Limits...
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

Gene ontology (GO) analysis. a GO analysis of differently expressed mRNA with p < 0.05, b GO analysis of differentially regulated target mRNA. The horizontal axis indicates the names of the clusters in cellular component (CC), biological process (BP) and molecular function (MF), respectively. The vertical axis displays the numbers of targets. The GO terms were sorted by the enrichment P-value, in an ascending order of p-value from bottom to top
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Gene ontology (GO) analysis. a GO analysis of differently expressed mRNA with p < 0.05, b GO analysis of differentially regulated target mRNA. The horizontal axis indicates the names of the clusters in cellular component (CC), biological process (BP) and molecular function (MF), respectively. The vertical axis displays the numbers of targets. The GO terms were sorted by the enrichment P-value, in an ascending order of p-value from bottom to top

Mentions: A functional analysis was conducted based on Gene Ontology. Some gene ontologies of biological processes, molecular functions and cellular components were selected, which were enriched by the features of the transcriptomic and proteomic datasets with p value < 0.05. In the study, GO enrichment analysis was performed through functional annotation clustering of microRNA and target genes, respectively. Totally, 11 biological processes (BP), 4 molecular functions (MF) and 14 cellular components (CC) GO terms were enriched in target genes. 10 BP, 4 MF and 13 CC GO terms in microRNA are shown in Fig. 4 a–b, respectively. BP includes translation, mRNA processing, nucleocytoplasmic transportation, vesicle-mediated transportation, cell motility and so on. MF involves structural constituent of ribosome, translation factor activity, nucleic acid binding, rRNA binding, mRNA binding. CC contains nucleolus nucleus, ribosome, Golgi apparatus, chromosome, endoplasmic reticulum, cytoplasmic membrane-bounded vesicle, mitochondrion, cytoplasmic membrane-bounded vesicle and so on. According to the biological process, the microRNA and target genes are mostly involved in BP GO terms, structural constituent of ribosome of MF GO term and nucleus of CC GO terms.Fig. 4


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)

Gene ontology (GO) analysis. a GO analysis of differently expressed mRNA with p < 0.05, b GO analysis of differentially regulated target mRNA. The horizontal axis indicates the names of the clusters in cellular component (CC), biological process (BP) and molecular function (MF), respectively. The vertical axis displays the numbers of targets. The GO terms were sorted by the enrichment P-value, in an ascending order of p-value from bottom to top
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig4: Gene ontology (GO) analysis. a GO analysis of differently expressed mRNA with p < 0.05, b GO analysis of differentially regulated target mRNA. The horizontal axis indicates the names of the clusters in cellular component (CC), biological process (BP) and molecular function (MF), respectively. The vertical axis displays the numbers of targets. The GO terms were sorted by the enrichment P-value, in an ascending order of p-value from bottom to top
Mentions: A functional analysis was conducted based on Gene Ontology. Some gene ontologies of biological processes, molecular functions and cellular components were selected, which were enriched by the features of the transcriptomic and proteomic datasets with p value < 0.05. In the study, GO enrichment analysis was performed through functional annotation clustering of microRNA and target genes, respectively. Totally, 11 biological processes (BP), 4 molecular functions (MF) and 14 cellular components (CC) GO terms were enriched in target genes. 10 BP, 4 MF and 13 CC GO terms in microRNA are shown in Fig. 4 a–b, respectively. BP includes translation, mRNA processing, nucleocytoplasmic transportation, vesicle-mediated transportation, cell motility and so on. MF involves structural constituent of ribosome, translation factor activity, nucleic acid binding, rRNA binding, mRNA binding. CC contains nucleolus nucleus, ribosome, Golgi apparatus, chromosome, endoplasmic reticulum, cytoplasmic membrane-bounded vesicle, mitochondrion, cytoplasmic membrane-bounded vesicle and so on. According to the biological process, the microRNA and target genes are mostly involved in BP GO terms, structural constituent of ribosome of MF GO term and nucleus of CC GO terms.Fig. 4

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