Charged particles can achieve better dose distribution and higher biological effectiveness compared to photon radiotherapy. Carbon ions are considered an optimal candidate for cancer treatment using particles. The National Institute of Radiological Sciences (NIRS) in Chiba, Japan was the first radiotherapy hospital dedicated for carbon ion treatments in the world. Since its establishment in 1994, the NIRS has pioneered this therapy with more than 69 clinical trials so far, and hundreds of ancillary projects in physics and radiobiology. In this review, we will discuss the evolution of carbon ion radiotherapy at the NIRS and some of the current and future projects in the field. Particle therapy (PT), especially with heavy ions such as carbon, is an attractive radiation modality with significant physical and biological advantages over photon irradiation and deserves special attention with respect to patient selection, treatment planning, and delivery. Since the middle of the twentieth century, however, particle therapy has been the subject of considerable debate. There is a consensus among radiation oncologists, physicists, and radiobiologists that heavy particles have superior physical and biological properties over photons ,2,3. TheUnfortunately, little data comparing PT to photon radiation has been produced over the past decades. Despite the lack of any phase III randomized trials data, the so-far reported oncologic outcomes and side effect profiles of carbon ion radiotherapy (CIRT) are very encouraging ,4. Some The Heavy Ion Medical Accelerator in Chiba (HIMAC) was built by the National Institute of Radiological Sciences (NIRS) to be the first heavy ion medical accelerator system specifically dedicated for clinical research and patient care. Since its inception, the HIMAC has adopted a patient-centered approach to treating patients with cancer while maintaining a safe environment for its staff. In this review, the history of CIRT at the NIRS, the evolution of the technology and clinical practice, and the future advances in the field will be discussed.-NIRS was founded in 1957.-Fast neutron therapy started in 1974 and proton therapy in 1979.-HIMAC planning and construction started in 1984.-HIMAC construction was completed at the end of 1993.-HIMAC commissioning was completed and first patient was treated in June 1994. Only passive beam irradiation was available until 2011.-Hospital for charged particle therapy opened in 1996–1997.-CIRT was approved as advanced medical technology by the Japanese government Ministry of Health, Welfare and Labor in 2003. As such, the NIRS could receive reimbursement for CIRT. Cost was fixed per treatment independent of the number of fractions.-Construction of the New Particle Therapy Research Facilities started in 2006.-NIRS collaborated with Gunma University to construct a compact CIRT center with 1/3 the size and cost of the HIMAC at Gunma University in 2010.-Active scanning treatment started in May 2011 in the new facility. A benchmark of 6000 patients were treated by 2011.-Respiratory-gated phase-controlled rescanning irradiation of moving targets started in March 2015.-A benchmark of 10,000 patients were treated by 2015 (one fourth were patients with prostate cancer).-Construction of the superconducting rotating gantry was completed in 2015 .-Commissioning of the gantry was completed in 2016  center in Germany).-The NIRS has established a workflow system allowing the treatment of >800 patients annually.-In April 2016, the National Institutes for Quantum and Radiological Science and Technology (QST) was established by merging the NIRS with the quantum beam and nuclear fusion departments of the Japan Atomic Energy Agency (JAEA).The NIRS was established in 1957 with the mission to advance basic and applied research into radiological sciences in Japan. The current organizational structure of the NIRS is shown in Japan has adopted a universal health coverage system since 1961. Almost all medical services are typically covered, with co-payments in the 10–30% range depending on patient’s age and income. When it opened in 1994, all patients were treated on clinical trials and the NIRS did not receive any reimbursement for these treatments. As results started to show the efficacy of CIRT, the Ministry of Health, Welfare and Labor declared CIRT as advanced medical technology in 2003. As such, the NIRS could be reimbursed for treating patients. However, the national health insurance did not reimburse those who wanted to receive CIRT. Since 2016, unresectable sarcoma became the only tumor type where CIRT is covered under the national healthcare insurance in Japan. The current available evidence suggests that, in addition to unresectable sarcoma, the superior benefits of CIRT are evident in patients with locally advanced pancreas cancer, recurrent rectal cancer, high-risk prostate cancer and non-squamous cell carcinomas of the head and neck. The Japanese healthcare reimbursement system is undergoing significant changes and a modification of CIRT reimbursement is expected in the near future with more cancer sites expected to be covered by the national insurance.Some definitions are important in order to explain the reasons for choosing carbon ions for therapy at the NIRS. Linear energy transfer (LET) represents the ionization density of a particular beam per unit track length. Relative biological effectiveness (RBE) is defined as the ratio of a reference radiation dose  to a test radiation dose in achieving the same biological endpoint under the same conditions. Oxygen enhancement ratio (OER) is defined as the dose of a particular radiation needed to result in an equivalent biological endpoint in the absence or presence of oxygen. In simplified terms, beams with high RBE and low OER are good candidates for particle therapy.RBE typically increases with increasing LET until about 100 keV/µm, beyond which it plateaus or decreases . OER, onBuilding CIRT centers is costly and complicated . Design 2 and houses the synchrotron which consists of ion sources, a linear accelerator cascade made of a radiofrequency quadrupole (RFQ) and an Alvarez linear accelerator (that can accelerate ions up to 6 MeV/u), dual synchrotron rings (which accelerate ions to 73% the speed of light), and independent horizontal and vertical high-energy transport beam lines which deliver the accelerated carbon ions to three treatment rooms with fixed ports: room A , room B  and room C   . In addiizontal) .The New Particle Therapy Facility was designed to allow for the adaptive radiotherapy of tumors which are constantly changing in size, shape and location during treatments . Thus, fThe construction of the rotating gantry at the New Particle Therapy Research Facilities was completed towards the end of 2015 . It is tThe success with the use of superconducting magnet technologies in the NIRS gantry has opened the field of superconducting technology to the design and construction of smaller and cheaper synchrotrons. The SUPERconducting Magnet INstalled Ion Medical Accelerator in Chiba  is the future vision of the NIRS in facility design with superconducting magnets in both the accelerator and gantry systems with significantly less cost (<50 million USD around year 2030) and a treatment capacity of 500 patients per year.Passive beam irradiation has been employed since 1994. The narrow “pencil” beam is received from the synchrotron rings by the beam delivery systems and is widened to create a uniform “broad beam” by the wobbler-scattering method. The wobbler magnets are located 9.9 and 11.7 m upstream of the isocenter in the vertical and horizontal ports, respectively. The wobbler magnets and scattering boards are able to generate different sizes of uniform irradiation fields with less loss compared to the double-scattering method. These beams are, however, mono-energetic and pristine. Ridge filters  are used to create spread out Bragg peaks (SOBP) of varying widths (maximum SOBP of 15 cm is able to cover most patients) by superimposing many pristine Bragg peaks. A range shifter  and patient-specific compensator (polyethylene) are used to conform the depth of the SOBP to the distal edge of the tumor. Multi-leaf collimators (MLC) and patient-specific (brass) collimators are used to cut the lateral beam edges to the shape of the tumor. Patient-specific collimators and compensators are individualized for each patient and for every irradiation direction, thus increase treatment cost and delay treatment start. Passive irradiation increases neutron scattering and integral dose ,19, has In contrast to passive techniques, scanning irradiation utilizes beams with narrow Bragg peaks created using mini ridge filters  to “paint” dose continuously over tumors. Transverse scanning is modulated by two scanning magnets. The distance from the first magnet to the isocenter is 8.4 m. Depth, on the other hand, has been modulated with different technologies over the past few years. Initial scanning technology in 2011 used range shifters for depth modulation. The use of range shifters, however, increased lateral beam size and neutron contamination. In 2012, a hybrid system of energy modulation and range shifters was implemented . Full enx) and 50 mm/ms (Vy), at the isocenter, up to 30 cm deep at 430 MeV/u) [Scanning irradiation was initially limited to static tumors because it is extremely sensitive to target motion and, thus, elaborate motion management schemes are needed for thoraco-abdominal tumors. To treat moving tumors, the NIRS had to develop an extremely fast scanning magnet, while at the same time maintain the ability to monitor beam quality and control scanning speeds. The NIRS has developed the world’s fastest phase-controlled 3D scanning irradiation system (up to 100 mm/ms (V0 MeV/u) ,24,25 wh0 MeV/u) ,26.While motion management is important in all radiation treatments, it acquires a special significance in CIRT given the sharp distal fall off after the Bragg peak. Indeed, a 1 cm shift may cause significant dose difference in CIRT compared to an insignificant difference in photon radiation. In addition to set-up variations and inter-fractional motion, target shifts are governed by intra-fractional tumor motion. For this review, we will focus on the efforts within the NIRS to manage irradiation in tumors with intra-fractional motion. Thoracic, abdominal and pelvic tumors are affected by respiration, heart beats, and peristalsis to varying degrees. The NIRS has performed extensive studies on the intra-fractional motion of tumors in these locations and accordingly developed techniques to mitigate the effect of such motion on treatment delivery ,28,29.Prior to scanning irradiation, respiratory gating was used for target motion management . Nearly Currently, markerless tracking, using deep learning networks in image processing, can track tumors without the need of fiducial markers . MarkerlThe NIRS has realized the importance of accurate treatment planning in CIRT and has developed its own software (HIPLAN) . The goaThe method of dose prescription at the NIRS has been previously described ,39,40 an-Step 1, Determining biological RBE: To create an SOBP with uniform cell kill, the linear-quadratic model variables α and β had to be determined. Cell survival curves were initially created for HSG cells using multiple monoenergetic pristine carbon ion beams and the corresponding α and β values were calculated as a function of depth. An SOBP, however, is characterized by a distribution of LET values. Consequently, α′ and β′ values were determined for a mixed beam of multiple Bragg peaks. Accordingly, survival  of HSG cells could be determined at any depth in a given SOBP. This allowed the creation of a ridge filter that could achieve a uniform cell kill effect of HSG cells along the SOBP. Because LET generally increases towards the end of the SOBP, the ridge filter design reduces the weight of the physical dose with depth. This was tested experimentally by irradiating HSG cells using a 6 cm SOBP of 290 MeV/u carbon ion beams, and the results allowed the determination of the biological RBE and RBE-weighted dose distribution [ribution . Clearly-Step 2, Determining the neutron equivalent point: The NIRS has a long history using fast neutrons in the clinic since 1974. The NIRS team aimed to find a depth in the carbon beam SOBP at which neutrons would exhibit the same RBE. Using an average of in vitro  and in vivo (mouse skin dry desquamation) data, it was decided that the depth corresponding to an LET value of 80 keV/µm, 8 mm upstream of the distal fall off of a 6 cm SOBP of carbon ion beams of 290 MeV/u, to be the neutron equivalent point. At this point, it was thought that neutron clinical RBE would be equivalent to carbon ion clinical RBE.-Step 3, Determining clinical RBE: The NIRS team decided to initially use a similar fractionation schedule for CIRT as was being used for neutrons. The neutron clinical RBE has been previously determined to be 3.3 for a 20-fraction treatment and thus, an RBE of 3 for an 18-fraction regimen was selected. The flat top of the biological dose is first determined by the treating physician. Using a clinical RBE of 3, the physical carbon ion dose is determined at the neutron equivalent point. The physical dose distribution is then normalized to the neutron equivalent point which allows the determination of the physical dose at the center of SOBP. The RBE values at the center of the SOBP are obtained by dividing the biological dose by the physical dose. A table of clinical RBEs at the center of the SOBP is created for each SOBP width.-Step 4, Updating the biophysical model: Given the lack of reliable experimental evidence in 1994, some assumptions had to be made to simplify the above biophysical model. LET was assumed to be an accurate predictor of RBE. In reality, LET does not fully explain the distribution of energy deposition around a particle track and RBE is also dependent on other variables such as, but not limited to, tissue type, fractionation and dose level. With the accumulated knowledge, it was necessary to update the biophysical model to account for the improved understanding of the mechanisms of biological effects of carbon beams. Hence, the Microdosimetric Kinetic Model (MKM) [el (MKM) , which ael (MKM) ,46. Compel (MKM)  but the el (MKM) . Variablel (MKM) . A diffeel (MKM) ,50,51,52el (MKM) ,54.The first patients expected to receive CIRT at the NIRS were patients with salivary gland tumors, and thus the initial dose prescription method built on results using in vitro data of human salivary gland (HSG) cell survival curves and the NIRS experience using fast neutrons in the clinic. Later experiments showed little variability in RBE of carbon ions among different cell lines . The folFor every treatment to be realized, the radiation oncologist and dosimetrist need to make important decisions about beam number and beam angles. For patients not treated in the gantry room, beam arrangement should be decided prior to CT simulation since patients need to be set-up accounting for beam angles and couch tilts. In addition to beam arrangements, other decisions need to be made regarding uncertainty margins and optimization algorithms. Similar to photon therapy, treatment plans should account for set-up errors and inter- and intra-fractional target motion using planning target volume (PTV) expansion. In addition however, CIRT planning needs to account for range uncertainty and thus another margin needs to be added in the longitudinal (depth) direction of each beam. Thus, for every treatment plan in CIRT, an initial PTV is added isotropically for set-up uncertainty and an additional field-specific margin is added for each beam for range uncertainties. The task of selecting the latter is more cumbersome for moving targets and a field-specific treatment volume (FTV) is created. In determining the optimal number of particles for each beam , the dosimetrist aims for a homogeneous dose in each beam using Single Field Uniform Dose (SFUD), or a non-uniform dose in individual beams whose sum will bring a uniform overall plan such as in Intensity Modulated Particle Therapy (IMPT). Only a limited number of patients have been treated with IMPT at the NIRS so far.The initial dose-escalation phase I/II clinical trials conducted at the NIRS aimed to establish the safety of CIRT, and to achieve local control for rare malignancies and for common cancers with poor outcomes with conventional treatments. In the first year between June 1994 and August 1995, a total of 55 patients were treated for various indications: head and neck (14), brain (10), lung (13), liver (5), prostate (2), cervix (3), and others (8). Subsequently, clinical trials for bone and soft tissue sarcomas started in 1996, renal cell carcinoma in 1997, pancreas cancer and recurrent rectal cancer in 2000, uveal melanoma in 2001, and esophageal cancer in 2004. The number of patients treated at the NIRS per indication is shown in From a clinical point of view, the high LET of CIRT improves the peak-to-plateau ratio, thus allowing a higher chance of tumor cell kill/control while maintaining a low risk of normal tissue complications. The average number of fractions per treatment at the NIRS was 18 in 1995 but it dropped quickly to become 12 fractions in 2016 . Most of the drop occurred before the year 2000. With hypofractionated regimens, the NIRS can manage more patients and the cost effectiveness for CIRT can be established. Moreover, patients, who travel to the NIRS from all over Japan and neighboring countries, will be more satisfied as they require fewer trips to finish their treatment. For intractable radio-resistant tumors , CIRT has been applied in 16 fractions over 4 weeks. Conversely, some common cancers  have been irradiated in 1–2 fractions. Other common cancers such as stage I esophageal cancer, pancreatic cancer and prostate cancer were treated in 8–12 fractions see .A detailed explanation of the physical and radiobiological advantages of CIRT has been previously discussed in details ,90. In bThe differential response of tumors and normal tissue to photon radiation is best explained using the classic “4R” biological factors . Repair, reassortment, and reoxygenation improve the therapeutic ratio in fractionated treatments whereas repopulation does the opposite. These factors are more important in low-LET radiation (such as photon radiotherapy or the low LET entrance region of CIRT) but less important in the high-LET regions. Thus, a theoretical framework can be hypothesized explaining the benefits of using hypofractionation in CIRT based on these factors acknowledging that research on these factors in the context of CIRT is weak. Sub-lethal damage repair is expected in the normal tissue low-LET regions with CIRT compared to the high-LET tumor region which is strategically located within the Bragg peak. Additionally, unlike photons where cells are more sensitive in the M/G2 phases, carbon ions are able to induce tumor kill independent of the cell cycle phase ,93. GiveTo date, there are no reported results comparing carbon to proton or photon therapy to support the wide-spread expansion of CIRT centers. In theory and in most reported phase I/II trials, CIRT has shown an advantage over historical controls with photon irradiation either due to improved tumor control (such as in chordoma) or reduced adverse events (such as in high-risk prostate cancer). Despite this however, the high cost of constructing and maintaining CIRT centers remain the major barrier for their widespread adoption especially in the absence of high level cost effectiveness data and the absence of reimbursement models in the United States, for example. Very few studies evaluated the cost effectiveness of CIRT, although the initial verdict support the economic rationale at least in chordomas, recurrent rectal cancer and possibly non-small cell lung cancer ,7,96,97.In addition to the ongoing efforts in hypofractionation, dose escalation and the ongoing testing of the scanning irradiation and gantry treatments, several improvement projects are either ongoing or being planned. The following topics summarize some subjects of current clinical/preclinical research at the NIRS.Given the increased biological effectiveness of CIRT, concurrent systemic therapies are not needed for radio-sensitization. However, in some cancers with high metastatic potential such as pancreas cancer or mucosal melanoma, concurrent chemotherapy is now a standard adjunct to CIRT to reduce the risk of distant failures ,99. WhilOne major indicator of cancer treatment resistance is hypoxia. Conventional photon fractionation allows for reoxygenation, but local relapses due to hypoxia continue to be a major cause of treatment failure. Low OER CIRT is an excellent candidate for treating hypoxic tumors. Unfortunately, however, OER is not 1 with CIRT. Using imaging techniques to delineate hypoxic regions , LET paiRadiation oncology is a data-driven field with unique dependence on technology and statistical modeling. Hence, there is a special interest in using artificial intelligence and machine learning techniques to promote research in radiation oncology . Given tThe NIRS is invested in developing and promoting CIRT in Japan and globally. Successive CIRT centers have built on technological advances from the NIRS with special emphasis on reducing size and cost. Gunma University, for example, has installed its own accelerator in collaboration with the NIRS with one third the size of the HIMAC while maintaining comparable capability. Hyogo Ion Beam Medical Center (HIBAC) in Hyogo since 2002, Gunma Heavy Ion Medical Center (GHMC) in Gunma since 2010 , Saga HeThe NIRS has also become an international research institution where hundreds of distinguished researchers and clinicians from all over the world have visited, and many even stayed for months or years developing projects. Moreover, the NIRS allows its international collaborators to use carbon beams for their experiments. The NIRS aims to establish international groups for collaboration on trans-national clinical trials. The CIPHER trial, comparing IMRT to CIRT for locally advanced pancreatic cancer, is an example of remarkable collaboration efforts between the NIRS, the department of radiation oncology at the University of Texas Southwestern Medical Center in the United States, the Centro Nazionale di Adroterapia Oncologica (CNAO) in Italy, Peking Union Medical College in China, and the CIRT project of Yonsei University Health System in South Korea.In 2015, the Lancet Oncology published a series of recommendations to advance CIRT after a panel of Japanese and international radiation oncologists, radiobiologists and medical physicists performed a complete review of the NIRS . A summaThe NIRS continues to strive to provide the best patient-centered care for individuals with rare and common cancers in Japan, and to disseminate the accumulated knowledge in accelerator technology and radiation treatments locally across Japan and globally. Optimization of treatment regimens will continue to progress as the NIRS steps into its second generation mode of operation and technologies and as it develops more compact and cheaper accelerators/gantries with improved specifications. Within the National Institutes for Quantum and Radiological Science and Technology (QST), the NIRS will integrate quantum technologies into its core mission and thus help incorporate quantum applications in CIRT.


Flaviviruses are enveloped arthropod-borne viruses with a single-stranded, positive-sense RNA genome that can cause serious illness in humans and animals. The 11 kb 5′ capped RNA genome consists of a single open reading frame (ORF), and is flanked by 5′ and 3′ untranslated regions (UTR). The ORF is a polyprotein that is processed into three structural and seven non-structural proteins. The UTRs have been shown to be important for viral replication and immune modulation. Both of these regions consist of elements that are essential for genome cyclization, resulting in initiation of RNA synthesis. Genome mutation studies have been employed to investigate each component of the essential elements to show the necessity of each component and its role in viral RNA replication and growth. Furthermore, the highly structured 3′UTR is responsible for the generation of subgenomic flavivirus RNA (sfRNA) that helps the virus evade host immune response, thereby affecting viral pathogenesis. In addition, changes within the 3′UTR have been shown to affect transmissibility between vector and host, which can influence the development of vaccines. Flaviviruses are arthropod-borne viruses that have been categorised into four groups: tick-borne flaviviruses (TBFV), mosquito-borne flaviviruses (MBFV), no-known vector flaviviruses (NKFV), and non-classified flaviviruses (NCFV). A more divergent genus known as insect-specific flaviviruses (ISFV) has also been recently identified ,2. TBFV Flaviviruses are enveloped viruses with a single stranded, positive sense RNA genome that is approximately 11 kb in size. Flaviviruses are taken up via receptor-mediated endocytosis by recognition of the envelope protein. Alternatively, flaviviruses such as DENV can opsonize with non- or sub-neutralizing levels of antibody, allowing entry into cells such as monocytes, macrophages, and dendritic cells via Fc gamma receptor-mediated endocytosis . After vFlaviviral RNAs contain a type I cap at the 5′ terminus, but lack a polyA tail at the 3′ terminus. The RNA genome consists of a single open reading frame (ORF) that is flanked by 5′ and 3′ untranslated regions (UTRs) . The UTRThe flavivirus genome is flanked by 5′ and 3′UTRs. The non-coding 5′UTR of flaviviruses spans around 100 nucleotides in length, whereas the 3′UTR ranges from 400 to 700 nucleotides in length, depending on the virus species. The interaction between 5′ and 3′UTRs are critical for viral RNA replication. These interactions are necessary for the recruitment and correct positioning of the NS5 RNA polymerase for initiation of minus strand RNA synthesis ,10,11,12The cHP element that lies within the coding region is conserved among flaviviruses, and has been shown to aid in start codon recognition and viral replication. Mutations to this element abrogated WNV and DENV replication, as cHP may function to recruit or stabilize initiation factors . It was Genome cyclization is necessary for RNA synthesis, and it requires several complementary regions between the 5′ and 3′UTR to form a panhandle—namely, the 5′–3′ cyclization sequence and 5′–3′ UAR sequences ,26. The The 5′UAR is located within SLB, and 3′UAR overlaps the sHP at the bottom of the 3′SL . These c7GpppAmpN1 cap structure and two conserved stem-loop regions—SLA and SLB  and two dumbbell (DB) structures and within these structures lie conserved sequences (CS) and repeated conserved sequences (RCS) ,52. The The length of the domain 1 variable region (VR) varies between arthropod-borne flaviviruses, and the functional reason for this variation is currently unknown. Repeat sequences are found in the VR, and spontaneous deletions have been observed during serial passaging. In fact, deletion in the VR of the TBEV genome increased the virulence of this virus in mice ,61. In aThe SL and DB structures of 3′UTR are further stabilized by pseudoknots (PK) ,63,64. TDirect repeats of CS/RCS (which are found on the DBs) are 20–45 nucleotides in length that are known to be mostly conserved in flaviviruses . CS1 andOver the years, multiple studies have attempted to identify and determine the region of 3′UTR that is critical for viral replication. Viruses made via reverse genetic techniques that delete distinct but limited regions of the 3′UTR have provided us with information regarding the necessity of the 3′UTR nucleotide sequence and RNA structures for virus replication ,79,80. RIn addition to the critical role 3′UTR plays on RNA synthesis, the RNA structures have also been shown to be important for stalling the host XRN1 exoribonuclease to form sfRNA. Subgenomic flavivirus RNA was first characterized in Murray Valley encephalitis viral infections, followed by JEV and WNV; it was later substantiated in YFV, DENV, and more recently, Zika ,85,86,87The folding of xrRNA1 is critical for XRN1 resistance, and the PK that stabilizes the helical arrangement further prevents XRN1 from unwinding the RNA . MutatiosfRNA has multiple roles, including effects on cytopathology in cell culture and viral pathogenesis in mice, suggesting its role in disease outcome ,92. sfRNCulex quinquefasciatus mosquitoes showed that sfRNA mildly suppressed the RNAi response, resulting in decreased virus transmission without compromising mosquito fitness [Studies on mosquitoes during Kunjin virus infection of  fitness . The aut fitness . Further fitness . These a fitness ,101,102.A recent study by Chapman et al. compared xrRNA1 and xrRNA2 for XRN1 stalling activity for sfRNA production . Kieft eThe antibody-dependent enhancement (ADE) hypothesis which has been a widely used explanation for the clinical association between secondary DENV infection and increased risk of severe dengue, was recently demonstrated clinically in a trial . ADE occThis was first demonstrated when a deletion of 30 nucleotides (Δ30) within domain II in the 3′UTR of DENV4 was found to attenuate infection in rhesus monkey . A studyThe use of chimeric viruses has been useful in generating DENV vaccines. Genomes of vaccine strains PDK53 or YF17D have served as backbones to deliver the prM/E genes of DENV1, DENV2, DENV3, and DENV4. Curiously, a tetravalent dengue vaccine (TDV) that uses PDK53 genome as backbone and chimeric yellow fever/dengue vaccine (CYD-TDV) that uses a YF17D backbone produced highest level of protection against DENV2 and DENV4, respectively. It is tempting to speculate that an interplay between the prM/E and 3′UTR region could be an important consideration for optimized vaccine candidates ,114. IntThe untranslated regions of flaviviruses serve important functions for virus replication and survival. These highly structured regions play an important role in forming the pan-handle structure for genome circularisation required for RNA synthesis and viral translation. The 5′UTR is highly conserved among flaviviruses, pointing to the importance of this region for virus replication. In addition to mediating viral replication, the 3′UTR also assists the virus in evading host innate immune responses, hence contributing to infection outcome in the vector and/or non-vector hosts. Furthermore, the variable region in the 3′UTR allows for changes to occur that enable virus adaptation as it switches between vector and non-vector hosts. Detailed functional understanding of the flaviviral UTRs could provide further insights into viral evolution, fitness, and vaccine design.


While RNA editing by A-to-I deamination is a requisite for neuronal function in humans, it is under-investigated in single cells. Here we fill this gap by analyzing RNA editing profiles of single cells from the brain cortex of living human subjects. We show that RNA editing levels per cell are bimodally distributed and distinguish between major brain cell types, thus providing new insights into neuronal dynamics. Deep-sequencing technologies have allowed the characterization of patterns of transcriptome variation across individuals and tissues at unprecedented resolution—facilitating substantial advances in our understanding of transcriptional regulation and post-transcriptional processing . Recent A-to-I RNA editing is widespread in humans, affecting coding and noncoding transcripts at thousands of sites, and has a plethora of biological effects depending on the RNA context modified. RNA editing alters codon identity, creates or eliminates splice sites, and interferes with base-pairing interactions within higher-order RNA structures . A-to-I We recently profiled A-to-I editing at the genome-wide level in various bulk human tissues and confirmed its pervasive nature, detecting more than 3 million events . Our comTo capture and characterize the complexity of RNA editing at single-cell resolution, we exploited existing single cell RNA sequencing (scRNA-seq) data from adult human cortex cells obtained from living subjects . Our stuWe explored cell inosinome profiles using a comprehensive and nonredundant collection of known RNA editing events, including A-to-I changes from our catalog  as well Supplemental Table 1), calling RNA editing events using REDItools . Indeed, the number of A-to-I changes per cell was strongly correlated with the number of uniquely aligned reads  . Strikingly, RNA editing levels (proportions of reads supporting an editing event at each known editing site) for individual cells showed a bimodal distribution with peaks close to extreme values (0 and 1) . Furthermore, raw and de-duplicated data sets shared, on average, 95% of candidate editing sites and by position, comparison of A-to-I levels showed a remarkable positive correlation  . After removal of PCR duplicates, A-to-I editing levels of single cells continued to exhibit an extreme bimodal distribution .The vast majority of RNA editing sites detected here and in previous studies  reside iADAR gene , while ADARB1 (known as ADAR2) was expressed at very low levels but a bit more in neurons . Interestingly, the ADARB2 gene, with questionable editing activity . However, the expression levels of ADARs did not correlate with RNA editing levels and AEI values as well. In contrast, the AEI index showed strong correlation with the mean editing levels per cell  or the normalized sum of editing levels per cell . The absence of significant correlation between the expression levels of ADARs and RNA editing levels or AEI values in single cells may be due to specific regulation mechanisms acting on ADAR enzymes or to a biased detection of gene expression levels. Indeed, many factors, such as cell cycle or cell size, may affect the correct quantification of gene expression levels, especially in cases of low expressed genes . The data set used in this study also included a new hypothetical cell type indicated as “hybrid” that displayed characteristics of both neurons and astrocytes . Thus, our results support the view of “hybrid” cells as a potential new cell type related to neurons.Historically, most interest has been directed toward recoding sites, where RNA editing results in amino acid substitutions. Many of the best-characterized recoding sites are in brain-specific transcripts coding for membrane receptors and ion channels . For 183trocytes . The anatrocytes . “Hybridtrocytes . In addiSupplemental Fig. 8A). MDS analysis of RNA editing profiles confirmed three main groups, distinguishing adult from fetal neurons . Notably, RNA editing activity at recoding sites was higher in adult than fetal neurons . In particular, the Q/R site in Gria2, linked to neurological disorders, was edited to high levels in fetal quiescent neurons but not in neuronal progenitors as previously assessed in vitro  using the accession number GSE67835. They comprise 332 cells from dissociated adult brain cortex and 134 cells from fetal brain tissue. Single cell capturing has been performed using Fluidigm C1 technology and RNA sequencing has been carried out on an Illumina NextSeq platform by RNA-seq data of human brain single cells were downloaded from the Gene Expression Omnibus (GEO) database , excluding reads with final length less than 50 bases. In addition, we removed read pairs displaying positive alignments with known human rRNA sequences. In brief, human rRNA annotations were downloaded from UCSC (hg19 human assembly) and indexed for STAR. Next, RNA reads were aligned onto rRNAs by STAR and only unmapped reads were retained for downstream analyses.RNA-seq reads in FASTQ format were inspected using the FASTQC program. Adaptors and low quality regions (phred cutoff of 25) were trimmed using TrimGalore .Supplemental Table 1). Cells with fewer than 1 million uniquely aligned reads and a mapping rate <70% were excluded from further analyses.Unique and concordant alignments in BAM format were processed by the CollectRnaSeqMetrics.jar tool from the Picard package to obtain basic statistics  was carried out in R using the metaMDS function of the Supplemental material is available for this article.


RNA modifications are involved in a broad spectrum of biological and physiological processes. To reveal the functions of RNA modifications, it is important to accurately predict their positions. Although high-throughput experimental techniques have been proposed, they are cost-ineffective. As good complements of experiments, many computational methods have been proposed to predict RNA modification sites in recent years. In this review, we will summarize the existing computational approaches directed at predicting RNA modification sites. We will also discuss the challenges and future perspectives in developing reliable methods for predicting RNA modification sites. Since the first kind of RNA modification was discovered 60 years ago , more thDue to the lack of effective methods, studies on RNA modifications have been hindered for a long period of time. Recent advances in next-generation sequencing technology have opened doors for the detection of RNA modifications. By using high-throughput sequencing methods ,16,17,186-methyladenosine (m6A), pseudouridine (ψ), 5-methylcytosine (m5C), and other types of RNA modifications [A high quality dataset is the primary requirement for developing machine learning models. Although various high-throughput experimental techniques have been developed to predict diverse RNA modifications, the generated data are scattered separately and thus inconvenient for the scientific community until the appearance of the RMBase . By collications , which iBased on high-throughput experimental data and RMBase, a host of computational approaches have been developed for the identification of RNA modifications in the past four years ,34,35,366A, ψ, and N1-methyladenosine (m1A). Current challenges facing the computational prediction of RNA modifications and future perspectives are also discussed.This review will summarize the representative computational approaches developed for the identification of RNA modifications that have been mapped transcriptome-wide, i.e., m6A occurs at the 6th N position of the adenosine residue in the consensus sequence motif RRACH  [6A is catalyzed by multicomponent methyltransferase complex, i.e. methyltransferase like 3 (METTL3) methyltransferase like 14 (METTL14) and Wilm’s tumor 1 associating protein (WTAP) and is reversed by demethylases fat mass and obesity-associated protein (FTO) and alkylation repair homologue protein 5 (ALKBH5) [mC, or U) ,38. As a(ALKBH5) ,40.6A controls these processes is still a major challenge. Therefore, knowledge about the positions of m6A site will be helpful for understanding its mechanisms and functions.Although a broad set of its biological functions have been revealed, how m6A-seq experiment, Schwartz et al. proposed the first computational model to predict m6A site in the yeast transcriptome [6A, no web server was provided for their method.Based on the data from mcriptome . Althoug6A sites in yeast, namely, iRNA-Methyl [6Apred [6Apred not only considers the accumulated frequency of nucleotides surrounding m6A sites, but also integrates nucleotide chemical properties. For the convenience of biologists, a web server for m6Apred has been provided at http://lin.uestc.edu.cn/server/m6Apred.php, which is the first free online tool for predicting m6A sites. Later on, by incorporating RNA sequences using the pseudo nucleotide composition [http://lin.uestc.edu.cn/server/iRNA-Methyl. Both methods have become a useful starting point for developing computational tools for predicting m6A sites.In 2014, Chen et al. established two efficient computational models to predict the mA-Methyl  and m6Ap [6Apred , respectposition ,42, the http://www.cuilab.cn/sramp) to predict m6A sites [6Apred. In addition, SRAMP is not only applicable for predicting m6A sites in yeast, but is also able to predict m6A sites in human and mouse transcriptomes, which is superior to both m6Apred and iRNA-Methyl.Inspired by Chen et al.’s works ,29, Zhou6A sites , in whichttp://lin.uestc.edu.cn/server/methyrna) to predict m6A sites in both human and mouse transcriptomes [Benefiting from the RMBase , Chen etriptomes , in whicriptomes , respect6A sites in plants until 2016, by using the same encoding scheme as the previous work [6A sites in the Arabidopsis thaliana transcriptome [http://lin.uestc.edu.cn/server/M6ATH.Considering that there was no computational tool available for predicting mous work , an onlicriptome . The onl6A modification, ψ is not reversible [http://lyh.pkmu.cn/ppus/. Each RNA sequence sample in PPUS was formulated using the orthogonal binary coding scheme [ψ is the isomer of uridine , which hversible . In 2015versible , which ig scheme , i.e., thttp://lin.uestc.edu.cn/server/iRNA-PseU. iRNA-PseU is trained based on the data collected from the RMBase [For predicting the Ψ modification sites with higher success rates and being able to cover more species, Chen et al. developed a more powerful predictor called iRNA-PseU , which ie RMBase , and in 1A was first discovered in RNA 50 years ago [1A has lagged behind as a result of lack of effective methods for detecting m1A sites. Recently, two high-throughput experimental techniques, i.e., MeRIP-seq [1A-ID-seq [1A sites. However, their resolutions are not fully satisfactory, as they cannot pinpoint which adenosine residue is modified. Therefore, it is necessary to develop new methods for studying the distribution of m1A sites.mears ago . HowevereRIP-seq  and m1A-A-ID-seq , have be1A-ID-seq experiments, Chen et al. proposed the RAMPred server for predicting m1A sites [1A sites so far and is freely accessible at http://lin.uestc.edu.cn/server/RAMPred. The RAMPred is able to predict m1A sites in human, mouse, and yeast transcriptomes [1A sites.Experimental data has provided unprecedented opportunities. Based on the data from the MeRIP-seq and m1A sites , which iriptomes . It has As a new level of gene regulation, RNA modifications participate in diverse biological processes, ranging from regulating RNA splicing, inducing RNA decay to altering the genetic code. Although high-throughput sequencing methods have been proposed to detect RNA modification sites, most of them are still cost-ineffective and could not precisely pinpoint out which ribonucleic acid is chemically modified.In the past four years, computational approaches have provided useful strategies for efficiently detecting RNA modification sites. Although impressive progress has been achieved by computational methods, there still exist some challenges that need to be considered in future work.6A sites may be important for identifying m6A sites. This point has been proved in our recent study [6A sites was improved [Since most of the existing approaches are based on sequence context, they cannot accurately predict changes in position and stoichiometry of RNA modifications. As an epigenetic modification, RNA methylation is also a complicated progress. Besides sequence context and nucleotide chemical properties, other factors may also be helpful for RNA modification site identification. Recently, Patil et al. reported that RNA-binding motif protein 15 (RBM15) and its paralogue RBM15B binding sites are in near the methylated m6A sites in DRACH consensus, while the non-methylated DRACH neighborhood is poor in the RBM15/15B binding sites [nt study . By inteimproved . We beliReminiscent of the regulation of gene expression by histone modifications, it is also possible that a combination of different types of RNA modifications might mediate biological functions together . Thus, i


Reconstruction of ancestral protein sequences using phylogenetic methods is a powerful technique for directly examining the evolution of molecular function. Although ancestral sequence reconstruction (ASR) is itself very efficient, downstream functional, and structural studies necessary to characterize when and how changes in molecular function occurred are often costly and time-consuming, currently limiting ASR studies to examining a relatively small number of discrete functional shifts. As a result, we have very little direct information about how molecular function evolves across large protein families. Here we develop an approach combining ASR with structure and function prediction to efficiently examine the evolution of ligand affinity across a large family of double-stranded RNA binding proteins (DRBs) spanning animals and plants. We find that the characteristic domain architecture of DRBs—consisting of 2–3 tandem double-stranded RNA binding motifs (dsrms)—arose independently in early animal and plant lineages. The affinity with which individual dsrms bind double-stranded RNA appears to have increased and decreased often across both animal and plant phylogenies, primarily through convergent structural mechanisms involving RNA-contact residues within the β1–β2 loop and a small region of α2. These studies provide some of the first direct information about how protein function evolves across large gene families and suggest that changes in molecular function may occur often and unassociated with major phylogenetic events, such as gene or domain duplications. Understanding how proteins evolve novel functional repertoires remains an important goal of molecular and evolutionary biology . Emerginde novo gene origination . DRBs coordinate the first steps of the RNA interference (RNAi) process, working with Dicer to select dsRNA targets and generate RNA fragments for loading onto the RNA-induced silencing complex . VertebrTo begin examining the molecular-functional evolution of double-stranded RNA-binding proteins (DRBs), we identified any protein sequence from NCBI’s NR database encoding 2–3 double-stranded RNA-binding motifs (dsrms) and no other annotated functional domains, consistent with the characteristic domain architecture of DRBs from well-studied model organisms , whereas animal DRBs encode 2–3 dsrms . No majoWe found that all animal dsrms from DRB proteins were monophyletic (SH-like aLRT = 0.98), all plant dsrms were monophyletic (support = 0.99), and dsrms from animal and plant DRBs were separated from dsrms from other proteins with maximal support . Even giTogether, our results support a model in which a single ancestral dsrm domain duplicated independently in animal and plant lineages, suggesting that the 2–3 dsrm domain architecture of animal and plant DRBs is a case of convergent evolution. Although we feel the structural alignment is probably more accurate than sequence-based alignments in this case, similar results were obtained using three different sequence alignment strategies, indicating these results are generally robust to alignment ambiguity  online. P < 0.05). Nearly all of these possible gene-conversion events (83) were among closely related mammal TARBP2 sequences, with only three among mammal PRKRA, two among arthropod DRBs, and three among plant DRBs. These results argue against widespread gene conversion affecting the major branching pattern of the dsrm phylogeny, although it may impact the branching pattern within mammalian TARBP2 sequences.Alternatively, the canonical domain architecture could have evolved before the animal–plant split, and partial-gene conversion events or phylogenetic artifacts may be responsible for the apparent respective monophyly of animal and plant dsrms. We did not observe strong evidence for widespread gene conversion among extant DRBs  online. The finding that animal and plant dsrm domains duplicated to produce DRB domain architectures independently in these lineages suggests our initial approach aligning full-length animal and plant DRBs could have introduced potential phylogenetic artifacts . To addrKd = −log10(Kd)] using a previously developed statistical machine learning approach .DRB dsrms from model organisms have been observed to play two different functional roles: they bind double-stranded RNA molecules and/or facilitate protein–protein interactions, primarily with Dicer, mammalian PKR or by forming dimers . To begiAlthough maximum-likelihood ancestral sequence reconstruction (ASR) is typically considered robust , some coKd estimates across these posterior-draw sequences to the pKd of the maximum-likelihood ancestral sequence, averaged over multiple structural replicates . Nodes for which the predicted RNA affinity of the maximum-likelihood ancestral sequence was not significantly different from the distribution of RNA affinities over random draws were considered robust to ancestral sequence uncertainty; we then expressed the maximum-likelihood protein and measured its affinity for short dsRNA experimentally .As affinity prediction approaches do not suffer from the same efficiency limitations as laboratory analyses, we examined the robustness of affinity estimates to ASR ambiguity by reconstructing multiple “random draws” from each ancestral sequence’s posterior distribution and comparing pP > 0.05), whereas only 6/10 ancestral plant dsrms were robust to ASR uncertainty . Ancestral low-affinity for RNA was retained in ancAnimal dsrm2  and at least one of the ancestral plant dsrm1 lineages . High affinity for dsRNA (∼10-fold increase) evolved at least once in plants, along the branch leading to ancPlant dsrm2  and at least twice in animals, independently along branches leading to ancAnimal dsrm3  and ancAnimal dsrm1 . Finally, ancPlant DRB6 dsrm2 re-evolved low affinity for dsRNA after it diverged from ancPlant dsrm2 .As The dsrm structural fold is highly conserved across animals and plants, and structural studies of dsrm–RNA interactions have indicated that dsrms form stabilizing interactions with RNA through two primary interfaces, a loop between β1 and β2, which inserts a canonical histidine into the RNA minor groove, and a cluster of basic residues at the start of α1, which appear to stabilize the RNA backbone .Kd = 17.17 μM. Along the branch leading to ancAnimal dsrm3, Q31H, and ΔSTA52RSKK substitutions occurred, which were collectively sufficient to increase dsRNA affinity 4.3-fold in the ancAnimal dsrm background (P = 0.011), making its RNA affinity indistinguishable from that of ancAnimal dsrm3 (P = 0.46). Independent Q31H and ΔSTA52ΔSKK substitutions along the branch leading to ancAnimal dsrm1 were sufficient to increase dsRNA affinity 3-fold (P = 0.013), which was also statistically indistinguishable from the full ancAnimal dsrm1 sequence (P = 0.11). These results suggest that both the ancestral animal dsrm1 and dsrm3 evolved high dsRNA affinity from a low-affinity ancestor through similar structural mechanisms.Consistent with this model, we found that specific historical substitutions in the β1–β2 loop and the α1 region were responsible for observed changes in dsrm–RNA affinities in animals and plants  online. Phylogenetic analysis suggests that the evolution of high-affinity dsrm–RNA interactions in animal DRBs occurred through convergent mechanisms, with the H31 substitution arising independently in ancAnimal dsrm1 and dsrm3 as well as along the dsrm2 lineage see  online. Kd = 59.2 μM). Introducing the derived ancPlant dsrm2 α2 region (KNKK53) into the ancestral plant dsrm background was sufficient to increase dsRNA affinity 9.1-fold (P = 0.021), which was similar to the affinity of ancPlant dsrm2 (P = 0.11). Following the evolution of high RNA affinity in ancPlant dsrm2, an H31L substitution along the branch leading to ancPlant DRB6 dsrm2 re-evolved low RNA affinity . Together, these results suggest that concerted amino-acid substitutions in the dsrm β1–β2 loop and α1 region were responsible for repeated gains and losses of dsRNA affinity during the early evolution of animal and plant DRBs.Although the ancestral plant dsrm had the canonical high-affinity H31 residue  online, P > 0.22). These results suggest that RNA affinity measurements are likely robust to ancestral sequence ambiguity at key residues , and in some cases, alternative reconstructions with >0.1 probability were identified  online. see also .Together, our results suggest that the canonical tandem-dsrm architecture of animal and plant DRB proteins was pieced together independently in early animal and plant lineages from an ancestral dsrm that had relatively low affinity for double-stranded RNA. Following early dsrm-domain duplications, independent but similar substitutions in the β1–β2 loop and α1 region of animal  and plant (dsrm2) dsrms produced domains with higher RNA affinity.Although these results demonstrate quantitative changes in an important component of DRB molecular function, the biological consequences of these changes in dsrm–RNA affinity are difficult to determine. Increases in RNA affinity during early animal dsrm evolution were relatively small (4.3- to 5.4-fold), whereas the change in RNA affinity along the plant dsrm2 branch was more substantial (11.4-fold). Animal and plant DRB proteins coordinate key aspects of the RNA interference process, but how changes in dsrm–RNA affinity might impact RNAi is not known. RNAi plays important roles in animal and plant antiviral immunity by directly targeting viral RNA , suggestTo the best of our knowledge, all existing ancestral reconstruction studies have identified particular nodes on the protein family tree to examine based on phylogenetic patterns and/or limited functional analyses of extant proteins. Although productive, existing studies are limited to examining a small number of nodes on the tree and cannot take a comprehensive, unbiased view of how molecular function may have evolved. As a complementary approach, we reconstructed maximum-likelihood ancestral sequences at every node on the dsrm phylogeny, built structural models of each sequence bound to dsRNA, optimized protein–RNA interactions by molecular dynamics and used statistical machine learning to directly infer affinities from the resulting structural complexes . Although computational—rather than experimental—this approach provides a direct assessment of protein–RNA affinity across the entire evolutionary history of DRB dsrm domains, providing a largely unbiased view of how molecular function may have evolved across a large phylogeny.Kd estimate was 3.33 , and the largest was 6.53 (Kd = 0.295 μM), with an average of 4.79 (Kd = 16.2 μM) and a median of 4.75. Kernel density estimation revealed that the overall distribution of pKd estimates was slightly skewed toward marginally smaller values (mode = 4.65), with a noticeable excess of estimates having pKd > 5.5 , suggesting that, overall, ancestral sequence ambiguity did not have a strong effect on pKd prediction.Dsrm–RNA affinity prediction used structural information about the dsrm–RNA complex, which we inferred by homology modeling and molecular dynamics . Any errors in ancestral sequence reconstruction that impact protein folding or stability could therefore impact psequence . If pKd Kd estimates using combined structural templates were plotted on the dsrm phylogeny  and maintained high affinity across all extant and ancestral dsrm3s . Animal dsrm1 also appears to have evolved a relatively stable and high affinity for dsRNA , except in the mammalian TARBP2 lineage, which lost affinity for RNA, according to our analysis . Animal dsrm2’s RNA affinity appeared generally lower than dsrm1 and 3 .When phylogeny  online, Kd predictions were slightly lower than those of animal dsrms , and we observed fewer large clades with consistently high or low RNA affinities in the plant lineage . Within plant dsrm1 groups, DRB1 had the highest affinity for RNA , and DRB4 had the lowest , but there was only a 2.2-fold variation in average RNA affinities across the major dsrm1 clades , and DRB4 dsrm2 had the lowest average affinity across the entire clade . There were some smaller plant clades with consistently high RNA affinities (Kd = 4.93 (SE = 0.195). Aside from Brassicaceae and Rosaceae, the second dsrm domain of eudicot DRB1 also had relatively high affinity for dsRNA .Overall, plant p lineage  online. 1 clades  online. finities . For exaIn order to characterize the rate at which dsrm–RNA affinity evolved across the phylogeny, we treated affinity similar to a quantitative phenotypic trait, applying a Brownian-motion model to infer changes in the rate of affinity evolution across extant and ancestral dsrm domains . In geneWe found that—with the exception of early branching dsrm1 sequences from plant DRB6 and DRB2/3/5—plant dsrms had a higher coefficient of proportionality than animal dsrms  online, As a whole, these results suggest that animal and plant dsrm sequences likely evolved under different dynamics. Animal dsrms appear to have differentiated into low- and high-affinity RNA receptors earlier, and affinity was more consistently maintained across larger taxonomic groupings, with an overall reduced rate of affinity change  online. Kd prediction of each ancestral protein to that of its immediate descendent, thereby identifying particular branches on which dsrm–RNA affinity has changed . This approach may not detect slow changes in dsrm–RNA affinities that occur across multiple branches, and it is unlikely that this approach will have equal power on all branches of the phylogeny. Nonetheless, this simple approach does provide a means for identifying strong, abrupt changes in protein-ligand affinities not linked to specific topological events, such as gene- or domain-duplications.Prediction of dsrm–RNA affinities across a large phylogeny of ancestral and extant proteins presents an opportunity to directly identify significant shifts in RNA affinities by comparing the pP < 0.05; After correcting for multiple tests, we identified 13 branches across the dsrm phylogeny exhibiting significant support for a shift in RNA affinity, using maximum-likelihood ancestral sequence reconstruction , and the other a 3.79-fold increase in PRKRA dsrm2’s affinity for RNA (P < 0.036).One of the inferred shifts in animal dsrm–RNA affinity—the shift to high affinity along the branch leading to the dsrm3 lineage—was observed in our earlier analysis  and was P > 0.43; The loss of RNA affinity in mammalian TARBP2 dsrm1 occurred at the base of the Boreoeutherian lineage. We hypothesized that the insertion of a pair of residues upstream of the RNA-contacting H31 were primarily responsible for the observed loss of RNA affinity by repositioning H31 out of favorable RNA contact . Introduction of the H31 substitution into this background increased affinity 8.1-fold, which was marginally higher than the derived Fabaceae DRB4 dsrm1  were identified by rpsblast search of the NR database using an  of 0.01 . Double-Full-length DRB protein sequences were aligned using Clustal Omega v1.2.3 , MUSCLE Initial maximum likelihood phylogenies were constructed from each alignment using FastTree v2.1.7 with default parameters . Initiale-value cutoff of 0.1. E-values were –log10-tranformed and capped to ≤200. Node degrees were capped at 280, which was the smallest maximum node degree that maintained a fully connected graph. MCL clustering was performed at various inflation parameters  after pre-inflating the graph (-pi 3) to improve contrast between high and low edge weights. Annotated DRBs from H. sapiens, D. melanogaster, and A. thaliana genomes were mapped to clusters, and we selected the optimal MCL clustering as that which maximized the number of annotated DRBs per cluster. All sequences within any cluster containing at least one annotated DRB were considered potential closely related DRB homologs.We identified all dsrm functional domains from the RefSeq database  using thH. sapiens, D. melanogaster and A. thaliana with SH-like aLRT > 0.9 were considered closely related, and we combined closely related dsrms from Markov clustering and phylogenetic analysis.Dsrm sequences closely related to those from DRBs were also identified phylogenetically. All dsrm protein sequences were aligned using the methods described above, and maximum-likelihood phylogenies were inferred from each dsrm alignment. Any dsrm sequences grouping with annotated DRBs from D. melanogaster and A. thaliana DRBs as queries and an e-value cutoff of 0.01. Resulting X-ray and NMR structures were aligned using the cealign algorithm in Pymol v1.8.1. We used the mafft –add parameter to align dsrm protein sequences to the structure-based alignment. We inferred the maximum-likelihood dsrm domain tree from the structure-based alignment, collapsed nodes with <0.8 SH-like aLRT support and reconstructed ancestral dsrm sequences at each node on the phylogeny by maximum-likelihood  as templates ] were predicted using a support vector regression model previously trained using a large number of protein–RNA and protein–DNA complexes with associated experimental affinity measurements. We report the mean of predicted affinities across the five complexes sampled from each dsrm structural model. Differences in predicted pKds were assessed using a two-tailed unpaired t test, assuming unequal variances and correcting for multiple tests using an FDR correction  or only using affinity predictions from extant sequences. Standard errors in affinity predictions were included in all Brownian motion models.We modeled the evolution of dsrm–RNA affinity using a Brownian motion process , in whicin vitro using T7 RNA reverse transcriptase and synthetic dsDNA as template. Complementary purified single-stranded RNAs were annealed to produce double-stranded RNA by combining at 1:1 ratio, heating to 95 °C for 5 min and then cooling to 25 °C. Blunt-ended dsRNA was produced by exposure to alkaline phosphatase. The 3′ end of one RNA strand was biotinylated to facilitate kinetics assays using the Pierce™ 3′ End RNA Biotinylation Kit (Thermo).We generated blunt-ended GC-rich 28-bp RNA molecules E. coli Rosetta™ 2(DE3)pLysS cells using pET-22b(+) constructs, which were verified by Sanger sequencing. Proteins were purified by His-affinity purification and visualized by SDS-page stained with 1% coomassie. Protein concentrations were measured using a linear-transformed Bradford assay  by fitting a one-site binding curve to the steady-state laser wavelengths measured across dsrm concentrations at saturation, using nonlinear regression. We additionally fit 1-site association/dissociation curves to the full time-course data in order to estimate the initial rates of RNA binding across dsrm concentrations and used these rates to calculate the dsrm concentration at which the ½-maximal RNA-binding rate was achieved (Km). Kds and Kms were –log10 transformed to facilitate visualization, and standard errors across three experimental replicates were calculated. We calculated the statistical significance of differences between Kds and Kms using the two-tailed unpaired t test, assuming unequal variances.For each replicate experiment, we estimated the dsrm concentration at which ½-maximal steady-state RNA binding was achieved . The structural alignment of dsrm domains and all phylogenetic trees reconstructed in this study are available in Molecular Biology and Evolution online.Supplementary DataClick here for additional data file.


Many more are expected to be revealed with accumulation of RNA-seq data from diverse types of normal and diseased tissues. However, discovering novel lncRNAs and accurately quantifying known lncRNAs is not trivial from massive RNA-seq data. Herein we describe UClncR, an Ultrafast and Comprehensive lncRNA detection pipeline to tackle the challenge. UClncR takes standard RNA-seq alignment file, performs transcript assembly, predicts lncRNA candidates, quantifies and annotates both known and novel lncRNA candidates, and generates a convenient report for downstream analysis. The pipeline accommodates both un-stranded and stranded RNA-seq so that lncRNAs overlapping with other genes can be predicted and quantified. UClncR is fully parallelized in a cluster environment yet allows users to run samples sequentially without a cluster. The pipeline can process a typical RNA-seq sample in a matter of minutes and complete hundreds of samples in a matter of hours. Analysis of predicted lncRNAs from two test datasets demonstrated UClncR’s accuracy and their relevance to sample clinical phenotypes. UClncR would facilitate researchers’ novel lncRNA discovery significantly and is publically available at  LncRNAs account for roughly 68% of transcribed genes4. The unique features of lncRNAs, for example, short lengths, regulatory functions and tissue specific expression, make them potential better biomarker candidates for disease diagnosis and prognosis6. As most of the newly discovered lncRNAs are from a limited number of tissue or cell types, it is expected that many new lncRNAs are yet to be characterized, particularly in diverse and heterogeneous human diseased tissues such as different types of human cancer.Long non-coding RNAs (lncRNAs) are a new and large class of gene transcripts that do not code proteins. The number of known lncRNAs has significantly increased in recent years due to the broader adoption of high throughput sequencing platforms7. It takes pre-assembled transcripts (such as from Cufflinks) and predicts “novel” long intergenic non-coding RNAs (lincRNAs) for a sample. The major feature of this tool is the filtering of less reliable lincRNA candidates by their expression level using statistical models. Additionally, single exon lincRNA candidates are filtered out if they overlap too much with a repeat region of a reference genome. The non-coding potential evaluation is achieved by iSeeRNA8.New lncRNA candidates (or loosely “novel” lncRNAs) are generally discovered from RNA-sequencing (RNA-seq) data. The sequencing data has to undergo multiple processing steps, starting from sequence read alignment, transcript assembly, non-coding potential evaluation to noise filtering and expression quantification. There have been no streamlined and easy-to-use public pipelines for the process, particularly for a large number of samples. Sebnif (self-estimation based novel lincRNA filter pipeline) provides a certain degree of solution9 or Scripture10 is commonly used but they are very slow and not scalable for a large number of samples. Sebnif can only process one sample at time, which is not only cumbersome for a large study but also difficult to compare predicted lincRNAs across multiple samples as each sample has a different set of candidates. An integrated pipeline that combines all steps and works efficiently is needed.While Sebnif is a useful tool for one or few samples, it becomes inconvenient for a larger study with many samples. The tool needs pre-assembled transcript candidates, which is the most time consuming and computationally intensive operation. CufflinksUltrafast and Comprehensive lncRNA detection and quantification pipeline named UClncR by taking advantage of a super-fast transcript assembly tool, Sebnif’s features, and parallel computing. Firstly, UClncR incorporates the most recent and super-fast StringTie11 for transcript assembly as an integral part of the pipeline, which was demonstrated with better performances than its predecessors11. Secondly, in addition to iSeeRNA8 for noncoding potential evaluation, we also  added the internally developed and widely used CPAT12 for combined use of coding potential assessment (intersection or union) for increased sensitivity or specificity. Thirdly, UClncR predicts lncRNA candidates but keeps known  lncRNAs intact so that they can all be analyzed together. This is important as years of research have characterized many well-known lncRNAs and pure digital prediction may break known lncRNA structure and the link between studies, making across-comparison difficult. Fourthly, UClncR can process both stranded and non-stranded RNA-seq data. For non-stranded RNA-seq, it is only possible to predict and quantify lincRNAs; however, for stranded RNA-seq all lncRNAs (including overlapping transcripts) are characterized. Fifthly, when UClncR processes multiple samples together, it merges all predicted candidates and generates a unified lncRNA annotation for next step expression quantification. This allows easier comparison across samples such as differential expression between conditions. Lastly, UClncR can distribute jobs on a cluster of computers to process multiple samples simultaneously. Tens or hundreds of samples can be completed in a matter of hours, which dramatically facilitates the analysis for a large project.To overcome these limitations and provide a convenient tool, we have developed an 11 is used to perform transcript assembly. Once the transcript candidates are built, the pipeline predicts potential lncRNA through two different routes depending on the RNA-seq protocol of non-stranded or stranded as they are handled very differently. The third step of the pipeline is to quantify both known and predicted lncRNAs and the final step is to generate consolidated reports across samples with annotations. For non-strand specific RNA-seq, only lincRNAs are predicted and quantified; however for stranded RNA-seq, lncRNAs overlapping other transcripts in opposite strands are also predicted and quantified.The pipeline has four major modules: de novo transcript assembly, lncRNA candidate prediction, known and lncRNA candidate quantification, and reporting Fig. . The de 13. This step is skipped if only one sample is provided. (4) Quantifies both newly predicted and known lincRNAs (defined in GENCODE) by featureCounts14, which generates read level (or fragment) digital expression. The combined known and lincRNA candidates can be analyzed by common tools such as edgeR15 or DESeq16. A normalized lincRNA expression by RPKM (reads per kilobase per million mapped reads)17 is also provided for quick and across gene comparison. (5) Annotates a lincRNA with a nearby protein coding gene along with the genomic distance between them. (6) Generates a summary html report with predicted lincRNA information and links to various reports.Non-strand specific RNA-seq is commonly used for short read sequencing; however, the coding strand information of a transcript is not kept in this data. As many lncRNAs are in the coding region of other genes but on the opposite strand, it is impossible to distinguish the expression of these lncRNAs from their overlapping genes. Therefore, only lncRNAs in the intergenic regions (or lincRNAs) can be predicted or quantified correctly in this data because they don’t have any overlap with other transcripts. UClncR performs following tasks for non-stranded RNA-seq data Fig. : (1) Conhttp://genome.ucsc.edu/cgi-bin/hgTables) are also filtered out. As transcript candidates at very low or high expression may be technical noises, the pipeline utilizes the statistical models of Sebnif to remove those transcripts not fully constructed (for multi-exon transcripts) or at extremely low or high expression (for single exon candidates) using the known multi-exon and single-exon transcripts as reference7. Noted is that the expression cut-off for single exon candidates is dynamically generated for each sample and is thus sample specific. Samples with low sequence depths would have lower absolute expression cut-offs than samples with higher sequence depths. The final step of the lincRNA prediction is protein coding potential evaluation. In addition to using Sebnif’s iSeeRNA module8, we provides CPAT, a popular tool developed by our group12 to allow users to choose the combination of the two for increased sensitivity (the union of non-coding transcripts from both) or specificity (intersection of the two). The reason we implemented these two tools was motivated by their observed complementarity. By default, we set non-coding potential greater than 0.9 for both programs; however, this can be reduced to as low as 0.5 for increased sensitivity as we learned from the known lincRNAs.The step 2 is the key for lincRNA discovery. Once transcripts are assembled, the pipeline compares the candidate transcripts with the provided known gene annotation (GENCODE by default) and selects those without any overlap with the known transcripts as novel transcript candidates. These candidates then go through several filters. The candidates that are shorter than 200 bp or single exon transcripts greater than 10000 bp are removed. For single exon candidates, those overlapping with repeat/low complexity regions of the genome  can be more accurately quantified. It also can help to discover a new lncRNA candidate overlapping with other transcripts on the opposite strand. To utilize the features, the RNA-seq aligners have to have the capability to handle the strand specific data and a correct parameter is set in the alignment step. Both Tophat (2) and HISAT (2) can be used but HISAT2 is generally recommended as it is dramatically faster.Strand specific RNA-seq protocol provides the coding strand information of each transcript. This protocol provides several advantages over the non-strand specific RNA-seqThe pipeline goes through the similar steps as the non-stranded RNA-seq data except for the following: (1) the lncRNA candidate prediction would include the assembled transcripts that are overlapping with coding transcripts; (2) in the known lncRNA quantification, it includes all lncRNAs defined in the GENCODE annotation, which doubles the number of lincRNAs from non-stranded RNA-seq protocol.LncRNAs are largely involved in the regulation of nearby or overlap protein coding genes (cis), although they can also regulate distant genes located on a different chromosome (trans). Hence, it is important to identify nearby genes with which lncRNAs may interact with, for downstream analyses. For this, we annotate all lncRNAs, both known and predicted, with nearby protein coding genes including their distance from its transcription start site (TSS).http://bioinformaticstools.mayo.edu/research/UClncR). To avoid tedious installation for simple testing or to accommodate those without access to a cluster environment, a virtual machine version is also available where all dependencies are pre-installed.The pipeline is developed in Linux environment using Java and shell scripts with third party modules . All needed modules and parameters are specified in configure files which allow users to customize their analysis. The workflow takes advantage of paralleled computing using Sun Grid Engine (SGE) for processing multiple samples simultaneously. The entire computing time and maximum memory usage are generally determined by the sample with the highest sequence depth and do not change too much to the number of input samples (but may be affected by the cluster load and performance). Thus, it normally takes a few hours to process hundreds of samples. Comprehensive log files are generated to monitor processes and report errors which help users to identify problems. It has been developed and fully tested with human reference genome hg19 but also works with hg38. The UClncR package and a test dataset can be downloaded from our website (https://www.encodeproject.org/experiments/ENCSR000AEF), which was generated from stranded and PolyA enrichment protocol and sequenced at a high depth of 250 million pair-end 100 base reads. Sequence reads were aligned to the human reference genome (hg19) using HISAT220 . The aligned bam file was then processed by UClncR twice, one with intact GENCODE annotation (v19) to predict new potential lncRNAs not defined in the database and another with one third of lincRNAs removed to recover the known lincRNAs for performance evaluation.To evaluate the performance of UClncR, we used an ENCODE RNA-seq sample GM12878 . The individual histone modification marks of H3K4me1, H3K27ac, H3K27me3 and H3K9me3 were also downloaded and compared with both known and predicted novel lncRNAs of this cell line.This test was run with the complete GENCODE gene annotation (v19) and was used to evaluate the complete functionality of UClncR, as described above. When the complete annotation was provided to the pipeline, UClncR treated any transcript in it as known and predicted novel ones not defined in that annotation, which were either located in intergenic regions (lincRNAs) or in genic/intronic regions but on the opposite strand of known transcripts. The expression patterns of these predicted lncRNAs were compared with the known lncRNAs. The histone modification states of the coding regions of these lncRNAs were checked with the genome segmentation file generated by chromHMM algorithmEvaluating the accuracy of lncRNA prediction is very challenging since true lncRNAs are not known in a particular sample being predicted. To provide a solution, we applied a validation strategy where one third of known lincRNAs in GENCODE annotation (v19) were removed for UClncR to predict these lincRNAs using sample GM12878. To get a clean lincRNA set  for the validation, we first ran all lincRNAs through the filtering of coding length, repeat region overlap, protein coding potential score and overlap with other transcripts and only kept those meeting the criteria, of which one third were withheld and remaining were passed to the pipeline along with all other genes. Among the withheld lincRNAs, we checked their coverage and only those with at least one read coverage were used as “truth set” to compare with the prediction result from the pipeline.To illustrate the usability of the UClncR for a project with a large number of samples, we processed 583 TCGA LUAD (lung adenocarcinoma) RNA-seq samples of which 524 are from tumors and 59 from adjacent normal lung tissues. The downloaded fastq files were aligned using HISAT2 (2.0.4) with the non-stranded option as they were generated from non-stranded PolyA enrichment protocol. The aligned bam files were provided to UClncR for processing to identify both known and predicted lincRNAs.UClncRNA output (GM12878 as example). UClncR completed processing the high sequencing depth (~250 M reads) sample in less than an hour in our computing environment with 33GB max memory usage. The result is summarized in an html index page that starts with project description, configuration settings, and analytical workflow. The summary table provides information for number of transcripts assembled, number of lncRNA candidates after removing known transcripts, number of predicted single exon/multi-exon lncRNA transcripts after various steps of filtering and evaluations Fig. . The htmUClncR predicted 22,121 lncRNA transcript candidates from this ENCODE sample, 800 multi-exon and 21,321 single exon ones Fig. , which rWhen the distances of the predicted lncRNAs to the closest protein coding genes were compared to those from known lncRNAs, we found similar distribution, i.e., the vast majority of them are within 250Kb of TSS of coding genes Fig. . We alsoOut of the 1793 lincRNA transcripts withheld for novel prediction, 102 had some read coverage in GM12878 RNA-seq data and therefore could be potentially predicted. Upon completion of the pipeline, 86 transcripts had sufficient expression and were successfully assembled by StringTie, of which UClncR reported 66 “novel” lincRNA transcripts and 12 lncRNAs overlapping with nearby genes . The missed transcripts included 1 that was filtered out due to overlap with a repeat region of the genome and 7 that did not pass the expression level filter of the pipeline Fig. , which iUClncR completed the processing and analysis of the 583 TCGA LUAD samples in less than 3 hours in our parallel computing environment. As the RNA-seq is non-stranded, only lincRNAs were quantified and predicted. The highly variable sequencing depths from sample to sample (ranging from 24 to 136 million pair end reads) led to varied novel predictions in each sample , underlying the impact of sequencing depth on novel lincRNA discovery Fig. . The totWhen compared with the known lincRNAs, the predicted lincRNAs had lower overall expression Fig. . Based oHerein we describe UClncR, a comprehensive lncRNA discovery and analysis pipeline, which takes advantages of the fast transcript assembly tool, more complex lncRNA prediction algorithms, and parallel computing. Both predicted and known lncRNAs are reported for comprehensive lncRNA profiling. When RNA-seq is stranded, not only lincRNAs but also overlapping transcripts  are detected, which doubles the number of transcripts. For a project with many samples, UClncR can complete analysis in a matter of hours and generate a consolidated report for all samples so that further analysis will be dramatically facilitated using analytical tools routinely used for protein coding genes.25, none of them have an implemented software package in the public domain that can be easily accessible for comparison. Noted is that almost all use the older Cufflinks as transcript assembler, which is extremely slow  and the new StringTie provides significant performance gains in terms of speed and assembling accuracy26. In our testing for the NA12878 RNA-seq sample, StringTie completed assembly in 40 minutes while Cufflinks took 4 days and 5 times more usage of memory  were commonly assembled by all three tools  is also recommended for more accurate alignment and transcript assembly. RNA-seq library preparation methods/protocols also have a significant impact. While PolyA selection method is clean and highly enriched for mRNA transcripts, lncRNAs that are not PolyA tailed do not get sequenced for discovery and quantification as a result. Total RNA with ribosome RNA removal method provides a better opportunity for novel discovery; however, even higher sequence depth is required as data from this protocol tends to contain more unusable reads . Stranded RNA-seq is more preferred to detect and quantify overlapping transcripts.RNA-seq data quality may also have an impact on novel lncRNA discovery. The pipeline itself does not have capability to distinguish true transcripts from artifacts such as DNA contamination. Transcript assembly does not work well on degraded and fragmented RNAs as it is very difficult to assembly original and complete transcripts.25. UClncR just relieves the daunting work of initial discovery and allows investigators to spend more time on further analysis and interpretation of these findings.Lastly but importantly, one has to bear in mind that UClncR provides the first step of novel discovery for uncharacterized lncRNAs; however, they are only lncRNA candidates and they need to be further analyzed, filtered, and validated either by a different technology or in a new set of samples. The initial candidate list can be very long when a fair number of samples are predicted together, particularly for single exon ones. In our lung cancer dataset, over 50,000 candidates were predicted but only 5,000 were multi-exon lincRNAs. One of the common approaches is to limit those detected in multiple samples or focus on multi-exon lincRNAs only as single exon candidates can be artefactual background noise, un-spliced pre-mRNA or gene extensionsUClncR is a fast and convenient pipeline for users to detect and analyze long non-coding RNAs from RNA-seq, both known and predicted ones. It works with both stranded and un-stranded RNA-seq protocols and is particularly useful for a project with many samples so that they can be analyzed together swiftly.Supplementary Information