Exploring the transcriptional landscape of plant circadian rhythms using genome tiling arrays

BACKGROUND Organisms are able to anticipate changes in the daily environment with an internal oscillator know as the circadian clock. Transcription is an important mechanism in maintaining these oscillations. Here we explore, using whole genome tiling arrays, the extent of rhythmic expression patterns genome-wide, with an unbiased analysis of coding and noncoding regions of the Arabidopsis genome. RESULTS As in previous studies, we detected a circadian rhythm for approximately 25% of the protein coding genes in the genome. With an unbiased interrogation of the genome, extensive rhythmic introns were detected predominantly in phase with adjacent rhythmic exons, creating a transcript that, if translated, would be expected to produce a truncated protein. In some cases, such as the MYB transcription factor AT2G20400, an intron was found to exhibit a circadian rhythm while the remainder of the transcript was otherwise arrhythmic. In addition to several known noncoding transcripts, including microRNA, trans-acting short interfering RNA, and small nucleolar RNA, greater than one thousand intergenic regions were detected as circadian clock regulated, many of which have no predicted function, either coding or noncoding. Nearly 7% of the protein coding genes produced rhythmic antisense transcripts, often for genes whose sense strand was not similarly rhythmic. CONCLUSIONS This study revealed widespread circadian clock regulation of the Arabidopsis genome extending well beyond the protein coding transcripts measured to date. This suggests a greater level of structural and temporal dynamics than previously known

Adherent Use of Digital Health Trackers Is Associated with Weight Loss

<div><p>We study the association between weight fluctuation and activity tracking in an on-line population of thousands of individuals using digital health trackers (1,749 ≤ <i>N</i> ≤ 14,411, depending on the activity tracker considered) with millions of recorded activities (119,292 ≤ <i>N</i> ≤ 2,221,382) over the years 2013–2015. In a first between-subject analysis, we found a positive association between activity tracking frequency and weight loss. Users who log food with moderate frequency lost an additional 0.63% (CI [0.55, 0.72]; <i>p</i> < .001) of their body weight per month relative to low frequency loggers. Frequent workout loggers lost an additional 0.38% (CI [0.20, 0.56]; <i>p</i> < .001) and frequent weight loggers lost an additional 0.40% (CI [0.33, 0.47]; <i>p</i> < .001) as compared to infrequent loggers. In a subsequent within-subject analysis on a subset of the population (799 ≤ <i>N</i> ≤ 6,052) with sufficient longitudinal data, we used fixed effect models to explore the temporal relationship between a change in tracking adherence and weight change. We found that for the same individual, weight loss is significantly higher during periods of high adherence to tracking vs. periods of low adherence: +2.74% of body weight lost per month (CI [2.68, 2.81]; <i>p</i> < .001) during adherent weight tracking, +1.35% per month (CI [1.26, 1.43]; <i>p</i> < .001) during adherent food tracking, and +0.60% per month (CI [0.44, 0.76]; <i>p</i> < .001) during adherent workout tracking. The findings suggest that adherence to activity tracking can be utilized as a convenient real-time predictor of weight fluctuations, enabling large-scale, personalized intervention strategies.</p></div

Average weight change during adherent and non-adherent periods for various definitions of adherent period.

<p>We observe that non-adherent periods have higher weight loss, regardless of the value of max gap or period length. The secondary analysis was performed with a max gap of 4 days, and including periods of 7–28 days in length.</p

Flowchart of inclusion/exclusion criteria for the primary analysis.

<p>Flowchart of inclusion/exclusion criteria for the primary analysis.</p

Amount of additional weight loss associated with increased logging rate, for each activity.

<p>Amount of additional weight loss associated with increased logging rate, for each activity.</p

Histogram of average weight change for users during adherent and non-adherent periods of activity tracking.

<p>The histograms depict the association between logging adherence and weight change while controlling for all user variation (gender, age, weight, etc.). For each user in the secondary analysis, average weight change is computed first for their adherent tracking periods and then for their non-adherent periods. Both averages are histogrammed for each activity type. The graph shows a positive association between logging adherence and weight loss, as the adherent weight-change distribution is shifted left relative to the non-adherent weight-change distribution.</p

Average weight change during adherent and non-adherent periods for various definitions of adherent period.

<p>We observe that non-adherent periods have higher weight loss, regardless of the value of max gap or period length. The secondary analysis was performed with a max gap of 4 days, and including periods of 7–28 days in length.</p

Plot of the modeled association between weight change and activity tracking frequency for both genders, over all activities, and for different monitoring durations.

<p>Note that that frequency on the <i>x</i> axis is log scaled. The 10, 50, and 90 percentiles of tracking duration were chosen to represent users who monitored their activity for short, medium, and long durations, and to demonstrate how the association between weight change and activity tracking frequency varied with monitoring duration. In general, increased monitoring duration is associated with decreased weight loss per month and a weaker association between tracking frequency and weight change. The confidence bands are 95% confidence intervals. We note that the three lines intersect in every graph at the point where <i>F</i> = −<i>β</i>₃/<i>β</i>₄, recalling that <i>F</i> is the mean-centered logarithm of recordings per week.</p

Mean per-user weight change during adherent and non-adherent periods in the secondary analysis: mean (standard deviation).

<p>Mean per-user weight change during adherent and non-adherent periods in the secondary analysis: mean (standard deviation).</p

Distribution of number of periods per user and median period length per user for all three log types in the secondary analysis.

<p>Distribution of number of periods per user and median period length per user for all three log types in the secondary analysis.</p