DExH-box helicase 9 modulates hippocampal synapses and regulates neuropathic pain

Yang, Li; Liu, Qiaoqiao; Zhao, Yaxuan; Lin, Ninghua; Huang, Yue; Wang, Qihui; Yang, Kehui; Wei, Runa; Li, Xiaotong; Zhang, Ming; Hao, Lingyun; Wang, Hongjun; Pan, Zhiqiang

doi:10.1016/j.isci.2024.109016

Cited by 2 publications

(1 citation statement)

References 59 publications

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“…Synaptic plasticity in the hypothalamus is important for adaptation to changes in physiological states and the environment (Dietrich, Horvath, 2013; Serrenho et al, 2019; Bains et al, 2015; Horvath, 2006). Consistent with this categorization, patternMarker genes for M17 are associated with synaptic plasticity, including AMPA receptor regulation ( Cnih2 , Herring et al (2013)), dendritic spine development ( Ddn, Ncdn , Yang et al (2024) and Nicolas et al (2022)), and synaptogenesis ( Nptxr , SJ Lee et al (2017)) (Table S1).…”

Section: Resultsmentioning

confidence: 65%

Multi-sample non-negative spatial factorization

Wang,

Woyshner,

Sriworarat

et al. 2024

Preprint

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It is important to model biological variation when analyzing spatial transcriptomics data from multiple samples. One approach to multi-sample analysis is to spatially align samples, but this is a challenging problem. Here, we provide an alignment-free framework for generalizing a one-sample spatial factorization model to multi-sample data. Using this framework, we develop a method, called multi-sample non-negative spatial factorization (mNSF) that extends the one-sample non-negative spatial factorization (NSF) framework to a multi-sample dataset. Our model allows for a sample-specific model for the spatial correlation structure and extracts a low-dimensional representation of the data. We illustrate the performance of mNSF by simulation studies and real data. mNSF identifies true factors in simulated data, identifies shared anatomical regions across samples in real data and reveals region-specific biological functions. mNSFs performance is similar to alignment based methods when alignment is possible, but extends analysis to situations where spatial alignment is impossible. We expect multi-sample factorization methods to be a powerful class of methods for analyzing spatially resolved transcriptomics data.

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Section: Resultsmentioning

confidence: 65%

Multi-sample non-negative spatial factorization

Wang,

Woyshner,

Sriworarat

et al. 2024

Preprint

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The role of DEAD- and DExH-box RNA helicases in neurodevelopmental disorders

Lederbauer,

Das,

Piton

et al. 2024

Front. Mol. Neurosci.

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Neurodevelopmental disorders (NDDs) represent a large group of disorders with an onset in the neonatal or early childhood period; NDDs include intellectual disability (ID), autism spectrum disorders (ASD), attention deficit hyperactivity disorders (ADHD), seizures, various motor disabilities and abnormal muscle tone. Among the many underlying Mendelian genetic causes for these conditions, genes coding for proteins involved in all aspects of the gene expression pathway, ranging from transcription, splicing, translation to the eventual RNA decay, feature rather prominently. Here we focus on two large families of RNA helicases (DEAD- and DExH-box helicases). Genetic variants in the coding genes for several helicases have recently been shown to be associated with NDD. We address genetic constraints for helicases, types of pathological variants which have been discovered and discuss the biological pathways in which the affected helicase proteins are involved.

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DExH-box helicase 9 modulates hippocampal synapses and regulates neuropathic pain

Cited by 2 publications

References 59 publications

Multi-sample non-negative spatial factorization

Multi-sample non-negative spatial factorization

The role of DEAD- and DExH-box RNA helicases in neurodevelopmental disorders

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