RNA binding proteins in the control of autoimmune diseases

Yoshinaga, Masanori; Takeuchi, Osamu

doi:10.1080/25785826.2019.1655192

Cited by 32 publications

(19 citation statements)

References 106 publications

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“…The absence of TTP increased the expression and proliferation of its activation markers, and inhibited apoptosis in T cells ( Moore et al, 2018 ). Accumulating evidence pointed to the fact that TTP might be involved in the regulation of autoimmune diseases ( Sanduja et al, 2012 ; Ross et al, 2017 ; Yoshinaga and Takeuchi, 2019 ). Considering the role of TTP in immune system and its derivation in autoimmune diseases, analyzing the polymorphism of TTP gene in Chinese RA patients can better understand the role of RA susceptibility.…”

Section: Discussionmentioning

confidence: 99%

Tristetraprolin Gene Single-Nucleotide Polymorphisms and mRNA Level in Patients With Rheumatoid Arthritis

et al. 2021

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To observe and evaluate the correlation between single-nucleotide polymorphisms (SNPs) and messenger RNA (mRNA) level related to tristetraprolin (TTP) in Chinese rheumatoid arthritis (RA). TapMan SNP was used for genotyping analysis in 580 RA patients and 554 healthy people. Association between TTP gene polymorphisms (rs251864 and rs3746083) and RA was obtained. Quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) technology was applied for the detection of TTP mRNA level in peripheral blood mononuclear cells (PBMCs) in 36 RA patients and 37 healthy people. We observed that the allele T of TTP rs3746083 increased RA susceptibility (p = 0.019). A significant difference was found under the dominant model of rs3746083 (p = 0.037). Further analysis showed the allele distribution of rs3746083 was nominally correlated with RF phenotype of RA patients (p = 0.045). Nevertheless, the association between TTP rs251864 and the incidence of RA was no statistically significant (p > 0.05). The TTP expression level in PBMCs of RA patients was significantly reduced (p < 0.001). In conclusion, the results of this experiment support that TTP may be involved in the pathogenesis of RA.

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Section: Discussionmentioning

confidence: 99%

Tristetraprolin Gene Single-Nucleotide Polymorphisms and mRNA Level in Patients With Rheumatoid Arthritis

et al. 2021

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“…Yet, the production thereof must be tightly regulated, as excessive cytokine production can play a role in diverse autoimmune diseases, such as inflammatory bowel disease [3‐6], multiple sclerosis [7] and rheumatoid arthritis [8]. Furthermore, disproportionate cytokine production can also lead to immunopathology [9‐13]. However, lack of cytokine production by T cells or defects in downstream signalling pathways also pose risks for the host.…”

Section: Introductionmentioning

confidence: 99%

Post‐transcriptional control of T‐cell cytokine production: Implications for cancer therapy

Heeren¹

2021

Immunology

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As part of the adaptive immune system, T cells are vital for the eradication of infected and malignantly transformed cells. To perform their protective function, T cells produce effector molecules that are either directly cytotoxic, such as granzymes, perforin, interferon‐γ and tumour necrosis factor α, or attract and stimulate (immune) cells, such as interleukin‐2. As these molecules can also induce immunopathology, tight control of their production is required. Indeed, inflammatory cytokine production is regulated on multiple levels. Firstly, locus accessibility and transcription factor availability and activity determine the amount of mRNA produced. Secondly, post‐transcriptional mechanisms, influencing mRNA splicing/codon usage, stability, decay, localization and translation rate subsequently determine the amount of protein that is produced. In the immune suppressive environments of tumours, T cells gradually lose the capacity to produce effector molecules, resulting in tumour immune escape. Recently, the role of post‐transcriptional regulation in fine‐tuning T‐cell effector function has become more appreciated. Furthermore, several groups have shown that exhausted or dysfunctional T cells from cancer patients or murine models possess mRNA for inflammatory mediators, but fail to produce effector molecules, hinting that post‐transcriptional events also play a role in hampering tumour‐infiltrating lymphocyte effector function. Here, the post‐transcriptional regulatory events governing T‐cell cytokine production are reviewed, with a specific focus on the importance of post‐transcriptional regulation in anti‐tumour responses. Furthermore, potential approaches to circumvent tumour‐mediated dampening of T‐cell effector function through the (dis)engagement of post‐transcriptional events are explored, such as CRISPR/Cas9‐mediated genome editing or chimeric antigen receptors.

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“…These interactions are crucial for a wide spectrum of cellular functions, such as protein synthesis, posttranscriptional regulation and regulation of gene expression [3][4][5]. Recent works associate some of the RNA-binding proteins with human diseases, from neurodegenerative and cardiovascular disorders to cancer [6][7][8][9][10]. While many experimental methods are currently used to characterize the RNA-binding proteins [11], these approaches do not keep up with the rapid accumulation of protein sequence data.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type

Wang

et al. 2020

IJMS

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With close to 30 sequence-based predictors of RNA-binding residues (RBRs), this comparative survey aims to help with understanding and selection of the appropriate tools. We discuss past reviews on this topic, survey a comprehensive collection of predictors, and comparatively assess six representative methods. We provide a novel and well-designed benchmark dataset and we are the first to report and compare protein-level and datasets-level results, and to contextualize performance to specific types of RNAs. The methods considered here are well-cited and rely on machine learning algorithms on occasion combined with homology-based prediction. Empirical tests reveal that they provide relatively accurate predictions. Virtually all methods perform well for the proteins that interact with rRNAs, some generate accurate predictions for mRNAs, snRNA, SRP and IRES, while proteins that bind tRNAs are predicted poorly. Moreover, except for DRNApred, they confuse DNA and RNA-binding residues. None of the six methods consistently outperforms the others when tested on individual proteins. This variable and complementary protein-level performance suggests that users should not rely on applying just the single best dataset-level predictor. We recommend that future work should focus on the development of approaches that facilitate protein-level selection of accurate predictors and the consensus-based prediction of RBRs.

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RNA binding proteins in the control of autoimmune diseases

Cited by 32 publications

References 106 publications

Tristetraprolin Gene Single-Nucleotide Polymorphisms and mRNA Level in Patients With Rheumatoid Arthritis

Tristetraprolin Gene Single-Nucleotide Polymorphisms and mRNA Level in Patients With Rheumatoid Arthritis

Post‐transcriptional control of T‐cell cytokine production: Implications for cancer therapy

Comprehensive Survey and Comparative Assessment of RNA-Binding Residue Predictions with Analysis by RNA Type

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