A dual is formulated and duality results are proved by using concepts of geodesic semilocal E-preinvex functions, geodesic pseudo-semilocal E-preinvex functions, and geodesic quasi-semilocal E-preinvex functions.The authors define a class of functions on Riemannian manifolds, which are called geodesic semilocal E-preinvex functions, as a generalization of geodesic semilocal E-convex and geodesic semi E-preinvex functions, and some of its properties are established. Furthermore, a nonlinear fractional multiobjective programming is considered, where the functions involved are geodesic E- Convexity and generalized convexity play a significant role in many fields, for example, in biological system, economy, optimization, and so on –5.η-semidifferentiability. After that optimality conditions for weak vector minima were given [η-semidifferentiability were established [Generalized convex functions, labeled as semilocal convex functions, were introduced by Ewing  by usingre given . Also, oablished .Furthermore, some optimality conditions and duality results for semilocal E-convex programming were established . E-conveGeneralized convex functions in manifolds, such as Riemannian manifolds, were studied by many authors; see –17. Udrin-dimensional Riemannian manifold with Riemannian connection ▽. Let Assume that ℵ is a complete η if there is exactly one geodesic a geodesic E-invex (GEI) with respect to η if there is a geodesic local E-invex (GLEI) with respect to E such that, corresponding to each pair of points a geodesic local starshaped E-convex if there is a map A nonempty set η if A is a GEI set anda geodesic E-preinvex (GEP) on A with respect to η if A is a GEI set anda geodesic semi E-preinvex (GSEP) on η if, for any A is a GLEI set anda geodesic local E-preinvex (GLEP) on A function A function entclass1pt{minimaη is a GLEI set with respect to η, where Every GEI set with respect to A is a GLEI set with respect to η. However, when Put η if, for any B is a GLEI set andf is GSLEP on B.A function η is a GSLEP function. On the other hand, their converses are not necessarily true. The next example shows SLGEP, which is neither a GSLEC function nor a GSEP function.Any GSLEC function is a GSLEP function. Also, any GSEP function with respect to η. Then h is a GSLEP on η. However, when Assume that Similarly, taking η.Hence, S is a GLEI set, is said to be a geodesic quasi-semilocal E-preinvex (GqSLEP) (with respect to η) if, for all A function S is a GLEI set, is said to be a geodesic pseudo-semilocal E-preinvex (GpSLEP) (with respect to η) if, for all A function η is both a GqELEP function and a GpSLEP function.Every GSLEP on a GLEI set with respect to η- semidifferentiable at η, if A function η- semidifferentiable is E-η-semidifferentiable [If entiable .η-semidifferentiable is the η-semidifferentiability [If iability .η-semidifferentiable is the semidifferentiability [If iability .Assume thathis a GSLEP (E-preconcave) and a geodesic E-η-semidifferentiable atwhereSis a GLEI set with respect toη. ThenLethbe a GqSLEP (GpSLEP) and a geodesic E-η-semidifferentiable atwhereSis a LGEI set with respect toη. HenceThe above lemma is proved directly by using definitions  and  are established.Letandf, hbe GSLEP andgbe a geodesic semilocal E-preincave, and they are all geodesic E-η- semidifferentiable atκ̄. Further, assume that there areandsuch thatThenκ̄is a weak efficient solution for (VFP).κ̄ be not a weak efficient solution for (VFP), then there exists a point By contradiction, let mentclasspt{minimatilizing  and 13)κ̄ be noter hand,  and 10)κ̄ be notSimilarly we can prove the next theorem.Consider thatandf, hare geodesic E-η-semidifferentiable at κ̄. If there existandsuch that conditions .nditions –7) hold holdConsConsider thatandare all pSLGEP functions andare all GqSLEP functions andf, g, hare all geodesic E-η-semidifferentiable atκ̄. If there areandsuch thatthenκ̄is a weak efficient solution for (VFP).κ̄ is not a weak efficient solution for (VFP). Therefore, there exists Assume that By the pSLGEP of κ̄ is a weak efficient solution for (VFP). □By the GqSLEP of ence, by  and 21)\documentrelation  at \docuConsiderandAlso, assume thatf, g, hare geodesic E-η-semidifferentiable atκ̄. If there areandsuch that conditions .nditions –19) hol holConsiLetandFurther, letf, be all GSLEP functions, gbe a geodesic semilocal E-preincave function, andf, g, hbe all geodesic E-η- semidifferentiable atκ̄. If there existandsuch that conditions .nditions –19) hol holLet\The dual problem for (VFP) is formulated as follows:Denote the feasible set problem LetandIfis a GpSLEP function andis a GqSLEP function and they are all geodesic E-η-semidifferentiable atλ, thenFrom Consider thatandIfis a GpSLEP function and a geodesic E-η-semidifferentiable atλ, thenLetandwhereIfare all GSLEP functions and all geodesic E-η-semidifferentiable atthenκ̄is a weak efficient solution for (VFP).By using the hypotheses and Lemma Utilizing the first constraint condition for (VFD), κ̄ is not a weak efficient solution for (VFP). From κ̄ is not a weak efficient solution for (Consider that equality . The pro


The liver-specific microRNA-122 (miR-122) recognizes two conserved sites at the 5′ end of the hepatitis C virus (HCV) genome and contributes to stability, translation, and replication of the viral RNA. We show that stimulation of the HCV internal ribosome entry site (IRES) by miR-122 is essential for efficient viral replication. The mechanism relies on a dual function of the 5′ terminal sequence in the complementary positive (translation) and negative strand (replication), requiring different secondary structures. Predictions and experimental evidence argue for several alternative folds involving the miR-binding region (MBR) adjacent to the IRES and interfering with its function. Mutations in the MBR, designed to suppress these dysfunctional structures indeed stimulate translation independently of miR-122. Conversely, MBR mutants favoring alternative folds show impaired IRES activity. Our results therefore suggest that miR-122 binding assists the folding of a functional IRES in an RNA chaperone-like manner by suppressing energetically favorable alternative secondary structures. The liver-specific microRNA-122 is an essential proviral host factor of Hepatitis C virus replication. Here the authors show that microRNA-122 functions as an RNA chaperone that guides the formation of a functional internal ribosome entry site by preventing energetically more favorable secondary structures within the HCV RNA genome. The genome consists of ~9600 nucleotides (nt) and contains a single open reading frame (ORF), flanked by 5′ and 3′ untranslated regions (UTR). The 5′ UTR contains a set of secondary structures that compose the internal ribosomal entry site (IRES), allowing a cap-independent translation of the viral genome. Cleavage of the resulting polyprotein by host and viral proteases gives rise to ten individual proteins, three structural proteins and seven non-structural (NS) proteins. The NS proteins are crucial for the formation of the membranous web, a vesicular structure containing the viral replication complexes. Genome amplification by the viral RNA polymerase initiates at the respective 3′ terminal structures of positive and negative strands2. Except for nt 5–20, which fold into a small G:C rich hairpin structure , the current model suggests an extended single-stranded form of the remaining sequence. In contrast, the complementary 3′ region of the negative strand is engaged in an extensive stem-loop (SLIIz′), which is crucial for RNA replication4. Therefore, both strands contain a structural code in this region, which is necessary for either translation or replication.The HCV 5′ UTR consists of four distinctly structured domains. Domain I , which serve as a binding hub for the eukaryotic translation machinery. DII can adopt an L-shaped tertiary structure, which is essential for IRES function as it mediates the displacement of eukaryotic initiation factor 2 (eIF2) complex to facilitate attachment of the 60S ribosomal subunit. Furthermore, it contacts the 40S ribosomal subunit. DIII is mostly responsible for host factor recruitment, it binds eIF2 and competes for the association of eIF3 with the 40S ribosomal subunit. DIV includes a short segment of the core coding region and folds into a pseudoknot element, which positions the viral AUG start codon in the ribosomal P site6 by inhibiting translation and accelerating mRNA decay. miRs are incorporated into an RNA-induced silencing complex (RISC), with one of four Argonaute (AGO 1–4) proteins as the central element. Target specificity is conferred by full complementarity of the seed region (5′ nucleotides 2–8), while the 3′ end is often imperfectly bound7. The 5′ end of the HCV viral genomic RNA features two conserved microRNA-122 (miR-122) target sites8. MiR-122 is liver-specific and highly abundant in hepatocytes, with over 60,000 copies per cell. It regulates lipogenesis, tumor suppression, and immune response9. The impact of miR-122 binding on HCV genome, however, is in stark contrast to the canonical mode of action of miRNAs. Upon binding of miR-122, HCV genome translation12 and initiation of replication13 are increased. Mir-122 binding further masks the HCV RNA 5′ end, conferring resistance to the degradation by host nucleases Xrn1 and Xrn2 and thereby contributing to increased protein production15.MicroRNAs (miRNA or miR) are 20–24nt long non-coding RNAs, which fine tune the expression of the majority of human genescis functions in the complementary negative strand sequence. We show that mutations in the MBR favoring or disfavoring SLII formation indeed stimulate or abrogate translation, independent of miR-122. Our data therefore suggest that miR-122 binding suppresses alternative folds of the 5′ UTR that interfere with IRES function.In this study, we show that stimulation of IRES activity substantially contributes to the diverse functions of miR-122 in the HCV replication cycle. Our in silico analysis predicted that the miR-122-binding region (MBR) has a negative effect on the formation of SLII, due to its additional MiR-122 has been shown to be a vital host factor for HCV, as it enhances RNA stability, translation, and replication. We aimed to understand the contribution of translation enhancement to the miR-122 dependency of HCV, which is discussed controversially in literature, and to address the yet unknown mechanism.16) are the standard cell culture model for studying HCV replication cycle and contain high copy numbers of miR-1229  rendered Hep3B cells permissive for HCV replication to similar levels as those measured in Huh7.5 cells17 . To dissect the relative contribution of translation on miR-122 dependency of HCV, we co-transfected miRWT or miRmut with SG and FL replicons capable of miR-122 dependent and independent translation  viral genomes and subgenomic (SG) HCV replicons, lacking the viral structural protein coding regions, both harboring luciferase reporter genes (Luc) as integral parts of the viral polyprotein Fig. . We gene229 Fig. . To allo229 Fig. . Reconsts17 Fig. , whereass17 Fig. , miRmut.ion Fig.  into Hepion Fig.  and detemut Fig. . Therefo122 Fig. . We vali122 Fig. . Similar122 Fig. . Due to These results demonstrated that a moderate stimulation of HCV translation by miR-122 resulted in an overall substantial effect on viral RNA replication, indicating that translation significantly contributes to the miR-122 dependency of HCV.firefly luciferase (FLuc) fused to the HCV NS proteins NS3-5B was initiated by the HCV IRES contained in the second copy of the HCV 5′UTR (UTR2). Since translation of the replicase proteins NS3-5B is a prerequisite for RNA replication, UTR1 and UTR2 thereby allowed studying the relative contribution of miR-122 to genome stability/replication and translation, respectively, on genome replication.To further disentangle the contribution of miR-122 on the processes of translation and/or stability/replication, a bicistronic luciferase reporter construct with two copies of the HCV 5′UTR was designed  crucial for RNA amplification3. Since complementarity of sequence also favors similar structures (except for U:G rich sequences), a structure similar to SLIIz′ might also form in the positive strand 5′ UTR, which would consequently prevent correct IRES formation. As the MBR is part of this alternative structure, miR-122 binding to this sequence could prevent misfolding of the IRES. Based on this hypothesis in vitro folding experiments of HCV RNA and in silico prediction of the conformation of DI and II were performed. Selective 2′-hydroxyl acylation analyzed by primer extension (SHAPE) reactivity data was used as constraint to assist structure prediction by RNA structure  or DII (nt 40–120) to 1D-ure Fig. , we obseure Fig. . This coure Fig. , whereasure Fig. . Howeverure Fig. , furtherure Fig. , suggest18. In addition, not many changes can be expected in the SHAPE signal, since most single stranded regions that are accessible by SHAPE in SLIIalt and SLII are fully or partially overlapping , and another one was predicted to stabilize an SLIIz′-like fold , preventing the formation of SLII. As expected, all mutants were severely impaired in replication or did not replicate at all, most likely due to the destruction of essential parts of SLIIz′ in the negative strand , and ISL (strong decrease in translation efficiency). We assessed short-term RNA decay kinetics by northern blot and found no correlation between stability and translation efficiency , C , again demonstrating the important contribution of miR-122 to stability and replication of HCV genome. Yet, the mutants, all incapable of miR-122 binding, showed a divergent behavior: While the ISL mutant, impairing SLII formation, remained replication deficient even in the presence of miR-122, mutations Δ20 and C restored viral replication due to their capability to efficiently support SLII folding  were translated in vitro using miR-122-deficient HeLa cell extracts. Replicon translation efficiency was assessed by measuring lls Fig. . In vitrlls Fig. . Distriblls Fig. . GMP-PNPCHX Fig. .As predicted, addition of miR-122 to the WT replicon resulted in less HCV RNA present in the 40/48S fraction, concomitant with a higher amount in the 80S fraction, indicating a more efficient association with the 80S ribosome Fig. . In lineOverall, these results suggest that mir-122 binding stimulates the formation of SLII thereby favoring the assembly of the 80S ribosome and translation initiation. In addition, translation enhancement after miR-binding is mimicked by the SLII-forming mutant Δ20 but disabled in the ISL mutant.23. Interestingly, in silico folding predicted native SLII formation of the U3 mutant in absence of miR-122 binding, which was reflected by strongly enhanced miR-122 independent translation  RNA (U3) and replicated in presence of miR-122 inhibitor and in Huh7.5 miR-122 knock-out cells25. Both identified a single G to A point mutation in the 5′ UTR at position 28 , showed an increased translation efficiency in the absence of miR-122 and they are predicted to increase the formation of a functional SLII structure relative to the WT sequence. These data further corroborate our central hypothesis that miR-122 binding to the HCV 5′ UTR stimulates translation by suppressing unfavorable alternative secondary structure interfering with IRES activity.12, but since the discovery of the RNA stabilizing effect27, its relevance has been called into question. Our data now clearly demonstrate that translation stimulation by miR-122 can be functionally separated from RNA stabilizing effects by inserting heterologous, miR-122 independent IRES elements and by duplicating the 5′UTR to address stability and translation independently. Both approaches indicate that stability of the viral RNA of course indirectly impacts on translation, since protection from degradation will allow more sustained translation. However, we show that the direct stimulation of translation efficiency is an additional, independent effect of miR-122 in the range of 2–3-fold, in agreement with previous studies12. Importantly, this apparently minor effect substantially contributes to the miR-122 dependency of HCV. Our approach does not yet permit dissection of the stabilizing effects of miR-122 from additional RNA replication functions that have been proposed previously13. This can be addressed in future studies, comparing replication efficiency of the same series of constructs in cells lacking Xrn1 and Xrn2.In this study, we interrogated the mechanism by which the HCV IRES is activated in response to miR-122 binding. The process has been described extensively4. This dual function of complementary sequences prompted us to investigate a potential impact of miR-122 on IRES conformation. Interestingly, the thermodynamically most favorable structures predicted by folding algorithms involved sequences of DI , but not the active conformation of SLII required for IRES activity. Notably, a similar fold of this region was proposed earlier28. The structural diversity of DI and II was corroborated by NMR studies and in vitro SHAPE analyses in absence of miR-122 supported the inclusion of the MBR in a stem-loop-structure, rather than the canonical SLII fold. However, we were yet not able to directly demonstrate a structural change induced by miR-122. This was on one hand due the significant overlap in the predictions regarding single stranded regions between the different models. On the other hand, an active structural change might require the additional action of proteins, e.g. AGO, available only in cellulo. However, our attempts to show the presence of SLIIalt by in cell SHAPE failed, which is in line with literature18, suggesting that the 5′UTR of HCV is hardly accessible for unknown reasons. More elaborate in cell techniques will therefore be required in future studies to clarify whether miR-122 indeed prevents alternative RNA folds interfering with IRES activity.The sequence at 5′ end of the HCV genome serves two independent roles: IRES activity required for translation in the positive strand and essential replication signals in the complementary negative strand, both requiring very different secondary structures20. Accordingly, the ISL mutant, stabilizing SLIIalt showed an inverse phenotype of abrogated translation and a block at the 40/48S stage. These results corroborate a previous report of Filbin and colleagues, observing a similar behavior of a ΔSLII construct29. The data obtained with the DI mutants therefore are the strongest support for our hypothesis of a strong impact of DI on the folding of a functional SLII element. However, studies addressing HCV IRES structure often excluded DI, since it is not part of the IRES and thereby missed the negative impact on SLII formation30.We generated several mutants of DI predicted to either favor SLII formation or alternative structures interfering with IRES function. Alterations in DI that were designed to suppress deleterious structures, and in turn stabilize SLII, indeed activated early translation in absence of miR-122. Mechanistically, we could observe a shift towards 80S formation in ribosome profiling assays for the SLII stabilizing mutants Δ20 and C, comparable to the addition of miR-122 to WT genomes. These data argue for a stabilized functional SLII, as this feature promotes eIF2 dissociation, and enables 60S recruitmentOur data suggest that the predicted structural variation at the 5′ terminus of the HCV genome could simply be the result of the reciprocal evolutionary pressure on the active conformations of each strand (SLII vs. SLIIz′). Hence, miR-122 could assist the dual function of these complementary sequences and prevent deleterious folds. Currently it is not known whether miR-122 is already bound to the viral RNA within the virion or meets the 5′ UTR upon release of the genome into the cytoplasm of a newly infected cell. In the latter case, an incorrectly folded IRES will abrogate the infection process at an early stage. Even a 2–3-fold increase in the population of a functional IRES element, as suggested by the stimulating effect of miR-122 on translation, might have a tremendous effect on the efficiency of viral spread in vivo.32. Interestingly, all examined specimens showed liver tropism and at least one conserved miR-122-binding site33. It is interesting to consider how such an intricate relationship between microRNA and virus has evolved. In fact, miR-122 and its liver specificity is conserved throughout the vertebrates. Therefore, the use of miR-122 to facilitate the formation of alternative RNA structures serving different functions might already have evolved in ancestors of recent hepaciviruses and it may have been maintained throughout co-evolution of virus and host, enforcing liver tropism. In case of GBV-B, a mutant lacking the 5′ terminus including the MBR is also attenuated, but viable33.The closest relatives to HCV have been identified only recently in cattle, horses, bats, and rodents34. Additionally, over the last decade there have been several reports of cellular IRES elements35, which were experimentally validated. Some of them also need chaperones to attain their functional conformation, expose the ribosome-binding site and activate translation36. Moreover, increasing numbers of functional miR-binding sites in the 5′ UTR of eukaryotic genes have been discovered, and some of these interactions can increase translation activity by an unknown mechanism37. These reports and our data suggest the possibility that sequence-specific RNA chaperones based on microRNAs may have originally evolved for regulatory requirements in eukaryotic cells.Notably, a similar interplay between coding and non-coding RNA has been described in bacteria. Here, the ribosome-binding site of the RNA is occluded by an inhibitory structure, which is resolved by binding of an antisense RNA in complex with the bacterial AGO-analog HfqΔmiR cells were generated by CRISPR/Cas9 technology using a lentiviral vector system and clonal selection. The founder cells Huh7.516 were a gift from Prof. Charles M. Rice .Human Hep3B hepatoma cells  were used as main model in this study. Huh7.52 and 37 °C. Huh7.5ΔmiR cells were cultured with 2 µg/ml puromycin (Sigma Aldrich).Human fibroblast HEK293T  cells were used for generation of lentiviruses. All cell lines were grown in Dulbecco’s modified Eagle medium  supplemented with 10% fetal calf serum , 100 μg/ml penicillin, 100 μg/ml streptomycin (Sigma Aldrich), and 1% non-essential amino acids (Life Technologies). Cells were maintained in a humidified incubator at 5% CO4 transfection with pCMV-ΔR8.31 (HIV gag-pol), pMD2.G (VSV-G), and the pLenti-CRISPR-miR-122 vector in a 3:1:3 ratio, respectively. pCMV-ΔR8.31 and pMD.2G and were kindly provided by Prof. Didier Trono . Cell-free supernatants were harvested 48, 56 and 72 h post transfection and used for transduction of Huh7.5 cells. Transduced cells were selected by supplementing the culture medium with 2 μg/ml puromycin. Clones were produced by seeding single cells in a 96-well plate, using a limiting dilution approach.Lentiviruses were produced in HEK293T cells by CaPO38. To achieve better signal at early timepoints, the Renilla luciferase was exchanged for a smaller and brighter Nano luciferase (Promega). pCMV-ΔR8.31 (HIV gag-pol) and pMD2.G (VSV-G) were used as packaging plasmids for lentivirus production. pLenti-CRISPR-miR-122 was produced by cloning a duplex of miR-122-specific sgRNA oligos , designed with E-CRISP (www.e-crisp.de) into the pLenti-CRISPR plasmid (Addgene #52961) via BsmBI (NEB). pFK-I389Luc-ubi/NS3-3′/JFH1 (Luc-SG) was described before39. All mutants within the HCV 5′ UTR were generated by PCR-based mutagenesis and inserted with SbfI and XbaI (NEB). pFK-I341PILuc-ubi/NS3-3′/JFH1 (PI-Luc-SG) has been used in a previous study40. It was generated by replacing the HCV IRES of pFK-I389Luc-ubi/NS3-3′/JFH1 with the HCV 5′ UTR-Polio IRES cassette from pFK-I341PILuc/NS3-3′/JFH141 using SbfI and XbaI (NEB). pRL-CMV (GenBank AF025843) was used for in vitro transcription of Renilla luciferase mRNA, as spike-in template for translation assays. pUC18 vectors were used for transcription of HCV 5′ UTR truncations  for NMR analysis. The fragments were amplified from pFK-I389Luc-ubi/NS3-3′/JFH1 plasmids with primers harboring SbfI and SmaI sites.FL HCV luciferase reporter viruses  were based on JcR2a341PINLuc/I389FLuc/NS3-3′/JFH1 (Bi-Luc-SG) was generated by insertion of a fusion PCR product of the I341PI cassette from pFK-I341PILuc/NS3-3′/JFH1 and the Nano Luciferase gene from pNL1.1.TK[Nluc/TK] Vector (Promega) into pFK-I389Luc-ubi/NS3-3′/JFH1 before the HCV 5′ UTR. The HCV 5′ UTRs of the first and second cistrons were named UTR1 and UTR2, respectively. All mutants were generated by PCR-based mutagenesis and inserted with SbfI and AflII for UTR1, or AflII and XbaI (NEB) for UTR2. pFK-JcN2a (Luc-FL) was generated from pFK-JcR2a by amplifying the Nano luciferase gene from pNL1.1.TK[Nluc/TK] Vector (Promega) and adding the sequence of the 2A peptide. The NLUC-2A fragment was inserted into pFK-JcR2a with AscI and BsiWI (NEB).pFK-I389NLuc HCV IRES fragment from Luc-FL with the I341-PI-NLuc cassette from pFK-I341PINLuc/I389FLuc/NS3-3′/JFH1 with SbfI and BsiWI (NEB).pFK-JcPIN2A (PI-Luc-FL) was generated by replacing the Iwww.mirbase.org). However, an additional 3 U was added, since this sequence is the most abundant variant in hepatocytes9 . For the inactive mutant miR-122mut, A4 was changed to U, and the complementary exchange was performed on the passenger strand .miR mimics were purchased at Eurofins MWG. Sequences of hsa-miR-122-5p (guide strand) and hsa-miR-122-3p (passenger strand) were acquired from miRbase , according to the manufacturer’s instructions. cDNA for miR-122-5p and miR-23b-5p was generated in a single reaction with specific RT primers . qPCR primers  and dNTPs (0.5 mM) were preheated to 65 °C and flash-cooled on ice. 1× FirstStrand buffer (Thermo Fisher Scientific), 10 mM DTT, 1 U/µl RNAsin (Promega), and 2.5 U/µl SuperScript III (Thermo Fisher Scientific), and 0.5 µg of total RNA were added and samples were transferred into a TPersonal PCR cycler (Biometra) and cDNA synthesis was performed with a pulsed program: 30 min 16 °C, cycle 60× , 85 °C 5 min and then cooled to 4 °C. cDNA was diluted 1:20 and 3 µl were used for qPCR. Analysis was performed with the 2× iTaq Universal SYBR green supermix (Bio-Rad) on a CFX96 Touch real-time PCR detection system (Bio-Rad) with the following program: 95 °C for 3 min, 95 °C for 10 s, and 60 °C for 30 s. miR-23b was used as an internal reference, based on previous findings43. The 2−CT values of RT negative controls were subtracted for each sample. Relative gene expression was determined using the threshold cycle (2ΔΔCT) method44.A previously described qPCR detection method for mature miRNA was adapted, using SYBR green instead of TaqMan probes for detection2, 10 mM spermidin, 200 mM DTT), 12.5 µl 25 mM NTP-solution, 2.5 µl RNasin (40 U/µl), 0.1 U pyrophosphatase (Sigma Aldrich), and 6 µl of T7 polymerase. The reactions were performed over night at 37 °C. Subsequently, the input DNA was removed using RQ1 RNase-free Dnase (Promega). The RNA was purified by acid phenol:chloroform extraction and isopropanol precipitation. The pellet was washed with 70% ethanol and resuspended in RNAse free water.Sub-genomic reporter replicons: RNA for electroporation was generated from 5 µg of pFK-plasmid (linearized with MluI (NEB)) in a 100 µl reaction mix containing 20 µl of 5× rabbit reticulocyte lysate buffer (400 mM Hepes (pH 7.5), 60 mM MgClRenilla mRNA for translation experiments: For capped Renilla transcripts, the pRL-CMV vector (Promega) was linearized with BamHI (NEB). The setup of the transcription reaction was as stated above, except 12.5 mM m7G analog (NEB) were added to the reaction, and the GTP concentration in the rNTP stock was reduced to 12.5 mM.Capped 2, 34 µg of BSA, 3.6 U of pyrophosphatase from Becker Yeast, 125 µg of T7 RNA polymerase, 5 mM DTT, 1 mM spermidine, 0.01% Triton X-100, 80 mg of PEG 8000, and 40 mM Tris at pH 8, was incubated at 37 °C for 3 h in a Hybrigene HB-3D (Techne) at 20 rpm. Then, 50 units of RNAse-free DNAse (Euromedex) were added and incubated 30 min at 37 °C to stop the reaction. Reaction volumes were 1, 4, or 8 ml and RNA constructs were purified using Q-sepharose Hiload column  on DuoFlow (Biorad) in denaturing condition. Briefly, the reaction mixture was diluted 10-fold in 20 mM Tris buffer at pH 8, containing 10 mM EDTA and 8 M urea, and loaded on the column previously equilibrated with the same buffer. Then RNA was purified with a gradient from 0 to 1 M NaCl in the same buffer. Fractions containing RNA (identified using absorbance at 260 nm) were controlled using denaturing polyacrylamide (15%) gel electrophoresis. The fractions with the target transcript were pooled, dialyzed three times against 200 volumes of H2O, and lyophilized.5′ UTR RNA constructs (1–120 nt (DI + II), 40–120 nt (DII)) for NMR studies were transcribed from pUC18 vectors, linearized using SmaI (NEB). 100 µg of linearized DNA plasmids were used for 1 ml run-off transcription reaction. The reaction mix containing 4 mM NTP, 16 mM MgCl2 and 5% D2O. The final RNA concentration for each NMR sample was 0.9 mM. All NMR experiments were recorded using a 950 MHz Bruker Avance III HD spectrometer equipped with a 5 mM cryogenically cooled pulsed-field-gradient triple-resonance probe.The RNA samples for NMR studies were resuspended in water, heated at 95 °C for 5 min, and were rapidly cooled by the addition of ice-cold 2× NMR buffer. NMR buffer composition was 50 mM Hepes at pH 6.4, 100 mM NaCl, 5 mM MgCl45. SEC was performed with a Superdex 200 column  equilibrated with 50 mM Hepes buffer (pH6.4) containing 100 mM NaCl and 5 mM MgCl2. Fifty microliters of a DI + II and DII solution at 0.77 and 0.8 mM, respectively, was injected with or without 1.2 equivalents of miR-122. Separations were performed at room temperature with a flow rate of 0.5 ml/min. Online MALLS detection was performed with a DAWN-HELEOS II detector (Wyatt Technology Corp.) using a laser emitting at 690 nm, and RNA concentration was measured online by measuring the differential RI using an Optilab T-rEX detector (Wyatt Technology Corp.) and an averaged RI increment, dn/dc, of 0.180 ml/g. Weight-averaged molar masses (Mw) were calculated using the ASTRA software (Wyatt Technology Corp.).Size exclusion chromatography (SEC) combined with online detection by MALLS and refractometry (refractive index—RI) is a method allowing the measurement of the absolute molecular mass of each component of a sample in solution, regardless of its dimensions and shape2O and flash cooled on ice. 8 µl of 3× SHAPE folding buffer  were added and reactions were incubated for 10 min at the desired temperature. Samples were put on ice to terminate the folding reaction and split into two tubes. One was supplemented with 3 µl of native loading dye , the other with 12 µl LDII  as denaturing control.15 pmol of DI + II RNA fragment were denatured at 95 °C in 15 µl of H2) gels were run for 1 h at 100 V at 4 °C, denaturing urea TBE  gels 1 h, 100 V at room temperature. Gels were stained with a 1:10,000 dilution of diamond nucleic acid dye (Promega) in the respective running buffer for 10 min at room temperature and visualized on a UV transilluminator (INTAS).10% polyacrylamide gels were pre-run for 10 min at running conditions. Native TBM , 25 mM Hepes (Gibco), 2 mM EGTA, 5 mM MgCl2, 5 mM glutathione, 2 mM ATP) and electroporated  with 5 µg of reporter replicon RNA and 50 pmol wild type or mutant miR-122 mimic. For translation assays, 5 µg of a capped Renilla transcript were added as internal control for transfection efficiency. Electroporated cells were seeded onto six-well plates and incubated at 37 °C for the desired time.3*104, 4 mM EGTA, 10% glycerol) per well in a six-well plate. Plates were stored at −20 °C until measurement of luciferase activity.Activity of reporter replicons was measured after cell lysis in 350 µl luciferase lysis buffer .Measurements of firefly and firefly luciferase, 360 μl luciferase assay buffer  with freshly added 1 mM DTT, 2 mM ATP was mixed with 100 µl of lysate in a luminometer tube. After injection of 200 µl of 1 mM d-Luciferin (PJK) in 5 mM glycylglycine the sample was measured for 20 s. Renilla luciferase was measured by adding 100 μl luciferase assay buffer supplemented with 1.43 μM coelenterazine (PJK) to 20 µl of lysate, and an acquisition time of 10 s. Non-transfected cells were used to determine the background signal. Firefly luciferase from IRES-dependent translation, was normalized to the respective Renilla value to account for transfection efficiency and cell numbers. Note that capped in vitro transcripts encoding Renilla luciferase were not included in experiments primarily focusing on Northern blot analysis and in experiments using the Bi-Luc-SG replicon.For firefly luciferase signal. Then, firefly activity was quenched for 10 min using 40 µl of NanoDLR Stop& Glo buffer (Promega) before determining the Nano luciferase signal. Non-transfected cells were used to determine the background signal. Measurements were performed using a Mitras LB940 plate reader (Berthold).For the dual luciferase replicons, the commercial Nano-Glo Dual-Luciferase Reporter Assay System (Promega) was used. In brief, 40 µl of cell lysate were transferred from the six-well plate to a white walled 96-well plate  in duplicates. Next, 40 µl of ONE-Glo EX Reagent (Promega) were added, mixed and the reaction was incubated for 10 min at room temperature before measurement of the 6 Hep3B cells were electroporated with 10 µg of replicon RNA. Cells from one electroporation were seeded on a 10 cm dish, and incubated at 37 °C for 0, 30, 60, and 120 min. Cells were scraped in the medium and put on ice immediately. 1 ml of the 6 ml suspension was transferred to an Eppendorf tube for luciferase assay, to control for translation activity at each time point. The cells were pelleted washed with PBS and taken up in 200 µl of Luciferase lysis buffer. The luciferase assay was performed as described above. The remaining sample was spun down and washed with ice-cold PBS with 0.1 mM aurintricarboxylic acid. Total RNA was extracted with acid guanidinium thiocyanate–phenol–chloroform and were denatured by treatment with 5.9% glyoxal in 50% DMSO and 10 mM NaPO4 buffer, pH 7.0. Total RNA was separated by glyoxal agarose gel electrophoresis. Then, the RNA was vacuum blotted onto a nitrocellulose membrane and UV crosslinked twice in a Stratalinker 1800 (Stratagene) with the “Auto Crosslink” program. Blotting efficiency and RNA integrity was checked by methylene blue staining of the membrane. After washing the membrane with demineralized water, the blot was cut below the 28S rRNA band and blocked with salmon sperm DNA at 68 °C in hybridization buffer. The upper slice was hybridized with a 32P-body-labeled RNA probe, complementary to nucleotides 5979–6699 of the HCV genome and the lower with a β-actin antisense riboprobe to correct for total RNA amounts loaded in each lane of the gel. Hybridization was performed over night at 68 °C. The blot was washed twice with wash buffer 1  and three times with buffer 2  at 68 °C. Specific bands were quantified after exposure of a phosphor imaging screen using a Molecular Imager FX scanner (Bio-Rad). Uncropped versions of the blots can be found in Supplementary Figs. For each time point, 6*1046, mfold47 were used without constraints. RNA structure was also used for integration of SHAPE data. Phylogeny analysis and visualization of RNA secondary structures was performed with Infernal48 and R2R49. The sequences used are shown in Supplementary Data For in silico folding of the 5′ terminal truncations of HCV, the online versions of RNA structure50, with minor modifications. Briefly, 1 mmol 1,1′-carbonyldiimidazole (Sigma Aldrich) in 0.5 ml DMSO was added dropwise to an equal molar amount 2-methylnicotinic acid (Sigma Aldrich) in 0.5 ml DMSO, and the mixture was kept at room temperature for 1 h to generate NAI. In vitro transcripts or RNA isolated from cells were denatured for 2 min at 95 °C, flash-cooled on ice and refolded in presence or absence of miR-122-5p at 37 °C for 10 min in SHAPE buffer . 100 mM NAI or DMSO were added and incubated for another 10 min. 1 µl β-mercaptoethanol was added to quench the reaction. 0.2 M NaOAc and 20 µg glycogen were added, and RNA was extracted with phenol:chloroform. RNA was precipitated with three volumes of absolute ethanol and washed twice with 70% ethanol. The purified RNA was dissolved in 12 µl RNAse-free water. For reverse transcription, 4 µl were denatured for 2 min at 95 °C with 1 pmol of a ³²P-labeled primer  and 0.5 M Betaine (Sigma Aldrich). After 2 min incubation on ice, 1× FirstStrand buffer (Thermo Fisher Scientific), 10 mM DTT, and 0.5 mM NTPs were added and the reaction was prewarmed to 55 °C for 1 min. 20 U SSIV reverse transcriptase (Thermo Fisher Scientific) were added and the reaction incubated for 10 min at 55 °C. Next, 1 µl of 4 M NaOH were added to hydrolyze the RNA and incubated at 95 °C for 5 min. The samples were cooled and one volume LDII was added before the cDNA fragments were separated by denaturing urea PAGE on a 10% gel . The bands were quantified by phosphor imaging using a Molecular Imager FX scanner (Bio-Rad). SHAPE data were analyzed with SAFA51. The data were normalized to the mean of the top 10% reactivities. Intermediate reactivity (0.2–0.8) is indicated in yellow, highly reactive nucleotides (>0.8) are colored in red. For differential analysis of miR-bound versus free RNA the normalized values were subtracted and all differences of at least 0.3 were considered as significant. Less reactive nucleotides are depicted in green, more reactive in red.SHAPE is a method to address RNA structures in solution by chemical modification of flexible/unpaired nucleotides. For readout, an RT reaction with a labeled primer is performed, and the polymerase will stop at each labeled residue. The resulting cDNA fragments are separated on a denaturing gel, and each band stands for single stranded RNA. Generation of 2-methylnicotinic acid imidazolide (NAI) and SHAPE assays were performed as describe before18. Briefly, RNA was in vitro transcribed by runoff transcription with T7 RNA polymerase followed by treatment with RQ1 DNase (Promega). The RNA was then purified using RNeasy columns (QIAGEN) according to manufacturer’s protocol and eluted in ME buffer . The RNA was then adjusted to a final concentration of 250 ng/μL in folding buffer (50 mM K-HEPES (pH 7.4), 0.1 mM EDTA, 150 mM KCl, and 5 mM MgCl2) and heated to 65 °C followed by cooling to 37 °C over a 45-min time course.For SHAPE, dimethyl sulfate (DMS) and nicotinoyl azide (NAz) probing of the full-length HCV genome, RNA was transcribed, purified, and folded as previously describedFor DMS probing, DMS was added for a final concentration of 0.4%, or an equivalent volume of EtOH was added for unmodified control samples. Samples were incubated at room temperature for 10 min, and modification was quenched by addition of 2-mercaptoethanol at 10%. In case of NAz, the sample was exposed to UV light (310 nm) to activate the reagent.32P-end labeled by T4-PNK (NEB), and reverse transcription was performed with 1 µg RNA by SuperScript III (Thermo Fisher Scientific) according to manufacturer’s protocol and the addition of 10U Superasin (Invitrogen). Following reverse transcription, RNA was degraded with the addition of NaOH and incubation at 95 °C, and cDNA products were resolved by denaturing UREA PAGE on an 8% gel.Modified RNA was purified by phenol:chloroform extraction, LiCl precipitation, and resuspended in ME buffer at 1 µg/µl. Primers were 2.5 µg of reporter replicon RNA was added to 10 µl HeLa lysate  translation mix in a total volume of 15 µl, following the manufacturer’s instructions. When needed, miR-122 duplex RNA was incubated at 30 °C in the mix, 10 min prior to RNA addition. The translation reaction was performed for 30 min at 30 °C. Then, 235 µl of ice-cold lysis buffer, containing 200 µg/µl CHX was added to stop the reaction. 10 µl of the reaction were used as input sample for RNA extraction and western blot, 5 µl for luciferase assay. Tubes were kept on ice until fractionation. 5 mM GMP-PNP (Sigma Aldrich) or 500 µg/ml CHX (Sigma Aldrich) were added prior to the incubation for experimental determination of the 40/48S and 80S fraction, respectively.2, 300 mM NaCl and subjected to ultracentrifugation at 165,000×g at 4 °C using a SW60 rotor (Beckman) for 2.5 h. Gradients were then eluted from the top using a Teledyne ISCO gradient elution system. Polysome profiles were obtained by measuring the absorbance at 254 nm. In parallel, fractions were collected every 15 s . Before RNA extraction from the fractions, 20 µg of glycogen, 350 µl urea buffer  were added. Moreover, 1 ng of in vitro transcribed Renilla luciferase RNA was supplemented as internal control. Following phenol:chloroform extraction and ethanol precipitation, the pellets were resuspended in 40 µl of RNase-free water. Samples were stored at −80 °C until further processing.Lysates were loaded onto a linear gradient of 17.5–30% in 15 mM Tris–HCl pH 7.4, 15 mM MgClRenilla-specific primers , 0.27 µM of the corresponding probes , 3 µl of template RNA, and RNase-free water. To generate a standard curve, serial dilutions of an RNA standard (107–109 copies of HCV RNA and Renilla in vitro transcript) was prepared for each plate. Reactions were performed using the following program: 50 °C for 10 min, 95 °C for 1 min, and 40 cycles of amplification: 95 °C for 10 s and 60 °C for 1 min. The amount of HCV RNA was normalized to the Renilla control and the input sample.HCV genomic RNA extracted from the fractions was analyzed by one-step qRT-PCR using qScript XLT one-step RT-qPCR ToughMix (Quanta Bio-sciences) according to the manufacturer’s instructions. Briefly, 15 µl of reaction mixture contained 7.5 µl of 2× RT mix, 1 µM of JFH1-specific and 10 µl of each fraction were substituted with 2 µl of 6× Lämmli buffer with 5% β-mercaptoethanol and denatured for 10 min at 95 °C. Samples were separated on a 10% SDS polyacrylamide gel and wet-blotted on a PVDF membrane for 1 h at 400 mA. Blots were blocked with low-fat milk in PBS-T (0.1% Tween) for 30 min at room temperature. Primary antibody ), rabbit polyclonal RPS3 antibody Abcam, ab140688) was applied in a 1:1000 dilution in milk PBS-T for 1 h at room temperature. Blots were washed three times with PBS-T and species specific HRP-coupled secondary antibodies were added in a 1:5000  or 1:10,000  dilution. After another three washes, the blot was incubated with Clarity Western ECL Substrate (Promega) for 1–2 min and analyzed with an INTAS imager. Uncropped versions of the blots can be found in Supplementary Fig. 0688 was 52. The samples were heated to 65 °C for 5 min in loading buffer LDII  and flash cooled on ice. The denatured samples were applied to a 1% TAE agarose gel, containing 10 µg/L ethidium bromide. The gel was run for 30 min at 100 V in 1× TAE buffer and bands were visualized on a UV transilluminator (INTAS). Uncropped versions of the gels can be found in Supplementary Fig. 18S and 28S ribosomal RNA was detected for each fraction using a denaturing TAE gel approacht-test was performed using Prism 6 software (GraphPad). All data are presented as mean (±SD), the sample size is specified in the corresponding figure legend. P values of <0.05 were considered statistically significant. In graphs, statistical significance is indicated as follows: *P < 0.05; **P < 0.01; ***P < 0.001.Where appropriate, a paired, two-tailed Student’s The data that support the findings of this study are available from the corresponding author upon reasonable request.Supplementary Data 1Supplementary InformationPeer Review File


Generation of large amounts of genomic data is now feasible and cost-effective with improvements in next generation sequencing (NGS) technology. Ribonucleic acid sequencing (RNA-Seq) is becoming the preferred method for comprehensively characterising global transcriptome activity. Unique to cytoreductive surgery (CRS), multiple spatially discrete tumour specimens could be systematically harvested for genomic analysis. To facilitate such downstream analyses, laser capture microdissection (LCM) could be utilized to obtain pure cell populations. The aim of this protocol study was to develop a methodology to obtain high-quality expression data from matched primary tumours and metastases by utilizing LCM to isolate pure cellular populations. We demonstrate an optimized LCM protocol which reproducibly delivered intact RNA used for RNA sequencing and quantitative polymerase chain reaction (qPCR). After pathologic annotation of normal epithelial, tumour and stromal components, LCM coupled with cDNA library generation provided for successful RNA sequencing. To illustrate our framework’s potential to identify targets that would otherwise be missed with conventional bulk tumour sequencing, we performed qPCR and immunohistochemical technical validation to show that the genes identified were truly expressed only in certain sub-components. This study suggests that the combination of matched tissue specimens with tissue microdissection and NGS provides a viable platform to unmask hidden biomarkers and provides insight into tumour biology at a higher resolution. By comparing the transcriptomes, differential expression of genes in discrete cell populations can be easily identified. This approach has emerged as a useful tool for characterising the transcriptomes and molecular signatures of tissues-of-interest via RNA or protein profiling, which may reflect their functionality2. In recent years, large genomic consortiums such as The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) have been established1. With these publically available large-scale integrated data, much effort have been placed into identifying putative prognostic biomarkers using NGS, however, few have made it to clinical practice3. A plausible reason for this could be due to the limitations of conventional sequencing methods which analyse whole tumour biopsies4. Conventional sampling of a cancerous mass often results in a collection of varied cell types as tumour cells are frequently intermixed with stromal, inflammatory and epithelial cells. This leads to great difficulty in obtaining pure samples of malignant cells, and as a result complicate the genetic evaluation of these cells6. To gain deeper understanding of biological processes and disease mechanisms, it is vital to elucidate gene expression of discrete cellular populations found in whole tumour samples8.High-throughput next-generation sequencing (NGS) is a powerful strategy to study cancer progression at the molecular level, where ribonucleic acid sequencing (RNA-Seq) is becoming the preferred method for comprehensively characterising global transcriptome activity9. Using microscopy to identify specific compartments of tissue, laser is used to precisely dissect the samples-of-interest, following which nucleic acids can be extracted from these pure cellular populations and used in downstream experiments, revolutionizing molecular analyses of intricate tissue samples13. In conjunction with RNA extraction methods, LCM has been used to ensure effective processing of samples, and analyse transcriptomes of discrete tumour cell populations and low-quantity cellular populations in tissues14. In comparison to other techniques for isolation of cells, LCM allows for the retention of positional information of cells without requiring tissue dissociation. Additionally, there is no need for live cells with genetic labels15.Laser capture microdissection (LCM) is a unique method which allows the segregation of pure cell populations from defined anatomical locations instead of whole tumour biopsies17. Furthermore, tissue handling time plays a critical role in the preservation of RNA integrity. In a selected group of patients who undergo cytoreductive surgeries (CRS) that usually take numerous hours, it is particularly challenging to harvest specimens with optimal RNA integrity and would require a streamlined workflow to ensure that specimens are processed promptly after being surgically removed. Hence, a feasible approach using targeted LCM that precisely captures areas of interest to achieve accurate RNA sequencing with minimal starting material is pertinent in light of the challenges of procuring adequate amounts of tissue samples.Although LCM is widely utilized on a variety of tissue types, obtaining high-quality RNA remains difficult and necessitates for strict protocols which are critical for downstream analysesin situ and to assess the accuracy of the LCM. Finally, to determine if LCM samples performed better compared to whole tumour samples, qPCR analyses on both microdissected and non-microdissected matched trios of normal-primary-metastatic samples were conducted.In this protocol paper, we aim to demonstrate a validated workflow incorporating LCM for robust transcriptomics analysis in the era of NGS compared to the conventional bulk tumour sequencing. There is currently no literature on the use of LCM to reproducibly isolate high-quality RNA from systematically collected matched tumours and its downstream applications specifically in colorectal and Krukenberg tumours. Furthermore, there is a lack of publicly available data, such as on TCGA, which are matched. As such, differences in gene expression in unmatched normal and tumour samples may not be truly representative of upregulated genes or signaling pathways in malignant cells. We have established a methodology developed for RNA-Seq that couples LCM with Nugen Ovation FFPE RNA Multiplex System for efficient sequencing of RNA in microdissected clinical samples from colorectal and Krukenberg tumours which have not been demonstrated before. The optimized workflow for RNA isolation and sequencing from LCM samples of matched normal mucosal, colorectal and Krukenberg tumours was determined. qPCR verification on several chosen targets was then performed to evaluate the quality of RNA-Seq data. Further confirmation of the spatial expression by immunohistochemistry (IHC) was performed to validate the target The workflow consisting of major steps such as LCM, RNA isolation and RNA sequencing is depicted in Fig. Multisampling of colorectal and Krukenberg tumour samples in conjunction with the associated normal mucosa was performed Fig. . Normal Total RNA was extracted from the microdissected tissues. The quality and degree of degradation of extracted RNA was appraised via RNA integrity number (RIN) before amplification. The quality of RNA integrity is determined by an algorithm applied to electrophoretic RNA analysis and is expressed as the RIN that ranges between 1  to 10 (intact). The automated electrophoresis Bioanalyzer system provided the RIN for the 20 samples, with a mean value of 5.53 ± 1.73 (s.d.), indicating relatively good RNA quality , whereby all except two samples  were well above the recommended input quantity by the Manufacturers’ guidelines  and the quality of the sequencing data assessed using RSeQC. We first studied the distribution of GC content across all reads in each sample. Most samples had the expected normal distribution of GC content, with some samples from patient 2 having additional peaks, possibly due to contamination of sequencing adapters or other factors related to sample preparation Fig. . In addiTo compare the transcriptomic landscape of the samples, we applied unsupervised clustering analysis to the expression data generated from RNA-Seq. Figure Using the RNA sequencing data of the epithelial component of the tumours and normal mucosa, the 2D principal component analysis (PCA) plots revealed that the principal components (PC) 1 and 2 separated tumour versus non-tumour epithelium into two groups. This observation is further supported by the distance matrix of the epithelium that demonstrated two distinct clustering that corresponds to tumour and non-tumour samples in general  is associated with aggressive tumours, and poorer overall and disease-free survivals in breast cancer25. We subjected these differentially expressed genes, namely ERBB2, SPINK1, CEACAM1, SPARC, TIMP1, IGFBP7, COL1A1, ENO1, VEGFA, S100A11 and PLA2G2A, to validation by qPCR using specific primers  in both analyses. Interestingly, our results demonstrated that whole tumour biopsies may not provide enough resolution to identify important targets that might be hidden in sub-compartments of tissues. For example, expression of IGFBP7 was insignificant in whole tumour analysis while homogenous stromal analysis showed significantly higher expression of IGFBP7 in both primary tumour and metastasis with 2.719 (p ≤ 0.05) and 12.46 (p ≤ 0.01) fold increase respectively when compared to normal stroma. Analysis of stroma enriched samples also revealed that SPARC and COLIA1 expression in the stromal compartments of both primary and metastatic tumour were highly significant (p ≤ 0.001) while these targets were only significantly enriched in metastatic tumours in whole biopsy analysis.To investigate the impact of microdissection in target calling, we performed qPCR on microdissected samples as well as matched whole tumour biopsies which were not subjected to microdissection. Comparing gene expression data between microdissected samples and whole biopsies, merely 1 out of 9 genes had matched gene expression patterns across the two groups of comparison, whereby TIMP1 was unanimously identified as a metastasis specific gene (p = 0.0961)  core. Representative staining intensities are shown in Fig. 61) Fig. . Althoug28. In order to precisely characterize genomic aberrations in a tumour population, it is integral to acquire pure genetic material from cancerous cells with no or minimal contamination of neighbouring cells. LCM is a powerful high throughput technique which allows for the isolation of pure cell populations from fresh frozen or paraffin-embedded tissue sections and dissociated cultured cells29. It allows for the microscopic isolation of distinct cell types from tissue sections that may subsequently be utilized in gene expression analyses30. Additionally, LCM enables the isolation of homogeneous populations of cells from histologically complex tissues sections31. This approach results in satisfactory quality and quantity of RNA for subsequent experiments, such as microarray analyses33. It is a valuable technique that permits extraction of deeply embedded cells from tissues and has multiple advantages compared to conventional techniques.Whole tumour biopsies are typically used in conventional techniques of RNA-Seq, limiting the possibility of further differentiation of specific gene expression in various tumour cell types. Furthermore, conventional methods typically require the capture of a significant number of cells or numerous cycles of RNA amplification to generate ample quantities of RNA for downstream experiments, which may inadvertently introduce artifacts especially when initial RNA quantities are smallLikewise, in this report, we have described and validated a workflow that combines LCM with RNA-Seq for a robust and efficient approach to RNA sequencing for NGS. Through our optimization, we were able to obtain high-quality RNA from LCM-derived matched tumour samples validated by RNA quality assessment platform, qPCR and IHC. The methodology of sequencing minute amounts of RNA obtained from LCM and RNA-Seq data generated from in-depth sequencing of microdissected tumour specimens was able to demonstrate high-quality RNA with minimal degradation as reflected by high RIN values in conjunction with acceptable quality control performance. Furthermore, qPCR and IHC data highlighted the difference in the expression profile of selected targets between whole tumour biopsies and the same samples subjected to tissue microdissection.35. This technique potentially provides greater insights into early genetic changes found within distinct tissue compartments enabling the investigation of molecular events that underlie the progression of malignant lesions36. Hence, LCM-based analyses of patient-derived clinical samples may be a routine and useful method for the identification of underlying pathological causes of malignancies for diagnostic and therapeutic applications.We have successfully demonstrated that in-depth analysis of tumour compartments and normalisation using matched tissue samples from CRS allows for the potential of identification of putative therapeutically relevant biomarkers that would otherwise not have been identified from conventional genomic sequencing approaches. The novelty of this paper lies in the analysis of multiple sampling of normal mucosal, stromal and epithelial components in the same patient to correct for innate biological differences. Notably, there is currently no literature on the use of LCM to reproducibly isolate high-quality RNA from systematic collection of matched tumours and its downstream applications specifically in colorectal and Krukenberg tumours. Furthermore, there is a lack of publicly available data, such as on TCGA, which are matched. As such, differences in gene expression in unmatched normal and tumour samples may not be truly representative of upregulated genes or signaling pathways in malignant cells. For example, the differing results of SPARC and COLIA1 expression in the microdissected vs non-microdissected samples highlight the utility of LCM for accurate target calling in molecular studies. Using conventional whole biopsy sequencing, SPARC and COLIA1 were identified as metastatic markers, but were not significant in the primary tumour. Harnessing the strength of LCM, the enriched samples showed that both SPARC and COLIA1 are significantly enriched in both primary and metastatic tumours, highlighting that these markers are biological relevant in both settings. Whole tumour biopsies may not provide enough resolution to identify important targets that might be hidden in sub-compartments of tissues. Using whole tumour sequencing may lead to the overlook of common profiles in primary and metastatic tumours that reveal conserved signaling pathways. These observations suggest that key molecular markers may be masked by disparate gene expression levels in different tissue compartments, highlighting the importance of tissue composition in the generation of molecular data. LCM allows for specific components to be dissected and subsequently a more accurate gene expression profile in separate components of the tumour sample37, a possibility by consortiums such as TCGA.We would like to emphasize that this manuscript serves as a methods paper of utilizing LCM to uncover targets that would be missed with conventional bulk sequencing, with qPCR and IHC data for technical validation. We do not claim that we have identified key differentially expressed genes in peritoneal metastasis in general. The current study is statistically underpowered for the identification of robust differentially expressed genes that dictate the biology of peritoneal metastases, and doing so would in turn generate multiple false positive results. To identify key differentially expressed genes that define the transcriptomic landscape of peritoneal metastasis will require a different study design and a larger sample size based upon careful statistical power calculations39. RNA-Seq is mainly limited to snap frozen samples due to inherent problems with FFPE tissues where the fixation and embedding processes directly modify and degrade RNA40. Moreover, freshly acquired specimens are technically challenging due to size constrains41. As the amount of RNA isolated with LCM is minute, sample degradation needs to be kept to a minimum10. However, degradation may also occur during staining procedures which are critical for the identification of cells for capture. Studies have reported a substantial drop in RNA levels in LCM samples from tissues following exposure to staining solutions during immunocytochemistry42, although there was no difference in RNA yield when immunohistostaining was carried out in the presence of RNase inhibitors in all solutions except wash buffers43. Hence, given limited tissue quantity of early neoplastic lesions, maximising genetic and molecular information obtained from these tissue specimens represents a vital technical challenge.Nonetheless, there are limitations to our approach. Even if RNA-Seq is able to pick up differentially expressed targets, validation using qPCR still requires selection of targeted genes. As such, several key differentially expressed genes may not be detected and evaluated. Furthermore, availability of LCM equipment and downstream molecular applications are less efficient with formalin-fixed paraffin-embedded (FFPE) tissue samples compared to ethanol fixed and live cultured cells samplesThese findings suggest that in the era of NGS and large consortiums, a fraction of samples should be subjected to LCM before transcriptomics analysis to enable the identification of robust targets. We present here, in principle, a method that reliably produces high quality RNA from fresh-frozen colorectal and Krukenberg microdissected matched specimens that are amenable to transcriptome profiling that has not been demonstrated before. We were able to obtain high-quality RNA from LCM-derived tumour samples validated by RNA quality assessment indicators and qPCR amongst paired samples of primary, metastatic and normal tissue. We further showed that RNA-Seq from the combination of matched trio  samples allows for the potential identification of putative therapeutically relevant biomarkers that can provide insights into tumor evolution and improve data integrity as compared to public databases of TCGA which only describes the macroscopic findings of cancers. Hence, this study demonstrates the usefulness of LCM for transcriptome analysis that enables comparisons of discrete cell populations residing within malignant tissues procured from intact tissue sections, a methodology that could be harnessed for biomarker discovery. Moving forward, this workflow may be practiced on other tissue types and will enhance research on neoplastic progression, epithelial-stromal interactions and unique cancer biomarkers.Four patients with Krukenberg tumours of colorectal origin were identified and recruited for this study. Multiple samples of matched normal mucosa, primary colorectal tumours and ovarian metastasis (Krukenberg tumours) as described in Table Total RNA was extracted from all 20 microdissected tissues and 3 whole biopsies which were not subjected to LCM using the QIAgen RNAeasy Micro Kit , according to the manufacturer’s instructions. All 9 samples were stored in nuclease-free tubes and stored at −80 °C. To reduce sample degradation, the bench top and instruments required for all laboratory procedures such as sample preparation, microdissection, and RNA extraction were cleaned using RNase AWAY®  to minimise atmospheric RNase enzyme.Aliquots of each RNA pool were analysed for quality and quantity on a Bioanalyzer RNA Pico chip (Agilent). The quality and quantity of isolated total RNA were evaluated using the 2100 BioAnalyzer  with RNA 6000 Pico LabChip kit . 1 µl of each isolated RNA sample was analysed with RNA 6000 Pico Assay employing RNA Pico LabChips (Agilent Technologies). Briefly, the quality of each RNA sample was approximated by the fragment size distribution, including two peaks correlating to 18 S and 28 S ribosomal RNAs, and a signal in the small molecular weight area, corresponding to small RNAs. This distribution was correlated to an extensive database of RNA samples and a RIN was tabulated. The resultant electropherograms were used to determine RNA integrity and concentration. RIN was calculated by the instrument where a value of 10 denotes intact RNA and a value of 1 indicates complete degradation.Nugen Ovation RNA-Seq System  was used to prepare the sequencing library. The amount of RNA used ranged from 3–60 ng. As defined in the user’s manual, amplification of both mRNA and non-polyadenylated transcripts makes the Ovation FFPE RNA-Seq Multiplex System  ideal for amplification before the use of next generation sequencing (NGS). The libraries were checked using Agilent DNA1000 chip to confirm that all obtained profiles conform to what was expected. Six uniquely indexed libraries were pooled and loaded into each lane of flow cells. Sequencing was carried out via Illumina HiSeq 2500  at 150 base pairs, paired end, and 50 million paired reads per sample as per the manufacturer’s protocol.44. To assess the quality of read alignments, the sequencing reads were first aligned to the human reference transcriptome (hs37d5) using the STAR aligner using default settings45. The BAM files were then processed using the RSeQC tool to produce various RNA-Seq diagnostic plots47. Briefly, transcripts were grouped quartiles based on their expression level. For each quartile, the degree to which transcript expression levels differed when using a random subset of reads as compared to when using the full set of reads was determined. This resampling process was repeated using varying fractions of the total reads. If sequencing depth is adequate, the relative error should be low and stable even when a subset of reads are used, indicating that saturation has been achieved. In this case, transcripts below the 25th percentile of expression (“Q1”) display saturation, suggesting that even low-abundance transcripts have been accurately quantified. To assess the contamination rate of human ribosomal RNA (rRNA) in the libraries, sequencing reads were aligned to a reference comprising the sequences of known human ribosomal RNAs using BWA under default settings, and the extent of rRNA contamination inferred from fraction of reads in the library aligning to the rRNA reference48.For quality assessment of raw sequencing reads, FASTQ files were analysed using the FASTQC tool using default settingsβ-Actin was used as a reference gene. qPCR was performed on the CFX-96 system  in triplicates per sample using the iTaq universal SYBR Green supermix . The data was normalized to β-Actin and analysed using one-way ANOVA.Using DNA nexus applications, reads were mapped using TopHat2 v2.0.12 and CuffDiff v2.1.1 on the genome version ucsc_hg19. Fragments per Kilobase of transcript per Million (FPKM) and fragment counts for each transcript, primary transcript and gene in each sample was normalized as part of CuffDiff. Data was further analysed in R (v 3.4.2). FPKM values from CuffDiff were used for principal component analysis (PCA), and a distance matrix  was visualised using the hopach library. As this was an exploratory analysis, targets selected for qPCR validation was based on biological significance. To verify the expression data derived from the RNA-Seq, we applied qPCR to the RNA extracted from microdissected tissues as well as matched biopsies which were not subjected to LCM in order to evaluate the quality of RNA-Seq analysis. We selected genes from the RNA-Seq data to represent increased expression unique to certain components or combinations of components  antibody for 20 minutes, following which the slides were rinsed and exposed to polymer for 5 minutes. Slides were then washed, treated with DAB-Chromogen detection reagent for 7 minutes and then rinsed again. Sections were subsequently exposed to haematoxylin for 5 minutes to counterstain the nuclei. Finally, slides were rinsed, dehydrated and mounted. Colon samples were used as positive controls while the negative control was derived by replacing the primary antibody with BondTM Antibody diluent.A tissue microarray (TMA) was constructed using Krukenberg tumours collected from patients who were treated in the National Cancer Centre Singapore or Singapore General Hospital between 2004–2017 (n = 14). The constructed TMA block was sectioned into 5 µm sections and mounted onto slides. IHC staining of the TMA sections was performed using the BOND-MAX autostainer  as per the manufacturer’s instructions. Briefly, sections were deparaffinized and rehydrated. Antigen retrieval was conducted at 100 °C, following which the slides were cooled down to room temperature and washed. In order to block endogenous peroxidase activity, sections were incubated in 3–4% (v/v) HAll stained sections were assessed by two independent scorers (WHN and JH) who were blinded to the outcome. Scoring for the epithelial and stromal components was conducted by stratifying the intensity of the stain to a value of 0 (no staining) to 3 (strong staining) if at least 10% of the target cells were positively-stained Fig. . In situSupplementary Information


Introduction: The Afirma® Xpression Atlas (XA) detects gene variants and fusions in thyroid nodule FNA samples from a curated panel of 511 genes using whole-transcriptome RNA-sequencing. Its intended use is among cytologically indeterminate nodules that are Afirma GSC suspicious, Bethesda V/VI nodules, or known thyroid metastases. Here we report its analytical and clinical validation.Methods: DNA and RNA were purified from the same sample across 943 blinded FNAs and compared by multiple methodologies, including whole-transcriptome RNA-seq, targeted RNA-seq, and targeted DNA-seq. An additional 695 blinded FNAs were used to define performance for fusions between whole-transcriptome RNA-seq and targeted RNA-seq. We quantified the reproducibility of the whole-transcriptome RNA-seq assay across laboratories and reagent lots. Finally, variants and fusions were compared to histopathology results.Results: Of variants detected in DNA at 5 or 20% variant allele frequency, 74 and 88% were also detected by XA, respectively. XA variant detection was 89% when compared to an alternative RNA-based detection method. Low levels of expression of the DNA allele carrying the variant, compared with the wild-type allele, was found in some variants not detected by XA. 82% of gene fusions detected in a targeted RNA fusion assay were detected by XA. Conversely, nearly all variants or fusions detected by XA were confirmed by an alternative method. Analytical validation studies demonstrated high intra-plate reproducibility (89%-94%), inter-plate reproducibility (86–91%), and inter-lab accuracy (90%). Multiple variants and fusions previously described across the spectrum of thyroid cancers were identified by XA, including some with approved or investigational targeted therapies. Among 190 Bethesda III/IV nodules, the sensitivity of XA as a standalone test was 49%.Conclusion: When the Afirma Genomic Sequencing Classifier (GSC) is used first among Bethesda III/IV nodules as a rule-out test, XA supplements genomic insight among those that are GSC suspicious. Our data clinically and analytically validate XA for use among GSC suspicious, or Bethesda V/VI nodules. Genomic information provided by XA may inform clinical decision-making with precision medicine insights across a broad range of FNA sample types encountered in the care of patients with thyroid nodules and thyroid cancer. HRAS gene   , PTEN1, EN1, RET , 23, ROSET  that variant and fusion identification by RNA-sequencing was more significantly harnessed. The use of whole transcriptome sequencing by both the Afirma GSC and Afirma XA allows the same sample collection and shipping method to be used for both tests, and both tests are run on the same FNA sample. Using one sample for both tests facilitates successful test results despite the small genomic sample obtained by FNA. XA findings may predict tissue cellular morphology, clinical syndromes, cancer behavior (including mode of metastasis), prognosis, and facilitate the selection of effective targeted therapy in the appropriate clinical setting.Currently, an important tool to avoid unnecessary diagnostic surgery among cytologically indeterminate thyroid nodules is the Afirma Genomic Sequencing Classifier (GSC) , 26. In  surgery –31. Conv surgery –31. The BRAF V600E, RET/PTC1, and RET/PTC3 fusions using whole transcriptome RNA-seq data derived from FNA samples. We compared the XA results to a targeted DNA panel for nucleotide variants and compared XA results to a targeted RNA fusion panel for gene/gene fusion detection. The data demonstrate a high level of agreement between methods and that these variant and fusion methods alone cannot serve as rule-out test to exclude cancer/noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). When used among Bethesda III/IV nodules that are GSC suspicious, Bethesda V/VI thyroid nodules, or by extension known thyroid cancer metastases (data not shown), the genomic content provided by XA can provide additional information that may inform clinical decision-making.Here we describe the analytical and clinical validation of XA to report nucleotide variants and gene fusions beyond RNA and DNA were extracted from thyroid FNAs or control tissues  using the Qiagen AllPrep Micro Kit  according to manufacturer's instructions. RNA was quantitated using Quantiflour  and DNA was quantitated using PicoGreen . Fluorescence was read on a Tecan Infinite M200 Pro . RNA Integrity Number (RIN) was determined for RNA using RNA Pico Chips on the Bioanalyzer 2100 .The Ion AmpliSeq Designer  was used to generate a custom DNA panel against 568 targets described in Pagan et al.  according to manufacturer's instructions using 10 ng input material. RNA was first reverse transcribed with SuperScript IV VILO . Following cleanup, libraries were quantitated using qPCR on a QuantStudio 6 , normalized to 100 pM, and loaded onto the IonChef . Pooled libraries were loaded onto the Ion 540 Chip  and sequenced on the Ion Torrent S5XL .BRAF V600E positive thyroid tissue , and TERT C228T positive thyroid tissue .Each DNA plate included the Horizon Quantitative Multiplex Reference Standard , NA12878 , The Ion Torrent Suite Software version 5.6.0  was used to demultiplex, map reads to the reference genome hg19, and call variants. Specifically, tmap version 5.6.8 was used for reads mapping, and Torrent Variant Caller version 5.6–10 was used to detect variants with parameter settings optimized for low frequency variant detection with minimal false negative calls on Ion AmpliSeq experiments.The Ion Reporter version 5.6.0 was used to detect fusions. Key fusion detection parameters were set as follows: minimum 20,000 total valid mapped reads to qualify a sample for further analysis; minimum 20 reads required to call a fusion; medium sensitivity, which requires 70% overlap between reads and reference sequence with at-least 66.66% exact matches in the overlap.Taqman assays were obtained from Thermo Fisher or designed and synthesized by Integrated DNA Technologies . Ten nanogram of RNA was reverse transcribed with QuantiTect . qPCR reactions were performed in duplicate using Fast Advanced Master Mix , VIC-labeled primer-limited TBP , and the FAM-labeled fusion-specific taqman assay (Thermo Fisher or IDT). qPCR assays were run on the QuantStudio 6  and Ct values were determined with the QuantStudio Real Time Software v1.3.RNA-seq data was generated using the TruSeq RNA Exome kit  using 15 ng of total RNA as previously described . LibrariFastq files were aligned to hg19 using STAR aligner, version 2.4.1b . FusionsAll fusion partners are described in their 5′/3′ order. In the commercial Afirma XA report, all fusion partners are reported alphabetically except CCDC6/RET and NCOA4/RET, which are reported using their colloquial names of RET/PTC1 and RET/PTC3, respectively.n = 32, 217, 75, 35, 36 from Bethesda categories II-VI, respectively)  (BRAF V600E truth Bethesda V (n = 52) and Bethesda VI (n = 51) (n = 247).943 blinded FNA samples with sufficient DNA were utilized from the following sources: Afirma GSC Algorithm Training set (ctively) , the proda V/VI) , samples(n = 51) , and Betn = 634 Bethesda III/IV GSC suspicious and n = 61 Bethesda V/VI).An additional 695 blinded FNAs from the Veracyte CLIA laboratory with sufficient RNA were deidentified and examined for fusions, during which time the total rate of assay failure among all samples received was 3.85%. This blinded and consecutive cohort was chosen without bias to represent an XA intended use cohort , negative percent agreement (NPA) and confirmation were calculated following the FDA guidelineTo determine how many DNA variants could be detected in expressed RNA, RNA and DNA were first extracted from the same biological sample for direct comparison. Afirma GSC data was generated from the RNA, which utilizes whole-transcriptome RNA-seq data as an input to the machine learning algorithms. DNA was analyzed with a custom, targeted AmpliSeq panel that covers 761 variants that have been described in thyroid samples.Nine hundred forty-three FNA samples were analyzed with the custom DNA AmpliSeq panel. Four hundred forty-two samples were used for parameter tuning to ensure accurate detection of variants, and after the analysis pipeline was locked, the remaining 501 samples were used to evaluate the performance of calling a variant from RNA-seq data relative to DNA-based detection. Using a DNA variant allele frequency (VAF) cutoff of 5%, 181 DNA variants were observed, and the same variants were observed in 134 RNA-seq samples 74%; . Using aTo further investigate the role of transcription in the detection of expressed variants, a targeted variant panel using RNA as the template was employed rather than DNA. From the 943 FNA samples, a representative set of 102 FNAs that were variant positive by DNA AmpliSeq were tested for expressed variants with an RNA AmpliSeq variant panel. The PPA of RNA-seq whole-transcriptome variants rose from 76.5% vs. DNA in this subset to 88.9% vs. RNA . Next, RPAX8/PPARG (n = 16), ETV6/NTRK3 (n = 13), RET/PTC1 (n = 6), STRN/ALK (n = 6), RET/PTC3 (n = 3), AGK/BRAF (n = 3), SND1/BRAF (n = 2), and RBPMS/NTRK3 (n = 2)  . This an (n = 2) .To determine the reproducibility of the whole-transcriptome RNA-seq assay across laboratories and reagent lots, we examined the analytical validity of the assay. We compared the variant and fusion calls from RNA-seq data between two labs (R&D and CLIA) with the same lot of library prep reagents. Sixty-nine variant positive samples were used, and the between lab accuracy was 89.9% . For fusRAS variants were the most common, followed by TSHR. In the 45 histologically malignant samples, 51% had no variant or fusion observed (49% sensitivity). Overall, the RNA-seq PPV and NPV were 38% and 83%, respectively, in this cohort with a 24% cancer prevalence. The 49% of samples with a positive variant or fusion mostly harbored RAS variants, BRAF variants, or fusions. Of the variants observed more than once, only BRAF V600E was confined to malignant nodules. Conversely, 10 of 11 TSHR variants and 4 of 5 SPOP variants occurred in histologically benign nodules. The only gene fusion observed more than once was PAX8/PPARG and both occurrences were in histologically benign nodules. MKRN1/BRAF and ETV6/NTRK3 fusions were each identified once and were in malignant nodules (both PTC). PAX8/GLIS3 was identified in the one hyalinizing trabecular adenoma, consistent with a recent report . In the 41 histologically malignant nodules with GSC suspicious results (true positives), 46% had a variant, and 5% had a fusion (MKRN1/BRAF and ETV6/NTRK3). Finally, in the 4 GSC benign false negative nodules , only the HCC contained a variant (TSHR). Taken together, in the 190 thyroid nodules with definitive histology, malignant nodules were twice as likely to carry a variant or fusion relative to benign nodules . The most common genomic alteration identified among GSC suspicious nodules was a variant in a RAS family gene (present in 32% and conveying a PPV of 46%). Taken together, GSC suspicious nodules carried a variant or fusion in 48% compared with 16% of GSC benign nodules (p < 0.0001). The most common variants among GSC benign nodules were TSHR, and no variants or fusions known to be highly predictive of malignancy were identified among nodules with GSC benign results. In clinical use, variants and fusions are not reported among GSC benign nodules.We next examined the combination of Afirma GSC classifier prediction and variant/fusion results . In the p = 0.77 [χ2]).Among GSC suspicious nodules with a cancer prevalence of 47%, the sensitivity, specificity, and NPV of XA were 51, 54, and 56%, respectively. The overall PPV of an Afirma GSC suspicious nodule was 47%, regardless of variant/fusion status. The PPV was 50% among GSC suspicious nodules when a variant or fusions was identified, compared with 44% among GSC suspicious nodules when no variant or fusion was identified , PAX8/PPARG, and ETV6/NTRK3  of the 943 samples harbored a fusion. The most common fusions were V6/NTRK3 . RET/PTC by qPCR . 100% ofAKT, ALK, BRAF, HRAS, KRAS, MET, NRAS, NTRK, PPARG, PTEN, and RET were identified across multiple Bethesda categories. Similarly, 4 RET point mutations suggestive of MTC were identified among Bethesda III-V samples  and 4/5 were histologically benign. Additionally, there were 2 FNAs that were positive for TERT C228T in combination with KRAS Q61R or HRAS Q61R, which were histologically malignant and benign, respectively. Finally, five Bethesda V and VI FNAs were TERT C228T in combination with BRAF V600E, and all 5 were histologically malignant. Three were PTC and two were PTC-TCV. The Bethesda VI sample with TERT C228T in isolation was an FC-v.The DNA AmpliSeq panel included the tegories , and allathology . The BetThis study demonstrates the analytical and clinical validation in thyroid nodule evaluation of the Afirma Xpression Atlas (XA), which detects gene variants and fusions from a curated panel of 511 genes via dedicated FNA samples using whole-transcriptome RNA-sequencing. More than 96% of consecutive real-world samples received has sufficient quantity and quality to receive an XA result. The data show that agreement is high between fusion detection by XA and an alternative fusion detection method, and that a high proportion of variants observed in DNA are also detected in the expressed RNA. As expected, agreement is even higher when comparing variant detection between two different RNA-based methods. Intriguingly, we found the wild type allele to be preferentially expressed compared with the variant allele in some samples , explainBRAF V600E-like vs. RAS-like (or non-BRAF-non-RAS) pathway signaling, iodine metabolism, neoplasm histology, risk of lymph node metastasis, risk of recurrence, and risk of mortality  and in the vast majority of cases, found in tandem with a RAS variant. Moreover, we show that TERT promoter variants in combination with RAS variants can occur in benign lesions in Bethesda IV FNAs. While current opinion is that nodules with a RAS variant (with or without TERT promoter variant) should be surgically removed given their potential malignant or pre-malignant status, it is unclear if cancers harboring a TERT promoter variant plus a RAS or BRAF variant should be treated differently based on this genomic information independent from traditional prognostic factors for risks of recurrence and death, especially among lower-risk patients . A waiver of written informed consent was granted regarding de-identified biological materials from the CLIA laboratory. IRB approval and written Informed consent in accordance with the Declaration of Helsinki was provided by all patients whose samples were previously used for training and validation of the Afirma GSC as previously described , 38.GK, JB, JH, PW, SK, and YH conceived and designed the study. JB, LW, PW, RK, SK, TA, and YH analyzed and interpreted the data. EC, GK, JB, LW, MC, MS, RK, SW, and TA drafted and critically revised the work for important intellectual content. SK and YH performed the bioinformatics and statistical analyses. All authors contributed to manuscript revision, read and approved the submitted version.GK, JB, JH, PW, RK, SK and YH are Veracyte Inc. employees and equity owners. The remaining 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.


Template-directed RNA ligation catalyzed by an RNA enzyme (ribozyme) is a plausible and important reaction that could have been involved in transferring genetic information during prebiotic evolution. Laboratory evolution experiments have yielded several classes of ligase ribozymes, but their minimal sequence requirements remain largely unexplored. Because selection experiments strongly favor highly active sequences, less active but smaller catalytic motifs may have been overlooked in these experiments. We used large-scale DNA synthesis and high-throughput ribozyme assay enabled by deep sequencing to systematically minimize a previously laboratory-evolved ligase ribozyme. After designing and evaluating >10 000 sequences, we identified catalytic cores as small as 18 contiguous bases that catalyze template-directed regiospecific RNA ligation. The fact that such a short sequence can catalyze this critical reaction suggests that similarly simple or even simpler motifs may populate the RNA sequence space which could have been accessible to the prebiotic ribozymes. Although no direct molecular signatures exist, it is widely accepted that RNA preceded DNA and proteins in the hypothetical RNA world ,2 in whiin vitro selection from a large pool of random RNA sequences can catalyze template-directed RNA ligation very efficiently (kobs > 1 s−1) (One RNA ligase ribozyme (class I Bartel ligase) that was discovered through > 1 s−1) . This ra> 1 s−1) . However> 1 s−1) ,10. Consin vitro selection to design the DSL   . R3C was ligases . Althoug ligases –C. . R3C wain vitro selection and sequence analysis, they have likely overlooked many weakly active sequences that were outcompeted during selection experiments. Few efforts to minimize these ribozymes have relied on ad hoc examination of several variants based on intuitive trimming of terminal stem-loops  pose an intriguing question about the probability of the emergence of RNA ligase activity. One path to address this question is to define minimal sequence requirements of the ribozymes. Smaller ribozymes with fewer sequence constraints are more likely to have emerged during prebiotic evolution, therefore, lend more support to the RNA world even if they are less catalytically active. However, as the previous laboratory evolution efforts of these ribozymes focused on optimizing catalytic activity through em-loops .In this work, we performed two cycles of high-throughput assay of F1-based ribozyme variants based on deep sequencing to elucidate the minimal sequence requirements of the ribozyme. We identified key sequence requirements and base-base interactions in the F1-like ribozyme that were previously not reported, resulting in a modified secondary structure model of the ribozyme core. We also found that a catalytic core as small as 18 bases can catalyze the reaction at a moderate rate, which represents the smallest catalytic core for this class of ribozymes. Our systematic large-scale mutational analysis of a ribozyme is free from selection bias, therefore, should help advance our understanding of ribozyme sequence-function relationships.TAATACGACTCACTATA-[ribozyme sequence] 3′ (T7 promoter underlined). The oligo pools were amplified by PCR using primers Ligase-lib-f and Ligase-lib-r (in vitro transcription using HiScribe T7 High Yield RNA Synthesis Kit (NEB) or ScriptMAX Thermo T7 Transcription Kit (Toyobo) according the manufacturers’ instructions. Upon completion of the transcription reaction, an equal volume of DNase I (NEB) stock solution  was added and incubated for 10 min at 37°C. RNA was cleaned by a silica column  and eluted in nuclease-free water. The ligase ribozyme pools were further purified by denaturing (TBE-urea) polyacrylamide gel electrophoresis (PAGE). The gels were stained by SYBR Gold (Thermo Fisher) and visualized on a blue-light transilluminator. The bands of the expected sizes were excised and frozen at −80°C, crushed, and the RNAs were extracted in Tris/NaCl buffer  at 4°C for 4 h. The RNAs were precipitated by ethanol using Quick-Precip Plus Solution (EdgeBio), washed twice with 70% ethanol, and resuspended in nuclease-free water.DNA templates that encode the T7 promoter and ribozyme variants were synthesized as oligo pools (CustomArray Inc.) with the following sequence: 5′ CCse-lib-r  using Ph2, 8 U/μl RNase Inhibitor, Murine (NEB)) were separately incubated at 42°C for at least 2 min. The reaction was initiated by mixing 3 volumes of RNA solution and 1 volume of 4 × reaction buffer, followed by 30 min incubation at 42°C. The reaction was terminated by adding 2.25 volumes of cold stop solution (5:13 mixture of 0.5 M EDTA and RNA Loading Dye (2×) (NEB)) and kept on ice. The F1* library ligation was performed in 8 μl and the minF1 library in 24 μl.A ligase ribozyme pool (0.67 μM) was mixed with the appropriate substrate (F1*sub or F1*subA) at 6.7 μM in nuclease-free water. The RNA solution was heated to 72°C for 3 min and then cooled on ice. Prior to the start of the ligation reaction, the RNA solution and the 4 × reaction buffer  and the unligated (Nunlig) pools which were used to calculate the fraction ligated (FL) values. The minF1 library was analyzed similarly except that the adapter sequence and the constant ribozyme sequence was first removed from each raw sequence read, and then the remaining catalytic core sequence was quality filtered to have all base calls with QS ≥ 30.MiSeq sequence data (fastq files) were analyzed by custom Python scripts. For the F1* library, each raw sequence read was first quality filtered for having at least 70% of the base calls with quality core (QS) ≥ 20. After trimming the adapter sequence and the constant ribozyme sequence, the catalytic core sequence was further quality filtered to have all base calls with QS ≥ 20. The sequence reads that passed the quality check were sorted to either ligated or unligated pools based on the 8 or 10 nt barcode sequence embedded in the reverse transcription primers. The number of reads of each ligase variant were then counted in the ligated (TAATACGACTCACTATA-[ribozyme sequence] 3′ (T7 promoter underlined) were prepared by annealing and extending two synthetic oligonucleotides using OneTaq 2X Master Mix with Standard Buffer (NEB). The dsDNA templates were then purified by silica columns (DNA Clean & Concentrator-5) and were used for in vitro transcription reactions as described above. Ligation reactions were performed as described above except for using the ligase ribozyme in excess (2 μM) over the FAM-labeled substrate . Polyacrylamide gels were imaged using Typhoon FLA9500  and quantified with ImageJ software. Kinetic analyses of minF1 and 4d394 were performed similarly by sampling 4 μl of the reaction mixture at appropriate time-points and mixing it with 9 μl cold stop solution. FAM-F1*sub was used as the substrate for the kinetic analysis of F1*.DNA templates encoding the T7 promoter followed by individual ribozyme sequences 5′ CCin vitro transcription using corresponding DNA templates prepared by annealing and extending two oligonucleotides as described above. The RNA (12.5–27.6 ng) was mixed with 3.5 μM Dz8-17 . The cleavage was allowed to proceed for 90 min at 37°C. Dz8-17 was digested by adding 5 μl of TURBO DNase (Thermo Fisher) stock solution  and incubation at 37°C for 10 min. The reaction products were separated by denaturing PAGE as described above, stained with SYBR Gold, and imaged by LuminoGraph II (ATTO).minF1 and 4d394 were ligated with F1*subA and the ligations products were purified by denaturing PAGE as described above. As controls, the expected ligation products were prepared by M Dz8-17  in 4 μl The catalytic core of F1* derived from the F1 ribozyme of Robertson and Joyce  includesPrevious efforts to elucidate the sequence-function relationships of nucleic acid enzymes have relied on either statistical randomization of a parental sequence by doped oligonucleotide synthesis or error-prone PCR, or saturation mutagenesis of a defined set of positions via oligonucleotide synthesized containing degenerate bases ,17. Whilin vitro transcription template by PCR. Then the ribozyme mutants were obtained as a mixture after in vitro transcription by T7 RNA polymerase , all single and double mutants, and all single, double, and triple deletions in the main catalytic core and the GAA bulge  and unligated (Nunlig) barcodes. Fraction ligated (FL) was calculated as:The ribozymes were allowed to ligate with an excess amount of the substrate F1*sub Figure  for 30 mkobs of the parental ribozyme, RA cannot differentiate variants with kobs greater than ∼0.1 min−1 (see Discussion). Therefore, the RA should be interpreted as a semi-quantitative parameter with a limited dynamic range especially at high kobs.Relative activity (RA) of each mutant was calculated by dividing FL by that of the wild-type (0.826). It should be noted that the FL and RA values represent ribozyme activity measured at a single time-point (30 min). Due to the high Most of the 93 single mutants were highly active (85/93 with RA>0.90). Only three substitutions  resulted in RA falling below 0.50 Figure . The leaTolerance of the ribozyme to mutations was further confirmed by the analysis of 4185 double mutants . ApproxiSingle base deletions generally do not affect the ribozyme activity . Only A2Flexibility of on-chip oligonucleotide synthesis was exploited by including a series of arbitrarily designed sequences. We focused on the GAA bulge (positions 33–35) opposite to the ligation site, to test all possible bulge sequences with sizes 0–3 Table . All sinin vitro evolution  while all other mutants were inactive. Furthermore, A is the only active single-bulge mutant, and deletion of the bulge altogether renders the ribozyme inactive. During the laboratory evolution of F1, Robertson and Joyce discovered GA and AA bulge variants after 10 rounds of volution  which isIn another set of mutants, the length of P2 (4 bp) was reduced to 1–3 bp resulting in change in the relative distance between the main catalytic core and the ligation site. Partial activity was observed when P2 was reduced to 3 bp  while shorter P2  completely inactivated the ribozyme . All sinOverall, the large-scale mutational analysis of F1* points to its high tolerance to mutations (including base deletions). This is consistent with the extensive sequence variations observed during the evolution of the ancestral R3 ribozyme which lacks cytidines to R3C, and subsequently to F1 by the Joyce group ,17. Aparkobs = 0.48 min−1)  reported for F1 (16.6 min−1) using a rapid-quench device under the same reaction conditions , therefore, the RA values obtained by sequencing are highly reliable.We further characterized minF1 by performing another round of large-scale mutation/deletion analysis by sequencing . The parRNAfold web server . On the other hand, A5-A9, G11 and A22 are highly tolerant to base substitutions Figure . A closeb server  (22). DoThe bulge mutants of sizes 0–3 bases in minF1 display more stringent sequence requirements compared to F1* in which many bulge variants are tolerated. Only the GAN bulges (RA > 0.80) were highly active in 3-nt bulges, and AAA showing a moderate activity (RA = 0.48). The only other active (RA > 0.50) variant was the 2-nt bugle GA (RA = 0.84) .minF1 also showed a higher sensitivity to base deletions compared to F1* Figure . Single kobs = 0.49 h−1 which is ∼58-fold slower compared to minF1  F1*. The above results were visually summarized in Figure to minF1 . Neverthhemistry . Their rin vitro transcription. The ligation products and the corresponding controls were digested by excess Dz8-17, and the digestion products were analyzed by PAGE (Regiospecificity of minF1 and 4d394 were confirmed by cleaving the ligation products with deoxyribozyme 8–17 (Dz8-17) targeting the ligation junction. Dz8-17 specifically cleaves 3′-5′ linkage at GA junction but not 2′-5′ . The lig by PAGE . The digTemplate-directed RNA ligation of a 3′-OH and a 5′-triphosphorylated RNA fragments is of fundamental interest because the same chemistry is used by the modern DNA and RNA polymerases. However, as nonenzymatic polymerization (templated or nontemplated) of ribonucleotide triphosphates (NTPs) has not been reported, 5′-triphosphorylated RNA fragments may not have been available prior to the emergence of more complex RNA polymerase ribozymes. A separate ribozyme that triphosphorylates 5′-OH RNA fragments may have existed, as demonstrated by the laboratory-evolved ribozymes from the Müller group that use trimetaphosphate as a substrate ,26. AlthIn vitro selection of statistically mutated ribozyme variants generated by doped oligonucleotide synthesis or error-prone PCR followed by sequence analysis of the selected (active) mutants has traditionally been used to investigate the sequence constraints of ribozymes  or local randomization (using degenerate oligonucleotides)  . In the kobs >10 min−1. However, our sequencing-based ribozyme activity assay was performed at a single time-point of 30 min after reaction initiation. This implies that even a kobs as low as 0.1 min−1 would be sufficient for the majority (95%) of the ligase to have reacted with the substrate. In other words, the apparent mutational tolerance does not mean that mutations do not affect the absolute kobs values. Data from additional (earlier) time-points would be necessary to quantitatively analyze the effects of mutations, as demonstrated previously for a deoxyribozyme  which makes a smaller drop in kobs to be noticeable as a lower fraction of ligated ribozyme at 30 min. It can also be expected that the removal of P5 in F1* structurally destabilizes minF1, possibly resulting in increased sensitivity to mutations. We also identified few mutants with 4 additional base deletions displaying moderate activities. The functional minF1 variants and the deletion mutants represent compact ribozyme motifs defined by a catalytic core that is as small as 18 contiguous bases   . Slower gkz729_Supplemental_FilesClick here for additional data file.