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Maintaining normal lipid homeostasis is crucial for every biological system. Although the transport of lipids in circulation in the form of lipoprotein complexes is an elaborate process orchestrated mainly by the liver, the molecular underpinnings of hepatic lipoprotein regulation are not entirely resolved. In this study, we identify a
novel role for the p53 protein in regulating lipid and lipoprotein metabolism, a process not conceptually conceived as related to p53, which is known mainly in its tumor suppressive functions. Gene expression microarray analysis revealed a group of 341 genes whose expression was induced by p53 in the liver-derived cell line HepG2.
Twenty of these genes encode proteins involved in many aspects of lipid homeostasis, especially lipoprotein metabolism. The mode of regulation of three representative genes (pltp, abca12 and cel) was further characterized. In addition to HepG2, the genes were induced in a p53-dependent manner in other cell types namely Hep3B cells, mouse hepatocytes, human liver cells and fibroblasts. Furthermore, p53 was found to bind to their promoter in designated p53 responsive elements (as measured by chromatin immunoprecipitation) and was able to increase the transcription of a reporter gene located downstream of the genes' promoters. Of note, p53 induced a significant elevation in the protein level of PLTP and CEL. Importantly, p53 augmented the activity of secreted PLTP, which plays a major role in lipoprotein biology and atherosclerosis pathology. These findings expose another layer of p53 functions unrelated to tumor suppression and render it a novel regulator of hepatic lipid metabolism and consequently of systemic lipid homeostasis and atherosclerosis development.

In early sensory systems, such as retina and olfactory bulb in vertebrates or optic and antennal lobes in invertebrates, information about the world converges from a large number of receptors onto a much smaller number of projection neurons. Such bottleneck in the communication channel to the higher brain areas (Attneave, 1954, Barlow & Levick, 1976) can be overcome for sensory stimuli containing correlations by the predictive coding strategy (Srinivasan et al, 1982). In case of the retina, instantaneous subtraction of the least squares prediction compresses information and results in center-surround biphasic receptive fields. However, explaining variation of receptive fields with SNR (Srinivasan et al, 1982, Van Hateren, 1992, Atick & Redlich, 1990) would require circuit re-wiring which is unlikely on short time scales. Here we develop the predictive coding idea by proposing that a non-linear recurrent neuronal circuit can implement predictive coding adaptively: stimuli of different SNR result in different inhibitory surrounds. We solve the transient dynamics of this circuit in response to a step-like stimulus and demonstrate that it communicates a residual of the regularization path to higher brain areas. Thus, we are able to map a non-trivial computation on a concrete neuronal circuit and provide a theoretical framework to understand neural coding for many physiological experiments.

The In different neural systems, the collective activity of populations of neurons responding to natural-istic stimuli is well described by second order “maximum entropy” or Ising models. We asked, how should such interactions in the network be organized to maximize the amount of information represent-ed in population responses about the stimulus it was presented with? To this end, we extended the line-ar-nonlinear Poisson model of single neurons to include pairwise interactions, yielding a stimulus de-pendent, pairwise maximum entropy model. We found that as we varied the noise level in single neurons and the distribution of network inputs, the optimal pairwise interactions smoothly interpolated to achieve network functions that are usually regarded as discrete – stimulus decorrelation, error correc-tion, and independent encoding. These functions reflected a tradeoff between efficient consumption of finite neural bandwidth, and the use of redundancy to mitigate noise. Spontaneous activity in the optimal network reflected stimulus induced activity patterns, and single neuron response variability overes-timated network noise. Our analysis suggests that rather than having a single coding principle hardwired in their architecture, networks in the brain should adapt their function to changing noise and stimulus correlations. Initial results from the vertebrate retina indeed show how it relies on the network’s ‘ground states’ to encode information about the stimulus to the brain.