Nuclear RNA export pathways in eukaryotes are often linked to the fate of a given RNA. Therefore, the choice of export pathway should be well-controlled to avoid an unfavorable effect on gene expression. Although some RNAs could be exported by more than one pathway, little is known about how the choice is regulated. This issue is highlighted when the human immunodeficiency virus type 1 (HIV-1) Rev protein induces the export of singly spliced and unspliced HIV-1 transcripts. How these RNAs are exported is not well understood because such transcripts should have the possibility of utilizing CRM1-dependent export via Rev or cellular TAP/NXF1-dependent export via the transcription/export (TREX) complex, or both. Here we found that Rev suppressed TAP/NXF1-dependent export of model RNA substrates that recapitulated viral transcripts. In this effect, Rev interacted with the cap-binding complex and inhibited the recruitment of the TREX complex. Thus, Rev controls the identity of the factor occupying the cap-proximal region that determines the RNA export pathway. This ribonucleoprotein remodeling activity of Rev may favor viral gene expression. Different classes of RNA in eukaryotic cells are exported by distinct sets of export factors, that is, by distinct export pathways ,2. In bucis-acting RNA elements serve to overcome the nuclear retention mechanism for intron-containing transcripts , cellular and viral protein factors and nscripts ,16. HIV-nscripts : in the nscripts ,18 and anscripts ; therefoDespite the above elegant paradigm, issues regarding the HIV-1 RNA export still remain and need to be clarified. For example, if we consider how singly spliced and unspliced transcripts are actually exported, the situation is not at all simple. They should have both the TREX complex and Rev on the same RNA molecule, and, thus, have the possibility of utilizing either the TAP-dependent or CRM1-dependent export pathway, or both. This issue is important since accumulating evidence has shown that the export pathway often influences the nuclear/cytoplasmic fate of a given RNA . TherefoFor the pre-ftzRRE plasmid, the RRE260 fragment from pBS-RRE260  was clonEscherichiacoli BL21 (DE3) Codon Plus RIL (Stratagene) harboring pGEX-6P-1-T7-Rev or pGEX-6P-1-T7-RevM10, respectively, by the standard procedure, and the expressed protein in the bacterial lysate was bound to Glutathione Sepharose beads . The bound beads were washed three times with Buffer 1 , and were subsequently treated with PreScission Protease  at 4°C for 4 days. The mixture was spun down and the supernatant was recovered as a purified fraction. FLAG-UAP56 and GST-Aly/REF , and rotated at 4°C for 1 h. After the beads were washed five times with RSB100N buffer, they were incubated in Homomix  for 30 min at 50°C. RNA was recovered from the supernatants by phenol/chloroform extraction and ethanol precipitation, and analyzed by denaturing PAGE and autoradiography. IP using RNase-digested singly labeled RNA samples was performed as described (RNA immunoprecipitation (IP) was performed as described previously ,41. Brieescribed , except 32P-labeled RNAs were mixed with purified recombinant T7-Rev in the presence of yeast tRNA (1 mg/ml) and in the presence or absence of CBC, and incubated at 30°C for 30 min. Then RNA IP was performed as above.GST-TAP231 pull-down and RNase H digestion/protection assay were performed as described previously ,43. DNA Pre-ftzRRE RNA, singly labeled near the exon–exon junction, was generated  and the 2 in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum, 100 unit/ml penicillin and 100 μg/ml streptomycin. pNL4-3ΔRev (a kind gift from Drs M. Matsuoka and E. Kodama), pCI-FLAG-Rev  according to the manufacturer's instructions. RNAs were subsequently treated with RNase-free DNase  according to the manufacturer's manual. First-strand cDNA was synthesized using reverse transcription  with a random hexamer, and amplified by PCR using an appropriate set of primers. PCR products were separated by 2% agarose gel electrophoresis and stained with SYBR Gold (Life Technologies). Images were obtained with an imaging analyzer . Primers for PCR were as follows :unsplicPurified recombinant CBP80 (3 μg) was incubated with either GST, GST-T7-Rev, GST-Aly/REF or GST-hnRNP A1 (1 μg) in the presence of 1 mg/ml RNase A and in the presence or absence of T7-Rev  in 20 μl D’K100 buffer  at 30°C for 30 min. The reaction mixture was rotated with Glutathione Sepharose beads in 100 μl RSB100N at 4°C for 1 h. After washing five times with RSB100N, the bound material was recovered and analyzed by SDS-PAGE/western blotting.Xenopus oocyte microinjection system, in which RNA export can be separated from other steps of gene expression, making it possible to clearly demonstrate the function of Rev in RNA export  gene containing an intron, one of the most conventional splicing substrates used in Xenopus oocytes and mammalian cells  RNA from type D retroviruses, which was shown to specifically bind to TAP and thereby inhibit mRNA export when a saturating amount was injected ,51. The Xenopus oocytes recapitulated the export of the singly spliced HIV-1 transcript.When a saturating amount of CTE was injected together with RNAs in the absence of the Rev protein, the export of spliced ftzRRE RNA and elongated U1 RNA was inhibited as expected, which indicated that these RNAs utilized the mRNA pathway Figure ,53, it iThe export pathway of spliced ftzRRE RNA was almost completely shifted from the TAP-dependent mRNA pathway to the CRM1-dependent pathway by the presence of Rev. One might think that RNA could be exported by the TAP-dependent pathway similar to elongated U1 RNA in the presence of both Rev and BSA-NES; however, the export of RNA was actually inhibited Figure . Since MCo-injection of the wild-type Rev protein had the same effect as described above. However, co-injection of the same amount of the M10 protein markedly inhibited the export of spliced ftzRRE RNA relative to that with the buffer control, whereas export of the other RNAs was unaffected Figure  and humaSince the Rev protein suppressed TAP-dependent export of spliced RRE-containing RNAs, we next investigated whether the association of the mRNA export factor TAP with RNAs was affected by Rev. To this end, we performed GST-TAP231 pull-down experiments .Both spliced ftzRRE and elongated U1 RNAs were specifically precipitated by GST-TAP231, as expected, in the absence of Rev, which indicated that these two RNAs were in a state that was capable of TAP association in the nucleus Figure , lane 3.32P-labeled RNAs as in Figure in vitro splicing reaction mixture .To understand how Rev inhibited the association of TAP with spliced RRE-containing RNAs, we next examined the effect of Rev on the association of Aly/REF with RNA. RNA IP experiments were performed either from the nuclear fraction of oocytes that had been injected with the same in vitro splicing reaction with HeLa cell nuclear extracts, and adding the Rev protein to the reaction greatly reduced the association of Aly/REF  in the start codon of the Rev gene in pNL4-3, which is a silent mutation for the HIV-1 Tat gene Figure . pCMV-Rep55, translated from the transcript, and Gagp24, processed from Gagp55, and Rev co-overexpression canceled this reduction , the level of singly spliced  and unspliced (9-kb) transcripts was significantly reduced, whereas that of the fully spliced  transcript was not Figure . Consistn Figure . Viral pa Figure  25). Th. Thp55, We tried to investigate whether Rev inhibits the association of TAP with singly spliced and unspliced transcripts in HIV-1-expressing cells. However, we could not detect the association of TAP with the transcripts, regardless the Rev expression. This may be because TAP association destabilizes the transcripts.The above results show that Rev generally inhibits the association of the TREX complex with RRE-containing RNAs. However, the detailed mechanism for this was still unknown. RRE is located in the second intron of the HIV-1 primary transcript, while the TREX complex is recruited to a region near the 5′-terminus of mRNA because Aly/REF interacts with CBC. How distantly positioned Rev affected the association of the TREX complex remained to be elucidated. We speculated that Rev bound to RRE may interact with CBC bound to the cap of the same RNA, thereby competitively inhibiting the interaction between CBC and Aly/REF.Xenopus oocytes  also belongs to a retrovirus family and utilizes the homologous mechanism to export viral RNAs by the Rex-RxRE system (Rev-RRE in HIV-1). It is interesting to determine whether Rex has the same activity as the HIV-1 Rev protein to suppress TAP-dependent export of viral RNAs. Here we found that Rex interacted directly or indirectly with CBP80 . Targeting Rev/Rex's remodeling activity could introduce a novel useful anti-retroviral therapy for HIV and HTLV. Other viruses also take advantage of RNA export regulation for their gene expression . The Her7G-capped RNA, CBC binding proteins, such as hnRNP C, Aly/REF and PHAX, serve as a landmark of the RNA state. In other words, the identity of the factor that interacts with cap-bound CBC and occupies the cap-proximal region determines the RNA export pathway. Owing to this mechanism, if RNA is committed to one pathway, the association of the export factors of other pathways is competitively prevented. Rev may favor viral gene expression by occupying the region and determining the RNA export pathway.A plethora of evidence has already been obtained supporting the link between the RNA export pathway and nuclear/cytoplasmic fate of RNA . TherefoSupplementary Data are available at NAR Online.


Although self-rated health (SRH) and performance-based physical function (PPF) are both strong predictors of mortality, little research has investigated the relationships between them. The objective of this study was to evaluate longitudinal, bi-directional associations between SRH and PPF.We evaluated longitudinal associations between SRH and PPF in 3,610 adults aged 65–89 followed for an average of 4.8 (standard deviation [SD]: 4.4) years between 1994 and July 2011 in the Adult Changes in Thought study, a population-based cohort in the Seattle area. SRH was assessed with a single-item question in the ACT study. Participants were asked at each evaluation to rate their health as “excellent”, “very good”, “good”, “fair”, or “poor” in response to the question “In general, how would you rate your health at this time”. PPF scores  included walking speed, chair rises, grip strength, and balance.At the baseline visit, participants averaged 74.5 (SD: 5.8) years of age and 2,115 (58.6%) were female. In multivariable linear mixed models, PPF declined with age, with more rapid decreases associated with very good, good, and fair (vs. excellent) baseline SRH. Adjusted annual change in PPF was −0.17 points  for individuals with excellent baseline SRH and −0.21 points  for participants with fair SRH. In multivariable generalized linear mixed models, lower baseline PPF quartiles were associated with lower odds of excellent/very good/good SRH at age 75, however, differences between baseline PPF quartiles diminished with age.These results suggest that less than excellent SRH predicts decline in physical functioning, however, poor physical functioning may not predict change in SRH in a reciprocal fashion. SRH provides a simple assessment tool for identifying individuals at increased risk for decline in physical function. Self-rated health (SRH) is a widely used measure of general health status that is typically assessed by asking an individual to rate their health Poor SRH is associated with faster decline in functional ability in older adults Few studies have examined longitudinal relationships between SRH and physical function in older adults. Ratings of fair/poor SRH have been associated with future decline in gait speed The goal of this study was to evaluate bi-directional longitudinal relationships between SRH and performance-based physical function (PPF), a composite measure of tests that assess both upper and lower extremity function, among older adults Participants were enrolled in the Adult Changes in Thought (ACT) study, a population based prospective cohort study of incident dementia and Alzheimer's disease, previously described The current analysis included ACT participants aged 65–89 years old who were followed between 1994 and July 2011. The sample was restricted to participants with complete data on SRH, PPF, and covariates at one or more visits. For this study, we defined the baseline visit as the first visit with non-missing SRH, PPF, and covariate information. Follow-up of participants ended at time of dementia diagnosis, death or drop-out; and did not include the visit at which a participant was diagnosed with dementia due to concern that dementia may influence SRH and PPF measures.SRH was assessed with a single-item question in the ACT study. This item is similar to SRH questions used in other questionnaires, particularly in the U.S. The PPF score was created following methods developed by Wang and colleagues (2002) A score of 0 to 4 was determined for walking speed, chair rises, standing balance, and grip strength tests. For all tests a score of 0 was given if the participant could not complete the test. Scores of 1–4 for walking speed, chair rises, and grip strength tests were based on previously published sex-specific cutoffs, which corresponded to sex-specific quartiles in the ACT population The sum of the four test scores determined an individual's PPF score (range: 0–16); higher scores corresponded to better performance. In analyses with PPF as the outcome, we used the total PPF score at each visit as a continuous variable. In analyses where PPF was the primary exposure, we categorized the baseline PPF score based on the study sample quartiles. We used baseline PPF quartiles because the relationship between baseline PPF and change in SRH may not be linear. In exploratory analyses, individual PPF components were dichotomized based on study sample quartiles into scores of 3–4 (“better” function), which generally corresponded to 75% of the participants vs. scores of 0–2; except balance, which had a highly skewed distribution, and “better” function was limited to a score of 4.2) and categorized as underweight (<18.5), normal (18.5 to 24.9), overweight (25–29.9), and obese (30+). Participants were asked whether a doctor had ever told them they had cancer, cardiovascular disease, cerebrovascular disease, diabetes, hypertension, or rheumatoid or osteoarthritis. We did not obtain information on number of medications.At study baseline and follow-up visits, information was collected on demographics, health status, and chronic health conditions. Demographic factors measured included age, sex, self-reported race, and years of education. Health status-related covariates included cognitive functioning, functional status, depression, and body mass index, exercise , smoking , and alcohol use . Cognitive function was evaluated using the Cognitive Abilities Screening Instrument (CASI) We used descriptive statistics to characterize the study population according to SRH. We described trends in PPF and SRH via their association with age. First, we estimated associations between baseline SRH and age-related change in PPF using linear mixed models with random intercepts to account for within-subject correlation. The primary exposures were age, baseline SRH, and their interactions. The outcome was the PPF score at each study visit. The interaction between age and baseline SRH allowed us to make inference on modifications to the relationship between age and PPF that were attributable to SRH. As sensitivity analyses, we re-ran models with missing PPF imputed first as the lowest PPF score and then as the highest PPF score. We also explored whether baseline SRH was associated with individual component measures of PPF.Next, we estimated associations between baseline PPF and age-related change in SRH, repeating analyses but using a logistic link function in a generalized linear mixed model. Healthy SRH was the outcome and the primary exposures were age, quartiles of baseline PPF, and their interactions. Inference was based on the interaction between age and SRH, which described the extent to which the age-related differences in odds of healthy SRH were modified by baseline PPF. We also re-ran models with missing SRH values imputed as unhealthy SRH and then as healthy SRH.We investigated three levels of time-varying covariate adjustment: Model 1 was adjusted for participant age at baseline; Model 2 was additionally adjusted for sex, race, education, cognitive functioning, limitations in ADLs and IADLS, depressive symptoms, body mass index, exercise, smoking status, and alcohol use; and Model 3 was additionally adjusted for chronic health conditions . Potential confounders were selected a priori as factors previously found to be associated with SRH and PPF To describe longitudinal trajectories across follow-up we report the estimated mean outcome for each level of the primary exposure at age 75 (corresponding to the average age at baseline for the sample) and the estimated annual change in the outcome for each level of the primary exposure from each model. In Models 2 and 3, we estimated the mean at age 75 based on regression model estimates using indirect standardization to account for possible confounding by other covariates included in the model. We used graphical analyses to illustrate estimated age-related trends in SRH and PPF. Model fit was assessed with residual plots. Analyses were conducted using R (version 2.12.1). All tests were two-sided with α = 0.05.As of July 25, 2011, data were available on 4,513 participants without dementia who had ever been enrolled in the ACT study, of which 4,411 participants were aged 65–89. Age-eligible participants were excluded if they were missing SRH, PPF, or covariate data at all visits. The final study sample included 3,610 ACT participants  followed on average for 4.8 (SD: 4.4) years. Excluded age-eligible participants were primarily missing PPF (n = 326) and/or health conditions (n = 497); they tended to have higher education, more limitations in ADLs and IADLs, more health conditions, and shorter follow-up than included participants . At the Among participants with at least one follow-up visit , the average change in PPF score was −1.9 points over follow-up (SD: 2.7). Frequency and percentage of participants by trajectories of PPF are shown in P = 0.02)  . Compare = 0.02) . For par = 0.02) . Results = 0.02) . Very fe = 0.02) .In sensitivity analyses, inference on variation in rates of decline in relation to baseline SRH was unchanged when all missing PPF values were imputed as the minimum, but when all missing PPF values were imputed as the maximum the mean rate of decline of PPF was slower for individuals with less than excellent baseline SRH (data not shown).P<0.001)  remained stable in SRH  during follow-up . In multP<0.001) . For thoP<0.001) . In adjuP<0.001) . ResultsP<0.001) . SensitiWe investigated bi-directional longitudinal relationships between SRH and physical function among older adults without dementia, with adjustment for multiple health indicators. We found that lower baseline SRH was associated with lower PPF at age 75. Very good, good, or fair baseline SRH was associated with faster decline of PPF compared to excellent baseline SRH. Although we found that lower baseline PPF was associated with lower odds of healthy SRH at age 75, lower baseline PPF was associated with increasing odds of healthy SRH over time. This resulted in little difference in odds of healthy SRH at older ages.Our results are consistent with previous studies that found fair/poor SRH to be associated with faster decline in timed gait We provide new evidence describing the longitudinal change in healthy SRH according to baseline physical function. Our results complement prior findings that poor physical function did not predict decline in SRH Although our results suggest that SRH and physical function are interconnected, independent of demographics and health outcomes, worse baseline physical function did not predict decline in SRH in a reciprocal fashion as worse baseline SRH predicted decline in physical function. People with less than excellent SRH may be at an increased risk for future disability Our study has several limitations. Generalizability may be limited because participants were predominantly white and well educated. Participants in poor health may have been more likely to die, drop-out earlier, or skip visits, which may have attenuated associations between SRH and PPF. Due to our interest in the subjective processes behind SRH rather than global health status, our measure of SRH did not incorporate death, unlike an alternative coding of SRH This study also has several important strengths, including a large, representative sample of community-dwelling older adults with extensive follow-up with multiple measures of health indicators. We used a longitudinal analysis, which allowed us to average over fluctuations in individual measurements to help capture long-term trends in PPF and SRH. Since many participants had fluctuating PPF measures, longitudinal modelling of repeated PPF measures can help uncover trends that would otherwise be obscured. We recommend this approach for future studies of PPF and SRH. Additionally, we investigated the associations between SRH and an objective measure of overall physical function eliminating potential confounding arising in subjective measures of function In conclusion, we found longitudinal associations between SRH and PPF in a large prospective cohort study of older adults. However, there appears to be a complex relationship between SRH and PPF. Future research should also assess the mechanisms through which SRH may affect physical function as well as disentangle factors that influence longitudinal change in SRH. As a strong predictor of mortality and functional decline, SRH provides a simple tool that can be used to identify individuals at higher risk of future poor health outcomes. It is unclear whether future poor health outcomes can be prevented among individuals with lower SRH. However, interventions to maintain physical function may be especially beneficial for those with less than excellent SRH.Table S1Baseline Characteristics of Included and Excluded ACT Participants Aged 65–89 .(DOC)Click here for additional data file.Table S2Linear Mixed Model Results for the Association between Baseline SRH and Longitudinal PPF.(DOC)Click here for additional data file.Table S3a for Individual Components of PPF.Generalized Linear Mixed Effects Model Results for the Association between Baseline SRH and Odds of Better Function Estimates of individual PPF components at age 75 are standardized to the distribution of all covariates included in the model via indirect standardization.(DOC)Click here for additional data file.Table S4Generalized Linear Mixed Model Results for the Association between Baseline PPF Quartile and Odds of Excellent/Very Good/Good  SRH.(DOC)Click here for additional data file.


RNA-editing is a tightly regulated, and essential cellular process for a properly functioning brain. Dysfunction of A-to-I RNA editing can have catastrophic effects, particularly in the central nervous system. Thus, understanding how the process of RNA-editing is regulated has important implications for human health. However, at present, very little is known about the regulation of editing across tissues, and individuals.Here we present an analysis of RNA-editing patterns from 9 different tissues harvested from a single mouse. For comparison, we also analyzed data for 5 of these tissues harvested from 15 additional animals. We find that tissue specificity of editing largely reflects differential expression of substrate transcripts across tissues. We identified a surprising enrichment of editing in intronic regions of brain transcripts, that could account for previously reported higher levels of editing in brain. There exists a small but remarkable amount of editing which is tissue-specific, despite comparable expression levels of the edit site across multiple tissues. Expression levels of editing enzymes and their isoforms can explain some, but not all of this variation.Together, these data suggest a complex regulation of the RNA-editing process beyond transcript expression levels.The online version of this article (doi:10.1186/s12864-015-2291-9) contains supplementary material, which is available to authorized users. RNA-editing is an enzyme-mediated post-transcriptional modification whereby individual nucleotide residues are altered in the nascent RNA transcript molecule . In contThe most common RNA-editing alteration is enzymatic deamination of adenosine nucleotides to inosine, by the family of ADAR enzymes , 3. ThesThere are 3 members of the ADAR family in vertebrates: Adar, Adarb1, and Adarb2 . A number of substrates are reported to be edited by Adar and Adarb1 –6. HowevThe gene for Adar produces two main protein isoforms. Expression of the longer Adar isoform, p150, is inducible by interferon stimulation  and has Owing to the role of interferon in defense against microbial infection, the p150 isoform of Adar has been thought to function in the editing of double stranded viral RNA. In humans, double stranded RNA structures often form in the long 3’ UTR regions of genes when juxtaposed Alu repeat elements are present. These inverted transposable elements, which are remnants of ancient viral insertions, thus form the secondary structure substrate for hyper-editing [In contrast to the hyper-editing of many adenosine residues in close proximity, site-selective editing tends to occur consistently at specific residues, and even within the coding sequence of transcripts. Aside from the propensity of RNA substrate to form hairpin structures, little is known about other mechanisms regulating this more selective form of editing. The importance of site-selective editing events is highlighted by the necessity of an Adarb1 mediated edit in the neurotransmitter receptor protein GluR-B for developmental viability in mice .The ADAR gene family is highly conserved across metazoans, and highly expressed in the nervous system , 17, 18.RNA-editing has also been implicated in functions and disease outside the nervous system. Site-specific editing of AZIN1 is associated with more aggressive tumor-initiating potential in hepatocellular carcinoma , while aIt is clear from these examples that RNA-editing is functionally important, and must require tight regulation to prevent dysfunction. However very little is known about how this regulation occurs. We do not have a thorough understanding of the variability in editing between individuals, nor the intra-individual variability across tissues to properly address this question. Furthermore, there has not yet been a genome-wide study to address, in an unbiased manner, the scope of functionally relevant RNA-editing in healthy individuals.Previous high throughput RNA-editing surveys have been limited in the range of tissues studied, with a bias towards central nervous system (CNS) tissues, or in their genomic breadth. Transcriptome-wide RNA-seq analyses of RNA-editing in mice have largely been limited to whole brain and cerebral cortex , 27.A valuable study of RNA-editing was performed across seven distinct tissues from a single human individual. However, the study used specific padlock probes, not transcriptome wide RNA-seq, and resulted in only 569 putative RNA-editing sites .In order to investigate the mechanisms underlying the regulation of RNA-editing, we present an analysis of a novel transcriptome wide RNA-seq dataset from nine tissues in a single individual mouse. Additionally we present a comparison and combined analysis of 20 previously published RNA-seq samples from mice of the same strain across a variety of tissues. To the best of our knowledge, this is the only dataset for which we can address in a genome-wide fashion, questions both on the variability of RNA-editing across tissues in a single individual and within specific tissues across individuals. We then use these data to investigate the underlying mechanisms regulating the important process of RNA-editing.We obtained a high confidence set of RNA-DNA differences (RDDs) by analyzing the RNA and DNA from various tissues of a single adult mouse Fig. . In paraWe identified 13,798 RDDs within our dataset, including 5053 novel A-to-G changes not previously reported in the Darned or Radar databases of RNA-editing sites , 30. In 22% of A-to-I RDDs fall within coding exons, and only 159 would result in a non-synonymous change. Further inspection of the genic context of editing sites revealed consistent results across samples within the same tissue. For example, all eight brain tissue samples showed a much higher proportion of intronic RDDs to coding exon RDDs than any of the other tissues. Likewise, the two heart samples had the highest coding exon RDD to intronic RDD ratio. Therefore RNA editing preferentially occurs within intronic sequence, and the amount of intronic substrate available appears to be tissue dependent.To investigate the functional relevance of the A-to-G RDDs identified from our analysis, we used RefSeq gene annotations to determine the genic context and predicted functional consequence  of each RDD. Across all tissues, most of the RDD sites occurred within introns Fig. . This is%. However, the same six edited genes  are found to recur in most of the significant GO categories for the brain samples. Likewise for the testis results, three edited genes  account for the results. Thus there are few ontologies enriched for protein altering RNA-editing, and these are driven by a very small number of edited genes.We next considered whether certain classes of genes were preferentially edited within our samples as this might reveal a regulatory aspect of the editing process. Within each tissue, we collected genes containing protein altering RDDs and tested for enrichment of any functional category amongst the genes normally expressed within that tissue. We performed a Gene Ontology analysis for each tissue individually, where the universe of genes was simply the set of genes with an RPKM expression level ≥ 10 within that tissue. Additional file % of sites are shared between any two tissues, and even fewer (0.7 %) are shared across all 9 tissues. These numbers both increase slightly when only RDDs within coding exons are considered . This might suggest that more functionaly relevant RDDs, such as those within coding regions, are more likely to be shared across tissues. However, for RDDs predicted to be non-synonymous, and thus more likely to cause a functional change in the resulting protein product, the proportion that are shared across tissues decreases to 8 %. Thus, the recurrence of editing sites across tissues is low, but increases modestly when considering only synonymous editing events within coding exons.After examining aggregated trends in RNA-editing across tissues and individuals, we next surveyed the variation in editing per site. This finer scale analysis is driven by the assumption that functionally relevant editing sites should be recurrently edited across individuals, and possibly across tissues. We first looked at the number of A-to-I RDD sites that were shared among the 9 tissue samples from our single male mouse Fig. a. An ediWe next looked at the inter-individual variability of calling RDDs. If RDDs are functionally important, we assume that their presence will be consistent across individuals, when looking in the same type of tissue. Here we included the previously published RNA-seq samples  due to insufficient sequencing depth), to serve as biological replicates for four of our tissues. We had 7 adult brain samples, and 2 samples each for the heart, kidney, and liver tissues.Within the 7 brain samples we found a much higher proportion of RDD sites that were shared  than across tissues within a single individual Fig. b. Thus tWe next wanted to see if this pattern held true in the other tissues. Since we only had 2 samples per tissue for the heart, kidney, and liver, for consistency we subset our brain samples to include only 2 samples ). The brain samples had the highest consistency in RDD recurrence within coding and non-synonymous sites . Our RNA-seq data provides us with both the overall expression level estimates for the Adars, and isoform level estimates based on splice junctions Fig. .Fig. 6EAdar is most highly expressed in the brain and spleen samples, which is consistent with brain and spleen having higher number of observed RDDs after accounting for sequencing depth see Fig. a. Adarb1From the literature, it is expected that there are differences in Adar isoform usage across tissues, with the shorter p110 isoform more highly expressed in CNS tissues. Therefore, it is important to study gene expression at the isoform level as well. When we examined the data across our samples, we confirmed the elevated expression of the p110 isoform within brain samples Fig. b. The loWhile spleen and thymus have Adar expression levels similar to those found in brain, the difference in isoform usage may result in differential regulation of editing between these tissues. There are some reported motifs prefered by Adar and Adarb1, but little is known about the specificity differences between the p110 and p150 isoforms of Adar. Both isoforms contain a z-alpha DNA binding domain at their N-terminal end, but the alternative 5’ UTR and splice of the p150 isoform encodes a second z-alpha domain upstream of the first. This extra z-alpha domain could potentially direct the editing enzymes to prefer a subset of nascent transcripts with proximity to a particular DNA sequence that interact with the z-alpha domains. Thus differences in the resulting protein isoforms may provide a mechanism for different selectivity of editing substrates.We also discovered a correlation of the p110 (J1 junction) with an internal in-frame alternative splicing event (J9) that results in the addition of 26 amino acids between the second double stranded RNA-binding domain and the deamination domain. This region includes four potential phosphorylation sites. The functional effect of this change, in altering the relative position of the deamination domain to the bound RNA transcript, could play a role in the sequence context of RNA-editing events. Thus isoform usage may influence and correlate with specific motifs surrounding editing sites within a sample.To test this hypothesis, we analyzed the neighboring nucleotides surrounding edited sites across tissues to look for tissue specific patterns that might elucidate editing specificity of the Adar isoforms , and RPKM estimates for the mutually exclusive 5‘ exons that distinguish the p110 and p150 isoforms of Adar. These expression levels were used to look for correlations with per site editing frequencies across samples.Within coding exons we found 16 RDD sites whose variable editing frequencies had significant positive correlations with at least one of the editing enzymes or isoforms tested. After multiple testing correction the editing site within the gene Tmem63b remains significant. While the other 15 sites are not statistically significant after correcting for multiple testing, interesting trends were observed. Two distinct patterns may explain the regulation of editing Fig. . The firPrevious studies suggest that RNA-editing is prevalent within transcripts of the central nervous system. In fact, the most well known A-to-I editing sites occur within coding regions of neuronal signalling genes. Among these, the transcripts of several genes involved with ionotropic glutamate signalling are typically edited at 100 % frequency. This begs the question of why these sites are not simply changed in the genome rather than edited in the transcript. Several studies report the importance of these editing events in modulating receptor activity in a developmental manner , 32–36 aHere we have quantified that the number of sites which undergo A-to-I editing is remarkably higher in transcripts derived from the brain. While the ability to detect editing events increases with sequencing depth , 37, we Interestingly, in our study, the vast majority of edited sites within brain samples occurred in intronic regions of genes. This enrichment for intronic editing accounted for the abnormally high amount of editing sites in brain tissues. The abundance of editing events within intronic sequence is intriguing as we did not expect high levels of sequencing coverage over introns. The RNA-sequencing library prep used in this study was a standard poly-d(T) protocol which should enrich for fully mature, poly-adenlyated mRNA transcripts that have already undergone splicing. However, intron retention appears to be a more common phenomenon within neural transcripts . The recPrevious studies have found that RNA editing in mice occurs less frequently than in humans . This isRNA-editing patterns varied across all tissues examined and sites edited in multiple tissues were not common. We demonstrate that tissue specific editing is largely a consequence of the tissue specific expression of the edited transcript. While our data do not argue against the existence of tissue specific editing cofactors, it appears that a major regulator of editing is the expression of the edited transcripts.However, we did find evidence for a small number of transcripts expressed in multiple tissues but with variable levels of editing. In several of these cases a change in the expression or isoform usage of the editing enzymes correlates with the observed difference in editing. This suggests that the editing frequency is regulated by expression of a particular editing enzyme. In fact, the editing sites in both Flnb and Copa have been previously studied in the context of human hepatocellular carcinoma . Using bWhile the gene Tmem63b is expressed in multiple tissues, editing is only observed within brain. This simpler, binary editing status is consistent with the brain having the highest expression of both Adarb1 and nearly exclusive expression of the Adar p110 isoform. However, Adars can dimerize to form homo- and heterodimers –49. ThisThis study within mouse tissues is largely consistent with genome wide patterns of editing described in human datasets , and furAn 8 week old C57BL/6J male mouse was obtained from The Jackson Laboratory and housed according to the animal use guidelines of Genentech, conforming to California State legal and ethical practices. This animal was euthanized by exsanguination with phosphate buffered saline (PBS) and tissues were collected, flash frozen and stored at –80 °C until further processing. DNA and RNA were extracted with either the All Prep DNA/RNA mini kit , the RNeasy Fibrous tissue mini kit (heart and skin) or the RNeasy Lipid tissue mini kit (brain) according to manufacturers protocol (Qiagen).We generated 100 bp, paired-end standard RNA-seq libraries from the 9 tissues  of the single adult male mouse described above. Each RNA-seq library resulted in 28 million to 77 million uniquely mapped reads.Additionally, from this same mouse, we extracted genomic DNA and generated 75 bp paired end whole genome sequence libraries from the brain and spleen tissue. These produced 586 million and 445 million uniquely mapped reads, respectively.For biological replicates of our dataset from a single mouse, we analyzed previously published RNA-seq data from 3 relevant datasets , totalling 20 additional samples from 15 animals. The first dataset included 76 bp single-end RNA-seq data from 6 tissues  of adult C57BL/6 mice . This exThe second dataset included 80 bp single-end RNA-seq data from the cerebral cortex of embryonic (E17) and adult (3 - 4 months old) C57BL/6J female mice . This exFinally, the third dataset included whole brain derived 76 bp paired-end RNA-seq data from two adult (ages 2 – 3 months) C57BL/6NJ female mice . These d% of the cycles had Phred scores ≥ 23), and ribosomal RNA contamination. The remaining reads were then aligned to the mouse reference genome (mm9) using the GSNAP alignment tool [Our nine RNA-seq samples derived from a single individual mouse, and two whole genome DNA samples were sequenced on an Illumina HiSeq machine. The fastq sequence files for all RNA-seq samples, including the 20 previously published samples, were then filtered for read quality  were first called using the Bioconductor package VariantTools . To incrTo infer the strand of a transcript, we considered only RNA-editing events that occurred within annotated genes, and discarded SNVs that were located in more than one gene on opposite strands.To exclude spurious variants arising from regions of the genome that are problematic for short read alignment, we also discarded SNVs within homopolymer tracts, UCSC repeat masker track elements , UCSC multi-mappability regions, and self-chain regions  was then produced by stringent filtering of the SNVs from RNA using the whole genome sequence data. After all the filtering above, an SNV in the RNA was considered an RDD if the genomic data showed zero reads supporting the edited base, and had > 20X total coverage at the site in question. Although highly stringent, this gave us confidence in calling the remaining RDD sites RNA-editing events .RNA-seq data generated for this study are available from NCBI’s Gene Expression Omnibus (GSE74747). Whole genome sequence data generated for this study are available from the European Nucleotide Archive (ERP010840).


Dermatofibrosarcoma protuberans (DFSP) may recur locally but rarely metastasizes. Fibrosarcomatous transformation in dermatofibrosarcoma protuberans (FS-DFSP) is said to have worse prognosis compared with ordinary DFSP (O-DFSP). Since DFSP rarely metastasizes, there have been few reports summarizing data on distant metastasis cases at single institution. The aim of this retrospective study is to review DFSP cases in order to analyze risk factors for metastasis.This retrospective study involved 67 patients. We analyzed O-DFSP and FS-DFSP metastasis rates, metastasis sites, time to metastasis, the relationship between frequency of local recurrence and metastasis, and the relationship between primary tumor size and metastasis.p = 0.216). The primary tumor diameters in the metastatic cases were 15.0, 12.6, 20.5, 13.0, and 5.0 cm, respectively. The tumor diameters in metastatic cases were significantly larger (Fisher’s exact test: p < 0.0001).Distant metastasis was found in 5 (7.4 %) of 67 cases with DFSP. Of the five cases, the histopathological diagnosis was FS-DFSP in four cases and O-DFSP in one case. Out of five cases with metastasis, three had not recurred and two had recurred twice. No clear correlation was identified (Fisher’s exact test: In this study, we identified a stronger correlation between DFSP metastasis and tumor size. There was a high possibility that the cases with large tumors might be FS-DFSP, having high rate of metastasis and poor prognosis. In treatment of DFSP, early diagnosis before primary tumor growth and wide resection is considered important.Level of evidence V. Dermatofibrosarcoma protuberans (DFSP) is a tumor that develops in skin or subcutaneous tissue and is characterized by a protuberant growth pattern . DFSP ofSince DFSP rarely metastasizes to distant sites, there have been few reports summarizing data about distant metastasis cases at single institution. Therefore, with respect to DFSP, there has been a lack of confirmation about the poor prognostic factors and follow-up methods. In addition, if a patient is diagnosed with FS-DFSP, it has not been determined whether they can be monitored in the same way as a patient diagnosed with O-DFSP.Therefore, the aim of this retrospective study is to review DFSP (including O-DFSP and FS-DFSP) cases where wide resection of primary tumor was performed in our hospital in order to analyze the relationship between distant metastasis and the following factors: local recurrence, primary tumor size, and fibrosarcomatous transformation, and to compare cases with and without metastasis. Furthermore, we would like to identify appropriate DFSP follow-up methods.This retrospective study involved 67 patients with histopathological diagnosis of DFSP, either O-DFSP or FS-DFSP, who underwent wide resection between January 1977 and July 2013 at the Department of Orthopedic Oncology of our hospital. Of the 67 cases, 50 were male and 17 were female. The mean age was 37.9 years (range 7–70 years). The mean follow-up period was 56.6 months (range 4–263 months).The histopathological diagnosis before performing wide resection at our hospital was established as follows: 37 cases were diagnosed shortly after resection performed at another hospital, 10 cases were diagnosed at time of recurrence after surgery performed at another hospital, 2 cases underwent incisional biopsy at another hospital, 2 cases underwent excisional biopsy at our hospital, and 16 cases underwent needle biopsy at our hospital.FS-DFSP was diagnosed according to the criteria of Enzinger and Weiss, which means that FS-DFSP was identified by presence of fibrosarcomatous changes  in at least 5 % of tumor tissue . ConsequImaging analysis of primary tumor was conducted using electroradiography or computed radiography. Additionally, computed tomography (CT) scans were used from 1980 onward, and magnetic resonance imaging (MRI) from 1985 onward. Distant metastasis was analyzed using chest X-rays and, from 1980 onward, CT scans. Surgical margin assessment was conducted based on macroscopic and histopathological analyses. Postoperative follow-up observation included clinical assessment and chest X-rays or CT scans for detection of metastasis every 3 months within the first 2 years after surgery and every 6 months from more than 2 to 5 years after surgery.At the initial visit to the department of our hospital, distant metastasis was noted in 2 (3 %) of the 67 cases; the 2 cases were diagnosed with FS-DFSP. According to the American Joint Committee on Cancer (AJCC) Staging Protocol for Sarcoma of Soft Tissue classification, 46 cases were stage IA, 16 were stage IB, 3 were stage IIC, and 2 were stage IV.In all cases, surgical treatment was performed for the primary tumor at the department. In cases of initial surgical procedure, the tumor was excised with at least a 1-cm margin of surrounding healthy tissue. In cases treated with surgical resection at another hospital, additional wide resection was performed. As the method for additional wide resection, the original tumor was excised with at least a 1-cm margin of surrounding healthy tissue, including the surgical scar. For cases where it was predicted that the surgical margin could be insufficient or cases where the excised tissue indicated insufficient margin, radiotherapy was used concomitantly. Concomitant radiotherapy was actually administered in 15 cases. No adjuvant chemotherapy was performed.We analyzed metastasis rates, metastasis sites, time to metastasis, the relationship between frequency of local recurrence and metastasis, and the relationship between primary tumor size and metastasis in DFSP (including O-DFSP and FS-DFSP) cases.p-Value <0.05 was considered significant for all statistical analyses.The statistical analysis was conducted using JMP software version 10. Fisher’s exact test was applied to evaluate the relationship between tumor size and metastasis as well as between recurrence and metastasis. The Kaplan–Meier method was used to estimate overall and disease-free survival curves, with differences between groups assessed by log-rank test. p = 0.0002)  of 67 cases with DFSP. Of the five cases, the histopathological diagnosis was FS-DFSP in four cases and O-DFSP in one case. With respect to the case of O-DFSP with metastasis, the histological diagnosis of the metastatic lesion was also O-DFSP. Thus, the metastasis rate by histological type was 4 (57 %) of 7 cases for FS-DFSP and 1 (1.7 %) of 60 cases for O-DFSP. Comparing O-DFSP and FS-DFSP, the metastasis rate of FS-DFSP was significantly higher than that of O-DFSP.   had not recurred and two (40 %) had recurred twice. In addition, out of 62 cases without metastasis, 52 (84 %) had not recurred, 8 13 %) had recurred once, 1 (1.5 %) had recurred twice, and 1 (1.5 %) had recurred three times or more  of 67 cases presented with recurrence. Both of the cases had no metastasis, and there was no clear correlation between recurrence and metastasis.We examined the relationship between tumor size at start of treatment at our hospital and metastasis. In cases where additional wide resection was performed after resection at another hospital, the tumor size before resection at the other hospital was used. Tumor size was less than 5 cm in 46 (69 %) cases, 5 cm to less than 10 cm in 16 (24 %) cases, and 10 cm or larger in 5 (7 %) cases. Looking at the seven cases of FS-DFSP, tumor size was 5 cm to less than 10 cm in three cases, and 10 cm or larger in four cases  (Table We examined the cases with 10 cm or larger tumors. Four (80 %) of the five cases with metastasis had 10 cm or larger tumors, while only 1 (3 %) of the 62 cases without metastasis had a 10 cm or larger tumor. Comparing metastatic with nonmetastatic cases, the tumor diameters in metastatic cases were significantly larger (Fisher’s exact test: Dermatofibrosarcoma protuberans (DFSP), first reported in 1890 by Tylar et al. , is a cup < 0.0001, log-rank test)  Fig. a.Fig. 2Looking at DFSP metastasis, it has been reported that O-DFSP also metastasizes in rare cases. In 1996, Gloster et al. reported that lung metastasis was observed in 1 (1.1 %) of 89 O-DFSP cases . In 2012In 1967, McPeak et al. reported that, in four of the five cases of DFSP with metastasis, the tumor had attained a dimension in excess of 15 cm in diameter . Howeverp = 0.216) (Table Then, regarding recurrence and metastasis, we examined and compared cases with and without metastasis. Compared with O-DFSP, FS-DFSP has been reported to have higher risk of metastasis and recurrence, but no clear correlation between recurrence and metastasis was identified in this study (Fisher’s exact test: Looking at the metastasis site, Jared et al. reported that, out of four cases of FS-DFSP with metastasis, two were lung metastasis, one was bone metastasis, and one was both lung and bone metastases . In thisWhile Abbot et al. reported that the time to metastasis from FS-DFSP was 36–72 months, there have been some cases in which metastasis appeared 10 years or more after initial diagnosis [In summary, based on the above, postoperative follow-up methods for DFSP were as follows: In FS-DFSP cases, the rate of distant metastasis was high, so postoperative detection of distant metastasis was essential. Furthermore, since there was a tendency to metastasize to extrapulmonary sites, follow-up examinations such as abdominal CT or ultrasonography were required, taking into account the potential for extrapulmonary metastasis. In O-DFSP cases, follow-up examinations for all cases were not necessary, considering the low rate of distant metastasis of 0.4–1.6 %. However, regular follow-up was recommended for large tumors with potential for FS-DFSP.In this study, we identified a stronger correlation between DFSP metastasis and tumor size than between metastasis and frequency of recurrence. There was a high possibility that the cases with large tumors might be FS-DFSP, having high rate of metastasis and poor prognosis. In treatment of DFSP, early diagnosis before acceleration of tumor growth and wide resection were important.


Scaffold attachment factor B1 (SAFB1) and SAFB2 proteins are oestrogen (ER) corepressors that bind to and modulate ER activity through chromatin remodelling or interaction with the basal transcription machinery. SAFB proteins also have an internal RNA-recognition motif but little is known about the RNA-binding properties of SAFB1 or SAFB2. We utilised crosslinking and immunoprecipitation (iCLIP) coupled with high-throughput sequencing to enable a transcriptome-wide mapping of SAFB1 protein-RNA interactions in breast cancer MCF-7 cells. Analysis of crosslinking frequency mapped to transcript regions revealed that SAFB1 binds to coding and noncoding RNAs (ncRNAs). The highest proportion of SAFB1 crosslink sites mapped to ncRNAs, followed by intergenic regions, open reading frames (ORFs), introns, and 3′ or 5′ untranslated regions (UTR). Furthermore, we reveal that SAFB1 binds directly to RNA and its binding is particularly enriched at purine-rich sequences not dissimilar to the RNA-binding motifs for SR proteins. Using RNAi, we also show, for the first time, that single depletion of either SAFB1 or SAFB2 leads to an increase in expression of the other SAFB protein in both MCF-7 and MDA-MD231 breast cancer cells. Moreover, a role for SAFB1 in RNA splicing and metabolism has also been proposed. Nayler et al. [The growing interest in SAFB1 and SAFB2 proteins in relation to cancer is generated from their well described ability to bind to and modulate ER-r et al. . However TRA2B variable exon [ TRA2B exon skipping was independent of its RNA-binding ability [ TRA2B pre-mRNA to regulate exon skipping but could possibly mediate an indirect effect through its interaction with various splicing factors [ in vitro evidence has shown that the RRM domain of SAFB1 was able to bind RNA isolated from MCF-7 breast cancer cells, although the identity of the RNA targets was not described [SAFB1 and SAFB2 proteins share a highly conserved RNA-recognition motif (RRM) with 98% similarity in the central region, although until now their direct RNA-binding potential has remained unclear. SAFB1 has also been labelled as a novel hnRNP protein due to its similarity to the highly conserved RBD found in the hnRNP protein family . Subsequble exon , 7. Howe factors –6. In an2 of UV at 254 nm and cell pellets resuspended in lysis buffer treated with Turbo DNase I (Ambion) and high (1 : 10 dilution) or low (1 : 500 dilution) RNase I (Ambion). Dynabeads Protein A or Dynabeads Protein G (Invitrogen) were resuspended in lysis buffer containing 5 μg SAFB1 antibody (Sigma-Aldrich) and precleared lysate was added to the magnetic beads for immunoprecipitation at 4°C for 2 hours. RNA 3′ ends were dephosphorylated and RNA linkers ligated. Magnetic beads were then resuspended in PNK mix containing 32P-γ-ATP to radioactively label the RNA 5′ ends, as previously described [http://dx.doi.org/10.1155/2015/395816). Precipitated RNA was reverse transcribed in RNA/primer mix containing different Rclip primers with individual barcode sequences for each replicate. Three gel fragments corresponding to cDNA size were cut at 120–200 nucleotides (high), 85–120 nucleotides (medium), and 70–85 nucleotides (low) ). Three independent biological replicates were prepared for sequencing using the TruSeq Sample Preparation kit (Illumina) and sequenced on the Genome Analyser II system . Bioinformatic analyses were performed on the web-based iCount software (http://icount.biolab.si/). Mapping of SAFB1 crosslink sites to regions of respective genes was visualised in UCSC Genome Browser (http://genome.ucsc.edu/) and a graphical representation of the novel SAFB1 consensus binding motif was designed using the web-based WebLogo software (http://weblogo.berkeley.edu/). Potential target genes that contain SAFB1 binding sites were selected for further validation using qRT-PCR with TaqMan gene expression assays.CLIP with individual nucleotide resolution (iCLIP) was performed for SAFB1 using MCF-7 breast cancer cells based on a published protocol . In brieescribed . Protein Silencer Negative Control siRNA, Silencer Select GAPDH, and β-actin Positive Control siRNA (Life Technologies) using INTERFERin Transfection Agent (Polyplus transfection) according to the manufacturer's instructions. Following optimisation, cells were seeded at 3.5 × 104 cells/well in 24-well plates or 1.75 × 105 cells/well into 6-well plates. After 24 hours, cells were transfected with 5 nM siRNA with 4 μL/mL of INTERFERin transfection agent. RNA was collected 48 and 72 hours after transfection and protein collected 72 hours after transfection. In all experiments, levels of knockdown by RNAi were assessed at the RNA and protein level by PCR and immunoblotting.MCF-7 and MDA-MB-231 cells were reverse transfected with two independent sets of Silencer Select siRNA for SAFB1 and SAFB2,μg of total RNA was reverse transcribed using oligo (dT) primers and Superscript II Reverse Transcriptase (RT)  following manufacturer's instructions. Conventional PCR was performed using PCR master mix (Promega). qPCR was performed using TaqMan gene expression assays (Life Technologies) according to the manufacturer's instructions. Validated TaqMan probes were selected to target specific genes as follows: SAFB1 (Hs01561652_gl), SAFB2 (Hs01006796_g1), ITGB4 (Hs00236216_m1), SHF (Hs00403125_m1), MALAT-1 (Hs00273907_s1), and β-actin (Hs99999903_m1). Data analysis was performed using the comparative Ct method normalised against β-actin expression. Experiments were performed in triplicate and statistical analysis was performed using Student's t-test or Repeated Measures ANOVA with Dunnett's Multiple Comparison Test. All effects at P < 0.05 are reported as significant.Total RNA from cultured cells was extracted with RNeasy spin columns (Qiagen) or the SV Total RNA Isolation System (Promega) according to manufacturer's instructions. One μg of either rabbit anti-SAFB1 antibody (Genetex) or rabbit IgG control (Santa Cruz). Cell lysates and antibodies were then incubated with Dynabeads Protein G (Invitrogen) for a further hour at 4°C. Beads were then washed five times with lysis buffer before the immunoprecipitated RNA was collected in Trizol reagent (Invitrogen) according to manufacturer's instructions. The RT-PCR was performed as before but half of the RNA obtained from the immunoprecipitation was used in each reaction. Fold enrichment of target mRNA was determined after normalization to the input and rabbit IgG controls.MCF-7 cells were washed in ice cold PBS and then collected in lysis buffer (10 mM Tris-HCl (pH 7.5), 150 mM NaCl, 0.5% NP-40, and 1% Triton X-100) containing protease inhibitors (Sigma-Aldrich) and RNase OUT (Invitrogen). Equal amounts of cell lysate were then incubated for 3 hours at 4°C with 2 http://genome.ucsc.edu/) is shown in Supplementary Figure  1(C).Although the role of SAFB proteins in RNA processing has been speculated, the function of their highly homologous internal RRM has not been examined. We sought to identify possible direct RNA targets for SAFB1 in breast cancer cells, using iCLIP technology –12 combiiCLIP identified binding sites for SAFB1 across the whole transcriptome, where 100% of significant cDNA reads mapped to the sense orientation in annotated genes. This confirms the high strand specificity of iCLIP also observed in other studies , 13. Ana in vivo binding specificity of SAFB1 is still currently unknown. The advantage of single nucleotide resolution provided by iCLIP method enabled the assessment of sequence specificity for SAFB1 binding. To derive whether a consensus binding motif exists for SAFB1, enriched pentamer sequences surrounding the crosslink sites were identified. The frequencies of each pentamer were analysed to determine the top 20 pentamers for SAFB1. Strikingly, adenine appeared as the most frequent nucleotide in the top 20 pentamers and represents 68% of the enriched pentamers . This enabled the identification of several interesting RNA targets that were selected for validation. Further experimentations were performed using qRT-PCR or conventional PCR on RNAi transfected MCF-7 and MDA-MB-231 cells to verify the effect of loss of SAFB1 on the expression of these selected RNA targets. Since SAFB2 shares 98% sequence homology to the RRM of SAFB1, these cells were also depleted of SAFB2 and double knockdown of SAFB1 and SAFB2 was also included; interestingly, data shows that when MCF-7 cells are reduced of SAFB1 by RNAi, the levels of SAFB2 mRNA and protein increase . Likewis SHF mRNA, particularly accumulated around the alternative promoter ; the use of alternative promoters plays a significant role in gene expression control . Moriewed in . Therefo reduced . Loss ofpression .MALAT-1). MALAT-1 is a highly conserved long ncRNA enriched in nuclear speckles that regulates alternative splicing by modulating splicing factor phosphorylation [ MALAT-1 is overexpressed in many different cancers including breast and is considered an oncogenic long ncRNA [ MALAT-1 expression  tagged SAFB1 protein combined with total RNA from MCF-7 cells generated a PCR product when reverse transcribed and PCR amplified [The presence of the highly conserved RRMs within SAFB1 and SAFB2 proteins has been a subject of interest since their discovery, especially in relation to their RNA-binding potential. Despite the fascination, very little has been undertaken until now to describe their RNA-binding capabilities. Initialmplified . However in vivo on a global scale and identify the positions of crosslink sites at nucleotide resolution [iCLIP has been proven as a powerful method to determine protein-RNA interactionssolution . The ran in vivo targets by iCLIP enabled the mapping of transcript regions and RNA classes bound by SAFB1. An overview of the iCLIP results showed that the important class of RNAs bound by SAFB1 was ncRNAs. Interestingly, this binding distribution of SAFB1 is similar to the RNA-binding distribution of splicing factors SRSF3 and SRSF4 rather than hnRNP C protein; SAFB1 was initially classified as a novel member of the hnRNP protein family [The identification ofn family . Recent n family , while Kn family  showed tn family . This obThe term ncRNA is commonly used for RNA that does not encode a protein but appears to comprise internal signals that control various levels of gene expression, including chromatin organisation, transcription, RNA splicing, editing, translation, and turnover (reviewed in ). Consis in vivo consensus binding sequences for SAFB1 based on the enriched pentamer sequences surrounding the crosslink sites. This study is the first to report that SAFB1 binds a consensus adenine-rich sequence in vivo. Closer examination of the putative consensus binding sequence revealed the exclusion of thymine (uracil in RNA) at base position 4 and a strong inclusion of adenine at base position 5. Interestingly, the predicted SAFB1-binding motif is not dissimilar to purine-rich sequences found in RNA-binding motifs for other SR proteins . SHF, ITGB4, and MALAT-1.When analysing SAFB1 crosslink sites within protein-coding transcripts, SAFB1 binding density was also enriched in regions encompassing the ORF and 3′ and 5′ UTR. The list of RNA targets was filtered according to the region and density of SAFB1 binding to identify targets that are relevant to tumourigenesis. Several interesting genes were highlighted in this study including SHF significantly decreases the rate of growth factor-induced apoptosis in neuroblastoma cells [ SHF mRNA was highly expressed in nonmetastatic neuroblastoma compared to metastatic tumour samples [SHF is a member of a family of adaptor protein characterised by their ability to mediate protein-protein interactions through their Src homology 2 domain , 28. Altma cells . Subsequ samples . Another samples . SHF. As the aberrant expression of alternative promoters is linked to cancer, SAFB1 binding surrounding this region gathered an interest for further examination. Interestingly, the knockdown of SAFB1 in MCF-7 cells did not significantly alter SHF expression while the knockdown of SAFB2 or both SAFB proteins significantly increased SHF expression. This suggests that direct SAFB1 binding to the alternative promoter did not affect the expression of this gene. MDA-MB-231 cells were included in this part of the study for comparison and, in this cell type, increased SHF expression was observed in the loss of SAFB1 or SAFB2 and both SAFB proteins.Initial iCLIP data from this study revealed enriched SAFB1 binding sites at the alternative promoter of ITGB4, although the full function of most variants remains to be defined [ ITGB4 have not been elucidated, the discovery of SAFB1 binding sites in its exonic regions may provide a new perspective to further understand the mRNA processing of ITGB4.Multiple alternatively spliced transcript variants encoding distinct isoforms have been found for defined –33. Alte defined ). AlthouMALAT-1. Previous work shows that MALAT-1 colocalises with SRSF2 in nuclear speckles [ MALAT-1 [We also identified another potential novel target for SAFB2—speckles . Further MALAT-1 , 36. We, MALAT-1 , observe MALAT-1 , 7.Interestingly, single depletion of either SAFB1 or SAFB2 led to an increase in expression of the other; this pattern was mirrored at both mRNA and protein levels. Our study also suggests that SAFBI and SAFB2 may themselves have different and overlapping RNA targets. This observation supports previous speculations regarding the distinct molecular roles between SAFB1 and SAFB2 , 37, 38.Supplementary Figure 1: Generating an iCLIP dataset for SAFB1Supplementary Figure 2: Distribution of x-link sites and counts in RNAmaps.Supplementary Figure 3: Compensatory effect of SAFB proteinsSupplementary Figure 4: Distribution of SAFB1 crosslink sites on SHF mRNA.


Technological advances in the sequencing field support in-depth characterization of the transcriptome. Here, we review genome-wide RNA sequencing methods used to investigate specific aspects of gene expression and its regulation, from transcription to RNA processing and translation. We discuss tag-based methods for studying transcription, alternative initiation and polyadenylation events, shotgun methods for detection of alternative splicing, full-length RNA sequencing for the determination of complete transcript structures, and targeted methods for studying the process of transcription and translation. With the ensemble of technologies available, it is now possible to obtain a comprehensive view on transcriptome complexity and the regulation of transcript diversity. The transcriptome can be described as the complete collection of RNA molecules expressed in a specific cell type or tissue at a given time. It includes coding RNAs (mRNA) and a multitude of non-coding RNAs . RNA plays a central role in cell biology, where it not only serves as template for protein synthesis but also acts as a structural scaffold and as regulatory molecule during post-transcriptional control of gene expression , 2.The complexity of the transcriptome arises from different layers of regulation of gene expression, including regulation at transcriptional, post-transcriptional and translational level. At the level of transcription initiation, a gene may give rise to multiple transcripts with different transcription start sites and/or first exons . PrimaryThis review will discuss the major RNA profiling methods , mainly focusing on the class of mRNAs. For each method, we will address its utility for the study of specific RNA transcript regulation and processing events. For technical differences between the mentioned sequencing platforms and extensive descriptions of all the regulatory mechanisms touched in this review we refer to previously published reviews , 16–20.Numerous next-generation sequencing (NGS)-based RNA profiling methods are nowadays available to specifically investigate different levels of regulation.RNA sequencing methods have been adapted for the most common DNA sequencing platforms , Applied Biosystems SOLiD (Life Technologies), IonTorrent (Life Technologies)]. These platforms require initial reverse transcription of RNA into cDNA. Conversely, the single molecule sequencer HeliScope (Helicos BioSciences) is able to use RNA as a template for sequencing , 22 and http://res.illumina.com/documents/products/appnotes/appnote_hiseq2500.pdf), allowing an accurate quantitative analysis of high and low abundant transcripts, PacBio currently yields ~50,000 long reads (http://files.pacb.com/pdf/PacBio_RS_II_Brochure.pdf), restricting the number of transcripts that can be detected, unless multiple runs are performed [The sequencing platforms differ also in the number of reads generated, leading to a difference in sensitivity. While common short-read platforms can generate millions of reads  tail, others allow a full sampling of the transcriptome by capturing also non-capped and non-polyadenylated molecules. The transcripts detected by different techniques are therefore only partially overlapping. Another issue to consider is the transcript’s orientation. While all tag-based methods are strand specific, meaning that they preserve information about the transcript’s orientation, shotgun methods may be strand specific or not strand specific. Strand specificity is important to determine the exact gene expression levels in the presence of antisense transcription.These advanced RNA sequencing methods and platforms generate a huge amount of data, up to millions of reads, giving us the possibility to understand the complexity of the transcriptome and its fine regulation. To correctly interpret sequencing data and reach a full understanding of the hidden biological meaning in it, a parallel development of statistical and computational approaches is fundamental. Numerous algorithms have been developed to detect differentially expressed genes and spliced variants. For an extensive comparison of some of the most commonly used methods, and for a general overview of the computational challenges, we refer to , 31, 32.In tag-based methods, each transcript is represented by a unique tag. Initially, tag-based approaches were developed as a sequence-based method to measure transcript abundance and identify differentially expressed genes, assuming that the number of tags (counts) directly corresponds to the abundance of the mRNA molecules. The reduced complexity of the sample, obtained by sequencing a defined region, was essential to make the Sanger-based methods affordable. When NGS technology became available, the high number of reads that could be generated facilitated differential gene expression analysis. A transcript length bias in the quantification of gene expression levels, such as observed for shotgun methods , 38, is Recently, an increased interest in the determination of transcripts’ structure led to the development of numerous directed tag-based strategies which aim to precisely define 3′ and 5′ transcript ends. We will refer to them as 3′ end sequencing and 5′ end sequencing methods.3′ end sequencing methods specifically focus on the end of the transcript, allowing the detection of transcripts which differ in the 3′-terminal exon used or in the length of their 3′ untranslated region (3′-UTR). Different 3′ ends arise from alternative polyadenylation of pre-mRNAs –41. AlteThe complexity of the transcriptome highly increases through the use of alternative polyadenylation sites within different exons/introns or within the same 3′-UTR, the first giving rise to transcript variants coding for different protein isoforms and the second giving rise to transcript variants potentially differing in stability , 47–49.A variety of 3′ end sequencing methods have been developed in the last years, from serial analysis of gene expression (SAGE)-like methods to more dedicated protocols, where the detection of the actual polyadenylation site used is even more precise. We review some of these methods, and assess the level of precision in which polyadenylation sites are determined.DeepSAGE  represenDifferent studies have shown that SAGE-like methods are suitable to detect alternative polyadenylation events , 55–57. To overcome the limitations observed in all SAGE-like methods, several dedicated protocols have been developed to specifically characterize polyadenylation sites and quantify their relative usage genome wide , 59–70 b, c. TheLimitations in the detection of the exact polyadenylation site location and biased quantifications may arise due to various steps involved in the preparation of the sequencing library. Oligo(dT) priming, DNA or RNA ligase-mediated adapter ligation, reverse transcription and amplification represent the main sources of bias.The available poly(A) site sequencing protocols may differ in the level of precision in which the polyadenylation site is determined, in the number of possible biasing steps introduced and in the number of false polyadenylation sites detected, mainly arising from internal priming events.The main technical differences between the reviewed methods are summarized in Table Internal priming events remain one of the limitations of all methods based on oligo(dT) priming , 69, 71.Different approaches have been taken to minimize internal priming artifacts. In 3P-Seq , ligatioWang et al.  used a cNext to differences in dealing with the internal priming issue, protocols display different degrees of resolution in the identification of the exact polyadenylation sites. If sequencing starts from the 5′ end of the library construct , 64, 71,Another method which avoids sequencing through the poly(A) tail is described by Wilkening et al. . In thisA more direct approach is described by de Klerk et al. . Their mAn even less biased approach is described by Ozsolak et al. , 22, andAccurate detection of polyadenylation sites can also be achieved on the PacBio-RS single molecule sequencing platform. Here, transcripts are converted into a circular double-stranded DNA template capped by hairpin loops at both 3′ and 5′ ends . Since tMethods relying on enzymatic ligation of adapter sequences to RNA molecules  and  promoters. The knowledge of the exact position of a transcription start site can also be used to investigate promoter usage and to identify transcription factor binding sites in these promoters .The detection of the exact transcription start sites is highly important since alternative transcription start sites can lead to the formation of protein isoforms with totally different biological functions. Alternatively, shorter or longer 5′-UTRs may influence the efficiency of protein translation , 15.The number of 5′ end sequencing methods available is restricted compared to the number of 3′ end sequencing approaches. A possible reason might be that the first method published, named DeepCAGE –79, alreWhereas SAGE-like methods are restricted to the use of restriction enzymes and therefore to the presence and location of restriction sites, CAGE-like methods are based on the 5′ cap structure of a transcript, and can theoretically detect all capped 5′ ends of mRNA molecules. On the other hand, these methods are not suitable for non-capped transcripts.DeepCAGE represents an improved NGS version of the previously published CAGE protocols , 81. ThiDeepCAGE libraries have been analyzed on common DNA-based sequencing platforms  but also on the Helicos single molecule sequencer , 84. TheBoth DeepCAGE and HeliscopeCAGE are based on the cap-trapper method. A different approach is described by Salimullah et al.  in theirAll CAGE-like methods discussed so far are limited in their ability to correctly detect alternative transcription start sites, due to a phenomenon called ‘exon painting’ , 83, 87.The detection of alternative transcription start sites and alternative polyadenylation sites by tag-based methods, which focus on the 5′ and 3′ end of a transcript, respectively, is a proven method to characterize transcript structure. Nevertheless, the full information about transcript structure is missing. To overcome this limitation, tag-based methods able to detect the co-occurrence of a specific transcription start site and a polyadenylation site has been developed. Methods able to determine both ends are called RNA-PET  and TIF-In TIF-Seq full mRNAs are first ligated to a single-strand oligo by oligo-capping. Then mRNAs are converted to cDNAs by reverse transcription and amplified using biotinylated primers. The double-stranded cDNA molecules are circularized through an intramolecular ligation, and fragmented by sonication. Fragments containing both 3′ and 5′ ends are captured by streptavidin-coated beads and ligated to adapters for amplification and paired-end sequencing. An advantage of both paired-end tag approaches is the ability to detect fusion transcripts. On the other hand, generation of full-length cDNAs from long transcripts still represents a technical limitation for any 5′3-sequencing method.The advantage of a shotgun, sequence-it-all method, over a tag-based method, is the ability to quantify the expression level of each exon within a transcript, estimate their percent inclusion level and detect  alternative splicing events. However, it is difficult to identify the exact 3′ and 5′ ends of transcripts due to various technical biases (such as random hexamer priming or oligo dT priming) leading to underrepresentation of sequences near 5′ and 3′ ends , 91.+ RNAs are selected prior to fragmentation, or may also include non-polyadenylated RNAs if no selection is performed. In the latter case, ribosomal RNA , converRNA pool ) needs tRNA pool . This maWhereas all tag-based methods are by definition strand specific, the first RNA-seq methods were not strand specific , as the Strand-specific methods can be classified into two categories: (1) RNA-seq methods based on ligation of two different adaptors in a known orientation relative to the 5′ and 3′ ends, and (2) RNA-seq methods based on chemical modification of the RNA, either by bisulfite treatment or by the incorporation of dUTPs during the second-strand cDNA synthesis. In both cases, the non-modified strand is degraded enzymatically. According to a comparative study published by Levin et al. , where 1Shotgun sequencing methods have the potential to identify alternative splicing events. Algorithms deriving transcript structure from short reads mostly use a combination of coverage patterns and exon–exon spanning reads, and read pair information. To be able to detect alternative spliced variants, a certain coverage is necessary. Therefore, low expressed genes will give less information than highly expressed genes, unless a large number of reads are generated. A discussion of these algorithms falls outside the scope of this review. The reader is referred to , 104.One of the main limitations of all short-read shotgun methods is the inability to directly characterize the structure of a transcript and/or to discriminate different alleles. Additional computational and statistical approaches are required to reconstruct the transcript, and the short fragment sizes limit the reconstruction to local regions of the transcripts.The PacBio system is the only available platform potentially able to produce reads with a length up to ~30 kb. However, the limitation faced at the moment is the production of full-length double-stranded cDNAs .Different approaches are used to create full-length cDNAs suitable for full-length transcript sequencing. One of the possible approaches is based on template switching, consisting in the addition of a non-templated poly-cytosine tail to the 3′ end of the first-strand cDNA molecule through the terminal transferase activity of the MMLV reverse transcriptase. The addition of a poly-(C) tail allows the hybridization of an adapter with a poly(G) tail if the first-strand cDNA synthesis has reached the 5′ end of the transcript. A disadvantage of this approach is that degraded mRNAs containing a poly(A) tail will also be converted into cDNAs, simply due to the fact that cDNA synthesis starts at the poly(A) tail. Distinction between full-length transcripts and partially degraded transcripts will therefore be impossible.A different approach based on the isolation of properly 5′-capped RNA molecules is also extensively used. It is based on first-strand cDNA synthesis starting at the poly(A) tail, followed by digestion of unconverted RNAs and capture of the 5′-cap. Only molecules where the cDNA synthesis has reached the 5′ cap will be used for second-strand synthesis.Minor improvements in cDNA length have been observed in recent template switch-based methods like Smart-seq2 , where tIndependently from which approach is used to generate full-length cDNAs, for PacBio sequencing these are converted into a SMRTbell library , consistTaking into account the actual limitations observed in full-length cDNA preparation, full-length sequencing on PacBio still represents a unique approach to interrogate transcript structure on a single molecule level. Unfortunately, the number of reads offered by the PacBio technology is limited, and full characterization of a transcriptome requires performing of many runs , 28 and Whereas previous methods usually reflect steady-state RNA levels, there are also dedicated methods available to monitor active transcription. A first approach is the immunoprecipitation of genomic DNA bound by RNA Polymerase II . DependiRNA immunoprecipitation-based methods are also used to understand how protein–RNA complexes interactions regulate gene expression at transcriptional and post-transcriptional level. Various targeted approaches have been developed to investigate the interaction between RNA-binding proteins and their target RNA molecules Table .Table 3IHITS-CLIP  and CLIPCells or tissues can be irradiated in vivo, and after cell lysis the crosslinked RNA–protein complexes can be purified by immunoprecipitation using specific antibodies. To be able to map each binding site, RNA is digested up to a length of ~50 nt, reverse transcribed after RNA adapter ligation, and amplified prior sequencing. In the traditional CLIP method the resolution is low, since the mapped binding sites correspond to the total length of the fragmented co-purified RNAs. Another limitation is represented by the low efficiency of crosslinking using UV light at a wavelength of 254 nm. Different approaches, such as PAR-CLIP , 113 andPAR-CLIP , 113 . Moreover, even though a crosslinking at 365 nm is generally considered more efficient, the efficiency of a crosslink might differ from protein to protein .Most of the CLIP-based studies performed so far focus on splicing factors , 114.All methods discussed so far focus on measuring the abundance and characterizing the structure of a transcript, or defining its interaction with RNA-binding proteins. The information derived is therefore restricted to the composition of the transcriptome. However, transcript levels are not necessarily a good approximation of protein levels because the process of translation is also highly controlled, probably to the same extent as transcription or splicing . RibosomRibosome profiling  –119 has Ribosome profiling is a shotgun method based on deep sequencing of ribosome-protected mRNA fragments, which allow to determine which transcript is actively translated at a specific moment in the cell, the rate of translation, the reading frame used and thereby the exact protein product. The technique is based on the observation that ribosomes bound to mRNA molecules protect ~28 nt fragments from nuclease digestion (ribosome footprints). After halting translation, ribosome-bound mRNAs are digested and the ribosome:mRNA complexes (monosomes) are recovered by ultracentrifugation on sucrose gradients or by size-exclusion chromatography. The short protected fragments are released from the monosomes, and converted into a cDNA library, which can be amplified and sequenced. Different variants of the original protocol have been developed to study translational control at different levels. Using drugs arresting ribosome initiation complexes, such as harringtonine or lactimidomycin, it is possible to detect alternative translation start sites or regulatory upstream open reading frames. By inhibiting ribosome translocation with cycloheximide or by thermal freezing, it is possible to quantify the level of translation, to identify the translational reading frame, potential reading frame switches, and to investigate ribosome pausing.It has been shown that some of the methods commonly used to halt translation may lead to artifacts. Cycloheximide is known to cause a profound accumulation of ribosomes at the translation initiation codon, due do the fact that translation can still initiate while elongation is already blocked . HarringLarge required amounts of input material represent an obstacle when studying rare and heterogeneous cell populations, micro-dissected tissues, subcellular fractions or simply when there is a limited accessible quantity of RNA from patients. Therefore, some RNA profiling methods are limited to bulk transcriptome analysis of large numbers of cells or pieces of tissues.The targeted approaches, such as the immunoprecipitation-based methods and the ribosome profiling method, require the highest amount of input material, in the range of millions of cells. The suggested amount of RNA for a PAR-CliP experiment ranges between 100 and 400 million cells , but iCL+ RNA [PacBio long-read sequencing also requires a high amount of input RNA, in the range of hundreds of thousands of cells. Successful full-length libraries have been generated starting from ~10 µg of total RNA  or ~1 µg+ RNA .Tag-based and shotgun methods have been extensively improved with regards to the amount of starting material. While the older DeepCAGE approach required ~50 µg of total RNA , the sin+ RNA [The 3′ end sequencing methods generally require low amounts of input RNA. Even though some poly(A) sequencing methods requires between 10 and 50 µg of total RNA , 63, 66 + RNA , 64, oth+ RNA , the Hel+ RNA , PolyA-s+ RNA  and MAPS+ RNA , requireRNA-seq remains at the moment the only method which has been used for whole-transcriptome single-cell sequencing.One of the main challenges in single-cell RNA-seq is the ability to distinguish between biological variation and technical variation, which suffers from biases introduced during cDNA synthesis and amplification. Next to the ambiguity in the quantification, when the starting amount is lowered to single-cell level, it also becomes difficult to detect lowly expressed transcripts . RecentlGene expression of coding RNA molecules is a complex process regulated not only at transcriptional and post-transcriptional level, but also during and after translation. To fully characterize this process on a genome-wide scale and at a nucleotide level, numerous high-throughput RNA profiling sequencing methods have been developed. The determination of the actual structure of a transcript cannot be achieved without capturing different processing and regulatory events occurring in the same transcript. Capturing these events by combining different complementary methods comes with limitations, due to the uncertainty faced while trying to reconstruct the transcript. Technological advances in the sequencing field are leading to full-length transcript sequencing. From a technological point of view, it is already possible to sequence full-length cDNA molecules, even though future improvements in the production of cDNA molecules are still required to fully investigate the exact structure of each transcript variant. Full-length transcript sequencing will help defining any coupling between the different layers of regulation of gene expression and lead to a better understanding of the complexity of the transcriptome and its expression. Direct use of RNA as a template for sequencing will further reduce biases introduced in the sample preparation procedure. The final outcome of gene expression could not be fully characterized without information on the translatome. Ribosome profiling represents the newest, most exciting tool to study gene expression at the level of translation. The use of a combination of approaches focusing at transcriptional, post-transcriptional and translational level will help to comprehensively characterize gene expression regulation.


Gene expression inheritance patterns in Arabidopsis hybrid plants were investigated for correlation with the presence of transposable elements (TEs) and small RNA profile.The presence of TEs in a gene and the expression of small RNA matching a gene were both found to be associated with non-additive mRNA inheritance patterns in hybrids. Expression levels below mid-parent values in the hybrids were associated with low mRNA expression in parents, with the presence of small RNA from both strands, and with the presence of TEs. High-parent dominance of mRNA levels was found to be associated with high parental mRNA expression levels, the absence of TEs, and for some genes, with small RNA fragments that are predominantly from the sense strand. These small RNAs exhibit a broader size distribution than siRNA and reduced nucleotide end bias, which are consistent with an origin from degraded mRNA. Thus, increased as well as decreased gene expression in hybrids relative to the parental mean is associated with gene expression levels, TE presence and small RNA fragments with differing characteristics.trans repression by siRNA and TEs as the cause of low-parent dominance.The data presented here is consistent with a role for differential mRNA decay kinetics as one mechanism contributing to high-parent dominance in gene expression. Our evidence is also consistent with The online version of this article (doi:10.1186/s12870-015-0549-7) contains supplementary material, which is available to authorized users. Hybrid plants can show enhanced growth rate, novel phenotypes or adaptive advantages in diverse environments. They may even have enhanced potential to evolve into new species , 2. The cis-regulatory elements, and capturing and duplicating genes or gene fragments [TEs were discovered by McClintock and proposed as regulators of gene expression . Recent ragments , 28. Theragments . Anotherragments , 31, silragments . This spragments , 34. Theragments .Small RNA is composed mostly of miRNA and small interfering RNA (siRNA). The role of siRNA is generally considered to be the silencing of TEs and genes, which occurs largely by RNA-directed DNA methylation or post-transcriptional pathways , 37. BotTo study how mRNA and genic small RNA levels are inherited in hybrids, we sequenced mRNA and small RNA from four genotypes, Col, Ler and their reciprocal hybrids Col × Ler  and Ler × Col  from leaf tissues . Moreover, small RNA of the highly expressed genes predominantly or exclusively matched the sense strand (i.e. the mRNA itself), while sense and antisense strands were roughly equally expressed from genes where mRNA was expressed at lower levels  was found to be associated with increased gene expression. To provide further insight, we studied the relationship between genic small RNA and mRNA levels using the two parental genotypes, Col and Ler. We found that small RNA level was positively correlated with mRNA level, but only when mRNA expression was above a clearly defined threshold of around 1,000 normalized reads  or adenosine nucleotides (siRNAs) that is preferred for ARGONAUTE binding , or deviate from MP . The proportion of genes showing additive and non-additive expression usually varies substantially between studies , and thiFor genes that had both small RNA and TEs, the gene expression levels are generally low and the small RNAs possess the expected properties of siRNAs. These genes are often associated with a low-parent inheritance pattern, which is consistent with a role for siRNA in gene silencing in hybrid plants. We propose that siRNAs and TEs are therefore causally related to LP and LPMP inheritance. However, for genes with detectable small RNA but no TE, the gene expression levels are often very high, and small RNA sequencing identifies predominantly or exclusively sense-strand derived fragments with high proportions of 25-28 nt RNAs and little noticeable 5’ nucleotide bias. These features are correlated with HP inheritance in the hybrids. The fact that small RNA from different strands is associated with contrasting non-additive inheritance patterns suggests that small RNA strandedness should be considered in future studies on small RNA and gene expression pattern. We should note that while we observed strong correlations in this study, these are significant associations not qualitative relationships. Many exceptions exist, such as highly expressed genes that contain TEs, or additively expressed genes that produce siRNA. It is therefore possible that if these phenomena are causative of non-additive gene expression, they may not be the only mechanisms regulating non-additive expression in hybrids.Despite a long-known association between siRNA, DNA methylation and low levels of mRNA expression, it has previously been described that genes with body methylation can show higher expression than genes that are unmethylated , 50. OneImportantly, we observe a large number of highly-expressed genes that produce no detectable small RNA. Thus, sssRNAs do not appear to be a general outcome from mRNA degradation. One explanation for this difference is that not all highly expressed genes may produce sufficiently stable 21-24 nt degradation intermediates to be detected by the RNA-adaptor ligation approaches that are used to generate small RNA libraries. We also considered the possibility that ARGONAUTE binding could preferentially stabilize the subset of mRNA degradation products with 5’ U or A terminal nucleotides, but did not find evidence supporting this hypothesis  tail or cleaved by miRNA-targeted action, mRNAs are degraded by enzymes, largely exonucleases, that are not sequence specific. On this basis, all mRNA in the process of degradation would be part of a common pool, and thus, for two mRNAs with the same overall level of accumulation but different half-lives, degradation intermediates of the shorter-lived mRNA would be expected to be more common, since this transcript will have a higher rate of synthesis and thus more intermediates at each stage of degradation. We would also expect oligomer intermediates such as sssRNA to be biased towards certain sites, which are furthest from the site unprotected from exonuclease action by cleavage or tail/cap loss. Since we observe more sssRNA for longer-lived mRNAs, and sssRNA throughout the transcript, our results are not consistent with sssRNA being the expected degradation intermediates of mRNAs. One possible explanation is that certain mRNAs with longer half-lives are degraded differently, producing degradation intermediates with different positional bias and longer half-lives. This could be a result of varying affinities of the degradation machinery for different mRNAs, or be the consequence of a property of the mRNAs . Either way, this explanation requires different mRNAs to show different degradation rates and/or fragment sizes after degradation has been initiated by miRNA-directed cleavage, loss of poly (A) tail or loss of 5’ cap. In hybrids, mRNA degradation could therefore act differentially on a subset of highly expressed genes, leading to HP inheritance of those genes.A third, related possibility is that the sssRNA are an indication of degradation pathways becoming overloaded under certain conditions in hybrids, leading to HP inheritance through reduced degradation rates. A link was recently reported between cytoplasmic mRNA decay and suppression of post-transcriptional gene silencing (PTGS) . Disrupttrans repression of genes by siRNA inherited from one parent, much of which is initiated by the presence of TEs within gene bodies. For HP inheritance, a challenging phenomenon to interpret using currently understood mechanisms of gene expression control, we suggest that overloaded mRNA decay may be involved in mediating HP inheritance patterns for at least some highly-expressed genes.In summary, we describe significant associations between mRNA expression levels, small RNA fragments, TEs and non-additive gene expression in hybrids. We show evidence that sssRNA is associated with high mRNA expression in parents of a cross and with HP inheritance in hybrids. We also show that LP and LPMP inheritance are correlated with low mRNA expression in parents, with small RNA from both the sense and antisense strands, with the presence of TEs and with DNA methylation in all sequence contexts. Thus, known mechanisms of transcriptional silencing may account for LP and LPMP inheritance patterns via erecta (Ler), and their reciprocal hybrids Col × Ler , Ler × Col . The source of Col and Ler has been described before [Four genotypes were used in this study, namely Columbia (Col), Landsberg d before . The hybSeeds were sterilized, planted on MS medium, stratified at 4 °C and germinated at 20 °C under 24 h cycles, 16 h of light and 8 h of darkness. Plants were then transplanted into soil (sunshine mix : perlite : vermiculite = 2 : 1 : 1) and grown under same conditions until bolting. The four youngest mature leaves (defined as leaf with petiole) from a single plant were harvested, pooled and viewed as one biological replicate. Four biological replicates were collected for each of the four genotypes. All materials were collected at around the same time every day to minimize effect of circadian clock, which has been shown to affect gene expression . Some ofTotal RNA was prepared from a single plant using TRIzol according to manufacturer’s instructions (Invitrogen). As suggested by the Illumina protocol, 1 μg of total RNA was used to prepare each mRNA sequencing library using the TruSeq RNA sequencing library preparation kit (Illumina). For small RNA library preparation, small RNA with sizes between 10-nt and 50-nt was first isolated from 15 μg of total RNA using 15 % urea-PAGE (Invitrogen). The purified small RNA was then used to prepare a sequencing library according to the instructions supplied with the TruSeq small RNA library kit (Illumina). Both RNA and small RNA libraries were barcoded and pooled. Sequencing was performed on Illumina HiSeq 2000 instruments, single-end reads of 100-nt were generated for the mRNA libraries and 40-nt for small RNA libraries. On average, 15.7 and 8.1 million reads were obtained for each RNA and small RNA library, respectively.The RNA reads were mapped onto Arabidopsis Col genome version 10 (TAIR10) using tophat allowing at most 2 mismatches . Reads mFor small RNA, FASTX  was usedvice versa. If hybrid expression levels were different from all three of MP, HP and LP, the d/a value (“d” is defined as “F1 - (HP + LP) / 2”, and “a” is defined as “(HP - LP)/2”) was further used to group genes into AHP (above HP) if d/a was greater than 1, HPMP (between HP and MP) if between 0 and 1, LPMP (between LP and MP) if between −1 and 0, and BLP (below LP) if less than −1. Genes where the hybrid expression level was significantly different from MP, but not from both HP and LP, were placed in an unclassified category (UC).An R package, DESeq  was usedThe annotated TEs present in the Arabidopsis Col genome were downloaded from TAIR , and comThe RNA and small RNA sequencing data for the two accessions and their two reciprocal hybrids are available at NCBI under accession number SRP058781.