Analysis of the microRNA transcriptome and expression of different isomiRs in human peripheral blood mononuclear cells
© Vaz et al.; licensee BioMed Central Ltd. 2013
Received: 3 October 2012
Accepted: 17 September 2013
Published: 28 September 2013
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.
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.
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.
MicroRNAs (miRNA) are a major class of small non-coding RNAs of about 22 nucleotides that are involved in various cellular functions. miRNAs are transcribed as pri-miRNAs which are processed into pre-miRNAs by an RNase III enzyme, Drosha. Pre-miRNAs are exported to cytoplasm and processed by another RNAse III enzyme, Dicer to give rise to mature miRNAs. These two enzymes are important not only for processing of intermediates to mature miRNAs but also for introducing variations in miRNAs [1–3].
Several reports have established miRNAs as important post transcriptional regulators of gene expression through different mechanisms, such as translational repression and destabilization and cleavage of the target mRNA. The processes are initiated by binding of the miRNA to partially complementary sites at the 3′UTR of the target mRNA in animals and completely complementary sites in plants [4, 5]. A single miRNA can bind to several target mRNAs and similarly a multitude of them can bind a single target . Recent estimates suggest that a substantial fraction (up to 60%) of higher eukaryotic mRNAs is regulated by these small non coding RNAs . Therefore, miRNAs and mRNAs are part of intricate networks that regulate gene expression and cellular decision making [8, 9]. The existence of miRNA clusters and families also add to the intricacies of miRNA regulation . In order to understand these networks, knowledge of complete miRNA expression profiles is necessary. Expression profiles can also be helpful in identifying tissue, stage and disease-specific patterns. miRNAs have also been shown to be involved in cancer and tumorigenesis [11–13]. It is generally believed that some of the miRNAs can be useful diagnostic and prognostic markers of different cancers with applications in patient care and therapy .
A number of approaches, such as northern blotting, RNase protection assay, PCR, microarrays and RAKE assay have been used for expression profiling of miRNAs [15–20]. In general these methods are either cumbersome for scaling up and/or not sensitive enough to detect low levels. miRNAs are expressed at different levels, spanning more than five orders of magnitude and majority of methods are not suitable to get accurate expression profiles. Microarrays have problems associated with cross hybridization and creating a single hybridization condition suitable for all miRNAs . Most of these methods could detect only pre-miRNAs owing to the technical difficulty caused by short length of mature-miRNAs. Next generation sequencing (NGS) has provided an edge over other methods in generating, not just an in depth knowledge of known miRNA expression, but also helped in identifying tissue-specific, and rarely expressed miRNAs [21–25]. NGS approach has also been successful in new miRNA discovery leading to exponential increase in miRBase entries in the last few years (Release 19) [26, 27]. Moreover, analysis of small RNA (sRNA) sequencing data derived using NGS platforms has helped to identify alternate processing products of biogenesis. It is clear from some of the preliminary analysis that these alternate processing products are biologically relevant and can have functional role probably by binding different Argonaute proteins [24, 25, 28, 29]. The previously known as minor or “miR*” sequences were found to be expressed higher than the major counterparts raising the need for a change in the annotation. Therefore, the annotation of miRNAs as-5p/-3p rather than mature and star is now used by the latest miRBase Release 19.
In this report, a detailed analysis on human PBMC miRnome has been described. Our analysis involves different processing products that include isomiRs of both miR-5p and miR-3p thus forming a composite miRNA transcriptome. We have generated the reference miRNA profile comprising of the miRNAs in miRBase as well as the abundant isomiR profile using our NGS data. On comparing the reference miRNA and the abundant isomiR profiles we found that the abundant isomiRs differ in different cells suggesting that miRNA profiling must include all the variants for developing biological markers in different diseases.
A few potential novel miRNAs have also been detected during our analysis . Our report highlights intricacies in miRNA biogenesis machinery that can be used as a signature of physiological state of cells.
Annotation and apportionment
Details of the samples
Total number of reads
Total number of reads (> = 14nt)
Reads that matched to the human genome but not with any known RNA species (unannotated reads) were 5–7%. These were taken for novel miRNA prediction. Lastly, the total unmatched pool that failed to match with the human genome was around 7–9% (Figure 2).
Reference miRNA identification
In general, the stem part of the precursor sequences is processed with Dicer enzyme to give rise to mature miRNAs. Previously, one of the arms was considered to form the mature miRNA whereas the other formed the minor or the star sequence. The expression levels determined the major/minor nomenclature. Recently, several reports have shown that this nomenclature could vary with the tissue and experimental conditions causing the change in the nomenclature. These are now referred to as miR-5p and miR-3p depending on their location in the precursor sequence. The miRBase [26, 27] is a repository of miRNAs, and the mature form of miRNAs in miRBase is referred here as the reference miRNAs. The expression profiles of these reference miRNAs were obtained by identifying the reads from our NGS samples that showed an exact match with them.
IsomiR identification and examination
miRNAs are also known to have alternative forms called “isomiRs”, that differ from each other by a few nucleotides at the ends. These are thought to be generated by alternate Dicer cutting . Identification of isomiR family has been discussed in the “Methods” section. Briefly, sequencing reads of each sample were aligned to the human pre-miRNAs to obtain an isomiR family for each mature miRNA (-5p/-3p). The Additional file 2 shows an example of a hsa-let-7b isomiR family with their respective frequency or expression values. Quite often only one of these isomiRs displays dominant expression, that is, has highest number of reads among all the other isomiRs. This dominant isomiR is considered to be the active or mature form of miRNAs.
We generated the isomiR expression profiles of all miRNAs and determined the dominant isomiR for each expressed miRNA based on the highest frequency. The Additional file 3 lists the most abundant isomiR for every expressed mature miRNA (Abundant isomiR expression profile).
The clustering of the samples was once again checked using the Abundant IsomiR expression profile. The Hierarchical clustering plot computed using the Abundant IsomiR expression profile gave results similar to that obtained using the Reference miRNA expression profile. The Normal1 and Normal2 samples were closer to each other forming one group. Similarly, Patient1 and Patient2 samples were closer to each other forming another group (Figure 3).
We then answered the question whether the most abundant isomiR was the same as the reference miRNA by comparing the Reference and the Abundant isomiR expression profiles. If the most abundant isoform of a miRNA was not the reference miRNA, it was reported as “NO”, whereas if the abundant one matched the reference miRNA it was reported as “YES”. The Additional file 4 lists this comparison.
Comparison of the reference and abundant IsomiR expression profiles
Reference miRNAs that matched the most abundant isomiRs (YES cases)
Reference miRNAs that did not match most abundant isomiRs (NO cases)
MiRNAs not considered
The same was seen for the patients; around 619 miRNAs were expressed and displayed uniformity in being YES/NO among both the Patient samples. The remaining 1423 miRNAs had either a low expression (1293) or were non uniform (130). Out of the 619 miRNAs analysed, the reference miRNA sequences of only 190 miRNAs matched with the most abundant isomiR (YES cases), while for 429 miRNAs, the reference sequence did not match the most abundant isomiR (NO cases) (Table 2).
Few examples revealing differences in the expression profiles based on selection of the most abundant isomiR versus the reference miRNA
Reference miRNA frequency
Most abundant isomiR frequency
Reference miRNA frequency
Most abundant isomiR frequency
Reference miRNA frequency
Most abundant isomiR frequency
Reference miRNA frequency
Most abundant isomiR frequency
Expression profile comparison
Reference miRNA expression profile
Abundant IsomiR expression profile
On an average about 875 miRNAs including about 20–55 singletons were expressed in each sample. Around 255 more miRNAs were found using this abundant isomiR profile that was missed out in the Reference miRNA profile owing to the absence of the reference sequence for these miRNAs.
The level of expression of the miRNAs was found to be variable just like the reference miRNA (Additional file 3) profiles but of a higher level owing to more number of miRNAs detected by using this profile in Normals (Figure 4A) and Patients (Figure 4B). On using this profile we got a bigger list of highly expressed miRNAs (>10,000 frequency count). This included miRNAs found to be highly expressed using the reference miRNA profile as well as comprised of a few more miRNAs that failed to be picked out as highly expressed on using the reference miRNA profile owing to the presence of another isomiR sequence that had a higher frequency than the reference sequence. Some of these miRNAs were: hsa-miR-107, 140-3p, 21-5p, 23a-3p, 26b-5p, 3074-5p, 378a-3p.
Novel miRNA identification
Discussion and conclusions
The major goal of this study was to understand the complete miRNA transcriptome in the context of human PBMCs obtained using deep sequencing of small RNA through custom designed computational tools. The expression of the reference mature miRNAs is commonly considered to form the miRNA transcriptome. Since NGS allows identification of isomiRs of miR-5p and miR-3p, it is important to include these processing variants as part of the complete miRNA transcriptome [23, 27, 28]. In our previous study we had profiled miRNAs in Normal PBMC and in CML cell line K562 and identified a number of differentially expressed miRNAs . The study did not include human patient cells and isomiR profiling.
The presence of isomiRs was first reported by Morin et.al. 2008 based on their analysis of miRNA sequences obtained through deep sequencing of human embryonic stem cells . These miRNA variants or the IsomiRs have variable 5′ and 3′ extensions. The miRNA transcriptome consists of sum total of different isomiR populations of miR-5p and miR-3p. Among different methods of miRNA profiling, sRNA sequencing using NGS platforms is capable of identifying all these variants. Some recent studies have reported tissue specific accumulation of miRNAs and their counterpart sequences. Preferential expression of IsomiRs has also been reported in tissue specific manner . Profiling of miRNAs in different lymphoid cells has been carried out. For example, one study reported a comprehensive profiling of miRNAs and their isomiRs expressed in B-cells from Jewish centenarians . There is a manually curated database of extracellular circulating miRNAs called the miRandola which is connected to the miRNA knowledge base, making it useful for inferring the potential biological functions of circulating miRNAs .
We have used this extended definition of miRNA transcriptome to study the total miRNA transcriptome of Normal PBMCs and CML patients. Since this was a pilot study comprising of two Normal PBMC and two CML patient samples, no attempt was made to identify differentially expressed miRNA isoforms among the normal and patient samples. In general, distribution of miRNA isoforms in normal and patient tissues displayed similar patterns (Additional files 1, 2, 3, 4).
Our results show that mature miRNAs have to be defined with respect to a specific cell type, as relative levels of different isoforms are dynamic in nature. Therefore, we have taken into consideration different isomiRs and looked for the most abundant isomiR and compared with the reference miRNAs (as in miRBase). Our results show that the most abundant isomiR is not always the same as the reference miRNA sequence (submitted to miRBase). This could be due to various reasons, such as tissue specific expression of a specific isomiR or variable degradation rates of different isomiRs. Overall the results presented here suggest that strand selection and processing of miRNAs are likely to be regulated and may be related to phenotypic differences of tissues and cells.
This dynamic nature of isomiRs and their tendency of changing with different conditions can affect any analysis that relies on miRNA expression profile. The Reference miRNA expression profile may not provide an exact representation of the miRNA transcriptome. As our results showed that the Reference miRNA expression profile missed out nearly 255 miRNAs and also identified a fewer number of highly expressed miRNAs, this could lead to incomplete and sometimes a misleading analysis. The Abundant isomiR profile can provide a more appropriate and clearer picture of the miRNA transcriptome and should be used for further downstream analysis such as differential expression estimation and even for finding commonly expressed miRNAs among samples.
Our results on identification of novel miRNAs from sRNA sequences suggest that there are still many novel miRNAs that have not yet been identified. Therefore it is important to find these novel miRNAs in order to define the miRNA transcriptome.
In summary NGS based analysis of sRNA sequences allows complete deciphering of miRNA transcriptome that includes all the isoforms. We are still not clear about the mechanism by which a given cell or tissue decides the functional isoform of a given miRNA and regulation of isoform switching.
Cell line and blood samples preparation
Buffy coat of healthy blood donors (Normal1 and Normal2) were collected from volunteers. Red cell lysis buffer (0.144 M NH4Cl, 0.01 M NH4HCO3) was added to buffy coat to lyse the remnant RBCs and pure WBC population was obtained by centrifugation at 3000 g. Peripheral blood mononuclear cells were obtained at diagnosis from patients with CML after signed informed consent had been obtained from the patient in accordance with the Declaration of Helsinki. This study was approved by the hospital (AIIMS, New Delhi) according to the guidelines of the hospital’s ethics committee (Reference no. A-36:20/10/04).
RNA isolation and sequencing
Total RNA isolation was carried out from peripheral blood using TRIzol® Reagent (Invitrogen) as per manufacturer’s instruction. RNA preparations were stored at - 80°C till further use. Small RNA population was isolated by separating 10 μg of total RNA on denaturing polyacrylamide gel electrophoresis (PAGE) and cutting a portion of the gel corresponding to the size 18–30 nucleotides based standard oligonucleotide markers. Adapter (5′) was ligated to sRNA population and ligated RNAs (40–60 nt) were purified by running on urea PAGE. This was followed by 3′ adapter ligation and purification of adapter ligated RNAs (70–90 nt) in a similar manner. Modified sRNAs were reverse transcribed and then PCR amplified with adapter specific primers and the amplified cDNAs were finally purified on Urea PAGE to generate cDNA tag libraries for sequencing by illumina genome analyzer.
Data sets information
The sRNA sequencing data containing PBMC of two normal individuals (Normal1, Normal2) and two CML patients (Patient1, Patient2) were obtained from Illumina high throughput sequencing platform. The sequences shorter than the cut-off read length (14nt), as determined by the read length distribution plot (Figure 1), were removed from all the four samples. The total number of reads for all the samples before and after the application of the read length cut-off is mentioned in Table 1.
Annotation and data classification
The sRNA sequences obtained were annotated against the known databases using the Elimination pipeline as used in our previous work [24, 30]. The Elimination module was used for fast matching of the sequences with the databases. A mismatch of up to 2 nucleotides was allowed. The pool of unannotated sequences at the end of the pipeline served as a source of potential novel miRNAs.
Reference miRNA expression profile generation
To generate the expression profile of the reference miRNAs, the sRNA sequences of all the samples were matched against the known mature miRNA sequences in miRBase using BLASTN. The parameters used for BLAST were tuned to obtain maximum matches, such as the word size was set to 7 nucleotides, filtering was turned off and the number of alignments reported were increased. The profile comprises of a list of the “reference” miRNAs along with their frequency or expression value for all the 4 samples (Additional file 1).
Alignment of the reads and isomiR identification
As mentioned in the introduction section, isomiRs are variant forms of the miRNAs caused by alternative Dicer cutting . To obtain the isomiRs for every known miRNA, an alignment of the reads to the pre-miRNA hairpins is necessary. This alignment facilitates identification of isomiRs of mature miRNA sequences derived from both, the 5′ and the 3′ region of the pre-miRNA hairpin.
Alignment of the reads to the pre-miRNAs
The alignment was done by the Bowtie software using the default parameters . The pre-miRNAs were downloaded from miRBase Release 19 comprising of 1600 pre-miRNAs and 2042 mature miRNA sequences . Reads from each of the four samples were aligned to the reference pre-miRNAs. The output was an alignment of the deep sequencing reads to the Homo sapiens reference pre-miRNAs.
Obtaining clusters of the isomiRs
The alignment output was processed by a perl script developed specifically to get a cluster of isomiRs that are actually reads that match at the same location but differ by a few nucleotides at the 5′ and 3′ ends. Such clusters were obtained for every miRNA from both the regions (5′ and 3′ region) of the hairpin using the position information given in the output of the alignment. To demonstrate this, the isomiR family of hsa-let-7b is shown in Additional file 2.
Abundant isomiR expression profile generation
The isomiR clusters generated as mentioned earlier were analyzed to obtain the most abundant member. An abundant isomiR expression profile comprising of a list of the known miRNAs along with the frequency of the “most abundant isomiR” for all the samples was then created (Additional file 3).
Comparison of the reference miRNA profile and the abundant isomiR expression profile
The most abundant member could either be the reference miRNA or some other isomiR. Cases where the most abundant isomiR was the reference miRNA itself, was denoted by “YES”, whereas the cases where another isomiR was the abundant one was denoted by “NO” (Additional file 4).
Novel miRNA prediction
After the NGS datasets were passed through the elimination pipeline to sieve out all the known RNAs, the unannotated ones were matched with the intergenic and intronic regions of the genome to obtain exact matched sequences. Since the intergenic and intronic regions are known to be sources of the miRNA genes, the unannotated RNA sequences were matched for identifying potential novel miRNAs sequences. The exact matches were extended in length corresponding to the average length of a precursor miRNA and then subjected to the ab-initio miRNA prediction algorithms such as the SCFG based CID-miRNA  and CSHMM  that can predict miRNAs with high sensitivity and specificity.
Presence of the concerned sRNA in one of the arms of the stem region (-5p or-3p) of the hairpin.
Presence of one or more isomiR of that sRNA.
Presence of another sRNA in the other arm of the stem region (-5p or-3p) of the hairpin.
Matching and Extending
The unannotated sRNAs which had frequencies above or equal to 5 were matched to the intergenic/intronic regions. The exact matched sequences were extracted along with 70 nucleotides flanking both the ends to obtain sequences of length comparable to a precursor miRNA sequence.
Folding and Filtering
The extended sequences were tested by CID-miRNA  and CSHMM  prediction tools to find for potential pre-miRNAs. The potential pre-miRNAs reported by these tools were then checked to see if the concerned sRNA occurred in the folded putative precursor and was located in one of the arms of the stem region. Since mature miRNAs are known to be arising from the stem portion and not the loop, only those hairpins in which the sRNAs occurred in the stem were classified as correct cases and the remaining as prediction errors. These correct cases were further tested by MiPred .
Identifying isomiRs and counterparts
The sRNAs derived from common precursors that were predicted as correct cases, were grouped into a family. The most abundant member of a family was designated as the major mature miRNA. The sRNAs that differed from the representative by a few nucleotides were called its isomiRs and those that had a different, partially complementary sequence and were located in the other strand (stem of the hairpin loop) were treated as its putative counterparts. The Additional file 5 comprise of the potential novel miRNAs grouped into families on the basis of sRNAs falling within the same precursor. The scores from the 4 tools (CID-miRNA, CSHMM, miRDeep, MiPred) assigned to the corresponding precursors are also listed.
Finally, the novel miRNA candidates from all the samples were pooled and the redundancy was removed to get a final set of potential novel miRNAs. These were given a unique name and were listed along with their sample IDs. The Additional file 5 comprises the details of all the mature and precursor potential novel miRNAs. The sequence and the structures of two potential novel miRNAs is shown in Figure 6. The Additional file 6 comprises of the structures of all the potential novel precursors.
Availability of supporting data
The raw data sets supporting the results of this article are available in the Sequence Read Archive (SRA) repository. BioProject ID: PRJNA216976.
BioSample accessions: SAMN02333778, SAMN02333779, SAMN02333780, SAMN02333781.
The authors thank Department of Biotechnology, Government of India for their generous support and UPOE-supported HPCF for computing time. AB also thanks Department of Science & Technology for a JC Bose Fellowship. CV acknowledges fellowship from CSIR (CSIR SRF award no: 9/263/ (0772)/9).
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