Transcriptomic analysis links diverse hypothalamic cell types to fibroblast growth factor 1-induced sustained diabetes remission

n rodent models of type 2 diabetes (T2D), sustained remission of hyperglycemia can be induced by a single intracerebroventricular (icv) injection of fibroblast growth factor 1 (FGF1), and the mediobasal hypothalamus (MBH) was recently implicated as the brain area responsible for this effect. To better understand the cellular response to FGF1 in the MBH, we sequenced >79,000 single-cell transcriptomes from the hypothalamus of diabetic Lepob/ob mice obtained on Days 1 and 5 after icv injection of either FGF1 or vehicle. A wide range of transcriptional responses to FGF1 was observed across diverse hypothalamic cell types, with glial cell types responding much more robustly than neurons at both time points. Tanycytes and ependymal cells were the most FGF1-responsive cell type at Day 1, but astrocytes and oligodendrocyte lineage cells subsequently became more responsive. Based on histochemical and ultrastructural evidence of enhanced cell-cell interactions between astrocytes and Agrp neurons (key components of the melanocortin system), we performed a series of studies showing that intact melanocortin signaling is required for the sustained antidiabetic action of FGF1. These data collectively suggest that hypothalamic glial cells are leading targets for the effects of FGF1 and that sustained diabetes remission is dependent on intact melanocortin signaling

Mathematical Model for the Contribution of Individual Organs to Non-Zero Y-Intercepts in Single and Multi-Compartment Linear Models of Whole-Body Energy Expenditure

<div><p>Mathematical models for the dependence of energy expenditure (EE) on body mass and composition are essential tools in metabolic phenotyping. EE scales over broad ranges of body mass as a non-linear allometric function. When considered within restricted ranges of body mass, however, allometric EE curves exhibit ‘local linearity.’ Indeed, modern EE analysis makes extensive use of linear models. Such models typically involve one or two body mass compartments (e.g., fat free mass and fat mass). Importantly, linear EE models typically involve a non-zero (usually positive) y-intercept term of uncertain origin, a recurring theme in discussions of EE analysis and a source of confounding in traditional ratio-based EE normalization. Emerging linear model approaches quantify whole-body resting EE (REE) in terms of individual organ masses (e.g., liver, kidneys, heart, brain). Proponents of individual organ REE modeling hypothesize that multi-organ linear models may eliminate non-zero y-intercepts. This could have advantages in adjusting REE for body mass and composition. Studies reveal that individual organ REE is an allometric function of total body mass. I exploit first-order Taylor linearization of individual organ REEs to model the manner in which individual organs contribute to whole-body REE and to the non-zero y-intercept in linear REE models. The model predicts that REE analysis at the individual organ-tissue level will not eliminate intercept terms. I demonstrate that the parameters of a linear EE equation can be transformed into the parameters of the underlying ‘latent’ allometric equation. This permits estimates of the allometric scaling of EE in a diverse variety of physiological states that are not represented in the allometric EE literature but are well represented by published linear EE analyses.</p></div

Illustration of a first-order Taylor linearization of a hypothetical population allometric equation for resting energy expenditure (REE): when , and .

<p>The linearization well approximates expected population REE given total values of body mass in the vicinity of . Therefore, given ‘noisy’ sample data with mean , the linear regression model is an estimate of the first-order Taylor series for the true population model.</p

Influence of the organ-tissue scaling exponent on an organ-tissue’s contribution to the positive y-intercept and to REE in linear models.

<p>Panel A depicts the contribution to the y-intercept by the <i>i-th</i> individual organ-tissue REE in terms of , where is the value of total body mass about which the Taylor linearization is performed. The contribution to the y-intercept is expressed as a multiplier of calculated as . The individual organ-tissue’s contribution to the y-intercept is maximized given a fixed numerical value of when is ∼0.70 for an animal with  = 30 g, as predicted by Eq. 12. Importantly, there is a substantial range of values that more than double . Panel B depicts the hypothetical effect of varying the value on both the y-intercept and slope of the hypothetical liver REE – relationship assuming that the allometric scaling coefficient remains fixed at 0.36 (rescaled from 22.6 , the value reported by <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103301#pone.0103301-Wang1" target="_blank">[27]</a> and depicted in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103301#pone-0103301-g002" target="_blank">Figure 2A</a>). Note that the sensitivity of the slope to variation in suggests that group differences in the of the liver, a small organ with a big impact on whole-body REE, could contribute to the problem of differing between-group slopes of whole-body REE in phenotyping studies.</p

Instantiation of the model for the contribution of individual organs and tissues to the y-intercept in linear regressions of resting energy expenditure (REE) on total body mass ().

<p>Panels A through E depict individual organ-tissue contributions to REE scaled to in accordance with the approach and numerical values for allometric scaling coefficients units expressed in reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0103301#pone.0103301-Wang1" target="_blank">[27]</a>. Note that the y scales differ. The REE for each organ-tissue is expressed as a first-order Taylor linearization at a specific body mass of 0.03 kg (upper equation) of the parent allometric function (lower equation). Panel F reveals that the sum of the linearized equations equals total REE at  = 0.03 kg and very nearly equals total REE in the range 0.02≤ ≤0.04 kg. The aggregate y-intercept (1.66) is the sum of the individual organ-tissue y-intercepts, while the aggregate slope (123.73) is the sum of the individual slopes. Note the particularly large contribution to the y-intercept and to whole-body REE by the liver even though it represents only ∼5% of . Applying Eqs. 6 and 7 with  = 0.03 kg in the aggregate linear equation results in the parameters  = 60.58 and  = 0.69 of a single 2-parameter allometric equation for the whole-body EE curve. These parameter values are remarkably similar to those identified by standard log-log analysis or by non-linear regression (see text). To convert the units of a scaling coefficient to , divide by . To convert the slope of a Taylor series to units of , divide by 1000; the intercept remains unchanged.</p

Concentration-related metabolic rate and behavioral thermoregulatory adaptations to serial administrations of nitrous oxide in rats - Fig 2

<p><b>Nitrous oxide minus control gas differences in core temperature and heat production in each of the twelve 3-h exposure sessions (first 90 min).</b> 95% confidence intervals that do not include zero are significantly different from control at p<0.05. N = 12 per dose group. C.I., confidence interval; N₂O, nitrous oxide; Tcore, core temperature; HP, heat production.</p

Concentration-related metabolic rate and behavioral thermoregulatory adaptations to serial administrations of nitrous oxide in rats

<div><p>Background</p><p>Initial administration of ≥60% nitrous oxide (N₂O) to rats evokes hypothermia, but after repeated administrations the gas instead evokes hyperthermia. This sign reversal is driven mainly by increased heat production. To determine whether rats will behaviorally oppose or assist the development of hyperthermia, we previously performed thermal gradient testing. Inhalation of N₂O at ≥60% causes rats to select cooler ambient temperatures both during initial administrations and during subsequent administrations in which the hyperthermic state exists. Thus, an available behavioral response opposes (but does not completely prevent) the acquired hyperthermia that develops over repeated high-concentration N₂O administrations. However, recreational and clinical uses of N₂O span a wide range of concentrations. Therefore, we sought to determine the thermoregulatory adaptations to chronic N₂O administration over a wide range of concentrations.</p><p>Methods</p><p>This study had two phases. In the first phase we adapted rats to twelve 3-h N₂O administrations at either 0%, 15%, 30%, 45%, 60% or 75% N₂O (n = 12 per group); outcomes were core temperature (via telemetry) and heat production (via respirometry). In the second phase, we used a thermal gradient (range 8°C—38°C) to assess each adapted group’s thermal preference, core temperature and locomotion on a single occasion during N₂O inhalation at the assigned concentration.</p><p>Results</p><p>In phase 1, repeated N₂O administrations led to dose related hyperthermic and hypermetabolic states during inhalation of ≥45% N₂O compared to controls (≥ 30% N₂O compared to baseline). In phase 2, rats in these groups selected cooler ambient temperatures during N₂O inhalation but still developed some hyperthermia. However, a concentration-related increase of locomotion was evident in the gradient, and theoretical calculations and regression analyses both suggest that locomotion contributed to the residual hyperthermia.</p><p>Conclusions</p><p>Acquired N₂O hyperthermia in rats is remarkably robust, and occurs even despite the availability of ambient temperatures that might fully counter the hyperthermia. Increased locomotion in the gradient may contribute to hyperthermia. Our data are consistent with an allostatic dis-coordination of autonomic and behavioral thermoregulatory mechanisms during drug administration. Our results have implications for research on N₂O abuse as well as research on the role of allostasis in drug addiction.</p></div