Independent evolution of transcript abundance and gene regulatory dynamics

Krieger, Gat; Lupo, Offir; Levy, Avraham A.; Barkai, Naama

doi:10.1101/2020.01.22.915033

Cited by 3 publications

(4 citation statements)

References 61 publications

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“…Gene expression of S. paradoxus WT, tec1Δ, phd1Δ and rim101Δ, was taken from Krieger et al [62], using the average expression for two biological repeats for each mutant during exponential growth in YPD.…”

Section: Gene Expression Of Mutantsmentioning

confidence: 99%

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Mapping the architecture of regulatory variation provides insights into the evolution of complex traits

Lupo

Krieger

Jonas

et al. 2020

Preprint

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Background: Organisms evolve complex traits by recruiting existing programs to new contexts, referred as co-option. Within a species, single upstream regulators can trigger full differentiation programs. Distinguishing whether co-option of differentiation programs results from variation in single regulator, or in multiple genes, is key for understanding how complex traits evolve. As an experimentally accessible model for studying this question we turned to budding yeast, where a differentiation program (filamentous) is activated in S. cerevisiae only upon starvation, but used by the related species S. paradoxus also in rich conditions. Results: To define expression variations associated with species-specific activation of the filamentous program, we profiled the transcriptome of S. cerevisiae, S. paradoxus and their hybrid along two cell cycles at 5-minutes resolution. As expected in cases of co-option, expression of oscillating genes varies between the species in synchrony with their growth phenotypes and was dominated by upstream trans-variations. Focusing on regulators of filamentous growth, we identified gene-linked variations (cis) in multiple genes across regulatory layers, which propagated to affect expression of target genes, as well as binding specificities of downstream transcription factor. Unexpectedly, variations in regulators essential for S. cerevisiae filamentation were individually too weak to explain activation of this program in S. paradoxus. Conclusions: Our study reveals the complex architecture of regulatory variation associated with species-specific use of a differentiation program. Based on these results, we suggest a new model in which evolutionary co-option of complex traits is stabilized in a distributed manner through multiple weak-effect variations accumulating throughout the regulatory network.including size, growth pattern, and body morphology [2][3][4]. A compelling model is that complex traits do not evolve de-novo but emerge through recruitment (co-option) of existing gene expression program to new contexts. Evidence supporting co-option were presented in the context of morphological evolution, where major interspecies differences in the positioning of body appendages or wing patterns were linked to variations in cis-regulatory elements controlling the expression of major regulators, including homeobox transcription factors or developmental signaling proteins [5][6][7][8][9][10].Modulating the expression of single upstream regulators provides a genetic shortcut for the evolution of complex traits [11]. Complicating this view, however, is the fact that gene expression networks, such as these activating complex traits, are polygenic and include multiple activators and inhibitors that could act as drivers [12]. Further, due to the inter-connected nature of genetic circuits, variations in seemingly distant genes could propagate to influence the same phenotype [13]. This was exemplified recently in Drosophila, where expression variation in a Hox gene that correlated with morphological...

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Section: Gene Expression Of Mutantsmentioning

confidence: 99%

“…Double deletion of genotype fkh1Δfkh2Δ were generated by amplifying the LEU2 gene from plasmid pRS425 and replacing FKH2's ORF. Strains of genotype ace2Δ::KanMX and swi5Δ::KanMX were generated as reported at Krieger et al[62].…”

mentioning

confidence: 99%

Mapping the architecture of regulatory variation provides insights into the evolution of complex traits

Lupo

Krieger

Jonas

et al. 2020

Preprint

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“…Temporal analysis of gene expression is gaining importance in the analysis of complex dynamic processes such as disease progression. Besides gene dynamic pattern characterization, time course gene expression data are also used to infer regulatory and signaling relationships among genes (21,22). Integrating with other different types of measurements, such as pathology and infection over time helps disentangle the complex dynamic processes and possible underlying mediators (23).…”

Section: Discussionmentioning

confidence: 99%

Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19

Wang¹,

Sanborn²,

Dai³

et al. 2022

JCI Insight

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Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses.Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there is a lack of methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic datasets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal datasets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk and single-cell RNA sequencing time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways as well as impaired type I interferon (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets.

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“…Temporal analysis of gene expression is gaining importance in the analysis of complex dynamic processes such as disease progression. Besides gene dynamic pattern characterization, time-course gene expression data are also used to infer regulatory and signaling relationships among genes (19,20). Integrating with other different types of measurements, such as pathology and infection over time helps disentangle the complex dynamic processes and possible underlying mediators (21).…”

Section: Discussionmentioning

confidence: 99%

Systematic temporal analysis of peripheral blood transcriptomes usingTrendCatcheridentifies early and persistent neutrophil activation as a hallmark of severe COVID-19

Wang

Sanborn

Dai

et al. 2021

Preprint

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A recent analysis of endothelial cell (EC) gene expression suggests that ECs of distinct organs vary in their gene expression profiles and respond distinctly to the systemic inflammatory stimulus of endotoxemia (Jambusaria et al., 2020). There is a need to systematically define tissue-specific gene expression dynamics in response to inflammation but such an analysis is in part limited by the availability of appropriate algorithms to analyze differential expression across a time course. Here, we present TrendCatcher (https://jaleesr.github.io/TrendCatcher_1.0.0/), an R package designed for time course RNA-seq data analysis which identifies distinct dynamic transcriptional programs. When applied to ECs, we observed that approximately 85-95% of EC genes in all three organs followed a biphasic response following endotoxemia. The rapid upregulation of innate immune response and bacterial response genes occurred within 6 hours in all three vascular beds but the subsequent upregulation of reparative EC mitosis and cell cycle genes occurred most rapidly in lung ECs (24-48h) and was most delayed in cardiac ECs (72h-168h). The distinct kinetics of EC inflammatory injury and regeneration identify vascular-bed specific temporal windows for targeted therapeutics.

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Independent evolution of transcript abundance and gene regulatory dynamics

Cited by 3 publications

References 61 publications

Mapping the architecture of regulatory variation provides insights into the evolution of complex traits

Mapping the architecture of regulatory variation provides insights into the evolution of complex traits

Temporal transcriptomic analysis using TrendCatcher identifies early and persistent neutrophil activation in severe COVID-19

Systematic temporal analysis of peripheral blood transcriptomes usingTrendCatcheridentifies early and persistent neutrophil activation as a hallmark of severe COVID-19

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