Subcellular relocalization and nuclear redistribution of the RNA methyltransferases TRMT1 and TRMT1L upon neuronal activation

Jonkhout, Nicky; Cruciani, Sonia; Vieira, Helaine Graziele Santos; Tran, Julian M.; Liu, Huanle; Liu, Ganqiang; Pickford, Russell; Kaczorowski, Dominic; Franco, Glória Regina; Vauti, Franz; Camacho, Noelia; Abedini, Seyedeh Sedigheh; Najmabadi, Hossein; Pouplana, Lluı́s Ribas de; Christ, Daniel; Schönrock, Nicole; Mattick, John S.; Novoa, Eva María

doi:10.1080/15476286.2021.1881291

Cited by 20 publications

(30 citation statements)

References 95 publications

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“…Besides, in a tumor microenvironment, PLOD2 could modulate the collagen–cross-linking activity of cancer-associated fibroblasts and promote tumor cell migration and invasion ( Chen et al, 2015 ; Qi and Xu, 2018 ). TRMT1 encodes tRNA methyltransferase 1, an RNA methyltransferase responsible for N 2 , N 2 -dimethylguanosine (m2,2G) formation in cytosolic and mitochondrial tRNAs ( Dewe et al, 2017 ; Jonkhout et al, 2021 ). TRMT1 has been considered as the cause of autosomal-recessive intellectual disability ( Zhang et al, 2020 ), but its role in cancers has not yet been reported.…”

Section: Discussionmentioning

confidence: 99%

A Novel Prognostic Signature for Survival Prediction and Immune Implication Based on SARS-CoV-2–Related Genes in Kidney Renal Clear Cell Carcinoma

Huang

Chen

Xiao

et al. 2022

Front. Bioeng. Biotechnol.

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Kidney renal clear cell carcinoma (KIRC) is a common aggressive malignancy of the urinary system. COVID-19, a highly infectious and severe disease caused by SARS-CoV-2, has become a significant challenge for global public health. Cancer patients have been reported to be more vulnerable to SARS-CoV-2 infection and have a higher risk for serious complications than the general population. However, the correlation between KIRC and COVID-19 remains incompletely elucidated. In this study, we comprehensively investigated the expression and prognostic significance of 333 SARS-CoV-2 infection–related genes in KIRC using the TCGA dataset and identified 31 SARS-CoV-2–related differently expressed genes between KIRC and normal renal tissues. Based on these genes, we constructed and validated a 5-gene prognostic signature (including ACADM, CENPF, KDELC1, PLOD2, and TRMT1) to distinguish low- and high-risk KIRC patients of poor survival in TCGA and E-MTAB-1980 cohorts. Gene set enrichment analysis (GSEA) showed that some inflammatory/immune-related pathways were significantly enriched in the high-risk group. The ESTIMATE analysis indicated that patients in the high-risk group had higher stromal and immune cell scores, therefore lower tumor purity. Moreover, they presented higher proportions of macrophages M0, regulatory T cells (Tregs), and T follicular helper cells and higher expression of immune checkpoints CTLA-4, LAG-3, TIGIT, and PDCD1 than low-risk patients. Besides, we also developed a nomogram to expand clinical applicability, which exhibits excellent predictive accuracy for survival. In conclusion, we identified a novel prognostic signature and nomogram based on SARS-CoV-2–related genes as reliable prognostic predictors for KIRC patients and provided potential therapeutic targets for KIRC and COVID-19.

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Section: Discussionmentioning

confidence: 99%

A Novel Prognostic Signature for Survival Prediction and Immune Implication Based on SARS-CoV-2–Related Genes in Kidney Renal Clear Cell Carcinoma

Huang

Chen

Xiao

et al. 2022

Front. Bioeng. Biotechnol.

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“…Trmt is a tRNA-modifying enzyme, which acts as a di-methyltransferase using S-adenosyl methionine as a methyl donor to modify the 26th guanine residue of the tRNA. Trmt1 was associated with neurological dysfunction ( Jonkhout et al, 2021 ). Prdm16 displayed histone methyltransferase activity and functioned as a transcriptional regulator ( Nishikata et al, 2003 ).…”

Section: Resultsmentioning

confidence: 99%

Ontology Specific Alternative Splicing Changes in Alzheimer’s Disease

Yue²,

Xie³

et al. 2022

Front. Genet.

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Alternative splicing (AS) is a common phenomenon and correlates with aging and aging-related disorders including Alzheimer’s disease (AD). We aimed to systematically characterize AS changes in the cerebral cortex of 9-month-old APP/PS1 mice. The GSE132177 dataset was downloaded from GEO and ENA databases, aligned to the GRCm39 reference genome from ENSEMBL via STAR. Alternative 3′SS (A3SS), alternative 5′SS (A5SS), skipped exon (SE), retained intron (RI), and mutually exclusive exons (MXE) AS events were evaluated using rMATS, rmats2sashimiplot, and maser. Differential genes or transcripts were analyzed using the limma R package. Gene ontology analysis was performed with the clusterProfiler R package. A total of 60,705 raw counts of AS were identified, and 113 significant AS events were finally selected in accordance with the selection criteria: 1) average coverage >10 and 2) delta percent spliced in (ΔPSI) >0.1. SE was the most abundant AS event (61.95%), and RI was the second most abundant AS type (13.27%), followed by A3SS (12.39%), thereafter A5SS and MXE comprised of 12.39%. Interestingly, genes that experienced SE were enriched in histone acetyltransferase (HAT) complex, while genes spliced by RI were enriched in autophagy and those which experienced A3SS were enriched in methyltransferase activity revealed by GO analysis. In conclusion, we revealed ontology specific AS changes in AD. Our analysis provides novel pathological mechanisms of AD.

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“…SNP rs12046095 is located in the TRMT1L gene, which is involved in cognitive function. In fact, knocking out TRMT1L in mice has been proved to lead to changes in motor coordination and abnormal exploratory behavior, indicating that its activity is related to neurological function [ 36 ].…”

Section: Resultsmentioning

confidence: 99%

MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions

Sun

Ren

et al. 2022

Genes

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Epistatic interactions are referred to as SNPs (single nucleotide polymorphisms) that affect disease development and trait expression nonlinearly, and hence identifying epistatic interactions plays a great role in explaining the pathogenesis and genetic heterogeneity of complex diseases. Many methods have been proposed for epistasis detection; nevertheless, they mainly focus on low-order epistatic interactions, two-order or three-order for instance, and often ignore high-order interactions due to computational burden. In this paper, a module detection method called MDSN is proposed for identifying high-order epistatic interactions. First, an SNP network is constructed by a construction strategy of interaction complementary, which consists of low-order SNP interactions that can be obtained from fast computations. Then, a node evaluation measure that integrates multi-topological features is proposed to improve the node expansion algorithm, where the importance of a node is comprehensively evaluated by the topological characteristics of the neighborhood. Finally, modules are detected in the constructed SNP network, which have high-order epistatic interactions associated with the disease. The MDSN was compared with four state-of-the-art methods on simulation datasets and a real Age-related Macular Degeneration dataset. The results demonstrate that MDSN has higher performance on detecting high-order interactions.

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Subcellular relocalization and nuclear redistribution of the RNA methyltransferases TRMT1 and TRMT1L upon neuronal activation

Cited by 20 publications

References 95 publications

A Novel Prognostic Signature for Survival Prediction and Immune Implication Based on SARS-CoV-2–Related Genes in Kidney Renal Clear Cell Carcinoma

A Novel Prognostic Signature for Survival Prediction and Immune Implication Based on SARS-CoV-2–Related Genes in Kidney Renal Clear Cell Carcinoma

Ontology Specific Alternative Splicing Changes in Alzheimer’s Disease

MDSN: A Module Detection Method for Identifying High-Order Epistatic Interactions

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