Elevated polyadenylation of ncRNA substrates in RRMS

To initiate our studies, we determined levels of Y RNAs in different subject cohorts by quantitative PCR using either random hexamers or oligo-dT for synthesis reasoning that random hexamers should allow amplification of all Y RNAs independent of whether or not they were polyadenylated but that oligo-dT would allow amplification of only those Y RNAs that were polyadenylated consistent with previously established methodologies [31]. We also confirmed that >95% of total cellular Y RNAs were retained during the PaxGene total RNA isolation procedure. Sanger sequencing of the PCR products confirmed identity of all human Y RNAs. We found that levels of total Y RNAs (random hexamers) were not markedly different between CTRL and RRMS subjects (Figure 1A). In marked contrast, levels of polyadenylated Y1 RNA (oligo-dT), but not other Y RNAs, were increased by about 20-fold in RRMS relative to CTRL (Figure 1B). This elevation of polyadenylated Y1 RNA was not seen in systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), neuromyelitis optica (NMO), or Parkinson’s disease (PD) (Additional file 1: Table S1 for a description of subjects) (Figure 1C). In a cross-sectional analysis, we compared levels of polyadenylated Y1 RNA in blood samples obtained from subjects experiencing a first clinically isolated syndrome who went on to develop RRMS at a later date (CIS-MS), from subjects at the time of their diagnosis of RRMS but prior to onset of any therapies (RRMS-NAÏVE) and from subjects with RRMS of >1 year’s duration (RRMS) (Figure 1D). We found that levels of polyadenylated Y1 RNA were elevated in both RRMS-NAÏVE and RRMS cohorts but that levels of polyadenylated Y1 RNA were similar to CTRL levels in the CIS-MS cohort. Thus, increased polyadenylation of Y1 RNA, but not other Y RNAs, was observed in RRMS, but not other autoimmune diseases.

Figure 1 Increased levels of poly(A) + Y1 RNA in RRMS. (A) Total Y RNA levels in peripheral whole blood determined after cDNA synthesis using random hexamers in CTRL (N = 24) and RRMS (N = 22). (B) As in (A) except oligo-dT was used for cDNA synthesis. (C) As in (B) except different disease cohorts. Individual transcripts were normalized to GAPDH transcript levels in CTRL (N = 24), SLE (N = 24), RA (N = 18), NMO (N = 22), and PD (N = 19). (D) Total poly(A) + Y1 RNA levels were determined using oligo-dT in CTRL (N = 24), CIS-MS (N = 16), RRMS-NAÏVE (N = 24), and RRMS (N = 22). Error bars are S.D. *P <0.05, **P <0.005. Full size image

We used a similar approach to measure levels of total and polyadenylated 18S and 28S rRNAs. We found that levels of total 18S and 28S rRNAs were similar between the RRMS and CTRL cohorts when random hexamers were used for cDNA synthesis (Figure 2A). However, we found a marked increase in levels of both polyadenylated 18S and 28S rRNAs as measured by employing oligo-dT for cDNA synthesis (Figure 2B). We therefore determined if there was also an increase in misprocessed rRNAs in RRMS since rRNAs are polyadenylated in the presence of defective processing of the 18S and 28S rRNAs from their 47S rRNA precursor. We predicted that misprocessing of rRNAs should produce 18S and 28S rRNAs longer than the typical 1.9 kb and 5.0 kb, respectively. To test this hypothesis, we designed a series of PCR primers to probe lengths of 18S and 28S rRNAs. We used common primers within the 18S or 28S rRNA (black arrows) and four additional primers; one within the classic 18S or 28S rRNA (green and blue arrows) and three that extend 30, 60, or 90 bp beyond the length of the fully processed 1.9 kb 18S or 5 kb 28S rRNA transcript (orange and red arrows) (Figure 2C). Random hexamers were used for cDNA synthesis. We found that 18S and 28S transcript levels in CTRL and RRMS were approximately the same (normalized to 1), labeled 18S (Figure 2D, left) or 28S (Figure 2D, right), when PCR primers within the 1.9 kb 18S rRNA or 5.0 kb 28S rRNA were employed. However, when we employed an internal primer (black arrow) and external primers 30, 60, or 90 bp distal to the normal transcript size (orange arrows, red arrows) we found a marked increase in transcript levels of 18S rRNA with extended ends between 30 and 90 bp in RRMS compared to CTRL. These 18S rRNA extended ends were more frequent at the 5’ end of the 18S rRNA than the 3’ end of the 18S rRNA. The 28S rRNA had only a modest increase in extended ends in RRMS relative to CTRL at the 5’ end. For the 28S RNA species, the increase in the frequency of extended ends in RRMS relative to CTRL was more pronounced at the 3’ end. We interpret these results to demonstrate that both 18S rRNAs and 28S rRNAs were not accurately processed to their mature size in RRMS.

Figure 2 Increased polyadenylation and mis-processing of 18S and 28S rRNAs in RRMS. (A) Total rRNA levels in blood determined after cDNA synthesis using random hexamers in CTRL (N = 24) and RRMS (N = 22). (B) As in (A) except oligo-dT was used for cDNA synthesis. (C) Schematic illustrating PCR strategy to detect misprocessed 18S and 28S rRNAs. (D) Transcript levels of misprocessed rRNAs (-90, -60, -30) in RRMS (N = 12) relative to CTRL (N = 12). Results were normalized to total 18S or 28S RNA and are presented as fold difference between CTRL and RRMS. *P <0.05. Full size image

Next, we determined levels of U1 RNA in blood (PaxGene tubes) from CTRL subjects, subjects with RRMS or with other inflammatory diseases. As above, synthesis of cDNA was performed using random hexamers and oligo-dT and transcript levels were determined by quantitative PCR. We found a modest increase in total levels of U1 RNA (random hexamers) in RRMS relative to CTRL (Figure 3A). In contrast, poly(A) + U1 transcript levels were markedly elevated in subjects with RRMS relative to CTRL (Figure 3B). The CIS-MS cohort did not exhibit increased poly(A) + U1 RNA levels nor did subjects with other inflammatory diseases, SLE, RA, and NMO. We also extracted RNA-seq data and confirmed that poly(A) + U1 RNA levels were elevated in RRMS and that U2 and U4 RNA levels were also elevated in RRMS relative to CTRL (Figure 3C). We also examined expression of total and poly(A) + U11 and U12 transcripts, components of the minor spliceosome pathway, and found elevated poly(A) + U11 and U12 transcript levels in RRMS versus CTRL and increased total U11 transcript levels. Total levels of U12 snRNA were not significantly different between RRMS and CTRL (Figure 3D). Thus, multiple ncRNA species exhibit increased polyadenylation in RRMS relative to CTRL or other autoimmune diseases.

Figure 3 Increased total and polyadenylated U snRNAs in RRMS. (A) Total U1 RNA transcript levels were determined by quantitative PCR using random hexamers for cDNA synthesis in CTRL (N = 24) and RRMS (N = 22). Individual transcripts were normalized to GAPDH transcript levels. (B) As in (A) except total poly(A) + U1 snRNA levels were determined by quantitative PCR using oligo-dT for cDNA synthesis in CTRL (N = 24), CIS-MS (N = 16), RRMS (N = 22), SLE (N = 24), RA (N = 18), and NMO (N = 22). (C) As in (A), except transcript levels were determined by whole genome RNA-sequencing (RNA-seq) and normalized to CTRL = 1; CTRL (N = 8) and RRMS (N = 6). (D) As in (A, B) except transcript levels of U11 and U12 snRNAs were determined by quantitative PCR using oligo d(T) for poly(A) + or random hexamer primers for total U11 or U12 snRNA. Full size image

Alterations in mRNA processing in RRMS

The U1, U2, U4, U5, and U6 RNAs play critical roles in determining mRNA length and isoform expression via several mechanisms including protection from premature cleavage and polyadenylation of nascent pre-mRNAs [21,26,34]. Disruption of levels of individual U RNAs is sufficient to cause global alterations in alternative splicing and mRNA length. It is not known how imbalances or altered polyadenylation of multiple U snRNAs may impact alternative splicing and mRNA lengths. Since these defects include mRNA shortening by loss of 5’ or 3’ exons, choice of alternative 5’ and 3’ UTRs, choice of initial and final exons and partial retention of intron sequences, we searched our RNA-seq data for differences in splicing and intron retention between CTRL subjects and subjects with RRMS. For each chromosome, we counted the number of reads per exon across the genome in CTRL and RRMS subjects. We found a genome-wide loss of exon expression in RRMS relative to CTRL (Figure 4A). Genome-wide analysis of expressed genes demonstrated that 17% of transcribed genes exhibited loss of 5’ exons (5’ shortening) (Figure 4B). Loss of 3’ exons (3’ shortening, 3% of transcribed genes) was less frequent. Intron retention was also less frequent and only 4% of transcribed genes exhibited forms of intron retention. Visual inspection of 3,000 or 30% of expressed genes using the Integrative Genome Viewer (IGV) confirmed these results. We defined 5’ and 3’ shortening to mean that at least one exon at the 5’ or 3’ end of the transcript, respectively, exhibited a >5-fold reduction in expressed counts or FPKM relative to exons at the 3’ or 5’ end of the transcript, respectively, in RRMS subjects compared to CTRL subjects. Intron retention was similarly defined as an increase in read counts localized to an intron by >5-fold in RRMS compared to CTRL subjects. The majority of genes transcribed in mononuclear cells from RRMS compared to CTRL did not display differences in isoform distribution or retention of intron sequences.

Figure 4 Extensive mRNA isoform loss and intron retention in RRMS. (A) Total exon loss and intron gain events across the genome in RRMS (N = 6) and CTRL (N = 8) were determined from RNA-seq analysis (see Methods). Results are expressed as average normalized counts or reads across known exons and introns in CTRL and RRMS. (B) Percent abundance of transcript alterations in RRMS relative to CTRL PBMC determined from analysis of genome-wide RNA-seq data. (C) Example of intron retention in mRNA encoded by MBP. Green arrows identify exons. Red circles identify retention of intron sequences in mature mRNA in RRMS. Orange and blue arrows identify two isoforms. (D, E) Examples of 5’ mRNA shortening, CSF1R, and 3’ mRNA shortening, NFATC1 in RRMS. Red arrows identify loss of exons in RRMS and green arrows identify exons expressed at equal levels between CTRL and RRMS. (F) Expression levels of individual exons and the MBP intron in CTRL (N = 12) and RRMS (N = 12) was determined by quantitative PCR and normalized to CTRL = 1.0 after normalization to transcript levels of GAPDH, error bars are S.D. * = P <0.05. Full size image

We analyzed the number of read counts per intron and found that subjects with RRMS exhibited higher intron read counts across chromosomes compared to CTRL. Examples of these alterations included MBP, CSF1R, and NFATC1 (Figure 4C to E). We found that the myelin basic protein gene, MBP, which encodes both classic myelin basic protein expressed primarily by myelin forming cells and a second family of proteins, called golli proteins, expressed by T lymphocytes, neurons, and oligodendrocytes, displayed increased intron retention in the mature mRNA (Figure 4C, note that transcription is from right to left) [35-37]. Individual exons are labeled with green arrows and regions of intron retention seen in RRMS are labeled with red circles. Retained intron sequences were present in both ‘golli’ and ‘golli-MBP’ mRNAs; classic MBP gene depicted by the blue arrow, golli-MBP gene depicted by the orange arrow. Thus, U snRNA imbalance in RRMS was associated with intron retention in MBP mRNA.

We also found a marked reduction in transcript levels of the exons comprising CSF1R mRNAs. In CTRL subjects, each of the 22 exons was present at approximately equal abundance. However, in RRMS, there was decreased transcript abundance of exons 1 to 11 (green arrows identify exons expressed at similar levels in CTRL and RRMS and red arrows show exon loss) (Figure 4D). Thus, U snRNA imbalance in RRMS was also associated with 5’ shortening. 3’ shortening was also found in RRMS (Figure 4E). All nine exons of NFATC1 mRNA and the 3’ UTR exhibited similar transcript abundance assembled into a continuous mRNA in CTRL subjects. RRMS subjects exhibited a specific loss of the eighth and ninth exons (note that the ninth exon is continuous with the 3’ UTR, red arrows). The remaining expressed exons were present at similar levels in CTRL and RRMS (green arrows). We then validated these findings in a different cohort of CTRL and RRMS subjects using quantitative PCR (Figure 4F). Transcript levels of MBP exon 4, CSF1R exon 2, and NFATC1 exon 9 were significantly reduced in RRMS versus CTRL. Levels of CSF1R exon 22 and NFATC1 exon 2 were only modestly reduced in RRMS similar to our RNA-sequencing findings. We also found increased transcript levels of the MBP intron between MBP exons 3 and 4 in RRMS compared to CTRL. Thus, using both quantitative PCR and RNA-sequencing, we were able to confirm specific examples of intron retention, 5’ shortening, and 3’ shortening in this independent sample set of RRMS subjects. These shortened mRNA isoforms seen in RRMS are consistent with mRNA premature cleavage and polyadenylation, a property that is produced by imbalances of U snRNAs or loss of U snRNA function. U snRNA imbalance observed in RRMS may contribute to intron retention as well as exon loss and mRNA shortening.

Reduced expression of Ro60 and La in RRMS

Ro60 and La proteins are components of ribonucleoprotein particles, bind discrete structural ncRNAs, and are thought to play important roles in ncRNA processing and quality control. For these reasons, we measured TROVE2 (Ro60) and SSB (La) expression levels in blood samples harvested in PaxGene tubes from the following cohorts of subjects: CTRL, CIS-MS, RRMS, RA, SLE, NMO, and PD. We found that TROVE2 and SSB transcript levels were markedly reduced in the established RRMS cohort compared to CTRL. This difference was unique to RRMS and not observed in other autoimmune disease cohorts or in other inflammatory (NMO) or non-inflammatory (PD) neurologic conditions (Figure 5A). We replicated these findings by whole-genome RNA sequencing (RNA-seq) and obtained equivalent results (Figure 5B). We also determined levels of protein expression of Ro60 and La in PBMC by western blotting. We found that both Ro60 and La proteins were profoundly diminished in RRMS PBMC relative to CTRL PBMC (Figure 5C and D). Thus, both TROVE2 and SSB transcripts and Ro60 and La proteins were profoundly diminished in RRMS and these mRNA and protein expression differences were not seen in several other autoimmune diseases.

Figure 5 Ro60 and La proteins are depressed in RRMS. (A) Ro60 (TROVE2) and La (SSB) transcript levels in CTRL (N = 24), CIS-MS (N = 16), RRMS (N = 22), RA (N = 18), SLE (N = 24), NMO (N = 22), and PD (N = 19) were determined by quantitative PCR after cDNA synthesis using oligo-dT. Results are normalized to CTRL = 1.0 after normalization to transcript levels of GAPDH, error bars are S.D. (B) As in (A) using whole genome RNA-sequencing data. (C) Western blotting to determine Ro60 and La protein levels in PBMC from CTRL (N = 9) and RRMS (N = 8). (D) Quantitative estimates of protein abundance relative to β-actin. *P <0.05, **P <0.01. Full size image

Interferon-β1b (Betaseron) and structural defects in RRMS

For all analyses above, RRMS subjects were either on copaxone or no immunomodulatory therapy. Thus, we compared these subjects to RRMS on stable betaseron therapy. We found that levels of TROVE2, SSB, poly(A) + Y1 RNA and poly(A) + U1 snRNA were close to CTRL levels in RRMS subjects on betaseron therapy compared to RRMS subjects on either copaxone or no immunomodulatory therapy (Figure 6A, B). We examined responses of three individuals in longitudinal studies who initiated betaseron and found that correction of levels of TROVE2, SSB, poly(A) + Y1 RNA and poly(A) + U1 RNA was very rapid. We hypothesize that signaling pathways either directly or indirectly activated by betaseron interfere with signaling pathways driving defects in polyadenylation of structural RNAs in and expression of TROVE2 and SSB in RRMS and that betaseron will be a useful tool to identify underlying mechanisms. Further, measurement of polyadenylated species of ncRNAs may provide a useful means to monitor responses to betaseron or other immunomodulatory therapies in RRMS.

Figure 6 Interferon-β1b (IFN-β1b) therapy corrects aberrant levels of TROVE2, SSB, poly (A) + Y1 RNA, and poly(A) + U1 snRNA in RRMS. Blood samples were collected in PaxGene tubes from CTRL (N = 12), RRMS subjects not on IFN-β1b (RRMS - IFN-β1b, N = 12), and RRMS subjects on stable IFN-β1b therapy (RRMS + IFN-β1b, N = 4). Oligo dT was used for cDNA synthesis. Transcript levels of TROVE2 and SSB (A) or poly(A) + Y1 RNA and poly(A) + U1 RNA (B) were determined by quantitative PCR and normalized to CTRL = 1 after normalization to levels of GAPDH. Error bars are ± S.D. *P <0.05. Full size image

TROVE2 and SSB silencing disrupts structural RNA surveillance and alters mRNA length

To establish causal links between Ro60 and La imbalance and polyadenylation and processing of structural ncRNAs observed in RRMS, we designed siRNAs specific for human TROVE2 and SSB and transfected them into the human THP-1 monocyte or the Jurkat T cell line. Transfection with TROVE2 siRNA caused specific reduction of TROVE2 transcripts but not SSB transcripts (Figure 7A). Similarly, the SSB siRNA caused specific loss of SSB transcripts but not TROVE2 transcripts. We asked if reduced TROVE2 or SSB levels resulted in an increase in the amount of poly(A) + Y1 RNA in THP-1 cells. We found that knockdown of either TROVE2, SSB, or the combination effectively increased levels of poly(A) + Y1 RNA (Figure 7B). Additionally, knockdown of SSB resulted in marked accumulation of poly(A) + 18S rRNA (Figure 7C). Knockdown of TROVE2 resulted in only a modest increase in poly(A) + 18S RNA. In contrast, levels of poly(A) + 28S rRNA were largely unaffected by knockdown of either TROVE2 or SSB but were modestly increased by the knockdown of both TROVE2 and SSB. We also examined the impact of TROVE2 or SSB knockdown on levels of poly(A) + U1 RNA and found that knockdown of TROVE2, but not knockdown of SSB, caused a marked increase in levels of poly(A) + U1 RNA (Figure 7D). rRNA misprocessing was also analyzed as described above. In both THP-1 cells and Jurkat cells, transfection of TROVE2 and/or SBB siRNAs increased levels of misprocessed 18S rRNA and, to a lesser extent, misprocessed 28S rRNA (Figure 7E, F). Since the combination of TROVE2 and SSB knockdown increased the amount of poly(A) + U1 RNA and alterations in levels of U1 RNA result in isoform switching, we determined if TROVE2 and SSB RNA knockdown was sufficient to alter mRNA lengths in a cell [21]. We transfected TROVE2 and SSB siRNAs into THP-1 cells and employed a PCR strategy to test for different lengths of MBP, CSF1R, and NFATC1 transcripts (Figure 7G). Similar to what we observed in vivo, we found that reduced levels of either Ro60 or La by siRNA silencing were sufficient to increase expression levels of the MBP intron, to reduce expression of CSF1R exon 2 but not CSF1R exon 22, and also decrease the ratio of short to long NFATC1 isoforms (Figure 7H). Finally, we examined the ability of Ro60 and La to bind these target mRNAs using RNA immunoprecipitation and quantitative PCR. Only a modest fraction of total CSF1R, NFATC1, or MBP mRNA bound to Ro60 or La (Additional file 2: Figure S1). Taken together, these results demonstrate that Ro60 and La protein levels are critical for many aspects of proper RNA surveillance including maintaining low levels of the poly(A) + ncRNAs, Y1 RNA, rRNAs, and U RNA, rRNA processing, and proper mRNA splicing; key components of RNA function in the cell.