Correction: Evolutionary principles of modular gene regulation in yeasts

Thompson, Dawn; Roy, Sushmita; Chan, M.K.; Styczynski, Mark P.; Pfiffner, Jenna; French, Courtney; Socha, Amanda; Thielke, Anne; Napolitano, Sara; Muller, Paul; Kellis, M; Konieczka, Jay H.; Wapinski, Ilan; Regev, Aviv

doi:10.7554/elife.01114

Cited by 16 publications

(11 citation statements)

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“…We propose that the ancestral PUF4/5 protein regulated both mitochondrial and cytosolic ribosome-biogenesis functions but that this coregulation was lost in budding yeasts when the two networks specialized toward PUF3-and PUF4-dependent control, respectively. This may have facilitated a further decoupling of mitochondrial and cytosolic ribosomal protein gene expression in respiro-fermentative yeasts, as previously proposed to have occurred through upstream transcription factor rewiring (54,55). yeasts.…”

Section: Discussionmentioning

confidence: 71%

Recurrent rewiring and emergence of RNA regulatory networks

Wilinski

Buter

Klocko

et al. 2017

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

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Alterations in regulatory networks contribute to evolutionary change. Transcriptional networks are reconfigured by changes in the binding specificity of transcription factors and their cognate sites. The evolution of RNA-protein regulatory networks is far less understood. The PUF (Pumilio and FBF) family of RNA regulatory proteins controls the translation, stability, and movements of hundreds of mRNAs in a single species. We probe the evolution of PUF-RNA networks by direct identification of the mRNAs bound to PUF proteins in budding and filamentous fungi and by computational analyses of orthologous RNAs from 62 fungal species. Our findings reveal that PUF proteins gain and lose mRNAs with related and emergent biological functions during evolution. We demonstrate at least two independent rewiring events for PUF3 orthologs, independent but convergent evolution of PUF4/5 binding specificity and the rewiring of the PUF4/5 regulons in different fungal lineages. These findings demonstrate plasticity in RNA regulatory networks and suggest ways in which their rewiring occurs.

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Section: Discussionmentioning

confidence: 71%

Recurrent rewiring and emergence of RNA regulatory networks

Wilinski

Buter

Klocko

et al. 2017

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

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“…Selenite was chosen because it was shown to induce both iron starvation and oxidative stress responses in S. cerevisiae ( Salin et al, 2008 ; Herrero and Wellinger, 2015 ), two stress conditions that C. glabrata is facing when invading the human body and being internalized by the cells of the innate immune system ( Nevitt and Thiele, 2011 ; Brunke and Hube, 2013 ). A key challenge in comparative transcriptomics is to set up the growth conditions to make the physiological state of the different species as similar as possible and to minimize irrelevant differences in gene expression ( Kuo et al, 2010 ; Thompson et al, 2013 ). To do so, we adjusted the selenite doses used for each species to obtain the same 100% increase in the generation time.…”

Section: Resultsmentioning

confidence: 99%

“…For instance, the evolution of pregnancy in mammals was associated with transcriptional network rewiring driven by transposable elements ( Lynch et al, 2011 , 2015 ). In yeasts, the loss of an AT rich cis -regulatory element in the promoters of oxidative phosphorylation and mitochondrial ribosomal protein genes following a Whole Genome Duplication event (WGD) allowed for the appearance of a respiro-fermentative life style in extent post-WGD species ( Ihmels et al, 2005 ; Habib et al, 2012 ; Thompson et al, 2013 ). Comparative transcriptomics (i.e., the comparison of gene expression profiles in different species) has been extensively used in yeasts to identify changes in gene regulation that accompanied the appearance of new physiological properties ( Ihmels et al, 2005 ; Lavoie et al, 2010 ; Wapinski et al, 2010 ), to achieve model phylogeny for regulatory evolution ( Roy et al, 2013 ; Thompson et al, 2013 ) or to predict transcriptional regulatory networks in non-model species ( Koch et al, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Comparative Transcriptomics Highlights New Features of the Iron Starvation Response in the Human Pathogen Candida glabrata

et al. 2018

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In this work, we used comparative transcriptomics to identify regulatory outliers (ROs) in the human pathogen Candida glabrata. ROs are genes that have very different expression patterns compared to their orthologs in other species. From comparative transcriptome analyses of the response of eight yeast species to toxic doses of selenite, a pleiotropic stress inducer, we identified 38 ROs in C. glabrata. Using transcriptome analyses of C. glabrata response to five different stresses, we pointed out five ROs which were more particularly responsive to iron starvation, a process which is very important for C. glabrata virulence. Global chromatin Immunoprecipitation and gene profiling analyses showed that four of these genes are actually new targets of the iron starvation responsive Aft2 transcription factor in C. glabrata. Two of them (HBS1 and DOM34b) are required for C. glabrata optimal growth in iron limited conditions. In S. cerevisiae, the orthologs of these two genes are involved in ribosome rescue by the NO GO decay (NGD) pathway. Hence, our results suggest a specific contribution of NGD co-factors to the C. glabrata adaptation to iron starvation.

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“…This result suggested that the coding and noncoding regulatory regions might have co-evolved under common evolutionary pressure. To test this hypothesis, we composed a dataset of cis-regulatory regions of orthologous genes from a diverse set of 14 yeast species 48 (Table S2-3) and compared the mutation rates between the different regions of the yeast gene structure (Methods). The mutation rates of the promoter and terminator regions displayed a moderate positive correlation (Pearson's r = 0.423 and 0.471, p -value < 1e-16, respectively) with the mutation rates of the yeast coding regions (Fig 2E,F), supporting the co-evolution hypothesis.…”

Section: Coding and Cis-regulatory Regions Additively Contribute To Gmentioning

confidence: 99%

Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

Zrimec

Buric

Muhammad

et al. 2019

Preprint

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Understanding the genetic regulatory code that governs gene expression is a primary, yet challenging aspiration in molecular biology that opens up possibilities to cure human diseases and solve biotechnology problems. However, the fundamental question of how each of the individual coding and non-coding regions of the gene regulatory structure interact and contribute to the mRNA expression levels remains unanswered. Considering that all the information for gene expression regulation is already present in living cells, here we applied deep learning on over 20,000 mRNA datasets to learn the genetic regulatory code controlling mRNA expression in 7 model organisms ranging from bacteria to human.We show that in all organisms, mRNA abundance can be predicted directly from the DNA sequence with high accuracy, demonstrating that up to 82% of the variation of gene expression levels is encoded in the gene regulatory structure. Coding and non-coding regions carry both overlapping and orthogonal information and additively contribute to gene expression levels. By searching for DNA regulatory motifs present across the whole gene regulatory structure, we discover that motif interactions can regulate gene expression levels in a range of over three orders of magnitude. The uncovered co-evolution of coding and non-coding regions challenges the current paradigm that single motifs or regions are solely responsible for gene expression levels. Instead, we propose a holistic system that spans all regions of the gene structure and is required to analyse, understand, and design any future gene expression systems.

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Correction: Evolutionary principles of modular gene regulation in yeasts

Cited by 16 publications

References 0 publications

Recurrent rewiring and emergence of RNA regulatory networks

Recurrent rewiring and emergence of RNA regulatory networks

Comparative Transcriptomics Highlights New Features of the Iron Starvation Response in the Human Pathogen Candida glabrata

Gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure

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