Loss of the transcription factor Meis1 prevents sympathetic neurons target-field innervation and increases susceptibility to sudden cardiac death

Bouilloux, Fabrice; Thireau, Jérôme; Ventéo, Stéphanie; Farah, Charlotte; Karam, Sarah Arangurem; Dauvilliers, Yves; Valmier, Jean; Copeland, Neal G.; Jenkins, Nancy A.; Richard, Sylvain; Marmigère, Frédéric

doi:10.7554/elife.11627

Cited by 31 publications

(54 citation statements)

References 143 publications

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“…Experimental evidences converged to promote dopaminergic neurons of the hypothalamic A11 nucleus as a strong candidate likely involved in the promotion of PLMS and sympathetic activity 44 . Meis1 is widely expressed in the nervous system 7 9 10 and recent Magnetic Resonance Imaging studies of whole brain functional connectivity in RLS patients demonstrated alterations in cortical and subcortical network, suggesting potential involvement of cortical, subcortical, spinal and peripheral nervous areas 45 . We recently showed that during mouse sympathetic neurons development, Meis1 serves as a dedicated maintenance factor allowing correct innervation of target organs and Meis1 inactivation lead to altered retrograde transport 7 .…”

Section: Discussionmentioning

confidence: 99%

“…Among them, MEIS1 , a gene coding for a transcription factor of the TALE (Three Amino acid Loop Extension) Homeodomain family, is the largest risk factor identified so far 3 4 5 . In various species, Meis1 has been implicated in the development of several peripheral organs and nervous system structures including the striatum, the retina, the forebrain, the cerebellum and sympathetic neurons 6 7 8 9 10 . The SNPs (Single-Nucleotide Polymorphisms) identified in the eighth intron of the MEIS1 gene suggest that deregulations of mRNA transcription and/or processing participate in the etiology of RLS.…”

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confidence: 99%

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MEIS1 variant as a determinant of autonomic imbalance in Restless Legs Syndrome

Thireau

Farah

Molinari

et al. 2017

Sci Rep

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Restless Legs Syndrome (RLS) is a genetically complex neurological disorder in which overlapping genetic risk factors may contribute to the diversity and heterogeneity of the symptoms. The main goal of the study was to investigate, through analysis of heart rate variability (HRV), whether in RLS patients the MEIS1 polymorphism at risk influences the sympathovagal regulation in different sleep stages. Sixty-four RLS patients with periodic leg movement index above 15 per hour, and 38 controls underwent one night of video-polysomnographic recording. HRV in the frequency- and time- domains was analyzed during nighttime sleep. All RLS patients were genotyped, and homozygotes for rs2300478 in the MEIS1 locus were used for further analysis. Comparison of the sympathovagal pattern of RLS patients to control subjects did not show significant differences after adjustments for confounding factors in frequency-domain analyses, but showed an increased variability during N2 and N3 stages in time-domain analyses in RLS patients. Sorting of RLS patients according to MEIS1 polymorphism reconfirmed the association between MEIS1 and PLMS, and showed a significant increased sympathovagal balance during N3 stage in those homozygotes for the risk allele. RLS patients should be considered differently depending on MEIS1 genotype, some being potentially at risk for cardiovascular disorders.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

MEIS1 variant as a determinant of autonomic imbalance in Restless Legs Syndrome

Thireau

Farah

Molinari

et al. 2017

Sci Rep

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“…We now look at the co-factors in detail. MEIS1 is a homeobox encoding TF, known to be crucial for neuronal differentiation [ 37 ]. REST has been shown to repress MEIS1 expression in non-neuronal cells via recruitment of Polycomb Repressor Complexes [ 38 ].…”

Section: Resultsmentioning

confidence: 99%

DIVERSITY in binding, regulation, and evolution revealed from high-throughput ChIP

2018

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Genome-wide in vivo protein-DNA interactions are routinely mapped using high-throughput chromatin immunoprecipitation (ChIP). ChIP-reported regions are typically investigated for enriched sequence-motifs, which are likely to model the DNA-binding specificity of the profiled protein and/or of co-occurring proteins. However, simple enrichment analyses can miss insights into the binding-activity of the protein. Note that ChIP reports regions making direct contact with the protein as well as those binding through intermediaries. For example, consider a ChIP experiment targeting protein X, which binds DNA at its cognate sites, but simultaneously interacts with four other proteins. Each of these proteins also binds to its own specific cognate sites along distant parts of the genome, a scenario consistent with the current view of transcriptional hubs and chromatin loops. Since ChIP will pull down all X-associated regions, the final reported data will be a union of five distinct sets of regions, each containing binding sites of one of the five proteins, respectively. Characterizing all five different motifs and the corresponding sets is important to interpret the ChIP experiment and ultimately, the role of X in regulation. We present diversity which attempts exactly this: it partitions the data so that each partition can be characterized with its own de novo motif. Diversity uses a Bayesian approach to identify the optimal number of motifs and the associated partitions, which together explain the entire dataset. This is in contrast to standard motif finders, which report motifs individually enriched in the data, but do not necessarily explain all reported regions. We show that the different motifs and associated regions identified by diversity give insights into the various complexes that may be forming along the chromatin, something that has so far not been attempted from ChIP data. Webserver at http://diversity.ncl.res.in/; standalone (Mac OS X/Linux) from https://github.com/NarlikarLab/DIVERSITY/releases/tag/v1.0.0.

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“…Then, HRV was evaluated by power spectral analysis (ms 2 ) using the fast Fourier transformation (segment length of 2048 beats, linear interpolation with resampling to a 20 Hz interbeat time series and Hamming windowing). The cutoff frequency ranges for the LFr powers (HFr: 1.5-5 Hz) were chosen according to those used in the literature (106).…”

Section: Methodsmentioning

confidence: 99%