It is a challenge to automatically and accurately segment the liver and tumors in computed tomography (CT) images, as the problem of over-segmentation or under-segmentation often appears when the Hounsfield unit (Hu) of liver and tumors is close to the Hu of other tissues or background. In this paper, we propose the spatial channel-wise convolution, a convolutional operation along the direction of the channel of feature maps, to extract mapping relationship of spatial information between pixels, which facilitates learning the mapping relationship between pixels in the feature maps and distinguishing the tumors from the liver tissue. In addition, we put forward an iterative extending learning strategy, which optimizes the mapping relationship of spatial information between pixels at different scales and enables spatial channel-wise convolution to map the spatial information between pixels in high-level feature maps. Finally, we propose an end-to-end convolutional neural network called Channel-UNet, which takes UNet as the main structure of the network and adds spatial channel-wise convolution in each up-sampling and down-sampling module. The network can converge the optimized mapping relationship of spatial information between pixels extracted by spatial channel-wise convolution and information extracted by feature maps and realizes multi-scale information fusion. The proposed ChannelUNet is validated by the segmentation task on the 3Dircadb dataset. The Dice values of liver and tumors segmentation were 0.984 and 0.940, which is slightly superior to current best performance. Besides, compared with the current best method, the number of parameters of our method reduces by 25.7%, and the training time of our method reduces by 33.3%. The experimental results demonstrate the efficiency and high accuracy of Channel-UNet in liver and tumors segmentation in CT images. Automatic liver and tumors segmentation in medical images has great significance in qualitative analysis of hepatic carcinoma, which can facilitate surgeons to diagnose disease and plan the patient-specific surgery . ComputeTo accurately segment liver and tumors in CT images, numerous segmentation methods have been proposed, including intensity threshold, region growth, and deformation model. In recent years, the development of convolutional neural networks, especially fully convolutional neural networks (FCN) , has madTo tackle with the above difficulty, we need to locate the target tissue and extract the features of similar objects, and solve the problems of "inter-class difference" and "intra-class consistency" . To solvHowever, the above methods lack the pixel-level mapping relationship of spatial information between pixels, which would result in wrongly detecting the similar tissues as the target tissue, as well as failing to distinguish various sub-domains with difference of the target tissue. To address this problem, we consider to extract the mapping relationship of spatial information between pixels from different convolutional layers of UNet . The corThe contributions of this paper are as follows:We propose the spatial channel-wise convolution, a convolutional operation along the direction of the channel of the feature maps, which facilitates the neural network to learn the mapping relationship between pixels in feature maps and to distinguish liver and tumors from other similar tissues or background.We put forward an iterative extending learning strategy. The proposed learning strategy extends the receptive field of single spatial channel-wise convolution layer by layer, and obtains spatial channel-wise convolution at multiple scales by max-pooling and transpose convolution. Thus, the receptive field of spatial channel-wise convolution at multiple scales is increased and the mapping relationship of spatial information between pixels can be effectively optimized iteratively by back propagation.We propose the Channel-UNet, which takes UNet as the main framework. Each module in the UNet consists of the convolution layer and the spatial channel-wise convolution layer. By concatenating spatial channel-wise convolution and feature map along the direction of the channel of the feature maps, the Channel-UNet can realize multi-scale information fusion and effectively suppress the over-segmentation and under-segmentation problems in the process of segmentation.Convolutional neural networks have been applied to various medical image segmentation tasks. However, medical images contains various soft tissues with complex structures, we need to distinguish the target tissue from various soft tissues. Current research mainly focuses on adding multi-scale image information and optimizing the extracted image information by using attention-aware methods to achieve accurate segmentation.In image segmentation, the convolution network can successfully determine the coarse boundary of the target by extracting the abstract features of the image. Generally, the deeper the network is, the more complex and abstract the information can be fitted with the increase of parameters and receptive field of convolution kernel. This success can be partly attributed to the inherent invariance of local image transformation in the convolution network, which enables the network to learn higher-level feature. This invariance is helpful for higher-level tasks, but it may hinder low-level tasks. For example, in image segmentation, both the high-level abstract information and low-level pixel information are required for accurate segmentation. While with the increasing of the numbers of neural network layers, the information at low-levels is prevented from being transmitted to the output. To solve this problem, researchers propose two solutions. The first solution is to use low-level feature information to assist high-level feature to restore image details. Specifically, up-sampling operations are used to encode the information and recover the missing target details due to the down-sampling process by connecting the encoding network with the decoding network. For example, FCN  and UNetThe convolutional layer contains the spatial information of each pixel in the feature maps. Therefore, many researchers try to optimize the spatial information of convolutional layers. According to the location of the convolutional layers in the network structure, the optimization of spatial information extracted from feature maps can be divided into encoding, decoding and output stages. In the stage of network encoding and decoding, attention mechanism is often applied to optimize the extracted spatial information of feature maps . The basIn this paper, we consider extracting and optimizing the mapping relationship of spatial information between pixel in convolution layers to solve the problems of over-segmentation and under-segmentation. We propose the spatial channel-wise convolution, iterative extending learning strategy, and Channel-UNet framework, which can converge the optimized mapping relationship of spatial information extracted by spatial channel-wise convolution and the existing information extracted by UNet in the feature maps, thus achieving accurate liver and tumors segmentation in CT images.L2 regularization are added between convolution layers to effectively alleviate over-fitting.The lacking of mapping relationship of spatial information makes it difficult to perform iterative spatial mapping optimization, which would result in the problems of over-segmentation and under-segmentation. In this paper, we propose the spatial channel-wise convolution, a convolutional operation along the direction of the channel of feature maps, which extracts the mapping relationship of spatial information in convolutional layers and facilitates distinguishing liver and tumors from other similar tissues or background.x, y)-plane of the image, while in the spatial channel-wise convolution, the spatial channel-wise convolution kernel slides on the channel z-axis of the image. N convolution kernels, where N represents the number of convolutional kernels whose value is equal to the number of output images. In order to calculate its output, the 1 × 1 convolution kernel is multiplied by the corresponding pixel value in each input image, respectively, and then the sum is obtained. Secondly, the 1 × 1 convolutional kernel slides in -plane. Finally, the N convolution results are superimposed along the z-axis direction, which is the channel of feature maps, as shown in The difference between the spatial channel-wise convolution and the convolution is the sliding direction of the convolutional kernel. Specifically, in the traditional convolution, the convolution kernel slides on the  is the pixel value of the corresponding feature map, N is the number of channels of the feature map, w is the weight coefficient.where 2 convolutional kernels to calculate spatial channel-wise convolution with three input images (32 × 32). The 1 × 1 convolutional kernel is placed at the top left corner, and then multiplied by the pixel value of the first input image (red image). Then the convolutional kernel slides along the z-axis, multiplying the corresponding pixel value of the second (green) and third (blue) input images. Finally, the results of k convolution kernels are arranged in the -plane according to the original position of spatial channel-wise convolution kernels. The previous steps can be expressed in Equation 2.For spatial channel-wise convolution, as shown in l is the number of layers, b is the offset. δ is the activation function, zis the pixel value of the corresponding feature map, k is the number of channels of the feature map, w is the weight. coefficient, denotes Hadamard product.where By using spatial channel-wise convolution, we extract the mapping relationship of spatial information between pixels, then we need to optimize the extracted mapping relationship of spatial information for accurate segmentation of liver and tumors. In this paper, we put forward an iterative extending learning strategy to optimize the mapping relationship of spatial information between pixels at different scales and enables spatial channel-wise convolution to map the spatial information between pixels in high-level feature maps. The receptive field of spatial channel-wise convolution at multiple scales is iteratively extended and the mapping relationship of spatial information between pixels can be efficiently optimized iteratively by back propagation.l −1), the input feature of layer (l −1) is mapped to X. We denote Z as the output feature of layer l, which can be obtained by spatial channel-wise convolution calculation, as shown in Equation 3. To be more specific, when the input images pass through the spatial channel-wise convolution layer, the pixels in the image are activated by different convolution kernels and the mapping relationship is established between the pixels in the input images. For convolution layer  continuous epochs, the over fitting occurs and we stop training the network.Inspired by , before In the test, 10% of the training data were randomly extracted as the verification set, and the results were verified by 10-fold cross-validation. For measuring the performance of our proposed network, five metrics are used to measure the accuracy of segmentation results including the Dice, which refers to the same measurement as Dice per case in 3Dircadb dataset, average symmetric surface distance (ASD), volumetric overlap error (VOE), relative volume difference (RVD), and root mean square symmetric surface distance (RMSD). For the last four evaluation metrics, smaller values indicate better segmentation results.Iterative Extending Learning Strategy, the receptive field of spatial channel-wise convolution can be improved by adding convolutional layer before spatial channel-wise convolution layer. In order to further test its effect in practical application, in this section we test the effect of spatial channel-wise convolution on liver segmentation under different receptive field by changing the number of convolutional layers in front of spatial channel-wise convolution layer. The network structure is based on the network introduced in Network Architecture. The number of convolutional layers in branch 2 is {0,1,2,3,4} in the experiment. According to the analysis in x, y)-plane extracted by spatial channel-wise convolution is helpful to the recognition of tumors and liver, especially when the pixel values of tumors and background are close. To verify the validity of the spatial channel-wise convolution layer structure, we compared the segmentation effects of removing the spatial channel-wise convolution layer with Channel-UNet on liver and tumor. Compared with the network after removing the spatial channel-wise convolution layer, the Channel-UNet proposed in this paper achieves good segmentation results in two measurement indicators of liver and tumors segmentation, as shown in 6 and the training time is 9 h, while for Channel-UNet, the number of parameters is 45.6 × 106 and the training time is 6 h. Compared with , Shenzhen Science and Technology Program , HK RGC TRS project T42-409/18-R, the CUHK T Stone Robotics Institute, National Natural Science Foundation of China (No. 61802386) and Natural Science Foundation of Guangdong (No.2018A030313100).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Overall, direct viral RNA sequencing followed by bioinformatic analysis proves to be a promising approach for identification of the viral strain or strains involved in clinical infections, allowing for more precise prevention and control strategies during PRRSV outbreaks.Prompt detection and effective control of porcine reproductive and respiratory syndrome virus (PRRSV) during outbreaks is important given its immense adverse impact on the swine industry. However, the diagnostic process can be challenging due to the high genetic diversity and high mutation rate of PRRSV. A diagnostic method that can provide more detailed genetic information about pathogens is urgently needed. In this study, we evaluated the ability of Oxford Nanopore MinION direct RNA sequencing to generate a PRRSV whole genome sequence and detect and discriminate virus at the strain-level. A nearly full length PRRSV genome was successfully generated from raw sequence reads, achieving an accuracy of 96% after consensus genome generation. Direct RNA sequencing reliably detected the PRRSV strain present with an accuracy of 99.9% using as few as 5 raw sequencing reads and successfully differentiated multiple co-infecting strains present in a sample. In addition, PRRSV strain information was obtained from clinical samples containing 10 The swine industry plays an important role in feeding the world, as pork is one of the highest consumed animal proteins in the world ,2. EmergGlobally, PRRS remains a threat to the swine industry despite many years of combined efforts to combat and control infection and disease . One of Prompt detection of pathogens during an outbreak is essential for efficient disease control. Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) testing, which has the advantages of short turnaround time and high sensitivity ,28, is cConventional sequencing methods for RNA viruses usually includes reverse transcription and PCR amplification during library preparation which is then followed by amplicon sequencing. These extra steps not only introduce bias, but also increase the sequencing time, making rapid diagnosis difficult ,40,41. TA PRRSV type 2 isolate, VR2332 (GenBank: EF536003.1), was used as the main reference strain. A PRRSV type 1 isolate PRRSV1/USA/Lab6  and PRRSV type 2 isolate PRRSV2/USA/Lab3  were used for experiments examining the detection of multiple viral isolates in a single sample. All viral isolates were propagated on MARC 145 cells as previously described . ClinicaPRRSV RNA was extracted from cell culture supernatants, virus-negative pig serum spiked with PRRSV, and clinical PRRSV-positive serum samples using the QIAamp Viral RNA mini kit  following manufacturer’s instructions without the addition of carrier RNA and with a final elution in 50 μL nuclease-free water. A high concentration PRRSV stock (supernatants from virus grown in MARC-145 cells) was extracted to generate a large amount of high concentration RNA for whole genome sequencing. Generation of known concentrations of the virus in serum samples (spike-in samples) was performed by adding the PRRSV stock to virus-negative pig serum, half of which was used for sequencing and the other half for determining the number of viral copies present. For clinical samples, RNA was extracted from 300 μL of serum, two thirds of which was used for sequencing and the remaining third was used to determine the number of viral copies present. Viral copies were determined using an RT-qPCR assay as described previously using a standard curve to determine the number of viral copies and then calculating the total number of copies sequenced .Since MinION RNA sequencing requires a high amount of input RNA for library preparation (>500 ng), lower viral RNA concentration samples were supplemented with exogenous cellular RNA for sequencing library preparation. Although lower amounts of RNA can be used, adding exogenous mRNA allows for protection of the flow cells, consistency between samples, especially those with low amounts of RNA, and testing of the method for use with clinical samples such as cells or tissues which would contain cellular mRNAs. This exogenous cellular RNA was obtained by extracting total RNA from MARC-145 cells using the Qiagen RNeasy mini kit  according to the manufacturer’s protocol with the addition of on-column DNAse digestion. When needed, concentration of RNA was performed using a SpeedVac lab concentrator . A Qubit 3.0 fluorometer  and a Nanodrop1000 spectrophotometer  were used for quantitative and qualitative assessments. Sequencing libraries were generated from 600 ng of extracted viral RNA or a combination of viral RNA and exogenous cellular RNA using the direct RNA sequencing kit  according to the manufacturer’s protocol . Since tFor evaluation of whole viral genome generation from MinION direct RNA sequencing, two duplicate runs were performed starting with 600 ng PRRSV VR2332 genomic RNA. Sequencing of mixed-strain samples combined 300 ng of VR2332 RNA and 300 ng of strain 1-7-4 or SDEU RNA, or 600 ng VR2332 RNA total as a control. Other samples that contained less than 600 ng of PRRSV RNA, such as clinical samples, were supplemented with exogenous cellular RNA to obtain a total of 600 ng RNA for use in library preparation. Basecalling of raw reads was performed using Albacore  to generate FASTQ files. Total yield, total reads, read quality, and read length from whole genome sequencing were analyzed using NanoPlot . To obtaA consensus genome was generated using the longest PRRSV read from the sequencing data as a scaffold. The longest PRRSV read was extracted from the FASTQ file using an awk command, all other raw reads were then mapped to this sequence using minimap2 , and theThe analytical sensitivity of MinION direct RNA sequencing was analyzed by examining the sequencing yield needed for viral strain detection, as well as the number of viral copies needed to generate detectable viral sequence. The sequencing yield needed for viral strain detection was examined by generating datasets with targeted yields ranging from 3000 to 30,000,000 bases from the two whole genome sequencing runs. Specifically, the text summary of the sequencing file from basecalling was analyzed using R (version 3.4.0)  and grouE) < 10−50 to examine the PRRSV sequence reads. The PRRSV raw reads were compared to the custom PRRSV database using nucleotide BLAST (BLASTn) and the top match, based on bit score, was regarded as the strain detected in the sample. This detected sequence was then aligned to the known reference genome using Geneious software version R8.0.5 .,45.44,45−2/ site/year [Analytical sensitivity of MinION direct RNA sequencing was first evaluated by examining sequencing results over a range of sequence yields to determine the lowest sequencing yield at which the PRRS virus could be identified and at which a consensus genome could be generated. A range of sequence yields from 3 kilobases (kb) to 30,000 kb were obtained from the two whole genome sequencing runs above. Total reads were analyzed against a custom PRRSV database using BLASTn and the top match for each sequence yield, even those with only a few reads, was GenBank ID KC469618.1 . A 99.9% identity was observed between the known sequence of the VR2332 strain used in this experiment  and the top BLAST match, KC469618.1, with an alignment length of 15,183 bp and only 15 base changes, suggesting they are basically the same isolate, especially since PRRSV has a high mutation rate estimated at (4.71–9.8) × 10ite/year . 4 reads) allowed for accurate whole PRRSV genome generation  because of the low number of PRRSV reads present, so the longest PRRSV read was used for accuracy analysis instead of a consensus sequence  even though the percent of PRRSV reads per total reads was higher in these samples. Thus, more than 109 viral copies with perhaps 1500 PRRSV reads are recommended if the goal is to obtain a full-length genome sequence, but if identification of the viral strain involved in infection is all that is needed, then clinical serum samples need only have 106–107 viral copies to be successful  was observed. This preliminary result suggests that the knowledge of the number of viral copies in a sample can predict the approximate number of raw reads that will be obtained after sequencing allowing for more successful sequencing results and the number of reads obtained from sequencing can be used to estimate the number of viral copies present in a sample.A comparison between the number of viral copies and the number of viral reads from sequencing was performed to determine if there was a quantitative relationship between input PRRSV RNA amounts and output PRRSV sequencing reads. Of note, the total raw reads varied greatly even though the same amount of total RNA was used for library preparation , which wIn swine farms, PRRS outbreaks can occur even in herds that are vaccinated, therefore it is necessary to be able to differentiate the presence of infectious field strains from vaccine to aid in outbreak investigation ,14,16. TFrom this study we also noticed that the percentage of PRRSV reads that mapped to the first BLAST hit could be used as an indicator for the presence of other PRRSV strains . The samPRRSV has been a severe threat to the swine industry worldwide ever since it was first described in the late 1980s . ControlThis study was planned and performed to assess the feasibility of Oxford Nanopore MinION DRS in clinical PRRSV diagnostics to identify the viral strains involved in infection. The key interests addressed in this study included whether sequencing can detect PRRSV strains to identify an outbreak as occurring due to the introduction of a new strain or recirculation of a previous outbreak, whether sequencing can generate whole genome information to aid in further understanding of PRRSV epidemiology, and whether sequencing can detect and differentiate multiple strains in a single sample to investigate outbreaks that occur in vaccinated herds or co-infection of multiple field-strains. Previously, PRRSV whole genomes have been generated using Sanger and Illumina sequencing technologies ,47,72. WSequencing can be incorporated as a supportive tool for PCR to aid in diagnostic strain level PRRSV detection. It has been reported that both Sanger and Illumina sequencing can accurately detect PRRSV strains present in a sample, but both require transcription of RNA into cDNA followed by PCR amplification prior to sequencing ,72. Diff7 viral copies in an influenza virus study [4 to 106 viral copies, depending on the sample type, can be successfully sequenced to accurately identify strains after about 6 hours of sequencing. Although DRS is not as sensitive as PCR for use as a diagnostic tool identifying viral presence [Now knowing the potential of DRS for strain level detection of pathogens as determined through this study as well as others , we nextus study . Our respresence ,78, it cpresence . Interespresence , thus empresence ,81. ThisIn addition to a single strain infection, clinical situations have been shown to be more complicated, sometimes involving infection with multiple strains simultaneously, such as co-infection of multiple field strains or co-existence of field strain(s) with vaccine strain ,82. ThisThis study begins the process of developing rapid and high-resolution PRRSV diagnostics for use in clinical situations where genomic data is urgently needed. This includes situations of potential infection, outbreak investigation, vaccine design guidance, and producer desires for more specific information. The PRRSV RNA genetic material presents the same technical demands for extraction, processing, and sequencing as do influenza virus, coronaviruses, picornaviruses, rotaviruses, and many foreign animal disease viruses for which rapid pathogen identification and discrimination can be critically important. Knowledge gained from PRRSV in this study can be immediately translatable to aid in rapid diagnostic detection and strain-specific identification of an entire class of important swine pathogens. In fact, MinION sequencing technology might end up being a useful and affordable diagnostic tool for swine veterinary medicine in general. This technology can provide a complete readout of RNA viruses and RNAs from the host or other pathogens present in a sample without the need for pre-existing knowledge of what might be present .The current evaluation of this sequencing technology indicates that it can be used successfully along with qPCR for diagnosis of a pathogen, whole genome generation, strain-level pathogen detection and differentiation. As the DRS technology continues to develop and RNA isolations are optimized for use outside of a research laboratory, these methods can be further refined and optimized using updated materials and protocols. The future goal is to realize on-site infectious disease investigation using the Oxford Nanopore MinION portable sequencer to allow for quicker diagnosis and facilitation of more rapid decision-making, an important consideration in an industry in which delays in moving animals due to unknown health status can disrupt flow patterns and schedules, or cause disease outbreaks with great economic losses.


Preparation of high-quality sequencing libraries is a costly and time-consuming component of metagenomic next generation sequencing (mNGS). While the overall cost of sequencing has dropped significantly over recent years, the reagents needed to prepare sequencing samples are likely to become the dominant expense in the process. Furthermore, libraries prepared by hand are subject to human variability and needless waste due to limitations of manual pipetting volumes. Reduction of reaction volumes, combined with sub-microliter automated dispensing of reagents without consumable pipette tips, has the potential to provide significant advantages. Here, we describe the integration of several instruments, including the Labcyte Echo 525 acoustic liquid handler and the iSeq and NovaSeq Illumina sequencing platforms, to miniaturize and automate mNGS library preparation, significantly reducing the cost and the time required to prepare samples. Through the use of External RNA Controls Consortium (ERCC) spike-in RNAs, we demonstrated the fidelity of the miniaturized preparation to be equivalent to full volume reactions. Furthermore, detection of viral and microbial species from cell culture and patient samples was also maintained in the miniaturized libraries. For 384-well mNGS library preparations, we achieved cost savings of over 80% in materials and reagents alone, and reduced preparation time by 90% compared to manual approaches, without compromising quality or representation within the library. Metagenomic next-generation sequencing (mNGS) is becoming an increasingly useful tool in the field of biology and clinical medicine. Its applications are almost limitless—any nucleic acid can be turned into a library, amplified, and sequenced, making mNGS an appealing technology for labs and hospitals alike. As sequencers such as the Illumina NovaSeq increase throughput, hundreds to thousands of libraries can be sequenced in a single run. Although the per-base cost of sequencing has become less expensive over the last several decades, the cost and time associated with sample preparation remain disproportionately high ,2.Manual library preparation is tedious and is often the bottleneck for many sequencing projects. Numerous library preparation protocols have been adapted for automation through the use of various positive displacement tip-based liquid handler instruments, including the Beckman Coulter Biomek, Hamilton Star, Agilent Technologies Bravo, TTP LabTech Mosquito, and others –5. ThougRecently, acoustic liquid handlers with sub-microliter precision have become commercially available and have been used for a large range of applications, including RT-qPCR, mass spectrometry, drug discovery, and compound dosing assays –9. To dahttps://www.protocols.io/private/07DC2C6765A0D9665C227879D935EED3.All next generation sequencing workflows begin with isolation of nucleic acid; however, such protocols are highly dependent on the source of the sample and desired product, such as DNA, RNA, mRNA, cell-free RNA, and so on. The protocol described herein is independent of nucleic acid isolation methods, and for the purpose of this work, we assume an input of isolated total RNA. To optimize miniaturization of our laboratory’s current library preparation protocol, we prepared libraries from varying concentrations of HeLa RNA using the New England Biolabs Ultra II RNA Library Prep Kit (E7770S/L). The miniaturized and automated workflow is presented in To achieve miniaturized volumes, nucleic acid samples must first be dehydrated. Several vacuum evaporators were tested to dehydrate input RNA: Thermo Savant AES2010, Thermo Savant SC110A, Thermo Savant DNA110, and GeneVac EZ-2. 5uL of extracted RNA were loaded into a 384-well PCR plate (E&K Cat No EK-75009) and were spun at low (no heat/room temperature), medium (40°C), or high (65°C) temperatures until all wells were completely dry. Room temperature drying was prohibitively slow; at medium and high temperatures, drying times were comparable. At 40°C, sample plates dried fastest with the GeneVac E-Z2 (25 minutes) and ranged from 35–50 minutes using other machines. Of note, variable drying times were observed between brands of 384-well PCR plates. After drying RNA at 40°C for 25 minutes, samples were rehydrated in 5uL water. Parallel capillary electrophoresis  RNA Integrity Number showed no significant difference between the resuspended sample and the original sample  collection –13, whicOur laboratory has previously described a mNGS library prep protocol for RNA using the New England Biolabs Ultra II RNA Library Prep Kit –16. In tTo miniaturize and adapt this procedure to the Echo 525, we tested several protocol modifications involving reduced volume of reagents using variable HeLa RNA input . Reagents for each step, including ERCC spike-ins, were prepared as a master mix and miniaturized volumes were dispensed using the Echo 525. The ideal number of PCR cycles is input-dependent and should be optimized depending on sample characteristics; for this miniaturized protocol, 19 PCR cycles were used to achieve adequate amplification of very low input samples; however, samples with a higher concentration of RNA could be appropriately amplified either by diluting the input or lowering the number of PCR cycles.P with a 384 multichannel head to perform a simultaneous 384-well bead clean and size selection using AMPure XP beads (Beckman Coulter Cat. No A63881). Bead volumes were chosen to select for a final library size of ≥200 base pairs and to remove adaptor- and primer-dimers. An Alpaqua 384 Post Magnet Plate (SKU A001222), adjusted with a 3D-printed plastic adaptor (available for download at http://derisilab.ucsf.edu/index.php?3D=225), was used to minimize library elution volume. The adaptor raises the PCR plate when placed on the magnet plate, thereby lowering the height of the bead-DNA pellet on the side of each well and allowing for complete resuspension of beads in as low as 6uL of eluent.Although all dispensing steps utilized acoustic liquid handling in this protocol, magnetic bead-cleaning steps still required pipetting automation. To facilitate high-throughput bead-cleaning, we programmed the Beckman Coulter Biomek NXTo check the quality of libraries prepared using the miniaturized protocol, we used a parallel capillary electrophoresis assay to process up to 95 samples simultaneously . Libraries were assessed for distribution of cDNA fragment size, estimated molarity and concentration, and for the presence of primer- and adaptor-dimers. Library size distributions were consistent between libraries prepared with both full and miniaturized volumes .Final HeLa RNA libraries were sequenced on the Illumina MiSeq to an average depth of 2.5 million paired-end reads. Resultant data were processed through a pipeline for pathogen detection developed in our laboratory involving subsequent removal of duplicate reads, reads with low quality, and reads aligning to phage ,17,18. O2 = 0.93, 2 = 0.995, Analysis of reads aligning to ERCC transcripts showed strong correlation to their original molar spike-in concentrations, which spanned six orders of magnitude, indicative of a successful library preparation and reflected uniform PCR amplification across fragment size  from each sample of a set of 265 libraries. This pool was then sequenced on the Illumina iSeq to a total combined depth of 4.5 million reads (other existing options include a MiSeq). Use of the Echo to dispense nanoliter volumes allowed preservation of the bulk of each original sample library. The read distribution of the 265 libraries resulted in a normal distribution with each library occupying a mean of 0.377% ± 0.125% of the total reads per sample  into a final pool. The pool was sequenced on the Illumina NovaSeq to a total combined depth of approximately 11 billion paired-end reads. Sequencing results from the NovaSeq yielded a mean library proportion of 0.376% ± 0.087% of the total reads per sample . The significantly tighter standard deviation produced by this step and shown in By virtue of the internal ERCC controls, the number of reads belonging to each library was proportional to the partial concentration occupied by each library. This enabled estimation of the partial concentrations for each of the original 265 libraries. Next, the Echo 525 was used to dispense calculated ). Similarly, the automation of this library prep resulted in significant time savings. To complete 384 samples by hand with the manual protocol would have consumed an estimated 166 hours (assuming 16 samples are prepared at a time); whereas the time to complete the same number using the automated miniaturized protocol was approximately 10 hours . Using this miniaturized protocol, the cost per sample dropped to approximately $8 per sample , resulting in cost savings of over 80% . Similar10 hours . This siPatient #1: Endotracheal tube aspirate from a patient with respiratory failure was collected with IRB approval (UCSF IRB 14–13546) and informed consent. An aliquot was sent to the UCSF hospital clinical lab and another aliquot was placed immediately on dry ice. Standard bacterial culture of this sample produced heavy Enterobacter cloacae growth. Sequencing libraries produced by hand with full reagent volume, and again as described in the miniaturized protocol above, both demonstrated robust detection of E. cloacae RNA . Patient #2: bronchoalveolar lavage sample was collected from another patient with respiratory failure, with IRB approval (UCSF IRB 14–13546) and informed consent. Standard bacterial culture of this sample produced heavy Haemophilus influenzae growth. Sequencing libraries produced concurrently by hand with full reagent volume, and as described in the miniaturized protocol above, demonstrated comparable detection of H. influenzae  in both preparations. Human-stripped FASTQ files are available at BioProject Accession #PRJNA493096.As a demonstration of the applicability of this protocol, we used mNGS for pathogen detection in clinical samples. Sequencing libraries were prepared, as described above, from RNA isolated from two clinical samples. In this study, we present a miniaturized, highly automated, high-throughput protocol for preparation of high-quality mNGS sequencing libraries. Compared to the standard full-volume protocol, this protocol is faster, less expensive, produced data of equivalent quality for pathogen detection and human transcriptome coverage in HeLa preparations, and, for use-case patient samples, demonstrated correlation with clinical microbiology test results. To our knowledge, this is the first application of the Echo for metagenomic and metatranscriptomic sequencing analysis and adds to existing Echo-based protocols for DNA synthesis and plasmid sequencing ,22.This protocol has several advantages over alternative library preparation approaches. First, since the Labcyte Echo does not use pipette tips, tip contamination is eliminated as is the pipetting error inherent to transferring small volumes by volumetric displacement . Second,There are several limitations to this protocol. First, although this protocol reduces the time needed to prepare libraries for sequencing, it does not take into account the necessary upstream procedures to isolate nucleic acid . Second,In conclusion, we present an automated, miniaturized, high-throughput protocol to prepare RNA sequencing libraries using the NEB Ultra II RNA Library Prep Kit. With this workflow, it is possible to prepare 384 high-quality sequencing libraries with just 10% of the regular reagent volume, at less than 20% of the cost and in less than 10% of the time compared to the regular hand-prep. The workflow presented here may support the further advancement of clinical metagenomics as well as large scale sequencing projects.


Numerous human diseases arise from alterations of genetic information, most notably DNA mutations. Thought to be merely the intermediate between DNA and protein, changes in RNA sequence were an afterthought until the discovery of RNA editing 30 years ago. RNA editing alters RNA sequence without altering the sequence or integrity of genomic DNA. The most common RNA editing events are A-to-I changes mediated by adenosine deaminase acting on RNA (ADAR), and C-to-U editing mediated by apolipoprotein B mRNA editing enzyme, catalytic polypeptide 1 (APOBEC1). Both A-to-I and C-to-U editing were first identified in the context of embryonic development and physiological homeostasis. The role of RNA editing in human disease has only recently started to be understood. In this review, the impact of RNA editing on the development of cancer and metabolic disorders will be examined. Distinctive functions of each RNA editase that regulate either A-to-I or C-to-U editing will be highlighted in addition to pointing out important regulatory mechanisms governing these processes. The potential of developing novel therapeutic approaches through intervention of RNA editing will be explored. As the role of RNA editing in human disease is elucidated, the clinical utility of RNA editing targeted therapies will be needed. This review aims to serve as a bridge of information between past findings and future directions of RNA editing in the context of cancer and metabolic disease. Genetic complexity, or plasticity, is the foundation to develop complicated biological functions in living organisms. To maximize the versatility of limited amounts of genetic material, a variety of changes take place at the genomic level, including RNA metabolism and modification . RNA is The year of 1987 marked the first milestone for the journey of RNA-editing. A cytidine (C) to uridine (U) conversion in the mRNA of human apolipoprotein B (apoB) was identified to be responsible for the production of a shorter version of apoB (apoB48) by creating a new stop codon . This alXenopus laevis  changing amino acid sequences of proteins (recoding); (ii) altering splicing patterns of pre-mRNA; (iii) causing changes in seed sequences of microRNAs (miRNAs) or in sequences of miRNA targeting sites; and (iv) influencing the stability of targeted RNAs , 10  provide a summary of recently identified RNA-editing events that regulate both cancer development and metabolic dysfunctions, (2) highlight the existing gaps in our knowledge of RNA-editing mechanisms, and (3) describe the potential implications for the development of novel therapeutic approaches to regulate RNA editing.Biogenesis of RNAs and RNA-regulated functions have been well-established in playing important roles in tumorigenesis . With thThe consistent finding from these reports is that RNA editing is a common phenomenon in cancer helping to drive transcriptomic and proteomic diversity, and overall levels of RNA editing mirror the expression levels of editases in cancers compared to normal tissues  –15. In cThese conflicting reports suggest that the relationship between RNA editing and cancer development is complicated and potentially influenced by other factors such as the origin, stage and microenvironment associated with the studied cancer. Instead of attempting to connect an individual cancer with the global level of RNA editing, connecting specific RNA editing events to cancer-related functions could prove to be more informative.ADAR gene family members have been identified and studied for their RNA-editing functions: ADAR1 (encoded by ADAR), ADAR2 (encoded by ADARB1) and ADAR3 (encoded by ADARB2)  .The mRNAs of multiple proteins have been identified as direct targets of ADAR1 and undergo nonsynonymous amino-acid substitutions associated with cancer development Figure . In hepaA receptor alpha 3 (GABRA3) activates the Akt pathway and promotes cell migration, invasion and metastasis. ADAR1-mediated editing of GABRA3 (I342M) reduces its cell surface expression and suppresses Akt activation and metastatic potential of cancer cells  T cells. It presents an intriguing possibility to explore immunotherapeutic approaches utilizing information of RNA editing. Alternatively, it also poses a potential mechanism that cancers can hijack in order to avoid effective immune surveillance.A recent study revealed a novel function of ADAR1 in cancer-associated immune environment. In subsets of tumor samples, including ovarian cancer (OC), melanoma and BC, increased levels of ADAR1-edited peptides are presented by human leukocyte antigen (HLA) molecules . PresentThe boundary between pro-tumorigenic and anti-tumorigenic functions of ADAR1 can be blurry even with the same amino-acid-sequence-altering event. The glioma-associated oncogene 1 (GLI1) activates the Hedgehog (HH) signaling pathway to promote cell proliferation . In multIn metastatic melanoma, ADAR1 confers tumor-suppressive activities by editing miRNA sequences to alter their target specificity. ADAR1-mediated editing of miR455-5p results in lack of inhibition of the tumor suppressor cytoplasmic polyadenylation element-binding protein 1 (CPEB1), while edited miR378a-3p targets the oncogene, α-Parvin, for downregulation , 29. DurLIN28 and the editing of the primary miRNA pri-let-7 at multiple sites, both events reduce the production of let-7 family members , JAK2 activation and BCR-ABL1 amplification was shown to increase ADAR1 expression promoting leukemia stem cell (LSC) self-renewal . A follo members , 33.In addition to altering miRNAs directly, ADAR1 also edits miRNA targets to affect their susceptibility to miRNA-mediated repression. ADAR1 was shown to edit over two dozen sites on the three prime untranslated region (3'UTR) of dihydrofolate reductase (DHFR). In BC, edited DHFR becomes resistant to the targeting of miR25-3p and miR125a-3p. As a result, the protein levels of edited DHFR increase to promote cell growth and resistance to chemotherapeutic agents like methotrexate .prostate cancer antigen 3 (PCA3), an intronic long noncoding RNA, increases its stability and expression. PCA3 acts as a dominant-negative oncogene and forms a double-stranded RNA with precursor mRNA (pre-mRNA) of prune homolog 2 (PRUNE2), a tumor suppressor gene . In lung adenocarcinoma (LUAD), the most common form of non-small cell lung cancer (NSCLC), ADAR1-mediated editing of intron 26 of FAK results in increased stabilization of FAK mRNA and protein. Induction of FAK contributes to cell invasiveness and is associated with tumor recurrence in LUAD patients .HNRPLL (HNRPLL acts as an enhancer of oncogenic splicing factor serine/arginine rich splicing factor 1 (SRSF1), resulting in a positive feed-back loop to increase the abundance of E12A-containing HNRPLL transcript. E12A-containing HNRPLL regulates expressions of cyclin D1 and transforming growth factor beta receptor 1 (TGFBR1) to promote cell proliferation in kidney and bladder cancers. Interestingly, this editing event is also mediated by ADAR2, pointing to potential interactions between ADAR1- and ADAR2-mediated RNA editing.A recent study linking ADAR1 to intron-editing showed that ADAR1 edits the intron of heterogeneous nuclear ribonucleoprotein L-like (HNRPLL) to create an additional exon (E12A) for HNRPLL . E12A-coFirst cloned in 1996, ADAR2 is the second identified A-to-I RNA editase that is also capable of editing itself , 42. ADAKip1 and CDKN1A/p21Cip1/Waf1, to promote cell cycle progression and tumorigenesis.These early studies inspired mechanistic investigations to directly link ADAR2 with malignant brain tumors such as high-grade astrocytoma or glioblastoma multiforme (GBM). The same team that made the initial connection between ADAR2-mediated RNA editing and brain tumors identified several pathways downstream of ADAR2 regulating GBM pathogenesis. By editing multiple sites within intron 7 of the CDC14B phosphatase pre-mRNA, ADAR2 promotes upregulation of CDC14B, subsequently causing the degradation of E3-ligase S-phase kinase-associated protein 2 (SKP2) , 48. In Kip1  and p27Kip1 .ADAR2 was found to edit numerous miRNAs, regulating tumorigenesis by balancing the functions of oncogenic and tumor-suppressive miRNAs , 50. ThiA recent study revealed another ADAR2-controlled switch through miRNA editing in GBM. In normal brain tissues, ADAR2 edits nearly 100% of miR589-3p to target and reduce expression of disintegrin and metalloproteinase domain-containing protein 12 (ADAM12), a metalloprotease that promotes cancer metastasis. In high-grade GBM, editing of miR589-3p decreases dramatically and unedited miR589-3p targets protocadherin 9 (PCDH9), a tumor suppressor protein, instead . These sIn addition to GBM, ADAR2 also displays tumor-suppressive functions in other types of cancer. ADAR2 is downregulated in ~50% of HCC and overexpression of ADAR2 in ADARs-deficient HCC cells reduces their oncogenic potential . ADAR2'sin vitro  ADAR2 is overexpressed in a small subset of HCC ; and  lead to activation of Akt and inhibition of programmed cell death . In contA recent study described an interesting “tug-of-war” relationship between ADAR2 and ADAR1 in gastric cancer (GC). Unedited podocalyxin-like protein (PODXL) promotes GC tumorigenesis by regulating cell adhesion mechanisms. PODXL can be targeted by both ADAR1 and ADAR2 to cause synonymous  and nonsynonymous  amino acid substitutions, respectively. In normal tissues, high ratio of ADAR2/ADAR1-mediated editing keeps PODXL in its edited form (H241R) preventing oncogenic onset. In GC, amplification of ADAR1 and reduction of ADAR2 cause imbalanced editing of PODXL to accumulate unedited PODXL to drive tumorigenesis .Few studies have focused on investigating ADAR3's functional importance in RNA editing. ADAR3 shares significant sequence and structural similarities with ADAR1 and ADAR2, including nuclear localization, deaminase, and RNA-binding domains . DespiteIn the human genome, there are eleven genes that belong to the APOBEC protein family. Functionally, APOBECs are cytidine deaminases and evolutionally conserved in vertebrates. The majority of research activities regarding APOBECs have focused on their ability to restrict viral infections by creating mutational imbalances in the viral genome . Recent Cip1/Waf1 , APOBEC1 was found to edit the mRNA of neurofibromin 1 (NF1) to generate a truncated NF1 (R1306Stop) in a subset of peripheral nerve–sheath tumor (PNSTs) samples  . These mThe fact that RNA editases (ADARs and APOBEC1) are expressed in major metabolic organs, such as the liver and pancreas, offers an initial clue that RNA-editing might play an important role in metabolic regulation , 76, 77.There is evidence to suggest that the RNA editing machinery is one of the evolutionarily-adaptive mechanisms developed while living organisms were becoming more complex. A-to-I RNA editing occurs mainly in primate-specific Alu repetitive elements that form secondary structures of dsRNA , 81. APOIt has been suggested that ADAR1 expression in the liver is important for early embryonic development. The absence of ADAR1 in the mouse liver results in impaired embryonic erythropoiesis, liver disintegration, and early death of the fetus , 89. MecADAR1-mediated RNA editing has recently been shown to contribute to cardiovascular disease (CVD). One mechanism for ADAR1 to promote CVD is through the phenotypic modulation of smooth muscle cells (SMC), a pivotal step during the development of CVD. The signature characteristic for the phenotypic modulation of SMC is the downregulation of SMC-specific genes, such as smooth muscle myosin heavy chain (SM-MHC) and smooth muscle α-actin (ACTA2), often mediated by platelet-derived growth factor (PDGF)-BB. In response to PDGF-BB, ADAR1 edits pre-mRNA of these SMC genes to cause abnormal splicing and subsequent downregulation of their mRNAs .ADAR1's role to attenuate an aberrant innate immune response has been well established , 94. ItsDrosophila melanogaster as the model, demonstrated that deficiencies in ADAR1 results in synaptic dysfunction in glutamatergic neurons and sustained release of neurotransmitter to promote sleep . Interestingly, increased levels of uric acid were detected in the cortex of ADAR2-knockout mice . Hyperur2cR), correlate with ADAR2 expression in the brains of ADAR2-transgenic mice and other mouse models of obesity (2cR developed phenotypic characteristics of Prader-Willi syndrome (PWS), including hyperphagia , an actin crosslinking protein whose inactivation is linked to vascular abnormalities, is significantly reduced. ADAR2 was identified as the editase of FLNA, and ADAR2-edited FLNA (Q2341R) prevents cardiac remodeling and hypertension . This isLike ADAR1, ADAR2 was also recently found to play a role in regulating circadian rhythm. CLOCK (Circadian Locomotor Output Cycles Kaput) – ARNTL  were found to be associated with human longevity, using genetic information collected from centenarians in the US  in n the US . Moreoven the US . While tgulation .apoB encodes for a full-length form apoB100 and a truncated form apoB48, respectively. ApoB100 is synthesized in the liver and is a part of the assembly of low density lipoprotein (LDL) and very low density lipoprotein (VLDL), while apoB48 is mostly produced in the intestine and is required for chylomicron formation and fat absorption   leading to diabetes/obesity and apoB100 (unedited) leading to atherosclerosis. In an apoE-deficient mouse model, apoB48 promotes higher levels of cholesterol accumulation and atherosclerotic lesion formation. This phenotype is the manifestation of apoB48 being cleared exclusively through apoE, while apoB100 can be cleared through the LDL receptor alone , 125. OnThese studies highlight the significant connections between apoB regulation, thus APOBEC1-mediated RNA editing, and metabolic disorders. Despite efforts made to identify other RNA editing targets of APOBEC1, no other target has yet been shown to play a role connecting APOBEC1-mediated RNA editing with metabolic disease . Data frAn established link of “Enzyme-Target-Function” provides the clearest blueprint to plan effective interventions of the RNA editing machinery. Numerous examples mentioned in this review fit this description providing multiple intervention points, including modulation of the levels and activity of editases, as well as correction of the edited target(s). There are, however, plenty of ambiguities in the world of RNA editing.In the context of A-to-I editing, many disease-relevant editing targets lack clear identification of the responsible editase(s). Such examples include hyperediting-mediated alternative splicing of protein tyrosine phosphatase PTPN6 in AML, and hyperediting-activated ras homolog family member Q (RHOQ) in CRC , 131. InThe functional difference between the two ADAR1 isoforms, p150 and p110, is also an important factor to consider when determining ADAR1's role in human disease. Earlier studies characterized p110 as constitutively expressed, while p150 is interferon-inducible and the main isoform responsible for innate immune response modulation and AGS , 133. InAs mentioned previously, APOBECs are better known for their abilities to edit DNAs in viral and tumor genomes. Recent identifications of APOBEC3A-mediated G-to-A RNA editing and APOBEC3G as a novel RNA editase signaled that (i) other APOBECs could also engage in RNA editing activities; and (ii) APOBEC-mediated RNA editing is not limited to the conversion from C to U , 137. InThe effects between RNA and DNA editing can be distinguished through careful planning and execution of sequencing strategies. RNA editases, however, possess functions that are independent of their RNA-editing abilities. RNA editing-independent functions of ADARs were first noted in their effects on miRNA expression. Both ADAR1 and ADAR2 are capable of influencing miRNA expression by either directly interacting with miRNAs or affecting miRNA biogenesis through regulation of important factors, such as Dicer, Drosha or DGCR8  –143. RNAThe RNA editing-independent functions of ADARs have been demonstrated by utilizing their catalytically inactive forms. This approach has helped identify these non-catalytic functions beyond the confines of miRNA biogenesis. The MAPK-phosphorylated ADAR1 p110 isoform can be shuttled by Exportin-5 to the cytoplasm, where it protects the expression of anti-apoptotic genes by competitively inhibiting binding of Staufen1 to their 3′UTRs . In metaLittle information is available regarding RNA editing-independent functions of C-to-U editases, such as APOBEC1. However, APOBEC-mediated functions that don't require its deaminase domains have been reported in humans and other species , 149. AsRNA-editing events are subjected to highly precise regulatory mechanisms. Mechanisms that regulate general localization and expression of RNA editases have been well-studied , 10. Fun2cR, could result in opposite phenotypes (obesed vs. lean) in two different animal models  and mutant RPL39 . CPSF6 interacts with ADAR1 to stabilize its localization and enhance its RNA editing activity . MoreoveIn the brain, where functions of ADAR2 have been extensively studied, a splicing factor SRSF9 (serine and arginine rich splicing factor 9) was found to repress ADAR2-mediated RNA editing. SRSF9 interacts with ADAR2 and its editing targets in the nucleus to disrupt the formation of ADAR2 dimer, which is necessary for the editing of genes involved in controlling cell survival , 154. ThConsidering the complex relationship between ADAR1- and ADAR2-mediated RNA editing, it is not surprising that mechanisms exist to regulate their RNA-editing functions simultaneously. One recent example of this is the RNA helicase, DHX9 (DEAH box helicase 9). By using an overexpression system in an esophagus carcinoma cell line (EC109), DHX9 was found to preferentially promote and repress ADAR1- and ADAR2-mediated RNA editing, respectively . The endin vitro experiments , another TGCT risk factor that shares strong sequence homology with Apobec1-editosome member Acf  hinted strongly at such connections. Using 129/Sv inbred mice that develop spontaneous TGCTs, mber Acf . Pendingmber Acf .in vivo. In mice lacking LDL receptor, PPARα agonist ciprofibrate (a common cholesterol-lowering drug) reduces hepatic RNA editing of apoB by decreasing the expression of Acf , impacts APOBEC1-mediated RNA editing n of Acf . The resSince RNA editing plays a prominent role in cancer development, tumorigenesis-associated pathways and factors could prove to be important regulators of the machinery. For example, tumor suppressor protein TP53 is involved in nearly all aspects of tumorigenesis, but few connections have been made between TP53 and RNA editing . So far,There are stronger hints for roles of another TP53-related protein, ARF , in RNA editing. ARF is well known for its ability to activate TP53 by inhibiting MDM2 (mouse double minute 2), but many TP53-independnet functions of ARF have also been identified . One of As our understanding of RNA editing in cancers and metabolic disorders improves, it is likely that many other connections will be discovered between RNA editing and established factors associated with these human diseases.With greater understanding of the relationship between RNA editing and human disease comes the opportunity for innovative therapeutic approaches. RNA-based therapies, which include targeting both RNA itself and its modifications, are becoming viable options to slow down or even reverse the course of human disease –181. ThiIn principle, modulating the expression or activity of RNA editases is a reasonable strategy for treating diseases driven by dysregulated RNA editing. In cancer cells with elevated ADAR-mediated RNA editing, such as breast and lung cancers, downregulation of ADAR expression reduces their tumorigenic capacity , 39, 185In situations where the RNA editing level is inversely correlated with disease progression, promoting RNA editing could have beneficial effects. In cancer cells where ADAR2 is downregulated, overexpression of ADAR2 displays tumor-suppressive activity , 53. OveThe concept of creating a RNA-guided editase has been adopted to attempt target-specific RNA editing. Proof-of-principle studies have been conducted to demonstrate the potential to target disease-relevant genes and restore proper protein function through RNA editing , 191. ThAs intriguing as the idea to reverse disease conditions by modulating RNA editing levels, it is not without potential drawbacks. Since different human diseases are associated with either elevated or reduced levels of RNA editing, altering it one way or the other poses the risk of undesired consequences. For example, it is theoretically possible to modulate the overall levels of inosine-containing RNA by regulating ribonuclease V , 197. BuIn addition to impacting disease progression, RNA editing could also affect drug response. In MM, ADAR1-mediated RNA editing promotes immunomodulatory drug resistance . APOBEC1Man-made, RNA-guided, and target-specific RNA editing has the potential to become the next revolution in (epi)genetic therapy. It offers a unique opportunity to modulate protein functions without altering the sequence and integrity of the genome. Indeed, its unique characteristics haven't gone unnoticed and RNA editing has been incorporated into cutting-edge technologies, such as CRISPR-Cas genome editing . Recent Having mentioned the potential problems, it should be acknowledged that there are tremendous opportunities for RNA-editing-based therapies to treat human diseases. By engaging biological events at the RNA level, RNA-editing could be associated with many-to-one or one-to-many relationships between RNA editases/events and downstream effects. Examples of “many-to-one” include the “see-saw” effect of PODXL editing by ADAR1 and ADAR2 in GC, and the ability of both ADAR1 and ADAR2 to regulate biogenesis of miRNA let-7 , 49, 58.The relationship between cancer development and metabolic disorders has been strengthened in recent years, and it appears to be a two-way street. It is well-established that cancers often overcome unwanted stresses by hijacking metabolic pathways, and metabolic disorders like obesity and diabetes are strong predisposing factors to cancers , 207. ReAs this review shows, there has been an explosion of information regarding RNA editing in the last 3–5 years. This is indeed an exciting time to study this unique epigenetic phenomenon, with plenty of opportunities and challenges ahead. Some burning questions, as highlighted throughout this review, will need to be answered in the near future. How is new information used to reshape the central dogma of cell biology of “DNA to RNA to protein?” Are there more RNA editases waiting to be discovered? How do we reliably identify RNA editases, and corresponding editing events, in specific human diseases? Can part of the RNA editing machinery be targeted as a monotherapy, or is combining these interventions with other parallel treatments, such as immunotherapy, a better course of action?Outside the purview of this article, RNA editing also plays significant roles in other physiological conditions, such as infectious, inflammatory/autoimmune, and neurodegenerative diseases –213. It CK and JW conceived of the topic of the manuscript. CK drafted the manuscript and created images with BioRender. CK, LM, and JW revised the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In vitro biophysical modeling, live-cell microscopy, and unbiased proximity labeling proteomics reveal that annexin A11 (ANXA11), an RNA granule-associated phosphoinositide-binding protein, acts as a molecular tether between RNA granules and lysosomes. ANXA11 possesses an N-terminal low complexity domain, facilitating its phase separation into membraneless RNA granules, and a C-terminal membrane binding domain, enabling interactions with lysosomes. RNA granule transport requires ANXA11, and amyotrophic lateral sclerosis (ALS)-associated mutations in ANXA11 impair RNA granule transport by disrupting their interactions with lysosomes. Thus, ANXA11 mediates neuronal RNA transport by tethering RNA granules to actively-transported lysosomes, performing a critical cellular function that is disrupted in ALS.Long-distance RNA transport enables local protein synthesis at metabolically-active sites distant from the nucleus. This process ensures an appropriate spatial organization of proteins, vital to polarized cells such as neurons. Here, we present a mechanism for RNA transport in which RNA granules “hitchhike” on moving lysosomes.   •RNA granules “hitchhike” on motile lysosomes during long-distance transport•ANXA11 binds to RNA and lysosomes via phase separating and membrane binding domains•ANXA11 tethers RNA granules to lysosomes and is required for axonal RNA transport•ALS-associated ANXA11 mutations impair its tethering function and RNA transport Annexin A11, a protein with mutations associated with ALS, tethers membraneless RNA granules to actively-transported lysosomes via its intrinsic membrane-binding and phase separating properties, enabling efficient transport of RNA to distal regions of the neuron. Many proteins within cells are translated locally rather than trafficked from their site of synthesis to their final destination. Neurons, which have long axons and dendrites, rely on local translation for numerous cell-specific functions . Local tFor membrane-bound organelles such as mitochondria and endosomes, the microtubule-based motors kinesin and dynein interact either directly or indirectly with membrane proteins and lipids to enable long-range transport. RNAs, however, do not usually exist in membrane-enclosed structures. Instead, they interact with with RNA-binding proteins (RBPs), which self-organize into phase separated structures called RNA granules . RNA graMutations in RBPs, molecular motors, and microtubule components have all been linked to neurological diseases, highlighting the critical contributions of RNA transport and metabolism to long-term neuronal integrity . In partRecently, it has become clear that not all cargos directly interact with motor proteins during long-range transport. Rather, some cargos are indirectly transported along microtubule networks by docking onto other membrane-bound organelles such as endosomes, a process known as “hitchhiking” . Endosomin vitro assays, we then identify the ALS-associated protein ANXA11 as a molecular tether that can dynamically couple RNA granules with lysosomes. ALS-associated mutations in ANXA11 disrupt docking between RNA granules and lysosomes, consequently impeding RNA granule transport in neurons in vitro and in vivo. Together, these findings identify the lysosome as a key player in neuronal RNA transport, characterize how ANXA11 enables interactions between membraneless RNA granules and lysosomes, and provide mechanistic evidence for the involvement of altered RNA transport in ALS pathogenesis.In this study, we show that RNA granules hitchhike on lysosomes for long-distance trafficking in mammalian cells. Using a combination of proximity labeling proteomics, live-cell imaging, and Using live-cell microscopy, we explored whether RNA granules could move within mammalian cells through association with motile, membranous organelles. Following heat shock, G3BP1-labeled RNA granules predominately co-localized with markers for lysosomes (LAMP1) and ER (SEC61) in U2OS cells A and S1BNext, we analyzed RNA granule and LAMP1 dynamics in cultured primary cortical neurons, which constitutively transport these structures within axons. In neurons, LAMP1-positive vesicles include both degradative lysosomes and non-acidic endo-lysosomes , but forIn confocal images, RNA granules and lysosomes were closely apposed   , representing proteins that are specifically associated with lysosomes  proximity labeling proteomics  to labelNeurons)   A and 2B.artments C. We ideysosomes D. Gene Oysosomes E. To ideysosomes  , purified ANXA11 formed phase-separated droplets that grew in size and fused with each other over time  and ANXA11 C terminus . As predicted by our structural models, the N-terminal LCR region of ANXA11 was necessary and sufficient for phase separation , rapid exchange between phase-separated and soluble states, and stress-induced oligomerization  . We founWe then asked if ANXA11 co-localized with RNA stress granule markers in cells undergoing heat shock. Stress granules composed of G3BP1, TDP43 or mRNA labeled by Oligo-dT all contained ANXA11 signal D. BecausWe next examined whether ANXA11 interacted with lysosomes in living cells. Time-lapse imaging revealed that ANXA11-positive puncta localized to the surface of LAMP1-structures following heat shock in U2OS cells H and pri2+ release from lysosomes. Supporting this observation, treatment with BAPTA-AM, a selective, permeable Ca2+ chelator that removes free Ca2+ from the cytoplasm, decreased the FRET efficiency P2 for their interaction  near the FRET acceptor LAMP1-YFP, suggesting that ANXA11 and lysosomes tightly associate with each other J. Intereficiency J and 3K.eraction J and 3K.Since mutations in both the N- and C-terminal domains of ANXA11 are associated with familial ALS, we investigated whether these mutations altered properties of RNA granules in living cells. FRAP analysis of ANXA11 dynamics showed that granules containing p.D40G-ANXA11, p.R235Q-ANXA11 or p.R346C-ANXA11 A had impin vitro assays using purified WT, p.D40G, and p.R346C forms of ANXA11. We transitioned the temperature between 4oC and 25oC over repeated cycles and observed the proteins’ ability to phase partition into droplets. Both p.D40G and p.R346C mutants exhibited accelerated phase transitioning from soluble protein to insoluble gels upon warming, and an impaired ability to recover into liquid states upon re-cooling  to the N terminus of WT and mutant ANXA11 proteins (Opto-ANXA11), allowing us to precisely regulate assembly/disassembly of ANXA11 phase condensates using light. Exposure of wild-type Opto-ANXA11 to 488-nm light triggered oligomerization faster than Opto-mCherry D and 4E,To test whether mutations in ANXA11 altered its ability to interact with RNA granules, we quantified the extent of co-localization of ANXA11 and its mutants with RNA granules following heat shock in living cells. Whereas the N-terminal p.D40G-ANXA11 mutation had no effect on RNA granule co-localization, C-terminal p.R235Q-ANXA11 and p.R346C-ANXA11 mutations dramatically reduced co-localization of ANXA11 with RNA granules D and S4EWe next asked whether ALS-associated ANXA11 mutations altered lysosome interactions. Light-induced oligomerization of Opto-ANXA11 caused rapid association of Opto-ANXA11 condensates with lysosomes A and 5B.We further tested whether ALS-associated mutations altered axonal co-trafficking of ANXA11 with lysosomes in cultured primary neurons. As predicted, mutations in the C terminus of ANXA11 impaired ANXA11’s ability to interact with motile lysosomes in axons E and 5F Since ANXA11 interacts with both RNA granules and lysosomes, we speculated that ANXA11 might function as a molecular tether to couple RNA granules with lysosomes. To explore this possibility, we expressed Opto-ANXA11 in U2OS cells and monitored lysosome and RNA granule dynamics using time-lapse confocal microscopy. We reasoned light-induced oligomerization of ANXA11 might facilitate the docking of RNA granules with lysosomes, since phase-separated ANXA11 has an increased affinity for both structures. When we stimulated Opto-ANXA11 expressing cells with 488 nm light, we saw G3BP1-labeled RNA granules associating with Opto-ANXA11-bound lysosomes  and an iWe then co-imaged ANXA11, RNA granules and lysosomes in heat-shocked U2OS cells to further define their spatial relationships. ANXA11 was present in the core of G3BP1-labeled RNA granules and additionally displayed a peripheral localization pattern that extended beyond the boundary of G3BP1 into the region labeled by LAMP1 B, consisin vitro reconstitution assay. We purified stress-induced RNA granule cores from cells  in axons of live zebrafish neurons P2. Interestingly, PIP2 is enriched in late endosomes and lysosomes P2 dynamics, such as the phosphatidylinositol-5-kinase PIKfyve, myotubularin family 3-phosphatases, and the 5-phosphatase We observed that ANXA11 interacted with lysosomes only in the presence of both Caysosomes , and is ysosomes . Therefoin vitro. Additionally, we observed stress-induced ANXA11 foci within cells that did not contain other markers of RNA granules. These ANXA11 foci fused with larger stress granules positive for both ANXA11 and other stress granule markers. The explanation for these unexpected distribution characteristics of ANXA11 in granules remains to be further investigated, but various possibilities can be envisioned given the previously described hierarchical organization of proteins within stress granules . Numerous mutations in ANXA11 have now been described by several different groups, and may account for up to 6% of familial ALS in Chinese populations . PathogeThe overall impact of these mutation-induced biophysical changes had several consequences for RNA granule/lysosome behavior within cells. These mutations interfered with RNA granule/lysosome docking and increased the gel-like properties of ANXA11 and other associated RNA granule proteins in cells. C-terminal mutations had particularly deleterious effects on ANXA11 function, potentially because they altered both its phase separation properties and lysosomal interactions. Because we saw that ANXA11 knockdown reduced axonal RNA granule transport, it is likely that both loss-of-function and gain-of-function mechanisms contribute to ALS pathogenesis in the setting of ANXA11 mutations. We also showed that ANXA11 knockdown reduced delivery of essential mRNAs to distal regions of the neuron. We postulate that the consequences of even modestly-impaired RNA transport could, over time, lead to widespread disruption of neuronal homeostasis and potential dysregulation of synaptic activity. Concurrently, ANXA11 mutation-induced aggregates might sequester additional critical RNA granule proteins, including granule chaperones. This could lead to further dysregulation of RNA metabolism within affected cells.In summary, our study identifies a previously unrecognized relationship between lysosomal biology and RNA metabolism, and implicates dysfunctional RNA granule trafficking as a potential converging disease mechanism in ALS. Our findings further suggest the possibility of additional mechanistic relationships between other ALS-associated genes, including those regulating lysosomal homeostasis, docking and transport machinery, and/or the biophysical state of RNA granules.wardme@nih.gov). Plasmids generated in this study have been deposited to Addgene. iPSC lines will be distributed to interested parties upon request.Further information and requests for resources and reagents should be directed to the Lead Contact, Michael E. Ward  and fed every 1-2 days with Essential 8 medium (Thermo Fisher Scientific), as needed. Accutase (STEMCELL Technologies) was used to enzymatically dissociate hiPSCs into single cells, and 0.5mM EDTA was used for routine dissociation to maintain colony growth. To promote cell survival during passaging, cells were passaged with the p160-Rho-associated coiled coil kinase (ROCK) inhibitor Y-27632 (10 μM: Selleckchem). hiPSCs were frozen in 90% fetal bovine serum (HyClone) and 10% DMSO (Sigma).The control male WTC11 human induced pluripotent stem cells (hiPSC) line was obtained from Coriell. We adhered to NIH Intramural Research Program policies regarding the registration and use of this iPSC line. Karyotyping was used to authenticate that the line had a normal male karyotype. HiPSCs were maintained under feeder-free conditions in a 37WTC11 hiPSCs with single-copy integration of a doxycycline-inducible NGN2 cassette at the AAVS1 locus  were sinStable hiPSC lines was generated from the WTC11 genetic background with a doxycycline-inducible mNGN2 transgene at the AAVS1 locus. For differentiation, 25 million hiPSCs were seeded onto a 15cm tissue culture dish in Neuronal Induction Medium (NIM), composed of DMEM/F12 medium (ThermoFisher Scientific), N-2 supplement (ThermoFisher Scientific), Nonessential amino acids supplement (NEAA) (ThermoFisher Scientific), Gluta-MAX supplement (ThermoFisher Scientific), Y-27632 (10 μM), and doxycycline . Cells were maintained on NIM with daily full medium changes for 3 days.After the 3-day differentiation period, cells were dissociated with Accutase from the 15-cm dish and seeded onto final experimental plates coated with poly-L-ornithine (0.1 mg/ml). Cells were seeded and maintained in Cortical Neuron Culture Media, composed of BrainPhys Neuronal Medium (STEMCELL Technologies), B-27 supplement (ThermoFisher Scientific), brain-derived neurotrophic factor (10 ng/ml), neurotrophin-3 (10 ng/ml), and mouse laminin (1 μg/ml). Half-media changes were conducted every 3 days for the lifetime of the culture.oC, 5% CO2 tissue culture incubator on tissue culture treated dishes in DMEM + 10% FCS and passaged with Trypsin EDTA.The U2OS cell line used in these studies was the human osteosarcoma cell line, obtained directly from ATCC (HTB96). Cells were authenticated by morphological assessment under microscopy. Cells were grown in a 37Cortices were dissected from E17 Sprague-Dawley rat embryos of both sexes. Rat maintenance and care followed policies advocated by NRC and PHS publications, and approved by Institutional Animal Care and Use Committee (IACUC), Janelia Research Campus.o C, indicated virus were transduced at DIV 10-14 and imaged at DIV 17-21.Tissue were digested with papain and gently triturated and filtered through 70 micron strainer. Neurons were plated in poly-L-ornithine coated dishes and cultured in Nbactive4 medium at 37∗AB were maintained at 28.5oC and spawned according to standard protocols .Adult rotocols . Embryos methods . All in Q8N0X4) locus. This locus was chosen to permit stable expression in both the iPSC and neuron stages. Plasmids were generated with PCR and recombinase-based cloning (In-Fusion by Clontech or NEBuilder by NEB). Plasmids for virus transduction in neurons  were generated with the pLEX lentiviral vector (Thermo Scientific) and ligation-based cloning. Actin-24xMBS, MCP-NLS-HaloTag and MCP-NLS-2xEGFP plasmids were kindly provided by Dr. Young Yoon from Dr. Robert H. Singer lab. Opto-ANXA11 plasmids were made by insertion of CRY2olig-mcherry on the N-terminal of ANXA11 plasmids by Gibson cloning. ANXA11 shRNA expression cassettes were made by oligo annealing and ligation into pLKO.1 vector.All plasmids generated in this study have been deposited to Addgene. Plasmids for generating stable iPSC lines (e.g. APEX2 constructs) were designed with 1 kb left and right homology arms against the CLYBL  was used extensively for western blot (WB) and immunofluorescence (IF) studies, and mouse monoclonal M2 anti-Flag antibody  was used to detect Lamp1-APEX expression via IF. GST Tag monoclonal antibody  was used to detect ANXA11-GST in lipid strip assay. Polyclonal rabbit anti-ANXA11 (HPA027545) was used for WB in lysosomal isolation experiments, liposome floatation as well as for protein localization via IF. Polyclonal rabbit anti-TDP43  and anti-G3BP1  were used to detect stress granule formation via IF, and anti-G3BP1 was also used for stress granule isolation (IP) and WB.2O). Cells were transferred to 4C room and incubated on a nutator until all stimulations were finished. Cells were scraped into 1.5mL polystyrene microcentrifuge tubes and sonicated (QSonica Q800R2) for 14 minutes with alternating 1-minute on/30 seconds off cycles. Lysate was spun at 4C at max speed for 12 minutes, and the supernatant was diluted with one volume of 50mM Tris-HCl. Samples were dialyzed to remove excess biotin and detergents. Dialysis buffer composed as follows: 50mM Tris-Cl pH 7.4, 250mM NaCl, 0.1% SDS, 1% Triton X-100 in H2O. Samples were loaded into SnakeSkin dialysis tubing (Thermo Fisher Scientific) and placed within a 2L dialysis chamber with a spin bar at 4C. Samples were dialyzed at 4C for 4 hours, with dialysis solution replaced every hour. After dialysis, the protein concentration was quantified using a Bio-Rad DCA assay, and a bead titration was carried out to determine the optimal volume of beads to use for complete pulldown of biotinylated proteins. We used Streptavidin Sepharose High Performance  beads for Lamp1-APEX experiments, and Nanolink Streptavidin Magnetic beads (Solulink) for ANXA11-APEX experiments. After normalization of protein amount and determination of optimal bead volume for each sample, bead incubation occurred overnight with agitation at 4C. The next day, beads were washed with 4 sequential buffers . Then, proteins were reduced , alkylated , quenched , and digested on beads overnight for 14 hrs with 1ug of trypsin/LysC enzyme (Promega). The next morning, 0.5ug of trypsin/LysC was added and incubated for another 2 hours at 37C at 1200 rpm. Samples were separated from beads with either centrifugation or magnetic separation, the supernatant was acidified with 10% trifluoroacetic acid (TFA), and samples were desalted using C18 solid-phase extraction spin columns. After eluting samples in 50% acetonitrile (ACN)/0.1% TFA, they were dried down in a speedVac and then resuspended in 2% ACN/0.1% formic acid (FA) for subsequent LC-MS analysis.hiPSC-derived neurons were grown to a density of 10 million cells per 10cm dish. 4 biological replicates of each experimental condition were used. To provide a substrate for the APEX enzyme, cells were fed with 500uM phenol-biotin  and incubated at 37C for 30 minutes prior to enzyme stimulation. To stimulate APEX2, cells were treated with 1mM hydrogen peroxide and incubated at 37C for 1 minute. The reaction was terminated by aspirating the growth medium and adding 4mL of ice-cold quench buffer  3 times before lysing the cells with 600uL of Lysis buffer  with a 120 min LC gradient. Top 15 data-dependent acquisition (DDA) was conducted, and the MS was scanned from m/z 350 to1500 at a resolving power (RP) of 120K. Parent masses were isolated (m/z 1.4 window) and fragmented with higher-energy collisional dissociation (HCD) with a normalized collision energy (NCE) of 27%. Dynamic exclusion time was 22.5 s. Automatic gain control (AGC) targets were 1 × 106 for MS and 2 × 105 for MS/MS acquisitions. Maximum injection times (maxIT) were 30 ms for MS and 35 ms for MS/MS.Peptide samples were analyzed on a Dionex UltiMate 3000 RSLCnano system coupled with a Thermo Scientific Q-Exactive HF mass spectrometer. Mobile phase A was 0.1% FA, 5% DMSO in H2ZOC has the highest resolution (2.0 Å) and contains four resolved calcium ions. To construct models of ANXA11 C-terminal domain with and without bound calcium ions, we used the pairwise sequence alignment obtained from iTasser (which is gapless for residues 5-319 of 2ZOC to residues 192-505 of ANXA11). Models were built using Modeller 9v19, with and without calcium ions in the same positions as in the template. Distance restraints were included in Modeller to optimize the calcium-oxygen interaction distances in the calcium binding sites, using upper bounds extracted from the equivalent interaction distances in the template. In each case, 100 models were built and ranked according to their MolPDF scores, which provides an assessment of the extent to which the model satisfies the restraints. We filtered out models with MolPDF scores outside the standard deviation obtained over all 100 models . The remaining models were filtered to consider only the 20% models with the highest ProQ2 scores (> 0.928). The ProQ2 score of the template is 1.005, for reference. Finally, we identified two models with the fewest backbone dihedral angles in the disallowed region of the Ramachandran plot, using PROCHECK. The holo and apo model have 96.9% and 96.5% residues, respectively, in the allowed regions of the Ramachandran plot, compared to 93.8% for the template.The full-length sequence of human ANXA11 (SWISSPROT code P50995) was submitted to the iTasser web server on 05/23/18 to identify suitable templates for structural modeling. The predicted secondary structure and unfolded nature of the resultant structural models confirmed the assignment of the N-terminal domain (residues 1-199) as unstructured. Template structures with good coverage of the C-terminal segment were all annexins, of which the candidate with the highest similarity was human annexin A4 . Among the available structures of ANXA4, PDB code Electrostatic potential surfaces were computed as follows. Partial charges and atomic radii were obtained for the model coordinate files using the PDB2PQR server v2.1.1, with the CHARMM force field, which includes calcium parameters. Poisson-Boltzmann electrostatic potentials were computed using the APBS plugin within Pymol using default parameters.in vitro studies were conducted with this mutant. His-Sumo tagged proteins were expressed in E. coli BL21(DE3) in TB autoinduction media by an overnight incubation at 25°C. Briefly, cells were centrifuged and lysed using a high pressure cell disruption system. Clarified lysate was loaded onto a Ni-Sepharose Excel column and purified using standard procedure. Protein containing eluates were pooled, and dialysed in 50mM HEPES pH 7.4, 100mM NaCl, and 5% glycerol buffer after addition of ULP protease to remove the His-Sumo Tag. Protein was further purified on a second Ni-sepharose column to remove the His-Sumo tag followed by a size-exclusion column and the fractions containing purified protein were pooled for all subsequent experiments. mCherry tagged Annexin WT and its mutants constructs were expressed and purified from insect Sf9 cells using standard procedures. After 6 days of infection cells were harvested and lysed by homogenising into a resuspension buffer containing 50mM HEPES pH 7.4, 100mM NaCl, and 5% glycerol, 0.1% CHAPS. Cell Lysates were subjected to high speed centrifugation and the clarified lysate was subsequently purified using three steps purification protocol including, Ni- Sepharose Excel affinity resin, Amylose resin, followed by size exclusion chromatography in the buffer containing 50 mM HEPES, 225mM NaCl pH 7.4.Constructs encoding ANXA11 residues 1-505 and its mutants , Annexin LC (aa1-185) and CTF (aa186-505), were either cloned into pOPINS vector containing an N-terminal His-Sumo Tag and a ULP protease cleavage site or in pACEBac2 vector with a TEV cleavable N-terminal MBP tag and an mCherry-6xHis-C-terminal tag. ANXA11 (R235) mutant protein was highly aggregation prone and could not be purified, so no further Phase separation of ANXA11 WT and it mutants was initiated either by changing the temperature of the samples from 4°C to RT or by the addition of the crowding reagent,10% dextran. Purified ANXA11 protein concentration ranging from 0.1 μM-50 μM in a total volume of 20 μL were deposited on 8-well glass bottom Ibidi slides, incubated at room temperature for ≥30 minutes before being imaged on a Zeiss Axiovert 200M microscope with Improvision Openlab software using 100X magnification objective. ImageJ software was used in all image processing. For all purified proteins n ≥ 3.oC. Stress granule cores were incubated with liposomes in the presence of 1 μM ANXA11 only, 500 μM calcium only, or both 1 μM ANXA11 and 500 μM calcium at 37oC for 1 hour.Stress granule cores were purified according to a previously-published method  from SH-2 stream. The mixture was resuspended in a buffered salt solution  for 30 minutes, and then sonicated in a water bath for 3 minutes to complete resuspension. The lipid mixtures were then extruded into large unilamellar vesicles (LUVs) using an Avanti Mini-Extruder (Avanti #610023) that was heated to 60oC. For the liposome preparation for microfluidic assay, The composition of liposomes without PIP is POPC: 49%, POPS: 10%, POPE: 20%, SAPI: 15%, Cholesterol: 5% DOPE-ATTO647: 1%. The composition of liposomes with PIP3 is POPC: 49%, POPS: 10%, POPE: 20%, SAPI: 10%, PIP 5%, Cholesterol: 5% DOPE-ATTO647: 1%.Liposomes were prepared with commercially available lipid components from Avanti Polar Lipids. Liposome composition was 70% phosphatidylcholine (Avanti #850457C), 24% phosphatidylethanolamine (Avanti #850757C), 5% cholesterol (Avanti #700000), and 1% phosphatidyl-3',5'-bisphosphate (Avanti 850164P). Powdered lipids were individually resuspended in chloroform, then mixed together in a glass sample vial and evaporated with a dry NGiant unilamellar vesicles were prepared from 50% phosphocholine, 10% phospho-L-serine: 20% phosphoethanolamine, 10% phosphoinositol (Avanti #850144), 5% cholesterol, 5% phosphatidylinositol 3-phosphate, 0.05% ATTO488 labeled 1,2-dioleoyl-sn-glycero-3-phosphoethanolamine- dissolved in chloroform using electroformation method in a Nanion Technologies Ves Prep pro setup . The lipids were purchased from Avanti .2 added) was incubated for 1 hour at 4oC. Then, 100μL of 60% sucrose in PBS was added and mixed gently to yield a 30% final sucrose concentration. 250μL of 25% sucrose was overlaid atop the 30% sucrose layer, and then 50μL of PBS was added as the final layer in the sucrose gradient. The gradient was centrifuged at 174,000 x g at 4oC for 1 hour, and then the fractions  were separated. Sample buffer was added to each layer and resolved by electrophoresis.In a thick-wall polycarbonate tube, LUVs and recombinant annexin A11 protein were mixed to a final concentration of 0.5mM LUVs and 0.25uM protein in 100μL of buffered salt solution (see above). LUV-protein mixture .Membrane lipid binding analysis of GST tagged ANXA11 was conducted using membrane lipid Strips (Echelon Bioscience), with each spot containing 100 pmol of indicated lipids. Membrane were blocked by PBST with 3% BSA for 1hr at room temperature, followed by incubated with 0.25μg/ml ANXA11-GST (LSBio) for 1hrs in blocking buffer, 50μM CaCl2 and 5.27 ± 0.5 nm in the presence of 13.75 mM CaCl2. The radius of lipid vesicles was measured to be 14.7 ± 1.1 nm and did not significantly change in the presence of calcium concentrations used in the binding experiments.The microfluidics setups used here have been described previously. For theoC overnight. For single molecule FISH, rat ActB-Quasar570 smFISH probes were designed using the Stellaris Probe Designer software (Biosearch Technologies), smFISH was performed according to manufacturer’s instruction using Stellaris buffers . After hybridization, cells were washed with 4xSSC and 2xSSC once each, and incubated with primary antibody in 2xSSC+ 0.1% triton-X-100 at 4oC overnight, then washed 3 times with 2xSSC and incubated with fluorescence labeled secondary antibody in 2xSSC+ 0.1% triton-X-100 at room temperature for 1hr. Coverslips were mounted and imaged with Zeiss Airyscan.In brief, cells were fixed with 4% paraformaldehyde for 10 mins, then 100% cold methanol for 10 mins and followed by 70% ethanol for 10 mins for rehydration, cells were then incubated with 1M Tris pH8.0 for 5 mins before incubated with 1ng/μl Cy3-Oligo-dT (30) for rat beta-actin in hybridization buffer  in 37Live cell imaging was carried out in phenol red free normal culture medium. For U2OS cells, imaging was performed using Zeiss 880 LSM with Airyscan, plan-apochromatic 63x oil objective (NA=1.4), images were processed with Airyscan processing in ZEN software (Zeiss). Axonal trafficking imaging was performed using Nikon spinning disk equipped with 100x oil objective lens (NA=1.4). Time-lapse movies of axons were then acquired at 100ms/frame for 30 seconds and displayed in kymograph format.3Neurons stably expressing LAMP1-APEX2 were fixed with 2% glutaraldehyde (Electron Microscopy Services) in EM buffer  for 30 minutes. Cells were washed 3X with EM buffer and then exposed to ImmPACT DAB solution (Vector Labs) for 10 minutes. Samples were washed with EM buffer an additional 3X and then fixed with 2% glutaraldehyde for at least an additional 48 hrs. Samples were washed with buffer and treated with 1% reduced osmium tetroxide in 0.1 N cacodylate buffer at pH 7.4 for 1 h on ice, washed and en bloc stained with 0.25–1% uranyl acetate in 0.1 N acetate buffer at pH 5.0 overnight at 4°C, dehydrated with a series of graded ethanol and finally embedded in epoxy resins. Ultrathin sections (70 nm) were stained with lead citrate and imaged with a JEOL 1200 EXII Transmission Electron Microscope.ioC for 16hrs and postfixed in 2% osmium tetroxide-1.25% potassium ferrocyanide in cacodylate buffer for 30 min followed by 2% osmium in cacodylate buffer for another 30 min and processed for Epon embedding. Cells imaged by Airyscan were localized on the grid (imprinted in the Epon block). Ultrathin sections (60 nm) from the imaged cells were cut and post-stained with uranyl acetate/lead citrate and imaged in a Tecnai 12 electron microscope  operating at 80kV equipped with an Ultrascan 4000 digital camera .U2OS cells were plated on glass gridded coverslips  and transfected with indicated plasmids. 24hrs after transfection, cells were fixed in 2% glutaraldehyde, 2 mM CaCl2 in 0.08 M sodium cacodylate buffer, pH 7.2 at RT for 10 minutes and imaged on Zeiss Airyscan to collect light microscopy images. Cells were kept in fixative at 45kbneurod promoter  and n is number of cooperative Ca binding sites. OriginPro 2016 was used to fit the experimental data.Diffusional sizing experiments Q were anhttp://www.peptideatlas.org/PASS/PASS01313). Structural models of ANXA11 with and without bound calcium, and the input files to generate them, are available at DOI: https://doi.org/10.5281/zenodo.3368597. All other data are either available in the main article or in supplemental files.The accession number for the mass spectrometry-based proteomics datasets reported in this paper is PeptideAtlas: PASS01313. (