The link between dysregulated serotonergic activity and depression and anxiety disorders is well established, yet the molecular mechanisms underlying these psychopathologies are not fully understood. Here, we explore the role of microRNAs in regulating serotonergic (5HT) neuron activity. To this end, we determined the specific microRNA "fingerprint" of 5HT neurons and identified a strong microRNA-target interaction between microRNA 135 (miR135), and both serotonin transporter and serotonin receptor-1a transcripts. Intriguingly, miR135a levels were upregulated after administration of antidepressants. Genetically modified mouse models, expressing higher or lower levels of miR135, demonstrated major alterations in anxiety- and depression-like behaviors, 5HT levels, and behavioral response to antidepressant treatment. Finally, miR135a levels in blood and brain of depressed human patients were significantly lower. The current results suggest a potential role for miR135 as an endogenous antidepressant and provide a venue for potential treatment and insights into the onset, susceptibility, and heterogeneity of stress-related psychopathologies.

The etiology and pathophysiology of anxiety and mood disorders is linked to inappropriate regulation of the central stress response. To determine whether microRNAs have a functional role in the regulation of the stress response, we inactivated microRNA processing by a lentiviral-induced local ablation of the Dicer gene in the central amygdala (CeA) of adult mice. CeA Dicer ablation induced a robust increase in anxiety-like behavior, whereas manipulated neurons survive and appear to exhibit normal gross morphology in the time period examined. We also observed that acute stress in wild-type mice induced a differential expression profile of microRNAs in the amygdala. Bioinformatic analysis identified putative gene targets for these stress-responsive microRNAs, some of which are known to be associated with stress. One of the prominent stress-induced microRNAs found in this screen, miR-34c, was further confirmed to be upregulated after acute and chronic stressful challenge and downregulated in Dicer ablated cells. Lentivirally mediated overexpression of miR34c specifically within the adult CeA induced anxiolytic behavior after challenge. Of particular interest, one of the miR-34c targets is the stress-related corticotropin releasing factor receptor type 1 (CRFR1) mRNA, regulated via a single evolutionary conserved seed complementary site on its 3Ј UTR. Additional in vitro studies demonstrated that miR-34c reduces the responsiveness of cells to CRF in neuronal cells endogenously expressing CRFR1. Our results suggest a physiological role for microRNAs in regulating the central stress response and position them as potential targets for treatment of stress-related disorders.

In response to physiological or psychological challenges, the brain activates behavioral and neuroendocrine systems linked to both metabolic and emotional outputs designed to adapt to the demand. However, dysregulation of integration of these physiological responses to challenge can have severe psychological and physiological consequences, and inappropriate regulation, disproportional intensity, or chronic or irreversible activation of the stress response is linked to the etiology and pathophysiology of mood and metabolic disorders. Using a transgenic mouse model and lentiviral approach, we demonstrate the involvement of the hypothalamic neuropeptide Urocortin-3, a specific ligand for the type-2 corticotropin-releasing factor receptor, in modulating septal and hypothalamic nuclei responsible for anxiety-like behaviors and metabolic functions, respectively. These results position Urocortin-3 as a neuromodulator linking stress-induced anxiety and energy homeostasis and pave the way toward better understanding of the mechanisms that mediate the reciprocal relationships between stress, mood and metabolic disorders.metabolic disorders | mood disorders | corticotropin-releasing factor (CRF) | CRF receptor type 2 | stress response I n modern Western societies, the high stress load correlates with an increasing incidence of mood disorders and metabolic syndrome, both of which have reached epidemic proportions over the past decades (1, 2). Exposure to acute or chronic stress is associated with derangement of metabolic and behavioral homeostasis that contributes to the clinical presentation of visceral obesity, type 2 diabetes, atherosclerosis, and metabolic syndrome (2-4). The corticotropin-releasing factor (CRF) neuropeptide system is the primary central mediator of the stress response and contributes to the etiology of stress-related psychiatric illness (5-8). Studies conducted using CRF receptor type 2 (CRFR2)-null mice or Urocortin-2 (Ucn2)-null mice provided evidence that, in addition to its role in mediating stress-related behavior, central CRFR2 is important in modulating metabolic rate, appetite, and feeding behaviors (9-12).The Urocortin-3 (Ucn3) neuropeptide selectively binds and activates CRFR2. Ucn3 is expressed predominately within the hypothalamus, in the median preoptic nucleus and the rostral perifornical area (rPFA) (13-15). The major rPFA-Ucn3 terminal fields, the lateral septum (LS) and the ventromedial hypothalamus (VMH), express high levels of CRFR2 (15). Different stressors and homeostatic insults influence Ucn3 expression levels, suggesting its position as a potential modulator of the stress response (13,16).To assess the functional relevance of endogenous rPFA-Ucn3 neuronal pathways, activating the CRFR2 both in the LS and the VMH, in mediating behavioral and metabolic responses to challenge, we used a site-specific and inducible genetic approach in vivo. We report that rPFA-Ucn3 overexpression induces an anxiety-like behavior, increases the respiratory exchange ratio (RER) and heat production, an...

Proopiomelanocortin (POMC) neurons in the arcuate nucleus of the hypothalamus are central components of systems regulating appetite and energy homeostasis. Here we report on the establishment of a mouse model in which the ribonuclease III ribonuclease Dicer-1 has been specifically deleted from POMC-expressing neurons (POMC(ΔDCR)), leading to postnatal cell death. Mice are born phenotypically normal, at the expected genetic ratio and with normal hypothalamic POMC-mRNA levels. At 6 weeks of age, no POMC neurons/cells could be detected either in the arcuate nucleus or in the pituitary of POMC(ΔDCR) mice. POMC(ΔDCR) develop progressive obesity secondary to decreased energy expenditure but unrelated to food intake, which was surprisingly lower than in control mice. Reduced expression of AgRP and ghrelin receptor in the hypothalamus and reduced uncoupling protein 1 expression in brown adipose tissue can potentially explain the decreased food intake and decreased heat production, respectively, in these mice. Fasting glucose levels were dramatically elevated in POMC(ΔDCR) mice and the glucose tolerance test revealed marked glucose intolerance in these mice. Secondary to corticotrope ablation, basal and stress-induced corticosterone levels were undetectable in POMC(ΔDCR) mice. Despite this lack of activation of the neuroendocrine stress response, POMC(ΔDCR) mice exhibited an anxiogenic phenotype, which was accompanied with elevated levels of hypothalamic corticotropin-releasing factor and arginine-vasopressin transcripts. In conclusion, postnatal ablation of POMC neurons leads to enhanced anxiety and the development of obesity despite decreased food intake and glucocorticoid deficiency.

We develop the notion of double samplers, first introduced by Dinur and Kaufman [Proc. 58th FOCS, 2017], which are samplers with additional combinatorial properties, and whose existence we prove using high dimensional expanders.We show how double samplers give a generic way of amplifying distance in a way that enables efficient list-decoding. There are many error correcting code constructions that achieve large distance by starting with a base code C with moderate distance, and then amplifying the distance using a sampler, e.g., the ABNNR code construction [IEEE Trans. Inform. Theory, 38(2):509-516, 1992.]. We show that if the sampler is part of a larger double sampler then the construction has an efficient listdecoding algorithm and the list decoding algorithm is oblivious to the base code C (i.e., it runs the unique decoder for C in a black box way).Our list-decoding algorithm works as follows: it uses a local voting scheme from which it constructs a unique games constraint graph. The constraint graph is an expander, so we can solve unique games efficiently. These solutions are the output of the list decoder. This is a novel use of a unique games algorithm as a subroutine in a decoding procedure, as opposed to the more common situation in which unique games are used for demonstrating hardness results.Double samplers and high dimensional expanders are akin to pseudorandom objects in their utility, but they greatly exceed random objects in their combinatorial properties. We believe that these objects hold significant potential for coding theoretic constructions and view this work as demonstrating the power of double samplers in this context. of its elements and gets a short string w| S : S → {0, 1}. The resulting codeword is the sequence Enc G (w) := (w| S ) S∈V 1 which can be viewed as a string of length |V 1 | over alphabet Σ = {0, 1} m .If the string w came from an initial code C ⊂ {0, 1} n with minimum distance α > 0, thenOf course the length of words in Enc G (C) depends on the size of |V 1 |, so the shorter the better.This elegant transformation from C to Enc G (C) is very local and easy to compute in the forward direction (from w to Enc G (w)), and indeed it has been found useful in several coding theory constructions, e.g. [GI02,KMRS17]. In this work we study the inverse question, also known as decoding: given a noisy version of Enc G (w), find w. Moreover, we wish to recover from as many errors as possible.Decoding and list decoding A decoding algorithm for Enc G (C) gets as input a string ( f S ) S∈V 1 , and needs to find a word w ∈ C such that w| S = f S for as many S ∈ V 1 as possible. A natural approach is the "maximum likelihood decoding" algorithm: assign each vertex i ∈ [n] the most likely symbol, by looking at the "vote" of each of the subsets S ∋ i, w ′ i := majority S:S∋i [ f S (i)] and then run the unique decoding algorithm of C on w ′ . Assuming C is efficiently unique-decodable from ǫ errors, and assuming G is a good sampler, this gives a decoding algorithm for Enc G (C) that recovers from ...