Saccharomyces cerevisiae cells deficient for RNA degradation pathways revealed that about half of the pervasive transcripts are degraded by NMD, which provides a fail-safe mechanism to remove spurious transcripts that escaped degradation in the nucleus. Moreover, we found that the low specificity of RNA polymerase II TSSs selection generates, for 47% of the expressed genes, NMD-sensitive transcript isoforms carrying uORFs or starting downstream of the ATG START codon. Despite the low abundance of this last category of isoforms, their presence seems to constrain genomic sequences, as suggested by the significant bias against in-frame ATGs specifically found at the beginning of the corresponding genes and reflected by a depletion of methionines in the N-terminus of the encoded proteins.Nonsense-mediated mRNA decay (NMD) is a translation-dependent RNA quality-control pathway targeting transcripts such as messenger RNAs harboring premature stop-codons or short upstream open reading frame (uORFs). Our transcription start sites (TSSs) analysis of DOI:http://dx.doi.org/10.7554/eLife.06722.001 Eukaryotes such as animals, plants and fungi store their DNA within the nucleus of each of their cells. Genes within this DNA contain the instructions needed to make molecules of RNA; some of which can leave the nucleus and be decoded to build proteins. However, not all of the DNA that is copied into RNA actually codes for proteins. Instead, some RNA molecules are important parts of the cell's protein-making machinery in their own right, and others help to regulate the expression of genes as RNAs or proteins.Nevertheless, many non-coding RNAs don't have such clear roles. Often these RNAs—which are called ‘pervasive transcripts’—are quickly destroyed within the nucleus, but it is likely that some molecules will escape this quality-control mechanism. If the cell's protein-making machinery decodes these RNAs, it could lead to the production of faulty or harmful proteins. Recent research suggested that another quality-control mechanism, which typically eradicates incorrectly processed protein-coding RNAs, could also destroy unneeded or harmful pervasive transcripts. But it was not clear how common it was for this process—called ‘nonsense-mediated decay’—to be used for this purpose.Now Malabat, Feuerbach et al. have engineered yeast cells that lacked either the genes required to carry out nonsense-mediated decay or the ability to destroy RNA molecules in the nucleus. Experiments with these yeast cells revealed that about half of all pervasive transcripts can be destroyed via nonsense-mediated decay; this suggests that this mechanism serves as a fail-safe to prevent the build-up of these potentially harmful molecules.Malabat, Feuerbach et al. also revealed that the enzyme complex that copies gene sequences to make RNA molecules will often also copy some extra DNA sequence from before the start of the gene. On the other hand, it is also common for this enzyme complex to miss the start of the gene and produce an RNA molecule that lacks some of the instructions needed to build the correct protein. Further experiments showed that in yeast these two kinds of incorrectly made protein-coding RNAs could both be identified and destroyed by nonsense-mediated decay as well. The next challenge will be to see to what extent these phenomena are conserved in other eukaryotes.DOI:http://dx.doi.org/10.7554/eLife.06722.002 Saccharomyces cerevisiae (S. cerevisiae), hundreds of such pervasive non-coding RNAs (ncRNAs) were identified in addition to the stable ncRNAs such as transfer RNAs (tRNAs), small nucleolar RNAs (sn(o)RNAs), and ribosomal RNA (rRNAs)  . Even in (rRNAs) .S. cerevisiae, degradation-coupled transcription termination of pervasive transcripts relies on the Nrd1-Nab3-Sen1 (NNS) complex, which interacts with the carboxy-terminal domain (CTD) of the RNA polymerase II (RNAPII) phosphorylated on serine 5 and triggers termination upon recognition of short sequences on the nascent RNA (Yeast ncRNAs are transcribed from nucleosome-free regions (NFRs) present throughout the genome, most often at gene promoters and terminators, and part of these RNAs represent by-products of divergent transcription initiation . Differecent RNA . Early tcent RNA . Howevercent RNA . Some pecent RNA . The discent RNA . What macent RNA .Originally described more than two decades ago as a quality-control pathway targeting for degradation mRNAs containing a premature termination codon , NMD is S. cerevisiae, the techniques used to perform these analyses  will generate, for a given gene, mRNAs with different 5′-UTRs that may include uORFs. Although NMD targets were previously analysed genome-wide using NMD-deficient strains of analyses  did not analyses  to perfoOur analysis also showed that NMD restricts the accumulation of cryptic transcripts initiating inside transcribed ORFs as a consequence of altered chromatin structure and provides a fail-safe control mechanism for the removal of pervasive transcripts that escaped degradation by the nuclear quality-control pathway, reminiscent of what has been previously shown for some unspliced pre-mRNAs . This inupf1∆rrp6∆ double mutant strains. To minimize polymerase chain reaction (PCR) amplification biases and provide an internal control, the S. cerevisiae poly(A)+ RNAs, treated or not with TAP, were mixed with an equal amount of TAP-treated poly(A)+ RNAs from Schizosaccharomyces pombe just prior to the ligation step. After normalization using the S. pombe sequencing reads and removal of the ribosomal DNA ones, these experiments generated 2,844,877 reads when TAP was used compared with 72,435 reads when TAP was omitted. The ratio between these two numbers provides an upper limit for the false-discovery rate of TSS identification of 2.5%. Analysis of the cumulative 5′-end read counts per nucleotide around the start codon for all protein-coding genes showed that, genome-wide, 77%  of the reads obtained for TAP-treated samples could be mapped within a 200 nucleotide region upstream of the start codons with a maximum at around 30 nucleotides upstream from ATGs, while only 22% mapped to the same region when this treatment was omitted . Surprisingly, 58% of TSS reads starting with a pyrimidine when aligned on the genome (12% of the mapped TSSs) show a mismatch at their first nucleotide, most of the time an A instead of the encoded pyrimidine  sequences were aligned with the genomic sequence . Analysis of the TSSs and their surrounding sequences using the Web-LOGO algorithm  identifirimidine . MoreoveTo assess the relative contributions of the cytoplasmic NMD and nuclear RNA control pathways in shaping the yeast transcriptome, the genes encoding Upf1, an RNA helicase essential for NMD, or Rrp6, a nuclear exosome catalytic subunit, were deleted . Since, rrp6∆ background, respectively, indicating that NMD targets about half of the pervasive transcripts. The absence of both Rrp6 and Upf1 had an additive effect on the stabilization of pervasive transcripts. In the double mutant, 76% and 92% of the XUTs/SUTs and CUTs, respectively, were significantly stabilized, suggesting that NMD provides a fail-safe control mechanism for pervasive transcripts that escaped degradation in the nucleus, reminiscent of what has been previously shown for some unspliced pre-mRNAs  and were only detected in mutants cells . These n of RRP6 , ‘interg of RRP6 . FurtherUPF1 was surprising. The synthesis of such transcripts is normally repressed within transcribed regions unless the proper chromatin structure cannot be re-established in the wake of RNAPII  identified in cells lacking f RNAPII . In partB’ TSSCs . As expec’ TSSCs . In agrec’ TSSCs , the ‘A’c’ TSSCs . Analysic’ TSSCs , confirmrong NFR . Insteadrong NFR .10.7554/upf1∆set2∆ compared with set2∆ cells; Importantly, 55% of the A-TSSCs and 40% of the B-TSSCs, whether repressed by Set2 or not, were sensitive to Upf1  than upon deletion of RRP6 and deletion of both genes simultaneously did not show any additive effect 6PyPu; Detailed analyses of the sequencing reads and the corresponding upstream genomic sequences extended and validated the previously identified sequence consensus surrounding TSSs in We also identified cases in which transcription started on what appears to be a ‘slippery’ sequence (PyAAA), which resulted in the incorporation of an additional non-encoded A at the 5′-end of the transcripts. The simplest explanation for this observation is that the very short nascent RNA formed after two or three nucleotide incorporation is able to shift back by one nucleotide, probably together with the RNAPII, before transcription resumes , suggestupf1∆ cells when using the TSSCs from the TSS-sequencing experiments  cannot be distinguished from normal mRNAs and associate with the translation machinery. However, since they usually have short ORFs followed by relatively long 3′ UTR regions  were found to be associated with translating ribosomes and an estimated 17.4% of lncRNAs were found to be upregulated in the absence of ng genes .Our results also showed that XUTs, originally described as Xrn1-sensitive transcripts , are in UPF1 deletion on chromatin structure, the high number of intragenic transcripts (whether sense or antisense) identified in the upf1∆ single mutant suggests that the inhibition of transcription initiation by transcription-coupled chromatin modifications is not fully efficient and thus that some of these transcripts are produced as part of the normal transcription cycle in wild-type cells, with NMD playing an important role to eliminate them.Our analysis also revealed the prevalent role of NMD in the removal of cryptic transcripts initiating from intragenic promoters either in sense  or in antisense orientation . The strong impact of NMD inactivation on these transcripts indicated that they are exported to the cytoplasm and associate with the translation machinery, as is the case for the pervasive transcripts (see above). Even though we cannot rule out an indirect effect of the UPF1 and RRP6 had an additive effect on the accumulation of CUTs, SUTs and XUTs. However, a number of pervasive transcripts, in particular ‘A’ and ‘intergenic’ ones, were stabilized to substantial levels only when both the nuclear and the cytoplasmic RNA quality-control pathways were compromised, suggesting that they can act synergistically.Deletions of S. cerevisiae , which generates the poly-adenylated mRNAs that get exported and translated in the cytoplasm, and another, which involves the NNS complex and is shared by CUTs and sn(o)RNAs precursors (see ‘Introduction’). However, the demarcation between the two modes of termination is far from being strict as some terminators can often be recognized by both pathways depending on their distance from the TSS . In the revisiae , we cannNMD was previously shown to target a few hundred uORF-containing mRNAs . HoweverAltogether, transcript isoforms carrying a uORF or starting at an iTSS and targeted by NMD constituted the major transcript isoforms for only 446 mRNAs and minor transcript isoforms for almost half of the expressed genes. Therefore, although not quantitatively impacting the overall mRNA levels substantially , NMD affhttp://web.uni-frankfurt.de/fb15/mikro/euroscarf/) or generated by crossing with a can1∆ derivative of BY4741 25 magnetic beads  following the manufacturer's protocol. Northern blots were carried out on poly(A)+-RNA as described in 32P-labeled riboprobes except for ACT1 for which a 32P-labeled oligonucleotide was used.Total RNA was extracted using the hot acid phenol protocol . Poly(A)+-RNA was mixed with 10 units of Antarctic phosphatase (New England Biolabs) in a final volume of 50 μl. After 1 hr at 37°C, the reaction was treated with phenol/chloroform and ethanol precipitated. The RNA pellet was resuspended in 44 μl of water and 10 units (1 μl) of TAP  and 5 μl of 10× TAP buffer was added. The reaction was incubated 1 hr at 37°C followed by phenol/chloroform extraction and ethanol precipitation. The RNA pellet was resuspended in 5 μl of water. Next, the RNAs were ligated overnight at 16°C with 50 pmoles of the biotinylated oligonucleotide 3041 . After washing, the beads were resuspended in 20 μl of water and the bound RNAs eluted by incubation for 5 min at 90°C. This fraction is enriched for 5′-ends of capped RNA molecules. Note that the supernatant of the first step of the purification procedure containing RNA fragments corresponding to the body and the 3′-end of the gene not attached to the biotinylated oligonucleotide can be recovered and used to prepare independent libraries. This RNA population can be further fractionated using oligo (dT)25 magnetic beads to enrich for 3′-end of RNA molecules. The ∼18.5 μl eluate from the streptavidin beads was mixed with 50 pmoles of oligonucleotide 3038 (see +-RNA obtained by purification with oligo (dT)25 magnetic beads starting from S. cerevisiae or S. pombe  were dephosphorylated using Antarctic phosphatase as described. Then, S. pombe RNAs and one half of the RNAs extracted from S. cerevisiae were treated with TAP. The second half of S. cerevisiae RNAs was mock treated. Before ligation with the biotinylated oligonucleotide 3041, each half of S. cerevisiae RNAs was mixed with an equivalent quantity of calf intestinal alkaline phosphatase (CIP)/TAP-treated RNAs from S. pombe. The subsequent steps of the library preparation were identical to the ones described above.Approximately 500 ng of poly(A)ide 3041  in a 20 3038 see , heat dexrn1∆ mutant compared with the upf1∆ mutant, since the absence of Xrn1 leads to the accumulation of decapped RNAs (2 and subsequently phosphorylated with T4 polynucleotide kinase (Thermo scientific). The subsequent steps of library preparation were identical to the ones described above for the 5′-end. Moreover, since the absence of Xrn1 affects the overall mRNA content  [http://www.yeastgenome.org/]) and S. pombe genome  as reference genomes.Duplicated reads, identified using the random sequence tags within the ligated oligonucleotides (oligonucleotides 3041 and 3118 in For libraries L5p_01 to L5p_10 see  used forLibraries L5p_01 to L5p_03 and L5p_04 to L5p_06 were regrouped into clusters (TSSCs) using the peak calling method described in https://github.com/PF2-pasteur-fr/SARTools). For TSSCs, the mean TSS read counts in the region 50 to 10 nucleotides upstream of the ATG of each non-dubious ORF were used as an internal standard for size factor determination. For transcripts, the S. pombe transcripts read counts were used as the internal standard for size factor determination.TSSCs and transcript differential expression were calculated using DESeq2  within thttp://www.yeastgenome.org/). CUT and SUT coordinates were retrieved from TSSCs were assigned to annotations and sorted in a defined class according to their relative positions to the linked features. TSSCs overlapping the 5′-UTR mRNA sequences in the sense orientation were assigned to mRNAs  and TSS accuracy.SNR was defined as the log2 of the ratios of read counts of TAP samples vs no-TAP samples. For TSSC_upf1_rrp6 we used libraries L5p_07 and L5p_08 and for TSSC_upf1_set2 we used libraries L5p_09 and L5p_10. We choose a SNR limit of 0.55.TSS accuracy was defined as the proportion of TSSs within a TSSC mapped on a purine preceded by a pyrimidine vs the total count of TSSs mapped within the TSSC. We choose a minimum TSS accuracy of 80%.Individual TSS counts and read counts were used to determine the repartition of the TSSs around the ATGs of annotated ORFs (−200 to +100 nucleotides around ORFs ATGs). We used the ORF ATG positions retrieved from the SGD and corrected as described above in the ‘Identification of TSSCs’ section.For each TSS identified in the region from −200 to +100 nucleotides around ORF ATGs, we looked for the two subsequent ATGs. When the TSS was upstream of the annotated ATG start codon, we determined whether or not it was the first encountered one. When the TSS was within the ORF, we determined whether the ATGs were in the same phase as the natural ORF or not.For mRNAs, ORF start, ORF end and 3′-end coordinates obtained, as described above in the ‘Identification of TSSCs’ section, were used to calculate the sizes of the ORF and the 3′-UTR. For XUTs and SUTs, ORF start was determined as the coordinate of the first ATG after the annotated start obtained in We determined the proportion of A, T, G, and C at each position in the region spanning −200 to +100 nucleotides around the ATGs of ORFs according to the annotation found in the SGD and corrected as described above.We determined the number of ATGs within the first 1000 nucleotides after the ORF start codon based on the information available in the SGD. To avoid the overrepresented value of the start ATG codon, the first three nucleotides were not included in the analysis. The ratios between phased ATG and total ATG counts were computed in a window of nine nucleotides. The same method was applied for all other codons. To estimate the differential use of a codon between the start and the body of the ORFs, we normalized the value for each codon taking into account the mean ratio for this particular codon in the region comprised between 500 and 1000 nucleotides after the start codon.We used the protein sequences retrieved from SGD and omitted the first methionine of the proteins.We used the coverage of LT_01 and LT_02 libraries to calculate transcript expression levels of mRNAs, SUTs and XUTs. The coordinates of the different types of transcripts were obtained as described above. We calculated the expression levels for the four samples of both libraries and then used DESeq2 for normalization, estimation of fold change and mean signal calculation .The mean NFR density has been calculated using a nucleosome definition in GSE64139.The data reported here have been deposited in NCBI GEO under the accession number  review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.eLife posts the editorial decision letter and author response on a selection of the published articles . An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent  and two reviewers, one of whom is a member of our Board of Reviewing Editors.Thank you for sending your work entitled “Quality control of transcription start site selection by nonsense-mediated-mRNA decay” for consideration at The Reviewing editor and the other reviewer discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.Overall the study by the Jacquier laboratory is timely and contains very interesting information adding to our knowledge of how pervasive transcription is suppressed post-transcriptionally by RNA turnover.Before acceptance the following points should be addressed:Major points:+ RNA with phosphatase to prevent degradation intermediates with a 5' phosphate from ligating to the biotinylated adaptor oligo. Since transcripts with 5'ends originating downstream of their annotated TSSs  are identified, it seems crucial to control for the efficiency of the phosphatase treatment, as many of these transcripts may otherwise represent degradation intermediates rather than transcripts with alternative 5'ends. Finding the same consensus sequence motif as at canonical TSSs helps, but do the authors have any direct means to control for the effective exclusion of such species from the libraries?1) A technical concern relates to the TSS sequencing method, which relies on the treatment of poly(A)2) Differential expression analysis should be done according to today's best practice, which includes normalization of the data that accounts for differences in sequencing depths between samples .Minor points:1) The authors' seem somewhat biased towards the idea that every transcript that is repressed by some factor constitutes noise. Is this really always clear?2) The authors identify a consensus sequence, which basically is identical for all types of RNAPII transcripts: A(N)6PyPu, where the Pu is the TSS. Is it possible to investigate/estimate what fraction of such sites in the genome is functioning as TSSs based on the TSS sequencing data?3) The term “synergistic”, used in the subsection “Identification of additional pervasive transcripts” (“it had a synergistic effect on some ‘intergenic’…”): could this synergism simply be an additive effect that is hidden by the generally low expression of these RNAs?4) What is the reason for the overall rather low mapping rate of the sequencing data (sometimes as low as 30%)?5) Throughout the manuscript only few statistical tests are applied. While in many cases the displayed effects appear convincing, it would be useful to, e.g., evaluate whether the small difference in  Major points:1) A technical concern relates to the TSS sequencing method, which relies on the treatment of poly(A)+RNA with phosphatase to prevent degradation intermediates with a 5' phosphate from ligating to the biotinylated adaptor oligo. Since transcripts with 5'ends originating downstream of their annotated TSSs  are identified, it seems crucial to control for the efficiency of the phosphatase treatment, as many of these transcripts may otherwise represent degradation intermediates rather than transcripts with alternative 5'ends. Finding the same consensus sequence motif as at canonical TSSs helps, but do the authors have any direct means to control for the effective exclusion of such species from the libraries?We agree that, in order to use the mapping of RNA 5’-ends as marks of transcription start sites, it is critical to control that they originated from capped-RNA ends. Even though we have not controlled the efficiency of the phosphatase treatment step specifically, we think that the best control for this is the experiment we performed comparing the results obtained with or without treatment with tobacco acid pyrophosphatase (TAP) shown in 2) Differential expression analysis should be done according to today's best practice, which includes normalization of the data that accounts for differences in sequencing depths between samples .2 threshold of 1, to determine the transcripts, TSSs or TSSCs significantly expressed differentially in the different strains. Note that, although to two approaches are markedly different, the results were very similar and using this more rigorous approach did not changed the overall conclusions drawn.We agree with this comment . We have now re-analysed all the data using DESeq2. This allowed more rigorous statistical analyses of the data and all figures and numbers were changed accordingly. In particular, we now use the standard Wald-test provided in DESeq2 with the usual threshold for the p-value of 0.05, instead of a logMinor points:1) The authors' seem somewhat biased towards the idea that every transcript that is repressed by some factor constitutes noise. Is this really always clear?No, we certainly do not consider that all transcripts targeted for degradation by quality-control mechanisms constitute transcriptional noise. For example, in some cases, it has been clearly shown that the transcription of some non-coding RNAs can have a biological function, in particular for gene expression regulation by transcriptional interference. The analysis of such functions for the thousands pervasive transcripts identified in this work is out of the scope of this manuscript. Also, and as discussed in the manuscript, a substantial fraction of mRNAs is down regulated by NMD and it certainly does not correspond to transcriptional noise. The different use of TSSs has also previously been shown to be a mean of regulation. We have now added a short paragraph in the Discussion to mention this possibility .2) The authors identify a consensus sequence, which basically is identical for all types of RNAPII transcripts: A(N)6PyPu, where the Pu is the TSS. Is it possible to investigate/estimate what fraction of such sites in the genome is functioning as TSSs based on the TSS sequencing data?We identified 1,775,474 sites in the genome with the exact consensus A(N)6PyPu and 85,714 (4.8%) were used as transcription start sites. A sentence was added in the Discussion .3) The term “synergistic”, used in the subsection “Identification of additional pervasive transcripts” (“it had a synergistic effect on some ‘intergenic’…”): could this synergism simply be an additive effect that is hidden by the generally low expression of these RNAs?upf1∆rrp6∆ double mutant  What is the reason for the overall rather low mapping rate of the sequencing data (sometimes as low as 30%)?Before mapping there is a filtering step, as it is described in materials and methods. This step first collapse reads with the same sequence and the same 5’-end random tag to eliminate PCR duplicates. We added this information in Table1 in the revised version of the manuscript, to indicate the reads count after collapsing the reads. Duplication level can be high for some samples and sometimes only 30 % of the reads are really unique. On the contrary, mapping level, considering unique reads count as initial count, is around 95% for all the samples.5) Throughout the manuscript only few statistical tests are applied. While in many cases the displayed effects appear convincing, it would be useful to, e.g., evaluate whether the small difference inis statistically significant.The use of the three biological replicates in DESeq2 allowed a number of statistical analyses to be performed. For example, the number of differentially expressed transcripts are not defined by a threshold anymore but by the Wald-test provided in DESeq2. For


Membrane transporters play critical roles in moving a variety of anticancer drugs across cancer cell membrane, thereby determining chemotherapy efficacy and/or toxicity. The retention of anticancer drugs in cancer cells is the result of net function of efflux and influx transporters. The ATP-binding cassette (ABC) transporters are mainly the efflux transporters expressing at cancer cells, conferring the chemo-resistance in various malignant tumors, which has been well documented over the past decades. However, the function of influx transporters, in particular the solute carriers (SLC) in cancer cells, has only been recently well recognized to have significant impact on cancer therapy. The SLC transporters not only directly bring anticancer agents into cancer cells but also serve as the uptake mediators of essential nutrients for tumor growth and survival. In this review, we concentrate on the interaction of SLC transporters with anticancer drugs and nutrients, and their impact on chemo-sensitivity or -resistance of cancer cells. The differential expression patterns of SLC transporters between normal and tumor tissues may be well utilized to achieve specific delivery of chemotherapeutic agents. Membrane transporters play critical roles in moving a myriad of endogenous and exogenous substances across cellular and organelle membranes, including the major nutrient metabolites sugars, digested peptides, amino acids and nucleosides, rare elements such as hormones and neurotransmitters, and xenobiotics. These transporters are encoded by numerous gene families, accounting for approximately 4% of genes in human genome . ClinicaAs reviewed elsewhere, the ABC transporters, which are typical efflux drug transporters using energy from the hydrolysis of ATP as the driving force to move substrates against the electrochemical gradients outward or into intracellular organelles. The 49 ABC transporters can be classified into 7 subfamilies from A to G subfamily –5. In thMore than 400 SLC transporter genes have been identified and grouped into 55 families, including ion coupled transporters, exchangers and passive transporters located at the plasma membrane or in intracellular organelles  . Unlike via down-regulation of certain essential transporters for cancer cell survival. The major subfamilies of SLC members, which are, to different extents, explored in their association with cancer therapy, include the following: folate transporters (SLC19A1 and SLC 46A1), which are particularly important for antifolate chemotherapy of cancer and reviewed elsewhere  imatinib in HEK293 cells transfected with human OATP1B3 gene and in Xenopus laevis oocytes injected with OATP1B3 cRNA [OATP1B3 gene into mammalian cells potentiates their sensitivity to methotrexate [Xenopus laevis oocytes injected with OATP1B3 cRNA, the uptake of docetaxel and paclitaxel were 2.2-fold and 3.3-fold higher, respectively, than that of water-injected control oocytes [Hu B3 cRNA . Methotrtrexate . In Xenooocytes , 108, suoocytes , only SNoocytes . OverallAmong normal tissues, OATP1A2 (SLCO1A2) is expressed in the intestinal epithelium , the renin vivo evidence supporting methotrexate (MTX) as a substrate of OATP1A2 as well. When compared with wild-type mice, Slco1a/1b (-/-) mice exhibited a 3.4-fold increase in plasma and 30-fold decrease in hepatic levels of MTX after received a high dose of 500 mg/kg, suggesting an overall role of OATP function in MTX disposition [Paclitaxel has been characterized as an OATP1A2 substrate. When compared to wild-type mice, the transgenic mice overexpressing human OATP1A2 had a remarkable increased hepatic uptake of paclitaxel . Furtherosition . Moreoveosition , confirmosition .SLC31A1 gene, is localized in the plasma membrane and intracellular membranes [Copper is a vital mineral for humans as demonstrated by serious health concerns associated with its deficiency or excess accumulation. Copper transporter 1 (CTR1) is the major high-affinity copper uptake transporter in mammals . CTR1, embranes , 119. CTmbranes . It is ambranes . In addimbranes .Ctr1 gene deletion accumulated 35% and 70% less platinum and exhibited 4- and 8-fold more resistant to cisplatin cytotoxicity after a 2 hour cisplatin exposure, respectively, than the wild-type MEF cells [Abundant evidence has been consistently showed that CTR1 is an important determinant of cellular accumulation and toxicity of platinum-based anti-cancer drugs, such as cisplatin –123. StaF cells . Taken tMultidrug and toxin extrusion (MATE) proteins were first found in bacteria in 1998 . In 2005et al. reported that MATE1 mediates the excretion of cisplatin in the kidney [et al. that cisplatine significantly increased the levels of plasma creatinine and blood urea nitrogen (BUN), two major biomarkers for renal injury, in Mate1-deficient (Mate1-/-) mice when compared with wild-type mice [et al. later reported that much severer nephrotoxicity of cisplatin was observed in Mate1-/- mice than in wild-type mice [Otsuka  kidney . It has pe mice . Furtherpe mice . Li et ape mice . Thus, rpe mice .+ gradient across the membrane [The oligopeptide transporters  serve as integral membrane proteins for mediating the cellular uptake of di- and tripeptides into cells. Their driving force comes from an inwardly directed Hembrane . PEPT1 aembrane . PEPT1, embrane .PepT1 and PepT2 were interestingly found to be expressed in fibroblast-derived tumor cells but not in normal fibroblasts . High leet al. found that a newly synthesized dipeptide, 4-(4-methoxyphenyl)-L-phenylalanyl sarcosine which was an inhibitor of PEPT1, resulted in nearly complete suppression of the xenograft growth of human pancreatic cancer AsPC-1 cells that highly expressed PEPT1 [Bestatin, a potent aminopeptidase inhibitor and a known substrate of PEPT1, suppressed the growth of tumor (Hela cells) xenografts overexpressing PEPT1 by 4-week consecutive oral administration . The higd PEPT1 .Floxuridine, a clinically proven anticancer drug, is commonly used for the treatment of metastases from colon carcinomas and hepatocellular carcinoma . In ordeThe transporter-mediated entry of amino acids into cells is necessary for various cellular functions including protein synthesis, energy metabolism, glutathione synthesis, and others. Moreover, amino acids transporters play important roles in conferring not only drug sensitivity by mediating the uptake of amino acid analog drugs), but also drug resistance by promoting the uptake of essential amino acids for tumor growth and survival. There are 6 major families of amino acid transporters in the solute carrier (SLC) gene superfamily  and the orphan SLC16 monocarboxylate transporter which transports aromatic amino acids .Accumulating evidence in the past two decades has indicated that amino acid availability controls cellular physiology by altered gene expression levels and signal transduction pathways in cancer cells. For example, the mammalian target of rapamycin (mTOR) is a serine/threonine kinase that regulates fundamental biological processes and plays critical roles in cell growth regulation and tumorigenesis , 149. ThSLC38A2, mediates the transport of most small neutral amino acids, including alanine, serine, and glutamine. The mRNA level of ATA1 was markedly induced in human hepatoma cancer cell lines and in patient-derived hepatocellular cancer tissues as well. Enhanced expression of ATA1 is also positively related to the progression of cholangiocarcinoma. Furthermore, silencing ATA1 mRNA expression decreased the viability of HepG2 cells, suggesting that ATA1 is likely essential to tumor survival [Amino acid transporter system A1 (ATA1), encoded by urvival . These sSLC7A5, is responsible for the transport of large neutral, aromatic or branched amino acids from extracellular fluids into the cells. Acivicin is an antineoplastic antibiotic that targets glutamine-dependent amidotransferases in the biosynthesis of purines and pyrimidines [50 values for acivicin to inhibit the gabapentin (a LAT1 substrate) accumulation in the stable HEK-LAT1 cells ranged from 7.9 μM to 340 μM [et al. have recently demonstrated that LAT1 is functionally active in prostate cancer cells (PC-3). Hence, LAT1 transporter may be used as a target for improving the availability of poorly permeable but highly potent anticancer drugs at least in prostate cancer cells [L-type amino acid transporter 1 (LAT1), encoded by midines . The IC5 340 μM . The exp 340 μM . Petel er cells .via limiting nutrient supply to cancer cells and regulating their apoptosis and electrochemical gradients. The SLC transporters expressed in the intestine, liver and kidney are of particular importance as their activity may be critical to systemic exposure and disposition of various anticancer agents, serving as a common basis or determinant for drug-drug interaction, pharmacological effects, and side effects. The function of SLC transporters in anticancer drug disposition and action has been increasingly recognized. However, the major biological implication and pathophysiological function of these membrane proteins are far from clear and under extensive exploration. With advanced knowledge of SLC transporters, their role in the development, optimization, and personalization of anticancer medicine will be further underscored and merited.It is critical to target drugs to tumor cells in order to improve the clinical efficacy and avoid the adverse effects of anticancer drugs. The efficacy of chemotherapy may be largely dependent on the relative activity of transporters in normal and cancer tissues. In addition to already extensively investigated efflux transporters, multiple types of membrane influx transporters, in particular the SLC superfamily members play very important roles in conferring sensitivity and resistance to anticancer agents. These SLC transporters not only directly bring anticancer agents into cancer cells but also serve as the uptake mediators of essential nutrients for tumor growth and survival. The differential expression patterns of SLC transporters between normal and tumor tissues may be well utilized to achieve specific delivery of chemotherapeutic agents. The transporters may be also directly targeted in development of anticancer drugs to increase chemosensitivity, for example,


E. coli ribosomes, namely pseudouridine-modified helix 69 from 23S rRNA and the 790 loop of helix 24 from 16S rRNA. RNase T1 probing, MALDI mass spectrometry, and dimethyl sulfate mapping revealed platination at GpG sites. Chemical probing results also showed platination-induced RNA structural changes. These findings reveal solvent and structural accessibility of sites within bacterial RNA secondary structures that are functionally significant and therefore viable targets for cisplatin as well as other classes of small molecules. Identifying target preferences at the nucleotide level, as well as determining cisplatin-induced RNA conformational changes, is important for the design of more potent drug molecules. Furthermore, the knowledge gained through studies of RNA-targeting by cisplatin is applicable to a broad range of organisms from bacteria to human.Cisplatin is a clinically important chemotherapeutic agent known to target purine bases in nucleic acids. In addition to major deoxyribonucleic acid (DNA) intrastrand cross-links, cisplatin also forms stable adducts with many types of ribonucleic acid (RNA) including siRNA, spliceosomal RNAs, tRNA, and rRNA. All of these RNAs play vital roles in the cell, such as catalysis of protein synthesis by rRNA, and therefore serve as potential drug targets. This work focused on platination of two highly conserved RNA hairpins from  The experimental and expected masses of full-length RNA molecules are listed in 1, two molecular ion peaks ([M + Pt-H]+ or [M + 2Pt-3H]+) were observed for both unmodified and modified H69 (+) (3)2 moieties to the RNA, respectively, with loss of the aqua and chlorido ligands, indicating one or two platination sites for each RNA , was obtained from Alfa Aesar . Silver nitrate was purchased from Fisher Scientific. Dimethylformamide (DMF) was obtained from Acros Organics. The activated complex cis-[PtCl(NH3)2X]+/°, in which X is DMF or NO3−, was prepared by mixing 1:1 equivalents of cisplatin and AgNO3 dissolved in DMF and agitating in the dark overnight at 37 °C [1 was freshly prepared by dilution of cis-[PtCl(NH3)2X]+/° to intermediate stock concentrations with water just prior to the experiments.Cisplatin, at 37 °C . The res260 nm = 189,400 M−1·cm−1; 790 loop ε260 nm = 188,800 M−1·cm−1) -pCp  -pCp, 12 units of T4 RNA ligase, and 1 mM ATP in a 30 μL reaction at 4 °C overnight. Labeled RNAs were ethanol precipitated and purified on 20% denaturing (8 M urea) polyacrylamide gels. The labeled products were visualized by autoradiography, then excised and eluted by the “crush and soak” method in 350 mM NaOAc, pH 5.3, 0.1 mM EDTA buffer overnight at 4 °C. The extracted RNAs were desalted over C18 Sep-Pak cartridges .RNA constructs were radiolabeled at the 3′ end with T4 RNA ligase  and -ATP  and 3 units of T4 polynucleotide kinase (New England Biolabs) in a total volume of 30 μL [RNA constructs were 5′-end labeled in T4 polynucleotide kinase buffer . The RNA products were separated on 20% denaturing polyacrylamide gels. The RNA bands were visualized by UV shadowing and excised from the gel. Bands with slower mobility than the unmodified RNAs were assigned as the platinated products. The platinated RNA products were eluted in 550 mM NH4OAc, pH 5.5, 0.1 mM EDTA buffer and desalted in C18 Sep-Pak cartridges. RNase T1 digestion of platinated and unplatinated RNAs was performed in water for 20 min at 37 °C using 1 unit of enzyme (Sigma). Digested samples were dried and resuspended in deionized water, mixed with saturated 3-hydroxy picolinic acid in 50% acetonitrile, and spotted on a MALDI plate. MALDI-MS analysis was performed on a Bruker Daltonics TOF-300 MALDI Ultraflex instrument equipped with a nitrogen laser (λ 337 nm). The mass spectra of all RNAs were acquired in the positive-ion reflector mode. Peak masses were assigned using Flex Analysis version 2.0 software .The platination reactions of all RNA constructs for RNase T1 mapping studies were performed in buffer B. Prior to platination, the RNAs were renatured by placing the sample tubes in a boiling water bath for 2 min followed by placement on ice. Platination was carried out in the dark at 37 °C for 5 h with a 1:2 ratio of RNA:complex 5–10 × 105 cpm) and unlabeled RNAs (0.7 μM) were combined and renatured in buffer A. Platination reactions were initiated by adding complex 1  to RNA in a final reaction volume of 60 μL. The reactions were incubated in the dark at 37 °C for 3 h. Control RNA samples did not contain platinum complex. Platinated RNA products and unreactive RNAs were separated on 20% polyacrylamide gels, followed by elution with the “crush and soak” method.For chemical probing, gel-purified, radiolabeled RNAs (8 × 10v/v) DMS in water and incubating for 60 s at 90 °C, followed by immediate quenching with stop solution . The RNAs were ethanol precipitated, redissolved in 10 μL of 1 M Tris-HCl, pH 8.2, and then treated with 10 μL of freshly prepared 0.2 M NaBH4 and incubated on ice for 30 min in the dark. The RNAs were ethanol precipitated again and treated with 10 μL of 1 M aniline acetate, pH 4.5, at 60 °C for 30 min in the dark. The samples were dried, lyophilized twice in water, and resuspended in denaturing loading buffer. Samples (approximately 2 × 104 cpm) were run on high-resolution sequencing gels  and visualized by autoradiography.Guanosine-specific reactions  on the p4 cpm) was done by mixing with buffer  and boiling for 90 s followed by quick freezing on dry ice. The samples were thawed and mixed with denaturing loading buffer just prior to electrophoresis.Alkaline hydrolysis of RNA constructs labeled at the 3′ or 5′ ends (1 × 104 cpm) by mixing with buffer  and 0.5 units of RNase T1 followed by incubation at 55 °C for 20 min. The reaction products were mixed with denaturing loading buffer and loaded directly onto 20% denaturing gels.RNase T1 digestion was carried out on end-labeled RNA , and at G785-G786 of the 790 loop, most likely as bis-adducts, in which the chlorido ligands are displaced by two G residues. Previous studies with truncated tRNA molecules showed a preference for platination at neighboring Gs within a G-C-rich wobble-base-pair region [1. Due to the relative simplicity of the reactive motif (GpG), similar platination sites may occur on other RNAs such as mRNA, tRNA, and non-coding RNAs in vivo.The rRNA motifs investigated in this study are highly conserved throughout phylogeny and important for proper ribosome function ,26,33. Tr region . TherefoChemical probing studies also revealed structural alterations in the RNA constructs following drug coordination, as well as the influence of nucleotide sequence on such changes. Comparisons to previous reports on rRNA-cisplatin interactions indicate that the GpG locations within the complete folded secondary structure will impact the reactivity, and not surprisingly, the presence of proteins and RNA-RNA contacts will also play a role. Overall, the data provided here show many similarities of cisplatin coordination to DNA and RNA, such as target preference  and impact on structure. Further studies may reveal how these adducts could further impact the biological function of rRNAs.


Since equal amounts of RNA were loaded for electrophoretic analysis, the conclusion remains valid. Figure 6A now displays the actual actin RNA panels. tk2 mutant SEB is impaired in its ability to induce cytokine gene expression; reduced cytokine mRNA levels are reflected by reduced protein levels.The authors would like to clarify that for some of the actin RNA loading control panels in Figure 2A and those in Figure 6A, a temporary placeholder was left in inadvertently. In Figure 2A, actin RNA was not assayed for pCorrected versions of Figs


Recent evidence suggests that RNA interaction can regulate the activity and localization of chromatin-associated proteins. However, it is unknown if these observations are specialized instances for a few key RNAs and chromatin factors in specific contexts, or a general mechanism underlying the establishment of chromatin state and regulation of gene expression.Here, we perform formaldehyde RNA immunoprecipitation (fRIP-Seq) to survey the RNA associated with a panel of 24 chromatin regulators and traditional RNA binding proteins. For each protein that reproducibly bound measurable quantities of bulk RNA (90 % of the panel), we detect enrichment for hundreds to thousands of both noncoding and mRNA transcripts.For each protein, we find that the enriched sets of RNAs share distinct biochemical, functional, and chromatin properties. Thus, these data provide evidence for widespread specific and relevant RNA association across diverse classes of chromatin-modifying complexes.The online version of this article (doi:10.1186/s13059-016-0878-3) contains supplementary material, which is available to authorized users. Control of gene expression is mediated by transcriptional and post-transcriptional mechanisms. Standard models hold that DNA binding proteins  respond to sequence composition and chromatin context to promote transcription of RNA molecules , 58, 62.RNA’s role in chromatin formation has long been studied , 63. RecMultiple groups recently mapped the full spectrum of RNA interactions of one of Xist’s silencing partners, PRC2 , 39, 89.Here, we address this question by surveying RNA interactions of 24 proteins using the same cell type (K562) and cross-linking conditions. Our set includes both traditional RBPs and chromatin-associated proteins (CAPs) that lack classically defined RNA binding domains. We refined a formaldehyde cross-linking RNA immunoprecipitation technique followed by deep sequencing (fRIP-Seq) to perform triplicate experiments that showed high concordance, exceeding previous genome-wide surveys of individual CAPs. We detected widespread binding of CAPs to both lncRNAs and mRNAs, driven by a mix of gene structure and sequence composition preferences. We uncovered many intriguing examples of RNA binding relating to the local chromatin, suggesting that RNA indeed plays important roles in creating and/or maintaining chromatin states. Our data provide a powerful, novel resource towards further dissecting the interplay of RNA and epigenetic regulation across diverse chromatin regulatory complexes.To survey a broad panel of RNA–CAP interactions, we required an immunoprecipitation (IP) method optimized for maximal RNA and protein recovery that is specific, scalable, quantitative, reproducible, and similar to chromatin immunoprecipitation (ChIP) conditions known to readily isolate CAP complexes and recover DNA–CAP interactions. Existing cross-link IP (CLIP) methods for measuring direct RNA–protein binding require large amounts of input RNA, scale poorly for survey purposes across multiple antibodies, and are challenging to assess quantitatively , 42, 68.To address these specific needs, we modified existing RNA IP (RIP) and ChIP protocols that employ formaldehyde cross-linking to prevent post-lysis re-association or “mixing” of RNA-protein complexes similar to RIPiT-Seq , 77, 78.After cross-linking, a 90 second sonication was sufficient for nuclear lysis and chromatin shearing, but gentle enough to lightly fragment RNAs. Following incubation with a targeted antibody, we isolated bound proteins with magnetic beads and purified associated RNA , HNRNPU enriches for cytoplasmically localized transcripts, suggesting that HNRNPU interacts with these RNAs after cell lysis  that did not appear to enrich RNA above background as a negative control for background binding of RNA to protein. We also included one antibody that appeared to cross-react with many proteins (SETD2) as a negative control example of a non-specific antibody. To identify the captured RNA associated with our total panel of 25 fRIP experiments, we performed high throughput RNA sequencing (RNA-Seq) on the protein-associated RNA alongside input RNA collected from lysate.Each fRIP-Seq was conducted in biological triplicate  in K562 cells, allowing a thorough assessment of the reproducibility of the experiments. Replicates exhibited remarkable consistency, demonstrated by hierarchical clustering of log2 fold changes of fRIP-Seq over input RNA-Seq Fig. . For eveFurther, these data recapitulated known RNA–protein interactions. For example, we observed specific binding of ADAR to Alu sequences, for which they have a well-documented affinity  , 40, fRIWe observed slight clustering of sequencing reads over specific regions of RNAs, due to sonication shearing prior to protein–antibody pull down. From this coverage bias, we were able to broadly determine regions of protein interaction, but with lower resolution than would be needed for direct binding site detection. For instance, alignment coverage for PABP, a protein that binds polyadenylated transcript tails, was highest over the 3’ end of transcripts . Prior studies establish a precedent for negative controls unexpectedly binding specific RNA targets . As a seIn studying positional preferences along transcripts, we also observed that fRIP alignments from different proteins varied along a spectrum of the proportion that aligned to introns versus exons. We hypothesized that the proteins bind at different stages during the lifecycle of the RNAs’ post-transcriptional processing. To compare the proteins, we computed the percentage contribution to total gene FPKM (fragments per kilobase per million fragments) by purely exon isoforms versus unspliced pre-RNA isoforms see “”. In thep value < 1e-10). The intron binders were more enriched in the fRIP for low abundance genes, particularly those with <10 FPKM .To test this hypothesis, and rule out the possibility that the FPKM-dependence of fRIP/input fold change is an artifact of the challenge of estimating abundance from incompletely spliced RNAs, we examined single exon genes. If the same dependence of fold change on FPKM appears for single exon genes, where the challenge of quantifying intron reads is absent, we may proceed with more confidence in the functional relevance of our observations. Indeed, single exon genes demonstrated the same influence of abundance on fRIP/input fold change Fig. . FPKM veSubstantial previous work has identified important functional roles for lncRNAs interacting with CAPs , 85, 88.Low abundance mRNA versus lncRNA enrichment spanned a wide range across the panel Fig. . SurprisWe next explored the idea that lncRNAs, as potential “guides” for chromatin modifying complexes, might be more selective in their associations with CAPs compared with mRNAs. To determine the selectivity of lncRNAs for CAPs in our panel, we calculated a CAP binding specificity score for each transcript using an entropy-based metric that relies on Jensen-Shannon (JS) divergence “” 5]. Th. Th5]. TGiven that CAPs interact widely with mRNAs, we next asked whether these mRNAs belong to coherent gene expression programs. We took advantage of the fact that, unlike ncRNAs, mRNAs have vast collections of functional annotations. We clustered all genes into ten discrete groups using k-medoid clustering on their fRIP-Seq enrichments to isolate distinct patterns amongst genes and between fRIPs Fig. ”. StrongWe analyzed each cluster for enrichment of a variety of functional annotations using the Database for Annotation, Visualization and Integrated Discovery DAVID; “” 27]. W. W27]. WWhile the underlying biology driving the observed functional relationships between fRIP-enriched sets of genes is unclear, their existence argues that the interactions captured via fRIP-Seq are nonrandom and that the widespread mRNA-CAP associations may be biologically relevant.We next turned to exploring the RNA properties that determine protein binding. For example, it has been reported that EZH2 has greater in vitro affinity for long RNAs , 13. To Recent studies have uncovered a regulatory layer interfacing co-transcriptional RNA splicing and chromatin , 73, 91.To differentiate the role of length versus exon number, we computed a semipartial correlation with fRIP/input fold change for each. More specifically, we performed a regression for one attribute to predict fold change and computed the Spearman correlation between the residuals and second attribute. If only one attribute (such as length) truly mattered, the regression for length would model the data completely and no correlation with exon number would remain in the residuals. Comparing these two statistics, we found that numerous proteins that appear to depend on transcript length  respond far more to the number of exons Fig. . For theSUZ12 exemplifies this exon number preference. We observed that SUZ12 fRIP/input fold change has Spearman correlation 0.37 with exon number after length-normalization, but an insignificant 0.03 correlation with length after exon-normalization. To further demonstrate this property, we observed that a positive correlation between transcript length and SUZ12 fRIP/input fold change was absent among sets of transcripts with an equal number of exons Fig. , but greIn summary, structural properties of the genes affect their binding by CAPs. Though previous work has characterized a preference of PRC2 subunit EZH2 for longer transcripts, we found here that for PRC2 subunit SUZ12, the number of exons in the transcript, rather than its length, is a more dominant determinant of binding.We next asked whether our panel of CAPs and RBPs have sequence composition binding preferences in addition to the gene structure preferences described above. To this end, we performed a search for motifs whose presence in gene transcripts had high mutual information with the transcripts’ fRIP/input fold changes for each protein “”.Even though the fRIP-Seq protocol does not include shearing RNA down to binding site resolution, we discovered many significant motifs in transcript-wide searches. The sequence binding preferences of traditional RBPs HUR, HNRNPH1, and HNRNPU have been previously explored, and we recapitulated those preferences here with U-rich motifs for HUR , 48, 61 Having established that fRIP-Seq can find known binding motifs, we turned to the CAPs, for which knowledge of sequence binding preferences is sparse. As with the traditional RBPs, we found many motifs for the CAPs, which were significant at similarly high levels. SUZ12 had affinity for the motif GAAGMHGAW and other AG-rich motifs, exemplified by the EIF5B locus Fig. . SupportDNMT1 was enriched for a GC-rich motif, but only in lncRNAs  can serve as a source of sequence motifs with an inherent evolutionary history. Thus, we also asked whether specific classes of TEs in the transcripts affected protein binding. Mentioned above and well-known, ADAR binds Alu elements in both orientations  mapped in K562. Because some chromatin marks are more relevant in either the promoter or spanning body of genes, we computed promoter-based and gene body-based statistics to measure the magnitude of binding for each mark and gene see “”.First, we asked whether CAPs bind RNA from loci where they concurrently bind DNA, perhaps because the proteins bind the RNA due to its proximity. We examined the Spearman correlations across all genes between fRIP-Seq and ChIP-Seq for 11 proteins with both data types , fRIP-Seq did not map RNA interaction partners of two proteins known to function together in a complex with the concordance one might expect. There are many possible contributing factors for this observation, primarily revolving around the fact that the RNA binding properties of these proteins are poorly understood. The proteins exist in the cell both in and out of the complex, and there is no reason to believe that the proteins in isolation would bind to similar transcripts as each other. If fRIP-Seq is preferentially capturing these RNA interaction partners, then differing profiles would be the expected result.We observed RNA–protein interactions at various stages of RNA processing, indicated by the quantity of intron alignments in each fRIP-Seq. Known co-transcriptional binders ADAR, HNRNPH1, HNRNPU, and HUR had the most intron alignments. Co-transcriptional binding also led to different patterns relating gene abundance to fRIP/input fold change. These correlations with abundance were recalled and normalized for in downstream analyses, such as comparisons with local chromatin.Though fRIP-Seq does not pinpoint interaction sites to the same resolution as CLIP-Seq, we nevertheless discovered many binding preferences for the proteins measured using transcript-wide analysis. This included reproducing the known sequence motifs bound by ADAR, HNRNPH1, HUR, and HNRNPU from whole transcript motif searches. Preferred sequence motifs were found for CAPs as well, with similar degrees of evidence as those known motifs. For example, we discovered an AG-rich motif predictive of SUZ12 binding.In addition to sequence preferences, we found that fRIP-Seq enrichment for many proteins correlated with transcript length and number of exons. In particular, though a PRC2 preference for longer RNA transcripts had previously been observed, we found here that length correlation manifests through a far stronger preference by SUZ12 for transcripts with more exons. Current models for the role of PRC2–RNA interactions posit that PRC2 maintains gene silencing by writing the silencing mark H3K27me3 only in the absence of RNA , 30, 31.Much previous work on RNA binding partners of CAPs has focused on ncRNA. Here, we surprisingly detected substantial binding of CAPs to mRNAs, too. Although we observed weaker enrichments of lncRNAs by CAPs in comparison with mRNAs, we did detect that lncRNAs are more selective and associate with fewer CAPs on average than mRNAs. However, our data overall suggest that lncRNA-CAP binding is not the dominant feature of the RNA–CAP interactome.Instead, RNA may more generally provide a communication medium between the genome and CAPs. We observed widespread correlations between CAP fRIP-Seq enrichment and local chromatin state. Matching a previous analysis, which suggested that DNMT1 would not methylate DNA in the presence of RNA , the proOur analysis leaves open the question of what happens to mRNAs bound by CAPs. One could imagine these interactions are transient light disturbances to the mRNA on its journey to translation. Alternatively, a small proportion of transcribed mRNA copies may be diverted to permanent interaction with CAPs and sequestered away from translation. Follow-up work will be needed to differentiate these outcomes and clarify the role of mRNAs in chromatin modification processes.Our introduction of fRIP-Seq and panoramic profiling of RNA interactions with chromatin-associated proteins will enable many future analyses to further dissect the role of RNA in chromatin processes. The dual nucleic acid affinity of CAPs is an intriguing feature that, with further study, may unify the separate paradigms of RNA-mediated chromatin regulation of transcription with chromatin-mediated post-transcriptional regulation of RNA. While we have provided a static snapshot of the cell, the open questions of how chromatin is modified are most relevant to the dynamics of development and disease. The framework applied here provides an important lens with which to study the chromatin regulation of these cell state changes.K562 cells  were grown in RPMI 1640  with 10 % fetal bovine serum (FBS) and 1 % Antibiotic-Antimycotic 100× . We collected cells with a gentle 5 minute spin at 500 g and washed these with room temperature phosphate-buffered saline (PBS). We re-suspended at 5e6 cells per ml in room temperature RPMI media without FBS or antibiotic-antimycotic and added formaldehyde to a final concentration of 0.1 %. We cross-linked at room temperature for 10 minutes and then halted it by quenching for 5 minutes at room temperature after adding glycine to a final concentration of 125 mM at a medium pace. We spun cells for 5 minutes at 500 g and then washed them twice in 4 °C PBS. We flash froze pellets of 10e6 cells and stored them at −80 °C.HuR antibody . After addition of antibody, we rotated lysate at 4 °C for 2 hours before adding 50 μl of Dynabeads® Protein G. We rotated beads and lysate at 4 °C for 1 hour before washing twice with 1 mL of fRIP binding/washing buffer + 1× PIC and 100 U/mL RNaseOUT. After the final wash, we removed the supernatant and froze and stored the beads at −20 °C.We re-suspended frozen pellets in 1 mL of RIPA lysis buffer (50 mM Tris (pH 8), 150 mM KCl, 0.1 % SDS, 1 % Triton-X, 5 mM EDTA, 0.5 % sodium deoxycholate, 0.5 mM DTT (add fresh) + protease inhibitor cocktail  + 100 U/ml RNaseOUT™ ). We incubated cells at 4 °C for 10 minutes before lysing on a Branson® digital sonifier using 10 % amplitude for 0.7 seconds on and 1.3 seconds off at 30 second intervals for a total of 90 seconds. We used chilled tube holders and swapped them out between shearing runs to reduce temperature elevation. After lysis, we spun the lysate at 4 °C max speed for 10 minutes. We collected supernatant and diluted by adding equal volume of fRIP binding/wash buffer , 5 mM EDTA, 0.5 % NP-40, 0.5 mM DTT (add fresh), 1× PIC (add fresh), 100 U/mL RNaseOUT (add fresh)). At this point, we removed 50 μl of lysate for input sample and stored at −20 °C for later RNA purification and library construction. After dilution, we clarified the lysate by passage through a 0.45 μM syringe filter. We then “pre-cleared” filtered lysate by incubating with Dynabeads® Protein G  at a concentration of 25 μl of beads per 5 million cells for 30 minutes at 4 °C with slow rotation. We flash froze pre-cleared lysate in 1 mL aliquots of ~5 million cells and stored it at −80 °C. For fRIP, we thawed lysate on ice and added 6 μg of Qiagen miRNeasy® Mini Handbook. We eluted RNA in 15 μl of RNase-free water. To remove ribosomal RNA, we fed ≥70 ng of input and fRIP RNA into the Ribo-Zero™ Magnetic Gold Kit  followed by a cleanup using Agencourt RNAClean XP beads  and elution with 19.5 μL of Elute, Prime, Fragment mix from the TruSeq RNA Sample Preparation Kit . We performed library construction per the vendor’s instructions, starting with the “Incubate RFP” step. We pooled the resulting cDNA libraries and subjected them to paired-end sequencing on an Illumina HiSeq 2500 at a depth of 31 base pairs per read.We re-suspended the frozen beads in 56 μl of RNase-free water and added 33 μL of 3× reverse-crosslinking buffer (3× PBS (without Mg or Ca), 6 % N-lauroyl sarcosine, 30 mM EDTA, 15 mM DTT (add fresh)), 10 μl of Proteinase K , and 1 μl of RNaseOUT to both the re-suspended beads and input sample. We performed protein degradation and reverse-crosslinking for 1 hour at 42 °C, then another 1 hour at 55 °C. We added beads and reaction buffer to 1 mL of TriZol . After agitation, we added 200 μl of chloroform followed by ~15 seconds of vigorous agitation and a 20 minute microcentrifuge spin at 4 °C max speed. We collected the aqueous layer, added it to 750 μl of ethanol + 1 μl GlycoBlue™, and ran it over a Qiagen RNeasy® min-elute column . We extracted RNA using the buffer RWT/3× isopropanol modification detailed in “Appendix B: Optional On-Column DNAse Digestion…” of the We aligned sequencing reads to human genome assembly hg19 and GENCODE v18 reference annotation  using ToWe limited cluster analysis in Fig. We performed K-medoid clustering (using the R package PAM) on only genes that were called as significantly enriched by Cuffdiff and enriched at greater than twofold over input in at least one replicate. We clustered using k = 10 and Euclidean distance. To order the clusters for visual representation in a heat map Fig. , we perfWe used FIRE to search for motifs that have high mutual information with fRIP-Seq enrichment . FIRE reChoosing an appropriate seed size for motif searches on full transcripts of varying size is more challenging than the typical application of equally sized promoters. We sought to focus on a middle range of the transcript length distribution so that the chosen seed size was not wildly inappropriate for many transcripts. Accordingly, in an initial analysis we allowed only transcripts whose length is within a factor of sqrt(10) from the distribution median; thus, all included transcripts have length within a factor of 10 of all other transcripts. We then chose the smallest k-mer seed size for which one would expect every k-mer to occur by chance in <1 % of transcripts of that length. Because transcript lengths are log-normally distributed, half of the transcripts are longer and half are shorter than the median transcript length, for which the chosen seed was aimed. For mRNAs, the median transcript length in GENCODE v18 is 1997 nucleotides, suggesting 10-mer seeds. Because this large k-mer size might miss some of the smaller k-mer motifs typical of RBPs , we perfIf sequence preferences are driven by more general sequence composition preferences that cannot be so easily represented by regular expression or position weight matrix motif models, then fRIP-Seq enrichment of gene transcripts may be more effectively modeled by considering all k-mers. To this end, we performed a regression to assign weight coefficients to all k-mers for the same input datasets described above. To avoid overfitting, we performed ridge regression, which minimizes not only the distance between model predictions and actual values but also the magnitude of the weights. We chose the alpha parameter that varies the emphasis of these two competing objectives by evaluating fivefold cross-validated mean squared error over a parameter grid. More complex techniques  failed to yield significant gains.To assess the concordance between fRIP-Seq and CLIP-Seq, we downloaded six datasets mapping five proteins (HNRNPU , CTCF 7, HUR 40, 61, IMPhttps://www.encodeproject.org.We downloaded aligned sequencing reads in BAM format for all K562 ChIP-Seq experiments performed by the ENCODE project from We assigned every transcript two scores measuring the enrichment of ChIP alignments over input alignments. For the first score, we computed log2 ChIP/input alignments for a promoter region of 3 kb, centered at the transcription start site. For the second, we computed log2 ChIP/input alignments for the entire transcript span. We normalized alignment coverage by the total number of mapped reads in the ChIP-Seq experiment. To assign scores to genes consisting of multiple isoforms, we computed a weighted average of the isoform scores, weighting isoforms by their FPKM.To measure the relationship between the fRIP/input fold change and ChIP scores across all abundance levels, we first computed separate Lowess nonparametric regressions of FPKM versus ChIP score separately for genes bound and unbound in the fRIP-Seq experiment. Next, we integrated the difference between these two regression lines over the distribution of FPKM. This statistic is conceptually similar to computing the area of the region in between the two regression lines in the FPKM versus ChIP score plots, where we more heavily weight more likely FPKM levels.Not applicable.fRIP-Seq data are available through the Gene Expression Omnibus at accession GSE67963.CLIP-Seq data were obtained for IMP1 from GSE21918, HNRNPH1 from GSE23694, HUR from GSE28865 and GSE29780, HNRNPU from GSE34491, and CTCF from GSE3554.Depletion RNA-Seq data were obtained for CTCF from GSE44267, SUZ12 from GSE50177, HUR from GSE28865, and HNRNPU from ENCSR732ICL, and IMP1 from ENCSR629EWX.


The number of RNA-Seq studies has grown in recent years. The design of RNA-Seq studies varies from very simple  to very complicated . Most of these publically available RNA-Seq studies are deposited in NCBI databases, but their metadata are scattered throughout four different databases: Sequence Read Archive (SRA), Biosample, Bioprojects, and Gene Expression Omnibus (GEO). Although the NCBI web interface is able to provide all of the metadata information, it often requires significant effort to retrieve study- or project-level information by traversing through multiple hyperlinks and going to another page. Moreover, project- and study-level metadata lack manual or automatic curation by categories, such as disease type, time series, case-control, or replicate type, which are vital to comprehending any RNA-Seq study. Here we describe “MetaRNA-Seq,” a new tool for interactively browsing, searching, and annotating RNA-Seq metadata with the capability of semiautomatic curation at the study level. High-throughput gene expression studies are pivotal in functional biology and genomics research . Until lhttp://metarnaseqdb.screensifter.com/.The importance of biocurating biological databases was realized in recent years , 12. TexThe metadata for RNA-Seq studies from multiple resources at NCBI were retrieved using the NCBI entrez direct utility . ScriptsThe MetaRNA-Seq web tool provides enhanced utilization of metadata for RNA-Seq studies in NCBI resources with semiautomatic curation, restructures the presentation, and allows convenient browsing of all of the details in a single window with a study-level search. MetaRNA-Seq currently has metadata information for 1508 human RNA-Seq studies and is updated every quarter. The main web interface of MetaRNA-Seq is shown in In contrast to the NCBI SRA interface, which by default provides an experiment-level view of the results of a search query, MetaRNA-Seq presents a study-level browsing and filtering mechanism. All RNA-Seq studies are presented in a table in the MetaRNA-Seq web interface. The table contains important columns, such as study accession, name, title, number of samples, number of experiments, number of runs, and average number of reads and bases. The table is filtered based on the search. The studies table can be sorted by double clicking any column. Study details appear for any selected project, including title, name, abstract, material, method, bioproject description, GEO design, and summary. This table of RNA-Seq studies also provides manual annotation details for the RNA-Seq study if that study is annotated using the MetaRNA-Seq annotation interface. In addition, the MetaRNA-Seq interface provides hyperlinks to NCBI SRA or PubMed if the user needs additional information about a study.Suggestion: Cell Lines - -> Yes: Cell line derived from Merkel Cell Carcinoma (10 samples) Confidence: 25%” and hint text “Suggestion: Tissue - -> Yes: FFPE Merkel Cell Carcinoma (16 samples), FFPE Basal Cell Carcinoma (6), FFPE Normal skin (6), FFPE Squamous Cell Carcinoma (2) Confidence: 40%,” respectively  improve experimental settings by comparing different platforms under similar experimental conditions, (2) compare whole transcripts of different cell lines, tissues, diseases, and experimental conditions  to select the most suitable conditions for newly designed experiments, and (3) estimate the number of samples and number of biological and technical replicates needed to obtain significant and relevant data. However, NCBI database interfaces are unable to provide the study-level search and comparisons required by the end user. Using annotated studies with the guided search function provided by MetaRNA-Seq is strongly recommended to supplement NCBI RNA-Seq metadata resources. The annotation capabilities provided in MetaRNA-Seq can be utilized by end users to support the community and improve the search process in subsequent sessions. MetaRNA-Seq can easily be turned into a crowd sourcing-based annotated resource. Currently, the interface provides all of the annotation details for the studies with annotation completed by all biocurators, as the number of biocurators is limited to our lab at the time of writing this paper. In the future, if the number of biocurators increases or if multiple biocurators handle a single study, manual annotation study details can be presented based on crowd statistics. MetaRNA-Seq currently presents the metadata for RNA-Seq studies ofThe NCBI provides many options for finding RNA-Seq data. However, the large amount and complex nature of RNA-Seq data that can be retrieved using NCBI data resources present difficulties for researchers. An annotated resource of RNA-Seq metadata is needed to better serve the community. A tool such as MetaRNA-Seq can be a great resource for both annotating and browsing RNA-Seq metadata, and it can provide better tools for more effective data interrogation. Enhanced access to RNA-Seq metadata could also potentially allow the creation of a customized RNA-Seq metadata database and has the potential to be turned into a crowd sourcing-based annotated resource for the benefit of the research community.


Ectocarpus siliculosus. We describe a workflow comprising cultivation and fixation of algae on glass slides, laser microdissection, and RNA amplification. To illustrate the effectiveness of the procedure, we show qPCR data and metrics obtained from cell-specific transcriptomes generated from both upright and prostrate filaments of Ectocarpus.Laser capture microdissection (LCM) facilitates the isolation of individual cells from tissue sections, and when combined with RNA amplification techniques, it is an extremely powerful tool for examining genome-wide expression profiles in specific cell-types. LCM has been widely used to address various biological questions in both animal and plant systems, however, no attempt has been made so far to transfer LCM technology to macroalgae. Macroalgae are a collection of widespread eukaryotes living in fresh and marine water. In line with the collective effort to promote molecular investigations of macroalgal biology, here we demonstrate the feasibility of using LCM and cell-specific transcriptomics to study development of the brown alga  However, this technique has its own pitfalls, requiring intensive bioinformatic analysis of raw sequence reads in order to accurately extract gene expression data.Transcriptomics allows the expression profiles of large sets of genes to be monitored in a single experiment, and as a consequence its application has had an impact in virtually every field of biology. Transcriptomics has particularly revolutionized developmental biology because body plans are specified by complex regulatory networks of genes that are expressed in precise spatial and temporal domains. Previously examined with microarrays, transcriptomes are ever more frequently studied using next generation sequencing techniques, an approach termed RNA-Seq  has been used for this difficult task , and brown algae belong to the Stramenopile group Baldauf, . All thrE. siliculosus. Each technical step and its optimization is described and, as a proof of principle, we present metrics of transcriptomes obtained after isolation of three different cell-types from E. siliculosus thallus. Finally, we discuss the potential of this approach to address significant biological questions.Here we report the use of LCM to generate cell-specific transcriptomes of the brown alga, Ectocarpus is prostrate and filamentous  and then sections are mounted on special microscope slides covered with a polyethylene naphthalate (PEN) membrane. Sections are attached to the membrane, and after the laser beam has cut the relevant cell-type, both membrane and cell-type are lifted into the collection vessel. The main purpose of the membrane is to reduce the attraction between section and slide and thus to lower the energy required to lift the sample. Because the early sporophyte of s Figure , and lats Figure  , and cross-linking fixatives that create chemical bonds between proteins and lipids  . Consequently, we attempted to fix slide-grown Ectocarpus thalli by directly immersing the slides in a solution of 100% acetone for 30 min. The morphology of the fixed thalli was relatively well conserved but clear identification of cell-types was impaired by a thick precipitate of salts from the culture medium that surrounded the filaments  Ruzin, . As comp) Ruzin,  and thus) Ruzin, , Arabidos Figure . TherefoEctocarpus filaments expand by tip growth  in sterile Millipore-filtered water and then the slide edges dried with tissue prior to fixation in 100% acetone. As a consequence, a much clearer view of cell-types along the filaments was obtained Figure . HoweverEctocarpus in Farmer's fixative  which is the most commonly used fixative in LCM-based transcriptomic studies . Pretreatments with sorbitol greatly improved morphological preservation and cellular integrity, but unfortunately led to a layer of sorbitol sticking to the surface of the slide, rendering laser cutting and light pulse lifting ineffective. We also attempted to fix Fixed slides were stored at room temperature in boxes that had been washed with RNAaseZap (Life Technologies) and that contained silica gel. LCM was performed within a few days after fixation.Ectocarpus displays a high level of developmental plasticity, both during the growth of filaments  after cDNA second strand synthesis. This approach generates antisense RNA because the T7 promoter is linked to the 5′ end of the polyA hybridizing primer. A second round of amplification can be performed to further amplify the initial quantity of RNA. The relative quantities of each RNA species present in the initial extract are reportedly conserved in the final product, however, this method has several drawbacks. First, the products are notably 3′ biased due to the polyA priming, and this can severely impair de novo transcriptome assembly due to lack of representation of 5′ sequences. Second, the method cannot capture non-polyadenylated species of RNA, e.g., long and short non-coding RNA, and microRNAs  and Tubulin subunit α (TUA). These genes were shown to be expressed at a high level: ~2500 transcript copies present per ng of total RNA for EF1α and ~1200 for TUA  were reasonably close to the ratios reported for these genes in overall Ectocarpus tissues  were also measured in the amplified cDNAs. Although UBCE transcripts could be amplified in these samples, reliable quantification was not possible because transcript levels were too variable between the three replicates . Notably accumulation levels of UBCE transcripts are much lower than EF1α and TUA transcripts, as reported in Le Bail et al.  were represented. Reads were cleaned for low quality and adapter sequences, and approximately 13 million clean reads were obtained for each sample. Every read had an overall Q20 > 97% .We first examined the ribosomal RNA (rRNA) contamination in reads using SortMeRNA v1.7 . We noted the existence of highly repeated kmers at the beginning of reads. The kmers assembled as a 14 nucleotide long sequence (AACTTTGTGTTTGA) that was present at the beginning of ~0.3% of reads. Truncations of the sequence toward the 3′ end were also detected at high rates explaining the highly repeated kmers revealed by FastQC. When attempting to further extend the sequence, a 33 nucleotide long sequence was extracted that forms a hairpin with a loop of 5 nucleotides in the middle (TCAAACACAAAGTTACCTAAACTTTGTGTTTGA). This long sequence was associated with random reads, at no precise location along the reads, and was present in about 0.1–1.5% of the reads either before or after rRNA filtering. Finding this hairpin prompted us to check our other NuGEN amplified transcriptomes—both the hairpin and its 3′ moiety were identified . Similar sequences could not be identified in any of our other RNA-Seq data that was generated on the same Illumina platform but from non-amplified total RNA. Given the non- random location of the 3′ stem of the hairpin, i.e., at the beginning of the reads, and the specificity of the hairpin to the NuGEN amplified cDNA, we believe this sequence results from the amplification process. This result is in accordance with a previous study that hypothesized the formation of hairpins during NuGEN amplification based upon biochemical evidence  (Table Reads from A-type and B-type cells mapped to 10613 (64%) and 10583 (63.8%) transcripts respectively over the 16580 transcripts defined in the %) Table , a value%) Table . As expe%) Table . Sample %) Table  showed t%) Table , thus co−5 and represented as a MA-plot  to the respective mRNA sequence, and then calculating the ratio between the two . These findings confirmed that accurate quantification of lowly expressed genes is difficult to achieve after RNA-Seq of amplified cDNAs.Differential gene expression between the A- and B-type cells was analyzed using the EdgeR package  routinely cultured in a growth chamber in natural sea water supplemented with Provasoli medium (NSWp) (Starr and Zeikus, EF1α, TUA, and UBCE genes were amplified according to Le Bail et al. (Mito-spores were obtained from l et al.  except tThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


RNA sequencing (RNA-Seq) is often used for transcriptome profiling as well as the identification of novel transcripts and alternative splicing events. Typically, RNA-Seq libraries are prepared from total RNA using poly(A) enrichment of the mRNA (mRNA-Seq) to remove ribosomal RNA (rRNA), however, this method fails to capture non-poly(A) transcripts or partially degraded mRNAs. Hence, a mRNA-Seq protocol will not be compatible for use with RNAs coming from Formalin-Fixed and Paraffin-Embedded (FFPE) samples.To address the desire to perform RNA-Seq on FFPE materials, we evaluated two different library preparation protocols that could be compatible for use with small RNA fragments. We obtained paired Fresh Frozen (FF) and FFPE RNAs from multiple tumors and subjected these to different gene expression profiling methods. We tested 11 human breast tumor samples using: (a) FF RNAs by microarray, mRNA-Seq, Ribo-Zero-Seq and DSN-Seq (Duplex-Specific Nuclease) and (b) FFPE RNAs by Ribo-Zero-Seq and DSN-Seq. We also performed these different RNA-Seq protocols using 10 TCGA tumors as a validation set.The data from paired RNA samples showed high concordance in transcript quantification across all protocols and between FF and FFPE RNAs. In both FF and FFPE, Ribo-Zero-Seq removed rRNA with comparable efficiency as mRNA-Seq, and it provided an equivalent or less biased coverage on gene 3′ ends. Compared to mRNA-Seq where 69% of bases were mapped to the transcriptome, DSN-Seq and Ribo-Zero-Seq contained significantly fewer reads mapping to the transcriptome (20-30%); in these RNA-Seq protocols, many if not most reads mapped to intronic regions. Approximately 14 million reads in mRNA-Seq and 45–65 million reads in Ribo-Zero-Seq or DSN-Seq were required to achieve the same gene detection levels as a standard Agilent DNA microarray.Our results demonstrate that compared to mRNA-Seq and microarrays, Ribo-Zero-Seq provides equivalent rRNA removal efficiency, coverage uniformity, genome-based mapped reads, and consistently high quality quantification of transcripts. Moreover, Ribo-Zero-Seq and DSN-Seq have consistent transcript quantification using FFPE RNAs, suggesting that RNA-Seq can be used with FFPE-derived RNAs for gene expression profiling.The online version of this article (doi: 10.1186/1471-2164-15-419) contains supplementary material, which is available to authorized users. The development of massively parallel sequencing for use in gene expression profiling is known as RNA-sequencing (RNA-Seq). RNA-Seq has had an enormous impact on gene expression studies. Compared to hybridization-based technologies like DNA microarrays, it provides consistent quantification and manifests its superiority in terms of the dynamic range, sampling depth, and has independence from pre-existing sequence information , 2. RNA-0t-kinetics-based normalization method to deplete abundant sequences that reanneal quickly, such as those derived from the highly abundant rRNAs and tRNAs [To allow for mRNA/gene detection, highly abundant ribosomal RNAs (rRNAs) must be removed from total RNA before sequencing. One standard solution is to enrich for the polyadenylated (poly(A)) RNA transcripts  with oligo (dT) primers, similar to how DNA microarrays are primed; however, this method eliminates all non-poly(A) RNAs in addition to rRNAs. Recent studies suggested that certain non-polyA RNAs, either non-coding or protein coding, are functionally important –15. Mored tRNAs . In thisTo rigorously evaluate the feasibility of reproducible gene expression profiling using RNA from clinically relevant FFPE materials, we collected FFPE and fresh-frozen (FF) tumor RNAs for matched sets of tumors from two different sources (UNC and TCGA). Most tumors were subjected to gene expression profiling using six different methods that included: 1) Agilent DNA microarrays using FF RNA, 2) mRNA-Seq using FF RNA, 3) Ribo-Zero-Seq using FF RNA, 4) DSN-Seq using FF RNA, 5) Ribo-Zero-Seq using FFPE RNA, and 6) DSN-Seq using FFPE RNA; see Figure The efficiency of rRNA removal is a key factor to maximize reads mapping to transcripts, because if left alone, rRNAs make up >80-90% of the total RNA of an un-enriched sample . Due theThe precision of RNA-Seq gene quantification is directly dependent on the number of reads that are mapped to transcripts, thus we first assessed the fraction of reads aligning to the reference human genome UCSC hg19  relative coverage of exons, introns, and intergenic regions, and (b) uniformity of transcript coverage.In FF samples, bases mapping to transcripts (i.e. coding and UTR regions) constituted 62.3% total bases in mRNA-Seq, while a marked reduction was observed in the two rRNA-depletion protocols  for the read coverage of the 1000 most highly expressed transcripts  enrichment strategies yield an accurate and reproducible measurement of transcript abundance with a wide dynamic range , 20, 21.Additional quality assessments were made on the TCGA dataset, to account for the fact that a much smaller set of reads were mapped to transcriptome in RNA depletion protocols. We generated eight technical replicates with the Ribo-Zero-Seq-FFPE protocol to balance the total number of transcriptome reads for the comparison with FF mRNA-Seq. The assessment of technical reproducibility suggested that these FFPE replicates were indistinguishable (Pearson =0.991). The correlation between Ribo-Zero-Seq on FF and FFPE as well as between Ribo-Zero-Seq-FFPE replicate pairs has also been confirmed in Norton et al. .Lastly, Hierarchical clustering analysis provides a global examination whether biologically relevant expression signatures are consistently measured by distinct protocols. In this example, we tested whether the same sample assayed by different protocols “paired” or “partnered” together; if so, then this is a very high level of assay validation as not only are the overall subtype expression profiles maintained, but also the profiles that are unique to that sample are maintained. We performed hierarchical clustering analysis of the RNA-Seq data using a previously published ‘intrinsic gene list’   on three Basal-like and three Luminal samples of UNC dataset that were sequenced by mRNA-seq, Ribo-Zero-Seq and DSN-Seq respectively. Comparison of the top 500 most variably expressed genes between Basal-like and Luminal tumors revealed that each pair of RNA-Seq protocols shared about 350 differential expressed genes, and more than 300 genes were consistently identified by all the protocols . We identified 410 genes with a FDR of 0 that were differentially expressed between mRNA-Seq and Ribo-Zero-Seq  includes 20,531  genes. To reduce the noise, we only counted genes as present if there were 3 or greater read counts. Using the average number of genes detected on our Agilent microarrays as the baseline , 13.5 million reads from FF mRNA-Seq libraries would allow detection of the same number of genes Figure , which iThe growing popularity of RNA-Seq makes it one of the more desired methods to explore the transcriptome. Preparing RNA-Seq libraries with poly(A) enrichment provides an accurate method to characterize mRNAs, which is functionally equivalent to what DNA microarrays have been accomplishing for more than a decade. However, certain biologically relevant RNA species that do not possess poly(A) tails are largely undetected using a poly(A) selection protocol. In addition, FFPE samples, such as those collected as part of standard medical practice, also require library preparation methods that do not rely on the intact poly(A) structure due to the highly degraded nature of the FFPE RNA. In this study, we demonstrate that a Ribo-Zero-Seq protocol using either fresh-frozen (FF) or FFPE samples eliminates rRNA with good efficiency. In evaluation of a possible coverage bias, 5′-to- 3′ bias was reduced in FF Ribo-Zero-Seq libraries as it does not rely on poly(A) selection step.One major distinction across these various protocols is the coverage of the transcriptome. To more directly investigate the relationship between sequencing depth and transcriptome coverage, we performed a simulation approach where mRNA-Seq was the most cost effective strategy to equal a microarray in terms of total genes detected with a minimum of ~13.5 million reads needed. For the same transcriptome coverage, the reads required for Ribo-Zero-Seq in FF and FFPE and DSN-Seq in FF were 35-65 M reads. However, rRNA depletion protocols also appear to measure immature transcripts (pre-mRNAs) and therefore provide more information on splicing patterns and possible splice junctions. Thus to achieve the same level of exonic reads as FF mRNA-Seq, one needs to sequence 2–4 times the number of reads in rRNA-depletion on FFPE RNA libraries.Despite fewer of the total reads mapping to exonic regions and a greater number of transcripts being detected, we did not observe a marked decrease in the correlation between microarray and RNA-Seq in rRNA-depleted libraries, where RNA-zero-Seq and DSN-Seq were found to be highly consistent in gene quantification. Our evaluation of the quantitative consistency of RNA-Seq on FFPE with microarray may be limited in two aspects: (a) the quality of a few UNC FFPE samples was less satisfactory, and (b) not all the tumors have RNA-Seq data on matched FFPE samples that passed our quality control available for this analysis. Yet we still observed very good correlations with microarray data for those samples with complete FFPE data, which gave correlation values nearly identical to those seen when comparing an Agilent microarray versus an Affymetrix microarray .Given the consistent quantification, mRNA-Seq and rRNA depletion protocols exhibited their merits in different aspects. In the set of genes detected by all the protocols, mRNA-Seq provided the highest sensitivity in detecting differentially expressed genes, which was likely due to the greater fraction of reads mapping to the transcriptome. On the other hand, Ribo-Zero-Seq detected about 550 more annotated genes than mRNA-Seq  RNA. Moreover, the two rRNA depletion methods have consistent transcript quantification using FFPE RNAs and show high reproducibility.We constructed RNA-Seq libraries using eleven UNC breast tumor samples using different sample preparation protocols including: (a) FF RNA samples by mRNA-Seq, Ribo-Zero-Seq and DSN-Seq and (b) FFPE samples by Ribo-Zero-Seq and DSN-Seq Figure B. One ofhttp://supportres.illumina.com/documents/myillumina/7836bd3e-3358-4834-b2f7-80f80acb4e3f/dsn_normalization_sampleprep_application_note_15014673_c.pdf). Sequencing: All cDNA libraries were sequenced using an Illumina HiSeq2000, producing 48x7x48 bp paired-end reads with multiplexing.mRNA-Seq library: Illumina TruSeq™ RNA Sample Prep Kit (Cat# RS-122-2001) was used with 1ug of total RNA for the construction of libraries according to the manufacturer’s protocol. Ribo-Zero library: rRNA was removed from FF or FFPE total RNA using Epicentre’s Ribo-Zero rRNA Removal kit (Cat# RZH11042). For FF samples, 30-100 ng Ribo-Zero RNA was used for the construction of the library using the Illumina TruSeq™ RNA Sample Prep Kit (Cat# RS-122-2001) and followed the manufacturer’s instruction, except for omitting the purification step before fragmentation. For FFPE samples, 30-100 ng Ribo-Zero RNA was then incubated with Random Primers  at 65°C for 5 minutes then Illumina TruSeq™ RNA Sample Prep Kit (Cat# RS-122-2001) was used to construct the library according to the manufacturer’s protocol from the step of First Strand cDNA Synthesis. DSN library: Illumina TruSeq™ RNA Sample Prep Kit (Cat# RS-122-2001) was used with 100 ng of total RNA for the construction of libraries following the manufacturer’s protocol, except for omitting the purification of mRNA step in FF samples, and the purification and fragmentation step in FFPE samples. The total RNA libraries went through DSN treatment and PCR enrichment according to Illumina DSN Normalization Sample Preparation Guide (http://picard.sourceforge.net/). The aligned reads were sorted and indexed using SAMtools, and then translated to transcriptome coordinates and filtered for indels, large inserts, and zero mapping quality using UBU v1.0 (https://github.com/mozack/ubu). For the reference transcriptome, UCSC hg19 GAF2.1 for KnownGenes [All samples were processed and filtered as described in The Cancer Genome Atlas . Bases awnGenes  was usedwnGenes  v1.1.13.The RNA-Seq data was filtered by requiring the gross RSEM count to be ≥3 for each gene. For each protocol, the detected gene sets were defined as genes that were reported in >70% tumor lanes and with 3 or more reads. To determine the amount of input reads needed for sufficient transcriptome coverage, a simulation test was performed on the UNC data. A series of fixed number of reads were randomly selected from each protocol in a drawing without replacement method. For all the resampling levels, the simulated data followed the same alignment and filtering pipeline as described above. Gene sets detected were then identified for all the various levels.For all the FF tumors and the Common Reference Sample, Agilent 244,000 feature whole genome microarrays were hybridized with tumor RNAs (Cy5) and a human common reference (Cy3) and lowess normalized as described in Herschkowitz et al. . In the The RNA-Seq gene quantification data was next filtered by gene counts as above. The log2 transformed abundance of tumor samples was reported and was used to derive the correlation between RNA-Seq protocol pairs. Using R package MethComp, Deming regression was applied to compare the sensitivity in detecting differentially expressed genes. An unpaired two-class SAM analysis was used to identify genes that have differential expression level in a) mRNA-Seq versus Ribo-Zero-Seq, and b) Ribo-Zero-Seq versus DSN-Seq.https://cghub.ucsc.edu/) and DCC .Gene expression quantification by microarray and RNA-Seq for all samples new to this manuscript can be found in GEO database under accession GSE51783. Aligned BAM files are available at dbGaP under the series ID of phs000676.v1.p1. TCGA sample RNA-Seq data is available at cgHub  Read pile-up plots of GATA3 in Sample 020578B showing data for five different RNA-Seq libraries. (B) Close-up of the read mapping identifying reads that span exon-intron boundaries, which identify unspliced mRNA species. (PDF 447 KB)Additional file 2: Figure S2: Intrinsic gene set clustering analysis. Hierarchical cluster using a breast cancer intrinsic gene set (~2000 genes) and 88 breast tumor samples prepared using the multiple protocols, with an additional 816 samples from the TCGA Breast Cancer Project . The rows above the heat map identify the 88 samples from this study, their RNA-Seq protocol type, and the red arrows show the location of the few mismatched samples. (PDF 1 MB)Additional file 3: Figure S3: Comparison of the top 500 differentially expressed genes between Basal and Luminal tumors detected by mRNA-Seq, Ribo-Zero-Seq and DSN-Seq. (PDF 194 KB)Additional file 4: Table S1: Comparison of the top 500 differentially expressed genes between Basal and Luminal tumors detected by mRNA-Seq, Ribo-Zero-Seq and DSN-Seq. The list of 307 differentially expressed genes that are identified SAM analysis in all the three protocols. (XLSX 55 KB)Additional file 5: Table S2: Differentially expressed gene list across RNA-Seq protocols obtained from Significance Analysis of Microarray.  SAM analysis comparison of mRNA-Seq versus Ribo-Zero-Seq using an FDR = 0. (A) Uniquely expressed genes in Ribo-Zero-Seq. (B) Lowly expressed genes in Ribo-Zero-Seq.  SAM analysis comparison of Ribo-Zero-Seq versus DSM-Seq using an FDR = 0. (C) Uniquely expressed genes in DSN-Seq. (D) Lowly expressed genes in DSN-Seq. (XLSX 64 KB)Additional file 6: Table S3: Comparison of genes detected by mRNA-Seq and Ribo-Zero-Seq in FF samples. (A) Genes detected only by mRNA-Seq and (B) genes detected only by Ribo-Zero-Seq. (XLSX 62 KB)