Analysis of the microRNA transcriptome and expression of different isomiRs in human peripheral blood mononuclear cells.
BACKGROUND
MicroRNAs (miRNAs) have been recognized as one of the key regulatory non-coding RNAs that are involved in a number of basic cellular processes. miRNA expression profiling helps to identify miRNAs that could serve as biomarkers. Next generation sequencing (NGS) platforms provide the most effective way of miRNA profiling, particularly as expression of different isoforms of miRNA (IsomiRs) can be estimated by NGS. Therefore, it is now possible to discern the overall complexity of miRNA populations that participate in gene regulatory networks. It is thus important to consider different isoforms of miRNA as part of total profiling in order to understand all aspects of the biology of miRNAs.


RESULTS
Here next generation sequencing data of small RNAs derived from normal peripheral blood mononuclear cells (PBMC) and Chronic myeloid leukemia (CML) patients has been used to generate miRNA profiles using a computation pipeline which can identify isomiRs that are natural variants of mature miRNAs. IsomiR profiles have been generated for all the 5p and 3p miRNAs (previously known as major mature miRNA and minor or miRNA*) and the data has been presented as a composite total miRNA transcriptome. The results indicated that the most abundant isomiR sequence of about 68% miRNAs, did not match the reference miRNA sequence as entered in the miRBase and that there is a definite pattern in relative concentration of different isomiRs derived from same precursors. Finally, a total of 17 potential novel miRNA sequences were identified suggesting that there are still some new miRNAs yet to be discovered.


CONCLUSIONS
Inclusion of different isoforms provides a detailed miRnome of a cell type or tissues. Availability of miRnome will be useful for finding biomarkers of different cell types and disease states. Our results also indicate that the relative expression levels of different isoforms of a miRNA are likely to be dynamic and may change with respect to changes in the cell or differentiation status.
School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi 110067, India. alok.bhattacharya@gmail.com.
Candida
C
Vaz
Hafiz M
HM
Ahmad
Richa
R
Bharti
Priyatama
P
Pandey
Lalit
L
Kumar
Ritu
R
Kulshreshtha
Alok
A
Bhattacharya
D001483:Base Sequence; D016000:Cluster Analysis; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D007963:Leukocytes, Mononuclear / Q000378:metabolism*; D035683:MicroRNAs / Q000097:blood* / Q000235:genetics*; D058977:Molecular Sequence Annotation; D008969:Molecular Sequence Data; D059467:Transcriptome / Q000235:genetics*
D035683:MicroRNAs