Here, we explored the mechanism employed by the bacterial factor σ in promoter-independent initial transcription. We found that the RNAP holoenzyme lacking the promoter-binding domain σ4 is ineffective in de novo transcription initiation and displays high propensity to pausing upon extension of RNAs 3 to 7 nucleotides in length. The nucleotide at the RNA 3' end determines the pause lifetime. The σ4 domain stabilizes short RNA:DNA hybrids and suppresses pausing by stimulating RNAP active-center translocation. The antipausing activity of σ4 is modulated by its interaction with the β subunit flap domain and by the σ remodeling factors AsiA and RbpA. Our results suggest that the presence of σ4 within the RNA exit channel compensates for the intrinsic instability of short RNA:DNA hybrids by increasing RNAP processivity, thus favoring productive transcription initiation. This “RNAP boosting” activity of the initiation factor is shaped by the thermodynamics of RNA:DNA interactions and thus, should be relevant for any factor-dependent RNAP.All cellular genetic information is transcribed into RNA by multisubunit RNA polymerases (RNAPs). The basal transcription initiation factors of cellular RNAPs stimulate the initial RNA synthesis  After ill RNAPs , 3, 4, 5ll RNAPs , 9, 10. ll RNAPs , 7, 11. in vitro , 13, 14  (reviewed by ). Duringi.e., bacterial σ subunit, archaebacterial TFB, and eukaryotic TFIIB) stimulate the initial steps of RNA synthesis  stabilization of the template ssDNA in the RNAP active site ; (b) deci+1-site ; and (c)i+1-site , 23, 35.i+1-site , 36. As Escherichia coli (EcoRNAP) and M. tuberculosis (MtbRNAP). Compared with EcoRNAP, MtbRNAP presents several structure-specific features, particularly the lack of Eco-specific TL-insertion and the presence of the ∼90 amino acid-long Actinobacteria-specific insertion in the β' subunit (β'-SI) , located ∼60 Å away from the active site, strongly stimulates RNAP translocation and stabilizes short RNA:DNA hybrids in the RNAP active site. The combination of these activities provides the basis for the initiation-to-elongation transition regulation by the auxiliary transcriptional factors that binds to the σ subunit.Here, to discriminate among these different scenarios, we investigated how the σ subunit and RNA:DNA hybrid length affect branching between productive and nonproductive RNA synthesis during initial transcription by two RNAPs from phylogenetically distant bacterial lineages:  (β'-SI) , 38. To  (β'-SI) , 26. DNA (β'-SI) , 40. The (β'-SI) . We founA): a Short Duplex Template (SDT), which included the 9-bp downstream DNA (dwDNA) duplex, and a Long Duplex Template (LDT), which comprised the 18 bp dwDNA duplex. The dwDNA duplex of the LDT scaffold forms additional contacts with the β' subunit residues 202 to 247 that stabilize the RNAP–scaffold complex (70 subunit (E. coli (EcoRNAP) was inactive in de novo transcription initiation at SDT and LDT templates performed in the presence of [α-32P]-UTP, CTP, and GTP (B). Conversely, the σ70-EcoRNAP holoenzyme synthesized a single 3-nt RNA (pppC[α-32P]UpG) starting 8 nt downstream of the 3' end of the template DNA (designated as “+1”) (B). This start site assignment was validated by using the antibiotic rifampicin that inhibits the synthesis of the second phosphodiester bond. Indeed, rifampicin addition abolished the formation of 3-nt RNA and induced the accumulation of radiolabeled 2-nt RNA (pppC[α-32P]U) (C).To explore the role of the σ subunit in initial transcription, we used two types of minimal DNA scaffold templates A: a Shor complex , 42. Pre subunit . Here, w and GTP B. Converas “+1”) B. This sα-32P]U) C.Figure 70 regions that influence RNA synthesis initiation on minimal scaffolds, we generated a panel of σ70 mutants (D and E). The σΔ3–4 fragment (residues 1–448) in which the regions 3 and 4 were deleted is inactive in promoter-dependent transcription initiation (Δ4 fragment (residues 1–529) lacked part of region 3.2 and the entire σ4 domain. In the σΔ4.2 fragment (residues 1–553), only region 4.2 was deleted, but not the 4.1 α-helix, which binds inside the RNA exit channel (E). In agreement with previous studies . The σΔ3.2 subunit, in which residues 513 to 519 in the σ3.2-finger were deleted, was active in transcription initiation with both promoters. The EcoRNAP holoenzymes assembled with the mutant σ70 subunits were inactive in de novo transcription initiation on the SDT scaffold (B). We detected no synthesis of dinucleotide RNA products by the EcoRNAP core and by the holoenzyme, differently from what reported for initial transcription on the M13 minus-strand origin . We assumed that the first catalytic step, addition of [α-32P]-UTP to pRNA2 (synthesis of GpC[α-32P]U), does not require translocation of the RNAP active center because the 3' end of pRNA2 binds to the “product-site” , thus leaving the “substrate site”  available for incoming NTP . This hypothesis is supported by the structures of the RNAP initiation complexes with synthetic scaffolds observed in posttranslocated states (32P]UpG and GpC[α-32P]UpGpA products) require the translocation of the RNA 3' end from the i + 1 site to the i-site (A and B). As only the first incorporated NTP (U) was labeled, the fraction of the longest reaction product (RNA[N + 2]) reflected the overall “efficiency of RNAP translocation” from register +2 to +4.In promoter-dependent transcription initiation, short (≤3-nt) RNA primers (pRNAs) can rescue the defects linked to deletions in the σ3.2 and σ4 regions , 22, 23.Fig. 2d states , 39. The32P]-UTP, GTP, and ATP), the EcoRNAP core synthesized two [32P]-labeled RNA products (3-nt and 5-nt RNAs), with a bias toward the shorter one . In the same conditions, σ70 strongly stimulated [α-32P]-UTP incorporation and translocation. Consequently, the 5-nt RNA was the major reaction product synthesized by the EcoRNAP holoenzyme (95% translocation efficiency). The observed low efficiency of the [α-32P]-UTP-addition reaction by the EcoRNAP core might reflect its low affinity for pRNA2. Indeed, increasing pRNA2 concentration increased [α-32P]-UTP incorporation, but did not stimulate translocation . The C-terminal deletions in the σ70 subunit  hindered the synthesis of 5-nt RNA (<50% translocation efficiency) leading to RNA patterns similar to those of the EcoRNAP core. As deletion of the region 4.2 and deletion of the entire σ3 and σ4 domains led to the same effect, we conclude that σ4.2 is a principal determinant for the efficient synthesis of the 5-nt RNA product. It is unlikely that the translocation defect conferred by deletion of the region σ4.2 is due to a decreased affinity of the 3-nt RNA product (GpC[α-32P]U) for the RNAP active site because the overall [α-32P]-UTP incorporation was similar with full-length σ70 and σΔ4.2 . Our results suggest that RNAP translocation from register +2 to register +3 is a rate-limiting step for 5-nt RNA synthesis and is slower than [α-32P]-UTP addition and the 3-nt RNA product dissociation. The σ4 deletions had similar effects on translocation also when using the SDT scaffold and the 3-nt pRNA  . Moreover, in the SDT2+1 scaffold, the 5' end upstream edge of the DNA duplex was extended by one base pair (G:C). Thus, translocation from register +2 to register +3 on SDT2+1 requires the unpairing of 1 bp of dwDNA followed by the formation of the contact between G at position +3 of the nontemplate DNA and the CRE pocket of the RNAP core that is known to counteract transcriptional pausing , suggesting that dwDNA duplex melting is not a barrier for translocation and that the interaction with CRE stimulates translocation. We conclude that EcoRNAP pauses after the addition of the first NTP to the RNA primer and that the interaction with the downstream DNA and RNA promotes forward translocation. The region σ4.2 may act on translocation by strengthening this interaction.Like with promoter DNA templates, the initiation defect on DNA scaffolds upon σ3.2-finger deletion was rescued by pRNA2  . However pausing . Translo2+-agarose beads and tested their ability to retain RNAs by washing the complexes with transcription buffer. We used 2 to 6 nt-long pRNAs the 3' end of which was aligned to the same position of the template, designated as position “+1” (A) Control experiments in which complexes were formed by the core EcoRNAP on SDT and LDT scaffolds showed that after washing with the “high salt” buffer containing 1M NaCl . This experiment demonstrated that 2- to 4-nt pRNAs were weakly bound to ITCs compared with 5- to 6-nt pRNAs (B–E). Therefore, the 4-bp RNA:DNA hybrid is a conversion point between stable and unstable ITC states. Moreover, we observed a clear difference in the capacity to hold 4-nt pRNA by ITCs containing the full-length σ subunit (75% retention efficiency) and σ mutants  (E). The defect was stronger for σΔ4-EcoRNAP than σΔ3.2-EcoRNAP. We obtained similar result with the SDT scaffold (Δ3.2 and σΔ4 (E) was significant, we performed several washing steps on ITCs formed by σΔ3.2-EcoRNAP and σΔ4.2-EcoRNAP (F and G). After the third washing step, almost no bound RNA was left in σΔ4.2-ITC (∼10% retention relative to full-length σ70), while RNA retention was higher for σΔ3.2-ITC (∼40% retention relative to full-length σ70). RNA binding remained stable with wild-type σ70-ITC (60% retention relative to the “no washing” condition). We conclude that the σ70 subunit stabilizes 4-5-nt-long RNAs in the RNAP active site and that the region σ4.2 is a major determinant of this activity.To determine whether the σ subunit can stabilize short RNAs in the RNAP active site, we immobilized ITCs on Niion “+1” A Control 1M NaCl , 5-, 6-,ing step B. This ent pRNAs , B–E. Thiciency) E. The descaffold . To dete and σΔ4 E was sig-EcoRNAP , F and G32P]-UTP, GTP and ATP (A). In these experiments, to facilitate the detection of the initial pause, we used the SDT scaffold that displayed stronger σ-dependence in translocation. It should be noted that 3- to 4-nt RNA products displayed anomalous migration in denaturing PAGE, thus CGCU moved faster than GCU (B). The EcoRNAP core efficiently extended ≥ 8-nt pRNAs (>90% efficiency) (B and C), and its translocation efficiency decreased gradually with the RNA length shortening. This dependence on RNA length might be explained by the intrinsic instability of RNA:DNA hybrids and/or by the disengagement of the RNA 3' end from the active site. The translocation efficiency was independent from the RNA length when the wild-type EcoRNAP holoenzyme was used (B and C). The σΔ3.2-EcoRNAP holoenzyme displayed strong translocation defects with 3-nt pRNA (∼25% efficiency), moderate defects with 4- to 5-nt pRNAs (∼80% efficiency), and no defect with 6-nt pRNA (>90% efficiency). On the basis of the RNAP–promoter complex structure data, the 5' end of ≥ 5-nt-long RNAs clashes with the σ3.2-finger (D). Thus, the σ3.2-finger should hinder the extension of RNAs longer than 5 nt and favor abortive initiation (EcoRNAP structure (E) showed that 5-to 7-nt RNAs can be accommodated in the active site cleft. Yet, the remaining segment of the region σ3.2 can still contact the template DNA strand at positions −6/−7. Strikingly, in our experiments, “abortive” RNAs accumulated when using the EcoRNAP core and the σΔ3.2-EcoRNAP holoenzyme, but not with the wild-type EcoRNAP holoenzyme. This suggests that the σ3.2-finger is not the primary cause of abortive RNA formation and that abortive RNA synthesis is not an obligatory event in initiation when the transcription bubble expansion and the RNAP upstream promoter contacts are missing.To explore the relationship between translocation efficiency and RNA:DNA hybrid stability, we performed 2-min primer extension reactions with pRNAs of various lengths in the presence of -UTP addition rate was similar for ITC3 and ITC5 in the presence of the σΔ4.2 and was the highest in the presence of the full-length σ70 (B and C). Translocation from register +4 to +5 was at least 100-fold faster (t1/2 ∼ 1.7 s) in the presence of full-length σ70 than of the σΔ4.2 mutant (t1/2 ∼ 200 s) (B and D). Reactions were completed in 120 s, without any further incorporation of [α-32P]-UTP. Therefore, the labeled 5-nt RNA product remained bound to RNAP. Extension of the pRNA 5' end by 2 nucleotides (pRNA5) accelerated the forward translocation only by twofold. Therefore, in agreement with the conclusion drawn in the previous section, the overall RNA:DNA hybrid stabilization has little effect on pausing.To explore the impact of σ4.2 on pausing, we studied the kinetics of 5-nt and 7-nt RNA synthesis initiated with pRNA3  and pRNA5 , respectively A. The ovngth σ70 , B and C∼ 200 s) , B and DA) and initiated the primer extension with [α-32P]-CTP. Unlike UTP, the CTP addition rate with the σΔ4.2-EcoRNAP holoenzyme was close to that observed with the wild-type EcoRNAP holoenzyme (compare C and F). Thus, without σ4, EcoRNAP senses the difference between UTP and CTP, while CTP suppresses the effect of the σ4 deletion. Irrespectively of the RNA length (4-nt or 6-nt), translocation of the mutant σΔ4.2-EcoRNAP from the register +2 to the register +3 was accelerated by approximately tenfold on SDT-G DNA compared with SDT-A DNA. This suggests that the stability of base pairing at the 3' end, but not the RNA:DNA hybrid length, is crucial for forward translocation. As σΔ4.2-EcoRNAP translocation rate was significantly reduced even when the RNA 3' end was stabilized, compared with wild-type EcoRNAP, we conclude that the region σ4.2 may affect the active site cycling or the clamp opening–closing dynamics that control RNAP translocation.In our assay, the RNA chain elongation starts with addition of U that forms an unstable U:dA pair with the DNA template . TherefoFig. 570 subunit (residues 528–613) that was deleted in the σΔ4 mutant and disrupts the interaction between σ4.2 and β-FTH (A). If the σ4.2-β-FTH contact were essential for RNA synthesis, AsiA should fully inhibit initial transcription. To test this hypothesis, we performed de novo and primed transcription by the σ70-EcoRNAP and σΔ4.2-EcoRNAP holoenzymes, with and without AsiA, on the SDT2 template (B). As control, we used an abortive transcription assay on the lacUV5 promoter. AsiA inhibited lacUV5-dependent transcription initiation by 85% . Conversely, de novo initiation from the scaffold was much less sensitive to AsiA. Indeed, 3-nt RNA synthesis was inhibited only by 50%, which coincided with the accumulation of the short 2-nt RNA product . AsiA also influenced transcription initiated with the GpC primer . The amount of 3-nt RNA increased simultaneously with the increase in total [α-32P]-UTP incorporation. Such effect was consistent with the AsiA-induced destabilization of short RNAs, leading to accumulation of “abortive” transcripts. The finding that AsiA did not affect [α-32P]-UTP incorporation with the σΔ4-EcoRNAP holoenzyme  indicates that AsiA modulates RNA synthesis through σ4. However, the weak impact of AsiA on initial transcription was in striking contrast with the strong inhibitory effect of the σ4 deletion. The only possible explanation for this discrepancy can be that the σ4 physical presence in the RNA exit channel is essential for initial transcription. In the presence of AsiA, the σ4 domain remains bound inside the RNA exit channel (Region σ4.2 binds to the flap-tip-helix (FTH) of the RNAP β subunit , 48 thatnd β-FTH , 51 (modnd β-FTH A. If thetemplate B. As conMtbRNAP. We used the M. tuberculosis σB subunit that requires the activator protein RbpA to stabilize its active conformation in the MtbRNAP holoenzyme (D). First, we compared the kinetics of 5-nt pRNA extension by MtbRNAP and EcoRNAP on SDT2 DNA in the presence of [α-32P]-UTP and GTP  and then assessed how the translocation activity of the mutant and wild-type enzymes were influenced by the pRNA length (E and F). ITCs were assembled with 2, 3, 5, and 6-nt pRNAs (ITC2–6) in the presence of σB and RbpA, or without transcription factors, and supplemented with [α-32P]-UTP and GTP. As observed with EcoRNAP, MtbRNAP translocation efficiency increased gradually with the RNA length and reached 80% with the 6-nt pRNA. Like for σ70, the σB-RbpA complex stimulated the forward translocation with short pRNAs (3- to 6-nt in length). However, the σB subunit alone did not stimulate translocation, in agreement with the fact that its conformation in the MtbRNAP holoenzyme differs from that of σ70 in the EcoRNAP holoenzyme. The deletion of β-FTH abolished σB antipausing activity with ITC2 and ITC3 . Furthermore, the RNA amount produced by unstable ITC2/ITC3 formed by the MtbRNAPΔFTH mutant increased by approximately fourfold, compared with the amount produced by the stable ITC6. . The σ subunit region 4.2, which was implicated only in promoter binding, counteracts the initial transcription pausing and thus plays an essential role in organizing of the RNAP active center for efficient initiation of in vivo . Before the first phase, abortive transcription is predominant and the RNA:DNA hybrid stability depends on σ. Before the second phase, pausing is predominant and translocation depends on σ. These phases perfectly fit with the three ITC types observed at natural promoter templates: unstable ITCs with RNAs <4 nt, intermediate stability ITCs with RNAs of 4 to 8 nt, and stable productive ITC with RNAs >8 nt  transition from unstable to stable RNA:DNA hybrids when RNA length reaches 5 nt; and 2) transition from ITC prone to pause to productive ITC/EC when RNA length exceeds 8 nt B. Before transitiAs >8 nt . Our resAs >8 nt . Unlike cription . The lacemplates , 13. Takσ subunit stimulates de novo initiation, stabilizes short RNA:DNA hybrids, and selectively stimulates UTP addition to the RNA 3' end. These activities were previously attributed to the σ3.2-finger  through β-flap interaction , disordered β-flap  and M. tuberculosis (expression plasmid pMR4) were expressed in E. coli BL21(DE3) cells and purified as described before  were constructed and produced as described  was prepared by annealing two oligonucleotides  followed by purification on 10% PAGE. To assemble DNA scaffolds, oligonucleotides were heated in transcription buffer  at 65 °C for 5 min and then annealed by lowering the temperature to 16 °C for 30 min.Recombinant core RNAP  of d before , 64. Theescribed , 63, 64.32P]-UTP or [α-32P]-CTP and 22 μM of the indicated NTPs (HPLC purified). Transcription was performed at 22 °C for 2 min or for the indicated time and stopped by adding an amount of loading buffer  equal to the reaction volume. Samples were heated at 65 °C for 2 min, and RNA products were resolved on 26% PAGE (acrylamide: bis-acrylamide ratio 10:1) with 7 M urea and 1× TBE.Transcription reactions were performed in 5 μl of TB. In total, 240 nM RNAP core was mixed with 1 μM of full-length σ or 2 μM of σ mutants and incubated at 37 °C for 5 min In total, 1 μM RbpA was added when indicated. Samples were supplemented with 0.8 μM  scaffold DNA and 50 μM  pRNA. The primer extension kinetics were evaluated using 100 μM pRNA. Samples were incubated on ice for 5 min, then at 22 °C for 5 min, and supplemented with 0.4 μM -UTP  was added to all samples that were then incubated at 22 °C for 3 min. Reactions were quenched and analyzed as above.lacUV5 and galP1cons promoters was performed with 200 nM EcoRNAP, 500 nM full-length σ70 or 2 μM σ70 mutants, and 300 nM DNA template in TB. Samples were incubation at 37° for 10 min and supplemented with 100 μM ApA (lacUV5 assay) or CpA  and 0.4 μM [α-32P]-UTP. Transcription reactions were performed at 37 °C for 10 min.Transcription on the 70 was first mixed with 1 μM AsiA and then with 100 nM EcoRNAP core. Samples were incubated at 30 °C for 10 min. Next, DNA templates were added and samples were incubated at 30 °C (with lacUV5 promoter) and at 22 °C (with SDT2 scaffold) for 5 min. Transcription from the lacUV5 promoter (50 nM) was initiated by adding 100 μM ApA and 0.4 μM [α-32P]-UTP at 37 °C for 5 min. Transcription from scaffold DNA (0.8 μM) was performed in the presence of 0.4 μM [α-32P]-UTP and 25 μM NTPs or 50 μM GpC and carried out at 22 °C for 3 min.In total, 500 nM σ1/2 = ln2/k) were calculated by fitting the fractions of RNA in pause [PiPS]/[Pitotal] in function of the time t using the following single-exponential equation: [PiPS]/[Pitotal] = A0·exp(−k·t) + R, where [PiPS] is the RNA in pause at the time point i and [Pitotal] is the total RNA at the time point i. A0 is the amplitude, and R is the residual.The pause half-life times (tAll data are contained within the article.The authors declare that they have no conflict of interest.


Bombyx mori is an oligophagous economically important insect and has been used as an important lepidoptera model insect. By far, the knowledge about A-to-I RNA editing and ADAR members in B. mori (BmADAR) is very limited. In this paper, we present a first molecular comprehensive cloning, sequence analysis of BmADAR transcripts and subcelluar localization of BmADARa. As a result, we obtained six BmADAR transcripts encoding different amino and carboxyl termini, among which BmADARa is a mainly expressed transcript with complete open reading frame. Our further investigations showed that the majority of BmADARa protein exists in the nucleus and has editing function to a specific site of the silkworm synaptotagmin I gene. Overall, by molecular cloning and functional identifing, this paper introduces the first ADAR enzyme in B. mori and contributes to further exploration of the functional domain of BmADARa and its editing substrates and target sites.Adenosine deaminase acting on RNA (ADAR) is a key enzyme in the editing of adenosine into inosine (A-to-I). The loss or dysfunction of ADAR enzymes in higher eukaryotes affects the editing efficiency of target genes, leading to some neurological diseases. The silkworm Bombyx mori (BmADAR), is limited. In this study, a first molecular comprehensive cloning and sequence analysis of BmADAR transcripts was presented. A complete open reading frame (ORF) (BmADARa) was obtained using RT-PCR and RACE and its expression pattern, subcellular localization and A-to-I RNA-editing function on the silkworm synaptotagmin I (BmSyt I) were investigated. Subcellular localization analysis observed that BmADARa was mainly localized in the nucleus. To further study the A-to-I RNA-editing function of BmADARa, BmSyt I-pIZ-EGFP was constructed and co-transfected with BmADARa-pIZ-EGFP into BmN cells. The result demonstrates that BmADARa can functionally edit the specific site of BmSyt I. Taken together, this study not only provides insight into the function of the first ADAR enzyme in B. mori, but also lays foundations for further exploration of the functional domain of BmADARa and its editing substrates and target sites.The most common type of RNA editing in metazoans is the deamination of adenosine into inosine (A-to-I) catalyzed by the adenosine deaminase acting on the RNA (ADAR) family of proteins. The deletion or dysfunction of ADAR enzymes in higher eukaryotes can affect the efficiency of substrate editing and cause neurological disorders. However, the information concerning A-to-I RNA editing and ADAR members in the silkworm,  Xenopus laevis . T. TB. morX4 XM_0217298. Intd or not D. The sp subtype D.Drosophila melanogaster [ADA hydrolyzes adenosine as a neuromodulator to inosine in a manner similar to ADAR catalyzing the water-free amino reaction on dsRNA substrates . Some renogaster ,36,37. Hnogaster . The subnogaster . Vertebrnogaster ,38,39. Hnogaster , while tnogaster . In thisnogaster , while Anogaster . Togethe4f-rmp in Drosophila and eri-6 in nematodes) that can form complementary RNA pairing sequences that are affected by RNA editing [BmADARa and BmSyt I, one can judge whether an editing event occurs, and this is a straightforward and rapid way to find the A-to-I editing site of the target gene  [Drosophila synaptotagmin I [Drosophila ADAR gene was co-transfected with BmSyt I to S2 cells, A1098G (a putative editing site exists in the 8th exon of BmSyt I) transition took place, in which the more than 1000 bp 8th intron was used as ECS [BmSyt I (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi), and we found that a dsRNA-like stem-loop structure appeared at the A-to-I RNA-editing site of A1098 (data not shown). Then, by co-transfection of BmADARa and BmSyt I to BmN cells, the same A1098G edition was observed  Q/R site of pre-mRNA , GluR-B,2CR) . In additagmin I . In summd as ECS . In thisobserved C, provinBmSyt I of BmADARa, the results of this report indicate that BmADARa mainly exists in the nucleus , which are formed by selective splicing and RNA editing of BmADAR mRNA precursors during transcription. BmADARa is a mainly expressed transcript with complete ORF sequence length of 2154 bp, encoding 717 amino acids with two dsRBMs and an adenosine deaminase domain, highly homologous to the vertebrate ADAR2. BmADARa mainly exists in the nucleus and has editing function for a specific site of BmSyt I. This study provides insight into the function of the first ADAR enzyme in B. mori, also lays the foundations for further exploration of the functional domain of BmADARa and its editing substrates and target sites.The deamination of adenosine into inosine (A-to-I) is catalyzed by the adenosine deaminase acting on the RNA (ADAR) protein family. In this study, six ADAR transcripts encoding different amino and carboxyl termini were obtained in the silkworm,


O-acetyl) glucoside (23.84% to 49.74%), and malvidin-3-(6-O-p-coumaroyl) glucoside (26.77% to 34.93%) as compared to CE. Ultrasound reduced the extraction time (2.5-fold) and showed an increase of antioxidant potential by 76.39% (DPPH) and 125.83% (FRAP) compared to CE.This study presents the results of conventional aqueous (CE) and non-conventional ultrasound-assisted (UAE) extractions of polyphenolic compounds from lees extracts of red wine varieties (Merlot and Vranac). The effect of ultrasound extraction time , and amplitude  from a 400 W ultrasound processor with different ultrasonic probes diameters  on the amount and profile of polyphenolic compounds in the obtained extracts was investigated and compared to CE. The optimal conditions resulting in maximum extraction of phenolic compounds were: Probe diameter of 22 mm, amplitude 90% and extraction time for Vranac wine lees 1500 s and for Merlot wine lees extraction time of 1361 s. UAE proved to be significantly more effective in enhancing the extraction capacity of trans-resveratrol glucoside (30.57% to 300%), trans-resveratrol (36.36% to 45.75%), quercetin (39.94% to 43.83%), kaempferol (65.13% to 72.73%), petunidin-3-glucoside (41.53% to 64.95%), malvidin-3-glucoside (47.63% to 89.17%), malvidin-3-(6- V. vinifera L.), as well as red wines naturally contain more than 200 different polyphenolic compounds [Grapes are one of the fruit crops produced in largest quantities throughout the world. About 80% of all grapes are used in wine production . The annompounds ,6,7,8,9.ompounds ,11,12.Scientific studies have confirmed that polyphenolic compounds have a positive impact on human health, primarily due to their antioxidant effects that protect the body from harmful radicals ,15,16,17An important compound in red wine containing particular antioxidant properties is resveratrol  ,32,33. APolyphenol rich residues of red wines may be used to enrich several food products, thus attracting the interest of food producers ,41,42,43An innovative high-intensity ultrasound process has proven to be highly effective for extracting polyphenolic compounds ,66,67,68Recently, natural deep eutectic solvents (NADESs) such as choline chloride:malic acid (ChMa), choline chloride:oxalicacid (ChOa), and choline chloride:citric acid (ChCit) have been recognized as a novel class of sustainable solvents to replace common organic solvents. Combination of ultrasound-assisted extraction and natural deep eutectic solvents (NADESs) of wine lees anthocyanins to result higher efficiency of extraction have also been previously explored .Despite numerous potential applications, large quantities of lees generated as a by-product during the production of red wines are discarded ,75,76,77O-glucoside, petunidin 3-O-glucoside, procyanidin B1 and procyanidin B2; were purchased from Polyphenols ; while kaempferol, (+) catechin, (-) epicatechin, myricetin, trans-resveratrol quercetin and isorhamnetin were obtained from Sigma-Aldrich Chemie GmbH  for identification and quantification utilizing HPLC–mass spectrometry (MS) and HPLC– diode array detection (DAD).Ethanol from Pharmachem , formic acid from Sigma-Aldrich Chemie GmbH  and deionized water, purified using a Milli-Q water system  were used to prepare the extracts. Standards including malvidin 3-All chromatographic solvents (HPLC grade) and the remaining reagents  grade) were obtained from Sigma-Aldrich Chemie GmbH . The solutions were prepared using Milli-Q water . All sample preparations, extractions and chemical analyses were carried out at Biotechnical Faculty University of Ljubljana, Department of Food Science, Slovenia. The present study utilized wine lees from two varieties of red grapes i.e., Merlot grape varieties grown in Istria (Croatia) and Vranac grape variety grown in Mostar, (Bosnia and Herzegovina). The wine lees were sampled and immediately packed in impermeable polyethylene bags and frozen. Lees were then lyophilized, and subsequently packed in vacuum bags and stored at −80 °C before extraction.v/v) mixture containing 1.5% formic acid (v/v). The ratio of the dry matter to solvent was 1:60 (w/v) as previously described [Thawed samples of lyophilized lees were crushed in the mortar and sieved through 500 microns sieve. Samples were prepared in a 200 mL of 50% aqueous ethanol (escribed .Extraction of previously prepared wine lees samples as described was carried out using an aqueous bath (temp. 25 °C) with the external stirrer (40 rpm), for 1 h, as described ,78,79.Ultrasound-assisted extraction was carried out using ultrasonic equipment UP 400s, procured from Laboratory of Thermodynamics, Faculty of Food Technology and Biotechnology, University of Zagreb, Croatia. UAE of bioactive substances in the wine lees (Cw) was performed using ultrasonic processor with nominal power of 400 W at a constant frequency of 24 kHz. Five different amplitudes (A), 30%, 38.79%, 60%, 82.21%, and 90%, and treatment times (t) of 120 s, 322.10 s, 810 s, 1297.90 s, and 1500 s, with ultrasonic probes of diameter (Ds) 22 mm and 40 mm, at a full cycle were considered as optimization conditions in accordance with the central composite rotatable design (CCRD) for the experiments. Following extraction under these conditions, 50 mL of each extract was centrifuged at 4000 rpm/15 min. to separate the lees particles as residues. The extracts were then flushed with inert nitrogen gas and stored in dark at −80 °C till further analyses. The results obtained represent the mean value of three replicates.3 × 6H2O, 20 mM). The FRAP reagent was used as a blank, with final absorbance read at 593 nm after 10 min at room temperature. The ferric reducing antioxidant power of the samples (AOP FRAP) was determined in triplicate and expressed as mg of Trolox equivalents per gram of the dried wine lees sample (mg TEAC/g d.m.) [A standard solution of Trolox 100 mg/L was prepared in methanol and working solutions of 25−500 μM were used. Briefly, 0.1 mL of extracts (diluted 1:10) was mixed with 3 mL of a FRAP reagent (25 mL of acetate buffer 300 mM at pH = 3.6 (corrected with formic acid) + 2.5 mL of Fe (II)-TPTZ 10 mM in HCl 40 mM + 2.5 mL of FeCl/g d.m.) ,81,82,83v/v) and subsequently 100 μL of the diluted extracts was added to 2.9 mL of a methanol solution of the radical DPPH (with a concentration of 6 × 10-5 M) and an absorbance of 515 nm was measured after 25 min at room temperature. A calibration line (5 points plot) was freshly prepared before each assay from methanol solutions of Trolox ranging from 0.19 to 0.93 mM [DPPH) were expressed as mg of Trolox equivalents per gram of samples of dried wine lees (mg TEAC/g d.m.).The method utilizes scavenging potential of 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical with an absorbance maximum at 515 nm. The radical is reduced in reaction with an antioxidant or another radical . Determi 0.93 mM . The resv/v) in order to obtain a final volume of 1.400 mL and then mixed with 300 μL of freshly prepared Folin–Ciocalteu reagent diluted with water . The mixtures were vortexed and allowed to react for 5 min. Then, 300 μL of 20% sodium carbonate in water (w/v) was added and the tubes were vortexed. After 60 min of incubation, the absorbance was measured in a 1-cm cuvette at 765 nm using a UV–vis spectrophotometer  at a room temperature [The total phenolic compounds content (TPC) of the samples were determined using the Folin–Ciocalteu assay and the results were expressed as mg/g dry matter (d.m.) of gallic acid equivalents (GAE) ,87,88, wperature . The res-1. The injection volume was 1.0 μL with the UV detector set to an absorbance wavelength of 280 nm for phenolic acid, 520 nm for anthocyanins and 360 nm for flavonol glycosides. The mass spectrometer was equipped with electrospray ion source (ESI), parameters were as follows: nebulizer 35 psi; dry gas (N2) flow, 6 L min−1; and dry gas temp. 300 °C; capillary voltage, 4 kV where the ion trap mass spectrometer was operated in negative/positive ion mode with a scanning range from m/z 100 to m/z 1000. Individual phenolic compounds were identified by comparing their retention times MS/MS and UV/Vis spectra with those of authentic standards [O-p-coumaroyl) glucoside and malvidin-3-(6-O-acetyl) glucoside were quantified in equivalents of malvidin 3-O glucoside. Polyphenol analysis was performed according to method described by Bosiljkov et al.  and moditandards ,90,91. QA central composite rotatable design for the UAE experiments based on two numeric factors: A (%) and t (s), set to five levels i.e., ±1.414 , ±1 , center point (with five replicates for both probes and both wine lees varieties); and two categorical factors (±1): Ds (mm) and wine less cultivar (Cw) was prepared using the statistical software Design-Expert 9.0.6. . 0 and a1-a23 represent the intercept and regression coefficients for linear, quadratic, and interaction effects, respectively.The reduced quadratic model equation Equation (1) was used to express the investigated responses (Yn) as a function of the coded independent variables , where ap < 0.05. The adequacy of the models was evaluated based on the coefficient of determination (R2) and the model p-value. The intercept (a0) in the acquired models represents the mean value of the investigated dependent variable (Yn) under the conditions of the performed experiments, whereas a1-23 refers to the significant regression coefficients which enable determination of the most significant effects of the investigated variables  and their position and negative impact (sign + or -) on the investigated dependent variable. Response surface plots were generated using the design expert and were based on a function of two factors while keeping the others constant. Numerical and graphical optimization was carried out according to conditions for each response. Results of numerical optimization are described with desirability. Desirabilities range from zero to one for any given response. A value of one represents the case where all goals of optimization are met perfectly. A zero indicates that one or more responses fall outside desirable limits [The obtained results were statistically analyzed using ANOVA and backward elimination regression at the significance level of e limits .The average measured values of all investigated variables in wine lees extracts obtained using conventional extraction and UAE are given in p < 0.0001) on the AOPDPPH of extracts whereas increasing the diameter of the ultrasound probe had a negative and linear effect on AOPDPPH (p < 0.0001). As it is evident in Equation (2) the AOPDPPH of wine lees extract is a varietal characteristic (p < 0.0001).On the other hand, the AOP obtained with ferric reducing antioxidant power (FRAP) assay of wine lees extracts after conventional extraction 42.52 and 50.39 mg TEAC/g d.m. for Merlot and Vranac varieties  obtained after using 2-diphenyl-1-picrylhydrazyl (DPPH) assay of wine lees extracts from conventional extraction was 44.21 and 49.72 mg trolox equivalent antioxidant capacity (TEAC)/g d.m. for the varieties Merlot and Vranac, respectively . Moreovearieties . It shouFRAP there are significant linear and quadratic effects of the investigated variables as is for AOPDPPH (p < 0.0001), the AOPFRAP is subject to a significantly positive effect of the interrelation of A × t (p < 0.0414) and A2 × Ds (p < 0.0034), including the quadratic effect of A (p < 0.4394), whereas a negative effect is provided by the interrelation A × Ds (p < 0.0124), t × Ds (p < 0.0294), Ds × Cw (p < 0.0016) and quadratic effect of t (p < 0.0001).From the obtained regression equation Equation (3), it is evident that for AOPMerlot = 32.95 and TPCVranac = 41.22 mg GAE/g d.m., respectively) (p < 0.2530), Ds (p < 0.0019), Cw (p < 0.5570), interrelation A × t (p < 0.0029), A × Ds (p < 0.5090), A × Cw (p < 0.8688), t × Ds (p < 0.293), t × Cw (p < 0.0003), A2 × Ds × Cw (p < 0.0393). The TPC decreased with an increase in A (p < 0.3539), with the interrelation of Ds × Cw (p < 0.0346), A × Ds × Cw (p < 0.1570), A2 × Ds (p < 0.744), A2 × Cw (p < 0.0012) and A2 (p < 0.0151).The extracts of wine lees obtained after conventional extraction were found to contain a significant amount of total phenols (TPCctively) . From TaThe presented Equation (4) and O-glc was provided by Cw (p < 0.0001), meaning that the quantity of these compounds is a characteristic of wine lees variety. Regarding the amount of t-Res-3-O-glc in the lees obtained using UAE, a positive linear effect was observed using A (p < 0.0001), t (p < 0.0001), Ds (p < 0.0001) and quadratic impact of t (p < 0.3025). As well significant positive impact was observed from the interrelations A × Ds × Cw (p < 0.0336), t × Ds × Cw (p < 0.0001), A2 × Cw (p < 0.0249), A2 × Ds × Cw (p < 0.0004) and t2 × Ds × Cw (p < 0.0410). A negative linear effect on the quantity of t-Res-3-O-glc was provided by Cw (p < 0.0001), as well as a quadratic effect from A (p < 0.0350) and interrelations A × Ds (p < 0.0160), A × Cw (p < 0.0001), t × Ds (p < 0.0001), t × Cw (p < 0.0001), Ds × Cw (p < 0.6957), A2 × Ds (p < 0.0002), t2 × Ds (p < 0.0293) t2 × Cw (p < 0.3766).Equations (5) and (6) show that the most important effect on the quantity of t-Res-3-trans-resveratrol (t-Res) was subject to a significant positive linear effect from A (p < 0.0359), t (p < 0.0003) and Cw (p < 0.0001), quadratic A (p < 0.0180) and t (p < 0.0134), including the interrelations A × Cw (p < 0.3058), t × Cw (p < 0.0042), A2 × Ds (p < 0.0474), A2 × Cw (p < 0.0004), t2 × Ds (p < 0.0408), t2 × Cw (p < 0.0349), A2×Ds×Cw (p < 0.0300). The quantity of extracted t-Res was subject to a negative effect from Ds (p < 0.0001) and the interrelation A × Ds (p < 0.4459), t × Ds (p < 0.0252), Ds × Cw (p < 0.0006), A × Ds × Cw (p < 0.3178).The quantity of O-glc was obtained at high amplitudes and long extraction time when using the probe with a diameter of 22 mm , t (p < 0.0001) and Cw (p < 0.6653), including the quadratic effect of t (p < 0.6362) and interrelations A × Cw (p < 0.0616), t × Cw (p < 0.0457), A × Ds × Cw (p < 0.0210), t × Ds × Cw (p < 0.1689), A2 × Cw (p < 0.0001), t2 × Ds (p < 0.1143), t2 × Cw (p < 0.6340), A2 × Ds × Cw (p < 0.0001), t2 × Ds × Cw (p < 0.0265). A negative linear effect on the quantity of quercetine in the lees was provided by Ds (p < 0.0001) and a quadratic effect by A (p < 0.4833) and the interrelations A × Ds (p < 0.0001), t × Ds (p < 0.0001), Ds × Cw (p < 0.0002), A2 × Ds (p < 0.0051). The analyzed extracts showed that the largest quantity of quercetin for both varieties was obtained using high amplitudes and long extraction times when using a probe with a diameter of 22 mm. Equation (8) shows the significant effects of the investigated variables of UAE for wine lees of the Merlot and Vranac on the quantity of extracted kaempferol in the obtained extracts. The same equation indicates that a positive linear effect on the quantity of kaempferol in lees is obtained using UAE with A (p < 0.0079), t (p < 0.0001), and interrelations between A × Cw (p < 0.0616). A negative linear effect on the quantity of kaempferol in the lees was provided by Ds (p < 0.0069) and Cw (p < 0.0001), including the interaction between t × Ds (p < 0.0069), t × Cw (p < 0.2954) and Ds × Cw (p < 0.6695). The analyzed extracts showed that the largest quantity of kaempferol for both varieties was obtained using high amplitudes and long extraction times when using a probe with a diameter of 22 mm (Equation (7) shows the significant effects of the investigated variables of UAE for wine lees of the Merlot and Vranac variety on the quantity of extracted quercetin in the obtained extracts. The same equation indicates that a positive linear effect on the quantity of quercetin in lees is obtained after using UAE parameters A , t (p < 0.0011), whereas a linear negative effect on the quantity of extracted Pt-3-glc is provided by Ds (p < 0.0002) and Cw (p < 0.0001). The analyzed extracts showed that the largest quantity of Pt-3-glc for both varieties was obtained using high amplitudes and long extraction times when using a probe with a diameter of 22 mm.Equation (9) shows the significant effects of the investigated variables of UAE for wine lees of the Merlot and Vranac variety on the quantity of extracted Pt-3-glc in the obtained extracts. The conclusion from Equation (9) is that the quantity of Pt-3-glc in the extracts obtained using UAE is subject to a positive linear effect from A (p < 0.0009), t (p < 0.0001) and Cw (p < 0.0245), including the quadratic effect of A (p < 0.0312), t (p < 0.1852), and interactions A × Cw (p < 0.1879), t×Cw (p < 0.3030), A × Ds × Cw (p < 0.7606), A2 × Ds (p < 0.0061), A2 × Cw (p < 0.0058), t2 × Ds (p < 0.0086), t2 × Cw (p < 0.0143) and A2 × Ds × Cw (p < 0.0071). A negative linear effect on the quantity of Mv-3-glucoside in the wine lees from the Merlot and Vranac varieties was provided by Ds (p < 0.0001) and the interactions of A × Ds (p < 0.0705), t × Ds (p < 0.0245) and Ds × Cw (p < 0.0001).Equation (10) shows a significant effect of the investigated variables of UAE on wine lees of the Merlot and Vranac regarding the amount of extracted malvidin-3-glucoside (Mv-3-glucoside) in the obtained extracts. The same equation indicates a positive linear effect on the quantity of malvidin-3-glucoside in lees after using UAE parameters A  glucoside (Mv-acetyl-3-glc) in the obtained extracts.Equation (11) shows the significant effects of the investigated variables of UAE for wine lees of the Merlot and Vranac variety on the quantity of extracted malvidin-3-(6-O-acetyl) glucoside in lees is obtained after using UAE parameters t (p < 0.0617), as well as a quadratic effect of t (p < 0.0312) and a significant positive effect due to the interrelations A × Cw (p < 0.0799), t × Cw (p < 0.6348), A × Ds × Cw (p < 0.1639), t × Ds × Cw (p < 0.8928), A2 × Cw (p < 0.0001), t2 × Ds (p < 0.0204), t2 × Cw (p < 0.8610) and A2 × Ds × Cw (p < 0.0024). A negative linear effect on the quantity of extracted malvidin-3-(6-O-acetyl) glucoside from the wine lees of the Merlot and Vranac varieties was provided by A (p < 0.9113), Ds (p < 0.0015), Cw (p < 0.0001), A2 (p < 0.3868) and the interrelations A × Ds (p < 0.0158), t × Ds (p < 0.1784), Ds × Cw (p < 0.2999), A2 × Ds (p < 0.2802) and t2 × Ds × Cw (p < 0.2577). The analyzed extracts showed that the largest amount of malvidin-3-(6-O-acetyl) glucoside was obtained for the Vranac variety when high amplitudes, long extraction times and a probe with a diameter of 22 mm were used, whereas for the extracts from the Merlot variety similar or greater amounts of malvidin-3-(6-O-acetyl) glucoside were obtained when lower amplitudes and shorter treatment times were used.The same equation indicates that a positive linear effect on the quantity of malvidin-3-(6-O-p-coumaroyl) glucoside (Mv-3-p-coum glc) in the obtained extracts. The same equation indicates that a positive linear effect on the quantity of malvidin-3-(6-O-p-coumaroyl) glucoside is obtained after using UAE parameters A (p < 0.0010), t (p < 0.0017) and Cw (p < 0.0001), including the quadratic effect of A (p < 0.0414) and interactions of t × Cw (p < 0.7400), A × t × Cw (p < 0.0455). A negative linear effect on the quantity of extracted malvidin-3-(6-O-p-coumaroyl) glucoside from the wine lees of the Merlot and Vranac varieties was provided by Ds (p < 0.0001) and the interactions A × t (p < 0.3454), A × Ds (p < 0.0156), A × Cw (p < 0.41743868), t × Ds (p < 0.0110), Ds × Cw (p < 0.0411). The analyzed extracts showed that the largest amount of malvidin-3-(6-O-p-coumaroyl) glucoside from the Merlot and Vranac varieties was obtained using high amplitudes and long extraction times when using a probe with a diameter of 22 mm. Results show that the wine lees extracts from the Vranac variety contain greater quantities of malvidin-3-(6-O-p-coumaroyl) glucoside.Equation (12) shows the significant effects of the investigated variables of UAE for wine lees of the Merlot and Vranac variety on the quantity of extracted malvidin-3- glucoside and malvidin-3-(6-O-p-coumaroyl) glucoside, which prevail in the wine lees and have high individual AOP, we further discovered the presence of other phenolic compounds that contribute to the overall AOP, but are primarily responsible for the astringent taste of red wines.Besides the detailed analysis of conditions for UAE of the compounds DPPH and AOPFRAP, in order to determine the optimal UAE conditions for obtaining extracts with the greatest AOP value, and responses were maximized for t-Res-3-O-glc (Merlot), trans-resveratrol (Vranac), quercetin, kaempferol, malvidin-3-glucoside, malvidin-3-(6-O-acetyl) glucoside, and malvidin-3-(6-O-p-coumaroyl) glucoside for wine lees from Merlot and Vranac varieties in order to obtain the most optimized UAE conditions for each wine lees separately.Numerical optimization of the UAE for wine lees under research conditions, was performed by maximizing the response AOPThe five best optimized extraction conditions are shown in DPPH in obtained extracts was enhanced by 76.39% and AOPFRAP by 125.83% with respect to the CE obtained samples. AOPDPPH and AOPFRAP of wine lees extracts obtained using conventional extraction of the Vranac variety were higher than the Merlot variety, which may be a varietal characteristic or a consequence of the applied technologies. Equations (2) and (3), indicated that the highest AOPDPPH and AOPFRAP were found in the extracts obtained using the ultrasonic probe with a diameter of 22 mm, at a maximum amplitude and longest extraction time for both wine lees variety. In the wine lees extracts of the Vranac variety, a higher antioxidant capacity has been achieved than in the wine lees extracts of the Merlot variety.Depending on the applied conditions of UAE, the AOPtrans-resveratrol glucoside was dominant in the extracts of wine lees from the Merlot while trans-resveratrol content is 20-fold lower. In comparison to the wine lees of the Merlot, trans-resveratrol glucoside (t-Res-3-O-glc) was not found in the wine lees of the Vranac, and the amount of trans-resveratrol was approximately three times greater compared to Merlot variety as shown in The obtained results regarding AOP are in accordance with other authors ,73, who In accordance with results from other authors  that sucO-acetyl) glucoside from wine lees extracts, and found that malvidin-3-glucoside (Mv-3-glucoside) and petunidin-3-glucoside (Pt-3-glc) were the predominant anthocyanins in Merlot samples. There are twice as much malvidin-3-(6-O-acetyl) glucoside, but malvidin-3-(6-O-p-coumaroyl) glucoside was three times lower than Mv-3-glucoside. Mv-3-glucoside prevails in the extract from the Vranac as compared to Merlot. The proportion of malvidin-3-(6-O-acetyl) glucoside, malvidin-3-(6-O-p-coumaroyl) glucoside and Pt-3-glc in samples is two and half times less then Mv-3-glucoside but in similar quantities. Based on the results, it is concluded that for the greatest quantity of investigated polyphenolic compounds from wine less of the Merlot variety, the best most optimized parameters are use of probe with a diameter of 22 mm under the following UAE conditions: Applied amplitude of the ultrasonic processor ranging from 89.28% to 100% and an extraction time of between 921.81 s to 1492.15 s. To obtain the greatest quantity of investigated polyphenolic compounds from wine lees the Vranac variety, best optimized parameters are probe with a diameter of 22 mm under the following UAE conditions, applied amplitude of the ultrasonic processor ranging from 44.86% to 93.64%, and an extraction time of between 1176.81 s to 1500 s. The confirmed results are in accordance with previously reported findings which also depicted a higher yield of TPC, AOP and polyphenols such as quercetin, malvidin-3-glucoside (Mv-3-gluc), myricetin in the range from 19−20% when compared to the CE [Likewise, Pérez-Serradilla and Luque de Castro  successfo the CE ,73.trans-resveratrol glucoside, trans-resveratrol, quercetin, kaempferol, petunidin-3-glucoside, malvidin-3-glucoside, malvidin-3-(6-O-acetyl) glucoside and malvidin-3-(6-O-p-coumaroyl) glucoside extraction was enhanced significantly in wine lees from both the varieties after using UAE. Therefore, it is concluded that ultrasound processing can be successfully used to enhance the extraction of bioactive compounds from wine lees, a by-product of the wine industry. Considering that very limited studies have been reported about the composition and bioactivity of wine lees extracts, as well as the extraction of phenolics from lees, a more detailed investigation could pave newer ways to utilize in wine industry waste effectively.It is evident from the findings obtained in this work that ultrasound-assisted extraction (UAE) of polyphenols from lees of the Merlot variety (five best optimized extraction conditions) results in significantly higher yields of bioactive compounds in the extracts compared to conventional extraction (CE). As per the DPPH assay, it is observed that UAE of the Merlot variety enhanced AOP from 76.05% to 76.39% when compared to the CE. Similarly, AOP FRAP, total phenols,


This review aims to assess and recommend approaches for targeted and agnostic High Throughput Sequencing of RNA viruses in a variety of sample matrices. HTS also referred to as deep sequencing, next generation sequencing and third generation sequencing; has much to offer to the field of environmental virology as its increased sequencing depth circumvents issues with cloning environmental isolates for Sanger sequencing. That said however, it is important to consider the challenges and biases that method choice can impart to sequencing results. Here, methodology choices from RNA extraction, reverse transcription to library preparation are compared based on their impact on the detection or characterization of RNA viruses. RNA viruses are of a global health concern from a One Health perspective, which is the intersection of human, animal and environmental health. Environmental transmission of these viruses, whether it be through food, water or recreational activities poses a risk for humans, plants and animals. It is important to adopt One Health principles for the surveillance of RNA viruses as environmental samples can (a) indicate hot spots for viral recombination, (b) serve as an important source of virus transmission, and (c) sequencing these samples allows us to pre-empt new RNA viruses and their variants of potential clinical concern. Viral persistence in the environment increases the opportunity for inter and intra-viral family recombination and increases virus-host exposure, factors that all contribute to the emergence of new viruses; that have the potential to cause large scale outbreaks. Non-enveloped viruses demonstrate remarkable persistence in the environment. Trans-kingdom virus interactions are thought to aid viral persistence in environmental settings, though this has been difficult to investigate, due to a lack of suitable cell culture systems. Furthermore, RNA viruses have high mutation rates as, unlike their DNA counterparts, most do not have a proofreading polymerase, though there are notable exceptions  have been widely applied in clinical settings but have had limited success for viral surveillance and aside from Flaviviruses . There have been important contributions regarding RNA virology from environmental HTS applications  their genetic diversity, (b) lack of conserved regions across the genome of viruses and (c) short genome lengths.resolution. In addition, the quantity of input RNA/HTS), either a targeted or agnostic approach can be taken. Targeted sequencing infers that some level of knowledge is available with respect to the target in question and that the experimental design incorporates this prior knowledge, either through amplicon-based sequencing or probe capture hybridization. An overview of how the different approaches work can be seen in In High Throughput Sequencing (ITS) between them. Unlike the 16S rRNA of bacteria, viruses lack universally conserved markers and genome plasticity. In particular, with respect to RNA viruses, this further contributes to the associated challenge, requiring a Family specific priming PCR approach. Amplicon based sequencing approaches, (particularly tiling or “jackhammer”) have been widely applied for the detection of RNA viruses with varying levels of success . Often in this case, common PCR-based approaches target highly conserved rRNA genes, such as those encoding the 16S/18S and 28S subunits or the Internal transcribed spacer (RT-PCR) amplification, primers can be designed to anneal to the most conserved sequences of the RNA virus genome(s) in question. In this case, a certain degree of validation is required. Confirmation of the PCR products via Sanger sequencing should be implemented for new primer sets to ensure specificity. There is a high likelihood that degenerate primer sets are required in order to account for virus divergence  requires prior knowledge of the specific sequence variants to be detected. Most capture-based methods use a tiling array approach, where 80 to 120-mer DNA or RNA probes are used to cover the length of the target genome/genomes. The probes typically have 10–50 bp regions between them, adopting a similar approach to the overlapping/jackhammer amplicon approach described above. Target enrichment is based on the biotinylated (or otherwise labeled) probe annealing to complementary sequences in the sample(s). The probes attach to previously fragmented genomic CBPH was initially implemented to detect Single Nucleotide Variants (SNV) in human genomic exon studies. Various studies have applied this methodology to virus specific studies using widely available commercial kits with custom design options such as SureSelect XT Target Enrichment system, Illumina TruSeq RNA Access, or SeqCap Ez probe design with separate library preparation kit. More recently, the VirCapSeq-VERT and CATCH custom virus oligonucleotide panel have become available as a general tiling array for vertebrate viruses , matrices and target viruses. Studies attempting to capture a very wide viral diversity have used panels ranging from 300,000 to 2.1 million probes per assay  though hopefully an understanding of the matrix and expected virome in question. For example, if the objective is to characterize human viruses of clinical concern in sewage, concentration and extraction methods must consider that sewage as a matrix will contain PCR inhibitors and that concentration methods may not concentrate both enveloped and non-enveloped RNA viruses of interest. Method validation and the inclusion of appropriate controls is necessary for interpretation of results and for setting quality control thresholds. As agnostic sequencing is not targeted, non-viral RNA can be captured during the library preparation and this can cause downstream issues. Indeed, obtaining sufficient genome coverage of virus RNA genomes against a background of host rRNA and SISPA) is a random priming method developed by Reyes and Kim  to a target population of blunt ended DNA molecules. The common end sequence allows one strand of the double-stranded primer to be used in repeated rounds of annealing, extension and denaturation in the presence of a high-fidelity polymerase. SISPA has been used for the discovery of new viral agents, particularly in the veterinary field  tail enrichment using TruSeq RNA kit and Circular resequencing (CirSeq). Parras-Moltó et al.  compared to CirSeq or Illumina TruSeq RNA Access kit and SISPA-generated sequences demonstrated an increased number of transition events. The accumulation of these errors could falsely indicate sub-clonal diversity or veil true diversity. Parras-Moltó et al.  is an isothermal enzymatic process where a short DNA or RNA primer is amplified to form a long single stranded DNA or RNA using a circular DNA template and specific DNA or RNA polymerases, as can be seen in Rolling Circle Amplification , was the least error prone   and Not so random (NSR) sequencing . For a detailed summary of enrichment methods, refer to Ribosomal RNA (rRNA) is the most abundant species of RNA in most cells. For agnostic RNA virus sequencing of complex samples, its presence is problematic as a large number of non-viral reads can be generated, thereby greatly limiting the number of relevant, virus-related, sequences. To increase the number of reads mapping to viral RNA, several methods have been employed to either enrich non-ribosomal RNA or remove unwanted rRNA sequences. Enrichment methods include poly-A selection [TruSeq mRNA (Illumina)], Single Primer Isothermal Amplificaiton  primers attached to magnetic beads to isolate RNA. Non-polyadenylated RNA, such as rRNA, are not captured. This approach does result in a strong bias toward the 3' end of RNA targets, though this bias is alleviated by the reduced sequencing depth required to obtain high quality viral reads   . Several product . SPIA rePDD). An overview of the rRNA depletion methods can be viewed in As an alternative to enrichment methods, rRNA can be removed using subtractive hybridization [Ribo-Zero (Illumina)], exonuclease digestion [MICROBExpress (Ambion)], endonuclease digestion (RNase H), or duplex specific nuclease (DSN)/Probe directed degradation  probes and depleted with streptavidin beads , Silica membrane based spin column technology (SMSC), and silica coated Magnetic beads (MB). Due to the physio-chemical differences between these three extraction methods, the yield, purity, and specificity of the RNA obtained varies and this can have downstream impacts.There are three common methods used for RNA extraction: organic extraction using Acid guanidinium thiocyanate phenol chloroform  or specific capture probes. Silica coated MB non-selectively bind nucleic acids in the presence of chaotropic salts via electrostatic interactions. The silica-coated beads are most suitable for applications that require nucleic acids other than mRNA while the oligo (dT) beads are best-suited for mRNA targets that are polyadenylated. Specific capture-based systems are best suited for applications that do not tolerate high concentration of non-target nucleic acids  concentration (ii) Nucleospin RNA XS generated the highest read count for RNA viruses  and (iii) viral richness is strongly impacted by extraction method. In a similar fashion, Strubbia et al.  values and higher concentrations of RNA using organic extractions is mixed, with no clear trend in either direction.a et al.  found thSNV. To date only two studies have investigated reverse transcriptase (RT) fidelity impact on next generation sequencing results  applications (see Both the efficiency of reverse transcription and fidelity is important for the detection of virus quasispecies present at low abundances and the identification of ions see . Whilst SISPA (2.2.1). As noted above, they are often six nucleotides long and are usually referred to as random hexamers, N6, or dN6. Due to their random binding, they can potentially anneal to any RNA species in the sample. Therefore, these primers may be considered for reverse transcription of RNAs without poly(A) tails, degraded RNA and RNA with known secondary structures. Some random primer sets have been constructed with viral genomes in mind, preferentially priming viral RNA over ribosomal RNA  priming has also been applied as a viral RNA enrichment method as outlined earlier 2.2.3.Oligo(dT) primers consist of a stretch of 12–18 deoxythymidines that anneal to poly(A) tails of eukaryotic mRNAs, which make up only 1–5% of total RNA. Oligo(dT) primers target polyadenylated RNAs, whereas random sequence primers target all RNAs including the abundant rRNA fraction. Mixtures of random hexamers with oligo(dT) are predominantly used in Gene-specific primers offer the most specific priming in RT  priming strategies were popular. As most publications have not assessed the yield/fidelity post cDNA synthesis, it is not possible to compare these publications based on the RT experimental design. Strubbia et al.  for GC-rich templates or betaine for AT-rich templates can reduce amplification bias for such targets. Betaine may help to keep a GC-rich template single-stranded, but it may also cause premature dissociation of the newly synthesized strand from an AT-rich template, introducing knock on effects for virome analysis  should be optimized and validated internally. “Jackhammer” PCR allows greater room for error in this aspect, as the primers are targeted across the genome, increasing the probability of successful amplification. That said however, viral RNA is a moving target and “jackhammer” approaches require up to date sequence data to perform consistently. Aside from primer design and method validation, additional considerations given to amplicon generation in HTS platforms  + selection or rRNA depletion, RNA samples are fragmentated to a certain size range, owing to the limitations in the read length of many cDNA synthesis as determined by AzNTPs:dNTPs. Following chain termination, single-stranded cDNA fragments are generated with an azido-group at their 3' ends. 3'-azido-blocked cDNA molecules can be purified and “click-ligated” to 5' alkyne-modified DNA adaptors via copper-catalysed azide-alkyne cycloaddition (CuAAC). The products of the ClickSeq reaction can be amplified using PCR to generate a cDNA sequencing library. Viral RNAs and mRNA using ClickSeq produced unbiased HTS libraries with low error-rates compared to standard methods  or enzymatic digestion. Nebulization involves directing compressed nitrogen or air forces into a DNA sample repeatedly through a small hole, producing mechanically sheared random fragments, leading to a heterogeneous mix of double-stranded DNA molecules containing 3'- or 5' overhangs as well as blunt ends  guidelines, HTS/RNA-Seq has been to slow to include extensive controls, as per other molecular methods  and comparing controls to biological samples post sequencing, cross-contamination can be identified, thereby aiding the interpretation of sequencing results. Strand specific sequencing can be used to identify the source of contamination during subtractive hybridization or viral genome vs. complementary RNA intermediates , Spike in RNA variants (SIRVs) and External RNA Control Consortium (ERCC). RNA oligonucleotides, SIRVs and ERCC samples can be applied as internal controls, spiked into each sample, including the whole process negative control. RNA oligonucleotides and ERCC can be used to assess sample inhibition, which is important to consider in complex matrices, as well as confirm method specificity , common environmental taxa introduced through cross-contamination  viruses, makes it difficult to design reasonably sized CBPH panels that capture species diversity, while also being affordable and technically feasible  of the RNA extracts may be required as SMSC/MB can carry through genomic DNA.Virus-specific approaches increase the chance to detect less abundant species through cDNA, but outcomes are dependent on the target, background RNA, reagent concentrations and priming strategy. Few studies have compared priming strategy during cDNA synthesis, and even fewer have looked at the impact of primer choice on HTS output. Random hexamers tend to produce more variable yields and should be applied at high concentrations  richness of the transcript in question  the target(s) in question and (ii) the bias introduced though polymerase choice and cycling conditions. Overall trends from the relevant studies demonstrate that thermostable, high fidelity polymerases outperform the robust alternatives. AmpliTaq Gold has been commonly applied in molecular virology and yet performed poorly in all studies, regardless of target or matrix .qRT-PCR measurements, rather than the detection of viral quasispecies. Bias observed is dependent on when and what type of fragmentation was applied. For fragmentation of RNA, RNase III-based fragmentation demonstrates a preference for double-stranded RNA sequences. This can result in uneven fragmentation of RNA leading to differential representation of specific regions of RNA  in question.While it is challenging to obtain high quality sequences from environmental samples, the information that could be gleaned is essential for maintaining public health. From developing new PCR/qPCR assays based on recent sequencing data, to monitoring antigenic drift and recombination, identifying new transmission pathways, hosts and viruses or pre-empting RNA viruses and variants of clinical concern in a One Health paradigm, the list of potential benefits goes on. AF reviewed the literature and compiled the review. AR revised the initial drafts. SK, HO'S, PC, and FC reviewed the final draft. All authors contributed to the article and approved the submitted version.The 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.


Calotropis , this study focuses on the RNA editing activity of ndhB based on light time.RNA editing is common in terrestrial plants, especially in mitochondria and chloroplast. In the photosynthesis process, NAD dehydrogenase plays a very important role. Subunit 2 of NADH-dehydrogenase is one of the major subunits in NAD dehydrogenase complex. Using desert plant NdhB (NADH-dehydrogenase subunit 2) gene accession no. MK144329 was isolated from Calotropis procera genomic data (PRJNA292713). Additionally, using RNA-seq data, the cDNA of the ndhB gene of C. procera was isolated at three daylight periods, i.e., dawn (accession no. MK165161), at midday (accession no. MK165160), and pre-dusk (accession no. MK165159). Seven RNA editing sites have been found in several different positions  within the ndhB coding region. The rate of these alterations was deferentially edited across the three daylight periods. RNA editing rate of ndhB gene was highest at dawn, , less in midday , and less at pre-dusk , also all these sites were validated by qRT-PCR.ndhB gene across light periods may be led to a somehow relations between the RNA editing and control of photosynthesis.The differential editing of chloroplast  Arabidopsis . RNA editing is found in coding sequences, long non-coding RNAs, introns, tRNAs, microRNAs, and rRNAs [nadI, nad3, coxII, and coxIII [atp1, nad5, rps1, and rpl2 genes [RNA editing is one form of post-transcriptional changes, which include nucleotide insertion, deletion, or modification , 2. Thisnd rRNAs , 10. Mosnd rRNAs . The effnd rRNAs , 13. Mand coxIII , 15. In l2 genes .ndhB gene [In chloroplast, the activity of NADH dehydrogenase (NDH) complex increases under various environmental conditions, e.g., drought, temperature, and high light stress . RNA edidhB gene .Arabidopsis [In recent years, next-generation sequencing (NGS) has a rapid and economical way to find organelle RNA editing sites, and extensively examine the whole transcriptome , 10. Besbidopsis , 8, 19.Calotropis procera leaf tissues to explore these effects.In our study, we aim to explore the relationship between the light/heat conditions and RNA editing gene wild plants. So, we used RNA-seq data of C. procera were used in this study. Three biological leaf disks from each plant per time are mixed. Plants were collected from the KSA western region . Three samples were isolated at three different times . Light density/heat temperature was 10 lux/35 °C at dawn, 9000 lux/45 °C at midday, and about 95 lux/37 °C at pre-dusk. Total RNA was isolated according to Ramadan and Hassanein [Triplicates plants of assanein . RNA conC. procera were developed with Illumina Genome AnalyserIIX (GAIIx). A minimal of 50-bp read with a confidence interval of 95% was considered for the study. Libraries from leaf tissues were constructed, generating the following raw sequencing reads number 158,537,240 at dawn, 108,554,060 at midday, and 108,751,224 at pre-dusk with average number of reads that align to known reference bases of 128.72 bp, 128.69 bp, and 119.37 bp, respectively.Paired-end short-sequence reads from Calotropis procera (accession no. MG678914) [The RNA sequence reads were submitted to NCBI BioProject with accession numbers PRJNA531450 for dawn transcriptome, PRJNA531451 for midday transcriptome, and PRJNA531452 for pre-dusk transcriptome. RNA editing site was detected by CLC Genomic Workbench 3.6.5 . MappingG678914) . The lowG678914) .ndhB sequences, genomic as well as cDNA sequences, were analyzed for RNA editing and corresponding protein using multi-sequence alignment through CLC genomic workbench 3.6.5.All obtained ndhB gene sequence (http://www.ncbi.nlm.nih.gov/Structure/cdd/wrpsb.cgi).NCBI’s conserved domain database (CDD) was used to identify the functional domains by submitting the coding sequence of obtained To validate predicted editing sites, biological triplicates of leaf tissue samples were used. Total RNA was extracted from all the samples using Qiazol .Calotropis procera actin gene was used as a reference to normalize data. Applied Biosystems Real-Time PCR  are used to carry out the following conditions: 5 min at 94 °C, 40 cycles of 20 s at 95 °C, and 1 min at 56 °C. Dissociation curve analysis was carried out by heating at 95 °C for 60 s; 55 °C for 30 s, and 0.2 °C increase per cycle till 95 °C. PCR amplification was done using primers designed by Primer-Blast (https://www.ncbi.nlm.nih.gov/tools/primer-blast/). Two forward specific primers with different 3′ ends  and one reverse primer were designed for each editing site , 10 μl M-MuLV reaction Mix, 2 μl M-MuLV enzyme (New England Biolabs), and then incubation at 42 °C for 45 min. The RT-PCR reaction was enforced according to Mohammed et al. and Rodrigues et al. , 25. qRTP values < 0.01 were considered significant according to Tukey’s test.ANOVA test was conducted in SPSS  to assess the variation of editing levels per time. Modeling pairwise alignment was achieved by I-TASSER, (Iterative Threading ASSEmbly Refinement) for three ndhB proteins  as well as correspondence protein of DNA coding sequence. This approach predicts protein structure and function .Calotropis procera ndhB gene was characterized via this study (accession number MK144329) using short DNA sequence reads 71,349,934 paired ends (BioSample Accession Numbers PRJNA292713). Calotropis procera chloroplast gene ndhB (accession no. MG678914) of t was used as reference in CLC genomic workbench. Multi-sequence alignment was enforced using the best hits of BLAST search (Table The ndhB gene of C. procera at dawn (accession no. MK165161), at midday (accession no. MK165160), and at pre-dusk (accession no. MK165159) were characterized using RNA-seq raw data. A total of 215,841,902 paired-end short sequence reads of the RNA C. procera were generated. In a CLC genomic workbench, ndhB gene (accession no. MK144329) of C. procera was used as a template.cDNA ndhB sequences with 3-times cDNA  were compared with the amino acid profile of other species derived from cDNA. This comparison clearly demonstrated that the editing in ndhB gene in C. procera led to the formation of conserved amino acid  and CDD accession number cl00535, and Pfam, PF00507  were comparatively quantified in three light periods , 10 in rice, 13 in soybean [Crambe (Crambe abyssinica) [C. procera ndhB gene  were deferentially edited across three daylight periods. These sites were validated by qRT-PCR. The identified edited sites in C. procera were concordant with previous research achieved by [ndhB transcript in C. procera. Most of the non-editing sites were unchanged because they were T, not C, in the target gene. Furthermore, RNA editing rate of ndhB gene was highest at dawn, , less at midday , and lowest at pre-dusk . RNA editing can modify hydrophilic amino acids to hydrophobic amino acids and occur at locations necessary for 3D protein structure formation and protein folding [RNA editing in  decades . RNA edi soybean , 25, andssinica) . In this folding . The pro folding , 36.These results indicated that light period may affect the rate of RNA editing without induction of new editing sites. From current results, we do speculate that photosynthesis in this desert plant may reach its maximum during dawn daylight time. In fact, RNA editing may increase hydrophobicity of protein and its affinity to phospholipids in the chloroplast’s membrane , 36. HownhdB transcript in C. procera, comprehensive editing takes place at 7 sites; these editing sites were primarily conserved through plant species. The differential editing of this gene across light periods may be led to a somehow relations between the RNA editing and control of photosynthesis. The main limitation of the study was the lack of understanding of how the light may control the level of RNA editing in the chloroplast in C. procera, which will be the main focus of future research taking into consideration circadian changes can occur.In Additional file 1: Table S1. NdhB C-to-U editing events using reads derived from total RNA-seq.Additional file 2:. Fig. S1 Multi-sequence alignment was enforced using the best hits of BLAST search.Additional file 3: Fig S2. A comparison between ndhB sequences of the genomic DNA and 3 times cDNA revealed RNA editing sites.


N6-methyladenosine (m6A) is the most prevalent modification of RNA in mammals. m6A RNA methylation levels are dynamically regulated by m6A RNA methylation regulators. While increasing evidence has suggested that m6A RNA methylation is vital in the initiation and progression of human carcinoma, little is known about the expression and effect of m6A RNA methylation regulators in differentiated thyroid carcinoma (DTC). Herein, we demonstrate that most of the thirteen main m6A RNA methylation regulators are differentially expressed in DTC tissues and normal thyroid tissues. Based on consensus clustering of m6A RNA methylation regulators, DTC cases were divided into two subgroups (TC1 and TC2). Compared with the TC1 subgroup, the TC2 subgroup was associated with a poorer prognosis, older age, higher T grade, higher N grade and higher TNM stage. The results indicated that alteration of m6A RNA methylation regulators was closely related to DTC. We further established a risk signature of four m6A RNA methylation regulators that could evaluate prognosis and clinicopathological features in DTC. Finally, the results of the TCGA analysis were verified by other cohorts from Gene Expression Omnibus (GEO) database. In conclusion, m6A RNA methylation regulators play a crucial part in the progression of DTC and are potentially useful for evaluating the prognosis and providing potential novel insights into treatment strategies. Thyroid carcinoma is the most common malignant endocrine tumor with an incidence that has remarkably increased in the past 10 years [1‑5]. Thyroid carcinoma ranks ninth in cancer incidence worldwide and accounts for 5.1% of the total estimated cancer burden in females in 2018 RNA modification have widely studied in the proliferation and metastasis of human carcinoma The present study analyzed the expression of 13 m6A RNA regulators in DTC with RNA sequencing data from The Cancer Genome Atlas (TCGA) and evaluated the association between m6A RNA methylation regulators and clinicopathological characteristics in DTC patients. We further established a signature with four m6A RNA methylation regulators to predict the prognosis of DTC.https://portal.gdc.cancer.gov/) in June 2019. First, the RNA‑seq data files were merged into a matrix file using the merge script of the Perl language (http://www.perl.org/). Gene names were converted from the Ensembl ID to the matrix of the gene symbol through the Ensembl database version 84 (http://asia.ensembl.org/index.html). The downloaded data included 509 thyroid carcinoma samples and 58 normal thyroid samples. The data of 13 m6A RNA modification regulators were extracted and analyzed. The R (version 3.6.0) package 2 fold change| >2 set as the threshold. The gene expression level based on microarray data was calculated using R package limma  with robust multiarray average (RMA) correction.The RNA-seq transcriptome data and clinical information of DTC patients were obtained from the Genomic Data Commons Data Portal within TCGA  and R package glmnet  to identify optimal prognostic m6A RNA methylation regulators that impact progression-free survival (PFS) of DTC patients. An individual's risk score signature was established as follows:We clustered the DTC patients into different groups using R package ConsensusClusterPlus . To discover potential m6A RNA methylation regulators that affect the prognosis of DTC patients, least absolute shrinkage and selection operator (LASSO) Cox regression algorithm Here, GENEi is the identifier of the ith selected gene. The risk score signature was a measure of prognostic risk for each DTC patient.https://CRAN.R-project.org/view=Survival ROC). Univariate and multivariate analyses with Cox proportional hazards regression for PFS were performed to determine the prognostic value of the risk score and various clinical characteristics. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated.The risk score was calculated according to the predictive GENE signature model. Using the median risk score as the cutoff, DTC patients were classified into the high-risk group and low-risk group. Kaplan‑Meier analysis was used to generate PFS curves, and log-rank tests were performed to assess PFS differences between high-risk and low-risk groups. The prediction efficiency of the risk signature model was evaluated by calculating the area under the curve (AUC) of the receiver operating characteristic (ROC) curve using R package survival ROC . Three mRNA datasets of DTC  were selected in the present study and the expression profiles were normalized by log2‐conversion.The datasets of DTC patients were obtained from the Gene Expression Omnibus (GEO) database . The edger function was used to analyze 13 m6A RNA modification regulators in DTC and normal thyroid tissues. Patients were clustered into different groups by consensus expression of m6A RNA methylation regulators. Chi-square tests were used to evaluate the distribution of clinicopathological characteristics between the two risk groups. In addition, the association between risk groups and the prognosis of patients with DTC was evaluated with the Kaplan‑Meier method and log‑rank test. Prognostic performance was estimated by ROC analysis. Univariate and multivariate Cox regression analyses were used to identify factors that were independently related to the prognosis of DTC patients. The Student's t-test were performed to calculate the results of GEO datasets. P<0.05 was considered statistically significant.While m6A RNA methylation regulators play a crucial role in the evolution and progression of malignant tumors, little information is available on their expression in DTC. We thus systematically investigated the expressions of 13 m6A RNA methylation regulators in DTC tissue and normal thyroid tissue using gene expression information from TCGA database profiles. The results showed that the expressions of 12 m6A RNA methylation regulators were significantly different between normal thyroid and DTC tissue (P<0.001)  +  +  + . We calculated the risk score for each DTC patient based on the risk score model and separated them into high-risk (n = 212) and low-risk groups (n = 213) using the median risk score as the cutoff point. The PFS of the two groups was significantly different; patients in the high-risk group had a shorter PFS compared with patients in low-risk group , T grade (P<0.01), and TNM stage (P<0.001). However, there was no association with other clinicopathological features including gender, age, focus type or M grade (P>0.05). Furthermore, the expression of the four selected m6A RNA methylation regulators in high- and low-risk groups was shown in our study . The prognosis and clinicopathological features were significantly different between TC1 and TC2 subgroups. We further developed a risk signature with four m6A RNA methylation regulators that categorized DTC patients into high- and low-risk groups with significantly different PFS.et alIn eukaryotes, m6A is the most abundant and evolutionarily conserved modification in mRNA and plays a crucial role in several aspects of RNA metabolism To explore the role of m6A RNA methylation regulators in DTC patients, we comprehensively analyzed the expression of thirteen regulators in our study. All writers  were downregulated in DTC compared with normal thyroid tissues. The m6A methylation readers YTHDC1, YTHDC2, and YTHDF1 were expressed significantly lower in DTCs, but the expression of HNRNPC was increased. Both FTO and ALKBH5 m6A methylation erasers were decreased in DTCs. Furthermore, we identified four m6A RNA methylation regulators  that were related to the prognosis of DTCs and used these to develop a prognostic signature. In addition, we found that the high-risk group was associated with high N grade, high T grade and high TNM grade, which indicated poor outcomes of DTCs. Univariate and multivariate Cox regression analyses suggested that the four-gene risk signature was an independent prognostic factor for PFS in DTCs. We also found that DTCs with M grade 0 were categorized into high-risk group with shorter PFS or low-risk groups with longer PFS. A similar situation was found in DTCs with TNM stage III, and PFS was significantly different between high- and low-risk groups.et alet alet alZinc finger proteins are involved in the regulation of transcription or translation by specific binding of the target molecules. Different combined with DNA, RNA, DNA-RNA, protein, or zinc finger motifs, which result in zinc finger proteins present multifunctional in biological processes HNRNPC is aberrantly up-regulated in melanoma In conclusion, here we revealed the expression and prognostic value of m6A RNA methylation regulators in DTC. This study also provides crucial evidence supporting further research of the role of RNA m6A methylation in DTC.


Biological functions of biomolecules rely on the cellular compartments where they are located in cells. Importantly, RNAs are assigned in specific locations of a cell, enabling the cell to implement diverse biochemical processes in the way of concurrency. However, lots of existing RNA subcellular localization classifiers only solve the problem of single-label classification. It is of great practical significance to expand RNA subcellular localization into multi-label classification problem.In this study, we extract multi-label classification datasets about RNA-associated subcellular localizations on various types of RNAs, and then construct subcellular localization datasets on four RNA categories. In order to study Homo sapiens, we further establish human RNA subcellular localization datasets. Furthermore, we utilize different nucleotide property composition models to extract effective features to adequately represent the important information of nucleotide sequences. In the most critical part, we achieve a major challenge that is to fuse the multivariate information through multiple kernel learning based on Hilbert-Schmidt independence criterion. The optimal combined kernel can be put into an integration support vector machine model for identifying multi-label RNA subcellular localizations. Our method obtained excellent results of 0.703, 0.757, 0.787, and 0.800, respectively on four RNA data sets on average precision.To be specific, our novel method performs outstanding rather than other prediction tools on novel benchmark datasets. Moreover, we establish user-friendly web server with the implementation of our method.The online version contains supplementary material available at (10.1186/s12864-020-07347-7). Biological functions of biomolecules rely on various cellular compartments. One cell can be divided into different compartments that are related to different biological processes. Thus, the cellular role of one RNA molecular could be inferred from its localization information. What’s more, there has been a great deal of research on the protein subcellular localization –6. CurreWith the explosive growth of biological sequences in the post-genomic era, one of the most important but also most difficult problems in computational biology is how to express a biological sequence with a discrete model or a vector, yet still keep considerable sequence-order information or key pattern characteristic. This is because all the existing machine-learning algorithms, such as Optimization algorithm , CovariaCurrently, the biological technology capable of whole-genome localization is the subcellular RNA sequencing, called SubcRNAseq, which yields high-throughput and quantitative data. Large amounts of raw subcRNAseq data have recently become available, most notably from the ENCODE consortium. A lot of research work has established the resource to make RNA localization data available to the broader scientific community. Firstly, Zhang et al.  built a Considering expensive and inconvenient biological experiments , automatHowever, most existing RNA subcellular localization classifiers only solve the problem of single-label classification. In fact, a single primary RNA transcript is used to make multiple proteins –50. TherIn this study, we utilize different nucleotide property composition models to adequately represent important information of nucleotide sequences. In the most critical part, we achieve a major challenge is to fuse the multivariate information through multiple kernel learning–58, baseIn this section, we compare various nucleotide representations, integration strategies and classification tools on our novel benchmark datasets.AP), Accuracy (Acc), Coverage (Cov), Ranking Loss (Lr), Hamming Loss (Lh) and One-error (Eone). Ten-fold cross-validation is a statistical technique to evaluate the performance of models in turn. Six parameters are used to analyze the performance of model , includiD| represents the number of samples, |L| represents the number of labels, y in Y on the descending order, y predicted by the classifier, Y represents the real label set, Y, Δ stands for the symmetric difference between two label sets.where |For Coverage, Ranking Loss, Hamming Loss and One-error, the model can achieve the best performance with the smallest value. For Average Precision and Accuracy, the model can achieve the best performance with the largest value.We analyze seven different nucleotide property composition representations via 10-fold cross validation. Here, we compare single-kernel feature models on four RNA subcellular localization datasets, as shown in Table In order to further analyze characteristics, we make use of random forest (RF) to calculate the importantce score of each feature dimension. On four RNA datasets, feature scores of mRNAs have more balanced overall distribution, but feature scores of miRNAs and snoRNAs have irregular distributions, as shown in Fig. We study five different integration strategies with SVM model as base classifier via 10-fold cross validation, including binary relevance (BR) , ensemblHere, we compare five integrated SVM strategies on four RNA subcellular localization datasets, as shown in Table KKmer1234 has highest kernel weight, and KNAC has second highest kernel weight. On human miRNAs dataset, KNAC has highest kernel weight. On other six dataset, KDNC similarly has highest kernel weights.According to MK-HSIC strategy, we optimize all weights of effective kernels, in order to improve the correlation between optimal combined kernel and ideal kernel. All weights for seven kernels are shown in Fig. We compare the performance of different classifiers for solving multi-label classification problem via 10-fold cross validation. We use all feature sets for training SVM , RF 40]40], ML-KHere, we compare six classification methods on four RNA subcellular localization datasets, as shown in Table In order to analyze the stability, we perform T-check on MKSVM-HSIC via 10-fold cross validation. We calculate mean value and standard deviation of Average Precision, Accuracy, Coverage, Ranking Loss, Hamming Loss and One-error, as shown in Fig. Importantly, RNAs are assigned in specific locations of a cell, enabling the cell to implement diverse biochemical processes in the way of concurrency. To be specific, our novel method performs outstanding rather than other prediction tools on our novel benchmark datasets. Moreover, we establish user-friendly web server with the implementation of our method.http://lbci.tju.edu.cn/Online_services.htm, including four servers: LocmRNA, LocmiRNA, LocmiRNA and LocsnoRNA. Each one supports two prediction formats, an on-line input single sequence or an entire multiple sequence upload file. The sequence format must be.fasta. It will return the possibility of each label for RNA subcellular localization, and also give the suggested labels as final prediction result.A web server is built for the new proposed method in this paper, the URL is In this paper, we establish multi-label benchmark data sets for various RNA subcellular localizations to verify prediction tools. Furthermore, we design an integration SVM prediction model with one-vs-rest strategy to fuse a variety of nucleic acid sequence to identify RNA subcellular localization. Finally, we propose user-friendly web server with the implementation of our method, which is a useful platform for research community. However, we only consider the frequency information of the sequence, and more characteristic information can be added in the future.In addition, deep learning can be introduced to solve the problem of multiple tags and multiple classifications, which may have good results.In this study, we establish RNA subcellular localization datasets, and then propose an integration learning model for multi-label classification. The flowchart of our method is show in Figure S1.RNAs are generally divided into two categories. One is encoding RNAs, such as messenger RNAs (mRNAs), which play a very important role in transcription. Other is non-coding RNAs, including long non-coding RNA (lncRNA), microRNA (miRNA), small nucleolar RNA (snoRNA), which play an irreplaceable regulatory role in life. In order to study subcellular localization for Homo sapiens, we further establish human RNA subcellular localization datasets. Subcellular localizations of various RNAs in cells are shown in Fig. http://www.rna-society.org/rnalocate/) contains more than 42,000 manually engineered RNA-associated subcellular locali- zation and experimental evidence entries in more than 23100 RNA sequences, 65 organisms , localization of 42 subcells , and 9 RNA categories . Thus, RNALocate provides a comprehensive source of subcellular localization and even insight into the function of hypothetical or new RNAs. We extract multi-label classification datasets about RNA-associated subcellular localizations on four RNA categories . The flowchart of mRNA subcellular localization dataset construction framework is shown in Fig. We use the database of RNA subcellular localization in order to integrate, analyze and identify RNA subcellular localization for speeding up RNA structural and functional researches. The first release of RNALocate .We count the number of samples for each category of subcellular localization labels, and then select some categories with the sample size greater than a reasonable threshold .We count the number of samples for each category, and then select some categories with the sample size greater than a reasonable threshold , where sl denotes the l-th ribonucleic acid and L denotes the length of S. How to formulate varied length RNA sequences as fixed length features, is the key point to effective operational problem-solving. Many studies have shown that the RNA sequence can be encoded by nucleotide property composition representation [RNA sequence can be represented as follow: entation , which ck-mer descriptor, RNAs are represented as occurrence frequencies of k neighboring nucleic acids, which has been successfully applied to human gene regulatory sequence prediction and enhancer identification. The k-mer (e.g. k=2) descriptor can be calculated as follows. For N(t) is the number of k-mer type t, while N is the length of a nucleotide sequence.where k=1,2,3,4, there are four combinations together, each of which has 4k distinct types of nucleotide characteristics. Therefore, we extract 340-dimensional feature vector Fkmer1234.For 4 types of nucleotide characteristics. Therefore, we extract 256-dimensional feature vector Fkmer4.Only remaining 4-mer, there are 4k-mer (RCKmer) is a variant of k-mer descriptor, which is not expected to be strand-specific. For instance, there are 16 types of 2-mer , ‘TT’ is reverse compliment with ‘AA’. After removing the reverse compliment k-mer, there are only 10 distinct types of k-mer in the reverse compliment k-mer approach .The reverse compliment k-mer approach. Therefore, we extract 136-dimensional feature vector FRCKmer.For 4-mer with 256 types, after removing reverse compliment 4-mer, there are 136 distinct types in the reverse compliment The nucleic acid composition (NAC) encodes the frequency of each nucleic acid type in a nucleotide sequence, which is similar to 1-mer. The frequency of each natural nucleic acid  can be calculated as follows. N(t) is the number of nucleic acid type t, while N is the length of a nucleotide sequence.where FNAC.Therefore, we extract 4-dimensional feature vector The di-nucleotide composition (DNC) encodes the frequency of each 2-tuple of nucleic acid type in a nucleotide sequence, which is similar to 2-mer. The frequency of each 2-tuple of natural nucleic acid can be calculated as follows. Nij is the number of di-nucleotide type represented by nucleic acid types i and j.where FDNC.Therefore, we extract 16-dimensional feature vector The tri-nucleotide composition (TNC) encodes the frequency of each 3-tuple of nucleic acid type in a nucleotide sequence, which is similar to 3-mer. The frequency of each 3-tuple of natural nucleic acid can be calculated as follows. Nijk is the number of di-nucleotide type represented by nucleic acid types i, j and k.where FTNC.Therefore, we extract 64-dimensional feature vector k-spaced nucleic acid pair (CKSNAP) is used to calculate the frequency of nucleic acid pairs separated by any k nucleic acids . For each k-space, there are 16 types of nucleic acid pair composition .The composition of k=0,1,2,3,4,5, there are six different combinations, each of which has 16 distinct types of nucleic acid pair composition. Therefore, we extract 96-dimensional feature vector FCKSNAP.For We apply radial basis function (RBF) on above feature sets to construct corresponding kernels, respectively. The RBF kernel is defined as follows. xi and xj are the feature vectors of samples i and j, N denotes the number of samples, and γ is the bandwidth of Gaussian kernel.where The kernel set with seven distinct kernels is denoted as follows. We use multiple kernel learning (MKL) to figure out weights of above kernels, and then integrate them together “X∈RN×d and Y∈RN×1 be two variables from a data set of Prxy. HSIC measures the independence between x and y by calculating the norm of cross-covariance operator over domain X×Y.The main purpose of hilbert-schmidt independence criterion (HSIC)  is to meCxy is defined as follows. Hilbert-Schmidt operator norm “Z, empirical estimate of HSIC is computed as follows. Given set F is the RKHS of feature set X,G is the RKHS of label set Y,e=T∈RN×1,H=I−eeT/N∈RN×N (centering matrix), K,U∈RN×N are kernel matrices with Kij=k and Uij=l,I∈RN×N is the identity matrix. The stronger the dependence between K and U, the larger the value. K and U are independent between each other, when HSIC=0.where Enligthened by HSIC , we defiK∗∈RN×N is the optimal kernel of feature space, and β∈RP×1 is the kernel weight vector. We aim to maximize HSIC between K∗ and U.where Convex quadratic programming problem can be solved as follows. ν1 is L2 norm regularization term. The final training and testing kernels are linearly weighted by β, respectively.where xi,yi},i=1,2,...,N, the classification decision function realized by SVM is expressed as follows. Support vector Machine  was firsxi∈Rd1× and yi∈{+1,−1}.where αi. Solving the following convex Quadratic Programming (QP) problem can obtain the coefficient C is a regularization parameter that controls the balance between boundary and misclassification errors, and when the corresponding αj>0,xj is called support vector.where k binary classifiers.We use an indirect strategy to solve multi-label classification problem, which can be solved by converting multi-label problem into multiple binary classification problems. The one-vs-rest strategy is to treat one class as positive samples and the rest classes as negative samples. We can build a binary classifier for each class label, thus construct a total of Additional file 1 Supplemental charts for the article are in the supplemental data file and include 12 more comprehensive tables and a flowchart.