Next Article in Journal
Sevoflurane Dampens Acute Pulmonary Inflammation via the Adenosine Receptor A2B and Heme Oxygenase-1
Previous Article in Journal
Modulating the Ubiquitin–Proteasome System: A Therapeutic Strategy for Autoimmune Diseases
Previous Article in Special Issue
Chimeric RNA Design Principles for RNA-Mediated Gene Fusion
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

The Landscape of Novel Expressed Chimeric RNAs in Rheumatoid Arthritis

by
Rajesh Detroja
,
Sumit Mukherjee
and
Milana Frenkel-Morgenstern
*
Cancer Genomics and BioComputing of Complex Diseases Lab, Azrieli Faculty of Medicine, Bar-Ilan University, Safed 1311502, Israel
*
Author to whom correspondence should be addressed.
Cells 2022, 11(7), 1092; https://doi.org/10.3390/cells11071092
Submission received: 15 February 2022 / Revised: 20 March 2022 / Accepted: 22 March 2022 / Published: 24 March 2022
(This article belongs to the Special Issue Gene Fusions and Chimeric RNA in Cancers and Complex Diseases)

Abstract

:
In cancers and other complex diseases, the fusion of two genes can lead to the production of chimeric RNAs, which are associated with disease development. Several recurrent chimeric RNAs are expressed in different cancers and are thus used for clinical cancer diagnosis. Rheumatoid arthritis (RA) is an immune-mediated joint disorder resulting in synovial inflammation and joint destruction. Despite advances in therapy, many patients do not respond to treatment and present persistent inflammation. Understanding the landscape of chimeric RNA expression in RA patients could provide a better insight into RA pathogenesis, which might provide better treatment strategies and tailored therapies. Accordingly, we analyzed the publicly available RNA-seq data of synovium tissue from 151 RA patients and 28 healthy controls and were able to identify 37 recurrent chimeric RNAs found to be expressed in at least 3 RA samples. Furthermore, the parental genes of these 37 recurrent chimeric RNAs were found to be differentially expressed and enriched in immune-related processes, such as adaptive immune response and the positive regulation of B-cell activation. Interestingly, the appearance of 5 coding and 23 non-coding chimeric RNAs might be associated with regulating their parental gene expression, leading to the generation of dysfunctional immune responses, such as inflammation and bone destruction. Therefore, in this paper, we present the first study to demonstrate the novel chimeric RNAs that are highly expressed and functional in RA.

1. Introduction

Chimeric RNAs are produced by the fusion of exons/introns from two different genes [1]. Several recent studies have demonstrated the functional significance of various chimeric RNAs in cancer development and other genetic abnormalities [2,3,4,5,6,7]. Chimeric RNAs could appear in the later stages of cancer, promoting cancer heterogeneity and drug resistance [8,9,10,11]. As such, chimeric RNAs have been recognized as potential biomarkers and drug targets for different cancers [12,13]. Chimeric RNAs could be translated to produce novel fusion proteins, which could alter cell functionality by regulating the dynamics of protein interaction networks [7,14,15]. Chimeric RNAs could also act as long non-coding RNAs (lncRNAs) and play significant regulatory roles that would also help cells to generate new functionalities [16,17,18,19,20,21]. The generation of chimeric RNAs could also increase the functional expansion of cells by creating phenotypic diversity, which would help cells to survive in the face of novel stresses [22]. Therefore, understanding the appearance of chimeric RNAs and their functional association in particular diseases could help us to understand better the complex mechanisms behind disease development.
Rheumatoid arthritis (RA) is an auto-immune disorder exemplified by the chronic and persistent inflammation of the joint synovial tissue associated with the destruction of the affected joints [23]. The pathogenesis of RA is complex and is probably caused by unknown antigens, which are sensitive to specific inherited factors. The recent transcriptomic profiling of the affected synovium and peripheral blood mononuclear cells (PBMCs) from RA patients displayed significant differences in gene expression, which enabled the identification of distinct molecular mechanisms involved in RA pathogenesis [24,25]. RA presents significant clinical heterogeneity [26], as demonstrated by the presence of different auto-antibody specificities, such as rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibodies (ACPA) in the serum [27,28], and different responses to treatment [29,30]. The complexity behind RA pathogenesis, heterogeneity, and clinical response to initial drug therapy is still not well understood [31]. The generation of chimeric RNAs could be crucial towards the development of RA and its clinical heterogeneity. However, no study has to date attempted to investigate the landscape of chimeric RNAs expressed in RA.
To understand the potential impact of chimeric RNAs on RA development and heterogeneity, in this paper, we analyze chimeric RNA expression in publicly available RNA-seq data on synovial fluids from 151 RA patients and 28 healthy controls. We identified 37 recurrent and RA sample-specific chimeric RNAs whose parental genes are predominately involved in immune-related processes, such as “adaptive immune response” and the “positive regulation of B-cell activation”. Furthermore, we found that parental genes of 20 of 37 such chimeric RNAs are significantly differentially expressed in RA samples. This points to these chimeric RNAs being associated with the dysfunctional immune response generation that leads to inflammation and bone destruction. Significantly, our study provides the signature of chimeric RNA expression in RA and highlights the potential clinical importance of this signature in personalized treatments.

2. Materials and Methods

2.1. Collection of RNA-Seq Data

A total of 217 publicly available total RNA sequencing samples isolated from joint synovial biopsies were downloaded from the GEO database (GEO accession: GSE89408). These contained a total of 151 samples from RA patients, a total of 28 samples from healthy subjects (HT), and a total of 38 samples from other arthritis patients (Table 1). We also downloaded RNA-seq data from tissue samples of 122 healthy human individuals representing 32 different tissues from EBI ArrayExpress (accession E-MTAB-2836) [32] as controls to distinguish RA-specific chimeras. All of the raw sequencing data were initially subjected to quality control analysis using FastQC [33] and an in-house bash script. A total of ~87 million paired-end raw reads of 100 bp were generated for each sample. According to the quality control analysis results, Illumina universal adapter sequences and 11 bp at the start of reads with imbalanced A/T and G/C ratios were trimmed using the cutadapt [34] tool.

2.2. Identification of Chimeric RNAs from RNA-Seq Data

All 217 samples were further used to identify chimeric RNAs using our in-house reference-based method ChiTaH [35], previously demonstrated to be the most efficient reference-based tool, as compared to all other available tools used for chimeric RNAs detection. ChiTaH uses 43,466 non-redundant high-quality human chimeras from the ChiTaRS 5.0 database to perform mapping of RNA-Seq datasets and to predict potential chimeric RNAs in each sample.

2.3. Differential Gene Expression Analysis

RNA sequencing reads of all 217 samples were subjected to analysis by STAR aligner [36]; a human reference genome (hg38) was used for alignment. Next, the total number of reads mapped to each human gene was calculated using the featureCounts [37] tool. Finally, a read count table of samples from healthy subjects (HT) and RA patients were subjected to differential gene expression analysis using DESeq2 [38]. A gene was considered to be significantly up-regulated when log2foldchange ≥ 2 and Padj ≤ 0.05, whereas a gene was considered to be significantly down-regulated when log2foldchange was negative and Padj ≤ 0.05.

2.4. Annotation and Enrichment Analysis of the Parental Genes of Recurrent Chimeric RNAs in RA

Gene ontology, pathway, and gene enrichment analysis of the parental genes of recurrent chimeric RNAs were performed using the online Metascape server [39]. In this analysis, we studied the enrichment of the parental genes of chimeric RNAs in terms of specific biological processes, pathways, and diseases related to autoimmunity.

2.5. Classification of Recurrent Chimeric RNAs into Coding and Non-Coding RNAs

The functional classification of recurrent chimeric RNAs was performed based on their protein-coding abilities. Three different tools (CPAT [40], CNIT [41], and LncFinder [42]) were used for this classification. We classified a given chimeric RNA as protein-coding or as a lncRNA when the output of the three tools agreed.

3. Results

3.1. Identification of Chimeric RNAs across Normal and Arthritis Cohorts

A total of 2102 chimeric RNAs was identified from 151 samples of rheumatoid arthritis patients, as were 856 chimeric RNAs from 22 samples of osteoarthritis patients (Table 2). Moreover, we also found 833 chimeric RNAs in the joint synovial biopsies of 28 healthy individuals, while 2066 chimeric RNAs were identified in 199 samples, representing 122 individuals and 32 different normal tissues. Chimeric RNAs expressed in normal joint synovial biopsies and 32 different normal tissues (EBI samples) were considered as normal or population chimeric RNAs, which could be observed as a result of potential stress response.

3.2. Expression Analysis of Recurrent Chimeric RNAs in RA Patients

We performed a comparative analysis of all identified chimeric RNAs in different cohorts to curate uniquely expressed chimeric RNAs only in 151 RA patients. A total of 566 chimeric RNAs was found to be uniquely expressed in RA patients. Next, according to the manual validation of sequences of 566 chimeric RNAs, a total of 246 high-quality novel chimeric RNAs was obtained (Table S1). Finally, a total of 37 chimeric RNAs was found to be expressed in at least 3 RA patients, which were considered recurrent chimeric RNAs and used for downstream analysis (Figure 1).

3.3. Enrichment Analysis of the Parental Genes of RA-Specific Recurrent Chimeric RNAs

Next, a total of 56 unique parental genes of 37 chimeric RNAs was used for gene annotation and enrichment analysis using Metascape [39]. The enrichment of the 56 genes into biological processes showed the majority of genes to be involved in processes, such as “adaptive immune response” and the “positive regulation of B-cell activation” (Table S2). Some of the genes were also involved in “leukocyte differentiation”. Moreover, using Metascape [39], we also studied the enrichment of the 56 genes into different diseases. Disease enrichment analysis shows that these genes were significantly involved in gout arthritis, reactive arthritis, and rheumatism (Figure 2). Altogether, these results suggest that 37 recurrent chimeric RNAs play significant roles in the pathogenicity of rheumatoid arthritis by prompting a dysfunctional immune response in the associated tissues.

3.4. Differential Gene Expression Analysis of the Parental Genes of Recurrent Chimeric RNAs

Assessing differential gene expression among samples of joint synovial biopsies from 28 healthy individuals and 151 RA patients was performed using DESeq2 [38]. Such analysis revealed that the 27 parental genes of at least 20 of 37 recurrent chimeric RNAs were significantly differentially expressed (Tables S3–S7). Most of the parental genes were up-regulated, although some were down-regulated (Figure 3). The up-regulated genes are involved in biological processes such as “adaptive immune response” and the “positive regulation of B-cell activation”. Altogether, these results suggest that differentially expressed parental genes of recurrent chimeric RNAs in RA patients could trigger a dysfunctional immune response. Intermediate files for differential gene expression analysis can be retrieved from (https://github.com/Rajesh-Detroja/RA_Chimeric_RNAs/, accessed on 14 February 2022).

3.5. Functional Classification of RA-Specific Recurrent Chimeric RNAs

To understand further the structure and function of recurrent chimeric RNAs in RA, these were classified as “coding” or “non-coding”. For this analysis, we considered common predictions made using three tools, namely, CPAT [40], CNIT [41], and LncFinder [42]. We thus identified a total of five coding chimeric RNAs with the potential to translate into functional chimeric proteins (Table S8). In contrast, we found a total of 23 non-coding chimeric RNAs with the potential to play similar functional regulatory roles as lncRNAs [Figure 4]. Among these 23 non-coding chimeric RNAs, at least 1 gene of the parental genes of 13 chimeric RNAs is differentially expressed, supporting the potential regulation of parental gene expression. Altogether, these observations suggest that most of the recurrent novel chimeric RNAs in RA patients play a regulatory role as non-coding RNAs, although some can translate into a functional protein. Therefore, the appearance of these novel chimeric proteins or chimeric lncRNAs could be associated with dysfunctional immune response generation, leading to the development of clinical symptoms in RA.

4. Discussion

Recent studies identified several chimeric RNAs in different cancers that are associated with oncogenesis, cancer heterogeneity, and the evolution of cancer drug resistance [8,43]. The appearance of chimeric RNAs in a particular cell can promote functional expansion and increase phenotypic diversity to help the cell to survive in the face of new stresses. The process of production of chimeric RNAs has an advantage over point mutations as it involves two parental genes. Therefore, the generation of chimeric RNAs is important for creating the phenotypic plasticity of diseased cells that can provide a fitness advantage that allows a cell to adapt to new disease-related stresses [22]. RA is an auto-immune disorder characterized by synovitis, systemic inflammation, and the presence of auto-antibodies, resulting in progressive joint damage [44]. RA presents disease heterogeneity, leading to treatment failure and remission [45,46]. Therefore, understanding the landscape of chimeric RNA expression in RA could provide new insights into RA development, heterogeneity, and drug response.
In the present study, we analyzed publicly available RNA-seq data from disease-relevant and healthy synovial tissues. Our systematic integrative analysis identified 37 recurrent RA-specific chimeric RNAs and several RA sample-specific chimeric RNAs. Previous studies demonstrated recurrent cancer-specific chimeric RNAs to be involved in cancer development. Moreover, chimeric RNAs expressed in specific cancer samples are important for cancer heterogeneity. Therefore, our finding suggests that the expression of recurrent chimeric RNAs in RA patients could be associated with RA development, whereas the expression of RA sample-specific chimeric RNAs could be important for generating disease heterogeneity. Furthermore, to understand whether recurrent chimeric RNAs are associated with RA development, we analyzed the expression of their parental genes and the pathways to which these genes contribute. Interestingly, we observed the parental genes of the 37 recurrent chimeric RNAs to be mainly involved in the immune-related processes and adaptive immune responses, namely, “adaptive immune response” and the “positive regulation of B-cell activation”. We also found that at least one parental gene is differentially expressed in most cases. Therefore, this could indicate that the appearance of chimeric RNAs is associated with the generation of dysfunctional immune responses in RA. Further, we performed the disease enrichment analysis to check the most enriched diseases where these parental genes of RA-specific recurrent chimeric RNAs are involved. We found the most enriched diseases are gout, rheumatism, and reactive arthritis. Therefore, the dysregulation of these genes could induce the common clinical signature of gout and RA, such as pain and swelling and stiffness of joints. Next, the functional characterization of RA-specific recurrent chimeric RNAs predicted the majority of them could act as lncRNA and involved in regulatory functions. In summary, we hypothesize that these recurrent chimeric RNAs could regulate the expression of their parental gene, leading to the generation of dysregulated immune responses in affected tissues, the development of RA, and subsequent inflammation in osteoclast formation and bone destruction of affected tissues (Figure 5).
In summary, our study provides an expression landscape of chimeric RNAs in the affected synovium of RA patients. Our results also highlight the potential functional association of the novel chimeric RNAs in RA development and heterogeneity. The limitations of our study are that it lacks experimental evidence, due to the unavailability of synovium tissue samples from RA patients. Nonetheless, this study opens a new paradigm to explore further chimeric RNA-mediated transcriptional dysregulation leading to RA development and heterogeneity that could help in the design of new treatment strategies.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/cells11071092/s1, Table S1: Expressed chimeric RNAs in rheumatoid arthritis. Table S2: Gene Ontology Analysis of Recurrent chimeric RNAs in rheumatoid arthritis. Table S3: Significantly differentially expressed genes between RA vs. OA. Table S4: Significantly differentially expressed genes between RA vs. HT. Table S5: Significantly differentially expressed genes between OA vs. HT. Table S6: Significantly differentially expressed genes between AL vs. HT. Table S7: Significantly differentially expressed genes between AL vs. OA. Table S8: Classification of RA-specific recurrent chimeric RNAs into coding and non-coding gene.

Author Contributions

Conceptualization, R.D., S.M. and M.F.-M.; methodology R.D. and S.M.; software, R.D. and S.M.; validation, R.D., S.M. and M.F.-M.; formal analysis, R.D. and S.M.; investigation, R.D. and S.M.; resources, R.D., S.M. and M.F.-M.; data curation, R.D. and S.M.; writing—original draft preparation, R.D., S.M. and M.F.-M.; writing—review and editing, R.D., S.M. and M.F.-M.; visualization, R.D. and S.M.; supervision, M.F.-M.; project administration, M.F.-M.; funding acquisition, M.F.-M. All authors read and agreed to the published version of the manuscript.

Funding

S.M. was supported by the Israeli Council for Higher Education through the PBC fellowship program for outstanding postdoctoral researchers from China and India (2019–2021). M.F.-M. was supported by the Israel Innovation Authority (Kamin grant #66824, 2019–2021) and a COVID-19 Data Science Institute (DSI) grant from Bar-Ilan University (#247017, 2020).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Intermediate files and resources is available at https://github.com/Rajesh-Detroja/RA_Chimeric_RNAs/, accessed on 20 March 2022.

Acknowledgments

The authors thank members of the Cancer Genomics and Biocomputing of Complex Diseases Lab for multiple discussions at different stages of this project. We thank Yair Levy from the Meyer Medical Center (Kfar Saba, Israel) and Assaf Sagi (Birad Research and Development, Bar-Ilan University)for the valuable discussions.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Chwalenia, K.; Facemire, L.; Li, H. Chimeric RNAs in cancer and normal physiology. Wiley Interdiscip. Rev. RNA 2017, 8, e1427. [Google Scholar] [CrossRef] [PubMed]
  2. Mitelman, F.; Johansson, B.; Mertens, F. The impact of translocations and gene fusions on cancer causation. Nat. Rev. Cancer 2007, 7, 233–245. [Google Scholar] [CrossRef] [PubMed]
  3. Kannan, K.; Wang, L.; Wang, J.; Ittmann, M.M.; Li, W.; Yen, L. Recurrent chimeric RNAs enriched in human prostate cancer identified by deep sequencing. Proc. Natl. Acad. Sci. USA. 2011, 108, 9172–9177. [Google Scholar] [CrossRef] [Green Version]
  4. Asmann, Y.W.; Necela, B.M.; Kalari, K.R.; Hossain, A.; Baker, T.R.; Carr, J.M.; Davis, C.; Getz, J.E.; Hostetter, G.; Li, X.; et al. Detection of redundant fusion transcripts as biomarkers or disease-specific therapeutic targets in breast cancer. Cancer Res. 2012, 72, 1921–1928. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Suzuki, M.; Makinoshima, H.; Matsumoto, S.; Suzuki, A.; Mimaki, S.; Matsushima, K.; Yoh, K.; Goto, K.; Suzuki, Y.; Ishii, G.; et al. Identification of a lung adenocarcinoma cell line with CCDC6-RET fusion gene and the effect of RET inhibitors in vitro and in vivo. Cancer Sci. 2013, 104, 896–903. [Google Scholar] [CrossRef]
  6. Nome, T.; Thomassen, G.O.S.; Bruun, J.; Ahlquist, T.; Bakken, A.C.; Hoff, A.M.; Rognum, T.; Nesbakken, A.; Lorenz, S.; Sun, J.; et al. Common fusion transcripts identified in colorectal cancer cell lines by high-throughput RNA sequencing. Transl. Oncol. 2013, 6, 546-IN5. [Google Scholar] [CrossRef] [Green Version]
  7. Frenkel-Morgenstern, M.; Lacroix, V.; Ezkurdia, I.; Levin, Y.; Gabashvili, A.; Prilusky, J.; Del Pozo, A.; Tress, M.; Johnson, R.; Guigo, R.; et al. Chimeras taking shape: Potential functions of proteins encoded by chimeric RNA transcripts. Genome Res. 2012, 22, 1231–1242. [Google Scholar] [CrossRef] [Green Version]
  8. Mukherjee, S.; Heng, H.H.; Frenkel-Morgenstern, M. Emerging Role of Chimeric RNAs in Cell Plasticity and Adaptive Evolution of Cancer Cells. Cancers 2021, 13, 4328. [Google Scholar] [CrossRef]
  9. Zhu, Y.-C.; Wang, W.-X.; Zhang, Q.-X.; Xu, C.-W.; Zhuang, W.; Du, K.-Q.; Chen, G.; Lv, T.-F.; Song, Y. The KIF5B-RET Fusion Gene Mutation as a Novel Mechanism of Acquired EGFR Tyrosine Kinase Inhibitor Resistance in Lung Adenocarcinoma. Clin. Lung Cancer 2019, 20, e73–e76. [Google Scholar] [CrossRef]
  10. Ma, Y.; Miao, Y.; Peng, Z.; Sandgren, J.; De Ståhl, T.D.; Huss, M.; Lennartsson, L.; Liu, Y.; Nistér, M.; Nilsson, S.; et al. Identification of mutations, gene expression changes and fusion transcripts by whole transcriptome RNAseq in docetaxel resistant prostate cancer cells. Springerplus 2016, 5, 1861. [Google Scholar] [CrossRef] [Green Version]
  11. Christie, E.L.; Pattnaik, S.; Beach, J.; Copeland, A.; Rashoo, N.; Fereday, S.; Hendley, J.; Alsop, K.; Brady, S.L.; Lamb, G.; et al. Multiple ABCB1 transcriptional fusions in drug resistant high-grade serous ovarian and breast cancer. Nat. Commun. 2019, 10, 1295. [Google Scholar] [CrossRef] [Green Version]
  12. Li, Z.; Qin, F.; Li, H. Chimeric RNAs and their implications in cancer. Curr. Opin. Genet. Dev. 2018, 48, 36–43. [Google Scholar] [CrossRef] [PubMed]
  13. Neckles, C.; Sundara Rajan, S.; Caplen, N.J. Fusion transcripts: Unexploited vulnerabilities in cancer? Wiley Interdiscip. Rev. RNA 2020, 11, e1562. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Frenkel-Morgenstern, M.; Gorohovski, A.; Tagore, S.; Sekar, V.; Vazquez, M.; Valencia, A. ChiPPI: A novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer. Nucleic Acids Res. 2017, 45, 7094–7105. [Google Scholar] [CrossRef]
  15. Latysheva, N.S.; Babu, M.M. Molecular Signatures of Fusion Proteins in Cancer. ACS Pharmacol. Transl. Sci. 2019, 2, 122–133. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Wu, H.; Li, X.; Li, H. Gene fusions and chimeric RNAs, and their implications in cancer. Genes Dis. 2019, 6, 385–390. [Google Scholar] [CrossRef]
  17. Qin, F.; Zhang, Y.; Liu, J.; Li, H. SLC45A3-ELK4 functions as a long non-coding chimeric RNA. Cancer Lett. 2017, 404, 53–61. [Google Scholar] [CrossRef]
  18. Mukherjee, S.; Detroja, R.; Balamurali, D.; Matveishina, E.; Medvedeva, Y.A.; Valencia, A.; Gorohovski, A.; Frenkel-Morgenstern, M. Computational analysis of sense-antisense chimeric transcripts reveals their potential regulatory features and the landscape of expression in human cells. NAR Genom. Bioinforma 2021, 3, lqab074. [Google Scholar] [CrossRef] [PubMed]
  19. Day, C.P.; Merlino, G.; Van Dyke, T. Preclinical Mouse Cancer Models: A Maze of Opportunities and Challenges. Cell 2015, 163, 39–53. [Google Scholar] [CrossRef] [Green Version]
  20. Frenkel-Morgenstern, M.; Valencia, A. Novel domain combinations in proteins encoded by chimeric transcripts. Bioinformatics 2012, 28, i67–i74. [Google Scholar] [CrossRef] [Green Version]
  21. Frenkel-Morgenstern, M.; Gorohovski, A.; Lacroix, V.; Rogers, M.; Ibanez, K.; Boullosa, C.; Leon, E.A.; Ben-Hur, A.; Valencia, A. ChiTaRS: A database of human, mouse and fruit fly chimeric transcripts and RNA-sequencing data. Nucleic Acids Res. 2013, 41, D142–D151. [Google Scholar] [CrossRef] [Green Version]
  22. Mukherjee, S.; Frenkel-Morgenstern, M. Evolutionary impact of chimeric RNAs on generating phenotypic plasticity in human cells. Trends Genet. 2021, 38, 4–7. [Google Scholar] [CrossRef] [PubMed]
  23. Seldin, M.F.; Amos, C.I.; Ward, R.; Gregersen, P.K. The genetics revolution and the assault on rheumatoid arthritis. Arthritis Rheum. 1999, 42, 1071–1079. [Google Scholar] [CrossRef]
  24. Toonen, E.J.M.; Barrera, P.; Radstake, T.R.D.J.; Van Riel, P.L.C.M.; Scheffer, H.; Franke, B.; Coenen, M.J.H. Gene expression profiling in rheumatoid arthritis: Current concepts and future directions. Ann. Rheum. Dis. 2008, 67, 1663–1669. [Google Scholar] [CrossRef] [PubMed]
  25. Van Der Pouw Kraan, T.C.T.M.; Wijbrandts, C.A.; Van Baarsen, L.G.M.; Voskuyl, A.E.; Rustenburg, F.; Baggen, J.M.; Ibrahim, S.M.; Fero, M.; Dijkmans, B.A.C.; Tak, P.P.; et al. Rheumatoid arthritis subtypes identified by genomic profiling of peripheral blood cells: Assignment of a type I interferon signature in a subpopulation of patients. Ann. Rheum. Dis. 2007, 66, 1008–1014. [Google Scholar] [CrossRef]
  26. Zhao, J.; Guo, S.; Schrodi, S.J.; He, D. Molecular and Cellular Heterogeneity in Rheumatoid Arthritis: Mechanisms and Clinical Implications. Front. Immunol. 2021, 12, 790122. [Google Scholar] [CrossRef] [PubMed]
  27. Zendman, A.J.W.; van Venrooij, W.J.; Pruijn, G.J.M. Use and significance of anti-CCP autoantibodies in rheumatoid arthritis. Rheumatology 2006, 45, 20–25. [Google Scholar] [CrossRef] [Green Version]
  28. Hueber, W.; Kidd, B.A.; Tomooka, B.H.; Lee, B.J.; Bruce, B.; Fries, J.F.; Sønderstrup, G.; Monach, P.; Drijfhout, J.W.; Van Venrooij, W.J.; et al. Antigen microarray profiling of autoantibodies in rheumatoid arthritis. Arthritis Rheum. 2005, 52, 2645–2655. [Google Scholar] [CrossRef]
  29. Lipsky, P.E.; van der Heijde, D.M.F.M.; St. Clair, E.W.; Furst, D.E.; Breedveld, F.C.; Kalden, J.R.; Smolen, J.S.; Weisman, M.; Emery, P.; Feldmann, M.; et al. Infliximab and Methotrexate in the Treatment of Rheumatoid Arthritis. N. Engl. J. Med. 2000, 343, 1594–1602. [Google Scholar] [CrossRef] [Green Version]
  30. Edwards, J.C.W.; Szczepański, L.; Szechiński, J.; Filipowicz-Sosnowska, A.; Emery, P.; Close, D.R.; Stevens, R.M.; Shaw, T. Efficacy of B-Cell–Targeted Therapy with Rituximab in Patients with Rheumatoid Arthritis. N. Engl. J. Med. 2004, 350, 2572–2581. [Google Scholar] [CrossRef] [Green Version]
  31. Zhao, J.; Jiang, P.; Guo, S.; Schrodi, S.J.; He, D. Apoptosis, Autophagy, NETosis, Necroptosis, and Pyroptosis Mediated Programmed Cell Death as Targets for Innovative Therapy in Rheumatoid Arthritis. Front. Immunol. 2021, 12, 809806. [Google Scholar] [CrossRef] [PubMed]
  32. Athar, A.; Füllgrabe, A.; George, N.; Iqbal, H.; Huerta, L.; Ali, A.; Snow, C.; Fonseca, N.A.; Petryszak, R.; Papatheodorou, I.; et al. ArrayExpress update—From bulk to single-cell expression data. Nucleic Acids Res. 2019, 47, D711–D715. [Google Scholar] [CrossRef] [PubMed]
  33. Andrews, S. FastQC: A Quality Control Tool for High Throughput Sequence Data. Available online: http://www.bioinformatics.babraham.ac.uk/projects/fastqc (accessed on 14 February 2022).
  34. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011, 17, 10–12. [Google Scholar] [CrossRef]
  35. Detroja, R.; Gorohovski, A.; Giwa, O.; Baum, G.; Frenkel-Morgenstern, M. ChiTaH: A fast and accurate tool for identifying known human chimeric sequences from high-throughput sequencing data. NAR Genom. Bioinforma 2021, 3, lqab112. [Google Scholar] [CrossRef]
  36. Dobin, A.; Davis, C.A.; Schlesinger, F.; Drenkow, J.; Zaleski, C.; Jha, S.; Batut, P.; Chaisson, M.; Gingeras, T.R. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics 2013, 29, 15–21. [Google Scholar] [CrossRef]
  37. Liao, Y.; Smyth, G.K.; Shi, W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 2014, 30, 923–930. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  38. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 550. [Google Scholar] [CrossRef] [Green Version]
  39. Zhou, Y.; Zhou, B.; Pache, L.; Chang, M.; Khodabakhshi, A.H.; Tanaseichuk, O.; Benner, C.; Chanda, S.K. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 2019, 10, 1523. [Google Scholar] [CrossRef] [PubMed]
  40. Wang, L.; Park, H.J.; Dasari, S.; Wang, S.; Kocher, J.P.; Li, W. CPAT: Coding-potential assessment tool using an alignment-free logistic regression model. Nucleic Acids Res. 2013, 41, e74. [Google Scholar] [CrossRef]
  41. Guo, J.C.; Fang, S.S.; Wu, Y.; Zhang, J.H.; Chen, Y.; Liu, J.; Wu, B.; Wu, J.R.; Li, E.M.; Xu, L.Y.; et al. CNIT: A fast and accurate web tool for identifying protein-coding and long non-coding transcripts based on intrinsic sequence composition. Nucleic Acids Res. 2019, 47, W516–W522. [Google Scholar] [CrossRef] [Green Version]
  42. Han, S.; Liang, Y.; Ma, Q.; Xu, Y.; Zhang, Y.; Du, W.; Wang, C.; Li, Y. LncFinder: An integrated platform for long non-coding RNA identification utilizing sequence intrinsic composition, structural information and physicochemical property. Brief. Bioinform. 2019, 20, 2009–2027. [Google Scholar] [CrossRef] [PubMed]
  43. Taniue, K.; Akimitsu, N. Fusion genes and RNAs in cancer development. Non-Coding RNA 2021, 7, 10. [Google Scholar] [CrossRef] [PubMed]
  44. Firestein, G.S. Evolving concepts of rheumatoid arthritis. Nature 2003, 423, 356–361. [Google Scholar] [CrossRef]
  45. McInnes, I.B.; Schett, G. Mechanism of Disease The Pathogenesis of Rheumatoid Arthritis. N. Engl. J. Med. 2011, 365, 2205–2219. [Google Scholar] [CrossRef] [Green Version]
  46. Majithia, V.; Geraci, S.A. Rheumatoid Arthritis: Diagnosis and Management. Am. J. Med. 2007, 120, 936–939. [Google Scholar] [CrossRef]
Figure 1. Distribution of the identified chimeric RNAs across cohorts. A total of 566 chimeric RNAs was uniquely identified from 151 RNA-Seq samples of RA patients. After manual validation, a total of 246 RA-specific chimeric RNAs remained. Finally, 37 recurrent chimeric RNAs expressed in at least 3 RA samples were used for further downstream analysis.
Figure 1. Distribution of the identified chimeric RNAs across cohorts. A total of 566 chimeric RNAs was uniquely identified from 151 RNA-Seq samples of RA patients. After manual validation, a total of 246 RA-specific chimeric RNAs remained. Finally, 37 recurrent chimeric RNAs expressed in at least 3 RA samples were used for further downstream analysis.
Cells 11 01092 g001
Figure 2. Gene annotation and enrichment analysis of the parental genes of recurrent chimeric RNAs. (A) The majority of genes are involved in processes, such as “adaptive immune response” and the “positive regulation of B-cell activation”. Some genes are also involved in “leukocyte differentiation”. (B) Disease enrichment analysis shows that these genes are significantly involved in gout arthritis, reactive arthritis, and rheumatism.
Figure 2. Gene annotation and enrichment analysis of the parental genes of recurrent chimeric RNAs. (A) The majority of genes are involved in processes, such as “adaptive immune response” and the “positive regulation of B-cell activation”. Some genes are also involved in “leukocyte differentiation”. (B) Disease enrichment analysis shows that these genes are significantly involved in gout arthritis, reactive arthritis, and rheumatism.
Cells 11 01092 g002
Figure 3. Differentially expressed parental genes of recurrent chimeric RNAs in RA. The parental genes of at least 20 of 37 recurrent chimeric RNAs were differentially expressed. Most of the parental genes were up-regulated (green), while some were down-regulated (orange).
Figure 3. Differentially expressed parental genes of recurrent chimeric RNAs in RA. The parental genes of at least 20 of 37 recurrent chimeric RNAs were differentially expressed. Most of the parental genes were up-regulated (green), while some were down-regulated (orange).
Cells 11 01092 g003
Figure 4. Classification of RA-specific recurrent chimeric RNAs into coding and non-coding gene sequences using CPAT, CNIT, and LncFinder. A total of 5 coding and 23 non-coding chimeric RNAs were identified by all three methods.
Figure 4. Classification of RA-specific recurrent chimeric RNAs into coding and non-coding gene sequences using CPAT, CNIT, and LncFinder. A total of 5 coding and 23 non-coding chimeric RNAs were identified by all three methods.
Cells 11 01092 g004
Figure 5. Computational study of expressed and recurrent chimeric RNAs in joint synovial biopsies of RA patients. Genes involved in immune response were significantly dysregulated, which leads to the generation of chimeric RNAs with the potential to translate into function chimeric proteins and regulatory long non-coding RNAs, resulting in the dysfunctional immune responses that cause inflammation and bone destruction.
Figure 5. Computational study of expressed and recurrent chimeric RNAs in joint synovial biopsies of RA patients. Genes involved in immune response were significantly dysregulated, which leads to the generation of chimeric RNAs with the potential to translate into function chimeric proteins and regulatory long non-coding RNAs, resulting in the dysfunctional immune responses that cause inflammation and bone destruction.
Cells 11 01092 g005
Table 1. Description of the total RNA sequencing samples.
Table 1. Description of the total RNA sequencing samples.
DescriptionNo. of SamplesAverage Raw Paired-End ReadsAverage Trimmed Paired-End Reads
Rheumatoid Arthritis15186,568,97286,432,629
Healthy2884,069,63483,923,697
Osteoarthritis2286,614,84686,458,277
Arthralgia1090,722,88290,617,905
Undifferentiated Arthritis687,407,94887,278,313
Table 2. Distribution of the identified chimeric RNAs across the normal and arthritis cohorts.
Table 2. Distribution of the identified chimeric RNAs across the normal and arthritis cohorts.
DescriptionNo. of SamplesNo. of Chimeric RNAs
Rheumatoid Arthritis1512102
Healthy28833
Osteoarthritis22856
Arthralgia10783
Undifferentiated Arthritis6671
Healthy Human Tissues From EBI1992066
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Detroja, R.; Mukherjee, S.; Frenkel-Morgenstern, M. The Landscape of Novel Expressed Chimeric RNAs in Rheumatoid Arthritis. Cells 2022, 11, 1092. https://doi.org/10.3390/cells11071092

AMA Style

Detroja R, Mukherjee S, Frenkel-Morgenstern M. The Landscape of Novel Expressed Chimeric RNAs in Rheumatoid Arthritis. Cells. 2022; 11(7):1092. https://doi.org/10.3390/cells11071092

Chicago/Turabian Style

Detroja, Rajesh, Sumit Mukherjee, and Milana Frenkel-Morgenstern. 2022. "The Landscape of Novel Expressed Chimeric RNAs in Rheumatoid Arthritis" Cells 11, no. 7: 1092. https://doi.org/10.3390/cells11071092

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop