DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity

Xue, Xiangning; Zong, Wei; Huo, Zhiguang; Ketchesin, Kyle D.; Scott, Madeline; Petersen, Kaitlyn; Logan, Ryan W.; Seney, Marianne L.; McClung, Colleen A.; Tseng, George C.

doi:10.1093/bioinformatics/btad039

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…1). We analyzed differential gene rhythmicity using DiffCircaPipeline (DCP) (Xue et al, 2023) and differential gene expression using DESeq2 (Love et al, 2014) .…”

Section: Resultsmentioning

confidence: 99%

“…To identify candidate genes for SCN photoperiod-induced plasticity, we examined changes in the circadian transcriptome of the SCN from C3Hf+/+ mice raised in Long (LD 16:8) vs. Short (LD 8:16) photoperiods. We analyzed differential gene rhythmicity using DiffCircaPipeline (DCP) [43] and differential gene expression using DESeq2 [41].…”

Section: Resultsmentioning

confidence: 99%

“…To explore differences in rhythmic biomarkers (e.g. differential rhythmicity, phase, amplitude, MESOR) between photoperiod groups the log2CPM expression data was analyzed using the DiffCircaPipeline v0.99.9 framework (Xue et al, 2023). Briefly, DCP uses a cosinor fit model (Cornelissen, 2014) to estimate from the time sample data amplitude (A), phase (Φ), Midline Estimating Statistic Of Rhythm (MESOR, M) and the noise level (σ) of gene expression rhythms.…”

Section: Differential Rhythmic Analysis (Diffcircapipeline)mentioning

confidence: 99%

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Gene expression plasticity of the mammalian brain circadian clock in response to photoperiod

Cox,

Gianonni-Guzmán,

Cartailler

et al. 2024

Preprint

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Seasonal daylength, or circadian photoperiod, is a pervasive environmental signal that profoundly influences physiology and behavior. In mammals, the central circadian clock resides in the suprachiasmatic nuclei (SCN) of the hypothalamus and synchronizes, or entrains, physiology and behavior to the prevailing light cycle from retinal input. The process of entrainment induces considerable plasticity in the SCN, but the molecular mechanisms underlying SCN plasticity are incompletely understood. Entrainment to different photoperiods persistently alters the phase, waveform, period, and resetting properties of the SCN and its driven rhythms. To elucidate novel molecular mechanisms of photoperiod plasticity, we performed RNAseq on whole SCN dissected from mice raised in Long (LD 16:8) and Short (LD 8:16) photoperiods. Using differential rhythms analysis, we showed that fewer rhythmic genes were detected in Long photoperiod and that there was an overall phase advance of gene expression rhythms of 4-6 hours. However, a few genes showed significant phase delays, including GTP binding protein overexpressed in skeletal muscle (Gem), an SCN light responsive gene and light response modulator. Using differential expression analysis, we found significant expression changes in the clock-associated gene timeless circadian clock 1 (Timeless) and abundant changes in expression levels of SCN neural signaling genes related to light responses, neuropeptides, GABA, ion channels, and serotonin. Particularly striking were differences across photoperiods in the expression of the SCN neuropeptide signaling genes, prokineticin receptor 2 (Prokr2) and cholecystokinin (Cck), as well as convergent regulation of the expression of three SCN light response genes, dual specificity phosphatase 4 (Dusp4) and RAS, dexamethasone-induced 1 (Rasd1), andGem. Transcriptional modulation ofDusp4andRasd1,and phase regulation ofGem,are compelling candidate molecular mechanisms for photoperiod-induced plasticity in the SCN light response. Similarly, transcriptional modulation ofProkr2andCckmay critically support SCN neural network reconfiguration during photoperiodic entrainment. Our findings identify the SCN light response and neuropeptide signaling gene sets as rich substrates for elucidating novel mechanisms of photoperiod plasticity.

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“…1). We analyzed differential gene rhythmicity using DiffCircaPipeline (DCP) (Xue et al, 2023) and differential gene expression using DESeq2 (Love et al, 2014) .…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Differential Rhythmic Analysis (Diffcircapipeline)mentioning

confidence: 99%

See 1 more Smart Citation

Gene expression plasticity of the mammalian brain circadian clock in response to photoperiod

Cox,

Gianonni-Guzmán,

Cartailler

et al. 2024

Preprint

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“…Current methods primarily use statistical testing to detect differential rhythmicity and some have integrated their use with standard genomic analysis pipelines (Weger et al, 2021). These methods can be summarized as either performing model selection (Pelikan et al, 2022, Weger et al, 2021) or hypothesis testing (Ding et al, 2021, Parsons et al, 2020, Singer and Hughey, 2019, Thaben and Westermark, 2016, Xue et al, 2023). Broadly, model selection methods aim to identify sets of functions that best describe changes between two conditions, while hypothesis testing evaluates the presence or absence of differential rhythmicity.…”

Section: Introductionmentioning

confidence: 99%

Quantifying Differential Rhythmicity based on Effect Sizes with LimoRhyde2

Obodo,

Asiaee

2024

Preprint

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Current methods for assessing differential rhythmicity in genomic data focus on hypothesis testing and model selection, often assuming sinusoidal rhythms. A more appropriate approach is to estimate differences in rhythmic properties between two or more conditions using effect sizes. To address this gap, we extend LimoRhyde2, a method for quantifying rhythm-related effect sizes and their uncertainty in genome-scale data, to enable differential rhythmicity analyses. Through extensive testing, we validate the method for differential rhythmicity analysis and showcase how it improves biological interpretation for circadian systems biology.

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A single-cell transcriptomic atlas of the prefrontal cortex across the human lifespan

Yang,

Clarence,

Scott

et al. 2024

Preprint

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The dorsolateral prefrontal cortex is central to higher cognitive functions and is particularly vulnerable to age-related decline. To advance our understanding of the molecular mechanisms underlying brain development, maturation, and aging, we constructed a detailed single-cell transcriptomic atlas of the human dorsolateral prefrontal cortex, encompassing over 1.3 million nuclei from 284 postmortem samples spanning the full human lifespan (0-97 years). This atlas reveals distinct phases of transcriptomic activity: a dynamic developmental period, stabilization during midlife, and subtle yet coordinated changes in late adulthood. Modeling non-linear age trends across the lifespan shows ten distinct trajectories of the entire transcriptome from all cell types, with notable findings in neurons and microglia, linked to neurodevelopmental disorders and Alzheimer's disease risk, respectively. Moreover, excitatory neurons exhibit a convergence of gene expression patterns across the lifespan, suggesting the emergence of a common molecular signature of aging. Pseudotime analysis tracing the progression of cellular lineages throughout life reveals key gene clusters with dynamic expression changes that reflect development, maturation, and aging, as well as their connection to brain-related diseases. We uncover significant circadian rhythm reprogramming in late adulthood, characterized by disruption of core clock gene rhythmicity and the emergence of new rhythmic patterns, particularly within microglia and oligodendrocytes. This comprehensive single-cell atlas provides a baseline for understanding the molecular transitions from development through successful aging in the human dorsolateral prefrontal cortex.

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DiffCircaPipeline: a framework for multifaceted characterization of differential rhythmicity

Cited by 4 publications

References 14 publications

Gene expression plasticity of the mammalian brain circadian clock in response to photoperiod

Gene expression plasticity of the mammalian brain circadian clock in response to photoperiod

Quantifying Differential Rhythmicity based on Effect Sizes with LimoRhyde2

A single-cell transcriptomic atlas of the prefrontal cortex across the human lifespan

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