Caenorhabditis elegans. Loss of UNC-116 causes the axon to overshoot its target and UNC-116 gain-of-function causes premature axon termination. We find that loss of the UNC-16/JIP3 kinesin-1 cargo adaptor disrupts axon termination, but loss of kinesin-1 light chain function does not affect axon termination. Genetic analysis indicates that UNC-16 functions with the NEKL-3 kinase to promote axon termination. Consistent with this observation, imaging experiments indicate that loss of UNC-16 and UNC-116 disrupt localization of NEKL-3 in the axon. Moreover, genetic interactions suggest that NEKL-3 promotes axon termination by functioning with RPM-1, a ubiquitin ligase that regulates microtubule stability in the growth cone. These observations support a model where UNC-116 functions with UNC-16 to promote localization of NEKL-3 in the axon. NEKL-3, in turn, functions with the RPM-1 ubiquitin ligase to promote axon termination.KIF5C is a kinesin-1 heavy chain that has been associated with neurodevelopmental disorders. Although the roles of kinesin-1 in axon transport are well known, little is known about how it regulates axon targeting. We report that UNC-116/KIF5C functions with the NEKL-3/NEK6/7 kinase to promote axon targeting in  Summary: The kinesin-1 heavy chain promotes the localization and motility of the NEKL-3 kinase in axons. This process is required for axon targeting and its disruption may cause neurodevelopmental disorders. In recent years, genome sequencing has been used extensively to identify genes that are associated with these disorders. In most cases, this has been accomplished by identifying genes that are enriched for ulations . These sDefects in axon connectivity are thought to be a major underlying cause for NDDs. For example, functional MRI studies indicate that connectivity between brain regions is altered in individuals with autism relative to controls . Moreoveunc-116, the Caenorhabditis elegans ortholog of the KIF5C kinesin-1 heavy chain gene that has been implicated in NDDs including intellectual disability, epilepsy and autism . In neurons, KIF5C can function with the kinesin-1 light chains (KLCs) to transport vesicles and organelles distally along the axon. For example, in mammalian neurons, the KHC uses the KLCs as adaptors to recruit lysosomes and transport them distally along axons  cargo adaptor to interact with two regulators of microtubule stability, the kinase NEKL-3 and the E3 ubiquitin ligase RPM-1. We show that UNC-116, UNC-16 and NEKL-3 are all required for axon termination and that UNC-116 and UNC-16 are required for the localization and motility of NEKL-3 in the axon. Moreover, we also find that NEKL-3 functions with RPM-1 to promote axon termination. We also tested two mutations in UNC-116 that are equivalent to NDD-causing unc-116 (unc-116 loss of function on PLM axon termination (unc-116(e2310) mutation, a Tc5 transposon insertion that disrupts the C-terminal tail of UNC-116 (unc-116(e2310) mutation causes PLM axon overextension with a penetrance of 30% (unc-116(rh24sb79) mutation, which is a hypomorph and revertant of the unc-116(rh24) gain-of-function mutation (unc-116(rh24sb79) also causes PLM axon overextension with a penetrance of 28.5%  gain-of-function mutation  gain-of-function mutants, we observed an early termination phenotype with a penetrance of 44.5%, where the PLM axon terminates before reaching the center of the vulva (unc-116(e2310) or unc-116(rh24sb79) loss-of-function mutants (unc-116(rh24) mutation mostly caused early axon termination, we note that in ∼16% of cases it caused PLM axon overextension  mutation was lethal without maternal rescue, precluding analysis of homozygotes. However, we found that in heterozygous unc-116(E239K)/+ mutants, the PLM axon often failed to terminate normally and overextended past the ALM cell body (unc-16(R288W) mutation was also lethal, but showed maternal rescue, allowing us to analyze maternally rescued homozygotes. We found that maternally rescued unc-16(R288W) mutants had PLM axon overextension with a penetrance of 75% , which in turn bind other cargo, such as vesicles and organelles A Deng e. KinesinC. elegans UNC-16 JIP protein and both of the C. elegans KLCs. For these experiments, we used three different unc-16 alleles to disrupt the function of the UNC-16 JIP protein and found that all caused substantial axon termination defects (klc-1(ok2609) null allele and the klc-2(km11) hypomorphic allele. We found that axon termination defects were not caused by a klc-1(ok2609) mutation, a klc-2(km11) mutation, or by a klc-1(ok2609); klc-2(km11) double mutation (klc-2(km11) allele is hypomorphic, we note that it causes defects in synaptic vesicle localization as well as defects in transport and localization of mitochondria in axons that are comparable in penetrance with that caused by the unc-116(e2310) allele  allele . TherefoTo investigate the mechanism of UNC-16 in axon termination, we sought to identify proteins that function with UNC-16 to promote axon termination. We considered the NEKL-3 kinase as a candidate because ongoing work in our laboratory had suggested that NEKL-3 is required for PLM axon termination. Moreover, the NEK6 and NEK7 orthologs of NEKL-3 have been implicated in the regulation of microtubule stability during mitosis in the HeLa cell line . As micrnekl-3(gk506) null mutants at the late L1 stage. The nekl-3(gk506) allele is lethal and is maintained with an extrachromosomal array that contains copies of the nekl-3 gene. We analyzed nekl-3(gk506) homozygotes that had lost the extrachromosomal copies of the nekl-3 gene and found PLM axon termination defects with a penetrance of 36%  null allele were partially rescued by the Pmec-7::nekl-3::scarlet transgene  mutants and unc-16(e109); nekl-3(gk506) double mutants at the late L1 stage. We found that unc-16(e109); nekl-3(gk506) double null mutants had a penetrance that was not enhanced relative to unc-16(e109) single null mutants ; nekl-3(gk506) double mutant strain. As an alternative, we used the unc-116(rh24) gain-of-function allele to ask whether the early axon termination phenotype caused by this mutation can be suppressed by the loss of nekl-3 function. Consistent with this prediction, we found that the penetrance of early axon termination in unc-116(rh24); nekl-3(gk506) double mutants was significantly reduced relative to unc-116(rh24) single mutants (. This observation indicates that the early termination defects caused by the unc-116(rh24) allele are dependent on NEKL-3 function. Moreover, this observation supports a model whereby UNC-116 functions upstream of NEKL-3 to promote axon termination.To test the idea that NEKL-3 functions with UNC-116, we attempted, unsuccessfully, to construct an  mutants E. This oMovies 1-3). We observed bidirectional transport of NEKL-3::SCARLET puncta (Movies 1-3). We also noticed a marked difference in NEKL-3 motility between the proximal axon (50 µm closest to cell body) and distal axon (50 µm closest to the axon tip) segments  mutants (unc-116(e2310) mutants, we found a 65% reduction in puncta number relative to wild type (unc-116(e2310) mutation also caused a decrease in the proportion of distal axon segments that display motile NEKL-3::SCARLET puncta (unc-116(rh24sb79) loss-of-function mutation and found similar results (klc-1(ok2609); klc-2(km11) double mutants, consistent with our finding that this double mutant does not affect PLM axon termination  contained an average of 6.1±0.4 NEKL-3::SCARLET punta  gain-of-function mutants. As described above, this allele mostly produces early PLM axon termination defects but can sometimes produce PLM axon overextension defects. Therefore, we separately analyzed NEKL-3:SCARLET puncta in unc-116(rh24) mutants with early PLM termination and in unc-116(rh24) mutants with overextended PLM axons (unc-116(rh24) mutants that had PLM overextension defects. In these mutants, we found that the number of NEKL-3::SCARLET puncta was reduced relative to wild type in both the distal and proximal axon segments  and unc-16(e109) loss-of-function mutations caused a reduction of the number of NEKL-3::SCARLET puncta in the PLM proximal axon  allele and the nekl-3(gk506) allele, both of which are thought to be null alleles (nekl-3(gk506); rpm-1(ok364) double mutants was not significantly enhanced relative to rpm-1(ok364) single mutants. (rpm-1 functions in the same pathway as nekl-3 to promote axon termination.RPM-1 and its ortholog Phr1 (Mycbp2) are ubiquitin ligases that can regulate axon termination . The rol alleles . We founmutants. A. These nekl-3 functions with the genes that encode the FSN-1 and GLO-4 proteins, we constructed double mutants between nekl-3(gk506) and null alleles for fsn-1 and glo-4. We found that the penetrance of axon termination defects in nekl-3(gk506); fsn-1(gk429) was no greater than the penetrance of axon termination defects in fsn-1(gk429) single mutants (nekl-3(gk506); glo-4(ok623) was no greater than the penetrance of axon termination defects in glo-4(ok623) single mutants  to promote polarization . LikewisDrosophila neurons the KHC can transport mitochondria independently of the KLCs  hypomorphic allele causes defects in the localization of synaptic vesicles and mitochondria that are comparable with those caused by the unc-116(e2310) allele (klc-2(km11) allele does not cause any defects in axon termination. Together, these observations suggest that defects in the localization of synaptic vesicles and mitochondria do not underlie the axon termination defects that are caused by loss of unc-116 function. Moreover, they also suggest that the role of UNC-116 in axon termination might not involve the KLCs.Our results reveal a novel role for the KHCs in axon termination and suggest that this process might occur independently of the KLCs. We report that a loss-of-function mutation in the ) allele . DespiteThe best-known function of UNC-16 involves retrograde transport of vesicles and organelles. For example, UNC-16 binds directly to the retrograde motor dynein and can mediate the retrograde transport of APL-1 . MoreoveA less studied aspect of UNC-16 function involves anterograde transport. The role of UNC-16 in anterograde transport occurs as a result of its binding to kinesin-1. For example, dynein can be transported anteriorly by interaction with kinesin-1 and UNC-16 . Moreoveunc-16 mutants decrease the activity of NEKL-3 puncta in the distal axon without altering the number of puncta. In this regard, we note that in the proximal axon NEKL-3 is motile, whereas in the distal axon it is stable. We speculate that motile NEKL-3 in the proximal axon could be important to deliver new NEKL-3 molecules to the distal axon. If this proximal motility is compromised, the stable NEKL-3 in the distal axon could become older and less active.We report a novel role for UNC-16 in axon development that is consistent with anterograde transport of the NEKL-3 kinase. We find that UNC-16 is required for axon termination and that loss of UNC-16 decreases the number of NEKL-3 puncta in the proximal axon. Together, these observations suggest that UNC-16 can promote transport of NEKL-3 into the proximal axon. However, it is surprising that loss of UNC-16 function does not alter the number of NEKL-3 puncta in the distal axon. One possible explanation for this observation is that C. elegans -agar plates using standard procedures. All experiments were performed on hermaphrodites. Alleles used in the study include: wild-type N2, unc-116(syb4566))/hT2 [also known as unc-116(E239K)], unc-116(syb4669)/hT2 [also known as unc-116(R288W)], unc-116(e2310), unc-116(rh24sb79), klc-1(ok2609), klc-2(km11), unc-116(rh24), nekl-3(gk506) mnEx174 [Pnekl-3::nekl-3], rpm-1(ok364), fsn-1(gk429), glo-4(ok623), unc-16(e109) and unc-16(ce483). Double mutants were constructed using standard methods. Briefly, male worms were placed with hermaphrodites and allowed to mate. Mutations were selected in subsequent generations by phenotype or by PCR assay. The unc-116(syb4566) and unc-116(syb4669) mutant alleles were obtained from SunyBiotech. The unc-116(rh24sb79) mutant allele was obtained from Paul Mains . The nekl-3(gk506) allele was obtained from David Fay . The jsIs973 transgene was obtained from Michael Nonet . The cueEx31 transgene was created by injecting a mixture of DNA plasmids encoding Pmec-7::nekl-3::scarlet (2 ng/µl), Podr-1::rfp (50 ng/µl) along with an empty pBluescript vector (50 ng/µl). Pmec-7::nekl-3::scarlet plasmid was created by PCR and Gibson assembly. The Podr-1::rfp plasmid was obtained from Cori Bargmann . The CueSi33 transgene is a single copy insertion of Pmec-7::nekl-3::scarlet::unc-54 3′ untranslated region that was created by recombination-mediated cassette exchange using the pLF3FShC integration vector (Addgene plasmid #153083) and the jsTi1493 landing pad on chromosome IV (Pmec-7::nekl-3::scarlet::unc-54 plasmid was synthesized by Twist Bioscience. All other mutants and transgenic strains were obtained from the Caenorhabditis Genetics Center (CGC).osome IV . The PmeC. elegans were randomly picked from plates and mounted on a 5% agarose pad, anesthetized with levamisole, and observed with a 40× objective on a Zeiss Axio Imager M2 microscope. PLM axons were observed using the jsIs973 and muIs32 transgenes, which encode RFP and GFP fluorescent proteins, respectively, and use an mec-7 promoter to drive expression of these proteins in all mechanosensory neurons. A PLM axon was considered overextended if the axon tip terminated anteriorly to the ALM cell body. A PLM axon was considered to terminate early if the axon tip did not cross the center of the vulva. Data for each genotype was collected from two or more generations and all data collected were included in our analysis.To determine the PLM axon termination phenotypes, unc-116(R288W) and the unc-116(E239K) mutations were maintained over the hT2 balancer. For the unc-116(R288W) mutation, we analyzed homozygous progeny that had been rescued by a maternal contribution from heterozygous parents. For the unc-116(E239K) mutation, we were unable to obtain maternally rescued homozygotes, and therefore we analyzed unc-116(E239K)/hT2 heterozygotes. The nekl-3(gk506) mutation is lethal and was maintained as a homozygous strain rescued by an extrachromosomal Pnekl-3::nekl-3 transgene and progeny that had lost the transgene were analyzed.For analysis of axon phenotypes in lethal mutants, we used genetic balancers and rescuing transgenes to maintain the mutations. The cueEx31 transgene, which encodes Pmec-7::nekl-3::scarlet. The number and motility of NEKL-3 puncta were analyzed by mounting L4 stage C. elegans, anesthetized with levamisole, on a 5% agarose pad and observing with a 40× water objective on a Nikon ECLIPSE Ti microscope equipped with the X-Light V2 L-FOV spinning disk system within 30 min of levamisole application. To determine the percentage of PLM axons with movement of NEKL-3, videos were recorded using the Nikon Elements program at 40× magnification on the spinning disk confocal microscope. Videos were created by imaging a three-layered z-stack every 10 s for 2 min total. Puncta size and movement were analyzed using ImageJ software. Only puncta that were greater than 0.6 µm were analyzed. A punctum was considered motile when it moved anteriorly, posteriorly or in both directions at any time during the 2 min recording period. A PLM axon segment (50 µm segment closest to the cell body or 50 µm segment closest to the axon tip) was considered to have movement if at least one NEKL-3::SCARLET punctum within the segment moved within 2 min. To generate kymographs, videos were made by recording a single-layered image at 1 s intervals for 2 min total. Kymographs were generated using ImageJ software.To observe localization of NEKL-3::SCARLET in the PLM axon we used the Pmec7::nekl-3::scarlet expression, an unpaired two-tailed independent samples t-test was used at a 95% CI.Significant PLM axon termination defects were analyzed using the ‘N−1’ Chi-squared test at a 95% confidence interval (CI) to compare with the control. We analyzed 200 axons for each allele unless otherwise stated. To determine significant differences among strains with 10.1242/develop.201654_sup1Supplementary informationClick here for additional data file.


Penicillimonavirus gammaplasmoparae), a fungi-associated mymonavirus, in grapevine plants showing an unusual upward curling symptomatology on the leaves and premature decline. Mymonaviridae is a family comprising nine genera of negative-sense single-stranded RNA viruses infecting filamentous fungi, although few of them have been associated with oomycetes, plants, and insects. Although the first mymonavirus genome description was reported a decade ago, the genome organization of several genera in the family, including the genus Penicillimonavirus, has remained unclear to date. We have determined the complete genome of P. gammaplasmoparae, which represents the first complete genomic sequence for this genus. Moreover, we provide strong evidence that P. gammaplasmoparae genome is bipartite and comprises two RNA molecules of around 6150 and 4560 nt. Our results indicate that the grapevine powdery mildew pathogen, Erysiphe necator, was also present in the analyzed plants and suggest P. gammaplasmoparae could be infecting this fungus. However, whether the fungus and/or the mycovirus are associated with the symptomatology that initially prompted these efforts remains to be determined.In this study, we identified Plasmopara-viticola-lesion-associated mononegaambi virus 3 (recently classified as  Vitis vinifera L.) is one of the most important crops cultivated worldwide, with its efficient and sustainable production seen as a major goal for the agriculture and industry systems of many countries. Grapevine plants can be infected by many different pathogens that may cause important losses in grape quality and production, thus threatening viticulture. Among them, 35 viral species have been associated with major grapevine diseases, like infectious degeneration and decline, leafroll, rugose wood, fleck, red-blotch, leaf mottling and deformation, vein clearing, and Roditis leaf discoloration [Grapevine  technologies have heavily contributed to the exponential increase in plant virome knowledge achieved during the last two decades ,13,14,15Mymonaviridae is a recently described virus family, which comprises nine genera Auricularimonavirus, Botrytimonavirus, Lentimonavirus, Penicillimonavirus, Sclerotimonavirus, Hubramonavirus, Phyllomonavirus, Plasmopamonavirus, and Rhizomonavirus and 50 species [Mymonaviridae species have been reported infecting fungi, several species like Apple virus B, Gisinge virus, Kiln Barn virus, and Hubei rhabdo-like virus 4 have been found associated with plants or insects [ species . Althoug insects ,19,20, sSclerotimonavirus sclerotinae [Hubramonavirus, Penicillimonavirus, Plasmopamonavirus, Phyllomonavirus, and Rhizomonavirus) are therefore unknown since the available genomic sequences are incomplete. No bipartite genomes have been described in the Mymonaviridae family to date.The typical mymonavirus genome organization was described based on the first species reported, erotinae , and conerotinae . Other rerotinae . The prePenicillimonavirus gammaplasmoparae, a recently identified fungi-associated mymonavirus species. Further experiments demonstrated that the genome of this virus is bipartite, and we report here the first complete genomic sequence of Penicillimonavirus gammaplasmoparae, the first bipartite mymonavirus described to date, as well as efforts to identify its host and possible involvement in the studied syndrome.The present study was initiated in an effort to understand the etiology of an unusual grapevine leafroll syndrome. A total RNAseq analysis showed the presence in these plants of In 2018, five symptomatic grapevine plants, cultivar Crimson seedless, showing unusual leaf rolling symptoms, were collected from a private vineyard located in Alicante (Spain). The leaves showed a very unusual upward curling phenotype a–d as weThe five plants were monitored during three growth seasons (2019–2021) and retained the same unusual symptomatology for the whole period. They eventually suffered a decline and died.In 2019, total RNA was extracted from leaves from two of the symptomatic grapevines described above (Pin1 and IVIA 49.3) and analyzed using HTS. At the time they were sampled for RNA extraction, the two plants also showed conspicuous grapevine powdery mildew symptoms. HTS analysis yielded 60,597,198 and 49,645,466 reads, respectively, after trimming and quality control steps. A grapevine genome subtraction was then performed by mapping the reads against the grapevine genome and the 8,642,008/5,609,129 remaining reads subjected to de novo assembly. A total of 14,905 (Pin1) and 34,061 (IVIA 49.3) contigs larger than 200 nt were obtained and subsequently analyzed using BLASTN against the RefSeq viral database. The results indicated the presence in the samples of 12 known viruses, including grapevine viruses and mycoviruses , none ofErysiphe necator associated negative-stranded RNA virus 2 (NC_077044) and 99.82% nt identity with that of Plasmopara-viticola-lesion-associated mononegaambi virus 3 (NC_076453), indicating these two names to be synonymous. Indeed, this synonymy has been recognized, and following the transition to viral binomial nomenclature, the corresponding virus species has recently been named Penicillimonavirus gammaplasmoparae and included in the new genus Penicillimonavirus in the family Mymonaviridae [Among all assembled contigs, the bioinformatic analysis revealed the presence in both samples, Pin1 and IVIA 49.3, of one contig of 6155 nt , which showed 99.8% nucleotide identity with the genomic RNA of the recently described Mymonaviridae, raising the possibility that RNA 1 may represent the second genomic segment of a bipartite mymonavirus. Consistent with this hypothesis, BLASTP analysis of the putative codified protein (pII) shows a higher percentage of identity (39.2%) with the nucleoprotein (NP) of the more recently released Magnaporthe oryzae mymonavirus 1 genome (GenBank OL415836).Interestingly, BLASTX analysis also revealed the presence in both grapevine samples of a smaller contig of 4564 nt , referred to here as RNA 1, which encodes a protein showing 24.1% amino acid identity with hypothetical protein 2 of Hubei rhabdo-like virus 4 (NC_032783) a member of the family The analysis of RNA 1 sequence showed the presence of five putative non-overlapping ORFs separated by non-coding intergenic regions: ORF-I encoding a protein of 292 aa with a predicted molecular mass of 31.8 kDa (named pI); ORF-II encoding a protein of 415 aa with a predicted molecular mass of 46.9 kDa ; ORF-III encoding a protein of 148 aa with a molecular mass of 17 kDa (named pIII); ORF-IV encoding a protein of 202 aa with a molecular mass of 22.1 kDa (named pIV); and ORF-V encoding a protein of 256 aa with a molecular mass of 29.6 kDa (named pV). ORFs III-V are located in the +2 reading frame, while ORF-I and ORF-II are in the +1 and +3 reading frames, respectively.Sanger sequencing confirmation of the presence of RNA 1 and RNA 2 in the sampled grapevine tissues and of their sequence was performed using specific primers designed for this study . The simP. gammaplasmoparae was first identified using HTS [sing HTS , and theThe association of RNA 1 and RNA 2 is also supported by the finding of common sequence features. Interestingly, both RNAs contain a shared sequence (SS) of 23 nucleotides, with just one single nucleotide polymorphism, in their 3′ untranslated region (3′UTR) b.In addition, when IGRs and terminal non-coding sequences of both RNAs were analyzed, conserved sequences similar to gene junction sequences of other mymonaviruses, were found in the RNA1 IGRs. Alignment of these conserved sequences in a 3′-to-5′orientation and of similar sequences found in the terminal non-coding regions of both RNAs showed the presence of two conserved elements, named element 1 and element 2, in this study. Element 1 is characterized by the sequence 3′-UUU(U/A)(U/A/G)-5′, while element 2, separated by two nucleotides from element 1, contains the sequence 3′-AA(C/A)(A/U)(C/A)(C/A)-5′.Mymonaviridae.Elements 1 and 2 are thus present at the intergenic regions between ORFs I-V in RNA 1 and in the 5′ untranslated region (5′UTR) of RNAs 1 and 2 a. SimilaP. gammaplasmoparae by RT-PCR using specific primers targeting RNA 1 or RNA 2  were extracted and analyzed for the presence of or RNA 2 . Interesor RNA 2 .Transmission electron microscopy (TEM) performed on the RNA 1/RNA 2 positive gradient fractions revealed the presence of viral particles with a single morphology. These virions exhibited a quasi-spherical morphology with a diameter ranging between 39 and 55 nm and frequently showed crescent or horseshoe-shaped outer zones less dense to electrons .To further support our findings indicating the bipartite nature of the penicillimonavirus genome, we studied the phylogenetic relationships between several mymonavirus belonging to different genera using the amino acid sequence of the NP, the sole RNA1-encoded protein showing sequence conservation between the different viruses.Hubramonavirus, Sclerotimonavirus, Auricularimonavirus, Lentimonavirus, and Plasmopamonavirus.During the completion of the present study, several sequences that could correspond to the RNA1 of other Plasmopara-viticola-lesion-associated mononegaambi viruses became available  and were therefore included in the analysis. The sequences of NPs of other members of the following mymonavirus genera were also included: P. gammaplasmoparae NP clusters with those of other penicillimonaviruses, strongly supporting the notion of a bipartite genome for all members of this genus (The results show that the is genus .Plasmopara viticola (downy mildew) infection but were clearly infected by Erysiphe necator, the grapevine powdery mildew agent. In order to confirm this, we performed a bioinformatic analysis by mapping the reads obtained for the Pin 1 and IVIA 49.3 plants against the P. viticola and E. necator genomes. The results of this analysis indicated the presence of E. necator in both grapevine plants, with a high coverage of greater than 97% of the fungus genome  and the absence of Plasmopara viticola in both samples. Raw data from the pipeline results are presented in the Cladosporium sphaerospermum and Wallemia sebi and the oomycete Albugo laibachii  causal agent(s) for the symptoms that initially prompted these efforts. They, however, suggest that P. gammaplasmoparae might be a virus of E. necator. We cannot, however, rule out the possibility that it infects either grapevine or another unidentified organism associated with the analyzed plants. Unfortunately, the decline and premature death of the grapevine plants have prevented us from performing additional studies in order to explore these possibilities.This study was initiated investigating the etiology of unusual grapevine symptoms not previously reported, with a conspicuous upward rolling of leaves and eventual plant decline and death. The HTS analysis performed on two of the symptomatic plants showed the presence of several grapevine or fungal viruses not known to be associated with such symptoms, including Mymonaviridae members. This contig harbors 5 ORFs, one of which codes for a nucleoprotein (NP) with affinities to those of Mymonaviridae members. It also shows a typical genomic organization with conserved IGRs between the various genes that show homologies with those of other family members. On the other hand, no homologies could be identified with any protein in GenBank for the other four hypothetical proteins encoded on this RNA.In the course of this work, our bioinformatic analyses recovered a 4.6 kb contig with some structural and phylogenetic properties linking it to P. gammaplasmoparae genomic RNA and this novel contig are, in fact, the two RNA segments of a bipartite genome: (i) as outlined just above, the RNA 1 contig has structural and phylogenetic properties linking it to Mymonaviridae members (ii) both RNAs (RNA 1 and RNA 2) share conserved terminal regions including a shared sequence of 23 nucleotides nearly totally conserved, a feature typical of the genomic RNAs of viruses with divided genomes [P. gammaplasmoparae as Erysiphe necator associated negative-stranded RNA virus 2 and as Plasmopara-viticola-lesion-associated mononegaambi virus 3 [Penicillimonavirus gammaplasmoparaee, a member of the family Mymonaviridae with a bipartite genome. We predict this bipartite genome feature as a rule rather than the exception for the genus Penicillimonavirus.Here, we present strong evidence that supports the notion the known  genomes  (iii) th virus 3 ,23 accorMymonaviridae on the basis of sequence homologies between Trichoderma harzianum mycovirus 1 RNA and a molecule they tentatively associate with Trichoderma harzianum mycovirus 2 RNA [Penicillimonavirus or Plasmopamonavirus genus members were identified, suggesting that a bipartite genome might be a feature of these two genera .During the final redactional stage of this study, a publication also hypothesized the existence of divided genomes for some us 2 RNA . They alPenicillimonavirus gammaplasmoparaee and the demonstration that a single type of viral particles is associated with these two molecules provide, we feel, much more convincing elements in favor of a bipartite genome structure than the co-occurrence and homologies brought forward by these authors.The identification of conserved sequence elements between the two genomic RNAs of Rhabdoviridae family, with most genera having undivided genomes, but the Dichorhavirus and Varicosavirus genera being characterized by bi-segmented genomes [It should be noted that this situation of a group of negative-sense viruses that contains viruses with either a single genomic molecule or a genome divided between two genomic segments is exactly that found in the  genomes .Penicillimonavirus gammaplasmoparae, and conclusively demonstrates its bipartite genome nature, a novel feature in the Mymonaviridae family. As suggested by Pagnoni et al. [Penicillimonavirus.Overall, this study represents the complete genome characterization of i et al. , we predFive symptomatic grapevine plants, cultivar Crimson seedless, showing upward leaf rolling, were collected from a private vineyard located in Alicante, potted and maintained in a greenhouse, and monitored during three growth seasons.Leaf tissue from each symptomatic plant sample was placed in individual plastic bags  and ground with extraction buffer (PBS containing 0.2% DIECA and 2% PVP-10) in a 1:5 ratio (w:v). Total RNA was purified from 200 µL of homogenate using the Plant/Fungi total RNA purification kit  according to the manufacturer’s manual, including DNase treatment using the RNase-Free DNase I Kit . Purified RNA was quantified with a DeNovix DS-11 spectrophotometer  to determine the concentrations and stored at −80 °C until subsequent analysis.RNA quality control, library construction, and HTS sequencing on a NextSeq 500 platform (paired 2 × 150 nt) were performed at Macrogen Inc. . Complementary DNA (cDNA) was synthesized from each RNA extraction for library preparation using TruSeq Stranded Total RNA LT Sample Prep Kit (Plant). Library protocol preparation used for it was TruSeq Stranded Total RNA Sample Prep Guide, Part #15031048 Rev.−3 against a local virus database. Mapping analysis of the Pin1 and 49.3 reads against the reference the genomes of P. viticola (GCA_001695595.3) and E. necator (GCA_016906895.1) was performed with the Geneious Prime 2021 software . In addition, metagenomics analysis was performed using the open-source cloud-based platform IDseq v8.2 [HTS raw reads data obtained from samples Pin1 and IVIA 49.3 were analyzed as follows: trimming and quality control were performed with CLC Genomics Workbench v.20.0.4 software . Host genome subtraction was performed using the reference genome GCF_000003745.3 and including mitochondrion (FM179380) and chloroplast (DQ424856) complete organelle genomes. De novo assembly contigs were performed using CLC Genomics Workbench. De novo contigs larger than 200 nt were annotated using BLAST analysis (BLASTN/X) with an e-value cut-off of 10seq v8.2 .In order to confirm the sequences assembled from HTS data, 22 primer pairs were designed on the RNA 1 and RNA 2 HTS-recovered sequences. Total RNA from the IVIA 49.3 grapevine was used as a template. All RT-PCR reactions were performed on the Applied Biosystems Veriti™ thermal cycler  using AgPath-ID One-step RT-PCR kit  on a total volume of 25 µL, containing 3 µL of total RNA as template and 0.5 μM of each primer. RT-PCR protocol consisted of one step of 45 °C for 30 min and 95 °C for 10 min, followed by 45 cycles of amplification . Amplicons were directly sequenced by Sanger, and sequences were aligned using Mega X software .g for 30 min in a Beckman Coulter centrifuge (rotor type JA-20). The supernatant recovered from this low-speed centrifugation was transferred to a new tube and ultracentrifuged at 80,000× g for 1 h in a Beckman L-80 ultracentrifuge (rotor type 70.1 Ti). The supernatant was discarded, and the pellet resuspended in 3 mL of gradient buffer  and loaded on the top of 20 mL of a continuous sucrose gradient (30–70%) prepared in the same buffer and centrifuged 214,000× g for 20 h in a Beckman L-80 ultracentrifuge (rotor type TFT 50.38). Gradient fractions of 500 µL were collected using a peristaltic pump system CTP 100  and a fraction collector FRAC 100 . RNA from each fraction was extracted and analyzed using RT-PCR as described above, in order to detect the presence of RNA 1 and RNA 2 using primers RNA1-6F and RNA1-580R, as well as primers RNA2-1F and RNA2-554R  extraction buffer supplied with NaCl (150 mM) pH 7.5. First, low-speed centrifugation was carried out at 5000× NA2-554R .Transmission electron microscopy (TEM) was performed from the collected RNA 1 and RNA 2-positive gradient fractions at the microscopy department of Centro de Investigación Principe Felipe . Glow Discharge  was applied over Carbon-coated copper grids, and grids were immediately placed on top of sample drops for 10 min. After two brief distilled water washes, grids were contrasted with 1% uranyl acetate for 5 min. Excess fluid was removed, and grids were allowed to dry before examination with a transmission electron microscope FEI Tecnai G2 Spirit . Finally, photomicrographs were obtained under a transmission electron microscope (FEI Tecnai G2 Spirit Biotwin) using a digital camera Morada Soft Imaging System .A phylogenetic analysis was performed on 12 complete NP sequences belonging to different mymonaviruses: OP042367, WKE35255, WKE35269, WKE35277, OQ550534, NC_032783, MN744716, NC_076025, NC_076899, MF276904, NC_025383, ON746412). The multiple alignment was performed using MAFFT (V7.490) implemented in Geneious Prime 2021. The phylogenetic tree was reconstructed in MEGA X , using t


RNA features a highly negatively charged phosphate backbone that attracts a cloud of counter-ions that reduce the electrostatic repulsion in a concentration dependent manner. Ion concentrations thus have a large influence on folding and stability of RNA structures. Despite their well-documented effects, salt effects are not handled consistently by currently available secondary structure prediction algorithms. Combining Debye-Hückel potentials for line charges and Manning’s counter-ion condensation theory, Einert et al.  modeled the energetic contributions of monovalent cations on loops and helices.The model of Einert et al. is adapted to match the structure of the dynamic programming recursion of RNA secondary structure prediction algorithms. An empirical term describing the salt dependence of the duplex initiation energy is added to improve co-folding predictions for two or more RNA strands. The slightly modified model is implemented in the ViennaRNA package in such way that only the energy parameters but not the algorithmic structure is affected. A comparison with data from the literature show that predicted free energies and melting temperatures are in reasonable agreement with experiments.The new feature in the ViennaRNA package makes it possible to study effects of salt concentrations on RNA folding in a systematic manner. Strictly speaking, the model pertains only to mono-valent cations, and thus covers the most important parameter, i.e., the NaCl concentration. It remains a question for future research to what extent unspecific effects of bi- and tri-valent cations can be approximated in a similar manner.Corrections for the concentration of monovalent cations are available in the ViennaRNA package starting from version 2.6.0.The online version contains supplementary material available at 10.1186/s13015-023-00236-0. Nucleic acids are highly negatively charged molecules since their phosphate backbone carries one negative charge per nucleotide. Structure formation brings these charges into close proximity and thus incurs substantial electrostatic penalties on the secondary and tertiary structure level. Nucleic acid–ion interactions also provide large interaction energies and therefore contribute decisively to the folding of RNA and DNA and to their interactions with ligands and macromolecule partners . Counter account . Althoug account  and UNAf account  offer a ment}Mg+ ) offeredment}Mg+ , the eneCations affect RNA structure in two different ways. The electrostatic stabilization of the structure due to charge screening is at least conceptually independent of the chemical nature and charge of the individual cations. In addition, divalent cations and in particular ng sites . Quantitng sites  for a stIf three-dimensional structures are known, the nonlinear Poisson-Boltzmann equation can be solved to obtain electrostatic potentials of RNA molecules in solution –14. Thisument}4)  and tighument}4)  accountiic helix  and loopic helix  energy cTo derive a suitably simple model, Einert and Netz  proposedfunction .Here we implement in the ViennaRNA package  the DebyThe model of  considerL backbone bonds can be written, at the level of the Debye-Hückel approximation, in the form  res resAdditexes. In , the autrom from . Figure en model , 17. Tab


RNA sequencing (RNA-seq) can be applied to diverse tasks including quantifying gene expression, discovering quantitative trait loci and identifying gene fusion events. Although RNA-seq can detect germline variants, the complexities of variable transcript abundance, target capture and amplification introduce challenging sources of error. Here, we extend DeepVariant, a deep-learning-based variant caller, to learn and account for the unique challenges presented by RNA-seq data. Our DeepVariant RNA-seq model produces highly accurate variant calls from RNA-sequencing data, and outperforms existing approaches such as Platypus and GATK. We examine factors that influence accuracy, how our model addresses RNA editing events and how additional thresholding can be used to facilitate our models’ use in a production pipeline.Bioinformatics Advances online. RNA-seq is a widely used method for transcriptome analysis. The technology is often used to study gene expression in a variety of contexts. Expression can be examined over time  approaches. For example, alignment coverage, a proxy for gene expression, exhibits more dynamic range in RNA-seq than DNA-seq. Coverage can range from little to none to excessively high levels. Low coverage can reduce or eliminate evidence of germline variants and complicate genotype classification  that splits reads into multiple segments by removing their SKIP operations. This change allowed DeepVariant to process RNA-seq data efficiently . RNA-seq BAMs, in contrast to DNA-seq BAMs, contain SKIP CIGAR operations which are used to represent skipped regions  in the riciently . With thOnce we updated DeepVariant to process RNA-seq data, we trained models using two datasets. For the first model, we used samples from the Genome in a Bottle consortium (GIAB) (We used RNA-seq data from GTEx V8 (phs000424.v8.p2) to train our second model (‘DV RNA-seq [GTEx]’). The GTEx consortium has extensively characterized human tissue samples from postmortem donors, and provides whole-genome DNA-seq and RNA-seq from a large collection of tissues. RNA-seq data from the GTEx project was sequenced using the Illumina TruSeq non-stranded polyA+ selection protocol. We assigned GTEx donors to a train or test group, and randomly selected 1000 RNA-seq samples from our train donor group . Variants were required to have a minimum depth of 3× in the RNA-seq sample in order to be considered.Separately, we also sequenced HG002  and HG005 (GM26107) on the NovaSeq platform using an mRNA poly-A-based library preparation. These samples were processed using the nf-core/rnaseq pipeline , which was held out from training for both models. Performance was stratified across CDS, exon, transcript, gene, high-confidence GIAB regions (GIAB-hc), chr20  and regions with a minimum of 3× depth. Our initial analysis here examines F1 scores as they are sensitive to both precision and recall and enable comparison of models using a single value. Our subsequent analyses consider precision and sensitivity separately. The GTEx model outperforms the GIAB model, except for INDEL exon regions . We chosWe compared the performance of our GTEx and GIAB models with several other RNA-seq variant calling approaches using our GTEx-derived ‘silver truth’ dataset. Our comparison includes the warm-start model trained on whole-exome data and used for training  to see how much training on RNA-seq data improved our model. We also tested GATK HaplotypeCaller (v4.2.4.0) . We alsoWe used our GTEx test data to compare performance across variant callers. Similar to our model comparison analysis, we stratified across several regions intersected at a 3× minimum coverage. We observe that DV RNA-seq [GTEx] achieves the highest median F1 score across all region stratifications , performWe also evaluated the performance on chr20 that was held out from training and again found DV RNA-seq [GTEx] to outperform alternative variant callers .Next, we investigated factors that impact DV RNA-seq [GTEx] performance, focusing on the use case of calling SNPs in CDS regions. We examined F1 scores after restricting variant calling to sites at a series of coverage thresholds in CDS regions . For eacAssuming variants are detectable at a minimum depth of 3×, we asked what proportion of these detectable variants remain across minimum coverage thresholds . AlthougR = 0.26; P = 0.00015; R = 0.16, P = 0.021; We also examined the correlation between the RNA integrity number (RIN) and RNA-seq sample accuracy. An RIN score can be calculated for a given RNA sample using an algorithm that examines the ratio of 28S and 18S ribosomal RNA. RIN scores range from 1  to 10 (intact) , which can result in Adenosine-to-inosine (A-to-I) changes . These AWe asked how the variants compared with the label dataset based on the base change observed and whether they were present in the REDIportal database. Interestingly, we observe that the DV RNA-seq [GTEx] model has a large reduction in the number of FPs at REDIportal sites compared to the DV WES model. In When implementing a variant caller into a production analysis pipeline, it is convenient to select a cutoff based on quality scores to maintain a desired true positive rate (TPR) or false discovery rate (FDR) based on the application. For example, the 1000 Genomes Project FDR target was 5%  . In thisVariability between samples is expected due to both random sampling during the sequencing process and variation among samples. Therefore, cutoff scores should be established by examining multiple replicates or samples using validated variants. We examined 3 HG002 cell lines: GM24385, a lymphoblastoid cell line (LCL), and two induced pluripotent stem cell lines, GM26105 and GM27730. These cell lines were evaluated with the DV RNA-seq [GTEx] model in high-confidence GIAB CDS regions to identify an appropriate cutoff score that would maintain an FDR of 1.5% while retaining high sensitivity.We considered two quality scores to use for setting a cutoff: Genotype quality (GQ) and quality (QUAL). GQ describes the probability of a variant call being incorrect, whereas QUAL describes the probability a given site is not a variant. We found GQ worked well for both SNPs and INDELs for thresholding in comparison to QUAL scores, and decided on a cutoff of GQ ≥ 18 GQ: . This thFinally, we evaluated the GQ ≥ 18 threshold using an LCL line from HG005  to see how it would perform against unfiltered models and other RNA-seq variant callers. We again examined performance in high-confidence GIAB regions intersected with CDS regions. We find that DV RNA-seq [GTEx] GQ ≥ 18 is able to achieve a precision of 0.998 on SNPs and 0.989 on INDELs with similar levels of sensitivity we observed when deriving our cutoff .We examined the runtime performance of our DeepVariant RNA-seq models against existing approaches . All runThis study introduces DeepVariant RNA-seq, an extension of DeepVariant that enables germline variant calling using Illumina short-read RNA-seq data. To the best of our knowledge, this is the first deep-learning-based RNA-seq germline variant caller. However, an existing tool called SmartRNASeqCaller  is a macAdditionally, we have demonstrated that a ‘silver-truth set’ can be used to generate highly accurate models when used appropriately. The GTEx-based model was trained using label variants derived from the DeepVariant whole-genome sequencing (DV WGS). Note that we only use a fraction of these labels for training where we observe adequate RNA-seq coverage. Although these labels are likely of poorer quality than GIAB-based truth sets, we observed that this approach allowed for a substantially larger and more diverse training dataset than the GIAB-based model. This approach ultimately yielded improved performance compared with the GIAB-based training approach, suggesting that a silver truth may be appropriate in cases which can dramatically expand gold standard datasets of limited size.Our examination of RNA editing events suggests our model learns to ignore these types of events, because it is trained on germline variant calls only. Conversely, this suggests that we could train a model capable of calling these RNA-editing events by configuring our model to produce an additional output (e.g. probability of a site being an RNA editing event). Such functionality could integrate germline and RNA-editing events into a single variant call format file, and allow for investigations of RNA editing and germline variant interactions. Consider the result when a heterozygous variant occurs adjacent to an RNA editing event within a codon. For example, [heterozygous genotype: T/C] + [RNA edit: A/G] + [C] could produce four different protein isoforms: UAC (Tyr), UGC (Cys), CAC (His) and CGC (Arg). Although CDS editing is rare, this extension of our work would allow for these types of events to be identified, in addition to long-range combinatorial effects of genotype and RNA edits.We expect our models to be sensitive to sample type, library preparation and/or bioinformatic methods. For example, we observed considerable differences in terms of performance when Opossum was applied to preprocess RNA-seq bams. Similarly, we expect using alternative RNA-seq aligners (other than STAR) may impact results. We will note, however, that DeepVariant performs a realignment step that may mitigate this effect somewhat.By releasing the DeepVariant RNA-seq models, we hope to provide reliable methods to add RNA-seq variant detection to typical RNA analysis use cases. We welcome community feedback to further enhance DeepVariant RNA-seq.vbad062_Supplementary_DataClick here for additional data file.


The variants cluster within the highly conserved kinesin motor domain and are predicted to interfere with nucleotide binding, although the mechanistic consequences on cell signaling and function are unknown.Kinesin motor proteins transport intracellular cargo, including mRNA, proteins, and organelles. Pathogenic variants in kinesin-related genes have been implicated in neurodevelopmental disorders and skeletal dysplasias. We identified in vivo genetic mechanism of KIF5B variants, we modeled the p.Thr87Ile variant that was found in two patients in the C. elegans ortholog, unc-116, at the corresponding position (Thr90Ile) by CRISPR/Cas9 editing and performed functional analysis. Next, we studied the cellular and molecular consequences of the recurrent p.Thr87Ile variant by microscopy, RNA and protein analysis in NIH3T3 cells, primary human fibroblasts and bone biopsy.To understand the C. elegans heterozygous for the unc-116 Thr90Ile variant displayed abnormal body length and motility phenotypes that were suppressed by additional copies of the wild type allele, consistent with a dominant negative mechanism. Time-lapse imaging of GFP-tagged mitochondria showed defective mitochondria transport in unc-116 Thr90Ile neurons providing strong evidence for disrupted kinesin motor function. Microscopy studies in human cells showed dilated endoplasmic reticulum, multiple intracellular vacuoles, and abnormal distribution of the Golgi complex, supporting an intracellular trafficking defect. RNA sequencing, proteomic analysis, and bone immunohistochemistry demonstrated down regulation of the mTOR signaling pathway that was partially rescued with leucine supplementation in patient cells.KIF5B kinesin motor domain in individuals with osteogenesis imperfecta. This study expands the spectrum of kinesin-related disorders and identifies dysregulated signaling targets for KIF5B in skeletal development.We report dominant negative variants in the  KIF5B, the gene encoding the Kinesin-1 heavy chain, have been implicated in neurodevelopmental delay, skeletal dysplasia, myopathy, and dysmorphic features. We identified de novo, heterozygous missense variants in KIF5B kinesin motor domain in four individuals with osteogenesis imperfecta. Findings in animal model and cell studies point to a dominant negative mechanism, an intracellular trafficking defect, and alteration of mTOR signaling. The study identifies KIF5B variants as a novel cause for osteogenesis imperfecta and suggests that mTOR signaling may be potentially targeted for future therapy in KIF5B-related disorder.Kinesin-related gene defects are associated with multi-systemic disorders, also known as “Kinesinopathies”. Heterozygous variants in  KIF5 genes: KIF5B, which is ubiquitously expressed, or its neuron-specific heavy chain isoforms KIF5A and KIF5C  X and VC2010. unc-116 variant edits, control edits, null deletion and single copy integration strains generated in this study are described below. A full list of strains used in this study is in S1 Table. Some strains were provided by the CGC, which is funded by NIH Office of Research Infrastructure Programs (P40 OD010440).unc-116 edits were generated as previously described [dpy-10 crRNA and repair template (the co-conversion marker) [unc-116(gk5722udn86) is derived from unc-116(gk5722[loxP + myo-2p::GFP::unc-54 3’ UTR + rps-27p::neoR::unc-54 3’ UTR + loxP]). Selection cassette (myo-2p::GFP::unc-54 3’ UTR + rps-27p::neoR::unc-54 3’ UTR) in unc-116(gk5722) was removed by injecting a plasmid pDD104 expressing Cre recombinase  as described previously  II; unc-119(ed3) III; bqSi711 [mex-5pFLP sl2 mNeon Green] IV. Three independent integrants were obtained as previously described [unc-119(ed3) III and bqSi711 [mex-5pFLP sl2 mNeon Green] IV, three single copy unc-116(+) transgene lines were generated.escribed . In brie marker) ,79. The eviously . The sine (RMCE) ,30. The escribed . After ounc-116 wild type and transgene locus in RNA-seq analysis, we have introduced four synonymous substitutions in exon 3 at the endogenous locus to generate unc-116(udn42). We conducted the RNA-seq analysis in triplicates to compare the mRNA level/read counts between the unc-116 wild type locus (chromosome III) and single copy transgene locus on chromosome II, using the strain UDN100221. The UDN100221 harbors the unc-116 transgene allele jsSi1579 jsSi1606 and the edited allele und42 at the endogenous locus . In brief, 1-day old young adults were collected to isolate total RNA with Trizol reagent following the manufacturer’s instruction. Total RNA was further purified with DNase digestion followed by column extraction. The following Next Generation Sequencing (NGS) DNA library preparation and bioinformatic analysis were conducted by Genome Technology Access Center: ~10 μg of total RNA each was used to enrich mRNA before DNA library preparation, using Oligo-dT beads . mRNA was first fragmented in reverse transcriptase buffer by incubation at 94°C for 8 minutes. mRNA was then reverse transcribed to yield cDNA using the SuperScript III RT enzyme (Life Technologies) with random hexamers. A second strand reaction was performed to yield double-strand cDNA. cDNA was blunt ended, had an A base added to the 3’ ends, and then Illumina sequencing adapters were ligated to the ends. Ligated fragments were then PCR amplified for 12–15 cycles, using primers to integrate unique dual index tags for the different biological replicates. Fragments were sequenced on an Illumina NovaSeq-6000 using paired-end reads extending 150 bases. The resulting reads were then quasi-aligned and quantitated against the Ensembl WBcel235.90 transcriptome with copies of the wild type and transgenic unc-116 using the expectation-maximization algorithm in Salmon [unc-116 transgene is not significantly different from the endogenous locus, and thus can be considered as supplying an equivalent gene dose as the wild type unc-116 gene.To facilitate the comparison of mRNA level between the n Salmon  to generBody length and swimming speed were measured using the WormLab 2019.1.1 (MBF Bioscience) . Brieflyfem-1 RNAi plates to generate females. Adult females, identified by presence of stacked oocytes but not embryos, were selected to cross with males of appropriate genotype. Therefore, the yielding offspring were all cross progeny. Specifically, for unc-116/qC1::GFP, females were crossed with VC2010 males and heterozygote progeny without pharyngeal GFP were selected for phenotyping.To obtain heterozygotes, L4 stage animals were transferred to Fig 2, worms were imaged with Leica M165 FC Fluorescent Stereo Microscope. For confocal images in Fig 3, “Straighten” function of ImageJ was used to straighten curved ALM neurites.For imaging UNC-116 mediated transport of mitochondria along axonal microtubules, L4 worms with GFP tagged mitochondria in mechanosensory neurons were immobilized with 10 mM levamisole, mounted on 2.5% agarose pads, and sealed with Vaseline. 5 minutes time-lapse images (1fps) were acquired using 63x objective on a spinning disk confocal microscope (PerkinElmer). Time-lapse images were converted into 2D kymographs by KymoResliceWide plugin in ImageJ. Images were aligned with StackReg Plugin in ImageJ where worm movement was detected during imaging. 100 μm long neurite regions, starting from ALM soma, were used for quantification of mitochondria moving particles. Particles with more than 3 μm displacement were counted as moving particles. Anterograde moving particles were defined by particles moving away from the cell body. Net run length was calculated as the distance between the starting position and the stop position. For combined segmental velocity, each trajectory was manually tracked using segmented line tool and processed automatedly by KymoAnalyzer  in ImageC. elegans modeling: Data were shown as median value, except Figs 2B and S3A in which mean and SD were shown. For cell studies: Data were shown as mean value and SD. Statistical significance was determined as p value that is equal to, or less than 0.01.Statistical analysis was performed using GraphPad Prism (Version 8.0.2). Student’s t-test or Mann-Whitney test were used for comparing differences between two groups, while one-way ANOVA or Kruskal-Wallis test followed by Dunn’s multiple comparison was performed for comparing three or more groups. For S1 Movie(AVI)Click here for additional data file.S2 Movie(AVI)Click here for additional data file.S3 Movie(AVI)Click here for additional data file.S4 Movie(AVI)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.S2 Data(XLSX)Click here for additional data file.S3 Data(XLSX)Click here for additional data file.S1 FigType I collagen isolated from patient fibroblasts showed no difference in electrophoretic migration pattern between patient and control. Mass spectrometry analysis showed no difference in hydroxylation pattern of 3-Hydroxyproline at P986 between patient and control.(PDF)Click here for additional data file.S2 FigKIF5B and orthologs in other organisms. The highly conserved P-loop region of the ATP-binding domain is highlighted with a green box. The KIF5B p.Thr87Ile variant in proband 1 (red arrowhead) and corresponding C. elegans (worm) unc-116 Thr90 residue (blue arrowhead) are shown.Multiple sequence alignment of the N-terminal part of the motor domain of (PDF)Click here for additional data file.S3 Figunc-116 and unc-116 transgene on chromosome II. Rescue of body length (B) and thrashing speed (C) defects were demonstrated upon introduction of two additional copies of unc-116(+) transgene into unc-116 (del). The difference in length between wild type and unc-116(del) with 2 extra copies of unc-116(+) is less than 2% and while statistically different is not considered biologically significant. Student t-test was used for comparison; ns: not significant, **p<0.001.(A) RNA sequencing transcripts per million (TPM) of endogenous (PDF)Click here for additional data file.S4 FigKIF5B in transiently transfected NIH3T3 cells . (B) Real time RT-PCR showing efficient knock-down of Kif5b in NIH3T3 cells using siRNA. ns: not significant, *p<0.01, **p<0.001, ***p<0.0001.(A) western blot analysis (left) and real time RT-PCR (right) showing overexpression of Flag-tagged wild type and mutant (PDF)Click here for additional data file.S5 FigWestern blot showing reduced phosphorylated AKT (top panel) and mTOR (bottom panel) in serum-starved patient fibroblasts.(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S1 Acknowledgement(PDF)Click here for additional data file.


Cells have developed different strategies to cope with viral infections. Key to initiating a defense response against viruses is the ability to distinguish foreign molecules from their own. One central mechanism is the perception of foreign nucleic acids by host proteins which, in turn, initiate an efficient immune response. Nucleic acid sensing pattern recognition receptors have evolved, each targeting specific features to discriminate viral from host RNA. These are complemented by several RNA-binding proteins that assist in sensing of foreign RNAs. There is increasing evidence that the interferon-inducible ADP-ribosyltransferases  contribute to immune defense and attenuation of viruses. However, their activation, subsequent targets, and precise mechanisms of interference with viruses and their propagation are still largely unknown. Best known for its antiviral activities and its role as RNA sensor is PARP13. In addition, PARP9 has been recently described as sensor for viral RNA. Here we will discuss recent findings suggesting that some PARPs function in antiviral innate immunity. We expand on these findings and integrate this information into a concept that outlines how the different PARPs might function as sensors of foreign RNA. We speculate about possible consequences of RNA binding with regard to the catalytic activities of PARPs, substrate specificity and signaling, which together result in antiviral activities. In order to establish an innate immune response to invading viruses, cells need to be able to distinguish self from foreign. This is enabled in part by a repertoire of proteins that specifically sense foreign nucleic acids. These proteins belong to the so-called pattern recognition receptors (PRRs) that recognize and bind pathogen-associated molecular pattern (PAMPs), including different pathogen-associated nucleic acids ,2,3. In The nucleic acid-sensing PRRs can be subdivided into two groups, the compartmentalized, endosomal and the cytosolic nucleic acid sensors. A subset of Toll-like receptors (TLRs) belongs to the first subgroup, whereas the second group includes retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs), Protein kinase R (PKR), 2′–5′ oligoadenylate synthetase proteins (OAS1-3), nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs), absent in melanoma 2 (AIM2)-like receptors (ALRs) and cyclic GMP-AMP synthase (cGAS) ,7,8,9,10In addition to these classical PRRs, a growing list of nucleic acid sensor proteins or accessory proteins have been described. These include helicases, ubiquitin ligases and ADP-ribosyltransferases, that can sense certain nucleic acids, assist in, or mediate the recognition of foreign nucleic acids and accelerate downstream signaling thereby contributing to and modulating an antiviral immune response ,13,14,15PARPs as ISGs we refer to a recent excellent review . H. H119]. ytokines . This isytokines .PARP14 was found to be localized mainly in the cytosol and translocates to the nucleus upon LPS treatment . It alsoPARP10 is highly expressed in hematopoietic cells, supporting a functional role in innate immunity . Like PARecently, non-structural protein (nsP) 2 of CHIKV has been identified as PARP10 substrate. MARylation impairs proteolytic activity of nsP2, which is essential for replication . CHIKV nInterestingly, MARylation of CHIKV-nsP2 was only observed when mimicking a viral infection by transfection of an in vitro transcribed RNA replicon. Plasmid-based co-expression of GFP-nsP2 and PARP10 was not sufficient to induce MARylation . SimilarN-terminally PARP10 possesses two RRMs near the N-terminus. This is followed by an intrinsically disordered, glycine-rich domain . WhetherAs pointed out above, RNA has been identified as substrate for MARylation ,124,125.What can be drawn from these findings? Quite clearly, PARPs are involved in antiviral defense. There is increasing evidence linking this subset of IFN-responsive PARP enzymes to innate immunity, as summarized in recent reviews ,109,118.Besides induction by IFNs, we hardly understand how the expression of these PARP genes and the function of the encoded proteins are regulated. How is their catalytic activity regulated? Is a precise regulation of MAR activity needed? How is turnover of these proteins achieved? What are the functions of the diverse protein domains these PARP proteins are equipped with? Is there crosstalk between these different domains and, extending on this, do they provide functionality separated from MAR activity? Further, how do the individual enzymes synergize to contribute to the establishment of a robust antiviral response? What are substrate molecules (protein or nucleic acids) to fine tune an immune response to one or the other pathogen? How is specificity achieved? In this last section we like to speculate on possible answers to these questions.Based on their domain organization , we specA direct link to inflammasome activation has not been elucidated yet, but NLRP3, which is activated upon a broad range of RNAs does fully rely on accessory proteins, as it has no intrinsic RNA-binding capability . In suchRNA-binding might trigger the activation of PARP enzymes and contribute to specificity, suggested by findings with CHIKV-nsP2 or the N-protein of MHV  80,81].,81.80,81Further, promiscuity of PARP enzymes has been discussed recently . Promiscpro) (DPUP), has been shown to interact with ribosomes and polyadenylate-binding protein-interacting protein 1 (PAIP1) [The RNA-binding capacities of PARP-enzymes might interfere with viral translation. Alphaviruses, for example, contain high CpG-content and are therefore recognized and targeted by PARP13 . PARP13  (PAIP1) . This in (PAIP1) . The bin (PAIP1) . However (PAIP1) .Viral RNA itself could be a substrate . In vitrInteraction and collaboration between PARPs could also be mediated by RNA. Several of these PARPs, at least when overexpressed appear to form condensates in cells . RNA plaAfter this rather speculative ideas on PARPs as sensors of foreign RNA and potential consequence of this RNA-interaction, there is still one obvious question to be answered. Do the IFN-regulated PARPs need tight regulation? As they are involved in innate immunity, does deregulation or activating mutations link PARPs to autoimmune disorders? Of note is also that PARP enzymes might act as double-edged sword, not only playing an antiviral role but also being exploited by some viruses. One such candidate might be PARP11, as counterbalance for IFN signaling .The last years have created several lines of evidence indicating a subset of the MARylating ARTDs plays a role in innate immunity. With proteins and recently also RNA being substrates of MARylation potential mechanisms how they confer to a robust antiviral response are being discussed and evaluated. In addition to the ART domain and modulation by catalytic activity, potential roles of various other domains, with which the IFN-regulated PARPs are equipped, are coming in focus for their possible contributions to antiviral activities. Certainly, we are far away from understanding the necessary details of the functions of these PARP proteins to draw a comprehensive picture of their involvement in innate immunity. Nevertheless, in this review we present possibilities how the additional domains besides the ART domain might contribute to innate immune signaling. Obtaining a more complete understanding of their functions and interplay with viral and host factors, both protein and RNA, will most certainly define novel starting points for pharmacological intervention.


Scientific Reports 10.1038/s41598-023-45911-9, published online 31 October 2023Correction to: In the original version of this Article, the affiliation of the authors was incorrectly given as ‘Children’s Hospital of Shanghai, Shanghai, China’. The correct affiliation is:Department of Emergency, Shanghai Children’s Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, ChinaThe original Article has been corrected.


Thousands of RNA-binding proteins (RBPs) crosslink to cellular mRNA. Among these are numerous unconventional RBPs (ucRBPs)—proteins that associate with RNA but lack known RNA-binding domains (RBDs). The vast majority of ucRBPs have uncharacterized RNA-binding specificities. We analyzed 492 human ucRBPs for intrinsic RNA-binding in vitro and identified 23 that bind specific RNA sequences. Most (17/23), including 8 ribosomal proteins, were previously associated with RNA-related function. We identified the RBDs responsible for sequence-specific RNA-binding for several of these 23 ucRBPs and surveyed whether corresponding domains from homologous proteins also display RNA sequence specificity. CCHC-zf domains from seven human proteins recognized specific RNA motifs, indicating that this is a major class of RBD. For Nudix, HABP4, TPR, RanBP2-zf, and L7Ae domains, however, only isolated members or closely related homologs yielded motifs, consistent with RNA-binding as a derived function. The lack of sequence specificity for most ucRBPs is striking, and we suggest that many may function analogously to chromatin factors, which often crosslink efficiently to cellular DNA, presumably via indirect recruitment. Finally, we show that ucRBPs tend to be highly abundant proteins and suggest their identification in RNA interactome capture studies could also result from weak nonspecific interactions with RNA. The human genome encodes over 400 proteins that contain well-studied RNA-binding domains (RBDs)8, but genome-wide RNA interactome capture assays using mass spectrometry have collectively cataloged thousands of proteins that crosslink to mRNA and non-coding RNA11. Many of these proteins have no previously reported function in RNA-binding, regulation, or metabolism. These new “unconventional” RBPs (ucRBPs)13—also referred to as enigmRBPs14, “non-canonical”, “non-classical”, and “non-professional” RBPs15—lack canonical RBDs and represent a wealth of potential new factors in RNA biology. Despite their prevalence, it is unclear how many ucRBPs recognize specific RNA sequences and structures. Some well-known ucRBPs are clearly sequence-specific (e.g. CFI(m)/NUDT2116, Vts1p17, ZRANB218, and others listed below), but more than a decade after the initial mass spectrometry studies, most remain uncharacterized in this regard.RNA-binding proteins (RBPs) control diverse RNA-related processes, ranging from RNA splicing to anti-viral defense, significantly impacting cellular and physiological function19, there are relatively few types of classical sequence-specific RBDs, with most of the literature focused on RRM, KH, CCCH zinc finger (CCCH-zf), and Pumilio domains23. Many more types of protein domains are associated with RNA metabolism24, and thus presumably have affinity for RNA, but few have reported sequence specificity. A handful of domain types (e.g. NHL)26 appear to have evolved RNA-binding sequence specificity in some phylogenetic branches27, presumably derived from predecessors with other biochemical functions. Proteins that form ribonucleoprotein complexes, such as the ribosome, spliceosome, and telomerase, among others, represent a special case. These proteins are associated with a single major substrate, but there is evidence that many perform “moonlighting” functions beyond their well-established roles28.The existence of so many ucRBPs also raises the question of how many sequence-specific RBDs remain to be discovered. Relative to transcription factor DNA-binding domains, which number well over 100 among eukaryotesHere, we surveyed a panel of 492 ucRBPs to determine their intrinsic RNA sequence preferences, subsequently localizing several RBDs and exploring the sequence specificity of their homologs. We anticipated that many new sequence-specific RBPs and their associated RBDs would emerge but, instead, very few of either were identified beyond those that were already known. This outcome suggests that although some ucRBPs may have roles in RNA metabolism, they do not rely on RNA sequence specificity. Alternatively, there are other explanations for their detection in RNA interactome capture experiments; we suggest a few below.10. Starting from a merged list of approximately 1100 putative RBPs, we removed any that contained RRM, KH, CCCH-zf, or Pumilio domains. Additionally, we removed any that were greater than 600 amino acids long, as large proteins are less compatible with expression and purification from E. coli. Several of the remaining 525 ucRBPs were already known or have since been found to recognize specific RNA-binding motifs , and these served as internal controls. Others are known to interact with RNA but have more limited information on sequence specificity , but we did not exhaustively survey the literature on all 525 proteins in advance. Furthermore, the experiments were conducted in parallel with hundreds of additional proteins containing conventional RBDs .Initially, we curated a set of 525 ucRBPs from two initial studies that identified RBPs crosslinked to mRNA at a genome-wide level36. Briefly, in RNAcompete experiments, a purified GST-tagged RBP selects RNA sequences from a designed (non-randomized) RNA pool. This pool is generated from a custom Agilent 244 K microarray consisting of 241,399 30–41 base RNAs. Following the GST pulldown, RNAs bound to the RBP are isolated, labeled with fluorescent Cy3 or Cy5 dyes, and hybridized to another custom 244 K Agilent microarray. Afterwards, the fluorescent intensities of individual microarray spots are quantified and used to estimate the level of RNA-binding by RBPs to specific RNA pool sequences. Computational analysis of RNAcompete microarray data calculates Z-score values for an RBP of interest to all RNA 7-mer sequences, representing the preference of an RBP to individual RNA 7-mers (i.e. relative RNA-binding affinity). The 7-mers with the highest Z-scores, which represent 7-mers that are bound with the highest affinity, are then aligned, and used to generate RNA-binding motifs. A design feature of the RNA pool is that RNA sequences in the starting pool can be split computationally into two sets, “Set A” and “Set B”, which have a nearly equal distribution of 7-mers. We use this feature to produce an internal reproducibility control by comparing 7-mer scores and motifs calculated separately for each set.From our list of 525 ucRBPs, we successfully expressed and purified 492 full-length GST fusion proteins and analyzed them using RNAcompeteA schematic and example data from this study are shown in Fig. bona fide RBPs.RNAcompete generates data that is conceptually straightforward. A successful experiment is typically characterized by a subset of related 7-mers yielding relatively high Z-scores and clear RNA motifs that are shared between Set A and Set B . Overall, only 63 of the 492 displayed any indication of sequence specificity, however, and many had low Z-scores and/or poor correlation between the A and B sets. All 63 were replicated, and most were judged to be not reproducible. To ensure unbiased assessments for the ucRBP (and other) RNAcompete experiments, we developed an automated classifier that combined a panel of RNAcompete experimental outcomes into a (pass/fail/uncertain) scoring system  corresponds to ucRBPs that, to our knowledge, have not been previously shown to possess RNA-binding activity. Thus, a key outcome of this study is the identification of several novel bona fide sequence-specific RBPs.In total, after merging replicates, we obtained sequence-specific RNA-binding motifs for 23 unique ucRBPs  and PEG10 (CCHC-zf domain).The ucRBPs yielding motifs often contained annotated protein domains that are associated with RNA-binding, but the RNA sequence specificity of these domains, and their prevalence in RNA-binding, has not been extensively studied Fig. . We seleWe then expanded the scope of this analysis by assessing whether homologs of these ucRBDs also bind RNA in a sequence-specific manner Fig. . Here, w41, but the CCHC-zf domain is not generally considered to be among canonical sequence-specific RBD families . Strikingly, the motifs obtained from CCHC-zf domain proteins are mostly distinct, a notable exception being CPSF4 and RBBP6—both of which bind U-rich motifs and are involved in pre-mRNA cleavage and polyadenylation44. Altogether, this outcome indicates that sequence-specific RNA-binding is relatively common among CCHC-zfs.A particularly striking outcome of this analysis is that seven of the 25 human proteins with CCHC-zf ucRBDs yielded a clear primary sequence motif Fig. . CCHC-zf45, but it was not designed to do so. We reasoned that the association of ucRBPs with cellular RNA might be explained by binding to long and/or structured motifs, which should be detected in cellular binding sites. To test this hypothesis, we analyzed eCLIP data published as part of ENCODE46. We curated a dataset of 31 eCLIP experiments (encompassing 26 proteins and two cell lines) that correspond to ucRBPs analyzed by RNAcompete  bases, and once to identify long motifs (7–12 bases)  . In contrast to the models for the other ucRBPs, these models each contained long, structured motifs. The motifs in the PRIESSTESS SLBP model closely resemble the stem-loop sequence from which SLBP derives its name (Stem-Loop Binding Protein)8), suggesting that the sites captured by eCLIP are not dictated by the sequence specificity of the RBP.To explore the surprising observation that many different ucRBPs yield short motifs that are related to each other we performed an all-by-all comparison of 5-mer frequencies, thus removing motif modeling as a variable. We also expanded the analyses to incorporate eCLIP experiments for 34 conventional RBPs (46 experiments) (Supplementary Table 50. CSTF2 is known to recognize GU-rich sequences downstream of the cleavage and polyadenylation (CPA) site51. In both SUB1 and CSTF2 eCLIP data, the top enriched 5-mer is GUGUG and the peaks for both proteins are predominantly found at CPA sites . These data suggest that the high similarity between SUB1 and CSTF2 likely result from their known association in cells and co-purification during eCLIP experiments.In contrast, for most of the well-studied conventional RBPs outside of the main cluster, the most frequent 5-mers from eCLIP experiments almost uniformly display a close match to their known in vitro RNA-binding specificity, and form distinct clusters  6. Moreover, these proteins have significantly more amino acids in intrinsically disordered regions than proteins that are not captured  , which have been associated with promiscuous interaction between proteins and RNA63. As such, the new motifs identified in this study could be used to characterize pathological mutations and/or the molecular determinants of RBP-RNA interactions. Surprisingly, RNAcompete-based analyses revealed specific and conserved RNA-binding activity for domains that normally have other functions  in species that diverged hundreds of millions of years ago , which supports the idea that the sequence specificity is of functional importance.We used RNAcompete to identify RNA-binding preferences for 23 sequence-specific ucRBPs. As RNA-binding is an inherent property of RBPs, identification of RNA-binding motifs for these proteins is an important first step in deciphering their function in RNA processing, metabolism, or post-transcriptional gene regulation. Among these newly discovered sequence-specific ucRBPs are many new and unusual cases. For example, ILF2, a known regulator of IL2, recognizes GC-rich RNA sequences, while two DNA-binding proteins, PURA and SSBP1, recognize a GA-rich RNA sequence and an RNA sequence with an AUG core, respectively. Approximately a third of the sequence-specific ucRBPs identified are ribosomal proteins, and several others have roles in human disease and development 66. The RNA-binding specificities for most CCHC-zf domains, if any, have not been previously determined, however. Nearly a third of CCHC-zf domains in this study displayed sequence specificity. Interestingly, motifs from the different CCHC-zfs analyzed are generally distinct, indicating flexibility in sequence preference, reminiscent of RRM, KH, and CCCH-zf domains . Moreover, as at least seven CCHC-zf proteins display sequence-specific RNA-binding, CCHC-zf now represents the fourth largest class of sequence-specific RBDs in human . Taken together, these data suggest that inclusion of the CCHC-zf domain family among the canonical sequence-specific RBDs would be reasonable and appropriate.CCHC-zf proteins have roles in DNA-binding, protein–protein interactions, and are commonly associated with RNA-related processes68 as well as components of larger RNA sequences such as the CNGGN hairpin-pentaloop consensus site for Vts169 and the GGAG consensus partial binding site contained in let-7 pre-miRNA71. Additionally, binding to larger G-quadraplexes, as described for CNBP30, could be detected as short primary sequence motifs and indeed, the CNBP motif we obtained resembles the potential CNBP-bound G-quadraplexes described in Ref.30.A striking observation from this study is that the vast majority of ucRBPs identified through RNA interactome capture, whether analyzed by RNAcompete or eCLIP, did not display RNA sequence specificity. Technical reasons for failure in RNAcompete experiments include aberrant protein production, and possible shortcomings of the RNAcompete assay itself (e.g. the inability to detect complex motifs or RNA secondary structure). For the former, the proteins examined were affinity-purified and therefore soluble, consistent with proper folding. For the latter, RNAcompete is effective in capturing small RNA bipartite motifs for proteins such as hnRNPL and hnRNPLL72 used a very different strategy to analyze all ENCODE eCLIP data, but nonetheless made similar findings, including a large cluster of unrelated RBPs that crosslink to G-rich sequences. These sequences were proposed to represent common contaminants in eCLIP data. Thus, analysis of eCLIP data appears to confirm RNAcompete results for many RBPs.The ucRBPs could conceivably bind only to very long and/or completely structured sites, but we did not detect such sites in eCLIP data for the vast majority of ucRBPs, instead finding either no sequence specificity or sequences that are frequently shared across many unrelated experiments. In a separate study, Kuret et al.bona fide mRNA-binding RBPs74. In “enhanced” RNA interactome capture experiments74, DNA and 25S RNA contamination issues have been largely circumvented. 18S rRNA contamination remains, however, albeit at significantly reduced levels74. Given that many of the ucRBPs have known RNA-related functions, it is also conceivable that they interact with RNA via mechanisms that do not rely on intrinsic sequence specificity (e.g. recruitment). Indeed, for SUB1 and NIP7, cellular RNA associations seem to be mediated by interactions with CSTF2 and NHP2L1, respectively. Additionally, proteins identified through RNA interactome capture studies can crosslink to RNA due to non-specific RNA-binding or transient associations75. Analogous features have been observed for chromatin proteins, which are distinguished from transcription factors by their lack of DNA sequence specificity, but nonetheless crosslink effectively to cellular DNA in ChIP-seq experiments77.It is thus unclear whether the observed lack of sequence-specific RNA-binding is an inherent property of ucRBPs , or is a consequence of other confounding factors such as transient RNA-binding activity in cells, high protein abundance, and/or technical issues with RNA interactome capture experiments. DNA and rRNA contamination were common in early RNA interactome capture studies, suggesting a potential for false identification of DNA-binding or structural ribosomal proteins as Finally, we propose that greater precision in terminology would be beneficial. “RNA-binding protein” should be used only to describe proteins that bind RNA with high sequence or structure specificity, whereas “nonspecific RNA-binding protein (nsRBP)” should be used to describe proteins that bind RNA non-specifically, and “RNA-associated protein” would describe proteins that associate with RNA in cells but do not possess intrinsic RNA-binding activity. Different terms are already used for equivalent types of DNA-associated proteins: “transcription factors”, “low specificity DNA-binding proteins”, and “chromatin proteins”. We propose that, at the very least, the class of “all proteins that contact RNA in cells” should not be conflated with the  sequence-specific subset.71. Briefly, RNAcompete experiments employed defined RNA pools that are generated from 244 K Agilent custom DNA microarrays. The RNA pool is designed using a single de Bruijn sequence78 of order 11 that was subsequently modified to minimize secondary structure in the designed sequences and minimize intramolecular RNA cross-hybridization. After these modifications, not every 11-mer is represented but each 9-mer is represented at least 16 times. To facilitate internal data comparisons, the pool is split computationally into two sets: Set A and Set B. Each set contains at least 155 copies of all 7-mers except GCTCTTC and CGAGAAG, which are removed because they correspond to the SapI/BspQI restriction site used during DNA template pool generation. A φ2.5 bacteriophage T7 promoter initiating with an AGA or AGG sequence is added at the beginning of each probe sequence in the DNA template pool to enable RNA synthesis. The final RNA pool consists of 241,399 individual sequences up to 41 nucleotides in length. The microarray design can be ordered from Agilent Technologies using AMADID# 024519. During the pulldown component of RNAcompete assays, 20 pmol of full-length GST-tagged ucRBPs and RNA pool (1.5 nmoles) are incubated in 1 mL of Binding Buffer  containing 20 μL glutathione Sepharose 4B beads  for 30 min at 4 °C, and subsequently washed four times for two minutes with Binding Buffer at 4 °C. The RNA is then recovered by thermal elution and labeled with Cy3 or Cy5 using the Kreatech ULS Labeling Kit. The labeled RNA is denatured and hybridized to a fresh single-stranded Agilent array of the same design, using a Tecan HS4800 Pro Hybridization Workstation. Samples are hybridized for 20 h at 42 °C, washed, and scanned. Images are processed using Imagene software version 8.0, with manual spot flagging.The RNA pool generation, RNAcompete pulldown assays, and microarray hybridizations were performed as previously described71. In this study, however, logos were generated from PFMs using ggseqlogo79.Normalization of microarray probe intensities, calculation of 7-mer Z-scores, and derivation of motifs were performed as described in71, resulting in an expression construct N-terminally-tagged with GST. The vector map and sequence for pTH6838 can be found at http://hugheslab.ccbr.utoronto.ca/supplementary-data/RNAcompete_eukarya/. Constructs were either commercially synthesized by BioBasic or cloned “in-house” using the Superscript II One-Step RT-PCR System , FirstChoice Human Total RNA Survey Panel  as template, and gene-specific primers. For analysis of RBDs, up to 50 amino acids of flanking sequence was included (less if the end of the polypeptide or a neighboring domain is encountered). Construct sequences are provided in Supplementary Table Full-length  or truncated  ucRBP coding sequences were cloned into the AscI and SbfI restriction sites in a modified pDEST-Magic vector (pTH6838)Escherichia coli C41 cells (Lucigen), and protein expression was induced by adding IPTG  to log phase cell cultures and incubating overnight at 16 °C. Supplementary Table GST-tagged ucRBP expression constructs were transformed into 12. We annotated the rest of the experiments as “Fails”, resulting in 229 passes and 242 fails. Forty of these experiments (20 passes and 20 fails) were held out for testing, the majority of which were performed on RBPs with well-described motifs. The remainder were used to train the classifier . Each experiment was annotated as a “Pass” if it showed an obvious visible correlation in k-mer enrichment between the Set A and Set B probes, the two sets produced visibly similar motifs, and the motif was not composed of k-mers that are found in many unrelated experiments (e.g. simple repeat sequences). Similar quality control steps used in RNAcompete microarray data analysis have been outlined in more detail elsewhere80  were also used: the number of top ten Set A and Set B 7-mers containing each of the artifacts were used as individual features, along with the combined sum of all the artifact counts. Finally, features capturing information about the Set A and Set B motifs were added: the information content of each motif and the similarity between the two motifs as calculated by TOMTOM81 with BayesSearchCV from scikit-optimize (https://scikit-optimize.github.io) to determine the optimal L1  regularization strength. This resulted in a classifier with nearly perfect performance on the held-out test data (AUROC = 0.99). The LR probability estimate for passed RNAcompete experiments in the held-out set ranged from 0.43 to 1.00 (mean = 0.92) and for failed experiments from 7.8 × 10–5 to 0.47 (mean = 6.1 × 10–2)  model using the LogisticRegression function from scikit-learnWe applied the classifier to all ucRBP experiments, thresholding the results such that experiments with an LR probability estimate ≤ 0.35 were determined to have failed, experiments with an LR probability estimate ≥ 0.65 were determined to have passed, and experiments that fell between were manually checked  that were performed on proteins in our ucRBP set. This set of experiments was used for the PRIESSTESS47 analysis , and once with the motif size set to 7–12 (-minw 7-maxw 12). Further increasing motif length (13–20) in PRIESSTESS runs resulted in either no enriched motifs being identified or a model with worse predictive power for all experiments. Due to the small number of sequences in many of the experiments, the p-value threshold for significantly-enriched motifs identified by STREME was increased to 0.1. Note that while this increases the number of motifs used in the logistic regression step of PRIESSTESS, it will not lead to the creation of predictive models if the motifs are not representative of the binding specificity; either the LASSO regularization will set all motif weights to zero, or the final model will fail to identify bound sites in the held-out data. AUROC values on held-out data output by PRIESSTESS were compared (short motif model vs. long motif model) using a paired t-test.71 and RBNS motifs84, except in the case of CSTF2, for which the motif is known to be a GU-rich sequence85. Curated IUPAC motifs can be found in Supplementary Table .72 additional file 7.To compare k-mer similarity across ucRBP and conventional RBP eCLIP experiments, 5-mers were counted in peak sequences for each eCLIP experiment. Pearson correlations between 5-mer counts for each pair of experiments were calculated and experiments were clustered using hierarchical agglomerative clustering with centroid linkage. To identify the k-mer rank of the known in vitro motif, we curated IUPAC motifs from CisBP-RNA52) to survey the relative abundance of ucRBPs. Here, histograms corresponding to log10 values for protein copy number were plotted for ucRBPs, conventional RBPs and all “other” proteins identified  that have been reviewed. Each of the proteins was then annotated as belonging to (or not belonging to) the set of proteins identified in interactome capture experiments as curated on RBPbase6 (Supplementary Table To analyze the prevalence of IDRs in the RNA interacting proteome, we collected IDR data from MobiDBSupplementary Figures.Supplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.Supplementary Table S7.Supplementary Table S8.