Circadian mRNA expression: insights from modeling and transcriptomics

Lück, Sarah; Westermark, Pål O.

doi:10.1007/s00018-015-2072-2

Cited by 29 publications

(29 citation statements)

References 246 publications

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“…to classify gene expression profiles as ‘non-rhythmic’ (for example, because they fail imposed thresholds on amplitude or FDR) although the underlying temporal patterns may still be more similar to, and more likely to be, rhythmic than invariable. Of note, the lack of canonical methods to reliably determine true absence of rhythms is a common problem in the field (see recent review by [6] for discussion). Venn diagrams that simply overlap rhythmic gene sets hence need to be interpreted with caution.…”

Section: Resultsmentioning

confidence: 99%

“…using mRNA abundances as a primary readout. However, comparison of mRNA levels with datasets of genome-wide transcriptional activity and of protein abundances that have become available recently, has suggested that a surprisingly large fraction of gene expression oscillations may have post-transcriptional origins (reviewed in [6]). The many cases of protein rhythms that are independent of an underlying oscillating transcript (initially reported in a low-throughput mass-spectrometric study from mouse liver ten years ago [7] and recently confirmed at a comprehensive scale [8, 9]) point to important roles for translation, protein degradation and protein secretion in shaping time of day-dependent proteomes.…”

Section: Introductionmentioning

confidence: 99%

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Translational contributions to tissue specificity in rhythmic and constitutive gene expression

et al. 2017

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BackgroundThe daily gene expression oscillations that underlie mammalian circadian rhythms show striking differences between tissues and involve post-transcriptional regulation. Both aspects remain poorly understood. We have used ribosome profiling to explore the contribution of translation efficiency to temporal gene expression in kidney and contrasted our findings with liver data available from the same mice.ResultsRhythmic translation of constantly abundant messenger RNAs (mRNAs) affects largely non-overlapping transcript sets with distinct phase clustering in the two organs. Moreover, tissue differences in translation efficiency modulate the timing and amount of protein biosynthesis from rhythmic mRNAs, consistent with organ specificity in clock output gene repertoires and rhythmicity parameters. Our comprehensive datasets provided insights into translational control beyond temporal regulation. Between tissues, many transcripts show differences in translation efficiency, which are, however, of markedly smaller scale than mRNA abundance differences. Tissue-specific changes in translation efficiency are associated with specific transcript features and, intriguingly, globally counteracted and compensated transcript abundance variations, leading to higher similarity at the level of protein biosynthesis between both tissues.ConclusionsWe show that tissue specificity in rhythmic gene expression extends to the translatome and contributes to define the identities, the phases and the expression levels of rhythmic protein biosynthesis. Moreover, translational compensation of transcript abundance divergence leads to overall higher similarity at the level of protein production across organs. The unique resources provided through our study will serve to address fundamental questions of post-transcriptional control and differential gene expression in vivo.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-017-1222-2) contains supplementary material, which is available to authorized users.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Translational contributions to tissue specificity in rhythmic and constitutive gene expression

et al. 2017

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“…In mammals, circadian rhythms in the expression of thousands of genes in various metabolic tissues ensure up-or down-regulation of important metabolic processes in anticipation of daily activity and rest periods (1,2). Circadian gene expression in mammalian tissues depends on the circadian core clock, a genetic feedback oscillator inducing the transcription of thousands of clock-controlled genes.…”

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confidence: 99%

Principles for circadian orchestration of metabolic pathways

Thurley

Herbst

Wesener

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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Circadian rhythms govern multiple aspects of animal metabolism. Transcriptome-, proteome-and metabolome-wide measurements have revealed widespread circadian rhythms in metabolism governed by a cellular genetic oscillator, the circadian core clock. However, it remains unclear if and under which conditions transcriptional rhythms cause rhythms in particular metabolites and metabolic fluxes. Here, we analyzed the circadian orchestration of metabolic pathways by direct measurement of enzyme activities, analysis of transcriptome data, and developing a theoretical method called circadian response analysis. Contrary to a common assumption, we found that pronounced rhythms in metabolic pathways are often favored by separation rather than alignment in the times of peak activity of key enzymes. This property holds true for a set of metabolic pathway motifs (e.g., linear chains and branching points) and also under the conditions of fast kinetics typical for metabolic reactions. By circadian response analysis of pathway motifs, we determined exact timing separation constraints on rhythmic enzyme activities that allow for substantial rhythms in pathway flux and metabolite concentrations. Direct measurements of circadian enzyme activities in mouse skeletal muscle confirmed that such timing separation occurs in vivo.circadian rhythms | glucose metabolism | metabolic response analysis | mouse skeletal muscle C ircadian rhythms are ∼24-h cycles in behavior, physiology, and cellular processes that persist in the absence of external cues. In mammals, circadian rhythms in the expression of thousands of genes in various metabolic tissues ensure up-or down-regulation of important metabolic processes in anticipation of daily activity and rest periods (1, 2). Circadian gene expression in mammalian tissues depends on the circadian core clock, a genetic feedback oscillator inducing the transcription of thousands of clock-controlled genes. Additional rhythmicity in gene expression is generated by rhythmic posttranscriptional regulation (3). Circadian rhythms have been detected in mRNA and protein abundances (4-6), enzyme activities (7, 8), and concentrations of metabolites (9-11) in various mammalian tissues. Whereas the mechanisms underlying rhythmic gene expression are relatively well-studied, it remains unclear how circadian gene expression induces rhythms in the abundance of metabolites or metabolic fluxes.A naïve picture of circadian regulation (Fig. 1A) ("central dogma of molecular chronobiology") suggests that the circadian core clock drives rhythmic mRNA expression, resulting in rhythmic protein levels and directly leading to rhythmic enzyme activity (12) and thus, metabolic activity. Here, especially metabolic pathway flux (rate of conversion of substrate into product metabolites measured in concentration per time; an analogy is water flow through a floodgate) has been regarded as a crucial biological function under strong evolutionary selection pressure (13,14). However, it is unclear whether circadian abundances of enzyme trans...

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“…While the competitive advantage of a circadian clock in a periodic environment has been demonstrated experimentally for cyanobacteria (Woelfle et al, 2004), the precise adaptive value and the selective pressure resulting the evolution of a clock remains only partially understood. Reasoning about the possible fitness implications of a circadian clock necessarily involves considering the organisms as a whole, as exemplified in the “escape from light” hypothesis that circadian rhythmicity arose from the need to protect the organism’s DNA from ultraviolet (UV) radiation, at the time unfiltered by the Earth’s early atmosphere (Hut and Beersma, 2011; Lück and Westermark, 2016). A quantitative evaluation of such a hypothesis requires to contrast the energetic cost of the circadian clock with its benefits for survival and growth—a task where advanced computational models will allow for an increasingly quantitative evaluation.…”

Section: Conclusion: Putting the Parts Togethermentioning

confidence: 99%

Toward Multiscale Models of Cyanobacterial Growth: A Modular Approach

Westermark

Steuer

2016

Front. Bioeng. Biotechnol.

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Oxygenic photosynthesis dominates global primary productivity ever since its evolution more than three billion years ago. While many aspects of phototrophic growth are well understood, it remains a considerable challenge to elucidate the manifold dependencies and interconnections between the diverse cellular processes that together facilitate the synthesis of new cells. Phototrophic growth involves the coordinated action of several layers of cellular functioning, ranging from the photosynthetic light reactions and the electron transport chain, to carbon-concentrating mechanisms and the assimilation of inorganic carbon. It requires the synthesis of new building blocks by cellular metabolism, protection against excessive light, as well as diurnal regulation by a circadian clock and the orchestration of gene expression and cell division. Computational modeling allows us to quantitatively describe these cellular functions and processes relevant for phototrophic growth. As yet, however, computational models are mostly confined to the inner workings of individual cellular processes, rather than describing the manifold interactions between them in the context of a living cell. Using cyanobacteria as model organisms, this contribution seeks to summarize existing computational models that are relevant to describe phototrophic growth and seeks to outline their interactions and dependencies. Our ultimate aim is to understand cellular functioning and growth as the outcome of a coordinated operation of diverse yet interconnected cellular processes.

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Circadian mRNA expression: insights from modeling and transcriptomics

Cited by 29 publications

References 246 publications

Translational contributions to tissue specificity in rhythmic and constitutive gene expression

Translational contributions to tissue specificity in rhythmic and constitutive gene expression

Principles for circadian orchestration of metabolic pathways

Toward Multiscale Models of Cyanobacterial Growth: A Modular Approach

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