ECHO: an Application for Detection and Analysis of Oscillators Identifies Metabolic Regulation on Genome-Wide Circadian Output

Santos, Hannah De los; Collins, Emily; Mann, Catherine; Sagan, April; Jankowski, Meaghan S.; Bennett, Kristin P.; Hurley, Jennifer M.

doi:10.1101/690941

Cited by 15 publications

(34 citation statements)

References 59 publications

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“…After circadian oscillations were detected by ECHO [18] (Figure 1), our methodology began by entering the ECHO results into the ENCORE interface to automatically generate ontological gene-set enrichments for all genes that oscillate with a circadian period, as well as subsets of this group divided by amplitude change (AC) category. In order to calculate enrichments relative to a wide background, as by the traditional GSEA method [50], genes represented in the whole genome for that organism and unrepresented within the user’s data were appended, with a designation of not circadian.…”

Section: Methodsmentioning

confidence: 99%

“…While in the past, fixed-amplitude computational tools such as JTK_CYCLE [31] and eJTK_CYCLE [34] were used to categorize circadian genes, these methods largely ignored the prevalence of changing amplitudes in these data sets [4, 58]. To compensate for this, we designed the Extended Circadian Harmonic Oscillator (ECHO) program to take this amplitude change (AC) into account to robustly detect circadian genes [17, 18]. ECHO demonstrated that each of the AC categories (damped, harmonic, and forced) has specific biological functions, highlighting a new level of biological complexity in circadian regulation [17, 18].…”

Section: Introductionmentioning

confidence: 99%

“…To compensate for this, we designed the Extended Circadian Harmonic Oscillator (ECHO) program to take this amplitude change (AC) into account to robustly detect circadian genes [17, 18]. ECHO demonstrated that each of the AC categories (damped, harmonic, and forced) has specific biological functions, highlighting a new level of biological complexity in circadian regulation [17, 18]. However, while ECHO improved the identification of oscillating elements, it also demonstrated that a large portion of the transcriptome and proteome were found to oscillate, anywhere from 5–80%, making the functional impact of those oscillating elements difficult to discern from the considerable amount of data that is gathered.…”

Section: Introductionmentioning

confidence: 99%

“…While websites such as REVIGO and WEGO provide reduction and visualizations of GO terms, these do not provide deep dataset exploration, as they only summarize GO results [51, 59]. Finally, the identification of the biological relevance of AC categories suggests a need to include these in GO and STRING analysis, not reflected in previously mentioned tools [17, 18, 47, 49].…”

Section: Introductionmentioning

confidence: 99%

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Santos

Bennett

Hurley

2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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Circadian rhythms are 24-hour biological cycles that control daily molecular rhythms in many organisms. The cellular elements that fall under the regulation of the clock are often studied through the use of omics-scale data sets gathered over time to determine how circadian regulation impacts cellular physiology. Previously, we created the ECHO (Extended Circadian Harmonic Oscillator) tool to identify rhythms in these data sets. Using ECHO, we found that circadian oscillations widely undergo a change in amplitude over time and that these amplitude changes have a biological function in the cell. However, ECHO does not align gene ontologies with the identified oscillating genes to give functional context. Thus, we created ENCORE (ECHO Native Circadian Ontological Rhythmicity Explorer), a novel visualization tool which combines the disparate databases of Gene Ontologies, protein-protein interactions, and auxiliary information to uncover the meaning of circadianly-regulated genes. This freely-available tool performs automatic enrichment and creates publication-worthy visualizations which we used to extend previously-gathered data on circadian regulation of physiology from published omics-scale studies in three circadian model organisms: mouse, fruit fly, and Neurospora crassa.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Santos

Bennett

Hurley

2019

Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

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“…However, there are other outstanding detection methods not covered by MetaCycle, including RAIN [53] and BooteJTK [56]. In recent years, there have been improvements to usability (e.g., DiscoRhythm [66], and Nitecap https://nitecap.org/), differential rhythmic analysis (e.g., DODR, LimoRhyde, and CircaCompare) [67][68][69], and generalizability to other "omics" data (e.g., ECHO) [61]. When applying these rhythmic detection algorithms, setting period length as 24 h is suggested for identifying circadian genes, considering (1) it is hard to accurately calculate the period length (e.g., 22 h vs. 24 h) with timeseries data covering only two cycles; (2) this will improve the statistical power and computational efficiency without multiple testing series of period length values.…”

mentioning

confidence: 99%

Genome-wide studies of time of day in the brain: Design and analysis

Ruben

Lee

et al. 2020

Brain Science Advances

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Transcriptome profiling at different times of day is powerful for studying circadian regulation in model organisms and humans. To date, 24 h profiles from many tissue types suggest that about half of all genes are circadian-expressed somewhere in the body. However, few of these studies focused on the brain. Thus, despite known links between circadian disruption and neurological disease, we have virtually no mechanistic understanding. In the coming decade, we expect more genome-wide studies of time of day in different brain diseases, regions, and cell types. We expect just as many different approaches to the design and analysis of these studies. This review considers key principles of circadian tran scriptomics, with the goal of maximizing utility and reproducibility of future studies in the nervous system.

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Influence of circadian rhythm on drug metabolism in 3D hepatic spheroids

Narayanan

Rodrigues

Dordick

2022

Biotech & Bioengineering

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Circadian rhythms are characterized as oscillations that fluctuate based on a 24 h cycle and are responsible for regulation of physiological functions. While the internal clock synchronizes gene expression using external cues like light, a similar synchronization can be induced in vitro by incubating the cells with an increased percentage of serum followed by its rapid removal. Previous studies have suggested that synchronization of HepG2 cell line induced the rhythmic expression of drugmetabolizing enzymes (DME) most specifically the cytochrome P450 enzymes.However, there is a lack of evidence demonstrating the influence of threedimensional microenvironment on the rhythmicity of these genes. To understand this interplay, gene expression of the circadian machinery and CYP450s were compared using the model human hepatocarcinoma cell line, HepG2. Upon serum shock synchronization, gene and protein expression of core clock regulators was assessed and rhythmic expression of these genes was demonstrated. Further insight into the interrelations between various gene pairs was obtained using statistical analysis. Using RNA sequencing, an in-depth understanding of the widespread effects of circadian regulation on genes involved in metabolic processes in the liver was obtained. This study aids in the better understanding of chronopharmacokinetic events in humans using physiologically relevant 3D culture systems.

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ECHO: an Application for Detection and Analysis of Oscillators Identifies Metabolic Regulation on Genome-Wide Circadian Output

Abstract: 1 Elements from the main text are referred to by M.X, where X is the element number.

Cited by 15 publications

References 59 publications

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Genome-wide studies of time of day in the brain: Design and analysis

Influence of circadian rhythm on drug metabolism in 3D hepatic spheroids

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