Adipocyte Turnover: Relevance to Human Adipose Tissue Morphology

International audienceOBJECTIVE: Adipose tissue may contain few large adipocytes (hypertrophy) or many small adipocytes (hyperplasia). We investigated factors of putative importance for adipose tissue morphology. RESEARCH DESIGN AND METHODS: Subcutaneous adipocyte size and total fat mass were compared in 764 subjects with BMI 18-60 kg/m(2). A morphology value was defined as the difference between the measured adipocyte volume and the expected volume given by a curved-line fit for a given body fat mass and was related to insulin values. In 35 subjects, in vivo adipocyte turnover was measured by exploiting incorporation of atmospheric (14)C into DNA. RESULTS: Occurrence of hyperplasia (negative morphology value) or hypertrophy (positive morphology value) was independent of sex and body weight but correlated with fasting plasma insulin levels and insulin sensitivity, independent of adipocyte volume (beta-coefficient = 0.3, P < 0.0001). Total adipocyte number and morphology were negatively related (r = -0.66); i.e., the total adipocyte number was greatest in pronounced hyperplasia and smallest in pronounced hypertrophy. The absolute number of new adipocytes generated each year was 70% lower (P < 0.001) in hypertrophy than in hyperplasia, and individual values for adipocyte generation and morphology were strongly related (r = 0.7, P < 0.001). The relative death rate (approximately 10% per year) or mean age of adipocytes (approximately 10 years) was not correlated with morphology. CONCLUSIONS: Adipose tissue morphology correlates with insulin measures and is linked to the total adipocyte number independently of sex and body fat level. Low generation rates of adipocytes associate with adipose tissue hypertrophy, whereas high generation rates associate with adipose hyperplasia

Global parameter search reveals design principles of the mammalian circadian clock

Background: Virtually all living organisms have evolved a circadian (~24 hour) clock that controls physiological and behavioural processes with exquisite precision throughout the day/night cycle. The suprachiasmatic nucleus (SCN), which generates these ~24 h rhythms in mammals, consists of several thousand neurons. Each neuron contains a gene-regulatory network generating molecular oscillations, and the individual neuron oscillations are synchronised by intercellular coupling, presumably via neurotransmitters. Although this basic mechanism is currently accepted and has been recapitulated in mathematical models, several fundamental questions about the design principles of the SCN remain little understood. For example, a remarkable property of the SCN is that the phase of the SCN rhythm resets rapidly after a 'jet lag' type experiment, i.e. when the light/ dark (LD) cycle is abruptly advanced or delayed by several hours. Results: Here, we describe an extensive parameter optimization of a previously constructed simplified model of the SCN in order to further understand its design principles. By examining the top 50 solutions from the parameter optimization, we show that the neurotransmitters' role in generating the molecular circadian rhythms is extremely important. In addition, we show that when a neurotransmitter drives the rhythm of a system of coupled damped oscillators, it exhibits very robust synchronization and is much more easily entrained to light/dark cycles. We were also able to recreate in our simulations the fast rhythm resetting seen after a 'jet lag' type experiment. Conclusion: Our work shows that a careful exploration of parameter space for even an extremely simplified model of the mammalian clock can reveal unexpected behaviours and non-trivial predictions. Our results suggest that the neurotransmitter feedback loop plays a crucial role in the robustness and phase resetting properties of the mammalian clock, even at the single neuron level

Quantification of Circadian Rhythms in Single Cells

Bioluminescence techniques allow accurate monitoring of the circadian clock in single cells. We have analyzed bioluminescence data of Per gene expression in mouse SCN neurons and fibroblasts. From these data, we extracted parameters such as damping rate and noise intensity using two simple mathematical models, one describing a damped oscillator driven by noise, and one describing a self-sustained noisy oscillator. Both models describe the data well and enabled us to quantitatively characterize both wild-type cells and several mutants. It has been suggested that the circadian clock is self-sustained at the single cell level, but we conclude that present data are not sufficient to determine whether the circadian clock of single SCN neurons and fibroblasts is a damped or a self-sustained oscillator. We show how to settle this question, however, by testing the models' predictions of different phases and amplitudes in response to a periodic entrainment signal (zeitgeber)

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding ‘big data’ that is conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them

Linking Core Promoter Classes to Circadian Transcription

<div><p>Circadian rhythms in transcription are generated by rhythmic abundances and DNA binding activities of transcription factors. Propagation of rhythms to transcriptional initiation involves the core promoter, its chromatin state, and the basal transcription machinery. Here, I characterize core promoters and chromatin states of genes transcribed in a circadian manner in mouse liver and in <i>Drosophila</i>. It is shown that the core promoter is a critical determinant of circadian mRNA expression in both species. A distinct core promoter class, strong circadian promoters (SCPs), is identified in mouse liver but not <i>Drosophila</i>. SCPs are defined by specific core promoter features, and are shown to drive circadian transcriptional activities with both high averages and high amplitudes. Data analysis and mathematical modeling further provided evidence for rhythmic regulation of both polymerase II recruitment and pause release at SCPs. The analysis provides a comprehensive and systematic view of core promoters and their link to circadian mRNA expression in mouse and <i>Drosophila</i>, and thus reveals a crucial role for the core promoter in regulated, dynamic transcription.</p></div

Identifying strong circadian promoters (SCPs).

<p>A. Amplitudes and averages of transcriptional activities as measured by Nascent-Seq were quantified for the transcript corresponding to each promoter. Promoters were stratified as either TATA box-containing and/or LCpG, or TATA-less and HCpG, as well as exhibiting either no, or at least one CTF ChIP-Seq peaks (CTFs, circadian transcription factors BMAL1, REV-ERBα, REV-ERBβ, and E4BP4). TATA boxes and LCpG were strongly associated with high circadian transcriptional amplitudes, while CTFs rather were associated with high average transcriptional activities. Box plots show medians, 25/75% quantiles, and minimum/maximum values. Promoter groups from top to bottom: <i>n</i> = 665, 605, 330, 295. B. Nucleosome occupancies 101 to 1 bp upstream of the TSSs for SCPs and type I circadian promoters (with TATA box or LCpG but not CTF binding). Occupancies were computed from MNase-Seq data (Methods). Data are presented as kernel densities. C. Nucleosome occupancies around TSSs for different promoter classes. Pileups were computed from MNase-Seq data (Methods) and averaged over the promoter classes for each position relative to the TSS (excluding top and bottom 1% values, respectively, due to a few outlier promoters). Circ. = Circadian; Const. = Constitutive; Type I defined as in panel B, type II = CTF binding TATA-less and with HCpG. D. Fraction of TFIID-dependent promoters for different classes of circadian promoters, with 95% confidence intervals assuming binomial distributions. Bars from left to right: <i>n</i> = 721, 330, 605, 78, 160, 57. E. Mature mRNA expression levels and transcript half lives for different promoter classes. Mature mRNA expression levels were quantified from poly(A)⁺ RNA-Seq data for the transcript corresponding to each promoter; transcript half lives were compiled from two literature sources (Methods). Mean expression levels and half lives were median averaged over the promoter classes and are shown as points. Error bars represent 95% confidence intervals for the medians. Circ. = Circadian; Const. = Constitutive; TATA = with TATA box; type I and type II as in panels B and C.</p

Linking Core Promoter Classes to Circadian Transcription - Fig 1

<p><b>A. Overview of the study and the various data sources (listed in the callouts).</b> Data were compiled from published studies of mouse liver in animals held under rhythmic light-dark (LD) conditions (green), or of <i>Drosophila</i> (blue), or both (black). Rhythmic transcriptional activities were estimated from Nascent-Seq data as visualized for the <i>Saa1</i> transcript. Rhythmic transcriptional activities thought to be generated by circadian transcription factors (CTFs) such as REV-ERB α and β, E4BP4, and the CLOCK/BMAL1 heterodimer in mouse, or CLK in <i>Drosophila</i>. The core promoter (beige box) mediates rhythms in CTF binding to rhythmic transcription initiation. This mediation may involve chromatin and nucleosomal remodeling, interacting with rhythms in general transcription factor and Pol II binding to core promoter elements such as the TATA box. B. Overview of promoter classification. Promoters were classified according to whether they drive circadian or constitutive transcription, according to core promoter sequence features, and according to detected CTF binding at the promoter.</p