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310 results for “Circadian clock”
Temperature-dependent fold-switching mechanism of the circadian clock protein KaiB
<p>Derived data accompanying publication of <em>Temperature-dependent fold-switching mechanism of the circadian clock protein KaiB</em> (Zhang et al., PNAS 2024).</p> <p> </p> <p>This dataset contains data for fold-switching of KaiB from simulations performed using the Upside coarse-grained model (Jumper et al. PLoS Comput. Bio 2017). Files contained include collective variables, kinetic quantities (committors), and initial structures used to seed unbiased simulations. These data should be sufficient recreate the analysis shown in the associated publicaion. Raw trajectory files have not been deposited due to their size; contact the author (Spencer Guo) to request.</p>
Two metabolomics data sets (mouse kidney, mouse plasma), generated for the publication Bignon et al., 2023: "Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock".
<p><strong>Publication: </strong>Bignon Y, Wigger L, Ansermet C, Weger BD, Lagarrigue S, Centeno G, Durussel F, Götz L, Ibberson M, Pradervand S, Quadroni M, Weger M, Amati F, Gachon F, Firsov D. Multiomics reveals multilevel control of renal and systemic metabolism by the renal tubular circadian clock. J Clin Invest. 2023 Mar 2:e167133. doi: 10.1172/JCI167133. Epub ahead of print. PMID: 36862511.</p> <p> </p> <p><strong>Abstract: </strong> Circadian rhythmicity in renal function suggests rhythmic adaptations in renal metabolism. To decipher the role of the circadian clock in renal metabolism, we studied diurnal changes in renal metabolic pathways using integrated transcriptomic, proteomic, and metabolomic analysis performed on control mice and mice with inducible deletion of the circadian clock regulator Bmal1 in the renal tubule (cKOt). With this unique resource, we demonstrated that ~30% RNAs, ~20% proteins and ~20% metabolites are rhythmic in kidneys of control mice. Several key metabolic pathways including NAD+ biosynthesis, fatty acid transport, carnitine shuttle,and b-oxidation displayed impairments in kidneys of cKOt, resulting in a perturbed mitochondrial activity. Carnitine reabsorption from the primary urine was one of the most impacted processes with a ~50% reduction in plasma carnitine levels and a parallel systemic decrease in tissues carnitine content. This suggests that the circadian clock in the renal tubule controls both kidney and systemic physiology.</p> <p> </p> <p><strong>This record contains two separate mass-spectrometry metabolomics data sets associated with this study:</strong></p> <ol> <li>Metabolic profile of renal tubules, MS/MS data, Metabolon, Morrisville, NC (N=60)</li> <li>Metabolic profile of blood plasma, MS/MS data, Biocrates, Innsbruck, Austria (N=60)</li> </ol> <p>For each data set, original data as received from the platforms and processed data as used in the data analysis are provided. Preprocessing of kidney data included removal of metabolites with more than 80% missing data values, median normalization, imputation and glog2 transformation. Preprocessing of plasma data included filtering of metabolites with any missing data and log2 transformation. Details of data processing are available in the STAR*methods of the publication.</p> <p> </p> <p><strong>Data sets in other repositories associated with the same study:</strong></p> <p>Additional data sets (transcriptomics, proteomics) pertaining to the same study have been deposited in public repositories:</p> <ul> <li>Gene Expression Omnibus (NCBI GEO), GSE216252</li> <li>PRIDE Archive (EMBL-EBI), PXD036803</li> </ul> <p> </p>
Anabaena circadian clock behavior under nitrogen-poor conditions from single-cell measurements of fluorescence intensity
<p>Circadian clock arrays in multicellular filaments of the heterocyst-forming cyanobacterium Anabaena sp. strain PCC 7120 display remarkable spatio-temporal coherence under nitrogen-replete conditions. To shed light on the interplay between circadian clocks and the formation of developmental patterns, we followed the expression of a clock-controlled gene under nitrogen deprivation, at the level of individual cells. Our experiments showed that differentiation into heterocysts took place preferentially within a limited interval of the circadian clock cycle, that gene expression in different vegetative intervals along a developed filament was discoordinated, and that the circadian clock was active in individual heterocysts. Furthermore, Anabaena mutants lacking the kaiABC genes encoding the circadian clock core components produced heterocysts but failed in diazotrophy. Therefore, genes related to some aspect of nitrogen fixation, rather than early or mid-heterocyst differentiation genes, are likely affected by the absence of the clock. A bioinformatics analysis supports the notion that RpaA may play a role as master regulator of clock outputs in Anabaena, the temporal control of differentiation by the circadian clock and the involvement of the clock in proper diazotrophic growth. Together, these results suggest that under nitrogen-deficient conditions, the clock coherent unit in Anabaena is reduced from a full filament under nitrogen-rich conditions to the vegetative cell interval between heterocysts.</p>
Reprogramming feedback strength in gibberellin biosynthesis highlights conditional regulation by the circadian clock and carbon dioxide
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Anabaena circadian clock behavior under nitrogen-poor conditions from single-cell measurements of fluorescence intensity
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Dynamic physiological and transcriptome changes reveal a po-tential relationship between the circadian clock and salt stress response in Ulmus pumila
<p>Despite the important role the circadian clock plays in numerous critical physiological responses in plants, such as hypocotyl elongation, leaf movement, stomatal opening, flowering, and stress responses, there have been no investigations into the effect of the circadian clock on physiological and transcriptional networks under salt stress. <em>Ulmus pumila</em> L.—a major tree species used for timber, shelter, food, medicine, fodder, and ecological protection—has been reported to tolerate 100~150 mM NaCl treatment. We performed a time-course physiological and transcriptome analysis of 2-years-old <em>U. pumila</em> seedlings under salt treatment to dissect the physiological regulation and potential relationship between the circadian clock and the salt stress response. Seedlings in 150 mM NaCl treatment exhibited salt-induced physiological enhancement compared to the control group. A total of 7,009 unigenes were identified under salt stress, of which 283 unigenes were identified as circadian rhythm genes and 16 unigenes were differentially expressed (circadian rhythm-related DEGs). Further analysis of dynamic expression changes revealed that DEGs involved in four crucial pathways—photosynthesis, thiamine metabolism, abscisic acid synthesis and metabolism, and the hormone-MAPK signal crosstalk pathway—are closely related to the circadian clock. Finally, we constructed a co-expression network between the circadian clock and these four crucial pathways. Our results help shed light on the molecular link between the circadian clock and salt stress tolerance in <em>U. pumila</em>.</p>
Data from: Phosphorylation, disorder, and phase separation govern the behavior of Frequency in the fungal circadian clock
<p>Circadian clocks are composed of molecular oscillators that pace rhythms of gene expression to the diurnal cycle. Therein, transcriptional-translational negative feedback loops (TTFLs) generate oscillating levels of transcriptional repressor proteins that regulate their own gene expression. In the filamentous fungus Neurospora crassa, the proteins Frequency (FRQ), the FRQ-interacting RNA helicase (FRH) and Casein-Kinase I (CK1) form the FFC complex that represses expression of genes activated by the White-Collar complex (WCC). A key question concerns how FRQ orchestrates molecular interactions at the core of the clock despite containing little predicted tertiary structure. We present the reconstitution and biophysical characterization of FRQ and the FFC in unphosphorylated and highly phosphorylated states. Site-specific spin labeling and pulse-dipolar ESR spectroscopy provides domain-specific structural details on the full-length, 989-residue intrinsically disordered FRQ and the FFC. FRQ contains a compact core that associates and organizes FRH and CK1 to coordinate their roles in WCC repression. FRQ phosphorylation increases conformational flexibility and alters oligomeric state but the changes in structure and dynamics are non-uniform. Full-length FRQ undergoes liquid-liquid phase separation (LLPS) to sequester FRH and CK1 and influence CK1 enzymatic activity. Although FRQ phosphorylation favors LLPS, LLPS feeds back to reduce FRQ phosphorylation by CK1 at higher temperatures. Live imaging of Neurospora hyphae reveals FRQ foci characteristic of condensates near the nuclear periphery. Analogous clock repressor proteins in higher organisms share little position-specific sequence identity with FRQ; yet, they contain amino-acid compositions that promote LLPS. Hence, condensate formation may be a conserved feature of eukaryotic circadian clocks. </p>
Shedding light on the threespine stickleback circadian clock
<p>The circadian clock is an internal timekeeping system shared by most organisms, and knowledge about its functional importance and evolution in natural environments is still needed. Here, we investigated the circadian clock of wild-caught threespine sticklebacks (<i>Gasterosteus aculeatus</i>) at the behavioural and molecular levels. While their behaviour, ecology, and evolution are well studied, information on their circadian rhythms are scarce. We quantified the daily locomotor activity rhythm under a light-dark cycle (LD) and under constant darkness (DD). Under LD, all fish exhibited significant daily rhythmicity, while under DD, only 18% of individuals remained rhythmic. This interindividual variation suggests that the circadian clock controls activity only in certain individuals. Moreover, under LD, some fish were almost exclusively nocturnal, while others were active around the clock. Furthermore, the most nocturnal fish were also the least active. These results suggest that light masks activity (i.e. suppresses activity without entraining the internal clock) more strongly in some individuals than others. Finally, we quantified the expression of five clock genes in the brain of sticklebacks under DD using qPCR. We did not detect circadian rhythmicity, which could either indicate that the clock molecular oscillator is highly light-dependent, or that there was an oscillation but that we were unable to detect it. Overall, our study suggests that a strong circadian control on behavioural rhythms may not necessarily be advantageous in a natural population of sticklebacks and that the daily phase of activity varies greatly between individuals because of a differential masking effect of light.</p>
Data from: Natural Zeitgebers under temperate conditions cannot compensate for the loss of a functional circadian clock in timing of a vital behavior in Drosophila
<p><span>The adaptive significance of adjusting behavioral activities to the right time of the day seems obvious but is under debate. Our data provides evidence that proper timing of eclosion, a vital behavior of the fruit fly <em>Drosophila melanogaster</em>, requires a functional molecular clock under quasi-natural conditions. </span></p> <p><span>We compared eclosion profiles and assessed eclosion rhythmicity in wildtype flies (CS) and clock-related mutant strains <em>(per<sup>01</sup></em>, <em>pdf<sup>01</sup></em>, <em>han<sup>5304</sup></em>) under laboratory and outdoor conditions. In the laboratory, flies were entrained in either light-dark cycle (LD12:12) or warm (25°C)-cold (16°C) cycle (WC12:12), and tested under entrainment or constant conditions using TriKinetics Drosophila Eclosion Monitors. For outdoor assays, a WEclMon system was used and experiments were performed between July-Octobre 2014 and July-Octobre 2016. </span></p> <p><span>Flies with a defective molecular clock showed impaired rhythmicity and gating under natural temperate conditions in Würzburg/Germany even in the presence of a full complement of abiotic Zeitgebers. We also found that eclosion rhythmicity cannot be entrained by daily cycles in relative humidity. Low relative humidity also did not or only weakly affect the ability of the flies to eclose and unfold their wings.</span></p> <p><span>Our results suggest that the presence of natural Zeitgebers is not sufficient, and a functional molecular clock is required to induce stable temporal eclosion patterns in flies under temperate conditions with considerable day-today variation in light intensity and temperature. Temperate Zeitgebers are, however, sufficient to functionally rescue a loss of PDF-signalling.</span></p> <p><span>The data set belongs to the publication:</span></p> <p><em><span>Ruf F, Mitesser O, Mungwa ST, Horn M, Rieger D, Hovestadt T, and Wegener C (2021) Natural Zeitgebers Under Temperate Conditions Cannot Compensate for the Loss of a Functional Circadian Clock in Timing of a Vital Behavior in Drosophila. </span>Journal of Biological Rhythms 36: 271–285. DOI: 10.1177/0748730421998112.</em></p>
Data for: Pericytes' Circadian Clock Affects Endothelial Cells' Synchronization and Angiogenesis in a 3D Tissue Engineered Scaffold
<p>Raw data set and analysis files for Mastrullo et al., Frontiers in Pharmacology, 2022 <strong>DOI:</strong> 10.3389/fphar.2022.867070 </p>
The intestinal circadian clock drives microbial rhythmicity to maintain gastrointestinal homeostasis
<p><strong>Diurnal (<em>i.e.</em>, 24-hour) oscillations of the gut microbiome have been described in various species including mice and humans. However, the driving force behind these rhythms remains less clear. In this study, we differentiate between endogenous and exogenous time cues driving microbial rhythms.</strong> <strong>Our results demonstrate that fecal microbial oscillations are maintained in mice kept in the absence of light, supporting a role of the host’s circadian system rather than representing a diurnal response to environmental changes. Intestinal epithelial cell-specific ablation of the core clock gene <em>Bmal1</em> disrupts rhythmicity of microbiota. Targeted metabolomics functionally link intestinal clock-controlled bacteria to microbial-derived products, in particular branched-chain fatty acids and secondary bile acids. Microbiota transfer from intestinal clock-deficient mice into germ-free mice altered intestinal gene expression, enhanced lymphoid organ weights and suppressed immune cell recruitment. These results highlight the importance of functional intestinal clocks for circadian microbiota composition and function, which is required to balance the host’s gastrointestinal homeostasis. </strong></p>
Collective flow of circadian clock information in honeybee colonies (Data)
<p>This repository contains the data used in the paper "Collective flow of circadian clock information in honeybee colonies".</p> <p><strong> </strong></p> <p>Paper: Collective flow of circadian clock information in honeybee colonies</p> <p><strong> </strong></p> <p>Code and more details are provided in the README file of<a href="https://github.com/BioroboticsLab/speedtransfer.git"> speedtransfer</a> repository.</p> <p><strong> </strong></p> <h2>Description of included files</h2> <p>All data sets exist for the period 01.08.-25.08.2016 and 20.08-14.09.2019.</p> <p><strong> </strong></p> <h3><strong>mean_velocity_2016.csv and mean_velocity_2019.csv</strong></h3> <p>The mean velocity for each bee and age is averaged over 10-minute time windows.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>velocity: Mean euclidean distance of two consecutive points of a bee's hive position.</p> </li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format.</p> </li> <li> <p>age: Age in days. Can be NaN if the bee has no associated death_date.</p> </li> </ul> <p> </p> <h3><strong>velocity_2088_2019.csv and velocity_5101_2019.csv</strong></h3> <p>The movement speed [mm/s] of two individual bees with the bee id 2088 and the bee id 5101 for the period 2019.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>velocity: Euclidean distance of two consecutive points of a bee's hive position.</p> </li> <li>time_passed: Time [s] in between the datetime of that current and the last previous detection.</li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format.</p> </li> </ul> <p> </p> <h3><strong>cosinor_2016.csv and cosinor_2019.csv</strong></h3> <p>A cosinor fit of the velocity per bee for a time window of 3 consecutive days according to the method proposed by <a href="https://doi.org/10.1186/1742-4682-11-16">Cornelissen</a>.</p> <p><strong>Keys</strong>:</p> <ul> <li> <p>mesor: Rhythm-adjusted mean of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>amplitude: Amplitude of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>phase: Acrophase of a cosine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_value: P-value of an F-test for overall significance of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_mesor: P-value of the mesor coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_amplitude: P-value of the amplitude coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_acrophase: P-value of the phase coefficient of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_reject: P-value of a F-test for model validity.</p> </li> <li> <p>r_squared: R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>r_squared_adj: Adjusted R² value of a cosinor fit with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</p> </li> <li> <p>p_ks: P-value of a Kalgomorov-Smirnoff test of residual normality.</p> </li> <li> <p>p_hom: P-Value of F-test of variance homogeneity of cosinor fit.</p> </li> <li> <p>dw: Durbin-Watson statistic for the independence of the residuals.</p> </li> <li> <p>p_runs: P-value of runs test of independence of residuals.</p> </li> <li> <p>RSS: Residual sum of squared - the sum of squared differences between the data and the estimated values from the fitted model</p> </li> <li> <p>SSPE: Pure error sum of squares of cosinor fit.</p> </li> <li> <p>bee_id: Numeric unique identifier per individual bee.</p> </li> <li> <p>age: Age in days. Can be NaN if the bee has no associated death_date.</p> </li> <li> <p>date: Date and time in year-month-day hour:minute:seconds+ms:ns format. The hour is always 12.</p> </li> <li> <p>n_data_points: Number of data points per cosinor fit.</p> </li> <li> <p>data_point_dist_max: Maximum temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_min: Minimum temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_mean: Mean temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>data_point_dist_median: Median temporal distance between two consecutive timepoints of velocity data.</p> </li> <li> <p>day_mean: Mean velocity during the daytime defined as the time between 9 and 18 o'clock.</p> </li> <li> <p>day_std: Standard deviation of velocity during the daytime defined as the time between 9 and 18 o'clock.</p> </li> <li> <p>night_mean: Mean velocity during the nighttime defined as the time between 21 and 6 o'clock.</p> </li> <li> <p>night_std: Standard deviation of velocity during the nighttime defined as time between 21 and 6 o'clock.</p> </li> <li> <p>ad_fuller: P-value of augmented Dickey-Fuller test for testing whether the velocity data is stationary.</p> </li> <li> <p>fit_type: Median time bin in seconds used for fit, e.g. 3600 means that the median over a time window of 3600s is used for the fit.</p> </li> <li> <p>ci_acrophase_lower: Confidence interval lower bound for the acrophase fit value.</p> </li> <li> <p>ci_acrophase_upper: Confidence interval upper bound for the acrophase fit value.</p> </li> <li> <p>ci_mesor_lower: Confidence interval lower bound for the mesor fit value.</p> </li> <li> <p>ci_mesor_upper: Confidence interval upper bound for the mesor fit value.</p> </li> <li> <p>ci_amplitude_lower: Confidence interval lower bound for the amplitude fit value.</p> </li> <li> <p>ci_amplitude_upper: Confidence interval upper bound for the amplitude fit value.</p> </li> </ul> <p><strong> </strong></p> <h3><strong>interactions_side0_2016.csv, interactions_side0_2019.csv and interactions_side1_2016.csv, interactions_side1_2019.csv</strong></h3> <p>The bee interactions and their post-interaction velocity change. An interaction between two bees (bee0 and bee1) is defined when two bees are detected simultaneously in the hive with a confidence threshold of 0.25, the distance between the markings on their thorax bodies is no more than 14 mm. These interactions are combined into one interaction if the same detections occur within a time interval of 1 second or less between them. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>bee_id0: Numeric unique identifier per individual bee.</p> </li> <li> <p>bee_id1: Numeric unique identifier per individual bee.</p> </li> <li> <p>interaction_start: Timestamp indicating interaction start time point.</p> </li> <li> <p>interaction_end: Timestamp indicating interaction end time point.</p> </li> <li> <p>x_pos_start_bee0: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos_start_bee0: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>theta_start_bee0: Numeric angle of bee relative to hive at interaction start.</p> </li> <li> <p>x_pos_start_bee1: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos_start_bee1: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>theta_start_bee1: Numeric angle of bee relative to hive at interaction start.</p> </li> <li> <p>x_pos_end_bee0: Numeric x-position of bee relative to hive at interaction end.</p> </li> <li> <p>y_pos_end_bee0: Numeric y-position of bee relative to hive at interaction end.</p> </li> <li> <p>theta_end_bee0: Numeric angle of bee relative to hive at interaction end.</p> </li> <li> <p>x_pos_end_bee1: Numeric x-position of bee relative to hive at interaction end.</p> </li> <li> <p>y_pos_end_bee1: Numeric y-position of bee relative to hive at interaction end.</p> </li> <li> <p>theta_end_bee1: Numeric angle of bee relative to hive at interaction end.</p> </li> <li> <p>vel_change_bee0: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>rel_change_bee0: Numeric post-interaction relative change of velocity: rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>vel_change_bee1: Numeric post-interaction absolute change of velocity: abs = vafter-vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> <li> <p>rel_change_bee1: Numeric post-interaction relative change of velocity:rel = (vafter-vbefore)/vbefore with vbefore and vafter are calculated as the mean velocity 30s before and after the interaction.</p> </li> </ul> <p>Modeling bees as rectangular mask to determine if bees body overlap when interacting - see more details in <a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer repository</a>:</p> <ul> <li> <p>x_trans_focal_bee0: Translated and rotated x-position relative to hive.</p> </li> <li> <p>y_trans_focal_bee0: Translated and rotated y-position relative to hive.</p> </li> <li> <p>theta_trans_focal_bee0: Translated and rotated theta relative to hive.</p> </li> <li> <p>x_trans_focal_bee1: Translated and rotated x-position relative to hive.</p> </li> <li> <p>y_trans_focal_bee1: Translated and rotated y-position relative to hive.</p> </li> <li> <p>theta_trans_focal_bee1: Translated and rotated theta relative to hive.</p> </li> <li> <p>overlapping: Bool indicating whether rectangular masks modeling the body of bees overlap.</p> </li> </ul> <p>Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame:</p> <ul> <li> <p>amplitude_bee0: Amplitude of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>phase_bee0: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>p_value_bee0: P-value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>r_squared_bee0: R² value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>amplitude_bee1: Amplitude of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>phase_bee1: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>p_value_bee1: P-value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p>r_squared_bee1: R² of the cosinor fit per bee of the day of the interaction.</p> </li> </ul> <p> </p> <h3><strong>interactions_side0_null_model_2016.csv and interactions_side0_null_model_2019.csv</strong></h3> <p>A null model for bee interactions and their post-interaction velocity change. The interaction null model is created by taking the distribution of the start and end times of a given interaction dataframe and selecting two random bees at those times that the bees were detected in the hive at that time. These pairs of bees are considered as "interacting" and their post-interaction speed change is calculated. The resulting interaction data frames per bee are concatenated with the estimates of the cosinor dataframe. The position data is relative to pixels not to the hive coordinates.</p> <p>As this is a null model the following keys are the same as described in the <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Interaction</a> data frame.</p> <p><strong>Keys:</strong></p> <ul> <li> <p>bee_id0</p> </li> <li> <p>bee_id1</p> </li> <li> <p>interaction_start</p> </li> <li> <p>interaction_end</p> </li> <li> <p>x_pos_start_bee0</p> </li> <li> <p>y_pos_start_bee0</p> </li> <li> <p>theta_start_bee0</p> </li> <li> <p>x_pos_start_bee1</p> </li> <li> <p>y_pos_start_bee1</p> </li> <li> <p>theta_start_bee1</p> </li> <li> <p>x_pos_end_bee0</p> </li> <li> <p>y_pos_end_bee0</p> </li> <li> <p>theta_end_bee0</p> </li> <li> <p>x_pos_end_bee1</p> </li> <li> <p>y_pos_end_bee1</p> </li> <li> <p>theta_end_bee1</p> </li> <li> <p>vel_change_bee0</p> </li> <li> <p>rel_change_bee0</p> </li> <li> <p>vel_change_bee1</p> </li> <li> <p>rel_change_bee1</p> </li> <li> <p>age_bee0</p> </li> <li> <p>phase_bee0</p> </li> <li> <p>amplitude_bee0</p> </li> <li> <p>r_squared_bee0</p> </li> <li> <p>p_value_bee0</p> </li> <li> <p>age_bee1</p> </li> <li> <p>amplitude_bee1</p> </li> <li> <p>r_squared_bee1</p> </li> <li> <p>p_value_bee1</p> </li> <li> <p>phase_bee1</p> </li> </ul> <p> </p> <h3><strong>interaction_tree_paths_2016.csv and interactions_tree_paths_2019.csv</strong></h3> <p>Graph-theoretic interaction tree paths. By tracing back interactions that positively influenced the speed of a focal bee, we constructed a graph-theoretic tree structure starting from a young rhythmic bee and recursively adding activating (velocity change parent > 0) individuals. We examined the impact of sequential interactions among bees occurring between 10 am and 3 pm, focusing on a subgroup of n = 1000 bees that are significantly rhythmic, younger than 5 days old, and peak in activity after 12 pm. We limited the time window between interactions to 30 minutes and capped the cascade duration at 2 hours to ensure causal relevance. The resulting interaction trees are collected and each node of all paths in the interaction trees are concatenated to this dataframe.</p> <p><strong>Keys</strong>:</p> <ul> <li> <p>bee_id: Numeric unique identifier per individual bee which is a node in the tree.</p> </li> <li> <p>datetime: Date in year-month-day hour:minute:seconds+ms:ns format when the interaction takes place.</p> </li> <li> <p>x_pos: Numeric x-position of bee relative to hive at interaction start.</p> </li> <li> <p>y_pos: Numeric y-position of bee relative to hive at interaction start.</p> </li> <li> <p>vel_change_parent: <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.728ge538e768">Absolute velocity change</a> of bee of parent node.</p> </li> <li> <p>age: Age in days of node bee. Can be NaN if the bee has no associated death date.</p> </li> <li> <p>is_root: Bool indicating if node is root of tree.</p> </li> <li> <p>depth: Depth of node in tree. E.g. depth of root is 0.</p> </li> <li> <p>is_leaf: Bool indicating if node is leaf of tree.</p> </li> <li> <p>n_children: Number of children of the subtree of the node.</p> </li> <li> <p>parent: Numeric bee_id of parent node.</p> </li> <li> <p>tree_id: Numeric unique identifier of tree.</p> </li> <li> <p>time_gap: Python datetime.timedelta object of time delta in between the parent and child node interaction.</p> </li> <li> <p>path_id: Numeric unique identifier of path.</p> </li> </ul> <p>Cosinor fit parameters - see more detailed in <a href="https://docs.google.com/document/d/1PHJWc9HqbYgndjX9WjkfSQoks81AR-E9cHC25xZgP84/edit#heading=h.wjr1wzfvmipm">Cosinor</a> data frame:</p> <ul> <li> <p> r_squared: R² value of the cosinor fit per bee of the day of the interaction.</p> </li> <li> <p> phase: Phase of the cosinor fit per bee of the day of the interaction.</p> </li> </ul> <p> </p> <h2>Software used to acquire and analyze the data:</h2> <p><a href="https://github.com/BioroboticsLab/speedtransfer.git">speedtransfer: Cosinor fit and interaction calculation and further analyses.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_rhythm">bb_rhythm: Cosinor fit and interaction calculation and further analyses.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_behavior">bb_behavior: Database interaction and data (pre)processing, velocity calculation.</a></p> <p><a href="https://github.com/BioroboticsLab/bb_utils">bb_utils: Database settings and interaction.</a></p> <p><a href="https://github.com/walachey/slurmhelper">slurmhelper: A package for slurm script handling.</a></p> <p> </p>
Data associated with the manuscript "Complex epistatic interactions between ELF3, PRR9, and PRR7 regulates the circadian clock and plant physiology"
<p>Datasets associated with the figures in the paper entilted: "<strong>Complex epistatic interactions between ELF3, PRR9, and PRR7 regulates the circadian clock and plant physiology"</strong></p>
The circadian clock of the bacterium B. subtilis evokes properties of complex, multicellular circadian systems
<p>Circadian clocks are pervasive throughout nature, yet only recently has this adaptive regulatory program been described in non-photosynthetic bacteria. Here, we describe an inherent complexity in the <em>Bacillus</em> <em>subtilis</em> circadian clock. We find that <em>B. subtilis</em> entrains to blue and red light and that circadian entrainment is separable from masking through fluence titration and frequency demultiplication protocols. We identify circadian rhythmicity in constant light, consistent with Aschoff's Rule, and entrainment aftereffects, both of which are properties described for eukaryotic circadian clocks. We report that circadian rhythms occur in wild isolates of this prokaryote, thus establishing them as a general property of this species, and that its circadian system responds to the environment in a complex fashion that is consistent with multicellular eukaryotic circadian systems.</p>
Dataset: Disease-associated KCNMA1 variants decrease circadian clock robustness in channelopathy mouse models
<p><em>KCNMA1</em> encodes the voltage- and calcium-activated K<sup>+</sup> (BK) channel, which regulates suprachiasmatic nucleus (SCN) neuronal firing and circadian behavioral rhythms. Gain-of-function (GOF) and loss-of-function (LOF) alterations in BK channel activity disrupt circadian behavior, but the effect of human disease-associated <em>KCNMA1</em> channelopathy variants has not been studied on clock function. Here, we assess circadian behavior in two GOF and one LOF mouse lines. Heterozygous <em>Kcnma1</em><sup>N999S/WT</sup> and homozygous <em>Kcnma1</em><sup>D434G/D434G</sup> mice are validated as GOF models of paroxysmal dyskinesia (PNKD3), but whether circadian rhythm is affected in this hypokinetic locomotor disorder is unknown. Conversely, homozygous LOF <em>Kcnma1</em><sup>H444Q/H444Q </sup>mice do not demonstrate PNKD3. We assessed circadian behavior by locomotor wheel running activity. All three mouse models were rhythmic, but <em>Kcnma1</em><sup>N999S/WT</sup> and <em>Kcnma1</em><sup>D434G/D434G</sup> showed reduced circadian amplitude and decreased wheel activity, corroborating prior studies focused on acute motor coordination. In addition, <em>Kcnma1</em><sup>D434G/D434G</sup> mice had a small decrease in period. However, the phase-shifting sensitivity for both GOF mouse lines was abnormal. Both <em>Kcnma1</em><sup>N999S/WT</sup> and <em>Kcnma1</em><sup>D434G/D434G</sup> mice displayed increased responses to light pulses and took fewer days to re-entrain to a new light:dark cycle. In contrast, the LOF <em>Kcnma1</em><sup>H444Q/H444Q </sup>mice showed no difference in any of the circadian parameters tested. The enhanced sensitivity to phase-shifting stimuli in <em>Kcnma1</em><sup>N999S/WT</sup> and <em>Kcnma1</em><sup>D434G/D434G</sup> mice was similar to other <em>Kcnma1</em> GOF mice. Together with previous studies, these results suggest that increasing BK channel activity decreases circadian clock robustness, without rhythm ablation.</p>
The Clinical Utility of Measuring the Circadian Clock in Treatment of Delayed Sleep-Wake Phase Disorder
ClinicalTrials.gov study NCT03715465. IPD Sharing: NO. Countries: 1. Publications: 1.
Dataset: Disease-associated KCNMA1 variants decrease circadian clock robustness in channelopathy mouse models
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Shedding light on the threespine stickleback circadian clock
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Data from: Cyclin-dependent kinase 5 (Cdk5) activity is modulated by light and gates rapid phase shifts of the circadian clock
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Data from: Natural Zeitgebers under temperate conditions cannot compensate for the loss of a functional circadian clock in timing of a vital behavior in Drosophila
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