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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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High abundant expression signals of detected miRNAs in two independent replicates show similar frequency distributions. Distributions of the expression signal count (y-axis: representing the proportion of total transcript counted and the x-axis: representing the count of signals; data in a log scale) derived from biological replicates of intracellular samples show a high degree of similarity. The empirical distributions can be interpreted as a mixture of two essentially distinct distributions. The low-abundant count (presented on the left part of the distribution) provides noise-rich data. This data shows an exponential decline of the frequency distribution suggesting that a large portion of signal have low counts. This is followed by a fairly even distribution across the log scale of counts and ended with sporadic signal that have high counts at the far end of the log scale. The high-abundant count (presented on the right part of the distribution) has a long tail and shows log-normal-like frequency distribution. The last part of the distribution shows high level of similarity in the right-size trends, which is is exploited in statistical tests for determining significances/reliable signals from mostly noise signals.
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Figure 2: High abundant expression signals of detected miRNAs in two independent replicates show similar frequency distributions. Distributions of the expression signal count (y-axis: representing the proportion of total transcript counted and the x-axis: representing the count of signals; data in a log scale) derived from biological replicates of intracellular samples show a high degree of similarity. The empirical distributions can be interpreted as a mixture of two essentially distinct distributions. The low-abundant count (presented on the left part of the distribution) provides noise-rich data. This data shows an exponential decline of the frequency distribution suggesting that a large portion of signal have low counts. This is followed by a fairly even distribution across the log scale of counts and ended with sporadic signal that have high counts at the far end of the log scale. The high-abundant count (presented on the right part of the distribution) has a long tail and shows log-normal-like frequency distribution. The last part of the distribution shows high level of similarity in the right-size trends, which is is exploited in statistical tests for determining significances/reliable signals from mostly noise signals.

Mentions: One of the main challenges in interpreting deep sequencing data is the large number of transcripts with small read counts in the range of 1 to 10. Therefore there is a need to determine a threshold value for sieving out significant miRNAs. We address this issue by first examining the overall distribution of transcripts in our samples which are biological replicates. Plots were made to interpret the global expression distribution for these miRNAs as depicted in Figure 2. Such plots can be interpreted to give the probability of finding a particular miRNA given an associated count/abundance.


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)

High abundant expression signals of detected miRNAs in two independent replicates show similar frequency distributions. Distributions of the expression signal count (y-axis: representing the proportion of total transcript counted and the x-axis: representing the count of signals; data in a log scale) derived from biological replicates of intracellular samples show a high degree of similarity. The empirical distributions can be interpreted as a mixture of two essentially distinct distributions. The low-abundant count (presented on the left part of the distribution) provides noise-rich data. This data shows an exponential decline of the frequency distribution suggesting that a large portion of signal have low counts. This is followed by a fairly even distribution across the log scale of counts and ended with sporadic signal that have high counts at the far end of the log scale. The high-abundant count (presented on the right part of the distribution) has a long tail and shows log-normal-like frequency distribution. The last part of the distribution shows high level of similarity in the right-size trends, which is is exploited in statistical tests for determining significances/reliable signals from mostly noise signals.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: High abundant expression signals of detected miRNAs in two independent replicates show similar frequency distributions. Distributions of the expression signal count (y-axis: representing the proportion of total transcript counted and the x-axis: representing the count of signals; data in a log scale) derived from biological replicates of intracellular samples show a high degree of similarity. The empirical distributions can be interpreted as a mixture of two essentially distinct distributions. The low-abundant count (presented on the left part of the distribution) provides noise-rich data. This data shows an exponential decline of the frequency distribution suggesting that a large portion of signal have low counts. This is followed by a fairly even distribution across the log scale of counts and ended with sporadic signal that have high counts at the far end of the log scale. The high-abundant count (presented on the right part of the distribution) has a long tail and shows log-normal-like frequency distribution. The last part of the distribution shows high level of similarity in the right-size trends, which is is exploited in statistical tests for determining significances/reliable signals from mostly noise signals.
Mentions: One of the main challenges in interpreting deep sequencing data is the large number of transcripts with small read counts in the range of 1 to 10. Therefore there is a need to determine a threshold value for sieving out significant miRNAs. We address this issue by first examining the overall distribution of transcripts in our samples which are biological replicates. Plots were made to interpret the global expression distribution for these miRNAs as depicted in Figure 2. Such plots can be interpreted to give the probability of finding a particular miRNA given an associated count/abundance.

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