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Analysis of deep sequencing microRNA expression profile from human embryonic stem cells derived mesenchymal stem cells reveals possible role of let-7 microRNA family in downstream targeting of hepatic nuclear factor 4 alpha.

Koh W, Sheng CT, Tan B, Lee QY, Kuznetsov V, Kiang LS, Tanavde V - BMC Genomics (2010)

Bottom Line: We utilized these results of which directed our attention towards establishing hepatic nuclear factor 4 alpha (HNF4A) as a downstream target of let-7 family of microRNAs.Further results derived from visualization of our alignment data and network analysis showed that let-7 family microRNAs could affect the downstream target HNF4A, which is a known endodermal differentiation marker.This is in line with recent paradigm where microRNAs regulate self-renewal and differentiation pathways of embryonic stem cells by forming an integral biological network with transcription factors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Institute (BII), Agency of Science Technology and Research (A*STAR), Matrix, Singapore. winstonk@bii.a-star.edu.sg

ABSTRACT

Background: Recent literature has revealed that genetic exchange of microRNA between cells can be essential for cell-cell communication, tissue-specificity and developmental processes. In stem cells, as in other cells, this can be accomplished through microvesicles or exosome mediated transfer. However, molecular profiles and functions of microRNAs within the cells and in their exosomes are poorly studied. Next generation sequencing technologies could provide a broad-spectrum of microRNAs and their expression and identify possible microRNA targets. In this work, we performed deep sequencing of microRNAs to understand the profile and expression of the microRNAs in microvesicles and intracellular environment of human embryonic stem cells derived mesenchymal stem cells (hES-MSC). We outline a workflow pertaining to visualizing, statistical analysis and interpreting deep sequencing data of known intracellular and extracellular microRNAs from hES-MSC). We utilized these results of which directed our attention towards establishing hepatic nuclear factor 4 alpha (HNF4A) as a downstream target of let-7 family of microRNAs.

Results: In our study, significant differences in expression profile of microRNAs were found in the intracellular and extracellular environment of hES-MSC. However, a high level of let-7 family of microRNAs is predominant in both intra- and extra- cellular samples of hES-MSC. Further results derived from visualization of our alignment data and network analysis showed that let-7 family microRNAs could affect the downstream target HNF4A, which is a known endodermal differentiation marker. The elevated presence of let-7 microRNA in both intracellular and extra cellular environment further suggests a possible intercellular signalling mechanism through microvesicles transfer. We suggest that let-7 family microRNAs might play a signalling role via such a mechanism amongst populations of stem cells in maintaining self renewal property by suppressing HNF4A expression. This is in line with recent paradigm where microRNAs regulate self-renewal and differentiation pathways of embryonic stem cells by forming an integral biological network with transcription factors.

Conclusion: In summary, our study using a combination of alignment, statistical and network analysis tools to examine deep sequencing data of microRNAs in hES-MSC has led to a result that (i) identifies intracellular and exosome microRNA expression profiles of hES-MSC with a possible mechanism of miRNA mediated intercellular regulation by these cells and (ii) placed HNF4A within the cross roads of regulation by the let-7 family of microRNAs.

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Additional Venn diagram analysis. A) Considerable overlap in top ranked genes from comparing of highly-expressed microarray and sequencing data for intracellular miRNAs. B) Overlap in top ranked genes from comparing of highly-expressed microarray and sequencing data for extracellular miRNAs is low. C) Top-level sequence data shows strong agreement between intra and extra cellular samples. Expression microarray for miRNAs were performed and compared with our deep sequencing data. Ranking of the top miRNAs by their abundance from both techniques was performed. Top hundred miRNAs for the intracellular samples were compared using kappa correlation analysis and a large degree of overlap between studied sets was observed. Similarly for the extra cellular samples, the top 50 miRNAs were compared. The degree of overlap between studied sets was less than that of the intracellular samples. A final comparison involves that of the common genes amongst replicates of the intracellular samples with the extra cellular samples. In this case we found that most of the genes of the extracellular microRNA form a common subset of the genes of the intracellular miRNA.
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Figure 4: Additional Venn diagram analysis. A) Considerable overlap in top ranked genes from comparing of highly-expressed microarray and sequencing data for intracellular miRNAs. B) Overlap in top ranked genes from comparing of highly-expressed microarray and sequencing data for extracellular miRNAs is low. C) Top-level sequence data shows strong agreement between intra and extra cellular samples. Expression microarray for miRNAs were performed and compared with our deep sequencing data. Ranking of the top miRNAs by their abundance from both techniques was performed. Top hundred miRNAs for the intracellular samples were compared using kappa correlation analysis and a large degree of overlap between studied sets was observed. Similarly for the extra cellular samples, the top 50 miRNAs were compared. The degree of overlap between studied sets was less than that of the intracellular samples. A final comparison involves that of the common genes amongst replicates of the intracellular samples with the extra cellular samples. In this case we found that most of the genes of the extracellular microRNA form a common subset of the genes of the intracellular miRNA.

Mentions: In general, the distribution shows three distinct phases. A large number of unique transcripts have low count number and are distributed unevenly across the low count number range. This is followed closely by a subsequent phase where the numbers of unique transcripts are distributed evenly across a large range of count number magnitude. The last phase follows the previous with transcripts reverting to an uneven distribution of high count number. These phases can be categorised into different groups based on transcript counts. Figures 3 and 4 show this classification which reflects the degree of agreement and overlap in each case.


Analysis of deep sequencing microRNA expression profile from human embryonic stem cells derived mesenchymal stem cells reveals possible role of let-7 microRNA family in downstream targeting of hepatic nuclear factor 4 alpha.

Koh W, Sheng CT, Tan B, Lee QY, Kuznetsov V, Kiang LS, Tanavde V - BMC Genomics (2010)

Additional Venn diagram analysis. A) Considerable overlap in top ranked genes from comparing of highly-expressed microarray and sequencing data for intracellular miRNAs. B) Overlap in top ranked genes from comparing of highly-expressed microarray and sequencing data for extracellular miRNAs is low. C) Top-level sequence data shows strong agreement between intra and extra cellular samples. Expression microarray for miRNAs were performed and compared with our deep sequencing data. Ranking of the top miRNAs by their abundance from both techniques was performed. Top hundred miRNAs for the intracellular samples were compared using kappa correlation analysis and a large degree of overlap between studied sets was observed. Similarly for the extra cellular samples, the top 50 miRNAs were compared. The degree of overlap between studied sets was less than that of the intracellular samples. A final comparison involves that of the common genes amongst replicates of the intracellular samples with the extra cellular samples. In this case we found that most of the genes of the extracellular microRNA form a common subset of the genes of the intracellular miRNA.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2822534&req=5

Figure 4: Additional Venn diagram analysis. A) Considerable overlap in top ranked genes from comparing of highly-expressed microarray and sequencing data for intracellular miRNAs. B) Overlap in top ranked genes from comparing of highly-expressed microarray and sequencing data for extracellular miRNAs is low. C) Top-level sequence data shows strong agreement between intra and extra cellular samples. Expression microarray for miRNAs were performed and compared with our deep sequencing data. Ranking of the top miRNAs by their abundance from both techniques was performed. Top hundred miRNAs for the intracellular samples were compared using kappa correlation analysis and a large degree of overlap between studied sets was observed. Similarly for the extra cellular samples, the top 50 miRNAs were compared. The degree of overlap between studied sets was less than that of the intracellular samples. A final comparison involves that of the common genes amongst replicates of the intracellular samples with the extra cellular samples. In this case we found that most of the genes of the extracellular microRNA form a common subset of the genes of the intracellular miRNA.
Mentions: In general, the distribution shows three distinct phases. A large number of unique transcripts have low count number and are distributed unevenly across the low count number range. This is followed closely by a subsequent phase where the numbers of unique transcripts are distributed evenly across a large range of count number magnitude. The last phase follows the previous with transcripts reverting to an uneven distribution of high count number. These phases can be categorised into different groups based on transcript counts. Figures 3 and 4 show this classification which reflects the degree of agreement and overlap in each case.

Bottom Line: We utilized these results of which directed our attention towards establishing hepatic nuclear factor 4 alpha (HNF4A) as a downstream target of let-7 family of microRNAs.Further results derived from visualization of our alignment data and network analysis showed that let-7 family microRNAs could affect the downstream target HNF4A, which is a known endodermal differentiation marker.This is in line with recent paradigm where microRNAs regulate self-renewal and differentiation pathways of embryonic stem cells by forming an integral biological network with transcription factors.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Institute (BII), Agency of Science Technology and Research (A*STAR), Matrix, Singapore. winstonk@bii.a-star.edu.sg

ABSTRACT

Background: Recent literature has revealed that genetic exchange of microRNA between cells can be essential for cell-cell communication, tissue-specificity and developmental processes. In stem cells, as in other cells, this can be accomplished through microvesicles or exosome mediated transfer. However, molecular profiles and functions of microRNAs within the cells and in their exosomes are poorly studied. Next generation sequencing technologies could provide a broad-spectrum of microRNAs and their expression and identify possible microRNA targets. In this work, we performed deep sequencing of microRNAs to understand the profile and expression of the microRNAs in microvesicles and intracellular environment of human embryonic stem cells derived mesenchymal stem cells (hES-MSC). We outline a workflow pertaining to visualizing, statistical analysis and interpreting deep sequencing data of known intracellular and extracellular microRNAs from hES-MSC). We utilized these results of which directed our attention towards establishing hepatic nuclear factor 4 alpha (HNF4A) as a downstream target of let-7 family of microRNAs.

Results: In our study, significant differences in expression profile of microRNAs were found in the intracellular and extracellular environment of hES-MSC. However, a high level of let-7 family of microRNAs is predominant in both intra- and extra- cellular samples of hES-MSC. Further results derived from visualization of our alignment data and network analysis showed that let-7 family microRNAs could affect the downstream target HNF4A, which is a known endodermal differentiation marker. The elevated presence of let-7 microRNA in both intracellular and extra cellular environment further suggests a possible intercellular signalling mechanism through microvesicles transfer. We suggest that let-7 family microRNAs might play a signalling role via such a mechanism amongst populations of stem cells in maintaining self renewal property by suppressing HNF4A expression. This is in line with recent paradigm where microRNAs regulate self-renewal and differentiation pathways of embryonic stem cells by forming an integral biological network with transcription factors.

Conclusion: In summary, our study using a combination of alignment, statistical and network analysis tools to examine deep sequencing data of microRNAs in hES-MSC has led to a result that (i) identifies intracellular and exosome microRNA expression profiles of hES-MSC with a possible mechanism of miRNA mediated intercellular regulation by these cells and (ii) placed HNF4A within the cross roads of regulation by the let-7 family of microRNAs.

Show MeSH