Clinical metagenomics is a broad-range agnostic detection method of pathogens, including novel microorganisms. A major limit is the low pathogen load compared to the high background of host nucleic acids. To overcome this issue, several solutions exist, such as applying a very high depth of sequencing, or performing a relative enrichment of viral genomes associated with capsids. At the end, the quantity of total nucleic acids is often below the concentrations recommended by the manufacturers of library kits, which necessitates to random amplify nucleic acids. Using a pool of 26 viruses representative of viral diversity, we observed a deep impact of the nature of sample , the reverse transcription, the random amplification and library construction method on virus recovery. We further optimized the two most promising methods and assessed their performance with fully characterized reference virus stocks. Good genome coverage and limit of detection lower than 100 or 1000 genome copies per mL of plasma, depending on the genome viral type, were obtained from a three million reads dataset. Our study reveals that optimized random amplification is a technique of choice when insufficient amounts of nucleic acid are available for direct libraries constructions. A single cell can contain up to 60,000 RNA molecules (excluding tRNA) [Direct identification of viral genomes from clinical specimens using next-generation sequencing (NGS) remains challenging  because s per mL ,3,4,5. Ts per mL . In reces per mL ,9. One ong tRNA) . For RNAng tRNA) . Furtherng tRNA) .To enrich the proportion of viral targets relative to host nucleic acids, and therefore improve analytical sensitivity, a typical workflow comprises the digestion of free nucleic acids that are not protected by viral capsids, or the precipitation of virus particles before viral nucleic acids extraction ,14. As aIn this study, we compared the performances of virus detection of six methods designed for nucleic acids random amplification and subsequent sequencing library preparation with a method without pre-amplification on a panel of 26 known human viruses. To mimic a complex biological matrix, we further spiked a human plasma pool with a dilution of this virus panel, in order to evaluate the limit of detection of these methods from clinical samples. We also compared the impact of the nature of the nucleic acid extracts  in the detection of viral sequences. Following this comparative study, we determined the limit of detection (LOD) of the best methods by spiking in plasma the WHO reference virus stocks ) and evaluated vertical and horizontal coverage of the viral genomes resulting from these random amplification and high-throughput sequencing.A sample comprising a suspension in PBS of 25 human viruses with different genomic and structural characteristics was purchased from the National Institute for Biological Standards and Controls (NIBSC code: 11-242-001). A representative of a linear ssDNA virus, Parvovirus B19 (NIBSC code: 12/208), was diluted to get Ct = 24 and added to the mix to form the virus multiplex reference panel (VMRP). The complete list of the 26 viruses is presented in 4, 103 or 102 viral gc/mL of plasma pool. The plasma pool used in the two studies was derived from five human plasma samples from healthy blood donors purchased from ICAReB .WHO reference virus stocks WRVS,  were purA schematic overview of the experimental design for the evaluation of the different methods on the VMRP panel of 26 viruses, diluted or not in the plasma matrix, is given in ® centrifuge tube filter 0.45 µM (cellulose acetate membrane)  then treated by nucleases as described above.As summarized in 4, 103 or 102 viral gc/mLwere treated prior to extraction with benzonase (25 U/µL) for 2 h at 37 °C.Other plasma samples derived from the same pool and spiked with WRVS at a final concentration of 10® Cador® Pathogen kit  with the substitution of carrier RNA by Linear Acrylamide  (10 µg per extraction). To get the RNA fraction, the extract was digested with TURBO DNase (Ambion) (10 U for 40 µL of nucleic acids) and purified with a Qiagen RNeasy Mini Kit.Total nucleic acids were extracted by the QIAampAll VMRP sample extracts were below the detection limit of the Qubit quantification system .Before pre-amplification, reverse transcription  using random hexamers was carried out on both total nucleic acids (NA fraction) and RNA fraction, except for SMARTer and MATQ methods, which included their own reverse transcription step. The methods of pre-amplification were compared to a method without amplification, referred to as the control method. Libraries were prepared following the instructions of the kit manufacturer for both SMARTer and Accel methods. For the other methods, the NEBNext Ultra II DNA Library Prep kit  for Illumina was chosen because it allows a broad range of input amounts, from 500 pg to 1 µg DNA. Our strategy was to construct the different libraries from the maximum amount of amplified nucleic acids available, with an upper limit of 1 µg DNA. Except for SMARTer libraries, DNA samples were fragmented by Covaris M220 Focused-Ultrasonicator  using microTUBE-15. The adaptor concentration, the size selection post-ligation or clean-up only, and the PCR cycle number were adjusted according to the input material following the manufacturer recommendations. Libraries were analyzed for size distribution using the High Sensitivity DNA Kit  on a Bioanalyser Instrument. The individually indexed libraries were quantified using Qubit HS DNA and pooled at equal molar quantity. Sequencing was carried out on Illumina MiSeq platform at a depth of three million reads for crude VMRP and for WRVS spiked in plasma, and on HiSeq2500 at a depth of 30 million reads for the VMRP diluted in the plasma matrix.https://doi.org/10.6084/m9.figshare.7588592), and Diamond-blastp similarity search against the protein Reference Viral Database  followeGenome fraction is defined as the percentage of bases that align to the reference genome. Viral genome fractions were obtained by mapping against the reference genomes using CLC Genomics Workbench Version 9.5.3 Qiagen, with the following parameters: length fraction of 0.8 and similarity fraction of 0.9 for the whole study. Reference viral genomes used for read mapping of WRVS are presented in The DNA yields obtained by random amplification of NA and RNA fractions from the same extracts of non-quantifiable nucleic acids are shown in The highest amounts of DNA product were achieved with WTA and MALBAC, both for NA and RNA fractions. The number of cycles of quasi-linear pre-amplification and amplification used for MALBAC were respectively 12 and 25, as the default number of cycles (8 and 17) did not allow to amplify the RNA fraction above the negative control (PBS). In presence of plasma, default number of cycles were used and generated a smaller amount of amplified product.Sequencing of the libraries of VMRP treated by the seven methods generated an average of 2,476,120 total reads  in NA fraction and an average 2,106,476 total reads  in RNA fraction after quality preprocessing .Replicated reads (redundancy) are usually introduced during library sequencing and are cumulated with those generated during the previous random amplification step, if any. For a given number of reads, the degree of replication has an impact on the viral genome fraction obtained. Read mapping of target viruses showed that the percentage of replicated reads was variable between methods. The highest percentage was observed for RNA viruses with SMARTerV1 method (respectively 33% and 54% for NA and RNA fractions). The Accel method showed an intermediate percentage of replicated reads . Both methods included their own library preparation that required respectively 16 and 17 cycles of final PCR. The other libraries, all constructed using the NEBNext kit, required less cycles of final PCR  and showed the lowest number of replicated reads. Among these libraries, DOPlify reached about 14% replicated reads, suggesting the direct impact of the pre-amplification step within the library construction.The weighted number of contigs and singletons parameter (WNCS) was used as readout to compare the proportion of the five genomic groups of viruses as defined by the Baltimore classification of viruses  and ssRNA (−)) in each fraction for all methods . Both taIn the NA fraction B for allIn the RNA fraction D, dsRNA It was verified that the majority of the detected dsRNA viruses corresponded to the spike virus, the rest being Kadipiro virus, a known contaminant of nucleic acid extraction spin columns . For theMicroviridae, CRESS virus, Circoviridae, Parvovirus NIH-CQV, Anelloviridae, Parvoviridae (other than B19). The dsDNA viruses identified with all methods belongs to Caudovirales, Mimiviridae, and Phycodnaviridae. Iridoviridae were detected only with NoAmp, Accel and SMARTer. The ssRNA(+) virus, Bovine viral diarrhea virus 1 , a known contaminant of calf serum used in cell culture, was detected by all methods in both fractions, except for WTA.Comparison with WNCS of spiked viruses shows that the proportion of non-spiked ssDNA viruses increased more specifically in fraction NA and especially with WTA. This viral group includes The distribution of WNCS of targeted or untargeted viruses is shown as a boxplot . The besFor the RNA fraction the same trend was observed, with more dispersed data and the highest WNCS obtained for MATQ.The length of the contigs is an important criterion for the evaluation of methods, as it has a direct impact on the capability to identify distant sequences and to optimize horizontal coverage. In this study, the contigs were all generated by de novo MEGAHIT assembling from the same nucleic material. The size distribution of contigs for all methods and both fractions is shown as a boxplot in The heatmap of genome fractions of targeted viruses presented in As expected, the NA fraction allowed a better genome coverage of DNA viruses . The dsRNA virus was better covered in the RNA fraction.The largest number of identified target viruses in the NA fraction was obtained by the pre-amplification-free control method (24 viruses) followed by SMARTer (despite its dedication to RNA viruses) and MATQ . On the There was no relationship regarding the ratio of mapped viral reads and the genome fraction covered by the reads. Indeed, MALBAC allowed the highest percentage of mapped target virus to total reads for the NA fraction (20.7%)  while NoAn estimate by qPCR of each viral genome abundance was previously described and six viruses among the VRMP were not detectable . The thrThe most sensitive methods  were able to detect all viruses detected by qPCRs. In addition, they also detected reads from some viruses not detected by qPCR: AdV41 (genome fraction 20% for NoAmpNA), PIV-3 (genome fraction 12%) and few reads of CoV 229E, IFVA H3N2, and IFVB. Thus, some NGS methods  show better sensitivity compared to qPCR, even using a relatively low sequencing depth (around 3 million). Some RNA viruses were detected by most protocols in both fractions (PIV-1 (Ct 34.43), PIV-4 (Ct 31.83), HPeV3 (Ct 29.35)).Despite a high horizontal genome coverage, the methods revealed different coverage patterns. As an example of a virus detected by most methods in both fractions (WTA was omitted due to insufficient coverage), the genome coverages of PIV-1 were compared . PIV-1 iParamyxoviridae viruses are characterized by a transcriptional gradient of mRNAs decreasing from 3′ to 5′ genes), the mapping of reads produced with the directional method SmarterV1 was analyzed to distinguish between genomic RNA and mRNA/antigenomic RNA. All reads R1 (representative of the genomic strand) were mapped as the same sense than the original RNA, and all reads R2 mapped as sequences antisense to the original RNA in both fractions, according to the sequencing protocol. This indicates that most reads corresponded to transcripts and that the over-coverage region observed in the phosphoprotein gene did not originate from an amplification bias, but was more likely due to the transcriptional pattern of this paramyxovirus.Of note, all methods, including the NoAmp control, generated an over-coverage of the gene encoding the phosphoprotein in both fractions . To inveThe genome fraction of viruses detected in VMRP and in VMRP spiked in plasma sample at a volume-ratio 1:10 is compared in In the NA fraction, targeted mapping allowed to detect 16 out of 26 viruses of the plasma pool with NoAmp, followed by 9 to 11 viruses with MALBAC, DOPlify and Accel methods, six viruses for SMARTer V2 and only two for WTA A. Virus Paramyxoviridae in presence of the plasma matrix, despite the SMARTer technology depletes libraries containing ribosomal RNA sequences.In the RNA fraction, PIV-1 was only detected by MALBAC, while the other paramyxoviruses were not detected by any other method B. Reads Conversely, increasing nucleic acids input and cycles of amplification allowed to detect AstV with the DOPlify method. This was further confirmed by both nucleotide- and protein-based BLAST analyses. Interestingly, AstV was rarely and inefficiently detected by the different methods. In addition, we observed that dsRNA viruses were under-represented, compared to the SMARTer method. We assumed that this could be linked to the denaturation step preceding the reverse transcription. Indeed, dsRNA may be more difficult to denature than ssRNA, resulting in a potentially bias of detection.Paramyxoviridae, Orthoretrovirinae) were not affected by the increase of temperature while dsRNA viruses (Reoviridae) gained more than 3 logs of detection compared to the denaturation at 65 °C. This new protocol was thereafter named MALBAC-V2.Based on this comparative study, two methods -MATQ and MALBAC- were identified as the most efficient. Indeed, MATQ performed well on the majority of studied criteria , while MALBAC showed a great potential to amplify viral nucleic acids in the NA fraction. Both methods used MALBAC random primers . Since M2, 103 and 104 genomes copies/mL). Of note, the agnostic pipeline detected that the HHV-4 stock was contaminated by the squirrel monkey retrovirus (SMRV), expressed by the virus-producer B95-8 cells [To evaluate the analytical sensitivity of the methods, we compared MALBAC-V2 to the non-modified MALBAC and to the control method NoAmp on WHO reference virus stocks (WRVS) spiked in a plasma matrix at different concentrations  was used as a starting material.Several studies ,33,34 reIn addition, in order to evaluate the impact of background host nucleic acids on viral detection, the VMRP was diluted into human plasma. After this first step of evaluation, two of the methods were selected, combined, optimized and evaluated using five standard viral stocks endorsed as WHO 1st International Virus Reference Standards for Adventitious Virus Detection in Biological Products by NGS technologies (WRVS). Results were compared to a non-pre-amplification-based method in the NA fraction.WRVS and VMRP are not highly purified as they originated from cell cultures, egg passages or clinical specimens. The upstream sample processing, including a nuclease pretreatment, inactivation of nuclease before the lysis step and then nucleic acid extraction, was not evaluated in this comparative study, as it was common to all spiking experiments.The method NoAmp to which we compared the random amplification methods consisted in the reverse-transcription of total RNA into cDNA with random primers, followed by the synthesis of double-stranded DNA from ssRNA/cDNA and ssDNA. Finally, the library was PCR amplified. The method showed the best performance regarding the genome fraction of DNA target viruses and a good one for RNA viruses in the NA fraction, despite usage of a low sequencing depth. The drawback of the method for low nucleic acid input samples is that the concentration of nucleic acids available to construct the libraries is often below the one recommended by manufacturers. As shown in our experiments, it could nevertheless work in such degraded conditions. For example, in this study, the NGS library obtained from VMRP RNA was barely visible on the Bioanalyzer (data not shown). Therefore, it cannot be used in routine with low amounts of starting nucleic acids in conditions that must fulfill quality insurance .Circoviridae and Parvoviridae) in presence of both DNA and RNA. The systematic over-representation bias of small circular genomes was previously described [The random primer-based MDA procedure used in the WTA method, is based on the use of the highly processive Phi29 polymerase. It produces a huge amount of amplified DNA. However, in our study, it was able to detect the three types of RNA viruses in a DNA-free context  but was overwhelmingly biased towards dsDNA and ssDNA . Optimizations would probably be necessary to obtain better detection of viral genomes.SMARTerV1 and V2 rely on a reverse transcriptase with template-switching activity, and a step enabling removal of ribosomal and mitochondrial cDNA following cDNA synthesis and five cycles of PCR. The method includes the library construction. SMARTer outperformed the other methods in both fractions regarding the detection of dsRNA viruses. Unexpectedly, it performed better on the NA fraction than after a DNase treatment and column purification. The fact that SMARter detected also DNA viruses was very likely due to residual amounts of RNA transcripts in VRMP crude stocks. The rRNA and mtRNA depletion step did not improve the detection of RNA viruses in plasma matrix, compared to other methods without depletion (NoAmp). In addition, the weakness of the protocol lies in the first step, the fragmentation setup. Indeed, the fragmentation parameters are based on the RNA integrity number (RIN), which is obviously not available for the tiny viral fraction among total RNAs). Finally, the protocol does not include a control step before obtaining the library, as for the Accel protocol.MATQ-seq is a multiple annealing and dC-tailing-based quantitative single-cell RNA-seq, using MALBAC primers for single-cell sequencing of total RNA. The number of detected RNA target viruses in RNA fraction and also their genome fraction were higher with MATQ than with SMARTerV1 for most of them. Despite the good performances, the whole protocol is a time-consuming process. This is the only protocol of this study that is not available as a commercial kit.MALBAC is based on multiple annealing and looping-based amplification cycles of genomic DNA and cDNA. It utilizes primers containing a 27-nucleotide common sequence and an eight-nucleotide variable sequence to produce fragments of amplified DNA (amplicons) during a quasi-linear pre-amplification step followed by a regular PCR step targeting the conserved sequence. The method generated the highest percentage of viral reads in the NA fraction, the highest sequencing depth and showed a good sequence accuracy, but was not sensitive enough for RNA virus detection. MALBAC and MATQ-seq were selected for further study and combined to ensure the detection of all RNA viruses.Paramyxoviridae members, even SMARTerV2 that included a removal step of ribosomal and mitochondrial cDNA following cDNA synthesis. NoAmp and MALBAC were the only methods capable of detecting the two viruses that were already the best covered in the absence of plasma (PIV-1 and PIV-4) in the NA fraction. In the RNA fraction the number of RNA viruses detected was smaller than in NA fraction and only MALBAC detected PIV-1. The upstream treatment of plasma samples  may have played a role in the loss of these viruses, as suggested by Li et al., 2015 [In order to mimic the viral detection within a biological matrix, the VRMP was added to plasma from healthy donors at a ratio 10:1. In these conditions, none of the methods recovered all l., 2015 . AnotherDsRNA viruses may be relatively more difficult to denature and biased during reverse-transcription. With a higher temperature for the denaturation step before reverse-transcription, we improved the detection of REO1 with both NoAmp and MALBAC-V2 methods. The upstream treatment and nucleic acid isolation were identical for both methods. Therefore, the improved detection of the dsRNA virus is due to the higher temperature used in the denaturation step before reverse-transcription. The use of the denaturing reagent DMSO prior to cDNA synthesis is also known to increase the dsRNA virus reads .The MALBAC method was first described in 2012 by Zong et al.  for GenoBacillus stearothermophilus (Bst) DNA polymerase for isothermal strand displacement and Taq DNA polymerase for PCR [As performed in MATQ method, reverse-transcription was carried out at low temperature with ten cycles of annealing random MALBAC primers , without for PCR . ConversIn conclusion, amplification of genomic nucleic acids is a necessary step for the available sequencing technologies and MALBAC-V2 represents a useful method for analysis such biological fluids for low input samples, for example when relative enrichment for viral nucleic acids is conducted before extraction.


NF-κB repressing factor (NKRF) was recently identified as an RNA binding protein that together with its associated proteins, the 5′–3′ exonuclease XRN2 and the helicase DHX15, is required to process the precursor ribosomal RNA. XRN2 is a multi-functional ribonuclease that is also involved in processing mRNAs, tRNAs and lncRNAs. The activity and stability of XRN2 are controlled by its binding partners, PAXT-1, CDKN2AIP and CDKN2AIPNL. In each case, these proteins interact with XRN2 via an XRN2 binding domain (XTBD), the structure and mode of action of which is highly conserved. Rather surprisingly, although NKRF interacts directly with XRN2, it was not predicted to contain such a domain, and NKRF's interaction with XRN2 was therefore unexplained. We have identified an alternative upstream AUG start codon within the transcript that encodes NKRF and demonstrate that the full-length form of NKRF contains an XTBD that is conserved across species. Our data suggest that NKRF is tethered in the nucleolus by binding directly to rRNA and that the XTBD in the N-terminal extension of NKRF is essential for the retention of XRN2 in this sub-organelle. Thus, we propose NKRF regulates the early steps of pre-rRNA processing during ribosome biogenesis by controlling the spatial distribution of XRN2 and our data provide further support for the XTBD as an XRN2 interacting motif. Ribosomes are complex macromolecular machines that translate the genetic code into functional polypeptides. They are comprised of four ribosomal RNAs (rRNA) and ∼80 proteins, and their biogenesis is one of the most energy consuming cellular processes . RibosomRecently, using a combination of RNA affinity purification and proteomics approaches, a subset of RNA binding proteins (RBPs) were identified that showed reduced interaction with RNA following inhibition of RNAPI . This grC. elegans, which can be substituted by the functionally related human CDKN2AIPNL (C2AIL), regulates XRN2's stability ,9,298,9,It has been proposed that NKRF interacts with the pre-rRNA and contributes to ribosomal RNA processing . RNA intIn vertebrates, there are only two other proteins that contain an XTBD, CDKN2AIP and C2AIL . Interes


Only a small portion of the stem cells participate in the process of adventitious root formation and the cells/tissues types involved in this process is species-dependent. In olive, it is still unclear which type of cells acquire competence for rooting. Regardless, the entire stem nodal segment (containing a mixture of distinct cell types) continues to be used in studies related to the molecular mechanisms underlying this process. Laser microdissection (LM) technology has been applied to isolate specific tissue and cell types. However, it is difficult to find a standard LM protocol suitable for all plant species and cell types and, thus, LM procedures must be developed and optimized for each particular tissue. In this study, we aimed to evaluate the efficiency of a LM protocol in olive microcuttings stem-base samples. This work presents a simple, rapid and efficient LM procedure for harvesting specific tissue types used for further high-quality RNA isolation. This will encourage future cell type-specific transcriptomic studies, contributing at deciphering rooting-competent cells in olive stems and to better understand the molecular mechanisms underlying the process of adventitious root formation.Higher plants are composed of different tissue and cell types. Distinct cells host different biochemical and physiological processes which is reflected in differences in gene expression profiles, protein and metabolite levels. When omics are to be carried out, the information provided by a specific cell type can be diluted and/or masked when using a mixture of distinct cells. Thus, studies performed at the cell- and tissue-type level are gaining increasing interest. Laser microdissection (LM) technology has been used to isolate specific tissue and cell types. However, this technology faces some challenges depending on the plant species and tissue type under analysis. Here, we show for the first time a LM protocol that proved to be efficient for harvesting specific tissue types  from olive stem nodal segments and obtaining RNA of high quality. This is important for future transcriptomic studies to identify rooting-competent cells. Here, nodal segments were flash-frozen in liquid nitrogen-cooled isopentane and cryosectioned. Albeit the lack of any fixatives used to preserve samples’ anatomy, cryosectioned sections showed tissues with high morphological integrity which was comparable with that obtained with the paraffin-embedding method. Cells from the phloem, cortex and epidermis could be easily distinguished and efficiently harvested by LM. Total RNA isolated from these tissues exhibited high quality with RNA Quality Numbers  ranging between 8.1 and 9.9. This work presents a simple, rapid and efficient LM procedure for harvesting specific tissue types of olive stems and obtaining high-quality RNA. Higher plants are composed by a myriad of tissues and hence, of different cell types. Distinct cell types are likely to host different biochemical and physiological processes being accompanied also by changes in gene expression profiles, protein and metabolite levels ,2. There® (https://www.leica-microsystems.com (accessed on 30 June 2020)), target cells are harvested from tissue section by bonding them to a plastic film at sites activated by a near-infrared (IR) laser. In laser cutting microdissection, target cells are first cut free from the tissue section by an UV laser and collected by one of several methods. One of the methods is the laser pressure catapulting (LPC) method , in which the target area is cut free with an UV nitrogen gas laser and then catapulted toward a collection vessel. In one other method, the laser microdissection (LMD) developed by Leica Microsystems, the biological section is in an inverted orientation, and the UV-circumscribed target is collected in a tube by gravity (reviewed by )To the best of our knowledge there are only two studies on the molecular mechanisms of AR formation working at the cellular and tissue level. Almeida et al.  isolatedOlea europaea L. plantlets of cv. ‘Galega vulgar’ (clone 1441) [2 s−2 at culture height. The nodal segment from the basal portion (approx. 1 cm from the base) of each microcutting were sectioned (In vitro-cultured ne 1441) , in a dene 1441) ,31,32, wectioned A and useFive stem nodal segments were placed directly into cryomolds  filled with Tissue-Tek optimal cutting temperature (O.C.T.) compound (Sakura Finetek) kept at 4 °C B. Three v/v) ethanol (in DEPC water cooled to −20 °C) for 30 s, and finally by 100% ethanol for 2 min. Slides were then air dried at room temperature for 2 min and immediately used for microdissection  D. Beforessection E.2 dissected area was recovered . Tubes were then closed and kept upside down on ice for 2 min and then briefly spun down in a centrifuge at 10,000 rpm for 1 min and stored at −80 °C until RNA extraction. Microdissection was performed using a Leica LMD6500 laser microdissection system  with the following laser parameters: power 60, aperture 45, speed 10–30 , and specimen balance 0, using the Leica LMD software (V6.7.2.4295). Cells from the phloem A,B, cortecovered , in ordeTotal RNA was isolated with the RNeasy Micro kit (Qiagen) according to the ‘Purification of Total RNA from Microdissected Cryosections’ protocol and according to the supplier’s instructions, and eluted in 10 µL volume of RNase-free water. The concentration and integrity  of total RNA were evaluated with the Fragment Analyzer Automated CE System  with the High Sensitivity RNA Analysis Kit , according to the manufacturer’s instructions. In order to assess the integrity of the cryosectioned olive stem-base tissues prepared as described here, we established a comparison using the same tissues but prepared by paraffin-embedding method described by Macedo et al.  Figure . It was To date, there is no standard LM protocol suitable for every plant species, organ or tissue type of interest, meaning that many different procedures have been established and optimized ,45,46,47One method widely used, because it permits to get high level of histological morphology and cell integrity, is the paraffin-embedding method. Albeit paraffin-embedding protocols enabling a good structural cell maintenance, prior to embedding, samples are usually submitted to fixation procedures. Some of the fixatives used are acetone, ethanol, ethanolic acetic acid, and/or methacarn (methanol/chloroform/acetic acid) . HoweveCryosectioning is one method that has also been applied for sample preparation for LM which has been reported to be a good option to obtain RNA of high quality . In facTo overcome the problem related to plant cell structure damage due to freezing, plant material is usually infiltrated with cryoprotection agents prior to freezing, such as sucrose ,51. HoweA number of plant LM studies have utilized fresh-frozen, cryoembedded, and cryosectioned plant tissues ,26,53,54Other aspect that ought to be optimized, is the sections thickness during cryosectioning D. The thOnce cryosectioning is complete, cryosections are normally transferred to a glass or membrane frame slide and then subjected to fixation and dehydration steps. During these steps, the integrity of the tissue can also be compromised and therefore they must be also optimized depending on the tissue type. This study used a (PEN)-membrane glass slide to mount the sections E. The adIn the present study, applying the laser settings described in the material and methods section, it was possible to collect efficiently microdissected areas, meaning that the cut areas were easily released  from the2 than smaller, densely packed cytoplasmic cells found in undifferentiated and actively dividing tissues. Both animal and plant cells typically contain on the order of 10–100 pg of total RNA per cell, depending upon the tissue type  the resultant RNA was eluted in 10 µL volume of RNase-free water. The RNA concentration  ranged between 0.974 ng/µL to 3.364 ng/µL, and the collected surface area ranged between 411,020 and 797,119 μm2 . Olsen an et al.  utilizedn et al.  reportedRNA quality is usually assessed by micro-capillary electrophoresis (separation of RNAs by size), by using systems such as the Bioanalyzer (Agilent) or the Fragment Analyzer . These systems report a number indicator of RNA quality, the RNA Integrity Number  or RNA Quality Number . These numbers are based primarily on assessing the level of degradation of 18S and 28S rRNAs. An elevated threshold baseline and a low 28S:18S rRNA ratio is indicative of RNA degradation. A RIN value of 1.0 indicates total degradation of RNA, whereas a value of 10.0 suggests that the RNA is fully intact ,60. In tThis work presents a simple, rapid and efficient LM protocol for stem basal segments of olive microcuttings to isolate specific tissue types and obtain high-quality RNA. Here, there was a concern to limit as much as possible the handling of tissue sections during all procedure as well as the amount of time they stayed at room temperature, in order to avoid/minimize RNA degradation. This procedure uses flash-frozen  samples without any kind of fixation and cryoprotection. The protocol permits to obtain total RNA of high quality and in sufficient amount for further works on gene expression analysis and/or high-throughput transcriptomic analyses. As a future work, we propose to use this protocol for harvesting regions corresponding to the first cells divisions, the meristemoids structures and the morphogenic root zones observed after olive rooting induction  (for det


Lung cancer is the leading cause of cancer-associated mortality worldwide. DNA damage-regulated autophagy modulator 1 (DRAM1) plays an important roles in autophagy and tumor progression. However, the mechanisms by which DRAM1 inhibits tumor growth are not fully understood. Here, we report that DRAM1 was decreased in nonsmall-cell lung carcinoma (NSCLC) and was associated with poor prognosis. We confirmed that DRAM1 inhibited the growth, migration, and invasion of NSCLC cells in vitro. Furthermore, overexpression of DRAM1 suppressed xenografted NSCLC tumors in vivo. DRAM1 increased EGFR endocytosis and lysosomal degradation, downregulating EGFR signaling pathway. On one side, DRAM1 interacted with EPS15 to promote EGFR endocytosis, as evidence by the results of proximity labeling followed by proteomics; on the other, DRAM1 recruited V-ATP6V1 subunit to lysosomes, thereby increasing the assemble of the V-ATPase complex, resulting in decreased lysosomal pH and increased activation of lysosomal proteases. These two actions of DRAM1 results in acceleration of EGFR degradation. In summary, these in vitro and in vivo studies uncover a novel mechanism through which DRAM1 suppresses oncogenic properties of NSCLC by regulating EGFR trafficking and degradation and highlights the potential value of DRAM1 as a prognostic biomarker in lung cancers. Activated mutations have been identified in multiple oncogenes, including EGFR, ERBB2/3, ALK, KRAS, ROS, MET, AKT, and BRAF in NSCLC2. Aberrant EGFR or EGFR-activating mutations activate various downstream signaling pathways to promote the proliferation, migration, and invasion of NSCLC. Treatment with third-generation EGFR-tyrosine kinase inhibitors (TKIs) has markedly improved overall response rate (~79%) and progression-free survival (~64%) in EGFR-mutant NSCLC patients3, while long-term chemotherapy induces mutations of EGFR T790M and C797S, conferring the inevitable acquired resistance and frequent de novo resistance of NSCLC patients to TKIs4. The mutations of EGFR in NSCLC are concentrated in exons 18–20, encoding the tyrosine kinase domain, and they affect the ATP binding pocket of the tyrosine kinase domain and lead to ligand-independent activation of EGFR. Under a steady state or EGF stimulation, the internalized mutant EGFR travels via the canonical endosome-lysosome route for degradation5.Lung cancer is the most common cause of cancer death globally, and nonsmall-cell lung cancer (NSCLC) accounts for the most lung cancer cases6. DRAM1 directs newly synthesized amino acid transporters to lysosomes and drives lysosomal amino acid efflux7. In addition to lysosomes, DRAM1 isoforms are also partly localized to peroxisomes, autophagosomes, and the endoplasmic reticulum8. The reported mechanisms of DRAM1-triggered autophagy are mainly focused on promoting the generation of autophagy and increasing the fusion between autophagosomes and lysosomes. DRAM1 takes part in p62-dependent selective autophagy to engulf the entire mycobacteria to defend against mycobacterial infection9. In acute myocardial infarction (AMI), DRAM1 exerted cardiomyocyte protection by increasing ATG7 expression and interacting with ATG7; additionally, DRAM1 enhances the conversion of autophagosomes to autophagolysosomes11. However, the mechanism by which DRAM1 promotes the fusion between autophagosomes and lysosomes has not been completely elucidated.DNA damage-regulated autophagy modulator 1 (DRAM1) encodes a six transmembrane protein mainly located in lysosomes to induce autophagy and is downregulated in multiple human cancers12. DRAM1 also recruited Bax to lysosomes and caused the release of cathepsin B to initiate Bid-mediated apoptosis of tumor cells under stress conditions13. We also demonstrated that DRAM1 regulated the activation of the IGF-1 receptor and inhibited the downstream PI3K–AKT–mTOR pathway in the presence of growth factors, resulting in autophagy activation and decreased cell proliferation in several human cancer cells14. Moreover, we found that DRAM1 was involved in maintaining normal organization of the Golgi apparatus, suggesting that DRAM1 plays a role in Golgi functions15. It was recently reported that acidification of mycobacterium marinum (Mm)-containing vesicles was strongly reduced in DRAM1 mutants in tuberculosis (TB) model16. In the current study, we found that DRAM1 was decreased in NSCLC and negatively correlated with EGFR levels. In addition, overexpression of DRAM1 inhibited the proliferation, migration, invasion, and EMT of NSCLC cell lines harboring mutant EGFR in vitro and in vivo. We demonstrated that DRAM1 promoted EGFR endocytosis through interacting with EPS15, and lysosomal degradation of EGFR via recruiting V-ATP6V1 subunit to lysosomes. Hence, our findings uncover a novel role of DRAM1 in EGFR endocytic trafficking and lysosomal degradation, making this protein a potential prognostic marker and/or therapeutic target for NSCLC patients.We have previously demonstrated that DRAM1 augmented lysosomal acidification and promoted the fusion of lysosomes with autophagosomes5 H1975 cells or PC9 cells were plated in 6-well plates in medium with 10% FBS and 2 μg/ml polybrene and infected with 50 μl of virus. The medium was refreshed, and puromycin (2 μg/ml) was added for selection after 24 h. After 48 h of transfection, the expression of mCherry or GFP was detected with fluorescence microscopy.Cells were bought from Cell Bank of Shanghai Institute of Cell Biology. HEK293T cells and A549 cells were cultured in DMEM with 10% fetal bovine serum. NCI-H1975 cells and PC9 cells were cultured in RPMI-1640 containing 10% fetal bovine serum. To overexpress or reduce DRAM1, lentiviral mCherry vector-mediated and lentiviral GFP vector-mediated DRAM1-FLAG overexpression constructs, lentiviral mCherry vector-mediated DRAM1 shRNAs  and EPS15 shRNA and EPS15 siRNA (sh/si-EPS15: GCCCAGAATGGATTGGAAGTTTC) were synthesized by Genepharm . A total of 5 × 10The 3FLAG-DRAM1-GCaMP6 expression plasmid, TMEM192-3HA, and DRAM1-BioID2-FLAG were generated by homologous recombination. 3FLAG-DRAM1, DRAM1, and TMEM192-3HA were generated by PCR subcloning of the human DRAM1 and TMEM192 coding sequences, GCaMP6 and BioID2 were generated by PCR subcloning of GCaMP6 plasmid and BioID2 plasmid, respectively. 3FLAG-DRAM1 and GCaMP6 or DRAM1 and BioID2-FLAG were homologously recombined using the Hieff Clone® Plus Multi One Step Cloning Kit  according to the manufacturer’s instructions. Information on labeling organelle with plasmids is included in Supplementary Table HEK293T, A549 cells, H1975 cells, or PC9 cells were cultured at 70–80% confluence in 24-well plates on the day before transfection. Lipofectamine 2000 reagent (0.5 μl) was diluted with 25 μl Opti-MEM and gently blended with 25 μl Opti-MEM containing 0.5 μg plasmid. The mixture was placed at room temperature for 15 min and added to the cell culture medium. The medium was changed to fresh medium after 6 h, and immunofluorescence assay was conducted after another 42 h.17. All information on antibodies used in the study is included in Supplementary Table The tumor tissues and cells were harvested and lysed in RIPA lysis buffer for 30 min on ice, the proteins were then quantified and Western blotting was performed as previously describedg for 15 min at 4 °C. A volume of 0.5 mg/ml protein was added to 20 μl Anti-FLAG agarose beads , and they were slowly shaken on a rotating shaker at 4 °C overnight. After centrifugation 500 × g for 3 min, the pellet was washed with precooled PBS three times, and the beads were boiled in 2× loading buffer . Then, the supernatants were collected and subjected to Western blot analysis for EPS15, ATP6V1D, and ATP6V0D, respectively.Proteins of H1975 cells, DRAM1-overexpressing H1975 cells, A549 cells, and DRAM1-overexpressing A549 cells were extracted using cold RIPA lysis buffer followed by centrifugation at 14,000 × −ΔΔCt method.Quantitative real-time PCR was performed using SYBR Green PCR master mix  in a total volume of 20 µl on 7500 Real-Time PCR System (Applied Biosystems) as follows: 95 °C for 30 s, 40 cycles of 95 °C for 5 s, and 60 °C for 30 s. All results were normalized to the expression of α-Tubulin, and relative quantification was calculated by the 22O2 for 15 min. Sections were blocked with 5% BSA and incubated with antibody for 3 h at room temperature. After washing in 1× PBS, the slides were processed with a GTVisinTM antimouse/antirabbit immunohistochemical analysis kit  according to the manufacturer’s instructions.Sections were deparaffinized, rehydrated through graded ethanol, and washed in PBS. Antigen retrieval was performed in citrate buffer for 10 min followed by blocking endogenous peroxidase in 3% HH1975 control cells, DRAM1-overexpressing H1975 cells, PC9 control cells, DRAM1-overexpressing PC9 cells, PC9-negative cells, and DRAM1-knockdown PC9 cells were seeded into 96-well plates at a density of 3000 cells per well. After 24, 48, 72, and 96 h, 10 μl of CCK-8 was added into each well to measure cell proliferation.Cells were washed three times with PBS, fixed with 4% paraformaldehyde for 30 min followed by 0.5% Triton X-100 for 10 min, incubated with 3% bovine serum albumin for 1 h at room temperature, and then incubated with primary antibodies overnight. After being washed three times with 0.01 M PBS for 5 min, cells were incubated with secondary antibodies labeled with Alexa Fluor™ 488/555/633  for 1 h at room temperature. Cells were washed three times with 0.01 M PBS for 3 min. Nuclei were stained with DAPI (10 µg/ml) for 5 min at room temperature. Images were acquired on an LSM-710 confocal microscope  using a 60× objective.18. Cells in five 10 cm plates were quickly rinsed twice with PBS and then scraped using PBS  and centrifuged at 800 × g for 5 min at 4 °C. Pelleted cells were gently homogenized with a 1 ml Dounce homogenizer. The homogenate was then centrifuged at 800 × g for 5 min at 4 °C, and the supernatant containing lysosomes was incubated with 50 μl PBS-prewashed anti-HA magnetic beads  on a gentle rotator shaker for 20 min. The tube was placed on a magnetic separator, and the supernatant was removed. Beads were washed 2 times using 1 ml PBS and mixed with 50 μl 2× loading buffer and heated in boiling water for 5 min. The mixture was vortexed and centrifuged at 12,000 × g for 15 min at 4 °C, and the supernatant was collected for Western blot analysis.Lysosomes were isolated as previously describedLysoSensor Green DND-189  and DQ-Green BSA  were used to detect lysosomal pH. LysoSensor Green DND-189 accumulates in acidic environments to exhibit more fluorescence and is often used to measure the pH of lysosomes. The proteolysis of DQ-Green BSA in acidic compartments results in fragment formation with fluorescence dequenching, which is observed as an increase in fluorescence intensity. Cells were stained with 1 μM Lysotracker Green DND-189 diluted in RPMI-1640 at room temperature for 30 min or 10 μg/ml DQ-Green BSA for 6 h, washed twice with PBS and collected using a scraper. Flow cytometry using a FACSCalibur (BD) and FlowJo software were performed to detect and analyze the fluorescence intensity.19, the BioID2-mediated proximity-tagging system and affinity purification (AP)-MS approach were performed to identify DRAM1-interacting proteins. Briefly, 5 × 107 HEK293T cells transiently expressing DRAM1-BioID2 were stimulated with 30 μM biotin for 24 h, and the cells were collected and lysed in 2 ml of RIPA buffer for 30 min on ice. After centrifugation at 12,000 × g for 15 min, supernatant was collected and incubated with 50 μl avidin resin  at 4 °C overnight. Beads were collected by centrifuging at 500 × g for 5 min and washed twice with RIPA lysis. Finally, beads were resuspended in 100 μl elution buffer  in a shaker for 10 min and centrifuged at 500 × g for 5 min. The supernatants were collected and boiled in loading buffer followed by Western blot analysis.As described previously with slight modificationFor liquid chromatography-mass spectrometry (LC-MS), the collected elution buffer was reduced with 5 mM Tris (2-carboxyethyl)phosphine (TCEP) for 30 min at 37 °C and alkylated with 10 mM iodoacetamide, followed by digestion with trypsin at 37 °C overnight. Formic acid was added to the peptide solution before LC-MS/MS.There is rapid endocytosis at physiological temperature (37 °C), whereas at 4 °C, ligand-receptor internalization is inhibited without affecting binding. To measure EGFR endosomal-lysosomal trafficking and degradation, A549 cells, DRAM1-overexpressing A549 cells, PC9 cells and DRAM1-overexpressing PC9 cells were serum-starved overnight and stimulated with 100 ng/ml EGF  for 0 min, 15 min, 30, 60, or 180 min. Subsequently, the cells were harvested and subjected to SDS-PAGE and Western blotting to detect EGFR protein levels and its downstream pathway activity. For fluorescence-based EGFR trafficking and degradation, A549 cells, DRAM1-overexpressing A549 cells, PC9 cells and DRAM1-overexpressing PC9 cells were serum-starved overnight, incubated with 100 ng/ml Alexa Fluor 488-conjugated EGF  for 30 min at 4 °C, washed twice with PBS, and shifted to 37 °C for 0, 15, 30, 60, or 180 min. After fixation, fluorescence images were taken using an LSM-710 confocal microscope , and fluorescence intensities were measured by flow cytometry .20. To measure the amount of total internalized EGFR, cells were incubated with serum-free medium overnight and treated with 100 ng/ml Alexa Fluor 488-conjugated EGF for 30 min at 4 °C, washed twice with PBS and fixed with 4% paraformaldehyde. To measure the recycling of internalized EGFR, serum-starved cells were stimulated with 100 ng/ml EGF for 30 min at 4 °C to bind EGFR completely (pulse), washed and chased for 0, 30, or 60 min to allow EGFR recycling. Cells were then treated with Alexa Fluor 488-conjugated EGF for 30 min at 4 °C, washed and fixed as described above.The EGFR recycling assay was conducted as described5 PC9 or 5 × 105 DRAM1-overexpressing PC9 cells in 30 μl of RPMI-1640 on the right back flank. Two weeks later, tumor size was measured with electronic calipers every 4 days through blind outcome assessment. Tumor volumes (V) were calculated by the formula V = (X2Y)/2, where X and Y are the shortest and longest diameters of the tumor, respectively. Mice were sacrificed on day 41 after tumor cell injection. For determination of a role of DRAM1 in gefitinib chemotherapy, mice bearing PC9 cells or DRAM1-overexpressing PC9 cells were orally administered gefitinib , dissolved in 0.5% CMC-Na, at dose of 50 mg/kg/day for 13 days. Tumor size and weight were measured. Tumors were collected and fixed with 4% formaldehyde or stored at −80 °C.Five-week-old male nude BALB/c mice were obtained from Soochow University. Mice were housed in SPF conditions with food and water ad libitum, a 12 h light/dark cycle and controlled (22–23 °C) temperature. Animal welfare and experimental procedures were carried out strictly in accordance with the Guide for the Care and Use of Laboratory Animals  and the related ethical regulations of Soochow University. Mice were randomly injected subcutaneously with 5 × 10Surgical resections of lung cancer tissues were collected from first diagnosed patients  without chemoradiotherapy at The First Affiliated Hospital of Soochow University from 2017 to 2018 according to approval from the Ethics Committee of Soochow University. Written informed consent was obtained from all patients prior to enrollment. Human lung cancer tissue array (IWLT-N-70L43) was purchased from Wuhan Iwill Biological Technology Co., China.http://www.oncomine.org) is a cancer microarray database. We adopted the Oncomine database to further validate the expression of DRAM1 in lung cancer. The differential expression analysis was directly performed using Oncomine online analysis tools. DRAM1 expression between squamous lung cancer and paired normal tissue from five patients was analyzed using data extracted from the Gene Expression Omnibus (GEO) with accession number GSE3268. Overall survival data were obtained from Kaplan–Meier Plotter  for lung cancer. MS-identified proteins were analyzed for KEGG pathways and biological processes (BP) to determine whether these proteins had overlapping functions using the Database for Annotation, Visualization, and Integrated Discovery (DAVID)21.The Oncomine database (t-test (two groups). All experiments were successfully replicated three times, and a value of P < 0.05 was considered statistically significant.Data were analyzed using the statistical software GraphPad Prism  and are expressed as mean ± standard deviation (SD). Statistical significance was evaluated by one-way analysis of variance (ANOVA) followed by Tukey’s multiple comparison test (multiple groups) or the paired or unpaired . Moreover, DRAM1 protein in NSCLC cells  was lower than that in human lung microvascular endothelial cells (HLMVEC)  Fig. . GEO datEC) Fig. .Fig. 1DeTaken together, these results suggest that DRAM1 is negatively associated with poor outcomes in lung cancer progression.To test the role of DRAM1 in NSCLC progression, subcutaneous transplantation model using PC9 cells, an EGFR-mutant (exon 19 deletion) NSCLC cell line with a TKI-sensitizing mutation, and DRAM1-overexpressing PC9 cells in nude mice were established. Compared with control tumors, overexpression of DRAM1 inhibited PC9 tumor growth with a significant reduction in tumor growth rate, tumor size and weight Fig. . PCNA1 sDRAM1-overexpressing cells exhibited a cobblestone morphology, reflecting EMT morphological changes, and DRAM1-silenced cells had an elongated mesenchymal shape Fig. SI . EMT is Taken together, these results suggest that DRAM1 significantly inhibits the oncogenetic potential of NSCLC cells in vivo and in vitro, including the inhibition of growth, EMT, and increasing TKI sensitivity in vitro and in vivo.22, and CD63, ALIX, TSG101, and SDCBP are key proteins involved in the generation of exosomes. No significant changes of these proteins were observed between control cells and DRAM1-overexpressing cells , an inhibitor of V-ATPase, and chloroquine (CQ), an inhibitor of lysosomal acidification, were applied to A549 cells and DRAM1-overexpressing A549 cells before EGF stimulation. As depicted in Fig. Therefore, the above results indicate that DRAM1 facilitates EGFR lysosomal degradation through V-ATPase-mediated lysosomal acidification, suppressing EGFR signaling.The present work uncovered a novel mechanism of the tumor suppression by DRAM1: promoting EGFR endosomal-lysosomal trafficking and degradation in NSCLC in vitro and in vivo through interacting with EPS15 and promoting the assembly of lysosomal v-ATPase Fig. .Fig. 7Sc25. Tp53 mutations occur in many types of cancers, causing them to lose tumor suppressor activity and gain an oncogenic potential. However, no correlation has been observed between DRAM1 expression and Tp53 mutational status, and DRAM1 expression is also partly controlled by the RAS/MAPK pathway in glioblastoma stem cells26. Although DRAM1 alone fails to induce programmed cell death under physiological conditions14, DRAM1-induced autophagy is involved in stimulator-induced apoptosis, where it exerts either a positive or a negative impact on survival of cancer cells. In response to starvation-induced apoptosis and autophagy, DRAM-induced mitophagy or autophagy contributes to apoptosis in hepatocellular carcinoma and breast cancer28. DRAM1 mediated autophagy can degrade IKKα to aggravate arsenite-induced cytotoxicity29. However, DRAM1 is also beneficial to cancer. Highly-expressed DRAM1 is positively associated with shorter overall survival in GBM (glioblastoma multiforme) patients, and knockdown of DRAM1 inhibits p62-mediated autophagy, leading to decreased motility and invasion in GBM stem cells26. In addition, DRAM1 promotes the migration and invasion of HepG2 cells via the autophagy-EMT pathway30. These contradictory results also imply that the multiple functions of DRAM1 may differ among different types of tumors or different stages of tumorigenesis. Our current results demonstrated that DRAM1 was downregulated in NSCLC patient specimens, and DRAM1 protein was detected in Tp53-wild-type A549 cells, Tp53-null H1299 cells and Tp53-mutant H1975 cells, further indicating that there exists Tp53-independent expression of DRAM1 in NSCLC cells. DRAM1-mediated reduction of mutant EGFR enhanced the cytotoxicity of TKI, and the overexpression of DRAM1 inhibited the growth, migration and invasion of NSCLC in vitro and in vivo, indicating that decrease in DRAM1 expression was beneficial to EGFR mutant NSCLC cells, supporting that DRAM1 has a tumor suppressor activity.As a known target gene of Tp53, DRAM1 is induced in a Tp53-dependent manner after cellular or genotoxic stress. The ROS–Tp53–DRAM1 pathway is indispensable in mitochondrial dysfunction-triggered autophagy31, and EGFR itself becomes ubiquitinated, rapidly internalized by the endosomal sorting complex required for transport (ESCRT), then sorted into intraluminal vesicles at multivesicular endosomes (MVEs), and finally degraded in lysosomes following MVE fusion32. There are two major destinations for endocytosed EGFR trafficking from early endosomes: recycling to the cell surface or transporting to lysosomes. EGFR mutants impair the interaction with CbI, leading to defective ubiquitination and inefficient lysosomal degradation in NSCLC cells33. EGFRvIII, an oncogenic EGFR mutant without exons 2–7 that is universally observed in glioblastoma multiforme, is constitutively active and poorly ubiquitinated, conferring inefficient receptor trafficking to lysosomes and prolonged oncogenic signaling34. Reduction of mutant EGFR ubiquitination by CSN6 causes steadily elevated levels of EGFR, leading to proliferation, migration, invasion, and tumorigenesis35. In addition to reduced endocytosis of mutant EGFRs, mutant EGFRs are constitutively endocytosed and colocalized with the early/recycling endosomes and the late endosomes in NSCLC cell lines36. Mutant EGFRs are preferentially trafficked into the endocytic recycling compartments (ERC), allowing them to go back to the plasma membrane or to interact with Src. Exon-19-deleted EGFR is colocalized with endocytic compartments under steady-state conditions, indicating that the exon-19-deleted EGFR mutant is constantly internalized and sorted to lysosomes for degradation5. It has been reported that unliganded EGFR can be internalized at a much slower rate than EGF-stimulated EGFR37. Therefore, we used A549 cells with wild-type EGFR and PC9 cells harboring exon-19-deleted EGFR to detect EGFR trafficking and degradation. DRAM1 promoted activated EGFR degradation in PC9 cells and in EGF-stimulated A549 cells, limiting EGFR recycling to the cell surface, resulting in downregulated EGFR levels in EGFR-mutant NSCLC cells and in EGF-stimulated A549 cells.The receptor-mediated endosomal-lysosomal pathway plays a key role in regulating cell surface signaling and the degradation of intracellular components, which are composed of a set of dynamically interconverted intracellular membranous compartments, including early endosomes, recycling endosomes, late endosomes, and lysosomes. EGFR endocytosis was also carried out in the present study to measure the endosomal–lysosomal trafficking route. For wild-type EGFR, the activation and termination of EGFR signaling depend on ligand-stimulated endocytosis and intracellular trafficking. With higher concentrations of ligand (over 20 ng/ml of EGF), EGFR triggers a cascade of responses of downstream signaling pathways, such as RAS/RAF/MEK/ERK, PI3K/Akt/mTOR, and JAK/STAT338. To find the mechanisms that DRAM1 regulates EGFR trafficking and degradation, BioID2-mediated proximity labeling was performed. BioID2 was fused to the C-terminus of DRAM1 without breaking the signal peptide in the N-terminus. The reported DRAM1-interacted proteins such as SLC1A5, SLC7A5 and SLC3A27 are also included in the list. Intriguingly, we found that DRAM1 interacted with EPS15 , a substrate for the tyrosine kinase of EGFR. EPS15 containing the Ub-interacting motif can bind to ubiquitinated EGFR, promoting EGFR internalization and degradation and delaying PI3K-Akt signaling39. It has been reported that the prolyl hydroxylase PHD3 acts as a scaffold protein to interact with endocytic adapter EPS15, promotes the internalization of EGFR and decreases EGFR signaling to inhibit cell proliferation and survival of cancer cells40. In addition to the trafficking of EGFR, another major process redundantly regulated by EPS15 is the endocytosis of the transferrin receptor. Iron deficiency-induced anemia is present in hematopoietic-specific conditional Eps15/Eps15L1-double-KO mice41. The inhibition of EPS15 with an antibody blocks the endocytic pathway and inhibits the internalization of EGF and transferrin42. Based on the identified role of EPS15 in vesicle transport, we wondered whether DRAM1 could regulate EGF-mediated endocytosis and the results demonstrated that DRAM1 interacted with EPS15, thus promoted EGFR internalization and trafficking. The significance of EPS15 in DRAM1-mediated trafficking and degradation of EGFR was demonstrated by the EPS15 knockdown study in the present study. The siRNA-induced deficiency in ESP15 blunted DRAM1’s effects on EGFR.Protein distribution and structure are vital to predict and analyze a novel function. Compared with the co-IP assay, proximity labeling-based methods coupled with mass spectrometry (MS) offer a higher-throughput approach for systematic analysis of spatially restricted proteomes, especially those proteins localized to discrete subcellular compartments43. DRAM1 potentiates lysosomal destabilization and dell death caused by lysosomal membrane permeabilization (LMP) inducers. In HIV-infected T cells, highly expressed DRAM1 enhances lysosomal membrane permeabilization (LMP) and cathepsin release, leading to mitochondrial outer membrane permeabilization and apoptosis44. During 3NP (3-nitropropionic acid)-induced or doxorubicin-induced cell death, DRAM1 recruits BAX to lysosomes to release lysosomal cathepsin B and cleave BID, leading to mitochondria-mediated cell death13. The lysosome is a sophisticated signaling center and can be divided into three compartments: highly glycosylated lysosomal membrane proteins, lysosomal proteases in the lumen, and other proteins, such as mTOR, on the lysosomal surface. The present study revealed that as a lysosomal membrane protein, the N-terminus and C-terminus of DRAM1 faced the cytosol, as confirmed in LysoIP assay, TRPML-mediated Ca2+ release assay, and acid quenching of GFP.Lysosomes are the main organelles that DRAM1 localized in, and the effect of DRAM1 on lysosomal morphology/position and function was measured. In addition to cargo degradation derived from autophagy, lysosomes are involved in various cell processes, including secretion, plasma membrane repair, signal transduction, energy metabolism, and cell death45. Furthermore, cells harboring widely distributed lysosomes are characterized by impaired cathepsin maturation and a higher lysosomal pH compared with cells with only peripheral lysosomes46. In contrast to control cells, decreased lysosomal pH and increased cathepsin maturation were observed in DRAM1-overexpressing cells. Our previous research found that DRAM1 increased V-ATPase activity12, and DRAM1-increased lysosomal acidification was also confirmed in mycobacterial infection9. As a heteromultimeric enzyme present in the plasma membrane, endosomes, secretory vesicles and lysosomes, V-ATPase (vacuolar (H+)-ATPase) is composed of a peripheral catalytic V1 complex (components A to H) that hydrolyzes ATP and an integral membrane V0 proton pore complex  that pumps H+ into the lumen of acidic vacuoles or into the extracellular environment; V-ATPase is upregulated in several cancers47. To test whether DRAM1 increased lysosomal V-ATPase assembly, lysosomes were isolated, and the V1 and V0 complexes were measured using ATP6V1D and ATP6V0D. Although DRAM1 did not directly interact with the V-ATPase complex, it recruited the V1 subunit and promoted the assembly of V0 and V1 complex, increasing V-ATPase activity to acidify lysosomes. To determine whether DRAM1-induced elevation of lysosomal acidification was responsible for endosomal–lysosomal degradation of EGFR, Baf A1, an inhibitor of V-ATPase, and CQ, a neutralizer of lysosomal acidification, were used. The blockade of EGFR degradation in DRAM1-overexpressing cells was more sensitive to Baf A1 than to CQ.Research has shown that the peripheral lysosomes and central lysosomes are different in pH. The increased leak permeability to protons and reduced V-ATPase activity lead to reduced acidifying ability of peripheral lysosomes compared with juxtanuclear lysosomes48, androgen receptor49, and TLR4 have been reported to be degraded through the endosomal–lysosomal route, thus whether DRAM1 displays specificity for EGFR degradation needs to be further studied.In summary, this work has identified that DRAM1 is a novel regulator of the endocytosis and lysosomal degradation of EGFR through interacting with EPS15 and promoting the assembly of lysosomal v-ATPase in NSCLC cells. Our data also revealed that DRAM1 may be a potential prognostic biomarker for NSCLC and an indicator for TKI therapy in EGFR-mutant NSCLC. Similarly, other receptors such as PD-L1Supplementary material and SI figure legendsSupplementary table 1Supplementary table 2Sipplementary table 3Figure SI 1Figure SI 2Figure SI 3Figure SI 4Figure SI 5Figure SI 6Figure SI 7Figure SI 8Figure SI 9Figure SI 10


The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP.Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by  H. sapiens and can be divided in conventionally-defined RBPs and several classes of non-canonical RBPs [Since their discovery and until recently, RNA-binding proteins (RBPs) have been identified by the presence of one or more RNA-binding domains in their sequences . Howevercal RBPs ,11,12.in vitro techniques that allow to define kinetics and dynamic parameters for each binding pair [Large-scale identification of new potential RBPs can reveal unexpected biological and pathological functions and, when confronted with a novel RBP, the identification of its RNA binding partners is a critical step to define the protein's cellular and molecular roles. To achieve this goal, increasingly sophisticated high-throughput methodologies have been developed, spanning from methods that aim to preserve the native cellular RNA–RBP interactions  to fineling pair ,17. Howeing pair . The widing pair .catRAPID [In this short review, we propose a work pipeline for the identification of the RNA binding partners for novel putative RBPs. Starting from in-cell data harvesting, we would like to guide the reader through the employment of the different tools offered by catRAPID ,24, our stimuli or under pathological stress.The validation and training of computational algorithms for the prediction of protein-RNA interactions is strongly supported by experimental data . PredictThe main tool to obtain information about the RNA binding partners of a target protein in the cellular environment is immunoprecipitation (IP), a widespread technique to pull down the protein of interest together with its physiological RNA binding partners. RNA immunoprecipitation (RIP) requires incubation of cell lysates with an antibody raised against the target protein . RNA molTo overcome RIP's limitations , cross-linked RNA immunoprecipitation (CLIP) has been developed . CLIP prHigh-throughput sequencing of the RNAs isolated through RIP, CLIP and related protocols yields millions of short ‘reads’, which represent the sequenced portions of cDNA fragments obtained through RNase titration (a step omitted in standard RIP-Seq), followed by reverse transcription and PCR amplification. The RNase treatment allows to obtain fragments long enough to be uniquely mappable but short enough to identify the binding site with the highest possible resolution. Library preparation ends with the production of cDNA fragments flanked by adapters that allow amplification and sequencing, resulting in the generation of short reads that can undergo bioinformatic analysis aimed at identifying RNA targets for the RBP . Reads pTo identify the RNA molecules from which they derive, reads are aligned to a reference genome using splice-aware alignment programs commonly used to analyze standard RNA-Seq data, like TopHat2  or Star Post-processing of aligned reads is a mandatory step in RIP-Seq and CLIP-Seq data analysis. Reads aligning to multiple genomic positions are usually removed . HoweverIn RIP-Seq experiments, target RNAs can be identified either by transcript enrichment analysis of IP versus control samples , which ccatRAPID would integrate the results of a CLIP-Seq experiment with this information [The outcome of the data analysis obtained by means of CLIP-Seq experiments is of enormous value in approaching the study of a potential RBP with unknown RNA partners. However, there are two major drawbacks in limiting the investigation to CLIP-Seq approaches: the protein regions in direct contact with the target RNAs remain unknown, and there are not sufficient data to speculate on the strength of the interaction for each protein–RNA pair. The use of predictive algorithms such as ormation   [catRAPID can be employed to select the best targets based on calculated binding strength, but it could be also useful in predicting putative targets that are not expressed within the cellular system. If no information about the RNA targets is available, catRAPID can represent a promising tool for the investigation of the protein's genome-wide RNA-binding potential against an RNA sequence library. Here we suggest a pipeline that could be followed in such circumstance:actions) , throughactions)  and was actions) . For laractions)  or rank actions) . The outactions) , overcomcatRAPID signature [If the RNA binding potential of a given protein is unknown, ignature  can be ucatRAPID omics [ID omics  can thencatRAPID express [If the protein of interest is human, its co-expression with the putative interactors in different tissues can be evaluated using  express ;catRAPID strength can be employed to evaluate the strength of each interaction [Once the most promising binding partners have been identified, eraction ;catRAPID fragments [Global Score [omiXcore [Finally, ragments  can be ral Score  or omiXcomiXcore .catRAPID may fail in identifying valid RNA targets. Prediction accuracy depends on the set provided to train the algorithm. As more and more data become available, retraining of the algorithm will be necessary to achieve better performances.By narrowing down the number of potential targets and by suggesting the most likely binding sites, such approach can be employed to guide further experimental and computational analyses. Being a predictive tool, there is always the chance that catRAPID is only one of several possible computational methods developed for predicting protein–RNA interactions. We would like to mention here some valid alternatives:catRAPID in the employment of RNA secondary structure, hydrogen-bonding and van der Waals interaction propensities. It is designed to predict whether a specific long non-coding RNA interacts with one or more protein sequences. Propensities are calculated for both protein and RNA and a probability score ranging from 0 to 100 is generated;LncPro : similarRPISeq : proteinRPI-Pred : it combiDeepS : a deep Once the RNA targets of a protein have been determined, further analyses are necessary in order to verify the reliability of the results and to gain insights into the biological function of the RBP. Both tasks can be approached by looking at the function of target RNAs. A common way to do that is to perform an Over Representation Analysis (ORA) , which cde novo motif discovery [Another common analysis consists in scanning the identified binding sites in order to detect common patterns highlighting RBP binding preferences . Sequence motifs recurring in large sets of binding sites can be discovered using different tools, like MEME-ChIP  and SeAMiscovery .Sequence alone may not be sufficient to fully explain the binding specificity of an RBP: a sequence motif could be accessible only when put in a proper secondary structure context. Tools like GraphProt , ssHMM  and BEAMTo validate the prediction accuracy of the computational analysis proposed so far, it is ideal to evaluate each interacting pair within a controlled environment. A most accurate validation should start from the screening of potential binders, followed by the precise determination of binding sites and kinetic and thermodynamic parameters, and completed with structural insights into the drivers of the interaction . A comprkon) and dissociate (koff). Conventionally, the dissociation constant (Kd), which is the ratio between koff and kon at the chemical equilibrium, is used to express the binding affinity: the lower the Kd, the greater the affinity. It is however important to note that binding pairs with the same Kd may have different kon and koff and therefore different binding mechanisms. To add another layer of complexity, many RBPs have multiple binding regions that may vastly differ in their affinity towards the same RNA [As for other molecular interactions, the binding between a protein and an RNA molecule is kinetically characterised by the rate at which they associate , more reliable kinetic data may be obtained by other techniques. Examples of such methods are bio-layer interferometry (BLI), multi-channel surface plasmon resonance (SPR), microscale thermophoresis (MST), the employment of fluorescence resonance energy transfer (FRET), and the most recent switchSENSE (There are several established methods for determining the kinetic and thermodynamic parameters of binding . Techniqin vitro with isolated components often do not allow studies under physiologically relevant conditions. Isolated proteins can be sensitive to higher temperatures, especially under prolonged experiments that far exceed their in-cell lifespans. Since chemical equilibriums are temperature-dependent, in vivo kinetics may thus significantly differ from those measured in vitro. The thermodynamics of binding can also reveal the energetic landscape of protein–RNA interactions [Kinetic studies are essential for determining the binding propensity and validating different binding partners . Howeverractions . It is tractions . Using tKinetic and thermodynamic data obtained from interaction studies provide a good numerical description of the binding and, through the use of RNA-centric methods, a library of RNA sequences with high binding propensities . HoweverOur understanding of many physiological and pathological phenomena cannot be exempted from an in-depth knowledge of protein–RNA interactions underlying them.Such comprehension, which goes from the identification of targets RNAs to binding modes characterization, requires a multidisciplinary approach involving biochemistry, molecular biology, bioinformatics and physics.catRAPID, which is general enough to be applied to any protein–RNA pair, could also be employed to improve the specificity of omics studies.As new and more powerful high-throughput methods and predictors are being developed, an integrated and productive usage of both approaches becomes more and more feasible. For instance, predictive tools such as


Ribosomal RNA (rRNA) biogenesis takes place in the nucleolus, the most prominent condensate of the eukaryotic nucleus. The proper assembly and integrity of the nucleolus reflects the accurate synthesis and processing of rRNAs which in turn, as major components of ribosomes, ensure the uninterrupted flow of the genetic information during translation. Therefore, the abundant production of rRNAs in a precisely functional nucleolus is of outmost importance for the cell viability and requires the concerted action of essential enzymes, associated factors and epigenetic marks. The coordination and regulation of such an elaborate process depends on not only protein factors, but also on numerous regulatory non-coding RNAs (ncRNAs). Herein, we focus on RNA-mediated mechanisms that control the synthesis, processing and modification of rRNAs in mammals. We highlight the significance of regulatory ncRNAs in rRNA biogenesis and the maintenance of the nucleolar morphology, as well as their role in human diseases and as novel druggable molecular targets. The eukaryotic ribosome as a multimolecular machine composed of more than 80 proteins and four distinct types of ribosomal RNAs (rRNAs) requires a dedicated and reliable procedure to become efficient and functional. Therefore, to achieve a consistent coordination of ribosome’s components transcription, translation and assembly, the eukaryotic cell has allocated a separate compartment, the nucleolus, where most of ribosome’s maturation steps take place . The nucThe function and the structural organization of the nucleolus appear as two components intimately connected. Deregulation at any distinct step of ribosome biogenesis provokes the nucleolar stress response that leads to cell cycle arrest, and finally, to programmed cell death ,10. HencApart from the housekeeping rRNAs, mRNAs and tRNAs, over the past few years, novel ncRNAs exhibiting non-canonical functions have drawn the attention and opened new important biological questions. RNA-mediated mechanisms have been now described in all kingdoms of life with multiple regulatory roles in chromatin remodeling, cell cycle arrest, gene expression regulation at both the transcriptional and translational level and post-transcriptional modifications, usually providing a better understanding of human diseases, including cancer ,21,22. OSense or antisense long ncRNAs (lncRNAs) derived from intergenic spacer sequences (IGS) that separate the rDNA gene clusters appear as key players in regulating directly the structural integrity of the nucleolus, as well as the rDNA transcription. 0 regulatory element of the rDNA promoter which altogether redirects the DNA methyltransferase DNMT3b to methylate CpGs at the rDNA promoter  are ribosomal IGS transcripts that modulate epigenetically the rDNA transcription. pRNA is a dual-function lncRNA which production is driven by an alternative rDNA promoter located upstream of the main one, and requires RNA pol I and exosome action to be rendered functional ,30. The promoter  29,32].,32.0 regPAPAS (Promoter and pre-rRNA antisense) are another type of IGS transcripts transcribed by RNA pol II from the rDNA clusters in an antisense orientation. Mechanistically, PAPAS are expressed in response to stress conditions and by being complementary to both the rDNA promoter and rDNA coding sequences lead to inhibition of rDNA transcription and therefore, to cell survival .More specifically, PAPAS, instead of recruiting Suv4-20h2 at rDNA loci, interact with complementary rDNA sequence via the formation of a DNA-RNA triplex with the dephosphorylated by heat shock ATPase CHD4, a NuRD (Nucleosome Remodeling and Deacetylase) complex subunit . Once di16 and IGS22 lncRNA, while acidosis due to accumulation of IGS28, results in loss of nucleolar structure. Such IGS transcripts interact with the nucleolar detention signal (NoDS)  of various proteins. Thus, cell cycle regulators, E3 ubiquitin ligases and chaperons, are immobilized inside the nucleolus, causing a nucleolar reorganization (a characteristic “nucleolar caps” phenotype) and rRNA synthesis arrest .,60.CaenoCircular RNAs (CircRNAs) are a recently described and broadly studied class of ncRNAs. CircRNAs were first identified in the late ‘70s, but it was not until 2013 when they were considered as a new family of regulatory RNA molecules. They are single-stranded RNAs with covalently closed circular isoforms and can function as microRNA (miRNA) sponges ,62,63, pSaccharomyces cerevisiae 45 ψ and 55 2′-O methylation sites have been identified on rRNAs and around 100 of each are found in human rRNAs [The complex landscape of rRNA biogenesis is further enriched by several epigenetic and post-transcriptional events, mediated by small nucleolar RNAs (snoRNAs). SnoRNAs are 60–300 nucleotides in size and guide the maturation and post-transcriptional modification of rRNAs ,74,75,76an rRNAs ,79,80,81TCOF1 gene, a regulator of RNA pol I, are associated with the Treacher Collins syndrome [KRAS mutations in various other types of cancer, including cutaneous melanoma, breast and lung adenocarcinomas [More specifically, at the 5′ and 3′ ends of the box C/D family two conserved sequence elements exist: the box C (RUGAUGA) and D (CUGA) motifs, respectively. Τhis family is characterized by a kink-turn (k-turn) structure , which isyndrome . In addisyndrome ,93,94,95rcinomas ,97,98.Furthermore, H/ACA box snoRNAs adopt the “hairpin–hinge–hairpin–tail” secondary structure with the conserved box H “ANANNA” sequence (N represents any nucleotide) within the hinge region and the ACA box on the 3′ end of the last hairpin. The hairpins of H/ACA box snoRNAs are interrupted by an internal loop which possesses on each strand a short complementary sequence to the RNA substrate and create the pseudouridylation pocket ,74,83,99RAS expression promotes cancer [Moreover, deficiency of the catalytic snoRNP proteins is the cause of many diseases. For instance, mutations in DKC1 have been observed in X-linked Dyskeratosis congenita (X-DC), a disorder characterized by hematopoietic defects and cutaneous abnormalities . These ms cancer . Precises cancer .A distinct group of snoRNA with no characteristic primary sequence motif forms the RNA component of RNase MRP (RMRP). In human, RMRP is transcribed by RNA pol III, while its yeast homolog, NME1  is transcribed by RNA pol II ,107. In BRAF, NRAS or TP53 mutational status [Ribosome biogenesis is increased in cancer cells and coordination between the cytosolic and mitochondrial translational machineries is required to achieve a high proliferative cell rate . SAMMSONl status .E. coli, a fragment of domain I of 23S rRNA  was identified, and despite its short length, harbors two distinct non overlapping binding sites for the ribosomal proteins L4 and L24, suggesting its essential role on the rRNA folding during the early assembly of the bacterial 50S subunit [Recently, bioinformatics tools have provided information about a different, tRF-like (tRNA-derived fragment-like) group of ncRNAs derived from ribosomal RNA, termed rRNA-derived fragments (rRFs). The biological significance and the exact role of this novel class of ncRNAs are poorly understood and require further investigation. However, some current insights point to rRFs as possible gene expression regulators, similarly to miRNAs, or as contributors in genome stability . As yet, subunit ,124,125.Nucleolus integrity and rRNA biogenesis in higher eukaryotes, including man, appear far more complicated and diverse than previously thought and especially as compared to lower eukaryotes, like yeast. It is now evident that a large number of molecular species are involved and a plethora of others might participate. Among those, several important new non-coding RNAs play key roles in the rRNA biogenesis and the nucleolar assembly, including diverse rDNA intergenic spacers, snoRNAs, circular RNAs, other long non-coding RNAs or even microRNAs. Most importantly, there is now accumulating data that directly correlate the aberrant expression of several ncRNAs with the emergence of atherosclerosis, Alzheimer’s, Kawasaki disease, as well as several ribosomopathies. However, the lack of conservation and the reduced sequence similarity between closely related species makes precise RNA identification a difficult task and necessitates a focused effort to stimulate the discovery of novel human ncRNA modulators of rRNA biogenesis ,126. On


As the most common neoplasm in digestive system, hepatocellular carcinoma (HCC) is one of the most important leading cause of cancer deaths worldwide. Its high-frequency metastasis and relapse rate lead to the poor survival of HCC patients. However, the mechanism of HCC metastasis is still unclear. Alternative splicing events (ASEs) have a great effect in cancer development, progression and metastasis. We downloaded RNA sequencing and seven types of ASEs data of HCC samples, in order to explore the mechanism of ASEs underlying tumorigenesis and metastasis of HCC. The data were taken from the The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases. Univariate Cox regression analysis was used to determine a total of 3197 overall survival-related ASEs (OS-SEs). And based on five OS-SEs screened by Lasso regression, we constructed a prediction model with the Area Under Curve of 0.765. With a good reliability of the model, the risk score was also proved to be an independent predictor. Among identified 390 candidate SFs, Y-box protein 3 (YBX3) was significantly correlated with OS and metastasis. Among 177 ASEs, ATP-binding cassette subfamily A member 6 (ABCA6)-43162-AT and PLIN5-46808-AT were identified both associated with OS, bone metastasis and co-expressed with SFs. Then we identified primary bile acid biosynthesis as survival-related (KEGG) pathway by Gene Set Variation Analysis (GSVA) and univariate regression analysis, which was correlated with ABCA6-43162-AT and PLIN5-46808-AT. Finally, we proposed that ABCA6-43162-AT and PLIN5-46808-AT may contribute to HCC poor prognosis and metastasis under the regulation of aberrant YBX3 through the pathway of primary bile acid biosynthesis. Hepatocellular carcinoma (HCC) is the most common primary liver tumor and one of the most important lethal malignancies worldwide . In ChinAlternative splicing (AS) is an important post-transcriptional regulatory mechanism that increases protein diversity . AS of pAlthough multiple studies have revealed the critical role of many genes in the development and progression of HCC, most previous studies focused on alteration in transcriptome level. Analysis of post-transcriptional process is largely ignored, especially the potential roles of ASEs in the pathogenesis of HCC.In our study, a comprehensive analysis was performed to characterize AS profiling and we constructed a prognostic model based on metastasis-related and overall survival-related ASEs (OS-SEs) for HCC. Additionally, Pearson correlation analysis was used to identify metastasis-associated ASEs as well as the corresponding SFs and pathways to elucidate the latent metastasis mechanism in HCC. Our results proved that the hypothetical constructed model has an important significance in prognostic prediction of HCC, which might assist oncologists in clinical management. Furthermore, a new sneak molecular mechanism and three potential therapeutic targets for HCC metastasis (YBX3 (Y-box protein 3), ABCA6, PLIN5) were both identified among the present study. In the present study, the active search about AS profiling has identified three potential therapeutic targets for HCC metastasis . Meanwhile, the underlying molecular mechanism was also investigated.https://tcgadata.nci.nih.gov/tcga/). Meanwhile, we retrieved seven types of ASE data  from the TCGASpliceSeq database (https://bioinformatics.mdanderson.org/TCGASpliceSeq/). Samples with more than 25 percent of missing percent splicing (PSI) values were excluded.RNA-seq data and SFs of 390 HCC cases were downloaded from the The Cancer Genome Atlas (TCGA) Data Portal  curve. In addition, the identification of differences between high-risk and low-risk groups can also be resolved by Kaplan–Meier survival analysis.To assess whether the risk score, as well as age, gender, grade, stage and TNM stage was independent prognostic factor, univariate and multivariate Cox regression analyses can be applied by modifying the baseline information.P>0.001 and the absolute value of correlation coefficient < 0.75 [In the SpliceAid2 database, 377 splicing factors were retrieved . Pearsont < 0.75 . In the To identify the OS-SEs significantly associated with TNM stage, metastasis or both of them, Kruskal–Wallis test and Mann–Whitney–Wilcoxon test were performed, and results were displayed by beeswarm plots. Moreover, the regulation network also contains these metastasis-/stage-related OS-SEs.To screen for prognosis-associated signal pathways identified by Gene Set Variation Analysis (GSVA), univariate Cox analysis was performed . Then, tTo minimize bias, multiple databases were used to detect gene and protein expression levels of key biomarkers at the tissue levels, including the UALCAN , Kaplan–www.r-project.org) . Two-tailed P<0.05 was regarded statistically significant.All statistical analyses were applied by R version 3.5.1  and we identified a total of 34163 ASEs in 8985 genes among the HCC cases: 12327 ES events in 5343 genes, 8087 AT events in 3532 genes, 6352 AP events in 2566 genes, 2666 AA events in 1937 genes, 2331 AD events in 1663 genes, 2263 RI events in 1561 genes and 137 ME events in 135 genes. Therefore, multiple AS types could occur in the same genes (The flow chart of the present study is illustrated in me genes A. We useme genes B. Additime genes C and bubme genes A–G.P= .225E-7) , P<0.001)  D. The ri.225E-7) E. The gr.225E-7) F. Beside.225E-7) G. FurtheP<0.001) .P=0.041, P=0.048), bone metastasis  and co-expressed with YBX3   were correlated with 177 ASEs. Among 187 pairs of correlation cases, 104 pairs were negatively regulated (green lines) and 83 pairs were positively regulated (red lines). Of the 177 ASEs, 79 were high-risk events  and 98 were low-risk events  A. Among P<0.001) B–H.Through GSVA, we identified survival-related KEGG pathways with univariate regression analysis. With the co-expression analysis, some significant correlation patterns between survival-related KEGG pathways and ASEs intersecting of bone metastasis and regulatory network were shown in http://pathcards.genecards.org/). Then to enhance the validity of our results, we performed a multidimensional validation for verifying prognostic value of ABCA6, PLIN5, YBX3 and the five genes mentioned above. All the results are shown in The top five genes for pathway of primary bile acid biosynthesis were CH25H, AMACR, AKR1D1, CYP27A1, CYP46A1 which were identified by Pathcard database  co-expressing with SF YBX3 and pathway of primary bile acid biosynthesis. Based on the results, we supposed YBX3 regulated ABCA6-43162-AT and PLIN5-46808-AT of the primary bile acid biosynthesis pathway resulting in HCC metastasis.YBX3upregto the Y-box binding protein family. The Y-box binding proteins play diverse regulatory functions, including regulation of transpription and translation, and are considered to be contributing to cell proliferation and transformation . These pYB-1, as the prototype member of this family, its nuclear expression has been reported to be correlated with advanced stages of malignant diseases, such as breast cancer , non-smaATP-binding cassette (ABC) transporters have been acknowledged as important players in the processes of transmembrane transport which utilize ATP hydrolysis energy to transport various substrates including lipid, amino acids, peptides, ions and sugars . As an iSimilarly associated with lipid, PLIN5 belongs to the family of perilipins consisting of five distinct members (PLIN1-5), which are major structural proteins located on the surface of lipid droplets known to serve as factors for lipid homeostasis by modulating lipid storage, with crucial roles in diseases characterized with lipid manifestations ,46. In oThe co-expression between the ASEs of ABCA6-43162-AT, PLIN5-46808-AT and the pathway of bile acid biosynthesis in the present study has also proved both the ASEs were related to HCC metastasis by affecting lipid metabolism. Bile acids acted as an emulsifier and helped to absorb and transport lipids. They were regarded as key signal molecules involving in lipid metabolism. Our study found that the pathway of primary bile acid biosynthesis was co-expressed with HCC metastasis-related ASEs (ABCA6-43162-AT and PLIN5-46808-AT) and we inferred that the pathway was involved in metastasis of HCC. A multicenter international study showed that primary biliary cirrhosis significantly promoted HCC initiation and this effect might be related to the activation of inflammatory signaling ,55.Our data revealed that ABCA6, PLIN5 and the pathway were all associated with lipid in various aspects. The further result of String database also demonstrated that there are extensive connections among our hypothesis members. ABCC3, establishing broad links between ABCA6 and the pathway genes, may play a part on transportation of organic anions in biliary and intestinal excretion. Besides, it is associated with Extrahepatic Cholestasis . PPARA, There are incontrovertibly some limitations in our study. First of all, the data we collected from public databases are limited so that clinical information is not comprehensive and may cause potential bias and errors. Second, the present study is a multidimensional correlation study rather than a specific biological mechanism. Last but not least, single bioinformatics analysis lacks high credibility. Therefore, cell and animal experiments and clinical trials still need to be done to verify the findings. Although there are limitations, for all we know, our study was the first to link ASEs effects on lipid-to-lipid effects on HCC metastasis. In the future, we will concentrate on the splicing isoforms of ABCA6-43162-AT and PLIN5-46808-AT to further explore mechanism of HCC metastasis.In conclusion, risk score calculated by the predict model can be used as an independent prognostic factor for HCC. We proposed that ABCA6-43162-AT and PLIN5-46808-AT are regulated by YBX3 via the pathway of primary bile acid biosynthesis, and this regulatory relationship can lead to a poor prognosis and clinical outcome in the progression of HCC.Click here for additional data file.