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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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Different hES-MSC replicates show similar alignment peaks. Visualization of Seqmap mapping via UCSC genome browser reveals peaks along specific genomic regions that have large numbers of miRNA binding to these regions. In the top figure A Blue and Purple trend lines represent biological replicates of samples derived from intracellular environment. High degree of correlation is observed between the replicates as can be observed from the similarity in locations where peaks were found. The height of each such peak corresponds to the number of transcripts detected from deep sequencing. Each peak now represents genomic locations where a large number of specific transcripts bind to. The bottom figure B depicts the extra cellular sample transcripts that are aligned to the human genome using Seqmap. Peaks occur in similar region after mapping across the replicates. Each replicate is visualized with a different color and the salient feature reveals peaks from different sample aligning in similar locations.
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Figure 5: Different hES-MSC replicates show similar alignment peaks. Visualization of Seqmap mapping via UCSC genome browser reveals peaks along specific genomic regions that have large numbers of miRNA binding to these regions. In the top figure A Blue and Purple trend lines represent biological replicates of samples derived from intracellular environment. High degree of correlation is observed between the replicates as can be observed from the similarity in locations where peaks were found. The height of each such peak corresponds to the number of transcripts detected from deep sequencing. Each peak now represents genomic locations where a large number of specific transcripts bind to. The bottom figure B depicts the extra cellular sample transcripts that are aligned to the human genome using Seqmap. Peaks occur in similar region after mapping across the replicates. Each replicate is visualized with a different color and the salient feature reveals peaks from different sample aligning in similar locations.

Mentions: To achieve this quantitative threshold value for further analysis, we began by mapping reads to the human genome. Seqmap was used to map known human miRNA transcript sequence data with 3 mis-matches in alignments to the human genome. The resulting genomic graphs (Figure 5) combined with read counts when observed in the UCSC genome browser reveals peaks along genomic locations. Histograms of the peaks magnitude distribution (Figure 6) reveal that, after an initial transient uneven distribution phase, peaks are similar amongst our biological replicates. To achieve a threshold value, an adaptive thresholding method using KS statistics is deployed (Figure 7). This value came to a read count of 32. Transcripts with a read count of more than 32 are thus deemed to be significantly different from noise. This threshold value of 32 read counts coincide with the point of inflexion in the distributions of Figure 2 that marks the separation of the initial transient uneven phase with the later stable phase.


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)

Different hES-MSC replicates show similar alignment peaks. Visualization of Seqmap mapping via UCSC genome browser reveals peaks along specific genomic regions that have large numbers of miRNA binding to these regions. In the top figure A Blue and Purple trend lines represent biological replicates of samples derived from intracellular environment. High degree of correlation is observed between the replicates as can be observed from the similarity in locations where peaks were found. The height of each such peak corresponds to the number of transcripts detected from deep sequencing. Each peak now represents genomic locations where a large number of specific transcripts bind to. The bottom figure B depicts the extra cellular sample transcripts that are aligned to the human genome using Seqmap. Peaks occur in similar region after mapping across the replicates. Each replicate is visualized with a different color and the salient feature reveals peaks from different sample aligning in similar locations.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 5: Different hES-MSC replicates show similar alignment peaks. Visualization of Seqmap mapping via UCSC genome browser reveals peaks along specific genomic regions that have large numbers of miRNA binding to these regions. In the top figure A Blue and Purple trend lines represent biological replicates of samples derived from intracellular environment. High degree of correlation is observed between the replicates as can be observed from the similarity in locations where peaks were found. The height of each such peak corresponds to the number of transcripts detected from deep sequencing. Each peak now represents genomic locations where a large number of specific transcripts bind to. The bottom figure B depicts the extra cellular sample transcripts that are aligned to the human genome using Seqmap. Peaks occur in similar region after mapping across the replicates. Each replicate is visualized with a different color and the salient feature reveals peaks from different sample aligning in similar locations.
Mentions: To achieve this quantitative threshold value for further analysis, we began by mapping reads to the human genome. Seqmap was used to map known human miRNA transcript sequence data with 3 mis-matches in alignments to the human genome. The resulting genomic graphs (Figure 5) combined with read counts when observed in the UCSC genome browser reveals peaks along genomic locations. Histograms of the peaks magnitude distribution (Figure 6) reveal that, after an initial transient uneven distribution phase, peaks are similar amongst our biological replicates. To achieve a threshold value, an adaptive thresholding method using KS statistics is deployed (Figure 7). This value came to a read count of 32. Transcripts with a read count of more than 32 are thus deemed to be significantly different from noise. This threshold value of 32 read counts coincide with the point of inflexion in the distributions of Figure 2 that marks the separation of the initial transient uneven phase with the later stable phase.

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