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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

Data analysis overview. Stage 1: SLE-iPSCs and NC-iPSCs were used for the extraction of protein and RNA. Stage 2: Proteins were identified with iTRAQ-coupled LC–MS/MS; the libraries of mRNA and miRNA were constructed by using the Illumina TruSeq RNA Sample Prep Kit v2-Set A and TruSeq Small RNA Sample Prep Kit Set A, respectively, and then sequenced by using the Illumina HiSeq™ 2000 System. Stage 3: Protein dates were submitted to the ProteinPilot analysis software for peptide identification and quantification, subsequently, differentially expressed proteins were identified and quantified. Gene expressions based on sequencing were measured by RPKM values, ‘FDR(false discovery rate) ≤0.001 and the absolute value of log2-Ratio ≥1’ as the threshold. Differentially expressed mRNAs and miRNAs were obtained. Stage 4: GO enrichment analysis facilitated the mappings of all differentially expressed proteins, mRNAs and miRNAs to GO terms in the database (http: //www. geneontology.org/). With PicTar, miRanda v5, TargetScan 5.1, differentially expressed target proteins and mRNAs were predicted. Stage 5: Differentially expressed proteins and mRNAs were classified according to the GO database. With Cytoscape software,the regulation network of microRNA-target protein and microRNA-target mRNA were analyzed. Stage 6: An integrated analysis of microRNA, target mRNAs and proteins was carried out using Cytoscape software. Representative miRNA, mRNA and proteins were verified using Q-PCR and western blotting
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Fig1: Data analysis overview. Stage 1: SLE-iPSCs and NC-iPSCs were used for the extraction of protein and RNA. Stage 2: Proteins were identified with iTRAQ-coupled LC–MS/MS; the libraries of mRNA and miRNA were constructed by using the Illumina TruSeq RNA Sample Prep Kit v2-Set A and TruSeq Small RNA Sample Prep Kit Set A, respectively, and then sequenced by using the Illumina HiSeq™ 2000 System. Stage 3: Protein dates were submitted to the ProteinPilot analysis software for peptide identification and quantification, subsequently, differentially expressed proteins were identified and quantified. Gene expressions based on sequencing were measured by RPKM values, ‘FDR(false discovery rate) ≤0.001 and the absolute value of log2-Ratio ≥1’ as the threshold. Differentially expressed mRNAs and miRNAs were obtained. Stage 4: GO enrichment analysis facilitated the mappings of all differentially expressed proteins, mRNAs and miRNAs to GO terms in the database (http: //www. geneontology.org/). With PicTar, miRanda v5, TargetScan 5.1, differentially expressed target proteins and mRNAs were predicted. Stage 5: Differentially expressed proteins and mRNAs were classified according to the GO database. With Cytoscape software,the regulation network of microRNA-target protein and microRNA-target mRNA were analyzed. Stage 6: An integrated analysis of microRNA, target mRNAs and proteins was carried out using Cytoscape software. Representative miRNA, mRNA and proteins were verified using Q-PCR and western blotting

Mentions: In order to comprehensively reveal mRNA, microRNA and protein interactions, we obtained high quality, complete information and estimated the expression levels of mRNA, miRNA and protein between the SLE-iPSCs and control-iPSCs (Fig. 1).Fig. 1


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)

Data analysis overview. Stage 1: SLE-iPSCs and NC-iPSCs were used for the extraction of protein and RNA. Stage 2: Proteins were identified with iTRAQ-coupled LC–MS/MS; the libraries of mRNA and miRNA were constructed by using the Illumina TruSeq RNA Sample Prep Kit v2-Set A and TruSeq Small RNA Sample Prep Kit Set A, respectively, and then sequenced by using the Illumina HiSeq™ 2000 System. Stage 3: Protein dates were submitted to the ProteinPilot analysis software for peptide identification and quantification, subsequently, differentially expressed proteins were identified and quantified. Gene expressions based on sequencing were measured by RPKM values, ‘FDR(false discovery rate) ≤0.001 and the absolute value of log2-Ratio ≥1’ as the threshold. Differentially expressed mRNAs and miRNAs were obtained. Stage 4: GO enrichment analysis facilitated the mappings of all differentially expressed proteins, mRNAs and miRNAs to GO terms in the database (http: //www. geneontology.org/). With PicTar, miRanda v5, TargetScan 5.1, differentially expressed target proteins and mRNAs were predicted. Stage 5: Differentially expressed proteins and mRNAs were classified according to the GO database. With Cytoscape software,the regulation network of microRNA-target protein and microRNA-target mRNA were analyzed. Stage 6: An integrated analysis of microRNA, target mRNAs and proteins was carried out using Cytoscape software. Representative miRNA, mRNA and proteins were verified using Q-PCR and western blotting
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: Data analysis overview. Stage 1: SLE-iPSCs and NC-iPSCs were used for the extraction of protein and RNA. Stage 2: Proteins were identified with iTRAQ-coupled LC–MS/MS; the libraries of mRNA and miRNA were constructed by using the Illumina TruSeq RNA Sample Prep Kit v2-Set A and TruSeq Small RNA Sample Prep Kit Set A, respectively, and then sequenced by using the Illumina HiSeq™ 2000 System. Stage 3: Protein dates were submitted to the ProteinPilot analysis software for peptide identification and quantification, subsequently, differentially expressed proteins were identified and quantified. Gene expressions based on sequencing were measured by RPKM values, ‘FDR(false discovery rate) ≤0.001 and the absolute value of log2-Ratio ≥1’ as the threshold. Differentially expressed mRNAs and miRNAs were obtained. Stage 4: GO enrichment analysis facilitated the mappings of all differentially expressed proteins, mRNAs and miRNAs to GO terms in the database (http: //www. geneontology.org/). With PicTar, miRanda v5, TargetScan 5.1, differentially expressed target proteins and mRNAs were predicted. Stage 5: Differentially expressed proteins and mRNAs were classified according to the GO database. With Cytoscape software,the regulation network of microRNA-target protein and microRNA-target mRNA were analyzed. Stage 6: An integrated analysis of microRNA, target mRNAs and proteins was carried out using Cytoscape software. Representative miRNA, mRNA and proteins were verified using Q-PCR and western blotting
Mentions: In order to comprehensively reveal mRNA, microRNA and protein interactions, we obtained high quality, complete information and estimated the expression levels of mRNA, miRNA and protein between the SLE-iPSCs and control-iPSCs (Fig. 1).Fig. 1

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