TY - JOUR
AB - Loss of functional cardiomyocytes is a major determinant of heart failure after myocardial infarction. Previous high throughput screening studies have identified a few microRNAs (miRNAs) that can induce cardiomyocyte proliferation and stimulate cardiac regeneration in mice. Here, we show that all of the most effective of these miRNAs activate nuclear localization of the master transcriptional cofactor Yes-associated protein (YAP) and induce expression of YAP-responsive genes. In particular, miR-199a-3p directly targets two mRNAs coding for proteins impinging on the Hippo pathway, the upstream YAP inhibitory kinase TAOK1, and the E3 ubiquitin ligase β-TrCP, which leads to YAP degradation. Several of the pro-proliferative miRNAs (including miR-199a-3p) also inhibit filamentous actin depolymerization by targeting Cofilin2, a process that by itself activates YAP nuclear translocation. Thus, activation of YAP and modulation of the actin cytoskeleton are major components of the pro-proliferative action of miR-199a-3p and other miRNAs that induce cardiomyocyte proliferation.
AU - Torrini, Consuelo
AU - Cubero, Ryan J
AU - Dirkx, Ellen
AU - Braga, Luca
AU - Ali, Hashim
AU - Prosdocimo, Giulia
AU - Gutierrez, Maria Ines
AU - Collesi, Chiara
AU - Licastro, Danilo
AU - Zentilin, Lorena
AU - Mano, Miguel
AU - Zacchigna, Serena
AU - Vendruscolo, Michele
AU - Marsili, Matteo
AU - Samal, Areejit
AU - Giacca, Mauro
ID - 7128
IS - 9
JF - Cell Reports
KW - cardiomyocyte
KW - cell cycle
KW - Cofilin2
KW - cytoskeleton
KW - Hippo
KW - microRNA
KW - regeneration
KW - YAP
SN - 2211-1247
TI - Common regulatory pathways mediate activity of microRNAs inducing cardiomyocyte proliferation
VL - 27
ER -
TY - JOUR
AB - We show that statistical criticality, i.e. the occurrence of power law frequency distributions, arises in samples that are maximally informative about the underlying generating process. In order to reach this conclusion, we first identify the frequency with which different outcomes occur in a sample, as the variable carrying useful information on the generative process. The entropy of the frequency, that we call relevance, provides an upper bound to the number of informative bits. This differs from the entropy of the data, that we take as a measure of resolution. Samples that maximise relevance at a given resolution—that we call maximally informative samples—exhibit statistical criticality. In particular, Zipf's law arises at the optimal trade-off between resolution (i.e. compression) and relevance. As a byproduct, we derive a bound of the maximal number of parameters that can be estimated from a dataset, in the absence of prior knowledge on the generative model.
Furthermore, we relate criticality to the statistical properties of the representation of the data generating process. We show that, as a consequence of the concentration property of the asymptotic equipartition property, representations that are maximally informative about the data generating process are characterised by an exponential distribution of energy levels. This arises from a principle of minimal entropy, that is conjugate of the maximum entropy principle in statistical mechanics. This explains why statistical criticality requires no parameter fine tuning in maximally informative samples.
AU - Cubero, Ryan J
AU - Jo, Junghyo
AU - Marsili, Matteo
AU - Roudi, Yasser
AU - Song, Juyong
ID - 7130
IS - 6
JF - Journal of Statistical Mechanics: Theory and Experiment
KW - optimization under uncertainty
KW - source coding
KW - large deviation
SN - 1742-5468
TI - Statistical criticality arises in most informative representations
VL - 2019
ER -
TY - THES
AB - A major challenge in neuroscience research is to dissect the circuits that orchestrate behavior in health and disease. Proteins from a wide range of non-mammalian species, such as microbial opsins, have been successfully transplanted to specific neuronal targets to override their natural communication patterns. The goal of our work is to manipulate synaptic communication in a manner that closely incorporates the functional intricacies of synapses by preserving temporal encoding (i.e. the firing pattern of the presynaptic neuron) and connectivity (i.e. target specific synapses rather than specific neurons). Our strategy to achieve this goal builds on the use of non-mammalian transplants to create a synthetic synapse. The mode of modulation comes from pre-synaptic uptake of a synthetic neurotransmitter (SN) into synaptic vesicles by means of a genetically targeted transporter selective for the SN. Upon natural vesicular release, exposure of the SN to the synaptic cleft will modify the post-synaptic potential through an orthogonal ligand gated ion channel. To achieve this goal we have functionally characterized a mixed cationic methionine-gated ion channel from Arabidopsis thaliana, designed a method to functionally characterize a synthetic transporter in isolated synaptic vesicles without the need for transgenic animals, identified and extracted multiple prokaryotic uptake systems that are substrate specific for methionine (Met), and established a primary/cell line co-culture system that would allow future combinatorial testing of this orthogonal transmitter-transporter-channel trifecta.
Synthetic synapses will provide a unique opportunity to manipulate synaptic communication while maintaining the electrophysiological integrity of the pre-synaptic cell. In this way, information may be preserved that was generated in upstream circuits and that could be essential for concerted function and information processing.
AU - Mckenzie, Catherine
ID - 7132
SN - 2663-337X
TI - Design and characterization of methods and biological components to realize synthetic neurotransmission
ER -
TY - CONF
AB - It is well established that the notion of min-entropy fails to satisfy the \emph{chain rule} of the form H(X,Y)=H(X|Y)+H(Y), known for Shannon Entropy. Such a property would help to analyze how min-entropy is split among smaller blocks. Problems of this kind arise for example when constructing extractors and dispersers.
We show that any sequence of variables exhibits a very strong strong block-source structure (conditional distributions of blocks are nearly flat) when we \emph{spoil few correlated bits}. This implies, conditioned on the spoiled bits, that \emph{splitting-recombination properties} hold. In particular, we have many nice properties that min-entropy doesn't obey in general, for example strong chain rules, "information can't hurt" inequalities, equivalences of average and worst-case conditional entropy definitions and others. Quantitatively, for any sequence X1,…,Xt of random variables over an alphabet X we prove that, when conditioned on m=t⋅O(loglog|X|+loglog(1/ϵ)+logt) bits of auxiliary information, all conditional distributions of the form Xi|X