants of Open Complex Lifetime and Structure

Ruff

Drennan

Michael

et al. 2015

Journal of Molecular Biology

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In transcription initiation by Escherichia coli RNA polymerase (RNAP), initial binding to promoter DNA triggers large conformational changes, bending downstream duplex DNA into the RNAP cleft and opening 13 base pairs to form a short-lived open intermediate (I2). Subsequent conformational changes increase lifetimes of λPR and T7A1 open complexes (OC) by >105-fold and >102-fold, respectively. OC lifetime is a target for regulation. To characterize late conformational changes, we determine effects on OC dissociation kinetics of deletions in RNAP mobile elements σ70 region 1.1 (σ1.1), β’ jaw and β’ sequence insertion 3 (SI3). In very stable OC formed by WT RNAP with λPR (RPO) and by △σ1.1 RNAP with λPR or T7A1 we conclude that downstream duplex DNA is bound to the jaw in an assembly with SI3, and bases −4 to +2 of the nontemplate strand discriminator region are stably bound in a positively-charged track in the cleft. We deduce that polyanionic σ1.1 destabilizes OC by competing for binding sites in the cleft and on the jaw with the polyanionic discriminator strand and downstream duplex, respectively. Examples of σ1.1-destabilized OC are the final T7A1 OC and the λPR I3 intermediate OC. Deleting σ1.1 and either β’ jaw or SI3 equalizes OC lifetimes for λPR and T7A1. DNA closing rates are similar for both promoters and all RNAP variants. We conclude that late conformational changes that stabilize OC, like early ones that bend the duplex into the cleft, are primary targets of regulation, while the intrinsic DNA opening-closing step is not.

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Make Them, Break Them, and Catch Them: Studying Rare Ubiquitin Chains

Abstract: Two studies in this issue, Kristariyanto et al. (2015) and Michel et al. (2015), describe innovative ways to produce large quantities of atypical K29 and K33 ubiquitin chains and report structures and mechanisms of chain-specific recognition.

Cited by 14 publications

References 8 publications

Generation and filtering of gene expression noise by the bacterial cell cycle

Generation and filtering of gene expression noise by the bacterial cell cycle

Different promoter affinities account for specificity in MYC-dependent gene regulation

E. coli RNA Polymerase Determinants of Open Complex Lifetime and Structure

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