The packaging of genomic RNA in positive-sense single-stranded RNA viruses is a key part of the viral infectious cycle, yet this step is not fully understood. Unlike double-stranded DNA and RNA viruses, this process is coupled with nucleocapsid assembly. The specificity of RNA packaging depends on multiple factors: (i) one or more packaging signals, (ii) RNA replication, (iii) translation, (iv) viral factories, and (v) the physical properties of the RNA. The relative contribution of each of these factors to packaging specificity is different for every virus. In vitro and in vivo data show that there are different packaging mechanisms that control selective packaging of the genomic RNA during nucleocapsid assembly. The goals of this article are to explain some of the key experiments that support the contribution of these factors to packaging selectivity and to draw a general scenario that could help us move towards a better understanding of this step of the viral infectious cycle. Picornaviridae  viruses occur in the cytoplasm. In almost all (+)ssRNA viruses, nucleocapsid assembly requires packaging of the genomic RNA (gRNA) in an infected cell as nucleocapsids cannot be assembled without RNA being packaged. However, there are at least two exceptions to this rule. In vivo assembly of some viruses from the aviridae ,2,3 and oviridae  familiesoviridae ,19,20,21process) ,26,27,28proteins ,30 and fproteins ,32,33.For future reference, it should be noted that in this article we refer to in vitro experiments as those carried out in a test tube and involving purified components and not in cultured cells. The reason for this distinction will become clear later on. In the simplest scenario, in vitro approaches have shown that RNA packaging selectivity depends on highly specific interactions between the gRNA and the capsid protein (CP) ,35,36,37In vivo and in cultured cells experiments have shown that specific gRNA-CP interactions are necessary but not sufficient to explain packaging specificity observed during an infection. These experiments have demonstrated that there are other factors that contribute to the specificity of this process: (i) the interaction between the RNA-dependent RNA polymerase (RdRp) and gRNA/CP complexes , (ii) thOne of the reasons that makes the study of (+)ssRNA viruses so attractive is that even viruses that are evolutionarily very distant from each other can share a large number of similarities . Therefore, it is too tempting to extrapolate findings from a particular virus to all members of its family, or even to all viruses. However, the specific details of RNA replication, assembly sites, assembly pathways and packaging specificity can differ even between closely related viruses.do packaging signals exist? To what extent can packaging signals explain specific packaging in an infected cell? How do packaging signals work? What other mechanisms can explain packaging selectivity?The goal of this article is to summarize some key experiments that can help us to understand the interactions that contribute to the ability of (+)ssRNA viruses to selectively package their genomes during an infection see . It is iLeviviridae family  have a second gene product (maturation protein) that is required for in vivo 47]). MemPackaging signals (PSs) have long been associated with the specific incorporation of the genome of (+)ssRNA viruses, retroviruses and hepadnaviruses. While retroviruses and hepadnaviruses switch their genome from ssRNA to dsDNA, the molecule that is packaged during assembly is ssRNA; thus most of the packaging-related aspects described for (+)ssRNA viruses are applicable to these two types of viruses.Leviviridae family) [Caliciviridae [Togaviridae [Coronaviridae [Bromoviridae [Tombusviridae [Packaging signals have been studied in ssRNA bacteriophages  ,126, aniiviridae ,128, Togaviridae ,132,133 aviridae  familiesoviridae ,136,137 sviridae  familiesOne of the best-known PSs and assembly processes is that of tobacco mosaic virus (TMV) ,138,139.Sorger and co-workers used in vitro disassembly experiments to identify the PS of turnip crinkle virus (TCV) . They foTogaviridae family) do not package their sgRNA [The Schlesinger group showed that a 132 nt-long fragment from the genome of the alphavirus Sindbis (SINV) had a high-binding affinity for the SINV CP . A few yir sgRNA ). Their ir sgRNA ,149,150.Alphaviruses can be divided into SINV and SFV clades. By using a bioinformatic approach the Frolov group proposed that the PS for the SINV clade ) is located in the nsP1 gene . Also, tSystematic Evolution of Ligands by Exponential Enrichment; for more information about SELEX read [As mentioned before, studying the role of a PS during a viral infection is complicated. The Stockley and Twarock groups came up with an elegant methodology to find PSs by using an in vitro approach ,36,84. FLEX read ) with a LEX read . On the LEX read ,156) butLEX read ,157,158.LEX read .One possible explanation for the apparent lack of specificity  observed in the experiments from Kim, D.Y. et al.  and KuhnPicornaviridae family) [Flaviviridae family) [Nodaviridae family) [Togaviridae family) [Bromoviridae family) [As we have extensively discussed, the selective packaging of the gRNA is often attributed to the presence of a PS and/or multiple PS-like elements. However, there is sufficient evidence that RNA replication, by the RdRp, is necessary for gRNA packaging of poliovirus ( family) , Kunjin  family) , Flock h family) ,133,160, family) ,161 and  family) ,44,162 vNugent and co-workers used a replication-competent poliovirus RNA to investigate whether RNA replication by an active RdRp is required for selective packaging . This RNBy using a slightly different approach Khromykh, A. A. et al. showed that the packaging of Kunjin RNA is coupled to RNA replication . To demoCoupling between RNA replication and packaging has been observed in Venezuelan equine encephalitis virus (VEEV) . By usinIt is possible that when RNA replication  is inhibited, cellular RNAs are packaged because they are far more abundant than the viral RNA. This scenario is not unique to (+)ssRNA viruses. It has been observed that when HIV-1 and Moloney murine leukemia virus Gag proteins  are expressed in cells in the absence of the gRNA , cellulaBromoviridae family belong to the Bromovirus genus: brome mosaic virus (BMV) and cowpea chlorotic mottle virus (CCMV). A distinctive characteristic of these viruses is that their genome is divided into three molecules. RNA 1 and 2 code for the two proteins that constitute the RdRp. RNA 3 has two ORFs: the first one codes for the movement protein and the second one for the CP. Only the movement protein is translated from RNA 3. During minus to plus-strand synthesis there is a moment in which the RdRp binds to the intergenic promoter and synthesizes an RNA that contains only the ORF2 of RNA 3, thus making the sgRNA. The CP is not translated from RNA 3 but from the sgRNA (RNA 4). In order to understand some of the general features of these two viruses Allison, R. F. et al. infected plants with CCMV and BMV in vitro transcripts [Another possibility that involves RNA replication during RNA packaging is a “three-body interaction” between the RdRp, gRNA and the CP. The Rao group has had a long-time interest in studying the relationship between RNA replication and packaging in Bromoviruses and Nodaviruses ,167,168.nscripts . They shnscripts ,68. On tnscripts ,137,169.nscripts . This isnscripts . Furthernscripts . This suAgrobacterium tumefaciens system to infect plants with BMV cDNAs and decouple replication from packaging [A few years later, Annamalai, P. et al. used an ackaging . They shBimolecular Fluorescent Complementation (BiFC) approach they characterized the interactions in planta of BMV RdRp proteins 1a (p1a) and 2a (p2a) (together they make the RdRp). They concluded that BMV CP has a physical and direct interaction with p2a but not with p1a and proposed that this interaction is necessary for packaging specificity.How does RNA replication contribute to selective packaging? As we have already mentioned, the simplest mechanism is that in which the RdRp increases the local concentration of the viral RNAs so that it dominates the RNA population. Nonetheless, this is not the only possibility. Chaturvedi, S. and Rao, A.L.N. carried out a series of very impressive experiments to test the hypothesis that genome packaging specificity requires a physical and direct interaction between the RdRp and CP . By usinNodaviridae family. Unlike in Bromoviruses, both gRNAs (RNA 1 and 2) are packaged into a single virion [Flock house virus (FHV) is a bipartite (two-molecule segmented) insect virus from the e virion . By usine virion . As in te virion . Later, e virion .So far it is clear that PSs are necessary but not sufficient for most viruses. In such cases, replication and/or translation are required to make a viral RNA suitable for packaging. As it turns out, RNA replication modifies cellular membranes. This rearrangement is mostly caused by the viral non-structural proteins; therefore, we will now discuss the role of such cellular modifications on packaging specificity.RNA replication of (+)ssRNA viruses that infect eukaryotic cells results in virus-induced membrane remodeling ,47. The Virus factories have been described as spherule invaginations  are localized around endosomes and lysosomes and create cytopathic vacuoles (CPV). In these cases, the remodeled membranes act as assembly sites. From the work by Zhao, H. et al. it is obvious that assembled SFV virions can be found inside endosomes . As in SN. bethamiana is sufficient to produce ER-derived invaginations. However, the role of the viral replication factories in BMV selective packaging remains unclear. Based on the fact that the p1a and p2a are present in the invaginations and that a direct interaction between p2a/CP is required for specific packaging, one could conclude that membrane remodeling is necessary for the selective packaging of BMV gRNA. However, Schwartz et al. found that, at least in a yeast system, only RNA 3 is inside the spherules [BMV ER-derived invaginations are the result of the interaction between the nonstructural protein p1a and cellular proteins . This aspherules . The lacpherules . It is pThe gRNA of (+)ssRNA viruses is like a Swiss Army knife in that (i) it contains the minimal genetic information required for the infectious cycle, (ii) is the template for RNA replication, (iii) is a messenger RNA, (iv) can act as a scaffold during virion assembly, and (v) has to be able to present different RNA structures to regulate these processes during the infectious cycle. On the one hand, it has to have an architecture similar to cellular RNAs so that it can be translated, evade the protective cellular response against dsRNA and interact with host-cell factors. On the other hand, it has to be different enough from the cellular RNAs to be identified by the RdRp and selectively packaged during assembly.what makes the gRNA different from cellular and viral-derived RNAs so that only the full-length viral RNA is packaged? How does a packaging signal work? Are packaging signals sufficient for packaging selectivity? What other factors, aside from a packaging signal, contribute to selective packaging?It is important to keep in mind that assembly and RNA packaging occur in the presence of a large and diverse population of cellular and viral-derived RNAs, yet only the gRNA is packaged (with a few exceptions like Aura virus and retroviruses). This observation leads to a series of questions: The first question is most commonly answered by attributing selective packaging to the presence of a single RNA element that is unique to the gRNA and that establishes highly specific interactions with the viral structural proteins. This has been the working hypothesis for most (+)ssRNA viruses and retroviruses. However, based on the evidence presented here this scenario is not always precise. An improvement to this view is that for some viruses there is not one PS, but of a series of PS-like elements ,107,186.The data presented here suggest that there is no universal PS-mediated packaging mechanism see . It is gTogaviridae family) there is no concrete evidence that these viruses utilize a PS in vivo (the activity of Bromovirus PSs has only been studied in vitro). The evidence supports the idea that the ability of the gRNA to be replicated by the RdRp is a key factor that determines if this molecule can be packaged. For example, the high degree of cross-packaging efficiency observed in alphaviruses by Kim et al. [The renewed interest in PS has overshadowed the contribution of RNA replication, translation and compartmentalization to RNA packaging. It is clear that at least in Picornaviruses, Togaviruses, Nodaviruses, Flaviviruses and Bromoviruses, a PS is not sufficient to explain selective packaging. With the exception of Alphaviruses  and if more than one RNA molecule is packaged. For example, virus replication in Togaviruses (SINV and RRV), Betacarmoviruses (TCV), Nodaviruses (FHV) and Bromoviruses (BMV and CCMV) requires the synthesis of sgRNAs. Bromoviruses always co-package their sgRNA, while Betacarmoviruses, Nodaviruses and Togaviruses (with the exception of Aura virus) do not package this molecule. This difference suggests that these are secondary mechanisms to control packaging of the sgRNA. It is possible that the amount of packaged nucleotides is a key factor (see next paragraph). In the case of the Togaviruses the sgRNA does not contain the PS (again Aura virus being the only exception); therefore, it is likely that selective packaging of the gRNA could depend on the presence of a PS, as well as on the ability of an RNA to be replicated. It is not completely clear how sgRNA packaging is regulated in Bromoviruses, or how the sgRNAs in the Betacarmoviruses and Nodaviruses are excluded during packaging. On the one hand, in Bromoviruses RNA 3 and 4 (sgRNA) are about 2/3 and 1/3 the length of RNA 1, respectively. Therefore, by co-packaging RNA 3 and RNA 4 the total number of nucleotides in the virion is almost the same as capsids containing either RNA 1 or 2. On the other hand, the sgRNA of Nodaviruses has a mass that is about 9% of the RNA mass contained in the virion (RNA 1 + 2). Thereby, the sgRNA might be too small to be packaged. In other words, it is likely the sgRNA of Nodaviruses cannot be packaged because is too small compared to the other two RNAs and cannot assemble viruses with an RNA mass similar to the normal viral particles. This scenario is consistent with the in vitro packaging efficiency experiments with CCMV CP and truncated RNAs that showed that there is an optimal RNA length and number of total nucleotides required for high packaging efficiency .Belyi and Muthukumar found that the genome size per capsid is directly proportional to the inner charge of the virion . In otheSo far, no PS for Flaviviruses have been identified in cultured-cells experiments. However, the presence of RNA replication and assembly sites seem to be crucial for these viruses. It is plausible that selective packaging in Flaviviruses is primarily due to RNA replication/co-translational packaging within the assembly sites and not by a PS-mediated mechanism. Although, at this point this is only one of the possible explanations. Nonetheless, the CP has to be able to at least distinguish between the (+) and (−)ssRNA and thus a weak PS may be required for this selection.Leviviridae family) are also likely candidates to fall into this category. The binding affinity of CP to PS for these viruses is in the low nanomolar regime (≈2.5–0.4 nM) [Are there any viruses with a packaging mechanism that depend only on the presence of a PS? This is a very complicated question that perhaps cannot be fully answered. However, there are a few cases in which a PS can be the primary, if not the only, determinant for assembly. For example, TMV in vitro assembly occurs only the presence of a PS ,112,113.–0.4 nM) ,126; the–0.4 nM)  or HIV-1–0.4 nM) . One expThe molecular mechanism that controls selective packaging in (+)ssRNA viruses is very complex and depends on a series of interactions and characteristics that are specific to each species. There are instances in which some of these characteristics can be shared among viruses that belong to the same genus or family, or even between unrelated viruses. However, the specific details of these molecular mechanisms can differ greatly even between species that are evolutionarily very close to each other. Viruses can solve the same problem by using very different solutions.Finally, the lack of knowledge of all the interactions that control packaging of the gRNA has led us to neglect this process as a possible target for antiviral therapies. Only a deep and comprehensive understanding of the similarities and differences between these processes among different viruses will allow us to develop novel therapies aimed at one of the most crucial steps of the viral infectious cycle. Remember, a virus that packages the wrong RNA is a dead end.


Compounds 1 (IC50 = 9.20 ± 0.32 µM), 5 (IC50 = 9.47 ± 0.16 µM), 7 (IC50 = 14.7 ± 0.19 µM), 8 (IC50 = 15.4 ± 1.56 µM), 11 (IC50 = 19.6 ± 0.62 µM), 12 (IC50 = 30.7 ± 1.49 µM), 15 (IC50 = 12.0 ± 0.16 µM), 21 (IC50 = 13.7 ± 0.40 µM) and 22 (IC50 = 22.0 ± 0.14 µM) showed promising β-glucuronidase inhibition activity, better than the standard .Phenoxyacetohydrazide Schiff base analogs  H-pyrazole-5-carbohydrazide hydrazones were found to be proliferation inhibitors of A549 cells acetohydrazide (1). Yield: 68%; 1H-NMR (DMSO-6d) δ: 8.04 , 7.63 , 7.34 , 7.23 , 7.12–7.20  6.85–7.06 , 6.80 , 5.17 , 3.84 , 2.23 ; EI MS: m/z (%) 358 , 327 (100),189 (40), 175 (55), 159 (100), 125 (75.0), Anal. Calcd for C19H19ClN2O3, C = 63.60, H = 5.34, N = 7.81. Found: C = 63.55, H = 5.31, N = 7.80.2-(4-Chloro-2-methylphenoxy)-N'-[(3-(4-methoxyphenyl)-2-propenylidene]acetohydrazide (2). Yield: 70%; 1H-NMR (DMSO-6d) δ: 8.02 , 7.55 ,7.12–7.23 ,7.01 , 6.76–6.87 , 6.92 , 5.03 , 3.76 , 2.21 ; m/z (%) 358 , 189 (40), 175 (85), 159 (100), 125 (60). Anal. Calcd for C19H19ClN2O3, C = 63.60, H = 5.34, N = 7.81. Found: C = 63.57, H = 5.32, N = 7.79.2-(4-Chloro-2-methylphenoxy)-N'-[(4-ethoxyphenyl)methylidene]acetohydrazide (3). Yield: 80%; 1H-NMR (DMSO-6d) δ: 8.20 , 7.62 , 7.23–7.12 , 6.84 , 6.84 , 5.13 , 4.04 , 2.22 , 1.32 ; EI MS: m/z (%) 347 , 205 (95), 155 (89), 147 (92), 119 (73). Anal. Calcd for C18H19ClN2O3, C = 62.34, H = 5.52, N = 8.08. Found: C = 62.33, H = 5.50, N = 8.03.2-(4-Chloro-2-methylphenoxy)-N'-[ methylidene]acetohydrazide (4). Yield: 72%; 1H-NMR (DMSO-6d) δ: 8.43 , 7.56 , 7.23 , 7.13 , 6.92, 6.83 , 5.13 , 3.82 , 2.22 ; EI MS: m/z (%) 392 , 251 (15.5), 193 (100), 179 (88.9), 155 (26.8). Anal. Calcd for C19H21ClN2O5, C = 58.09, H = 5.39, N = 7.13. Found: C = 58.05, H = 5.37, N = 7.11.2-(4-Chloro-2-methylphenoxy)-N'-[(2-nitrophenyl)methylidene]acetohydrazide (5). Yield: 65%; 1H-NMR (DMSO-6d) δ: 8.09, 8.29 , 7.98  7.24 , 7.13 , 6.89 , 5.22 , 2.22 ; EI MS: m/z (%) 347 , 206 (100), 155 (83.3), 125 (75.8). Anal. Calcd for C16H14ClN3O4, C = 55.26, H = 4.06, N = 12.08. Found: C = 55.23, H = 4.02, N = 12.04.2-(4-Chloro-2-methylphenoxy)-N'-[(3-nitrophenyl)methylidene]acetohydrazide (6). Yield: 70%; 1H-NMR (DMSO-6d) δ: 8.40 , 8.51 , 8.12–8.25 ,7.76 , 7.12-7.24 , 6.90 ,5.22 , 2.23 ; EI MS: m/z (%) 347 , 206 (100), 178 (28.4), 141 (17.5), 125 (39.9). Anal. Calcd for C19H21ClN2O5, C = 58.09, H = 5.39, N = 7.13. Found: C = 58.06, H = 5.37, N = 7.12.2-(4-Chloro-2-methylphenoxy)-N'-[-(4-chlorophenyl)methylidene]acetohydrazide (7). Yield: 63%; 1H-NMR (DMSO-6d) δ: 8.27 , 7.73 , 7.51 , 7.13–7.23 , 6.89 , 5.15 , 2.19 ; EI MS: m/z (%) 336 , 195 (35), 155 (55), 125 (80), 89 (100). Anal. Calcd for C16H14Cl2N2O2, C = 56.99, H = 4.18, N = 8.31. Found: C = 56.95, H = 4.15, N = 8.28.2-(4-Chloro-2-methylphenoxy)-N'-[-(2-chlorophenyl)methylidene]acetohydrazide (8). Yield: 66%; 1H-NMR (DMSO-6d) δ: 8.26 , 7.77 , 7.48 , 7.13–7.24 , 6.89 , 5.15 , 2.23 , 2.19 ; EI MS: m/z (%) 336 , 195 (30), 155 (40), 125 (75), 89 (100). Anal. Calcd for C16H14Cl2N2O2, C = 56.99, H = 4.18, N = 8.31. Found: C = 56.96, H = 4.15, N = 8.27.2-(4-Chloro-2-methylphenoxy)-N'-[methylidene]acetohydrazide (9). Yield: 70%; 1H-NMR (DMSO-6d) δ: 8.25 , 7.96 , 7.68 , 7.12–7.23 , 6.88 , 5.08 , 2.22 ; EI MS: m/z (%) 371 , 228 (100), 155 (87.1), 125 (89.8). Anal. Calcd for C16H13Cl3N2O2, C = 51.71, H = 3.53, N = 7.54. Found: C = 51.68, H = 3.50, N = 7.52.2-(4-Chloro-2-methylphenoxy)-N'-[methylidene]acetohydrazide (10). Yield: 60%; 1H-NMR (DMSO-6d) δ: 8.48 , 7.57 , 7.44 , 7.24–7.11 , 6.78 , 5.09 , 2.18 ; EI MS: m/z (%) 370 , 229 (39.5), 155 (60.1), 125 (100), 89 (62). Anal. Calcd for C16H13Cl3N2O2, C = 51.71, H = 3.53, N = 7.54. Found: C = 51.66, H = 3.51, N = 7.52.2-(4-Chloro-2-methylphenoxy)-N'-[-(2-fluorophenyl)methylidene]acetohydrazide (11). Yield: 74%; 1H-NMR (DMSO-6d) δ: 8.52 , 7.95  7.48 , 7.25-7.31 , 7.12–7.21 , 6.90 , 5.17 , 2.23 ; EI MS: m/z (%) 320 , 179 (80), 125 (100), 89 (75.8). Anal. Calcd for C16H14ClFN2O2, C = 59.91, H = 4.40, N = 8.73. Found: C = 59.89, H = 4.39, N = 8.72.2-(4-Chloro-2-methylphenoxy)-N'-[3-thienylmethylidene]acetohydrazide (12). Yield: 68%; 1H-NMR (DMSO-6d) δ: 8.07 , 7.12–7.24 , 6.73–9.92 , 5.10 , 2.22 ; EI MS: m/z (%) 308 , 199 (10), 155 (45), 125 (100). Anal. Calcd for C14H13ClN2O2S, C = 54.46, H = 4.24, N = 9.07. Found: C = 54.45, H = 4.22, N = 9.04.2-(4-Chloro-2-methylphenoxy)-N'-[-2-furylmethylidene]acetohydrazide (13). Yield: 70%; 1H-NMR (DMSO-6d) δ: 8.17 , 7.11–7.23 , 6.90 , 6.81, 6.62 , 5.07 , 2.22 ; EI MS: m/z (%) 292 , 155 (50), 151 (80.), 125 (100). Anal. Calcd for C14H13ClN2O3, C = 57.44, H = 4.48, N = 9.57. Found: C = 57.44, H = 4.46, N = 9.56.2-(4-Chloro-2-methylphenoxy)-N'-[-(5-methyl-2-furyl)methylidene]acetohydrazide (14). Yield: 72%; 1H-NMR (DMSO-6d) δ: 8.07 , 7.11–7.23 , 6.88 , 6.80 , 6.24 , 5.07 , 2.21  2.19 ; EI MS: m/z (%) 306 , 65 (30), 137 (100), 125 (40). Anal. Calcd for C15H15ClN2O3, C = 58.73, H = 4.93, N = 9.13. Found: C = 58.70, H = 4.96, N = 9.10.2-(4-Chloro-2-methylphenoxy)-N'-[-methylidene]acetohydrazide (15). Yield: 76%; 1H-NMR (DMSO-6d) δ: 8.41 , 7.24 , 7.13 , 6.89 , 6.83 , 6.71 , 5.15 , 2.19 ; EI MS: m/z (%) 334 ,300 (9), 175 (89.7), 193 (35), 179 (50), 137 (65), 125 (60). Anal. Calcd for C16H15ClN2O4, C = 57.41, H = 4.5, N = 8.37. Found: C = 57.40, H = 3.50, N = 8.36.2-(4-Chloro-2-methylphenoxy)-N'-[-methylidene]acetohydrazide (16). Yield: 68%; 1H-NMR (DMSO-6d) δ: 8.06 , 7.12-7.19 ,7.24 , 6.85–6.92 , 6.76 , 5.10 , 2.22 ; EI MS: m/z (%) 334 , 193 (23.9), 155 (71.6), 125 (88.8), 77 (100). Anal. Calcd for C16H15ClN2O4, C = 57.41, H = 4.5, N = 8.37. Found: C = 57.39, H = 3.50, N = 8.37.2-(4-Chloro-2-methylphenoxy)-N'-[1-(4-hydroxyphenyl)ethylidene]acetohydrazide (17). Yield: 70%;1H-NMR (DMSO-6d) δ: 7.65 , 7.23 , 7.12 , 6.90 , 6.77 , 5.16 , 2.19 ; EI MS: m/z (%) 332 , 191 (100), 177 (24.5), 149 (34.5), 134 (37.9). Anal. Calcd for C17H16ClN2O3, C = 61.36, H = 5.15, N = 8.42. Found: C = 61.34, H = 5.14, N = 8.40.2-(4-Chloro-2-methylphenoxy)-N'-[(3-hydroxyphenyl)methylidene]acetohydrazide (18). Yield: 61%; 1H-NMR (DMSO-6d) δ: 8.18 , 7.04-7.24 , 6.79–6.89 , 5.14 , 2.22 ; EI MS: m/z (%) 318 , 177 (100), 155 (71), 125 (65.3). Anal. Calcd for C16H15ClN2O3, C = 60.29, H = 4.74, N = 8.79. Found: C = 69.27, H = 3.49, N = 8.34.2-(4-Chloro-2-methylphenoxy)-N'-[1-ethylidene]acetohydrazide (19). Yield: 62%; 1H-NMR (DMSO-6d) δ: 8.16 , 7.21 , 7.16–7.23 , 6.86 , 4.98 , 2.18 , 1.93 ; EI MS: m/z (%) 254 , 155 (91.2), 142 (75.5), 125 (100), 113 (100), 99 (86.6).2-(4-Chloro-2-methylphenoxy)-N'-[(4-hydroxyphenyl)methylidene]acetohydrazide (20). Yield: 72%; 1H-NMR (DMSO-6d) δ: 7.88 , 7.52 , 7.24 , 7.13, 6.88 , 6.79 , 5.11 , 2.22 ; EI MS: m/z (%) 318 , 199 (10.78), 177 (27.43), 155 (44), 125 (67.50), 77 (100). Anal. Calcd for C16H15ClN2O3, C = 60.29, H = 4.74, N = 8.79. Found: C = 69.26, H = 3.50, N = 8.34.2-(4-Chloro-2-methylphenoxy)-N'-[-1-naphthylmethylidene]acetohydrazide (21). Yield: 68%; 1H-NMR (DMSO-6d) δ: 8.66 , 8.83 , 8.61 , 8.02 , 7.93  7.65 , 7.14–7.25 , 6.95 , 5.24 , 2.26 ; EI MS: m/z (%) 352 , 211(65), 199. Anal. Calcd for C20H17ClN2O2, C = 68.09, H = 4.86, N = 7.94. Found: C = 68.06, H = 4.84, N = 7.92.2-(4-Chloro-2-methylphenoxy)-N'-[-2-naphthylmethylidene]acetohydrazide (22). Yield: 70%; 1H-NMR (DMSO-6d) δ: 8.43 , 8.16 , 7.94  7.56 , 7.22 , 7.17 , 6.92 , 5.23 , 2.23 ; EI MS: m/z (%) 352 , 211 (60) 199 (35), 169 (45), 153 (100), 127 (60). Anal. Calcd for C20H17ClN2O2, C = 68.09, H = 4.86, N = 7.94. Found: C = 68.04, H = 4.85, N = 7.91.2-(4-Chloro-2-methylphenoxy)-N'-[(5-Chloro-2-hydroxyphenyl)methylidene]acetohydrazide (23). Yield: 66; 1H-NMR (DMSO-6d) δ: 8.23 , 7.70 ,7.12–7.23 , 7.31 , 6.93 ,6.86 , 5.17 , 2.22 ; EI MS: m/z (%) 352 , 210 (7.4), 197 (62.6), 155 (100), 141 (46.8). Anal. Calcd for C16H14Cl2N2O3, C = 54.41, H = 4.00, N = 7.93. Found: C = 54.39, H = 4.00, N = 7.92.2-(4-Chloro-2-methylphenoxy)-N'-[1ethylidene]acetohydrazide (24). Yield: 70%; 1H-NMR (DMSO-6d) δ: 8.23 , 7.17–7.23 , 6.89 , 6.51 , 4.82 , 2.26 , 2.21 ; EI MS: m/z (%) 392 , 251 (72.1), 237 (100), 125 (89.9), 77 (68.5). Anal. Calcd for C16H14ClN3O6, C = 50.60, H = 3.72, N = 11.07. Found: C = 50.57, H = 3.70, N = 11.05.2-(4-Chloro-2-methylphenoxy)-N'-[(4-hydroxy-3-methoxyphenyl)methylidene]acetohydrazide (25). Yield: 59%; 1H-NMR (DMSO-6d) δ: 8.11 , 7.12–7.23 , 7.03 , 6.86–6.96  6.83 , 5.12 , 3.78 , 2.23 ; EI MS: m/z (%) 348 , 207 (51), 193 (100), 155 (43), 125 (46.9). Anal. Calcd for C17H17ClN2O4, C = 58.54, H = 4.91, N = 8.03. Found: C = 58.53, H = 4.90, N = 8.02.2-(4-Chloro-2-methylphenoxy)-N'-{-[4-(methylsulfanyl)phenyl]methylidene}acetohydrazide (26). Yield: 63%; 1H-NMR (DMSO-6d) δ: 8.22 , 7.63 , 7.31 ,7.23 , 7.13 , 6.89 , 5.15 , 2.19 ; EI MS: m/z (%) 348 , 207 (25), 149 (100), 125 (35), 118 (35). Anal. Calcd for C17H17ClN2O2S, C = 58.53, H = 4.91, N = 8.03. Found: C = 58.51, H = 4.90, N = 8.02.2-(4-Chloro-2-methylphenoxy)-N'-[(2-methylphenyl)methylidene]acetohydrazide (27). Yield: 67%; 1H-NMR (DMSO-6d) δ: 8.25 , 7.78 , 7.30–7.21 , 7.13-7.17 , 6.84 , 5.15 , 2.23 , 2.19 ; EI MS: m/z (%) 316 , 199 (78.9), 175 (89.7), 155 (100), 141 (93.2), 118 (94.2). Anal. Calcd for C17H17ClN2O2, C = 64.46, H = 4.41, N = 8.84. Found: C = 64.45, H = 4.40, N = 8.83.2-(4-Chloro-2-methylphenoxy)-N'-[(4-methylphenyl)methylidene]acetohydrazide(28). Yield: 63%; 1H-NMR (DMSO-6d) δ: 8.23 , 7.59 , 7.24 ,7.13 , 7.17 , 6.85 , 5.15 , 2.32 , 2.19 ; EI MS: m/z (%) 316 , 199 (10.80), 175 (36.26), 155 (47.70), 125 (71.62). Anal. Calcd for C17H17ClN2O2, C = 64.46, H = 4.41, N = 8.84. Found: C = 64.44, H = 4.40, N = 8.82.1, 5, 7, 8, 11, 12, 15, 21, and 22 have been identified as β-glucuronidase inhibitors and it is anticipated that by slight synthetic modification in these molecules, some new most active β-glucuronidase inhibitors can be developed.A number of potential lead molecules


Covalently closed single-stranded circular RNAs (circRNAs) consist of introns or exons and are widely present in eukaryotic cells. CircRNAs generally have low expression levels and relatively stable structures compared with messenger RNAs (mRNAs), most of which are located in the cytoplasm and often act in cell type and tissue-specific manners, indicating that they may serve as novel biomarkers. In recent years, circRNAs have gradually become a hotspot in the field of RNA and cancer research, but the functions of most circRNAs have not yet been discovered. Known circRNAs can affect the biogenesis of cancers in diverse ways, such as functioning as a microRNA (miRNA) sponges, combining with RNA binding proteins (RBPs), working as a transcription factor and translation of proteins. In this review, we summarize the characteristics and types of circRNAs, introduce the biogenesis of circRNAs, discuss the emerging functions and databases on circRNAs and present the current challenges of circRNAs studies. In the past few decades, the field of RNA, especially the non-coding RNA(ncRNA)field,has benefitted from the rapid development and application of high-throughput RNA sequencing (RNA-seq) technology . The majCircRNAs are covalently closed, single-stranded circular transcripts with no 5′ caps and 3′ poly(A) tails; this structural characteristic makes circRNAs resistant to the digestion of ribonucleases, such as RNase R and exonuclease, and confers a longer half-life than that of linear mRNAs . In addiCircRNAs are produced from precursor mRNA (pre-mRNA), and they are transcribed by RNA polymerase II . The curThe ceRNA(competing endogenous RNA) hypothesis indicates that miRNA can affect mRNA stability and transcription at the post-transcriptional level by binding to target genes; thus, RNA can also affect miRNAs . This hyMany circRNAs have different types and amounts of miRNA binding sites that can specifically bind to miRNAs, thereby reducing miRNA activity and upregulating the expression of miRNA-related target genes  Fig. 2aa2a. ReseAlthough multiple reports support the role of circRNAs as sponges for miRNAs, some scholars have indicated that some intronic circRNAs and EIciRNAs can regulate protein production by regulating gene expression in a transcriptional or post-transcriptional manner , 14 Figb. EIciRNJust as linear RNAs have been revealed to bind to proteins, several studies have shown that certain circRNAs can work as protein decoys, which sequester proteins by harbouring binding sites for specific proteins and block protein activity by working as competing elements Fig. c. For exAs non-coding RNAs, circRNAs were once considered to fail to translate via cap-dependent mechanisms due to their lack of a 5’cap structure and a poly (A) tail. Additionally, previous polysome gradient analysis and ribosome profiling showed that most circRNAs are not associated with polysomes . TherefoInterestingly, the translation of circRNAs can be mediated not only by IRES but N6-methyladenosine (m6a) can also drive the translation of circular RNA Fig. d. Yang eWith the development of circRNA field, many databases about circRNAs have been built to facilitates the analysis of circRNAs Table . The CirTo date, there has been multiple functional cancer-associated circRNAs screened and identified by different research groups Table . These cSeveral studies have found that multiple circRNAs play a tumour suppressor role in hepatocellular carcinoma. CircMTO1 is underexpressed in hepatocellular carcinoma and affects the expression of downstream P21 protein by targeting miR-9 . It was Derived from exon 4 and exon 5 of the CCNB1 gene, the circRNA circ-Ccnb1 was downregulated in breast cancer and mainly localized in the nucleus in breast cancer cells . Based oMultiple types of tumour cell genomes have chromosomal translocations and rearrangements, resulting in the formation of irrelevant gene recombination and fusion mRNAs that are ultimately translated into fusion proteins. Studies found that in acute promyelocytic leukaemia cells, the PML-RARa fusion gene can not only translate the fusion protein but also form a fusion circRNA f-circM9 . A gain Dai et al. found that circRNA circ0006916 acts as a tumour suppressor in lung cancer by binding to miR-522-3p and inhibiting PHLPP1 activity, thus inhibiting cell proliferation via slowing down the cell cycle process rather than by promoting apoptosis . In addiYang et al. revealed that the endogenous circRNA circFBXW7, comprised of exon 3 and exon 4 of the FBXW7 gene, could encode a novel 21-kDa protein, named FBXW7-185aa, driven by the IRES . This prRecently, a study found that the circular RNA circCCDC66 is upregulated in a variety of tumour cells and is associated with poor prognosis of colorectal cancer . CircCCDStudies from Li et al. revealed that circRNA circHIPK3 was significantly downregulated in bladder cancer tissues and cell lines and was negatively correlated with bladder cancer grade, invasion as well as lymph node metastasis, respectively . CircHIPWith the development of sequencing technology and bioinformatics, an increasing number of circRNAs are gradually being discovered and have become hotspots in the field of RNA. However, there are still quite a few challenges that need to be addressed. First, there is no uniform naming convention for circRNAs at present, which means researchers in different fields use different names when studying the same circRNA molecules, thus causing confusion. For example, cirs-7 indicates that this circular RNA acts as a sponge for miR-7, and CDR1as refers to a circRNA that is related to the CDR1 gene, but both actually refer to the same circRNA. Second, the current databases of circRNA are still not perfect. Although researchers can predict the function of circRNA by using multiple databases, there is no circRNA database related to tumour prognosis, which makes it difficult to screen functional circRNA after RNA-seq. In addition, although sponging miRNAs is the most classical functional model of circRNA, whether this effect between miRNA and circRNA is universal is questioned. It has been reported that, based on the results of bioinformatics and experimental verification, the circRNA-miRNA functional model has a negative result . Most ofThe discovery of circRNA enriches cognition of biological evolution, increases understanding of RNA, and makes it a hotspot in the field of cancer research, which has given researchers a new direction in which to investigate the occurrence and development of tumours. Although knowledge of the precise mechanism of the formation, transportation and function of circRNAs in tumours is still very limited, with the application of new technologies and the constant efforts of scientists, the mystery of circRNA will eventually be solved.


Additionally, aspirates from 31 euthanised dogs were collected for standardising the procedure. Each aspirate was separated into two subsamples: for cytology and RNA extraction. Additional prostate tissue samples served as control for RNA quantity and quality evaluation, and differential expression analysis. The US-FNA sampling procedure was feasible in 95% of dogs. RNA isolation of US-FNA samples was successfully performed using phenol-chloroform extraction. The extracted RNA of 56% of a subset of US-FNA samples met the quality requirements for RNA-Seq. Expression analysis revealed that only 153 genes were exclusively differentially expressed between non-malignant US-FNAs and tissues. Moreover, only 36 differentially expressed genes were associated with the US-FNA sampling technique and unrelated to the diagnosis. Furthermore, the gene expression profiles clearly distinguished between non-malignant and malignant samples. This proves US-FNA to be useful for molecular profiling.Ultrasound-guided fine-needle aspiration (US-FNA) biopsy is a widely used minimally invasive sampling procedure for cytological diagnosis. This study investigates the feasibility of using US-FNA samples for both cytological diagnosis and whole transcriptome RNA-sequencing analysis (RNA-Seq), with the ultimate aim of improving canine prostate cancer management. The feasibility of the US-FNA procedure was evaluated  However, there is still a lack of information on the molecular mechanisms underlying canine prostate diseases, which is especially relevant to the early diagnosis and management of canine prostatic tumours4. Dogs with prostate cancer (PCa) are often diagnosed at an advanced stage, which is associated with poor prognosis and limited therapeutic options5. Although the reported prevalence of prostate malignancies is low (0.2–0.6%)7, the incidence is likely to be underestimated due to the absence of reliable biomarkers and the lack of biopsies on asymptomatic dogs4. In addition, aside from humans, dogs are the only non-human species to naturally develop PCa sharing many clinical properties8 and will often have metastases10, similar to men11. This highlights the importance of characterising the transcriptomic landscape of canine PCa. Next-generation sequencing (NGS) of RNA (RNA-Seq) has enabled comprehensive insights into cancer-relevant mechanisms13 and is a well-established method for differentiating between neoplastic diseases14. Compared to conventional “gene by gene” approaches, RNA-Seq has several advantages: it is fast, cost-efficient and provides simultaneous information on several thousands of genes13. There is little doubt that RNA-Seq has the potential of becoming a routine diagnostic tool – at least in human medicine15. And even if the cost and complexity of the analysis may prevent this in veterinary practice in the immediate future17, RNA-Seq data already serve as a solid basis for fostering new diagnostic and therapeutic approaches16. Indeed, RNA-Seq has already been utilised in dogs to address clinical issues in different organs22. And while no genome-wide transcriptome analysis have been conducted for canine PCa, there is a growing body of information on the transcriptomic landscape of human PCa26. NGS allows the analysis of the transcriptome from minimal amounts of sampling material27. This is of special interest in clinical routine diagnostics, where representative sample material from tumours or organs must be obtained intra vitam. Meeting the requirements of a minimal invasive sampling procedure2, aspirates collected by ultrasound-guided fine-needle aspiration (US-FNA) biopsy are regularly employed in the cytological diagnosis of canine prostate diseases28 and other diagnostic approaches, such as flow cytometric phenotyping29. In this context, ultrasonographic parameters such as prostate size and differences in echogenicity within the parenchyma are used to identify lesions3 and guide the fine-needle to aspirate cells from representative areas30. The cells targeted and collected by the US-FNA procedure normally undergo cytological diagnosis28, but are also viable for molecular profiling32.Numerous diagnostic approaches for canine prostate diseases have been developed in the last few decades, substantially improving the detection of benign conditionsThe aim of the present study was to determine the suitability of aspirates derived from canine prostate US-FNA for molecular diagnostic. In pursuing this aim, the following questions were investigated: (a) the technical feasibility of the canine prostate US-FNA procedure as a sampling tool for cytological diagnosis and laboratory workflow; (b) the quantity and quality of the RNA isolated from US-FNA subsamples; (c) the impact of methodological contamination  in US-FNA aspirates on the transcriptome analysis; and (d) the similarities and differences in the molecular profiles of malignant and non-malignant US-FNA samples compared to those of prostate tissue samples.post mortem FNA (PM-FNA) training set  and histopathologically diagnosed as normal to hyperplastic , inflammation , prostatic atrophy , prostate carcinoma  or metastasis  from a transitional cell carcinoma of the urinary bladder or lymphoma  but failed due to the presence of clotted blood. Semi-quantitative macroscopic evaluation of the remaining 40 US-FNA samples  revealed that 65% showed moderate to strong blood contamination, 28% had weak to nearly invisible blood additions and 8% exhibited no signs of blood contamination. For this reason, RNA isolation from these samples was performed using the phenol-chloroform extraction method, which performed well in all but one US-FNA sample, resulting in a mean of 70.8 ng/µl of total RNA , RNA isolation from tissue resulted in a mean of 789.0 ng/µl of total RNA , which was significantly higher (p < 0.05) than those obtained for the US-FNA samples and PM-FNA training set .Based on clinical data, histopathological and cytological diagnosis see Fig. , 16 US-FThe RNA integrity numbers (RIN) were more consistent for tissue samples  performed on the expression values of the DEGs see Fig.  indicateMYC), catenin beta 1 (CTNNB1), prostate transmembrane protein, androgen induced 1 (PMEPA1), proliferating cell nuclear antigen (PCNA) and marker of proliferation Ki-67 (MKI67). The remaining 450 (42%) down-regulated genes included kallikrein-related peptidase 2 (KLK2), kallikrein-related peptidase 4 (KLK4), NKX3 homeobox 1 (NKX3-1) and acid phosphatase prostate (ACPP). These have been reported as genes with potential diagnostic value in the literature36 and their consistent expression in US-FNA and tissue samples supports the use of US-FNA samples for transcriptomic profiling. In addition, only 36 genes (1% of the DEGs) were associated with both non-malignant and malignant aspirates of US-FNAs  and 2 (HBA2), ENSCAFG00000029518 and ENSCAFG00000030286 as an orthologue of haemoglobin subunit delta (HBD) and haemoglobin beta (HBB) and ENSCAFG00000029224 as haemoglobin subunit mu. Therefore, molecular profiling of sampling-associated differences appears to reflect blood contamination in US-FNA samples.Moreover, hierarchical clustering of the fold-changes of the expression values of the DEGs relative to the control group confirmed this result, revealing a largely common molecular profile between malignant US-FNA and malignant tissue samples see Fig. . Specifi4. Having the opportunity to further use samples collected for routine diagnostic for molecular profiling is paramount to improve our molecular understanding of diseases16. An ideal biopsy procedure should be minimally invasive and allow rapid sampling and comprehensive clinical diagnostics, including molecular and cellular marker analysis27. In general, the FNA procedure has been shown to meet these requirements37. In particular, the present study confirms that FNA samples are a reliable sampling tool for both cytological and molecular diagnosis.Canine prostate diseases are currently unsatisfactorily characterised at molecular level, hampering early detection strategies38. In order to address the aggressiveness of human PCa using the Gleason Score, the FNA procedure has been largely replaced in routine diagnostic by multiple core needle biopsies39. Nevertheless, aspirates collected by FNA are acknowledged as samples with molecular diagnostic potential43. In dogs, the collection of canine prostate samples is challenging due to the anatomical localisation of the prostate gland44. Although histopathological biopsy is described as the gold standard for diagnosis of canine prostate diseases44 and a histopathological standard terminology is available45, the collection of tissue samples has several disadvantages, such as the need for general anaesthesia and the relatively high complication rates46. A practicable minimally invasive sampling procedure would be preferable both veterinary research and, especially, in clinical routine16. Based on the current state of knowledge, this is the first study to evaluate the feasibility of using samples of the canine prostate obtained intra vitam by US-FNA with 22-gauge needles for both cytological diagnosis and RNA-Seq.In human oncological medicine, different types of biopsies – including FNA – have been comparatively assessed as samples for NGS applications aimed at optimising the management of patients based on their molecular profiles28, the US-FNA procedure was well tolerated by 95% of the dogs in the study without the need of general anaesthesia. Complications associated with the US-FNA procedure were transient and mild and in concordance with previous studies47. The US-FNA procedure proved more challenging in neutered healthy dogs due to the dense and firm prostate tissue structure48 caused by androgen deprivation49 and resulted in generally low quantities of RNA. Nonetheless, the US-FNA procedure yielded adequate quantities of RNA in the case of neutered dogs with PCa, which typically exhibit disease-related changes of the prostate. This is particularly important since neutered dogs have an increased risk to develop canine PCa50. These findings indicate that the US-FNA procedure is feasible in diagnostic routine.In agreement with the literature51. In the present study, 88% of the cytological smears exhibited sufficient cellularity for cytological diagnosis. This was similar to the fraction of the subsamples that met the requirements for RNA-Seq: 93% of US-FNA samples. In concordance with these results, previous studies described the remaining aspirates as a viable source for advanced diagnostic procedures after the preparation of cytological smears53. In general, the results of this study support the idea of using subsamples to bridge the gap between clinical and molecular diagnosis.In this study, US-FNA samples were split into two subsamples to address conventional and molecular diagnostic questions. An adequate cellularity is crucial for cytology and RNA extraction. The strategy of splitting the aspirates into subsamples generally decreases the cellularity. Varying cellularity can be additionally explained by the non-standardisable conditions during collection and subsampling in the clinical routine. The cellularity of aspirates has been previously analysed in different tumour samples and shown to be affected by the application of suction, the number of needle passes and cell exfoliation54. In fact, in a previous study, the researchers opted for not using highly blood contaminated canine FNAs of different tumour entities for RNA extraction51. In the present study, 65% of US-FNA samples had moderate to strong blood contaminations, probably related to the aspiration technique51 and the use of intravenous needles55. Processing and pipetting of contaminated US-FNA samples was challenging and might have led to a decrease in the number of cells. However, US-FNA samples were successfully used for RNA purification by phenol-chloroform extraction. Based on the results in this study, the use of column-based isolation should be avoided without any further lysis steps or reagents. Interestingly, and consistent with the macroscopic findings, five globin genes, which can be associated with erythroid cells56, were more highly expressed in US-FNA samples compared with tissue samples. Therefore, blood contamination was detectable at molecular level, thus indicating that RNA-Seq enables intra vitam sampling associated effects to be identified. Additionally, there was no evidence that 0.1% of DEGs identified as globin genes in anyway impaired the actual diagnostic for gene expression profiling.Cytological findings on the sample quality such as necrosis and blood contamination appear to provide important information regarding the viability of molecular analysis. Large amounts of blood are known to influence the quality of cytological smears, hampering an adequate cytological diagnosis57. Of US-FNA samples, 56% met the requirements of RNA-Seq. It has been shown before that RIN values of FNA samples are not always detectable51. For precise assessment of RNA quality the Agilent RNA 600 Nano assay requires a minimum RNA concentration of 25 ng/µl58. Of the US-FNA samples, 31% (n = 5) selected for quality control were below this threshold, which could explain some of the poor RIN values. Furthermore, the RIN value and the corresponding electropherogram needs to be evaluated. In addition, the nucleases and factors associated with the FNA procedure itself, such as the needle length, can cause RNA degradation59. Moreover, this study evaluated the RNA integrity in non-malignant and malignant canine prostate tissue samples with no evidence of a difference. Similar to the results of the present study, one recent study on canine tumour FNA found no significant difference analysing the integrity of RNA from readily exfoliative tumour and non-readily exfoliative tumour FNA. However, non-malignant samples were not part of the above mentioned study51.In general, RNA-Seq demands high-quality total RNA60. To evaluate the molecular diagnostic potential of canine prostate US-FNA subsamples for further gene expression studies, seven US-FNA samples were subjected to RNA-Seq and their whole transcriptome profiles were compared to those of 18 prostate tissue samples. The main advantage in using NGS is the possibility to comprehensively characterise the transcriptome, allowing rapid profiling of neoplastic diseases for downstream diagnostics of therapeutic intervention61. The findings of the present study indicate that the sampling procedure has a minor impact on the sample’s gene expression profile, since only 36 (1%) of 3,587 DEGs were specifically identified in all US-FNA samples relative to the non-malignant prostate tissue control. Differences on the gene expression level between US-FNA and tissue samples can be explained based on the variability of the individual samples and the diagnosis. In particular, tumour heterogeneity has been described in different cancer types62, including canine PCa63. To minimise these effects and ensure representative material for RNA-Seq analysis as well as adequate diagnosis, the samples were collected under ultrasound guidance and characterised by cytological examination. The molecular profiling of aspirates collected by FNA samples in dogs has been previously performed and compared to matched tissue samples for lymph nodes using microarray technology31. Nevertheless, that study differs from the present one in that FNA samples were i) collected only from euthanised dogs (post mortem), and ii) not separated into two subsamples for cytological and molecular analysis. Overall, the authors described FNA samples as a reliable source for molecular profiling31, which agrees with the results presented here. In this context, US-FNA transcriptomics becomes increasingly attractive as a diagnostic tool. Indeed, malignant and non-malignant US-FNA samples were perfectly separable based on their molecular profiles, matching the cytological diagnosis. In addition, genes that are well known to be deregulated in cancer such as MYC, MKI67, ACPP, CTNNB1, KLK2, KLK4, PCNA, PMEPA1, NKX3-168 were also significantly deregulated (and in the same direction) in US-FNA samples, confirming the similarity of US-FNA samples and their prostate tissue counterparts. These genes and other genes with similar molecular profiles are interesting from a diagnostic point of view. In particular, genes like MYC, MKI67, CTNNB1, PCNA, NKX3.1, ACPP and kallikreins are warranted further investigation as diagnostic markers for cancer and cell proliferation. The combination of US-FNA and RNA-Seq opens up a wide array of possibilities in clinical diagnostics.One previous investigation on human PCa emphasises the importance of biopsy based gene-expression studies and successfully used prostatectomy samples for molecular profiling and additional needle core biopsies for identification of candidate genesIn conclusion, the data presented here underline the suitability of the US-FNA procedure as a sampling tool for cytological and molecular diagnostic purposes. To optimise the laboratory workflow, details on cytological examination may be helpful for identifying and processing adequate samples prior to sequencing. This study highlights the strengths of US-FNA as a sampling tool for molecular profiling of the canine PCa. Validation on a larger number of US-FNA samples is warranted to confirm these conclusions. Beyond the use of US-FNA for whole transcriptome analysis, this study provides a solid basis for approaches such as targeted sequencing or qPCR screening of single genes, opening new perspectives for biomarker discovery for early detection and follow-up diagnosis and, eventually, for developing and establishing individualised therapeutic strategies.Intra vitam aspirated cells, collected by US-FNA were referred to as US-FNA, while aspirates collected by FNA after prostatectomy from euthanised dogs were referred to as PM-FNA training set  after the PM-FNA procedure from euthanised dogs  attached to a 5 mL Luer syringe . The needle was inserted into prostate tissue and moved several times while aspirating. A fraction of the aspirated cells were used to prepare a smear for cytological examination; the remaining cells were expelled to a 1.8 mL Nunc CryoTube . The needle was then rinsed with 50 to 100 µl sterile phosphate-buffered saline (PBS) for collecting the remaining aspirate in the CryoTube. Matched prostate tissue samples were collected from macroscopically representative areas for molecular biological procedures and histopathological classification.A training set of 31 PM-FNA samples was collected from euthanised dogs during necropsy after prostatectomy  was isolated in accordance with the manufacturer’s protocol of Qiagen AllPrep™DNA/RNA Micro Kit . For lysis and homogenisation, Buffer RLT Plus and QIAshredder spin column  were used and purified RNA was eluted RNAse-free water.intra vitam collected US-FNA samples (n = 42), the method of RNA isolation was adjusted to phenol-chloroform extraction (n = 40), since column-based isolation of total RNA from two US-FNA samples failed due to clotted blood. A semi-quantitative scoring was used prior to RNA isolation to assess blood contamination in 40 US-FNA samples macroscopically: Score 0 was used for samples without macroscopically visible contaminations of blood; score 1 comprised samples with almost invisible contamination of blood; score 2 included light red coloured samples for weak but clearly visible blood contamination; score 3 included red coloured samples, describing a moderate contamination and score 4 deep red samples indicating strong blood contamination.For Phenol-chloroform extraction of RNA from remaining aspirates collected by US-FNA (n = 40) was performed using QIAzol Lysis Reagent with Qiagen miRNeasy Micro Kit  eluted RNase-free water. RNA quantity of US-FNA and PM-FNA samples was determined using the Qubit® RNA HS Assay Kit  with Qubit 2.0 Fluorometer.TM Multi-Volume Plate by Synergy™II multi-mode reader, evaluated by Gen5™ Microplate Data Analysis Software .For isolating total RNA from 10-20 mg prostate tissue samples (n = 43), AllPrep® DNA/RNA/miRNA Universal Kit  was used in accordance with the manufacturer’s protocol. Tissue samples were disrupted utilising Buffer RLT Plus and 5 mm stainless steel beads by TissyeLyser II . Total RNA was eluted in 30 µl RNase free-water and RNA quantity was measured on a Take3US-FNA and prostate tissue samples for RNA-Seq were selected in accordance with multiple criteria. First, they were required to contain a minimum of 10 ng and a maximum of 1 µg of total RNA. To ensure their comparability, the samples were further selected based on their histopathological and cytological diagnosis see Fig. . The PM-The RNA quality was measured using the RNA 6000 Nano LabChip on an Agilent Bioanalyzer 2100 , based on the 18S to 28S ribosomal ratio provided as electropherogram. The RIN value was determined with the Agilent 2100 Expert software and ranged from 1 (most degraded RNA) to 10 (most intact RNA). For samples with low RIN values, details in the electropherogram were used for further evaluation of RNA degradation.Statistical analysis of RNA quantity and quality was performed using SAS Enterprise Guide 7.1 . The Shapiro-Wilk test was applied to confirm the normal distribution of the data. The influence of neutering and diagnosis on RNA quantity in prostate tissue samples was evaluated with the two-sample t-test. Wilcoxon’s signed-rank test was used to compare the differences between US-FNA aspirates and the PM-FNA training set. A two-sample t-test was performed to analyse differences of RIN-values between non-malignant and malignant tissue samples. Significance level was defined as p < 0.05.72.Samples with a RIN value ≥ 5.5  were then subjected to NGS library preparation  with the following parameters: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36. Trimmed reads were then mapped to the CanFam3.1 assembly of the dog genome using TopHat275 with default parameters and supplying the Ensembl GTF annotation (Ensembl release 8576) through the -G option. This resulted in an average of ~14 million pairs of mapped reads per sample.Raw sequencing reads were quality checked using FastQC77 with default parameters. The sample groups: non-malignant US-FNA, malignant prostate tissue and malignant US-FNA were each independently tested against non-malignant prostate tissue (control) for differential expression with the R/Bioconductor package DESeq278. p-values were adjusted for multiple testing using the false discovery rate (FDR). Genes with an FDR < 0.00001 in any of the three comparisons were considered as DEGs.Read counts for each protein-coding gene were calculated for each sample using the htseq-count tool of the Python package HTSeq76 not associated with any Official Gene Symbol were aligned for identifying sequence homologies based on Ensembl IDs using the online BLAST  interface provided by NCBI (https://blast.ncbi.nlm.nih.gov/Blast.cgi 80).Nucleotide sequences of Ensembl gene IDsDEGs were used to perform a PCA to summarise and visualise variances across all samples. Specifically, PCA was performed on library size-normalised regularised log-transformed expression values, which were computed using the rlogTransformation function in the R/Bioconductor DESeq2 package.Fold-changes relative to non-malignant prostate tissue of DEGs were hierarchically clustered using complete linkage and Euclidean distance (genes) or a Pearson correlation-based distance measure (samples). On basis of fold-changes, a list of well-studied genes was used for further profiling of US-FNA in comparison with prostate tissue samples.82), using the 19,856 Ensembl IDs corresponding to all protein-coding genes annotated in the dog genome (Ensembl release 8576) as background and a FDR ≤ 5% cut-off for significance.DEGs identified exclusively in US-FNA samples were analysed on biological pathway. This pathway analysis was performed with DAVID (Database for Annotation, Visualization and Integrated Discovery Functional Annotation Tool


From the formulation of the Central Dogma of molecular biology to the discovery of novel non-coding RNA (ncRNAs) classes, the focus of Genetics involved DNA variants with the scope of elucidating biological pathways perturbed in disease. Recently, considerable attention has shifted toward the study of RNA modifications dynamically occurring in both protein-coding as well as non-coding RNAs, in both animal and plant kingdoms. This, in turn, has gradually led to the exciting exploration of the novel frontier of “Epitranscriptomics,” revealing an additional, finer layer of complexity in gene regulation.The application of the fast-growing high-throughput sequencing technology (HTS) has allowed the unprecedented opportunity to unveil millions of RNA modifications in human genes, such as resulting from methylation , pseudourylation (Ψ) and deamination , counting to date more than 140 different forms of RNA modifications.The aim of this Research Topic was to collect both original research articles and reviews in addressing the bioinformatics approaches and wet lab methodologies necessary to aid in the detection of novel RNA modifications and characterization of their biological functions, as well as contribute to the identification of the molecular protagonists involved in the regulation of such phenomena. Among the articles embracing the scope of the Research Topic, we have collected five reviews, four original research and methods articles, and a technology article.Romano et al. surveyed the main types of RNA methylation in the context of ncRNAs with their associated functions. They also described the methodologies to identify and profile such RNA modifications. On the other hand, Zhao et al. instead, focused on the role of the pseudouridylation of ncRNAs in nuclear gene expression events. In particular, they reviewed how, in the nuclear context, ncRNA pseudouridylation contributes to transcription and pre-miRNA splicing.Two reviews focused on RNA modifications in ncRNAs. Menezes et al. reviewed current discoveries of the functional roles for 3′ RNA uridylation, particularly focusing on mammalian biology.Another important RNA modification is the non-templated addition of uridine(s) to the terminal end of RNA, termed 3′ RNA uridylation, which has been described having a role in the regulation of both mRNAs and ncRNAs. In this collection, Angelova et al. discussed the emerging findings that show indicate the modifications of RNA impact the development of the brain and their involvement in neurological disorders. One of the most abundant internal modifications in messenger RNAs (mRNAs) is represented by N6-methyladenosine (m6A), which has been discovered to play important roles in multiple biological processes. Liu et al. reviewed how the malfunctions of m6A machineries lead to several cancer types, including solid and non-solid tumors.RNA modifications add a novel and fine-tuning layer to gene expression regulation, having recently shown how the dysregulation of these phenomena can lead to the onset of disease, such as neurological diseases and cancer. In this collection, Rodrigues et al. introduced a method to profile RNA editing from chloroplast by using small RNA (sRNA) libraries form Arabidopsis, soybean and rice. The results obtained in this study encourage using sRNA libraries in order to identify novel RNA editing events and confirm previous detected ones, as well as profiling RNA editing in different plants under different biological conditions.RNA modification phenomena are also present in the plant kingdom. It has been observed that post-transcriptional modifications, such as C-to-U RNA editing events, can occur in chloroplasts. In this collection, Lo Giudice et al. presented the third release of REDIdb, a freely available database for RNA editing events in plants organelles. The current release of REDIdb contains more than 26K RNA editing modifications, together with a new web interface allowing users to contextualize editing events in their genomic, biological and evolutionary contexts.The improvements of high-throughput sequencing (HTS) techniques allowed us to unveil thousands of editing events in plants. In this collection, Song et al. presented PEA-m5C, a transcriptome-wide machine learning-based predictor of m5C modification in Arabidopsis. PEA-m5C was developed by employing a random forest algorithm with sequence-based features and optimized window, obtaining an average AUC of 0.939 in 10-fold cross-validation experiments.Together with the improvements of HTS technologies, we have observed remarkable progress in the development of algorithms and computational methods able to analyze and study data in the emerging field of Epitranscriptomics. Chmielowska-Bak et al. concerning the influence of short-term cadmium stress on two RNA oxidation-dependent modifications: 8-hydroxyguanosine and apurinic/apyrimidinic sites. The authors reported an increased level of RNA oxidation in plants in stress conditions.The collection also includes a study conducted in Soybean seedlings by Finally, the collection also includes a study on the effect of centrifugal force in quantification of colorectal cancer-related mRNA in plasma using targeted sequencing. We know that the pre-analytical factors are critical for the measurement of analytes in the blood. This study investigated two common centrifugation protocols on plasma mRNA quality and quantity. The authors concluded that more targeted mRNAs could be found by double centrifuges of 1,600xg followed by 16,000xg than a single centrifuge of 3,500xg.Epitranscriptomics represents a thrilling, vast and novel field in Cellular Biology. The articles we finally accepted for our Research Topic address some of its most exciting challenges, with the intention of providing a useful resource that will peak the interest of many researchers investigating the RNA modification phenomena. We would like to take this opportunity to thanks the contributions of the authors and the professional support from the editorial staffs.All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.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.


Autophagy is an evolutionarily conserved process that degrades subcellular constituents. Mammalian cells undergo two types of autophagy; Atg5-dependent conventional autophagy and Atg5-independent alternative autophagy, and the molecules required for the latter type of autophagy are largely unknown. In this study, we analyzed the molecular mechanisms of genotoxic stress-induced alternative autophagy, and identified the essential role of p53 and damage-regulated autophagy modulator (Dram1). Dram1 was sufficient to induce alternative autophagy. In the mechanism of alternative autophagy, Dram1 functions in the closure of isolation membranes downstream of p53. These findings indicate that Dram1 plays a pivotal role in genotoxic stress-induced alternative autophagy. Macroautophagy (hereafter described as autophagy) is a cellular mechanism by which cells digest their own cellular components, including proteins, lipids, and even entire organelles. The molecular mechanisms of autophagy have been extensively studied regarding starvation-induced autophagy, in which autophagy-related (Atg) proteins induce the formation of autophagosomes and autolysosomes 1341However, in addition to Atg5/Atg7-dependent autophagy, we discovered an alternative type of autophagy that occurs even in cells lacking Atg5 and Atg7 Most of the molecules required for the conventional autophagy have been identified and characterized. In contrast, only a few molecules associated with alternative autophagy have been identified. One such molecule is Ulk1, which drives the initial steps of both types of autophagy 8Fig. 1A). Representative autophagosomes (double-membrane vesicles)  and autolysosomes degrading cellular constituents  were observed. However, we did not observe any such structures in Atg5/p53 DKO MEFs . Quantitative analysis of autophagic area per cell confirmed the induction of alternative autophagy by etoposide exposure in Atg5 KO MEFs, but not Atg5/p53 DKO MEFs . Note that autophagic cells were defined as cells in which autophagic vacuoles exceed 6% of the cytoplasmic area, which was the upper limit in untreated healthy cells. Alternative autophagy can be assessed more easily by the immunostaining of the lysosomal protein Lamp2 (or Lamp1), because lysosomal fluorescence is usually seen as small dots that become large puncta by the induction of autophagy (owing to the fusion of lysosomes with autophagic vacuoles). The correspondence of large Lamp2 (or Lamp1) puncta to autolysosomes was previously shown by correlative light electron microscopy (CLEM) analysis Fig. 3D). Using the Lamp2 immunostaining assay, we observed large Lamp2 puncta (autolysosomes) in Atg5 KO MEFs, but not Atg5/p53 DKO MEFs , consistent with the EM observations . These results indicated that p53 is necessary for the induction of etoposide-induced alternative autophagy.To clarify the molecular mechanisms of genotoxic stress-induced alternative autophagy, we investigated the requirement of p53, because p53 is a molecule involved in various genotoxic stress-induced responses 1011Fig. 2A), given that no anti-mouse Dram1 antibody for Western blotting was available. Dram1 has already been reported as a molecule involved in conventional autophagy 8Fig. 2B). When we assessed etoposide-induced alternative autophagy using EM, many autolysosomes were observed in control Atg5 KO MEFs , whereas such structures were not observed in Dram1-silenced Atg5 KO MEFs . Consistent results were also observed when Atg5 KO MEFs were transiently transfected with different siRNA for Dram1 . The crucial role of Dram1 in alternative autophagy was also shown by another autophagy assay using Keima. Keima is a fluorescent protein that enables the detection of autolysosomes by its emission of different-colored signals at acidic and neutral pHs Fig. 2E, F), consistent with the EM observations . Detailed EM analyses of etoposide-treated Atg5 KO MEFs expressing shDram1 showed a large number of curved and swollen isolation membranes which were at a step before their enclosure of subcellular constituents . These membranes were localized close to the Golgi membrane . This morphology suggested that etoposide-induced alternative autophagy originates from Golgi membranes, and that loss of Dram1 blocks autophagosome generation by inhibiting the elongation and closure of isolation membranes. Note that camptothecin, another DNA-damaging reagent that induces genotoxic stress via a mechanism different from etoposide, also induced Dram1-dependent alternative autophagy . In contrast, staurosporine (STS), a pan-kinase inhibitor, induced alternative autophagy, but it was not affected by Dram1 silencing . Therefore, Dram1 is only crucial for genotoxic stress-induced alternative autophagy.Because p53 is a transcription factor, molecules that are transcriptionally upregulated by p53 are expected to be crucial for alternative autophagy. Thus, we compared gene expression profiles of Atg5 KO MEFs and Atg5/p53 DKO MEFs after etoposide treatment, and identified Dram1 as one of the most upregulated molecules in Atg5 KO MEFs, but not Atg5/p53 DKO MEFs. We confirmed this upregulation by real-time PCR , including representative autophagosomes (double-membrane compartments enclosing cargo) and autolysosomes (single-membrane compartments digesting cargo)  in Dram1-overexpressing Atg5 KO MEFs. Autophagosomes were localized close to the Golgi membrane . Analyses using Lamp2 immunofluorescence demonstrated that only cells efficiently expressing Dram1 showed large Lamp2 puncta formation , and these large puncta were actually merged with autolysosomal structures as assessed by the CLEM analysis . Furthermore, the number of large Lamp2 puncta-containing cells increased in a time-dependent manner in Dram1-expressing Atg5 KO MEFs, but not control MEFs , indicating that the induction of Dram1 is sufficient to induce alternative autophagy. We also analyzed whether Dram1 was sufficient to induce alternative autophagy using Cyto-ID, which is a novel dye that selectively labels accumulated autophagic vacuoles. CLEM analysis confirmed that Cyto-ID puncta are identical to autophagic vacuoles . As shown in Fig. 3F and 3G, Cyto-ID puncta were observed upon the overexpression of Dram1 in Atg5 KO MEFs, and their intensity increased in a time-dependent manner. Keima analysis showed consistent results , indicating that the simple induction of Dram1 is sufficient to induce alternative autophagy.We next analyzed whether Dram1 is not only necessary but also sufficient to induced alternative autophagy. To this end, we expressed Flag-tagged Dram1 in Atg5 KO MEFs, and analyzed alternative autophagy using EM. We observed many autophagic vacuoles , whereas others merged with Lamp2  at 12-24 hours after Dram1 overexpression. This Golgi localization is reasonable because Dram1 functions in the closure of isolation membranes, which originate from the Golgi membrane , and Dram1 generates autophagosomes next to the Golgi membrane . At 48 hours after transfection, most Dram1 signals were separate from GS28 signals, and were completely merged with ring-like structures of Lamp2, namely, autolysosomes .Dram1 is a hydrophobic protein with 6 transmembrane domains, and was reported to localize on the Golgi membrane, early and late endosomes, and lysosomes Fig. 5A, LC3-II (a lipid conjugate) formation was observed in Dram1-expressed MEFs, but not in vector-transfected MEFs in the absence of bafilomycin A1 (BafA), which prevents autophagosome-lysosome fusion. Assessment of autophagic flux, a dynamic process of autophagy that can be measured by differences in the levels of LC3-II with/without BafA, confirmed the induction of conventional autophagy by Dram1 . Puncta formation of GFP-LC3, another marker of conventional autophagy, was also increased in Dram1-expressing MEFs . Interestingly, we observed GFP-LC3 puncta and GFP-LC3/Dram1 colocalized puncta in Dram1-expressing MEFs . Because of the lysosomal localization of Dram1, these puncta are thought to be autophagosomes/isolation membranes and autolysosomes, respectively. Consistent results were also observed when HeLa cells were used instead of WT MEFs . These data indicated that Dram1 is sufficient to induced conventional autophagy. We also analyzed whether Dram1 is necessary for etoposide-induced conventional autophagy using shDram1-transfected WT MEFs . As shown in Fig. 5E, LC3-II formation, and autophagy flux were unaffected in etoposide-treated WT MEFs irrespective of the reduction of Dram1. Therefore, Dram1 has the potential to activate conventional autophagy, but its role is marginal in etoposide-induced conventional autophagy, at least in WT MEFs. Dram1 was also reported to regulate genotoxic stress-induced apoptosis Fig. 2B, 5D), Dram1 silencing suppressed alternative autophagy , but not conventional autophagy  and apoptosis , suggesting that Dram1 is more important in alternative autophagy than other cellular functions, at least in MEFs.Dram1 was originally reported as a molecule associated with conventional autophagy 89Fig. 6A). Because Dram1 blocked alternative autophagy by inhibiting the closure of isolation membranes , Dram1 is thought to function downstream of Ulk1 in the alternative autophagy pathway. This was confirmed by the fact that alternative autophagy was increased by Ulk1 overexpression, but was suppressed by Dram1 silencing . Furthermore, Ulk1 upregulation, which is essential for alternative autophagy Fig. 6C "Ulk1"). Therefore, Dram1 is thought to function at the downstream of Ulk1. Taken together, we here showed the crucial role of Dram1 in the closure of isolation membranes during alternative autophagy.Ulk1 is another crucial molecule for DNA damage-induced alternative autophagy We previously demonstrated the presence of genotoxic stress-induced alternative autophagy. Although alternative autophagy involves similar morphological features as conventional autophagy, neither Atg5 nor the LC3-conjugation pathway is required. Recently, conventional autophagy was reported to be induced even in the absence of Atg5 in a manner dependent on Syntaxin17. However, genotoxic stress-induced alternative autophagy was not categorized into this type of conventional autophagy, because Syntaxin17 was colocalized with LC3 in starvation-induced conventional autophagy , but it did not colocalize with alternative autophagic structures in genotoxic stress-treated Atg5 KO MEFs . Molecularly, we previously reported several crucial molecules, including Ulk1, Beclin1, and Rab9 8Fig. 6C). Therefore, alternative autophagy does not appear to be regulated by lysosome-localized Dram1. Instead, Golgi-localized Dram1 appears to be a key player in alternative autophagy. This is because (1) some Dram1 localizes on the Golgi membrane , consistent with a previous report Fig. 2G), and (3) Dram1-induced autophagosomes were characteristically associated with Golgi membranes .Dram1 has six membrane spanning regions, and largely localizes on lysosomal membranes. In the autophagy pathway, lysosomes function at the final step to generate autolysosomes. If Dram1 functions on lysosomes, it is expected to function at the final step. However, our ultrastructural analyses showed that Dram1 functions at the earlier step of isolation membrane closure. Furthermore, Dram1 did not alter lysosomal protein expression and maturation . Furthermore, Dram1 expression did not alter lysosomal function, as assessed by lysosensor, nor lysosomal protein expression and maturation . Thus, in the case of Dram1 overexpression, enrichment of Dram1 on the Golgi membrane might be the trigger of autophagosome generation, although the precise mechanisms involved are still to be determined.The mechanism as to how overexpressed Dram1 induces alternative autophagy also remains unclear. When we found the induction of large Lamp2 puncta by Dram1 expression, we first considered them as swollen lysosomes, rather than autolysosomes, and assumed that they were generated by Dram1-induced alteration of lysosomal function. However, this idea was denied by ultrastructural analyses, because Dram1-expressing Atg5 KO MEFs contained isolation membranes and autophagosomes enclosing cargos, rather than swollen lysosomes , anti-Flag , anti-GS28 , anti-p62 , anti-LC3 , anti-Ulk1 , anti-cathepsinL , and anti-V-ATPase G2 . Other chemicals were obtained from Nacalai Tesque .Atg5 KO and p53 KO mice have been described previously 176 cells) using the Neon transfection system (Thermo Fisher Scientific) according to the manufacturer’s instructions.For retrovirus transfection, the retroviral plasmids pLMP-shDram1, pLMP-shUlk1, pLMP-Luciferase (shControl), and MSCVhygro-Keima were introduced into MEFs by retroviral infection. Briefly, Plat-E, retroviral packaging cells were plated, and transfected with retrovirus plasmids by the calcium phosphate transfection method 24 hours later. Virus-containing supernatants were collected at 48, 60, and 72 hours post-transfection and MEFs were infected consecutively three times every 12 hours with 4 μg/mL polybrene. Hygromycin 200 μg/mL) or puromycin (2 μg/mL) selection was started 48 hours after the last infection 0 μg/mL oSynthetic oligo shRNAs for the targeted genes were cloned into the retroviral shRNA expression pLMP vector. The following shRNA target sequences were used.shDram1: AAGAGTTCCTAGTAGTTCAAT; shUlk1: GGGUGGACACAUGCUAAUA; and shLuciferase: ACAAACGCCCTGATCGACAAG.The siRNA sequences used were as follows: mouse Dram1 5'- AAGAGTTCCTAGTAGTTCAAT -3' and control siRNA (Dharmacon siGENOME Non-Targeting siRNA).4). Fixed samples were embedded in Epon 812, and thin sections (70-80 nm) were then cut and stained with uranyl acetate and lead citrate for observation under a JEOL-1010 electron microscope (JEOL) at 80 kV. Fixed adherent cells were sectioned up to 3 μm from the base. The extent of autophagy was assessed on electron micrographs that contained both the nucleus and cytoplasm of individual cells. The area of every autophagic vacuole and total cytoplasmic area were calculated on the enlarged photographs using a planimeter (Planix). For each cell, the autophagic area (%) was calculated as the total area of autophagic vacuoles relative to the cytoplasmic area, and cells with an autophagic area greater than 6% were defined as autophagic cells . The number of autophagic structures was counted in cells that had a long axis of more than 0.8 μm. Autophagy was quantitated in at least 25 cells for each sample, and was confirmed by two additional independent experiments.Attached cells were fixed by a conventional method  and were fixed with 1.5% paraformaldehyde/3% glutaraldehyde in 0.1 M phosphate buffer (pH 7.4). Subsequently, the samples were visualized by confocal microscopy; and were fixed using 1% OsOCells were fixed in 4% formaldehyde, permeabilized in 0.1% Triton X-100 or 0.1% saponin PBS, and stained with primary antibodies followed by Alexa Fluor 488 or Alexa Fluor 568 secondary antibodies (Thermo Fisher Scientific). Cells were mounted in mounting medium with propidium iodide or ProLong Gold antifade reagent with DAPI (Thermo Fisher Scientific) and examined by fluorescence microscopy .Keima-expressing cells were incubated in phenol red-free DMEM. After observing the cells using phase-contrast microscopy, Keima fluorescence was acquired using multiple wavelengths (405 and 555 nm). Ratio (555/405) images of Keima were created and merged with the phase-contrast images. Cells were also stained with Cyto-ID autophagy dye (Enzo) for 30 min at 37°C, followed by fixation in 4% formaldehyde. Cells were mounted in ProLong Gold antifade reagent with DAPI (Thermo Fisher Scientific) and examined using a confocal microscope . The extent of Cyto-ID fluorescence was also analyzed using a flow cytometer (FACS Canto).For microarray analysis, total RNA was purified using TRIzol reagent (Invitrogen) and utilized for preparing cRNA by Message AmpII (Ambion). cRNA was hybridized to Mouse Whole Genome 4 × 44 K microarrays (Agilent Technologies) according to the manufacturer’s instructions. Two biological replicates were performed for each set of experimental conditions. Data were analyzed using Feature Extraction Software (ver. 9.5.3.1) (Agilent). For quantitative RT-PCR analysis, total RNA was purified on an RNeasy column (Qiagen) and utilized for mRNA purification by Turbo Capture8 (Qiagen). Synthesis of cDNA and PCR amplification were performed using the iScript One-step RT-PCR kit with SYBR Green (Bio-Rad Laboratories). Quantitative analysis was performed with the CFX96 real-time PCR system (Bio-Rad). All samples were normalized using ß-actin levels.post hoc test. A p-value of less than 0.05 was considered to indicate a statistically significant difference between two groups. Statistical analyses of nonrandom associations between two categorical variables were analyzed using the Fisher’s exact test.Results are expressed as the mean + standard deviation (SD). Statistical analysis was performed using Prism (GraphPad) software. Comparisons of multiple datasets were performed using one-way ANOVA followed by Tukey’s Click here for supplemental data file.http://www.cell-stress.com/researcharticles/dram1-regulates-dna-damage-induced-alternative-autophagy/.All supplemental data for this article are also available online at