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Focuses on a study which examined the potential roles of fat-derived hormone adinopectin in alcoholic and non-alcoholic fatty liver diseases in mice. Background to the study; Methods; Results; Discussion.

Across the scientific realm, we find ourselves subtracting or dividing stochastic signals. For instance, consider a stochastic realization, $x$, generated from the addition or multiplication of two stochastic signals $a$ and $b$, namely $x=a+b$ or $x = ab$. For the $x=a+b$ example, $a$ can be fluorescence background and $b$ the signal of interest whose statistics are to be learned from the measured $x$. Similarly, when writing $x=ab$, $a$ can be thought of as the illumination intensity and $b$ the density of fluorescent molecules of interest. Yet dividing or subtracting stochastic signals amplifies noise, and we ask instead whether, using the statistics of $a$ and the measurement of $x$ as input, we can recover the statistics of $b$. Here, we show how normalizing flows can generate an approximation of the probability distribution over $b$, thereby avoiding subtraction or division altogether. This method is implemented in our software package, NFdeconvolve, available on GitHub with a tutorial linked in the main text.

When tracking fluorescently labeled molecules (termed "emitters") under widefield microscopes, point spread function overlap of neighboring molecules is inevitable in both dilute and especially crowded environments. In such cases, superresolution methods leveraging rare photophysical events to distinguish static targets nearby in space introduce temporal delays that compromise tracking. As we have shown in a companion manuscript, for dynamic targets, information on neighboring fluorescent molecules is encoded as spatial intensity correlations across pixels and temporal correlations in intensity patterns across time frames. We then demonstrated how we used all spatiotemporal correlations encoded in the data to achieve superresolved tracking. That is, we showed the results of full posterior inference over both the number of emitters and their associated tracks simultaneously and self-consistently through Bayesian nonparametrics. In this companion manuscript we focus on testing the robustness of our tracking tool, BNP-Track, across sets of parameter regimes and compare BNP-Track to competing tracking methods in the spirit of a prior Nature Methods tracking competition. We explore additional features of BNP-Track including how a stochastic treatment of background yields greater accuracy in emitter number determination and how BNP-Track corrects for point spread function blur (or "aliasing") introduced by intraframe motion in addition to propagating error originating from myriad sources (such as criss-crossing tracks, out-of-focus particles, pixelation, shot and camera artefact, stochastic background) in posterior inference over emitter numbers and their associated tracks. While head-to-head comparison with other tracking methods is not possible (as competitors cannot simultaneously learn molecule numbers and associated tracks), we can give competing methods some advantages in order to perform approximate head-to-head comparison. We show that even under such optimistic scenarios, BNP-Track is capable of tracking multiple diffraction-limited point emitters conventional tracking methods cannot resolve thereby extending the superresolution paradigm to dynamical targets.

Alpha-calcitonin gene-related peptide (alphaCGRP) is released mainly from sensory and motor nerves in response to physiological stimuli. Despite well-documented pharmacological effects, its primary physiological role has thus far remained obscure. Increased lipid content, particularly in skeletal muscle and liver, is strongly implicated in the pathogenesis of insulin resistance, but the physiological regulation of organ lipid is imperfectly understood. Here we report our systematic investigations of the effects of alphaCGRP on in vitro and in vivo indices of lipid metabolism. In rodents, levels of alphaCGRP similar to those in the blood markedly stimulated fatty acid beta-oxidation and evoked concomitant mobilization of muscle lipid via receptor-mediated activation of muscle lipolysis. alphaCGRP exerted potent in vivo effects on lipid metabolism in muscle, liver, and the blood via receptor-mediated pathways. Studies with receptor antagonists were consistent with tonic regulation of lipid metabolism by an endogenous CGRP agonist. These data reveal that alphaCGRP is a newly recognized regulator of lipid availability and utilization in key tissues and that it may elevate the availability of intramyocellular free fatty acids to meet muscle energy requirements generated by contraction by evoking their release from endogenous triglyceride.

Adiponectin is a fat cell-secreted hormone with antidiabetic and anti-inflammatory activities. The reduced adiponectin levels are associated with obesity-related metabolic syndrome. Replenishment of this hormone into animal models can improve insulin sensitivity, decrease blood glucose and lipid levels, and prevent the development of atherosclerosis and fatty liver injury. Despite these findings, the underlying molecular mechanisms remain largely unknown. Here, we have used affinity chromatography to purify the protein complexes that are associated with adiponectin in human serum. The nature of these adiponectin-binding proteins was analyzed by MS/MS. Eight proteins from the adiponectin-containing protein mixtures have been identified. Many of them, including thrombospondin-1 (TSP-1), histidine-rich glycoprotein, kininogen 1, and alpha 2 macroglobulin (alpha2M), are well-known glycoproteins involved in the regulation of inflammation, angiogenesis, and tissue remodeling. Coimmunoprecipitation and radioligand competitive-binding assays confirmed the direct interactions between adiponectin and alpha2M, or TSP-1. Moreover, these specific bindings were also detected in the serum samples derived from both healthy human subjects and patients with type 2 diabetes. In summary, our study demonstrated that, in the circulation, adiponectin forms protein complexes with other serum proteins. These proteins might serve as the physiological-binding partners of adiponectin and regulate its bioavailability and biological activities.