C. elegans. erp-1 mutants exhibited reduced transmission and a progressive reduction in optogenetically evoked muscle contraction, indicative of impaired SV recycling. This was confirmed by electrophysiology, where particularly endophilin A (UNC-57), but also endophilin B (ERP-1) mutants exhibited reduced transmission. By optogenetic and electrophysiological analysis, phenotypes in the unc-57; erp-1 double mutant are largely dominated by the unc-57 mutation, arguing for partially redundant functions of endophilins A and B, but also hinting at a back-up mechanism for neuronal endocytosis. By electron microscopy (EM), we observed that unc-57 and erp-1; unc-57 double mutants showed increased numbers of synaptic endosomes of large size, assigning a role for both proteins at the endosome, because endosomal disintegration into new SVs, but not formation of endosomes were hampered. Accordingly, only low amounts of SVs were present. Also erp-1 mutants show reduced SV numbers (but no increase in LVs), thus ERP-1 contributes to SV formation. We analyzed temperature-sensitive mutants of clathrin heavy chain (chc-1), as well as erp-1; chc-1 and unc-57; chc-1 double mutants. SV recycling phenotypes were obvious from optogenetic stimulation experiments. By EM, chc-1 mutants showed formation of numerous and large endosomes, arguing that clathrin, as shown for mammalian synapses, acts at the endosome in formation of new SVs. Without endophilins, clathrin formed endosomes at the PM, while endophilins A and B compensated for the loss of clathrin at the PM, under conditions of high SV turnover.Synaptic vesicle (SV) recycling enables ongoing transmitter release, even during prolonged activity. SV membrane and proteins are retrieved by ultrafast endocytosis and new SVs are formed from synaptic endosomes (large vesicles—LVs). Many proteins contribute to SV recycling, e.g., endophilin, synaptojanin, dynamin and clathrin, while the site of action of these proteins (at the plasma membrane (PM) vs. at the endosomal membrane) is only partially understood. Here, we investigated the roles of endophilin A (UNC-57), endophilin-related protein  and of clathrin, in SV recycling at the cholinergic neuromuscular junction (NMJ) of  Depolarization and voltage-gated Ca2+ channels (VGCCs) provide this signal. Following fusion, the process of ultrafast endocytosis retrieves the SV membrane and its protein constituents. This form of endocytosis  occurs within 50–100 ms after fusion, and at physiological temperature is independent of clathrin  maintained when UNC-57 was tethered to the PM, indicating that it must act at the PM, while full functionality may require its presence also at the endosome  and unc-57 mutants, LVs/endosomes were still formed after optogenetic stimulation. At the same time, the breakdown of these LVs into new SVs was strongly attenuated in both unc-26 and unc-57 mutants, arguing that the two proteins act at the step of SV formation from the endosome. Here, however, the unc-26 phenotype was more severe, resulting in extremely large, persistent LVs  mutants accumulate endocytosis intermediates at the PM, from which budding of new SVs can be observed; at the same time, disintegration of endosomes is delayed  exhibited enhanced formation of LVs, the end-product of (sustained) ultrafast endocytosis. Thus, ERP-1 appears to act in a partially redundant manner with UNC-57 at the stage of breakdown of synaptic endosomes to form new SVs, but is dispensable for bulk/ultrafast endocytosis. For CHC-1, we show that temperature-sensitive mutants also exhibit a SV recycling phenotype in behavioral assays, and that they accumulate unusually large LVs. Thus, as in mammals, C. elegans clathrin is not essential for bulk/ultrafast endocytosis, but acts mainly in de-novo generation of SVs at the synaptic endosome. To assess whether the remaining SV recycling in (erp-1 and) unc-57 mutants could be due to the function of CHC-1, we analyzed erp-1; chc-1 and unc-57; chc-1 double mutants. Our data suggest mutually redundant functions of the three proteins in SV recycling, with the strongest contribution by endophilin A/UNC-57. Loss of function can be partially compensated by each of these proteins, when the respective other two proteins are missing, thus enabling sufficient activity to maintain synaptic transmission and survival.Here, we used a combination of pharmacological, behavioral, optogenetic, electrophysiological and ultrastructural assays to probe the roles of UNC-57, ERP-1 and of clathrin heavy chain (CHC-1) in SV recycling in C. elegans Bristol strain N2 was used for some experiments and cultivated as previously described I, DH1230: chc-1(b1025ts)III, RB700: erp-1(ok462)X, ZX2088: erp-1(ok462)X; unc-57(e406)I. The following transgenic strains were generated and/or used: ZX460: wild type; zxIs6V I; zxIs6V, ZX1602: erp-1(ok462)X; zxIs6V, ZX1778: chc-1(b1025ts)III; zxIs6V, ZX2227: unc-57(e406)I; erp-1(ok462)X; zxIs6V, ZX2342: chc-1(b1025)III; unc-57(e406)I; zxIs6V, ZX2343: chc-1(b1025)III; erp-1(ok462)X; zxIs6V, BC12478: dpy-5(e907)I; sIs10597[rCes F35A5.8a::GFP + pCeh361]  and punc-17::ChR2(H134R)::YFP (strain ZX460) were analyzed on a Zeiss Axiovert 200 microscope , with a 40×/0.25 Zeiss oil objective and GFP filter set . Micrographs were taken with an ORCA-Flash4.0 Digital sCMOS Camera . Animals were transferred onto 10% agarose pads in M9 buffer  and immobilized with Polybead polystyrene 0.1 mm microspheres .Expression of p2, swimming-assay: 0.53 mW/mm2) from a 50 W mercury lamp, filtered through a GFP excitation filter (450–490 nm), on 5.5 cm diameter dishes, under a 10× objective in a Zeiss Axiovert40 microscope . Duration of illumination was defined by a computer-controlled shutter (Sutter Instruments). Worms were filmed with a Powershot G9 digital camera  at 640 × 480 resolution with 30 fps. Body length was determined as previously described .Transgenic worms were cultivated in the dark at 20°C on nematode growth medium (NGM) dishes with OP50 bacteria Brenner,  without For analyzing swimming behavior, worms were placed into 96-well plates containing 100 ml NGM and 100 ml of M9 buffer per well. Worms were left in the M9 buffer for 15 min before the first trial of the swimming-assay, then they were filmed for 60 s (swimming-assay before light stimulation). The second round (60 s) of the swimming-assay was filmed after a 90 s light-stimulation and an additional 90 s incubation time. The swimming cycles (the worm’s body bends twice per cycle), were counted manually. The swimming assays were repeated on three different days, with 8–10 worms/group, picked from different populations on different days.Worms were cultivated at 20°C on NGM dishes with OP50 bacteria. To study aldicarb sensitivity, 1.5 mM aldicarb dishes were prepared  and controlled by Patchmaster software . Data were analyzed by Patchmaster  and MiniAnalysis  software.For recordings from BWMs animals were immobilized with Histoacryl glue  and a lateral incision was made to access neuromuscular junctions (NMJs) along the ventral nerve cord. The basement membrane overlying muscles was enzymatically removed by incubation in 0.5 mg/ml collagenase for 10 s . Muscles were patch-clamped in whole-cell mode at 22°C using an EPC10 amplifier with head stage connected to a standard HEKA pipette holder for fire-polished borosilicate pipettes  of 4–7 MΩ resistance. The bath solution contained: NaCl 150 mM; KCl 5 mM; CaClE. coli OP50 −/+ (0.1 mM) ATR dishes 1 to 2 days before high-pressure freezing (HPF). Young adult animals were used for HPF fixation, based on methods previously described  for subsequent photostimulation. To prevent preactivation, all manipulations were done under red light. For light stimulation experiments, worms were continuously illuminated with a laser (~20 mW/mm2) for 30 s, followed by HPF at −180°C under 2100 bar pressure in a Bal-Tec HPM010 HPF machine. A ~6 s period of manual insertion of the sample after photostimulation is required with this high pressure freezer. For experiments involving the clathrin mutant, animals were kept at permissive temperature (15°C) at all times. Tools used were equilibrated to the respective temperatures. Worms were either loaded at room temperature to the planchette for freezing directly or incubated at non-permissive temperature at 30°C for 14 min before freezing.Transgenic L4 worms were transferred from regular NGM dishes to freshly seeded 4 for 39.5 h (in dry acetone) while slowly increasing the temperature up to room temperature. Afterwards, the process of embedding in Epoxy resin  was executed with increasing concentration from 50% to 100% at room temperature and 100% at 60°C over 48 h.After freezing, specimens were transferred under liquid nitrogen into a Reichert AFS machine (Leica) for freeze substitution. Tannic acid (0.1% in dry acetone) fixative was used to incubate samples at −90°C for 100 h. Then, a process of washing was performed, followed by an incubation of 2% OsO2-free chamber and subsequently washed in distilled water again. Images of regions in the ventral nerve cord were taken with an Erlangshen ES500W CCD camera (Gatan) in a Philips CM12 transmission electron microscope operated at 80 kV. Images were scored blind for each condition and tagged in ImageJ (NIH) for the PM (synapse perimeter). ImageJ ROIs were stored and then quantified based on methods described previously  and 40.2% (ERP-1 isoform a) identity to their human homologs . This suggests that ERP-1 is present in GABAergic neurons as well (as these are the only other cell type present in this part of the nervous system). We tentatively assigned the individual cell bodies  phenotype, while mutants in unc-57, and erp-1; unc-57 double mutants showed very strong ric phenotypes and did not paralyze at all during 5 h of exposure to aldicarb . This demonstrates that erp-1 mutants are somewhat affected for cholinergic transmission, however, much less than unc-57 endophilin A mutants.To probe for ERP-1 function in cholinergic cells, we first performed a simple pharmacological assay for malfunction of cholinergic transmission. Aldicarb is an ACh esterase inhibitor that leads to accumulation of ACh in the synaptic cleft and to progressive paralysis of the animals. In mutants with reduced cholinergic transmission, paralysis is delayed or does not occur  contraction of the body  measure the rate of cholinergic transmission in live animals while they are undergoing strong neuronal activity. Here, animals expressing transgene  Figures . This dit Figure . Howevererp-1 mutants , only unc-57 mutants showed slightly reduced amplitudes. In contrast, both unc-57 and unc-57; erp-1 double mutants showed significantly reduced mPSC frequency, as compared to wild type. This is likely a consequence of reduced SV numbers. Thus, endophilin A/UNC-57 appears to have much more pronounced functions in SV recycling and maintenance of synaptic transmission than endophilin B/ERP-1.To further investigate this, we used electrophysiology to measure spontaneous and optogenetically evoked transmission at the NMJ (excitatory post-synaptic currents—ePSCs). Also these assays can uncover SV recycling defects  led to drastic alterations in the synaptic profile: first, the synaptic profile area was significantly increased, which we interpret as a result of excessive SV fusion that adds membrane to the PM of the en passant synaptic bouton  and/or RP are reduced in these animals, and that a lower turnover of SVs occurs, in line with our behavioral and electrophysiological assays. erp-1 mutants also showed reduced docked SV numbers, that were not further reduced upon photostimulation. LV numbers in erp-1 were increased upon stimulation, just as for wild type. Next, we analyzed endophilin A/UNC-57 mutants: unc-57(e406) synapses had significantly reduced numbers of total and docked SVs , that were further depleted upon stimulation, indicating a strong defect in SV formation and/or recycling, consistent with the electrophysiological results  in the number of LVs per synaptic profile . In contrast to wild type and also to unc-57 single mutants, the double mutants showed highly increased numbers of LVs (~350% of wild type), already before stimulation. This indicates major difficulties of these synapses to break down the end-products of bulk endocytosis; yet, endocytosis per se does occur. LVs in erp-1; unc-57 double mutants were significantly larger than in wild type or erp-1 single mutants  mechanisms exist that enable some formation and recycling of LVs, such as to sustain a low level of SV exocytosis and recycling.To investigate this, we also analyzed n Figure , indicats Figure . After serp-1 and unc-57; erp-1 double mutants, neither under resting or stimulation conditions  is essential for survival, we could assess only a chc-1(b1025ts) temperature sensitive allele: these animals are comparably unaffected at the permissive temperature (15°C), but are deprived of CHC-1 function at elevated temperatures (30°C). First, we performed a time-dependent optogenetic contraction assay, comparing wild type to chc-1 mutants at elevated temperature. After the shift to 30°C, the animals showed a (mild) decline in contractions over the 1 min photostimulation period, that was significantly different from wild type and from the initial contraction, while wild-type animals showed comparable and consistent contractions both at 15°C (no hs) and at 30°C  mutants showed a mild SV recycling defect at non-permissive temperature.We next asked at which stage during SV recycling clathrin function is required. Clathrin was shown to affect SV size in chc-1 mutants, at permissive or non-permissive temperatures , as observed previously  in  contractions over the 1 min stimulation period  double mutants , however, somewhat reduced the extent of this phenotype , as well as for unc-57 single mutants. However, it was not the case in erp-1; chc-1 mutants , as long as endophilin A is present. Clathrin, however, can compensate for loss of endophilin A also at the PM. ERP-1/endophilin B has only a minor role , which can be both at the PM, as suggested by the residual recycling activity that we observed in the unc-57; chc-1 double mutant, and at the endosome, as suggested by the reduced number of SVs in this mutant. For clathrin, we found that, as in mammals, its function is also associated with the breakdown of synaptic endosomes under physiological conditions. In sum, we could clarify roles for endophilins A, B and clathrin in the SV recycling process in C. elegans, uncovering a capacity of each protein for mutual “rescue” of the lack of other two proteins in the different stages of SV recycling.Here, we analyzed the role of three proteins in SV recycling at cholinergic NMJs of the nematode unc-57 mutants , we suggest that UNC-57 is not absolutely essential for the PM-localized bulk endocytosis event, and that clathrin acts in a back-up pathway that helps in formation of endocytic structures at the PM. This mechanism may be augmented by high Ca2+, as we found that numbers of LVs were normal in photostimulated unc-57; erp-1 double mutants, while these synapses accumulated numerous and large LVs without stimulation. Likewise, a dependency of ADBE on Ca2+ and calcineurin was reported earlier  bulk endosomes are formed in erp-1 mutants, LVs are formed, likely excluding a primary role for ERP-1 at the PM. Yet, the amount of LVs formed is not increased when compared to wild type. Nonetheless, since erp-1 mutants have significantly reduced SV numbers, the bottleneck appears to be the resolution of the endosomes to form new SVs. Here, however, also UNC-57 must act, as otherwise we would expect stronger phenotypes in erp-1 mutants. The lower severity of the erp-1 mutant vs. the unc-57 mutant could indicate that unc-57 phenotypes are a consequence of its lack at both the PM and endosomal membrane, for which ERP-1 cannot compensate, or only when also clathrin is absent. The lack of endophilin A function may have much more severe consequences ; this may not only be because of its role at the PM and endosome in SV recycling, but likely also because it is required for processes involved in synapse formation. Cargo arriving from the soma, as part of transport vesicles, is thought to be first delivered to the PM by fusion of these vesicles  dependent process that is triggered by ChR2 stimulation could have augmented the compromised CHC-1 protein in forming normal size SVs. This will require future investigation.Not unexpectedly, the role of clathrin appears highly conserved between lower and higher animals also in the SV recycling process  facilitate such studies. However, details of the function of these proteins may be different between the species, thus comparisons across different experimental and organismal preparations will remain an important approach.In sum, we combined optogenetic, behavioral, electrophysiological and ultrastructural analyses in wild-type and mutant AG: acquired funding and wrote the manuscript, with the help of the other authors. S-cY, BJ, SW, JL and AG: conceived research and performed analyses. S-cY, BJ, SW and JL: performed experiments.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.


Next-generation sequencing (NGS) techniques have been used to generate various molecular maps including genomes, epigenomes, and transcriptomes. Transcriptomes from a given cell population can be profiled via RNA-seq. However, there is no simple way to assess the characteristics of RNA-seq data systematically. In this study, we provide a simple method that can intuitively evaluate RNA-seq data using two different principal component analysis (PCA) plots. The gene expression PCA plot provides insights into the association between samples, while the transcript integrity number (TIN) score plot provides a quality map of given RNA-seq data. With this approach, we found that RNA-seq datasets deposited in public repositories often contain a few low-quality RNA-seq data that can lead to misinterpretations. The effect of sampling errors for differentially expressed gene (DEG) analysis was evaluated with ten RNA-seq data from invasive ductal carcinoma tissues and three RNA-seq data from adjacent normal tissues taken from a Korean breast cancer patient. The evaluation demonstrated that sampling errors, which select samples that do not represent a given population, can lead to different interpretations when conducting the DEG analysis. Therefore, the proposed approach can be used to avoid sampling errors prior to RNA-seq data analysis. In addition, no more than two or three biological replicates are generally used as representative samples for a population of a given condition , due to the high cost of RNA-seq or difficulty obtaining samples. This means that individual RNA-seq data will greatly affect the outcome. Potential sampling errors can be avoided by carefully examining RNA-seq data prior to downstream analysis such as differentially expressed gene (DEG) analysis. Here, we proposed a simple approach that provides a systematic view of a given RNA-seq dataset using two different principal component analysis (PCA) plots. The gene expression PCA plot provides a map of the distances between samples from which the characteristics of RNA-seq data can be inferred. However, the transcript integrity number (TIN) score [Recent advances in DNA sequencing technology led by next-generation sequencing (NGS) have been generating various molecular maps, including genomes, transcriptomes, and epigenomes –3. Among=series) . Since a=series) . AlthougN) score  PCA plothttp://rseqc.sourceforge.net/#tin-py) [https://www.gencodegenes.org/releases/current.html) were estimated using Cuffnorm in the Cufflink application (version 2.2.1) [We had previously conducted RNA-seq with one tissue of invasive ductal carcinoma  from breast tissue and a corresponding adjacent normal tissue biopsied from a Korean breast cancer patient with informed consent. The data has been deposited under the gene expression omnibus (GEO) accession number GSE110114. We used this RNA-seq dataset to evaluate the impact of RNA quality and sample variations to identify differentially expressed genes (DEGs) in this study. The RNA-seq dataset was downloaded and processed using the Octopus-toolkit (version 2.1.0) . Brieflyhttps://github.com/sinhrks/ggfortify) [Principal component analysis (PCA) plots were generated using the ggfortify package in R (fortify)  with FPKhttp://metascape.org/) [Gene ontology (GO) analysis was performed using Metascape (pe.org/) . Box plope.org/) . GAPDH and ACTB, also confirmed that the C3 sample was distinct from the other samples  of their RNA-seq data in depth, which could lead to the misinterpretation of results. In this study, we proposed a simple method that can assess the characteristics of RNA-seq data with mapped read files . This approach used two estimated units, FPKM (or RPKM) and TIN score, which could be calculated easily using available applications such as Cufflinks  and RSeQ samples . In the  samples . Interes p value cutoff. Then, several downstream analyses such as gene ontology (GO) and network analysis are conducted to select the final gene set among identified DEGs. DEGs are generally defined as the genes that show FDR-adjusted p values less than a cutoff such as 0.05. We evaluated the effect of incorporating unintended samples that showed different RNA-seq qualities for DEG analysis. For this, all possible combinations of three cancer samples (or two cancer samples) were set as a cancer group and compared with three normal samples  using Cuffdiff with different FDR-adjusted p values . The result showed that sampling error was critical for DEG analysis   between given groups, which relies on statistical significance such as a false-discovery rate (FDR)-adjustedanalysis . For exaanalysis  was inclnd 0.01) . Next, wnd 0.01) . The majnd 0.01) . HoweverSince the quality of RNA-seq data can vary, we wondered whether the quality of publicly available RNA-seq datasets was acceptable according to the gene expression and TIN score PCA plots. The first RNA-seq dataset we evaluated contained 36 single-cell transcriptomes of Artemether-treated human pancreatic islets from one donor profiled by single-cell RNA-seq (scRNA-seq) technique . The genhttps://www.ncbi.nlm.nih.gov/geo/summary/) at the time of writing, 67% of available data  were gene expression profiling data. Among the gene expression profiling data, the volume of RNA-seq data is rapidly increasing due to the reduced cost as well as its higher coverage and the greater resolution of the transcriptome. However, there are no simple ways to intuitively assess the characteristics of RNA-seq data. We sought to resolve this issue by proposing a simple method that could accurately characterize a given RNA-seq dataset. Without determining the characteristics of the RNA-seq data, downstream analysis such as the identification of differentially expressed genes could be significantly biased. In general, the majority of RNA-seq data registered in GEO has two or three biological replicates per condition, which means that two or three replicates represent a population for a given condition. In this scenario, if one sample is of low quality or sampling error occurred, the impact of incorporating it is significant, as shown in Figures All living cells contain various types of RNA; therefore, most studies focus on RNA. According to the statistics of the gene expression omnibus (GEO) database ( p value cutoffs. Therefore, researchers should carefully determine whether a given RNA-seq could be a representative sample of a population for a given condition with the proposed PCA plots. Our evaluation of publicly available RNA-seq data found that a few low-quality RNA-seq data had been used in original studies (Figures Our simulation for DEG analysis highlights that the number of DEGs varies greatly when incorporating a low-quality sample or a sample from a spatially distinct region, regardless of different FDR-adjusted Figures  and 5, aThe proposed approach can distinguish the low-quality RNA-seq samples that should be avoided for the RNA-seq data analysis. Incorporation of low-quality RNA-seq data can lead to the misinterpretation of results as shown in the present study.


Kaposi's sarcoma-associated herpesvirus (KSHV) transcribes a long noncoding polyadenylated nuclear (PAN) RNA, which promotes the latent to lytic transition by repressing host genes involved in antiviral responses as well as viral proteins that support the latent state. KSHV also expresses several early proteins including ORF57 (Mta), a member of the conserved multifunctional ICP27 protein family, which is essential for productive replication. ORF57/Mta interacts with PAN RNA via a region termed the Mta responsive element (MRE), stabilizing the transcript and supporting nuclear accumulation. Here, using a close homolog of KSHV ORF57 from herpesvirus saimiri (HVS), we determined the crystal structure of the globular domain in complex with a PAN RNA MRE, revealing a uracil specific binding site that is also conserved in KSHV. Using solution NMR, RNA binding was also mapped within the disordered N-terminal domain of KSHV ORF57, and showed specificity for an RNA fragment containing a GAAGRG motif previously known to bind a homologous region in HVS ORF57. Together these data located novel differential RNA recognition sites within neighboring domains of herpesvirus ORF57 homologs, and revealed high-resolution details of their interactions with PAN RNA, thus providing insight into interactions crucial to viral function. Kaposi's sarcoma herpesvirus (KSHV) is a γ-herpesvirus that can cause malignancies including Kaposi's sarcoma (KS), Primary Effusion Lymphoma and Multicentric Castleman's Disease, primarily in immunocompromised individuals such as AIDS patients and transplant recipients . In commAs part of its RNA stabilization and nuclear export functions, ksORF57 interacts with other viral proteins including ORF50  and alsoThe sequence and structural features of RNAs that bind to ksORF57 have been studied; although a universal binding motif was not revealed, several contributory factors were identified. These include AT-content , and speDue to the functional importance of the RNA interactions with ORF57 homologues, particularly with PAN RNA in KSHV, here we have explored the nucleotide sequence specificity, protein structure and RNA binding sites by NMR and X-ray crystallography. We used NMR to explore the RNA binding properties and map binding sites within the ksORF57 N-terminal domain, revealing specificity similar to that of an equivalent region of hvsORF57. We used surface plasmon resonance to explore the similarities and differences in binding of a PAN RNA MRE oligonucleotide fragment to IHDs of ICP27, UL69, hvsORF57 and ksORF57. We also obtained the high-resolution structure of the C-terminal IHD from hvsORF57 in both the presence and absence of a PAN RNA MRE oligonucleotide fragment. The data revealed a RNA binding site at the homo-dimer interface conserved in ksORF57 and other γ-herpesviruses. Together the data provides detailed high-resolution structure of hvsORF57, the closest ksORF57 homologue, revealing the molecular origins of the specificity for PAN RNA. Comparison with the archetype ICP27 structure indicated differences that provided an explanation for the lack of MRE recognition by the homologous protein ICP27 from the α-herpesvirus subfamily. The site-specific characterizations of RNA binding sites will facilitate more targeted functional studies in the future to unravel the importance of these discovered sites for KSHV replication.E. coli. DNA fragments were each cloned into the NdeI and XhoI restriction sites of pET-21a(+) (Merek). Protein was expressed in Escherichia coli strain T7 express LysY (New England Biolabs). Terrific Broth (Sigma) supplemented with 50 μg/ml ampicillin and 0.1 mM ZnSO4 was inoculated with 1% (v/v) overnight pre-culture. Culture density was monitored at 600 nm until an absorbance of 0.6, at which point protein expression was induced with 0.25 mM IPTG and incubation continued for 18 h at 20°C. Cells were pelleted by centrifugation . Selenomethionine (SeMet) labelled protein was obtained by growing cells in M9 minimal media in place of terrific broth, using the protocol described by Van Duyne et al.  and KSHV ORF57 residues 153–455 (ksORF57Δ153) were obtained by gene synthesis (Invitrogen) in codon optimized form for expression in e et al. .68–178 was carried out as previously described by His-tag purification followed by size-exclusion chromatography  supplemented with 0.5% (v/v) Triton X-100, DNase, RNase and EDTA free protease inhibitor (Roche). l-Arg·l-Glu was added to improve protein sample solubility and stability  pre-equilibrated in gel filtration buffer . Purified ORF57 constructs were concentrated in Vivaspin 500 centrifugal devices with a 5 kDa MWCO (Sartorius Stedim Biotech GmbH) as necessary prior to further experiments.Purification of ksORF57tography . HCMV ULescribed ,48. ksORtability . The celFor free hvsORF57Δ146, purified selenomethionine (Se-Met) derivatized protein was concentrated to 300 μM in Vivaspin 500 centrifugal devices with a 5 kDa MWCO immediately prior to setting up crystal trials. The hvsORF57Δ146-RNA complex was obtained by mixing of 30 μM native (non Se-Met) protein with 90 μM HPLC purified RNA oligo PAN bases 34–50, sequence CACCUAUGGAUUUUGUG (Sigma) here abbreviated to PAN17, the solution was stored at 4°C for 60 min, then concentrated in a Vivaspin 500 centrifugal device with a 5 kDa MWCO 10-fold prior to crystal trials. Concentrated samples were used to setup 4  ×  96 crystal trials and screened by the sitting drop vapor diffusion method. A 200 nl drop of purified hvsORF57Δ146 or hvsORF57Δ146-RNA were mixed with 200 nl of the screen condition using a TTP Mosquito Crystal nanolitre pipetting robot. Following 24 h incubation at 4°C the plates were manually inspected and single crystals suitable for X-ray diffraction analysis were observed in a range of conditions. Selenomethionine derivatized RNA-free hvsORF57Δ146 SeMet crystals grew from reservoir solutions consisting of 0.12 M Alcohols , 0.1 M  Buffer System pH 6.5, 50% (v/v) GOL_P4K Mix (Morpheus HT96 D3 Molecular Dimensions). hvsORF57Δ146-PAN17 crystals grew from reservoir solutions consisting of 0.06 M divalents , 0.1 M  buffer system pH 6.5, 50% (v/v) GOL_P4K (Morpheus HT96 A3 Molecular Dimensions). Crystals were flash frozen by plunge freezing in liquid nitrogen prior to data collection at Diamond Light Source Ltd. Identical attempts at producing diffraction quality crystals using ksORF57Δ153 in place of hvsORF57Δ146 in both the presence and absence of PAN17 RNA were carried out without success.Data were collected from three single cryo frozen crystals. A high redundancy dataset was collected from a single selenomethionine (SeMet) derivatized crystal of free hvsORF57Δ146 at Diamond Light Source (DLS) (i03). This SeMet data was subsequently used to solve the structure by single wavelength anomalous dispersion (SAD). A high-resolution native data set was collected (DLS i03) to a resolution of 1.86 Å and subsequently used to refine and rebuild the ORF57 model. A third crystal was used to collect a native data set of the ORF57 RNA complex hvsORF57Δ146-PAN17 (DLS i04-1).All data were scaled and merged in Xia2 with phasing performed with the Big EP pipeline . The parFor size exclusion chromatography coupled with multi angle light scattering (SEC-MALS) analysis, samples (0.5 ml at 1 mg/ml) were loaded onto a Superdex 200 10/300GL column  and passed through a Wyatt DAWN Heleos II EOS 18-angle laser photometer coupled to a Wyatt Optilab rEX refractive index detector. Data were analyzed using Astra 6 software .l-Arg, 50 mM l-Glu, 1 mM EDTA, 2 mM TCEP, pH 6.2 and concentrated using Vivaspin centrifugal devices. 5% (v/v) D2O was added to samples for lock and data was acquired at 298 K on a Bruker Avance III 800 MHz spectrometer equipped with a cryoprobe. Topspin 3.2 was used for data acquisition and processing. Mapping of RNA binding sites was carried out by acquisition of HSQC spectra of 50 μM uniformly 15N labelled protein without binding partner and then addition of a small volume of unlabeled RNA oligo, either PAN17 (PAN bases 34–50 sequence CACCUAUGGAUUUUGUG) or 14merS (CAGUCGCGAAGAGG), from a concentrated stock in matching buffer resulting in an equimolar mixture of the two species. A second HSQC spectrum of the bound state was then acquired for comparison. The distance of signal movement were measured as described previously for the ICP27-REF1–155 interaction . Experiments were performed using the ProteOn XPR36 SPR instrument (Bio-Rad Laboratories). The ProteOn XPR36 is a multiplex system that can be used to provide simultaneous flow of up to six different analyte concentrations (channels A1–A6) over up to six different ligand channels (L1–L6). Running buffer (RB) was 250 mM NaCl, 20 mM HEPES, 0.05% (w/v) Tween-20, pH 7.4. All experiments were performed at 25°C. Immobilization of biotinylated RNA oligos was performed on a GLC chip (Bio-Rad Laboratories) in the vertical orientation. Four channels  were activated with 150 μl of a 1:1 mixture of 20 mM N-ethyl-N′-(3-dimethylaminopropyl) carbodiimide (EDC) and 6.5 mM sulfo-N-hydroxysuccinimide (sulfo-NHS) in water at a flow rate of 30 μl/min. NeutrAvidin was diluted in 10 mM sodium acetate buffer pH 5 to a final concentration of 50 μg/ml, and 150 μl was injected, followed by an injection of 150 μl of 1 M ethylenediamine–HCl, pH 8.5, at a flow rate of 30 μl/min. The immobilization level of NeutrAvidin was ∼2000 resonance units (RU). Then RNA oligos were bound on channels L2–4 to an immobilization level of ∼100 RU. The RNA oligos used were PAN17 (CACCUAUGGAUUUUGUG) and 14merS (CAGUCGCGAAGAGG) as well as a non-specific control 14mer1 (CCGUCCCCGCCGCU). The L1 channel (no RNA) was used as a reference. Measurements of binding were made using different concentrations of viral protein constructs in channels A2–A6, channel A1 was used as a buffer only control. A short pulse of 2 M NaCl (50 μl/min for 60 s) was used for regeneration between measurements. Non-specific binding of protein to the reference channel precluded the use of analyte concentrations above 1 μM. All binding sensorgrams were collected, processed and analyzed using the integrated ProteOn Manager software (Bio-Rad Laboratories). Short black segments on some sensorgrams represent artefact (spike) removal from the data. Sensorgrams were assessed qualitatively for RNA interactions.Purified proteins ksORF57Δ153, hvsORF57Δ146, UL69et al. , an intrinsically disordered region (68–178), which contained the region analogous to the hvsORF578–120 RNA binding site plus flanking sequences containing three NLSs also implicated in RNA interactions in experiments by Majerciak et al.  allowedry 27484 . Also inry 27484 . The datE. coli and purified to homogeneity. Similarly the proteins ICP27Δ241, UL69200–540 and ksORF57Δ153 were also purified  of the closely-related ORF57 proteins from KSHV (ksORF57) and HVS (hvsORF57) interacted with reporter RNAs, and in parallel we also carried out the same experiments with the distantly-related homologous IHDs from HSV-1 ICP27 and HCMV UL69. The regions containing the IHD of HSV-1 ICP27, HCMV UL69 and ksORF57 have already been determined ,43,48, bpurified ,43,48 . However only the hvsORF57Δ146 construct produced diffracting crystals. Initial phases were obtained from single wavelength anomalous dispersion (SAD) experiments carried out on selenomethionine derivatized crystals in Phenix.AutoSol . Model bThe C-terminal IHD of hvsORF57 forms a compact homo dimer, approximately 65  ×  60  ×  40 Å. Each polypeptide chain is composed of 11 α-helices and a small β-sheet formed by two parallel strands, folded into an individual globular domain Figure . One CHCTo investigate PAN RNA binding to ORF57 from the structural perspective, purified hvsORF57Δ146 mixed with a 3-fold molar excess of a 17-mer RNA oligo matching bases 34–50 of PAN (PAN17) was screened against crystallization conditions, and produced diffracting crystals. The hvsORF57Δ146-PAN17 diffraction data was solved by molecular replacement with Apo hvsORF57Δ146 coordinates. Data were deposited into the protein data bank (code 6HAT). Continuous electron density was assignable to residues 145–417 in both ORF57 polypeptide chains that formed a homodimer (residue 145 is a non-WT glycine introduced in cloning). The conformation of hvsORF57Δ146 was unchanged from that observed in the Apo form, with a Cα RMSD of 0.6 Å (residues 147–417). The RNA oligo was observed in association with the bridge formed by the two α6-helices at the dimer interface, and the electron density was assigned to the bases 44–47 of PAN, a UUUU sequence, plus an isolated nucleotide pair assigned to the 5′ CA sequence of the PAN17 oligo Figure ,B. WithiThe structure of the C-terminal IHD RNA binding site is suggestive of binding preference for pyrimidine and likely uridine bases, and the bent conformation of the RNA as it contacts ORF57 is indicative that the nucleotides should be flexible and not constrained by base-pairs. We therefore use the term ‘U-bend’ to describe this RNA motif. Considering steric factors, a cytosine base could also be accommodated in place of the uracil, however when the hydrogen bond patterns are also considered this would not be compatible in the site on chain B and only possible in chain A if the sidechain amide of Q308 adopts a different rotamer with the sidechain amide flipped 180 degrees. The steric bulk of larger purine bases could not be accommodated in the conformation observed. In the structure, any specificity determinants for the peripheral bases were not discernible, therefore the structure is suggestive of a single stranded RNA binding motif compatible with NYUN, where N is any base and Y is a pyrimidine. As an indirect probe for specificity, we were able to crystallize hvsORF57Δ146 in the presence of both shorter RNA oligos corresponding to sub-fragments of PAN17, namely CACCUAU and GAUUUUGU (PAN 34–41 and PAN 42–49 respectively), however electron density for RNA was only discernible in the latter, which matched the same U-bend interaction observed for PAN17 (data not shown).et al. , regions that share 19% sequence identity, indicates they clearly adopt the same core protein fold Figure  and the ing site , and theA low-resolution (3.5 Å) structure of ksORF57 (Protein Data Bank code 5ZB3) was recently published which revealed a dimeric fold with a domain swap mediated by an N-terminal arm . The ksOThe ICP27 homolog expressed by Kaposi's sarcoma herpesvirus (KSHV) named ORF57 (ksORF57) promotes the stabilization of a functionally important long non-coding polyadenylated (PAN) RNA as well as other nascent transcripts ,28,29,3468–178 to identify RNA interaction sites within the N-terminal domain using NMR signal perturbation mapping, which indicated residues 79–145 bind RNA with an apparent preference for an oligo containing the sequence motif GAAGRG over a PAN MRE derived oligo  is not present in ICP27 Figures , B whichet al. (The significance of the ORF57 U-bend binding site likely reaches beyond just the PAN RNA MRE studied here as several other ksORF57 interaction sites on PAN RNA were identified by Sztuba-Solinska et al. , which wet al. . We theret al. , we specet al. . Therefoet al. . The ORFet al. ,72. The Supplementary DataClick here for additional data file.


The advent of second-generation sequencing and its application to RNA sequencing have revolutionized the field of genomics by allowing quantification of gene expression, as well as the definition of transcription start/end sites, exons, splice sites and RNA editing sites. However, due to the sequencing of fragments of cDNAs, these methods have not given a reliable picture of complete RNA isoforms. Third-generation sequencing has filled this gap and allows end-to-end sequencing of entire RNA/cDNA molecules. This approach to transcriptomics has been a “niche” technology for a couple of years but now is becoming mainstream with many different applications. Here, we review the background and progress made to date in this rapidly growing field. We start by reviewing the progressive realization that alternative splicing is omnipresent. We then focus on long-noncoding RNA isoforms and the distinct combination patterns of exons in noncoding and coding genes. We consider the implications of the recent technologies of direct RNA sequencing and single-cell isoform RNA sequencing. Finally, we discuss the parameters that define the success of long-read RNA sequencing experiments and strategies commonly used to make the most of such data. The first decade of the new millennium has made it abundantly clear that most genes produce multiple distinct isoforms: Estimates of the fraction of multi-exon genes that are alternatively spliced rose from 42% in 2001  to 74% iThis abundance of alternative sites and events raises a number of key questions, for many  of which, sequencing of full-length isoforms is giving and is expected to yield significant insights. 1) Which combinations of the previously mentioned variable sites are actually being generated as RNA isoforms? In theory, all these alternative sites can specify an exponential number of distinct RNA molecules by exploiting distinct combinations of the previously discussed sites; however, recent data suggest that for many  genes, this is not the case. 2) Can we find the precise cell types that generate each isoform? As we will see later, single-cell approaches are beginning to offer a window into this field. 3) What is the relative timing of multiple alternative processing events within a gene? 4) With multiple long-read sequencing approaches now available, we must ask to which extent these may give different answers. This review will focus on the contributions made by long-read RNA sequencing to date and will also include a discussion of the technical challenges that have been overcome and that will need to be overcome in the future.We will only briefly touch on other important questions, such as “Which isoforms harbor function?”—a question whose negative is not easily assessed, given the variety of possible settings in which an event may be relevant. Here, we will limit ourselves to saying that there are many clear examples for the functionality of alternative RNA processing events. This is exemplified by the FAS receptor  and the High-throughput transcriptional profiling (“RNA-seq”) was pioneered in 2008, which enabled a transcriptome-wide survey of gene expression and alternative splicing in a quantitative fashion . Despite1) single cells   revealedSimultaneous with the previous results, the Wong laboratory’s hybrid sequencing approach  i.e., c, RefSeq n alternative sites with only two options each, which (for simplicity of the argument) are all used in 50% of the molecules  could produce 2n combinations at relative abundances of 1/(2n) through exhaustive random pairing. Under random pairing, short-read sequencing would be the method of choice because it would yield a usage probability for each variable site at the lowest cost. The frequency of each complete isoform could then be determined by multiplication over the associated probabilities. At the other end of the spectrum, perfectly coordinated exon pairing could result in just two isoforms . In this setting, short-read probabilities of variable sites would be uninformative for complete isoforms. The interest of combinations has been noticed and investigated for a long time  distributions across individuals cells . That isIn 2018, the same lab extended the single-cell long-read view to 96 cells , also deThe advantages and disadvantages of different long-read sequencing strategies can be summarized using several criteria (summarized in Completeness of reads: Completeness of reads describes the extent to which a long read represents the entire underlying RNA molecule. An interesting twist to this question is that a long read may represent a complete RNA molecule (which was turned into cDNA) but not a complete transcript, as the RNA molecule may have suffered damage in the cell or during the experiment. Whether a read is complete at its 3’ end is, in theory, relatively easily assessed by considering its polyA-tail. Of note, a cDNA molecule that is generated through reverse transcription with a polydT primer must contain a polyT (or polyA depending on its orientation) region at its end. This is the case even if the polydT primer annealed to a non-perfect genomic A-rich region because the sequence in the cDNA is determined by the primer, not by the transcript’s region that the primer bound to. Given these observations, it was a surprise that we initially, using a hidden Markov model, only found 67% of PacBio CCSs to contain a polyA-tail  with Illumina short reads  content, structure, to name only a few. Looking at the bias of representation fundamentally asks whether the molecules that are presented to the machine differ significantly in any of the previously discussed characteristics from those that are reported as long reads. On the PacBio machine, there is little to no bias of coverage in GC-rich region . The continued development of novel bioinformatic approaches designed specifically for long, noisy reads can be expected to lead to further increases in performance. It seems that in the near future, a lot of biological reasoning could be performed with the isoform as a unit, rather than with single exons, splice sites, RNA edits, and modifications. Eventually, this development will hopefully further the large body of knowledge on the interactions between the respective machineries and allow us to appreciate all variables on individual RNA molecules at once.All authors listed have made substantial, direct, and intellectual contribution to the work and approved it for publication.HT is a Leon Levy Research Fellow in Neuroscience and is furthermore grateful for a generous gift by Anita Garoppolo. SH acknowledges an Australian National Health and Medical Research Council (NHMRC) Early Career Fellowship (APP1156531). PF and AF acknowledge support from the National Human Genome Research Institute (U41HG007234), the Wellcome Trust (WT108749/Z/15/Z), and the European Molecular Biology Laboratory. PF is a member of the Scientific Advisory Boards of Fabric Genomics, Inc., and Eagle Genomics, Ltd.PF is a member of the Scientific Advisory Boards of Fabric Genomics, Inc., and Eagle Genomics, Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA. Here, we present a data-driven model for RPI prediction using a gradient boosting classifier. Amino acids and nucleotides are classified based on the high-resolution structural data of RNA protein complexes. The minimum structural unit consisting of five residues is used as the descriptor. Comparative analysis of existing methods shows the consistently higher performance of our method irrespective of the length of RNA present in the RPI. The method has been successfully applied to map RPI networks involving both long noncoding RNA as well as TERRA RNA. The method is also shown to successfully predict RNA and protein hubs present in RPI networks of four different organisms. The robustness of this method will provide a way for predicting RPI networks of yet unknown interactions for both long noncoding RNA and microRNA. At the turn of the century, the discovery that most of the human genome is transcribed into noncoding RNA (ncRNA) and is involved in regulation has led to renewed interest and a paradigm shift in the field of RPI10. Two of the most widely used techniques to determine high-resolution structures of biological molecules are X-ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy. Even though both methods have yielded highly informative atomic resolution structures of RNA-protein complexes, the number of available structures is abysmally low compared to the protein structures. Part of this problem can be attributed to the difficulty in obtaining well-ordered RNA crystals and the complex nature of NMR spectra due to a severe overlap of proton resonances and lower proton density of RNA residues than proteins. However, recent advances in low-resolution high throughput methods have shed light on the varied atlas of these interactions. With the advent of techniques such as HITS-CLIP11 and PAR-CLIP12, there is a better understanding of the role of ncRNA in biology and the role of RPI in general, however, these experiments are often expensive and time-consuming. This has led to the development of computational tools to predict RPI.Ribonucleic acids, RNA, have shown to be essential for a myriad of biological processes ranging from genetic information storage to being active enzymes due to their structural diversity. Many functions discovered for RNAs involve the binding of RNA binding proteins (RBPs) to specific sequences or structural motifs within the RNA. These RNA-protein interactions (RPI) govern the assembly and function of ribonucleoprotein particles (RNPs), such as ribosomes and spliceosomes, play principal roles in viral replication and translation, regulation of gene expression 13. Even though this method led the way for using computational tools for predicting RPI, it was limited due to its requirement for experimental data. Soon after, Muppirala et al., developed a similar method, RPISeq, but based solely on RNA and protein sequence information14. RPISeq is based on the curated RNA-protein interactions obtained from PRIDB15, a database of RNA-protein structures extracted from PDB, and used RF and SVM as the classifiers. Recently, Suresh et al., have proposed RPI-Pred, an SVM based prediction method utilizing protein and RNA secondary structural information as input features16. More recently, predicting RPIs using Deep Learning has been proposed17. Besides the sequence-based methods, RPI prediction methods such as catRAPID18 and lncPro19 use both sequence and structure information to predict RPI. Though all these methods have reasonable prediction accuracies on both cross-validation tests and external databases, the confidence of the predictions is very low and improvements are sought in dealing with RPI involving short RNA or protein sequences such as the ones present in the regulation of telomeres (TERRA RNA and the RBP interactome) and biogenesis of microRNA (microRNA and its interactions with RBPs).Nowadays, a widely used computational technique in predicting bio-macromolecular interactions is Machine Learning (ML), a type of Artificial Intelligence (AI) that enables computers to learn to perform a task without programming them explicitly. The ML models can be supervised, by providing the machine with the correct output for each corresponding input or unsupervised, wherein no outputs are provided and the machine learns the relationships between the inputs, an example of which would be clustering. RPI prediction is a type of supervised learning problem, specifically, a binary classification problem in which the goal is to predict whether a given RNA-protein pair is interacting or not. The first of its kind prediction method for RPI was developed by Pancaldi and Bähler using the Random Forest (RF) and Support Vector Machine (SVM) algorithms and using several parameters (features in ML parlance) such as protein localization, genetic interactions, chromosomal localization, and predicted protein structure as inputs to the modelet al.20. All these methods fail to address several key observations of RPIs:(1) The amino acid classification used in these methods based on PPI work will not be relevant to the RPI prediction as the amino acid interaction propensities are different for PPI and RPI23; (2) The conjoint triad method assumes three amino acids acting as a structural unit in the interactions but in RPI the minimum structural unit required for interactions is five (see results); and (3) the presence of modified bases in RNA that play an essential role in RPI are generally neglected since they are usually labeled as X in the fasta files used for generating the feature vectors. For example, the crystal structure of the T. thermophilus aspartyl tRNA-synthase bound to the tRNAAsp from E. coli clearly shows interactions between the Glu91 of the protein with the Queuosine, a modified guanosine base, at position 34 of the tRNA24. Modified residues have also been shown to provide stability to the overall structure of the tRNA which might influence the binding of proteins to the tRNA25.In binary classification, the model needs to be trained on positive and negative data, i.e. examples of both interacting and non-interacting sequence pairs. All the methods mentioned above use X-ray crystallography derived structures of RPI as a positive dataset. Since experimentally verified non-interacting RNA-protein pairs are scarce, if any, a negative dataset is created by random jumbling of RNA and proteins in the positive dataset with redundancy conditions applied to eliminate false negatives. One major hurdle in doing so is the lack of confidence in the negative dataset that might bias the model’s predictions. Another major component in the successful implementation of ML algorithms is the selection of features. Usually, the RNA is encoded using a window size of four nucleotides, making a 256-dimensional vector, and the protein encoding is different in different methods but essentially follows the conjoint triad method developed for predicting protein-protein interactions (PPI) by Shen XRPI, an ML-based method using the Extreme Gradient Boosting classifier, XGBoost, with features based on data-driven parameters, such as the amino acid interaction propensities and minimum structural unit (window size). Firstly, we have extracted biologically significant features from the RPI structural dataset itself. We have calculated the RPI specific amino acid interaction propensities and have scanned the entire RPI to obtain the length of the optimum binding interface. Interestingly, both the amino acid propensities as well as the optimum binding interface are different for RPI as compared to PPI. Though XGBoost has been applied in several data science challenges, its application in the field of computational biology has been limited26. We have optimized XGBoost to maximize its applicability and performance in dealing with biological systems. To overcome overfitting of the data, we have carefully designed a negative dataset consisting of pairs that are not similar to any known interacting pairs. Comparison with existing methods has shown that XRPI is the best performing method on NPInter, a database of curated RPI sourced from high throughput data and literature28, with 97.8% accuracy as well as TeloPIN, a network of RPI involved in telomere function29, with 99.4%. Our results indicate that XRPI does consistently well both in predicting RPI wherein the input sequence information is either large (NPInter) or very sparse (TeloPIN). Another advantage of XRPI is its computational efficiency allowing processing of large batches in seconds. We believe that the robustness of XRPI will greatly enhance the prediction ability of RPI and the associated RPI networks involving not only the long ncRNA but also microRNA and shorter non-coding RNA.To enhance the accuracy and confidence of predicting RPIs with sequence information alone we have developed XRPI is developed based on XGBoost, an optimized variant of the Gradient Boosting Machine, using data-driven parameters from high-resolution structures of RNA protein complexes. In this method, the amino acids are classified into four classes based on their respective interaction propensities in RPI. Each of the four monomers of RNA is considered separately along with any modified base belonging to the same class as the parent nucleotide. To account for the nearest neighbor effects, that control the structural context of the protein and RNA interactions, an interface size of five is considered for both protein and RNA.th May 2016, 953 RNA-protein complexes with a resolution of ≤3 Å were deposited in the Protein Data Bank30 (www.rcsb.org). Out of the 953 complexes, 351 complexes were rejected due to the absence of any valid RNA or protein chain (see Methods). Any RNA and protein chain in a given complex is classified as interacting if the distance between any pair of atoms of these two chains is less than 8 Å (this distance cut off was used to account for interactions involving bridging water or ion molecules)21. This resulted in a non-redundant dataset of 2825 unique RNA-protein interacting pairs (RPI2825). Since the RNA protein complex structures deposited in PDB are mostly ribosomal complexes, the RPI2825 is split into two parts: RPI2435 and RPI390 containing only the ribosomal RPIs and the non-ribosomal complexes respectively . The interface size for interactions or the window size (k) for the feature generation is defined as a Minimal Structural Unit (MSU) equal to five. The resulting k-mer representations of the RNA and protein sequences are concatenated to form a single feature vector which is then fed into the XGBoost model  has done better than the Shen et al., method and the sequence descriptor (conjoint triad representation) developed by Shen 31. To test the validity of choosing XGBoost over other tree-based boosting algorithms we constructed prediction models using these algorithms and conducted two tests: (1) with the same parameters as the XGBoost classifier, such as the learning rate and the number of trees/iterations, wherever applicable, and (2) with each algorithm’s parameters being independently optimized. Although all the algorithms (with the exception of AdaBoost) have comparable performance on RPI2825 and NPInter, they fail to perform well on the TeloPIN dataset with the GBTree and AdaBoost performing no better than random guessing. This suggests that these boosting methods have learned patterns specific to the RPI2825 dataset and are unable to generalize their predictions to the TeloPIN dataset, consisting of a central RNA molecule made of a repetitive hexamer sequence. The comparative analysis clearly showed the advantage of XGBoost both in the cross-validation as well as on external databases  does better than both XRPI2825 (88.4%) and RPISeq-SVM (90.1%). In contrast, RPISeq-RF performed poorly with accuracy less than random guessing (48.6%). RPI-Pred, where structural information is incorporated in the model building, has an accuracy of only 74.3% and lncPro which also uses structural information performed very poorly  outperforms RPISeq-SVM (88.4%) and RPI-Pred has an accuracy of 96.8% and able to correctly predict all RPIs in Drosophila melanogaster and Saccharomyces cerevisiae. lncPro again performs very poorly with an accuracy of 55.5%. One possible explanation for RPISeq RF and SVM models performing differently on different datasets may be due to the lack of regularization mechanisms in the Random Forest algorithm which may lead to the underperformance of these models in RPI containing sparse data as is the case with TeloPIN having a short hexamer repeat of RNA. Though RPISeq does perform well on the external datasets depending on the model (SVM vs. RF), the confidence of predictions is very low14. RPI-Pred performs reasonably well on NPInter dataset but the difficulty in obtaining the RNA secondary structure and protein structural blocks is a major hindrance in using this tool. On the other hand, lncPro doesn’t perform much better than random guessing in our test. Surprisingly, IPMiner16 fails to correctly predict any of the RPIs either in the NPInter or TeloPIN datasets  is the best performing method on NPInter with 97.8% accuracy and comes in a close second on TeloPIN with 88.4%. It is the only method that does consistently well on both datasets, in spite of vastly differing RNA compositions. The lack of long RNA sequences in the RPI390 (average length of RNA in RPI390 is 44 residues) and the predominance of ribosomal RNA in the comprehensive dataset RPI2825 (with the average length of RNA being 1768) might be the possible reasons for the better performance of RPI2825 in predicting long noncoding RNA protein interactions represented in the NPInter database, whereas TeloPIN interactions involve a repeat hexamer sequence where RPI390 is expected to do better. We provide the XRPI with the models trained on both the RPI2825 and RPI390 datasets allowing the user to choose based on the size and nature of the RPI to be predicted.XRPI over the other methods can be attributed to: (1) the RPI specific domain knowledge used in developing the prediction models and (2) the discriminatory power (positive vs. negative RPI) of XGBoost compared to other ML tools. When we have used the PPI based domain knowledge in generating the RPI prediction models using the current datasets, we have seen lesser prediction accuracies compared to the XRPI  between the protein MSUs chosen by XRPI to the MSUs often seen at the binding interface of the RPIs. In other words, XRPI is learning to discriminate based on biologically relevant features (http://universe.bits-pilani.ac.in/goa/aduri/xRPI). Another plausible explanation for the better performance of XRPI lies in the domain-specific knowledge provided to the ML.Possible reasons for the superior performance of 28 is downloaded from NPInter website (http://www.bioinfo.org/NPInter/) for the network analysis. This set of NPinter v3.0 contains all known functional ncRNA interactions, verified and curated from literature. We extracted only those entries having “ncRNA-protein binding” and wherever sequence information is available. The resulting entries were then segregated according to the source organism. The corresponding protein and RNA sequence data is downloaded from “the blast database in NPInter v3.0” section of NPInter. All the RPIs involving Xist and LincRNA from Mus musculus were used for creating the Xist and Linc RNA networks. Telomeric proteins interaction network (TeloPIN) database consists of data on telomeric protein interactions with telomeric repeat-containing RNA (TERRA). These interactions are downloaded from the TeloPIN database29 (http://songyanglab.sysu.edu.cn/telopin/). All the datasets used in the current study are available at XRPI webpage.The high confidence dataset referred to as “golden set” of NPInter v3.0These databases provide two kinds of networks: 1. single core networks with a star topology consisting of either a single RNA interacting with multiple proteins or a single protein interacting with multiple RNA and 2. Partial mesh networks with multiple interconnected RNA and protein components.32. The TeloPIN database currently has TERRA networks of two organisms - Homo sapiens and Mus musculus which we use to construct the networks. At the heart of this star network is the repetitive TERRA sequence - UUAGGG. Our method trained using RPI2825 successfully predicted 124 out of 140 potential TERRA-protein interactions in Humans and 36 out of 41 in Mouse giving a prediction accuracy of 88.4%  is involved in several regulatory processes such as telomere length control, the formation of telomeric heterochromatin, and telomere motion. TERRA performs these functions by interacting with specific proteins and recruiting them to the telomeres.4% Fig. . Interes33. Xist recruits several RNA binding proteins in a cascade of events to perform its function. The current method is successful in predicting 71 out of 74 Xist interactions with the proteins ins Fig. .8. Owing to their controlled interactions with regulatory proteins, these RNAs have been implicated in chromosome remodeling and gene expression34, pluripotency and differentiation35, and immune response36. We constructed the lincRNA interactome from M. musculus from the lincRNA RPI derived from NPInter golden dataset. Our method successfully predicted 142 out of 147 (with an average confidence of prediction being 81%) possible interactions involving lincRNA  are a kind of long noncoding RNA that do not overlap protein-coding genesRNA Fig. .S. cerevisiae, we were able to build the RPI network which revealed multiple hubs made of both proteins and RNA  with confidence levels of 88%. For D. melanogaster and M. musculus, the prediction accuracies are 94.7% and 94.9% respectively  of the RPI present in RNA Fig. . For H. www.rcsb.org). A minimum length cutoff of 25 and 15 residues per chain is used for proteins and RNA respectively to be considered as a valid chain in the complex. Any RNA and protein pair in a given complex is classified as interacting if the distance between these two chains is less than 8 Å. The resulting 15088 positive RNA-protein interacting pairs are checked for redundancy, and exact pairwise duplicates are removed to obtain a non-redundant dataset of 2825 unique positive RPI pairs (RPI2825). These chains are manually curated to incorporate the modified nucleotide bases by replacing the “X” with the corresponding parent nucleotide, for example, uridine for pseudouridine, in the fasta sequence, which is otherwise ignored in previous studies. There are a few modified amino acid residues in protein chains and are treated in the same manner as the RNA. To overcome the bias towards the ribosomal RNA protein structures, two more datasets: RPI2435 - consisting of all the known ribosomal complexes in the RPI2825 dataset, and RPI390 - consisting of only the non-ribosomal complexes from RPI2825 are also used in generating the prediction models. The PDB IDs of the RNA protein complexes used in the current study can be downloaded from http://universe.bits-pilani.ac.in/goa/aduri/xRPI.X-ray crystal structures of RNA protein complexes with resolution ≤3 Å are downloaded from PDB databank  and the multimeric complexes . For a pair in this generated set to be considered as truly negative: (1) it should not be in the positive RPI list; (2) the protein in a negative RPI must have less than 30% sequence similarity to all the proteins that the RNA of the given negative RPI is paired with in the positive RPI set , similar check is done for the RNA; and (3) In the negative RPI set itself, all the proteins/RNA that are paired with the same RNA/protein should not have more than 30% sequence similarity. This has resulted in 7561 unique negative RNA-protein interaction pairs, of which we have selected a subset of 2825 in order to balance the positive and negative datasets for training. This subset is chosen in such a way that the ratio of unique protein chains and unique RNA chains is roughly the same in both the positive set and the negative set.37. To calculate the amino acid interaction propensities in RPI, we have defined the binding interface of an RPI using a 5 Å distance cutoff between the RNA and protein chains. Once the binding interface(s) is identified, we have calculated the RPI interaction propensity of an amino acid, to interact with the RNA as given by Equation Pj is the RPI interaction propensity of amino acid j, j at the binding interface and j at the binding interface and It is well known that the amino acid interaction propensities for PPI are different compared to RPI and DNA-protein interactionsThis resulted in formation of four distinct groups (Supplementary Table 38 to predict RNA secondary structure is made of five residues. Based on these observations we choose a descriptor (window size) of five residues to represent both the protein and RNA sequences. This five residue window, named minimum structural unit, MSU, takes into account the effect of two adjacent nearest neighbors on each side of the central residue. This rationale is supported by the observation that the optimum binding interface in an RPI, as derived from the curated dataset obtained previously is found to be of five residues . Besides, In case of RNA, the minimum structural unit often used in dynamic programming algorithms such as Mfoldk-mer feature representation. This representation uses a window of size n to create a histogram of frequencies for each possible n-mer. We used a window size of five, an MSU representation, for both RNA and protein chains. This technique enables us to map sequences of variable length to a finite dimensional vector whose size depends on the width of the window and the classification used. The vectors are further normalized by scaling each feature to be in the range of  by subtracting the minimum frequency from each feature’s frequency and dividing by the maximum frequency of that feature for a given RPI, in order to balance the effect of high frequencies found in longer chains. Here each protein and RNA is represented by a 1024 (4 × 4 × 4 × 4 × 4) dimensional vector. One such vector is constructed for each RNA and protein and they are appended together to create the final vector representation of the RNA-protein pair  of 0.25 with each tree having a maximum depth of eight. The L1 regularization parameter was set using cross-validation to 1.12 and the L2 regularization parameter was set to 18.51 before training. The sub-sample ratio was set to 0.9 and the loss function to be optimized was the “binary:logistic” function, otherwise known as the log-loss function, which is well suited for binary classification tasks. The optimized XGBoost models were trained on a 16 core CPU to speed up the learning process. Parameter optimization and evaluation of the models is done using 10-fold nested cross-validation .We have used the following metrics to assess the performance of the method.Where, TP is true positives, TN is true negatives, FP is false positives, and FN stands for false negatives.XRPI is freely available at http://universe.bits-pilani.ac.in/goa/aduri/xRPI. All the datasets used in the current study, as well as the comparative analysis metrics, are also available at XRPI website. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful models41, we have developed a webserver that provides XRPI as an online service. A standalone Linux program has also been provided so that users can process multiple RNA-protein sequence pairs efficiently on their local systems. The links for the web server as well as the binary files are freely available at the XRPI website http://universe.bits-pilani.ac.in/goa/aduri/xRPI.XRPI, an RNA protein interaction prediction tool using sequence information alone as input, based on XGBoost, a boosting ML algorithm that hasn’t been explored in biological systems extensively, and features obtained from understanding the RPI at a molecular level.One way to improve ML-based prediction models of biological interactions is to apply novel ML techniques and/or through domain knowledge based representation of biological information. Here, we propose XRPI, we elucidated RPI specific knowledge about the optimum binding interface (MSU) and RPI specific amino acid/nucleotide interaction propensities which are then used to represent the RPI to ML algorithms. The RPI specific properties elucidated in the current study will definitely expand the knowledge of RNA protein interactions in general. We believe that using domain-specific knowledge (either readily available or needs to be derived as is the case here) greatly enhances the performance of ML-based prediction models. To the best of our knowledge, XRPI is the first of its kind sequence alone based prediction model that uses domain-specific knowledge. The use of XGBoost algorithm that has the ability to control overfitting and over-specialization also enabled superior performance of XRPI. This is particularly important in the context of biological sequence data where models perform well during the cross-validation step but fail to generalize to external testing datasets. Comparative analysis of XRPI with the existing RPI prediction tools has revealed the superior performance of XRPI in predicting RNA protein complexes involving both long noncoding RNA and smaller telomere complex RNAs.We have constructed 3 different predictive models; one is trained on only ribosomal RPIs (RPI2435), another on non-ribosomal RPIs (RPI390), and a comprehensive model that is trained on the complete dataset of 2825 RPIs (RPI2825) derived from the high-resolution x-ray crystallography structures deposited in the protein data bank. In developing XRPI surpasses the current state of the art in predicting RPI by using a superior classifier whose performance is demonstrated through evaluation on external datasets. Hence XRPI is an accurate and robust tool that can be used for reliably predicting RPIs with a high measure of confidence, especially in cases where the sequence information is limiting (eg. telomere network). We anticipate that the method proposed will greatly aid in predicting RPI involving not only long non-coding RNA (lincRNA) but also microRNAs.XRPI by incorporating structural information in our feature set. Creating a structure-aware model for location specific RNA-protein interaction prediction is another research area that we are currently exploring. Incorporating additional training data from newer RPI databases such as RAID42 is one more approach which might improve the predictive power of XRPI. In the future, we plan to combine the domain knowledge and graph theoretical approaches to further the performance of XRPI.We are currently applying the data driven parameter strategy outlined here to develop models for predicting other biomolecular interactions such as DNA-protein and protein-protein interactions. We also hope to enhance the quality of predictions from Supplementary Information


POLR3A, encoding the largest subunit of Pol III, cause POLR3-related hypomyelinating leukodystrophy (POLR3–HLD), characterized by deficient central nervous system myelination. Identification of the downstream effectors of pathogenic POLR3A mutations has so far been elusive. Here, we used CRISPR-Cas9 to introduce the POLR3A mutation c.2554A→G (p.M852V) into human cell lines and assessed its impact on Pol III biogenesis, nuclear import, DNA occupancy, transcription, and protein levels. Transcriptomic profiling uncovered a subset of transcripts vulnerable to Pol III hypofunction, including a global reduction in tRNA levels. The brain cytoplasmic BC200 RNA (BCYRN1), involved in translation regulation, was consistently affected in all our cellular models, including patient-derived fibroblasts. Genomic BC200 deletion in an oligodendroglial cell line led to major transcriptomic and proteomic changes, having a larger impact than those of POLR3A mutations. Upon differentiation, mRNA levels of the MBP gene, encoding myelin basic protein, were significantly decreased in POLR3A-mutant cells. Our findings provide the first evidence for impaired Pol III transcription in cellular models of POLR3–HLD and identify several candidate effectors, including BC200 RNA, having a potential role in oligodendrocyte biology and involvement in the disease.RNA polymerase III (Pol III) is an essential enzyme responsible for the synthesis of several small noncoding RNAs, a number of which are involved in mRNA translation. Recessive mutations in  POLR3A, POLR3B, and POLR1C, encoding subunits of the ubiquitous RNA polymerase III (Pol III),1–POLR3A mutations were also recently linked to hereditary spastic ataxia  is specifically affected in POLR3–HLD remains puzzling, as most Pol III transcripts are highly abundant and expressed ubiquitously. Indeed, the CNS appears to be particularly vulnerable to impaired Pol III and/or tRNA function, because mutations in a basal Pol III transcription factor also cause a rare cerebellar disorder , 10, whe11–23–POLR3A, POLR3B, and POLR1C encode subunits of Pol III, one of the three essential eukaryotic nuclear RNA polymerases. POLR3A and POLR3B are the two largest subunits of Pol III and form the active center of the enzyme  exclusively possess upstream promoter elements  mutation, located in the cleft domain of Pol III harboring the template DNA during transcription  of BC200 in an oligodendroglial-derived cell line results in major transcriptomic and proteomic changes, suggesting an important functional role of BC200 RNA in this cell type. Altogether, our results indicate that tRNAs, BC200 RNA, and its downstream targets may be important effectors of POLR3–HLD pathogenesis.In this study, we performed comprehensive profiling of the Pol III transcriptome in human cells carrying the Fig. S1), indicating that this mutation does not impair Pol III complex assembly or nuclear import as did the previously reported POLR1C mutations  and mutant M852V) POLR3A in HeLa cells and performed anti-FLAG affinity purification followed by MS as well as immunofluorescence. POLR3A–M852V pulled down similar levels of Pol III complex subunits compared with the WT and did not accumulate in the cytoplasm  mutation in the endogenous POLR3A gene in HEK293 cells using the CRISPR-Cas9 system . These clones mimic the genotypes observed in POLR3–HLD cases carrying the c.2554A→G (p.M852V) mutation, because four out of the five reported patients that are compound heterozygous for this mutation have a premature stop codon on the other allele  in patient-derived fibroblasts, resulting in decreased levels of POLR3A protein (b and c). POLR3A mRNA levels were reduced in clones M1 and M2 compared with WT clones but not in the homozygous mutant clone M3 . Upon inspection of the cDNA, we observed partial (∼40%) in-frame skipping of exon 19, carrying the M852V mutation, in clone M3 . It was previously shown that in-frame skipping of exons carrying an indel sometimes occurs following CRISPR-Cas9 editing . ChIP followed by quantitative PCR at three Pol III-transcribed genes also showed comparable POLR3A occupancy in the two groups . Although a direct correlation generally exists between Pol III occupancy and nascent RNA levels . We synthesized exogenous spike-in RNAs to confirm small RNA enrichment  and focused on precursor tRNAs to (i) overcome the quantification challenges associated with the strong secondary structure and RNA modifications of mature tRNA, and (ii) to more directly assess Pol III transcription levels, without the confounding effects of tRNA stability . In contrast to the drops in coverage expected at highly-modified positions in mature tRNAs , indicating that most reads originate from precursor tRNA molecules that have not yet acquired post-transcriptional modifications. For Pol III transcripts larger than 200 nucleotides, we performed standard rRNA-depleted RNA-seq. Pol III transcripts showed a global trend toward decreased expression in mutants compared with controls in both RNA-seq datasets (a). Furthermore, stratifying according to their promoter type revealed that this decrease was mostly attributable to Pol III transcripts possessing type 2 internal elements, whereas type 3 transcripts were less affected (b).Pol III occupancy levels measured by ChIP-seq have traditionally been used as a proxy for Pol III transcription levels , 39, becA levels , this core tRNAs , 42, we datasets a. Furtheaffected b.p < 0.0001, Wilcoxon rank-sum test) (a). However, although the majority of expressed precursor tRNAs showed a trend toward a decrease , at the individual gene level only four tRNA genes demonstrated a statistically significant decrease (FDR <0.05), due to the variability in the genotypes of the three mutants. To reduce variability and analyze the effects of the potentially most detrimental POLR3A mutations on tRNA expression, we also analyzed biological triplicates of mutant M2, the clone with the lowest POLR3A expression (c and d). Indeed, the large majority of nuclear-encoded tRNAs showed decreased expression in M2 compared with control C3 (c and d), with 77 tRNAs reaching statistical significance at the individual gene level . In contrast, levels of other categories of small RNAs (snRNAs and snoRNAs), the majority of which are not synthesized by Pol III, were unaffected, both in individual clones  and in replicates of clone M2 . Decreased tRNAs belonged to most tRNA isoacceptor families (a), suggesting a widespread effect on transcription of tRNA genes and arguing against a specific impact on tRNAs cognate for certain codons or amino acids. We next confirmed the decreased precursor tRNA levels by an independent method, measuring the expression levels of three tRNAs by qRT-PCR with primers specific to the precursor form for two of them (b). However, the down-regulation of precursor levels did not result in a decrease in levels of three mature tRNAs detectable by Northern blots (c), suggesting that the stability of tRNAs is sufficient to overcome the impaired tRNA synthesis in HEK293 cells. Altogether, these results provide the first evidence that a leukodystrophy-causing POLR3A mutation causes a global reduction in transcription levels of nuclear-encoded tRNA genes.Because nuclear-encoded tRNAs represent the majority of transcripts with a type 2 promoter, we examined their expression in the small RNA transcriptome data, consisting mostly of precursor tRNAs. Indeed, we observed a global reduction of precursor tRNA levels in the three mutants compared with controls (um test) a. Howevepression , c and dfamilies a, sugges of them b. Howevern blots c, suggesBC200  and genes encoding 7SL RNA were among the most significantly down-regulated genes in the large RNA transcriptome . In contrast, vault RNA levels were comparable across both groups, making them the only transcripts with type 2 internal elements that were not decreased in mutants, which could be due to their vault RNA-specific distal regulatory sequences . Taken together, these results strongly suggest that transcripts possessing type 2 internal promoter elements are the most vulnerable to the studied disease-causing POLR3A mutation.We next examined the other transcripts with type 2 internal promoter elements, which include BC200 RNA, 7SL RNAs, and vault RNAs , 29, 31.a and b), suggesting a particular sensitivity to Pol III hypofunction. To validate this finding, we performed Northern blotting for BC200 RNA in four controls and four POLR3A mutants  carrying the F849L mutation in POLR3A (see supporting Methods)). This confirmed that BC200 RNA levels are significantly decreased in mutants compared with controls (c). To determine whether this transcript was also affected in patient cells, we examined two independent datasets of patient-derived fibroblasts. First, in RNA-seq from four French Canadian POLR3A patients (see genotypes in Table S5), BC200 RNA was the top affected Pol III transcript in patients compared with controls , although not reaching statistical significance after multiple testing correction . Other large Pol III transcripts did not display significant differences between patients and controls . Second, using a custom microarray for all known Pol III transcripts in four American POLR3A cases , we found that BC200 RNA displayed a significant reduction in patients compared with controls . Altogether, we show that BC200 RNA is the top down-regulated large Pol III transcript in three separate datasets of POLR3A mutant cells, including carriers of several distinct mutations, emphasizing its vulnerability to Pol III hypofunction and suggesting a possible role in the pathophysiology of POLR3–HLD.Among the large RNA transcriptome, BC200 RNA, the only Pol III transcript observed to have a predominant CNS expression, was the most down-regulated transcript in HEK293 mutant clones , a and bcontrols c. To detPOLR3A mutant cells and patient-derived fibroblasts, we next investigated the effects of decreasing BC200 RNA expression. Because oligodendrocytes are likely to be one of the main dysfunctional cell types in POLR3–HLD , resulting in absent expression . Next, using the same approach as above, we generated MO3.13 cells carrying the POLR3A c.2554A→G (p.M852V) mutation in compound heterozygosity with a null allele . We verified that POLR3A mutant MO3.13 cells recapitulate the effects observed in HEK293 cells. Indeed, they display reduced levels of POLR3A protein . They also show significantly decreased levels of BC200 RNA . Consistent with our above results, type 3 Pol III transcripts RN7SK, RPPH1, and RMRP were unaffected in POLR3AM852V cells . However, contrary to what we observed in HEK293 cells, 7SL RNA levels were not decreased in mutant MO3.13 cells , suggesting cell type–specific effects that could be related to upstream regulatory sequences specific to 7SL genes (M852V and absent in BC200KO), which was confirmed by Northern blotting (a).Given the expression of BC200 RNA in the CNS and its consistent down-regulation in OLR3–HLD , 8 for tOLR3–HLD , 44. FirSL genes . In sum,blotting a.b), detecting a total of 1,272 proteins. We found that, compared with WT cells, BC200 KO had a more potent effect on the transcriptome and proteome than the POLR3A M852V mutation did, leading to a higher number of dysregulated mRNAs and proteins and to more of them showing a large effect size  . In particular, more proteins were up-regulated than down-regulated in BC200KO cells  . These observations point to a clear functional role for BC200 RNA in MO3.13 cells, because its inactivation leads to significant changes for more than 20% of detected proteins  and expressed mRNAs  (Tables S7 and S8). Gene Ontology analysis showed an over-representation of proteins located in the plasma membrane, endoplasmic reticulum, and cytoskeleton among the up-regulated proteins. In contrast, down-regulated proteins were enriched for ones involved in the G1/S transition, including members of the condensin and minichromosome maintenance complexes, suggesting slower cell growth or cell cycle defects.Given the potential role of BC200 RNA in mRNA stability, transport, and translation regulation , 45, 46,Fig. 6n = 1,200)  and searched for common effects in BC200KO and POLR3AM852V. Although only 29 proteins were consistently significantly dysregulated (log2 fold change >0.5) in both mutants (d), we observed that proteins affected in BC200KO  tended to be affected in the same direction if they were also significant in POLR3AM852V (FDR <0.05), albeit with a lower fold change (e). We also observed a similar effect at the mRNA level . This suggests that the changes in mRNA and protein levels observed in POLR3A mutant cells may be partially explained by reduced expression of BC200 RNA and further support a contribution of this ncRNA to POLR3–HLD pathogenesis.For the remainder of our analyses, we focused on mRNA/protein pairs that were properly detected in both the RNA-seq and SILAC datasets  that were significantly increased or decreased in BC200KO compared with WT but that did not display significant differences at the mRNA level , implying protein-level regulation. Furthermore, we found an additional 54 proteins  that had substantially greater fold changes compared with their mRNA counterparts . Thus, although the majority of deregulated proteins (63.0%) have similar fold changes in SILAC and RNA-seq, suggesting that their regulation occurs at the transcriptional/mRNA level, the remainder (37.0%) appears to undergo changes at the protein level only as a result of BC200 KO (f), perhaps through the direct action of this ncRNA on translation initiation (n = 65) than down-regulated ones (n = 37) in this category (f), which is consistent with the putative role of BC200 as a negative regulator of translation . This type of promoter is composed of control elements located within the transcribed sequence, called A- and B-boxes  with significant phenotype overlap with POLR3–HLD . For differentiation of MO3.13 cells, cells were switched to serum-free 1× DMEM supplemented with 100 nm phorbol 12-myristate 13-acetate (PMA), as described previously (Stable HeLa cell lines expressing FLAG-tagged POLR3A variants (WT or M852V) were produced by transfection with Lipofectamine according to the manufacturer's instructions. MO3.13 cells were purchased from Cedarlane CELLutions Biosystems (no. CLU301). Unless otherwise specified, all cells were grown in 1× DMEM (Wisent no. 319-005-CL) supplemented with 10% fetal bovine serum (Wisent no. 080-150) and 1% penicillin–streptomycin–eviously , and colAffinity purification, MS, and immunofluorescence in cell lines expressing FLAG-tagged POLR3A subunits were performed as described previously , 61.BC200 KO cell lines were generated using an approach adapted from Ref. BC200 gene . Detailed methods are described in the supporting Methods.POLR3A mutant cell lines carrying the c.2554A→G (p.M852V) or c.2547C→G (p.F849L) mutations were generated using CRISPR-Cas9 coupled with homology-directed repair, as described previously . BC200 KPOLR3A were amplified (see Table S10 for primers) and Sanger-sequenced (McGill University and Génome Québec Innovation Centre).For RT-PCR, 1 μg of RNA was reverse-transcribed using the Superscript III reverse transcriptase (Thermo Fisher Scientific). For detection of the M852V mutation, exons 17–21 of m Tris-HCl, pH 7.5, 150 mm NaCl, 1 mm EDTA, 1% Triton X-100, and protease inhibitors (Roche Applied Science)) and incubated on ice for 30 min, followed by centrifugation at 14,000 rpm for 10 min and collection of the supernatant. Protein concentration was determined using DCTM protein assay (Bio-Rad). Protein samples were separated onto a 4–12% NuPAGE BisTris gel (Thermo Fisher Scientific) and transferred onto a nitrocellulose membrane (Bio-Rad). Immunoblots were probed with anti-POLR3A (Abcam no. ab96328), anti-GAPDH (GeneTex no. GTX627408), and anti-actin (Abcam no. ab3280).Cells were homogenized in lysis buffer  were enriched from total RNA using Qiagen miRNeasy (no. 217004), directly followed by random priming, cDNA synthesis, and next-generation sequencing. To monitor the level of small RNA enrichment in each sample, we synthesized three spike-in RNAs  and added them at the beginning of the procedure. Libraries were prepared with the KAPA-stranded RNA-seq library preparation and sequenced on an Illumina HiSeq 2500 with 100-bp single-end reads at the McGill University and Génome Québec Innovation Centre, resulting in an average of 25 million reads/sample. Quality control and trimming were performed as described previously (supporting Methods).Detailed protocols and data analysis are described in eviously . Trimmedeviously , includieviously  and UCSCeviously  version p < 0.05 and a mean normalized expression of >100. Because Pol III transcripts are highly repetitive, alignment and subsequent analyses were repeated with the inclusion of all multimapping reads to confirm consistency in results. Only results that replicated when using either uniquely mapped or multimapping reads were reported.Total RNA was extracted using miRNeasy (Qiagen) or TRIzol (Thermo Fisher Scientific), treated with the RNase-free DNase set (Qiagen), and quantified on Nanodrop. RNA Integrity Number (RIN) was assessed on an Agilent Bioanalyzer and was routinely above 8. RNA extractions were submitted to Illumina TruSeq rRNA-depleted stranded library preparation (HEK293 cells and patient fibroblasts) or NEBNext rRNA-depleted stranded library preparation (MO3.13 cells). HEK293 libraries were sequenced on Illumina HiSeq 2500 with 125-bp paired-end reads at an average of 42 million reads/sample. Fibroblast libraries were sequenced on Illumina HiSeq 2000 with 50-bp paired-end reads at an average of 60 million reads/sample. MO3.13 libraries were sequenced on an Illumina HiSeq 4000 with 100-bp paired-end reads at an average of 61 million reads/sample. Quality control and trimming were performed as described previously . TrimmedPOLR3A-mutated cells were cultured to 80% confluence and cross-linked with 1% formaldehyde directly in the cell medium for 5 min followed by a 5-min quenching in 125 mm glycine. For ChIP experiments, nuclei from 6 × 106 cells were lysed and resuspended in sonication buffer . Chromatin was prepared by sonicating the nuclei in a Covaris E-Series E22 for 8 min at Duty 2. This generated chromatin fragments of 500 bp on average. Sonicated chromatin was immunoprecipitated using rabbit POLR3A antibody (Abcam no. ab96328) bound to magnetic beads (protein A and protein G Dynabeads) (Sigma) for 4 h. The eluates were loaded to a PCR purification kit (Qiagen). Sequencing libraries were prepared from input and ChIP eluates using the TruSeq DNA library preparation kit and sequenced on an Illumina HiSeq 2000 with 50-bp paired-end reads at an average of 18 million reads/sample for input libraries and 56 million reads/sample for ChIP libraries. Quality control and trimming were performed as described previously (read_count/inputread_count), where IP is immunoprecipitation, as described in Orioli et al.  according to the manufacturer's instructions. Real-time PCR was performed in technical duplicates with a 1:25 dilution (tRNAs and BC200 RNA) or 1:2 dilution (MBP) using the SYBR GreenERTM qPCR SuperMix (Thermo Fisher Scientific) on a Bio-Rad CFX96. The ΔΔCt method was used to calculate relative RNA expression, with normalization to genes PSMB6, SDHA, and COPS7A (HEK293 cells) or NDUSF2 and PMM1 (MO3.13 cells). All primers used are indicated in Table S10.Total RNA was extracted using miRNeasy (Qiagen), treated with the RNase-free DNase set (Qiagen), and quantified on Nanodrop. RIN was assessed on an Agilent Bioanalyzer and was routinely above 8. For qRT-PCR of tRNAs, BC200 RNA and m urea denaturing 8% PAGE and transferred to positively charged nylon transfer membranes . The resulting blots were hybridized with 32P-5′ end–labeled probes detecting BC200 RNA, tRNA–Ala–TGC, tRNA–Lys–TTT, tRNA–Leu–CAA and 5.8S rRNA in ExpressHyb hybridization solution (TaKaRa). All probes used are indicated in Table S10 and are complementary to the last 24 nucleotides in 3′ of mature tRNAs, including the CCA trinucleotide. 5.8S rRNA was used for normalization of BC200 RNA and tRNA levels.Total RNA was extracted using miRNeasy (Qiagen), treated with the RNase-free DNase set (Qiagen), and quantified on Nanodrop. RIN was assessed on an Agilent Bioanalyzer and was routinely above 8. RNA samples (3 μg) were separated on a 7 supporting Methods. Briefly, cells were grown in DMEM for SILAC  supplemented with 10% dialyzed fetal bovine serum  and light, medium, or heavy amino acids for MO3.13 WT, BC200KO, and POLR3AM852V, respectively (b). For each of the six independent replicate experiments, equal protein quantities from the three conditions were mixed. Samples were digested with trypsin and analyzed via LC-MS/MS. Data analysis was performed with MaxQuant (Detailed reagents, protocols, and data analysis are included in the ectively b. For eaMaxQuant  version K. C., G. B., A. V., R. S., M. T., B. C., B. B., and C. L. K. conceptualization; K. C. and D. F. data curation; K. C., D. F., E. M., M. T., and C. L. K. formal analysis; K. C., D. F., E. M., and M.-J. D. validation; K. C., D. F., E. M., M.-J. D., M. R. F., B. B., and C. L. K. investigation; K. C., D. F., M. R. F., and C. L. K. methodology; K. C., B. B., and C. L. K. writing-original draft; K. C. project administration; K. C., D. F., E. M., M.-J. D., G. B., A. V., R. S., M. R. F., M. T., B. C., B. B., and C. L. K. writing-review and editing; G. B., B. C., B. B., and C. L. K. funding acquisition; B. C., B. B., and C. L. K. supervision; C. L. K. resources.