Non-coding RNAs in Rheumatoid Arthritis: From Bench to Bedside

Twin concordance data for rheumatoid arthritis (RA) on their own provide only limited insight into the relative genetic and environmental contribution to the disease. We applied quantitative genetic methods to assess the heritability of RA and to examine for evidence of differences in the genetic contribution according to sex, age, and clinical disease characteristics. Data were analyzed from 2 previously published nationwide studies of twins with RA conducted in Finland and the United Kingdom. Heritability was assessed by variance components analysis. Differences in the genetic contribution by sex, age, age at disease onset, and clinical characteristics were examined by stratification. The power of the twin study design to detect these differences was examined through simulation. The heritability of RA was 65% (95% confidence interval [95% CI] 50-77) in the Finnish data and 53% (95% CI 40-65) in the UK data. There was no significant difference in the strength of the genetic contribution according to sex, age, age at onset, or disease severity subgroup. Both study designs had power to detect a contribution of at least 40% from the common family environment, and a difference in the genetic contribution of at least 50% between subgroups. Genetic factors have a substantial contribution to RA in the population, accounting for approximately 60% of the variation in liability to disease. Although tempered by power considerations, there is no evidence in these twin data that the overall genetic contribution to RA differs by sex, age, age at disease onset, and disease severity.

Various stable circular RNAs (circRNAs) are newly identified to be the abundance of noncoding RNAs in Archaea, Caenorhabditis elegans, mice, and humans through high-throughput deep sequencing coupled with analysis of massive transcriptional data. CircRNAs play important roles in miRNA function and transcriptional controlling by acting as competing endogenous RNAs or positive regulators on their parent coding genes. However, little is known regarding circRNAs in plants. Here, we report 2354 rice circRNAs that were identified through deep sequencing and computational analysis of ssRNA-seq data. Among them, 1356 are exonic circRNAs. Some circRNAs exhibit tissue-specific expression. Rice circRNAs have a considerable number of isoforms, including alternative backsplicing and alternative splicing circularization patterns. Parental genes with multiple exons are preferentially circularized. Only 484 circRNAs have backsplices derived from known splice sites. In addition, only 92 circRNAs were found to be enriched for miniature inverted-repeat transposable elements (MITEs) in flanking sequences or to be complementary to at least 18-bp flanking intronic sequences, indicating that there are some other production mechanisms in addition to direct backsplicing in rice. Rice circRNAs have no significant enrichment for miRNA target sites. A transgenic study showed that overexpression of a circRNA construct could reduce the expression level of its parental gene in transgenic plants compared with empty-vector control plants. This suggested that circRNA and its linear form might act as a negative regulator of its parental gene. Overall, these analyses reveal the prevalence of circRNAs in rice and provide new biological insights into rice circRNAs.

Data from the Encyclopedia of DNA Elements (ENCODE) project show over 9640 human genome loci classified as long noncoding RNAs (lncRNAs), yet only ∼100 have been deeply characterized to determine their role in the cell. To measure the protein-coding output from these RNAs, we jointly analyzed two recent data sets produced in the ENCODE project: tandem mass spectrometry (MS/MS) data mapping expressed peptides to their encoding genomic loci, and RNA-seq data generated by ENCODE in long polyA+ and polyA− fractions in the cell lines K562 and GM12878. We used the machine-learning algorithm RuleFit3 to regress the peptide data against RNA expression data. The most important covariate for predicting translation was, surprisingly, the Cytosol polyA− fraction in both cell lines. LncRNAs are ∼13-fold less likely to produce detectable peptides than similar mRNAs, indicating that ∼92% of GENCODE v7 lncRNAs are not translated in these two ENCODE cell lines. Intersecting 9640 lncRNA loci with 79,333 peptides yielded 85 unique peptides matching 69 lncRNAs. Most cases were due to a coding transcript misannotated as lncRNA. Two exceptions were an unprocessed pseudogene and a bona fide lncRNA gene, both with open reading frames (ORFs) compromised by upstream stop codons. All potentially translatable lncRNA ORFs had only a single peptide match, indicating low protein abundance and/or false-positive peptide matches. We conclude that with very few exceptions, ribosomes are able to distinguish coding from noncoding transcripts and, hence, that ectopic translation and cryptic mRNAs are rare in the human lncRNAome.