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Integrated analysis of mRNA, microRNA and protein in systemic lupus erythematosus-specific induced pluripotent stem cells from urine.

Tang D, Chen Y, He H, Huang J, Chen W, Peng W, Lu Q, Dai Y - BMC Genomics (2016)

Bottom Line: Renal tubular cells-derived iPSCs were successfully obtained from the urine of SLE patients and healthy controls.Representative miRNA, mRNA and proteins were verified.It was also expected that the knowledge gained from this study can be applied to assess the usefulness of pathogenesis and novel biomarker candidates of SLE, which may develop a new way for SLE diagnosis.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, People's Republic of China.

ABSTRACT

Background: In clinical practice, it is difficult to monitor the repeating relapse in patients who have been suffering from systemic lupus erythematosus (SLE). The underlying etiology remains largely unknown.

Methods: Aiming to understand the pathogenesis of SLE, a detailed study was conducted. Renal tubular cells-derived iPSCs were successfully obtained from the urine of SLE patients and healthy controls. With the purpose to identify simultaneous expression profiling of microRNA, mRNA and protein, Illumina HiSeq™ 2000 System and iTRAQ-coupled 2D LC-MS/MS analysis were utilized in systemic lupus erythematosus-specific induced pluripotent stem cells (SLE-iPSCs) and normal control-iPSCs (NC-iPSCs). The integration of multiple profiling datasets was realized since it could facilitate the identification of non-seed miRNA targets, as well as differentially expressed mRNAs and proteins.

Results: For this study, profiling datasets of 1099 differentially expressed mRNAs, 223 differentially expressed microRNAs and 94 differentially expressed proteins were integrated. In order to investigate the influence of miRNA on the processes of regulating mRNAs and proteins' levels, potential targets of differentially expressed mRNAs and proteins were predicted using miRanda, TargetScan and Pictar. Multiple profiling datasets were integrated to facilitate the identification of miRNA targets, as well as differentially expressed mRNAs and proteins. Through gene ontology (GO) analysis of differentially expressed mRNAs and proteins, biological processes that drive proliferation were identified, such as mRNA processing and translation. Western blot and Q-PCR confirmed AK4 protein and mRNA up-regulation. The findings also showed that TAGLN's protein and mRNA level were down-regulated in SLE-iPSCs, both miR-371a-5p and let-7a-5p in SLE-iPSC were down-regulated and verified using Q-PCR. The up-regulation of AK4 involved in nucleotide biosynthesis suggested a general acceleration of anabolic metabolism induced by down-regulated miR-371a-5p, which might contribute to SLE.

Conclusion: Based on high throughput analysis, integrated miRNA, mRNA, and protein expression data were generated. Differentially expressed dates were also adopted in conjunction with in-silico tools to identify potential candidates for SLE-iPSCs. Representative miRNA, mRNA and proteins were verified. It was also expected that the knowledge gained from this study can be applied to assess the usefulness of pathogenesis and novel biomarker candidates of SLE, which may develop a new way for SLE diagnosis.

No MeSH data available.


Related in: MedlinePlus

microRNA-target protein network. The mapping is composed of 48 differentially regulated target proteins and 36 differentially expressed miRNAs. Green circles represent down- regulated target proteins, and red triangles signify up-regulated miRNAs. The lines stand for coherent miRNAs-target proteins interaction pairs. A single miRNA can target multiple proteins, while a single protein can be targeted by several miRNAs. Multiple miRNAs can cooperatively repress a range of targets
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Fig5: microRNA-target protein network. The mapping is composed of 48 differentially regulated target proteins and 36 differentially expressed miRNAs. Green circles represent down- regulated target proteins, and red triangles signify up-regulated miRNAs. The lines stand for coherent miRNAs-target proteins interaction pairs. A single miRNA can target multiple proteins, while a single protein can be targeted by several miRNAs. Multiple miRNAs can cooperatively repress a range of targets

Mentions: In this study, a comparative proteome survey was performed on the SLE-iPSC and control-iPSC using iTRAQ technique. The identification and quantification of differentially expressed proteins were realized. In order to investigate the potential functional correlations among microRNA target proteins, an integrated systematic analysis was made on microRNA and protein data. In addition, Cytoscape software was applied to construct the regulation network of microRNA and target protein. Given a 95 % confidence level, the interaction network of 94 proteins in 207 differentially expressed proteins were classified (Fig. 5); 37 MicroRNAs which regulated 49 target proteins were predicted. MicroRNA-target proteins regulation network is shown in Fig. 5, which manifests that microRNA- target protein were mutually cross-regulated.Fig. 5


Integrated analysis of mRNA, microRNA and protein in systemic lupus erythematosus-specific induced pluripotent stem cells from urine.

Tang D, Chen Y, He H, Huang J, Chen W, Peng W, Lu Q, Dai Y - BMC Genomics (2016)

microRNA-target protein network. The mapping is composed of 48 differentially regulated target proteins and 36 differentially expressed miRNAs. Green circles represent down- regulated target proteins, and red triangles signify up-regulated miRNAs. The lines stand for coherent miRNAs-target proteins interaction pairs. A single miRNA can target multiple proteins, while a single protein can be targeted by several miRNAs. Multiple miRNAs can cooperatively repress a range of targets
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4940874&req=5

Fig5: microRNA-target protein network. The mapping is composed of 48 differentially regulated target proteins and 36 differentially expressed miRNAs. Green circles represent down- regulated target proteins, and red triangles signify up-regulated miRNAs. The lines stand for coherent miRNAs-target proteins interaction pairs. A single miRNA can target multiple proteins, while a single protein can be targeted by several miRNAs. Multiple miRNAs can cooperatively repress a range of targets
Mentions: In this study, a comparative proteome survey was performed on the SLE-iPSC and control-iPSC using iTRAQ technique. The identification and quantification of differentially expressed proteins were realized. In order to investigate the potential functional correlations among microRNA target proteins, an integrated systematic analysis was made on microRNA and protein data. In addition, Cytoscape software was applied to construct the regulation network of microRNA and target protein. Given a 95 % confidence level, the interaction network of 94 proteins in 207 differentially expressed proteins were classified (Fig. 5); 37 MicroRNAs which regulated 49 target proteins were predicted. MicroRNA-target proteins regulation network is shown in Fig. 5, which manifests that microRNA- target protein were mutually cross-regulated.Fig. 5

Bottom Line: Renal tubular cells-derived iPSCs were successfully obtained from the urine of SLE patients and healthy controls.Representative miRNA, mRNA and proteins were verified.It was also expected that the knowledge gained from this study can be applied to assess the usefulness of pathogenesis and novel biomarker candidates of SLE, which may develop a new way for SLE diagnosis.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou, 510632, People's Republic of China.

ABSTRACT

Background: In clinical practice, it is difficult to monitor the repeating relapse in patients who have been suffering from systemic lupus erythematosus (SLE). The underlying etiology remains largely unknown.

Methods: Aiming to understand the pathogenesis of SLE, a detailed study was conducted. Renal tubular cells-derived iPSCs were successfully obtained from the urine of SLE patients and healthy controls. With the purpose to identify simultaneous expression profiling of microRNA, mRNA and protein, Illumina HiSeq™ 2000 System and iTRAQ-coupled 2D LC-MS/MS analysis were utilized in systemic lupus erythematosus-specific induced pluripotent stem cells (SLE-iPSCs) and normal control-iPSCs (NC-iPSCs). The integration of multiple profiling datasets was realized since it could facilitate the identification of non-seed miRNA targets, as well as differentially expressed mRNAs and proteins.

Results: For this study, profiling datasets of 1099 differentially expressed mRNAs, 223 differentially expressed microRNAs and 94 differentially expressed proteins were integrated. In order to investigate the influence of miRNA on the processes of regulating mRNAs and proteins' levels, potential targets of differentially expressed mRNAs and proteins were predicted using miRanda, TargetScan and Pictar. Multiple profiling datasets were integrated to facilitate the identification of miRNA targets, as well as differentially expressed mRNAs and proteins. Through gene ontology (GO) analysis of differentially expressed mRNAs and proteins, biological processes that drive proliferation were identified, such as mRNA processing and translation. Western blot and Q-PCR confirmed AK4 protein and mRNA up-regulation. The findings also showed that TAGLN's protein and mRNA level were down-regulated in SLE-iPSCs, both miR-371a-5p and let-7a-5p in SLE-iPSC were down-regulated and verified using Q-PCR. The up-regulation of AK4 involved in nucleotide biosynthesis suggested a general acceleration of anabolic metabolism induced by down-regulated miR-371a-5p, which might contribute to SLE.

Conclusion: Based on high throughput analysis, integrated miRNA, mRNA, and protein expression data were generated. Differentially expressed dates were also adopted in conjunction with in-silico tools to identify potential candidates for SLE-iPSCs. Representative miRNA, mRNA and proteins were verified. It was also expected that the knowledge gained from this study can be applied to assess the usefulness of pathogenesis and novel biomarker candidates of SLE, which may develop a new way for SLE diagnosis.

No MeSH data available.


Related in: MedlinePlus