Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters

Sharon, Eilon; Kalma, Yael; Sharp, Ayala; Raveh-Sadka, Tali; Levo, Michal; Zeevi, Danny; Keren, Leeat; Yakhini, Zohar; Weinberger, Adina; Segal, Eran

doi:10.1038/nbt.2205

Cited by 458 publications

(565 citation statements)

References 42 publications

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“…We used sequencing, DNASeq and RNASeq, to obtain steady-state DNA and RNA levels, respectively, across the library (12). For obtaining protein levels, we used FlowSeq, which combines fluorescence-activated cell sorting and high-throughput DNA sequencing and is similar in design to recently published work (34,35). Briefly, we sorted cells into 12 log-spaced bins of varying GFP/mCherry ratios; isolated, amplified, and barcoded DNA from each of the bins; and then used high-throughput sequencing to count the number of constructs that fell into each bin ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Kosuri

Goodman

Cambray

et al. 2013

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

410

453

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The inability to predict heterologous gene expression levels precisely hinders our ability to engineer biological systems. Using well-characterized regulatory elements offers a potential solution only if such elements behave predictably when combined. We synthesized 12,563 combinations of common promoters and ribosome binding sites and simultaneously measured DNA, RNA, and protein levels from the entire library. Using a simple model, we found that RNA and protein expression were within twofold of expected levels 80% and 64% of the time, respectively. The large dataset allowed quantitation of global effects, such as translation rate on mRNA stability and mRNA secondary structure on translation rate. However, the worst 5% of constructs deviated from prediction by 13-fold on average, which could hinder large-scale genetic engineering projects. The ease and scale this of approach indicates that rather than relying on prediction or standardization, we can screen synthetic libraries for desired behavior.next-generation sequencing | synthetic biology | systems biology O rganisms can be engineered to produce chemical, material, fuel, and medical products that are often superior to nonbiological alternatives (1). Biotechnologists have sought to discover, improve, and industrialize such products through the use of recombinant DNA technologies (2, 3). In recent years, these efforts have increased in complexity from expressing a few genes at once to optimizing multicomponent circuits and pathways (4-7). To attain desired systems-level function reliably, careful and time-consuming optimization of individual components is required (8-11).To mitigate this slow trial-and-error optimization, two dominant approaches have taken hold. The first approach seeks to predict expression levels by elucidating the biophysical relationships between sequence and function. For example, several groups have modified promoters (12, 13) and ribosome binding sites (RBSs) (14-16) to see how small sequence changes affect transcription or translation. Such studies are fundamentally challenging due to the vastness of sequence space. In addition, because these approaches mostly look at either transcription or translation individually, they are rarely able to investigate interactions between these processes.The second approach uses combinations of individually characterized elements to attain desired expression without directly considering their DNA sequences (17-25). Current efforts have focused on approaches to limit the number of time-consuming steps required to characterize potential interactions and on identifying existing or engineered elements that act predictably when used in combination (26-28). However, these studies still suggest there are enough idiosyncratic interactions and context effects that it will be necessary to construct and measure many variants of a circuit to achieve desired function (29). For larger circuits, such approaches are necessarily limited in scope due to the difficulty in measuring large numbers of combinations (26, 27...

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Section: Resultsmentioning

confidence: 99%

Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Kosuri

Goodman

Cambray

et al. 2013

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

410

453

View full text Add to dashboard Cite

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“…Future work is needed to determine the functions of more of these uORFs. Testing large numbers of uORFs will require new high-throughput assays, perhaps similar to those used recently to test transcription factor binding sites (Sharon et al 2012;Kheradpour et al 2013). In addition, translation regulation is known to be particularly sensitive to environmental conditions, cell-cycle phases, and developmental transitions.…”

Section: Conserved Non-aug Uorfs In Yeastmentioning

confidence: 99%

Conserved non-AUG uORFs revealed by a novel regression analysis of ribosome profiling data

et al. 2017

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Upstream open reading frames (uORFs), located in transcript leaders (5′ UTRs), are potent cis-acting regulators of translation and mRNA turnover. Recent genome-wide ribosome profiling studies suggest that thousands of uORFs initiate with non-AUG start codons. Although intriguing, these non-AUG uORF predictions have been made without statistical control or validation; thus, the importance of these elements remains to be demonstrated. To address this, we took a comparative genomics approach to study AUG and non-AUG uORFs. We mapped transcription leaders in multiple Saccharomyces yeast species and applied a novel machine learning algorithm (uORF-seqr) to ribosome profiling data to identify statistically significant uORFs. We found that AUG and non-AUG uORFs are both frequently found in Saccharomyces yeasts. Although most non-AUG uORFs are found in only one species, hundreds have either conserved sequence or position within Saccharomyces. uORFs initiating with UUG are particularly common and are shared between species at rates similar to that of AUG uORFs. However, non-AUG uORFs are translated less efficiently than AUG-uORFs and are less subject to removal via alternative transcription initiation under normal growth conditions. These results suggest that a subset of non-AUG uORFs may play important roles in regulating gene expression.

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“…How transcription-factor binding sites contribute to gene expression is complicated, but systematic analyses are beginning to suggest that promoter activity is largely a function of binding-site location and multiplicity (MacIsaac et al, 2010;Segal et al, 2008;Sharon et al, 2012). We thus expect that many new computational models will be developed that link signalling dynamics to transcriptional signatures (Cheng et al, 2011;Huang and Fraenkel, 2009).…”

Section: Future Perspectivesmentioning

confidence: 99%

Models of signalling networks – what cell biologists can gain from them and give to them

Janes

Lauffenburger

2013

Journal of Cell Science

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SummaryComputational models of cell signalling are perceived by many biologists to be prohibitively complicated. Why do math when you can simply do another experiment? Here, we explain how conceptual models, which have been formulated mathematically, have provided insights that directly advance experimental cell biology. In the past several years, models have influenced the way we talk about signalling networks, how we monitor them, and what we conclude when we perturb them. These insights required wet-lab experiments but would not have arisen without explicit computational modelling and quantitative analysis. Today, the best modellers are crosstrained investigators in experimental biology who work closely with collaborators but also undertake experimental work in their own laboratories. Biologists would benefit by becoming conversant in core principles of modelling in order to identify when a computational model could be a useful complement to their experiments. Although the mathematical foundations of a model are useful to appreciate its strengths and weaknesses, they are not required to test or generate a worthwhile biological hypothesis computationally. Key words: Cell signalling, Computational biology, Systems biologyIntroduction A decade ago, we welcomed the first signalling-network models that were strongly grounded in wet-lab experiments (Hoffmann et al., 2002;Schoeberl et al., 2002). Excellent models now exist for many canonical signalling circuits in a variety of biological settings. However, such models should not be viewed as an end product but rather as a tool for addressing systems-level challenges in cell biology . Have models fulfilled this role and have they provided biological insights that experimentalists should bother to care about? Here, we answer 'Yes' to both questions and predict that signallingnetwork models will soon become indispensable for modern research in the field. Fortunately, the current wealth of dataintensive methods has primed today's cell biologists to embrace modelling, even though they may lack formal training in the underlying mathematics.In this Opinion, we propose that empirical cell biologists have much to gain from signalling-network models, and much to give by ensuring that these models stay in touch with reality. We begin with a brief primer on how computational models can be critically assessed from a biological standpoint. Then, we walk through a series of important insights about cell signalling that have stemmed from computational-systems modelling. We conclude with future perspectives on where signalling-network models are just beginning to have an impact and will continue to do so in the coming years.

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Inferring gene regulatory logic from high-throughput measurements of thousands of systematically designed promoters

Cited by 458 publications

References 42 publications

Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Conserved non-AUG uORFs revealed by a novel regression analysis of ribosome profiling data

Models of signalling networks – what cell biologists can gain from them and give to them

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