Long-Range Interactions in Neuronal Gene Expression: Evidence from Gene Targeting in the GABAA Receptor β2–α6–α1–γ2 Subunit Gene Cluster

2000 ◽  
Vol 16 (1) ◽  
pp. 34-41 ◽  
Author(s):  
M. Uusi-Oukari ◽  
J. Heikkilä ◽  
S.T. Sinkkonen ◽  
R. Mäkelä ◽  
B. Hauer ◽  
...  
1995 ◽  
Vol 5 (6) ◽  
pp. 550-560 ◽  
Author(s):  
Schahram Akbarian ◽  
Molly M. Huntsman ◽  
James J. Kim ◽  
Alireza Tafazzoli ◽  
Steven G. Potkin ◽  
...  

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Livia Eiselleova ◽  
Viktor Lukjanov ◽  
Simon Farkas ◽  
David Svoboda ◽  
Karel Stepka ◽  
...  

The eukaryotic nucleus is a highly complex structure that carries out multiple functions primarily needed for gene expression, and among them, transcription seems to be the most fundamental. Diverse approaches have demonstrated that transcription takes place at discrete sites known as transcription factories, wherein RNA polymerase II (RNAP II) is attached to the factory and immobilized while transcribing DNA. It has been proposed that transcription factories promote chromatin loop formation, creating long-range interactions in which relatively distant genes can be transcribed simultaneously. In this study, we examined long-range interactions between the POU5F1 gene and genes previously identified as being POU5F1 enhancer-interacting, namely, CDYL, TLE2, RARG, and MSX1 (all involved in transcriptional regulation), in human pluripotent stem cells (hPSCs) and their early differentiated counterparts. As a control gene, RUNX1 was used, which is expressed during hematopoietic differentiation and not associated with pluripotency. To reveal how these long-range interactions between POU5F1 and the selected genes change with the onset of differentiation and upon RNAP II inhibition, we performed three-dimensional fluorescence in situ hybridization (3D-FISH) followed by computational simulation analysis. Our analysis showed that the numbers of long-range interactions between specific genes decrease during differentiation, suggesting that the transcription of monitored genes is associated with pluripotency. In addition, we showed that upon inhibition of RNAP II, long-range associations do not disintegrate and remain constant. We also analyzed the distance distributions of these genes in the context of their positions in the nucleus and revealed that they tend to have similar patterns resembling normal distribution. Furthermore, we compared data created in vitro and in silico to assess the biological relevance of our results.


2006 ◽  
Vol 98 (1) ◽  
pp. 122-133 ◽  
Author(s):  
Mariangela Serra ◽  
Maria Cristina Mostallino ◽  
Giuseppe Talani ◽  
Maria Giuseppina Pisu ◽  
Mario Carta ◽  
...  

SLEEP ◽  
2013 ◽  
Author(s):  
Jui-Hsiu Tsai ◽  
Pinchen Yang ◽  
Hung-Hsun Lin ◽  
Kuang-hung Cheng ◽  
Yi-Hsin Yang ◽  
...  

1996 ◽  
Vol 23 (3) ◽  
pp. 235-244 ◽  
Author(s):  
Thomas Sander ◽  
Thomas Hildmann ◽  
Dieter Janz ◽  
Thomas F. Wienker ◽  
Amedeo Bianchi ◽  
...  

2021 ◽  
Vol 81 (1) ◽  
pp. 43-57
Author(s):  
Mona Faraz ◽  
Nastaran Kosarmadar ◽  
Mahmoud Rezaei ◽  
Meysam Zare ◽  
Mohammad Javan ◽  
...  

2020 ◽  
Author(s):  
Jeremy Bigness ◽  
Xavi Loinaz ◽  
Shalin Patel ◽  
Erica Larschan ◽  
Ritambhara Singh

Long-range spatial interactions among genomic regions are critical for regulating gene expression and their disruption has been associated with a host of diseases. However, when modeling the effects of regulatory factors on gene expression, most deep learning models either neglect long-range interactions or fail to capture the inherent 3D structure of the underlying biological system. This prevents the field from obtaining a more comprehensive understanding of gene regulation and from fully leveraging the structural information present in the data sets. Here, we propose a graph convolutional neural network (GCNN) framework to integrate measurements probing spatial genomic organization and measurements of local regulatory factors, specifically histone modifications, to predict gene expression. This formulation enables the model to incorporate crucial information about long-range interactions via a natural encoding of spatial interaction relationships into a graph representation. Furthermore, we show that our model is interpretable in terms of the observed biological regulatory factors, highlighting both the histone modifications and the interacting genomic regions that contribute to a gene's predicted expression. We apply our GCNN model to datasets for GM12878 (lymphoblastoid) and K562 (myelogenous leukemia) cell lines and demonstrate its state-of-the-art prediction performance. We also obtain importance scores corresponding to the histone mark features and interacting regions for some exemplar genes and validate them with evidence from the literature. Our model presents a novel setup for predicting gene expression by integrating multimodal datasets.


1994 ◽  
Vol 174 (1) ◽  
pp. 5-8 ◽  
Author(s):  
W. Kamphuis ◽  
T.C. De Rijk ◽  
F.H. Lopes^da Silva

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