Retinoic acid (RA) signaling is essential for enteric nervous system (ENS) development, since vitamin A deficiency or mutations in RA signaling profoundly reduce bowel colonization by ENS precursors. These RA effects could occur because of RA activity within the ENS lineage or via RA activity in other cell types. To define cell-autonomous roles for retinoid signaling within the ENS lineage at distinct developmental time points, we activated a potent floxed dominant-negative RA receptor α (RarαDN) in the ENS using diverse CRE recombinase–expressing mouse lines. This strategy enabled us to block RA signaling at premigratory, migratory, and postmigratory stages for ENS precursors. We found that cell-autonomous loss of RA receptor (RAR) signaling dramatically affected ENS development. CRE activation of RarαDN expression at premigratory or migratory stages caused severe intestinal aganglionosis, but at later stages, RarαDN induced a broad range of phenotypes including hypoganglionosis, submucosal plexus loss, and abnormal neural differentiation. RNA sequencing highlighted distinct RA-regulated gene sets at different developmental stages. These studies show complicated context-dependent RA-mediated regulation of ENS development. Theslionosis , confirm in mice . Consist in mice , with moutations . Furtherutations , 28, 32.utations , it is n RARαDN)  in combiRarαLoxP/+DN to +Wnt1Cre mice. RarαLoxP/+DN produce RARαDN after CRE-mediated DNA recombination. Wnt1Cre express Cre recombinase in the CNS and many neural crest derivatives, including essentially all fetal ENS precursors  mice, ENCDC had colonized the esophagus, stomach, small intestine, and half of the colon, as seen by TuJ1 (neuron specific β3-tubulin) antibody staining  neurons were readily detected in stomach, small bowel, and proximal colon of control animals  with RET antibody. A total of 100% of stomach EYFP+ ENCDC expressed RET in controls as expected , the other mice evaluated had very few RET+EYFP+ ENCDC   . RARαDN ato mice . EdU inccontrols ; therefolionosis , 40.RarαLoxP/+; Wnt1Cre+DN mice had smaller vagus nerves than control littermates . Enlarged images also demonstrate many TdTomato+ cells migrating along the TuJ1+ vagal nerve fibers in +; TdTomatoWnt1Cre and Rarα+; TdTomatoDN; Wnt1Cre mice  starting at E10.5 . To avoid analysis of dying mice, we examined E18.5 ENS. Although the RarαLoxP/+; TyrCre+; R26R-TdTomatoDN bowel looked grossly normal  to track CRE activity. Because Ret is on mouse chromosome 6 near the ROSA26 locus that drives expression of RarαDN, as well as most fluorescent reporters, we bred RarαLoxP/+DN mice to TandemRETCreERT2-EYFP mice to generate RarαLoxP/+;DNTandemRETCreERT2-EYFP and RarαLoxP/+DN controls. The RarαLoxP/+;DNTandemRETCreERT2-EYFP have undergone recombination so RETCreERT2 and a CRE-activated EYFP–Channelrhodopsin-2 (ChR2) protein are on the same chromosome. E13.5 control RarαLoxP/+DN lacking CreERT2 had the bowel fully colonized by ENS precursors after E10.5 tamoxifen treatment  at E11.5  at E 13.5  to characterize RARll types . Multiding ENCDC . Among aat E11.5  and 1140t E 13.5 , as reprt E 13.5 . The topt E 13.5  and E13.t E 13.5  and mainRarαDN;TandemRETCreERT2-EYFP compared with controls. We suspect that this occurred because Ret is expressed in the hematopoietic stem cell (HSC) lineage, where RAR supports erythropoiesis (TandemRETCreERT2-EYFP–lineage cells in the colon were stained with TER119 (erythroid lineage) and CD31  antibodies  and performed quantitative PCR (qPCR), which confirmed differences predicted by RNA-seq  to induce Ret expression in ENCDC as these cells migrate from neural tube to bowel  (αDN) expression selectively within ENCDC at specific times during development. Our RarαLoxP/+; Wnt1Cre+DN studies show that cell-autonomous RAR signaling within ENCDC is required to activate Ret expression early in development, consistent with data from zebrafish  that blocks all 3 RAR receptors  . By breeebrafish  and aviaebrafish . RAR sigping ENS , 48. In 493 data , 27. We 3 that bl studies , 45, 49.RarαLoxP/+;DN TyrCre+ and RarαLoxP/+; RETCreERT2-EYFPTandemDN mice closely resemble the colon ENS patterning defect we observed in BMS493-treated fetal gut organ cultures . We confirmed that these NOS1+ neurons had expressed CRE using an R26R-TdTomato reporter. This suggests that NOS1+ neuron differentiation is less dependent on RAR signaling than other neuron subtypes or that RAR turns off Nos1 expression in some enteric neuron subtypes. Finally, we found an almost complete loss of submucosal neurons in RarαLoxP/+; RETCreERT2-EYFPTandemDN mice treated with tamoxifen at E10.5, suggesting a critical role for RA signaling in radial migration of ENCDC to form submucosal plexus. The normal postnatal growth of these tamoxifen-treated RarαLoxP/+; RETCreERT2-EYFPTandemDN mice and normal appearance of the adult bowel suggests that a loss of approximately 90% of submucosal neurons is well tolerated, at least in mice. This is interesting, in part, because we know little about mechanisms controlling radial migration of ENS precursors to the submucosal plexus, with prior studies implicating only GDNF and netrin/DCC signaling in this process  where expression changed in the same direction at E11.5 and E13.5 for RARN+ cells . At E11.ogenesis , 54, andtterning –62. At Eelopment , at leaselopment , 65. How Xenopus . ConsistT+ ENCDC . In addielopment –70, RAR elopment .RarαLoxp–STOP–LoxP/+DN (previously described in ref. H2az2(Tg[Wnt1cre]11Rth) , TyrCre (RRID: MGI:358524_JAX:029788), SOX10Cre (MGI:3586900_JAX:025807), EYFPRosa26 , and Gt(ROSA)26Sortm9(CAGtdTomato)Hze  were from The Jackson Laboratory and maintained in C57BL/6J background. TandemRETCreERT2-EYFP mice, a gift from Wenqin Luo (University of Pennsylvania), were generated by breeding RETCreERT2 (RRID: MGI:4437245) to mice with ChR2-Lox-Stop-Lox-EYFP in the Rosa26 locus (Ret and Rosa26 loci are close together (about 5 million bp), but TandemRETCreERT2-EYFP recombined so that they are on the same chromosome. TGM/TGMRet  on C57BL/6J were previously described . Timed pregnant animals (E12.5) were injected (i.p.) with EdU (12.5 μg/gm body weight). Tissues were dissected 4 hours after EdU and fixed  before staining, using manufacturer’s protocol.3DISCO imaging was performed as described  with modFor whole-mount staining, fetal bowel was fixed as a tube. Adult bowels were opening along mesentery and pinned flat to Sylgard before fixation. Bowels were fixed with 4% PFA , washed , blocked , and then incubated overnight (4°C) with primary antibodies in 5% NDS in PBST. Secondary antibody staining, unless otherwise specified, was done by incubating samples in a 1:400 dilution of the appropriate antibody . All the primary and secondary antibodies are listed in RarαLoxP/+; Wnt1Cre+; R26R-TdTomatoDN and +; R26R-TdTomatoWnt1Cre (control) mice. E13.5 colon ENCDC were isolated from RarαLoxP/+;DNTandemRETCreERT2-EYFP or TandemRETCreERT2-EYFP (control) mice after treatment with tamoxifen  at E10.5. Individual E11.5 stomach and E13.5 colon were incubated with collagenase  and dispase  in Ham’s F12 media  with 1% BSA  and then triturated by pipetting up and down 15 times using a P1000 pipette tip before passing through a 30 μm strainer . Cells with TdTomato or EYFP fluorescence were purified by flow sorting , pelleted by centrifugation , washed once with PBS, and frozen immediately at –80°C until analysis. RNA isolation, library preparation, and RNA-seq analysis were performed by University of Pennsylvania Next-Generation Sequencing Core. RNA extracted using the GenElute Single Cell RNA Purification Kit  was analyzed on a 2100 Agilent Bioanalyzer using an RNA 6000 Pico Kit . The libraries were made using the combination of the Clontech (now Takara), SMART (cDNA synthesis and amplification), and Illumina Nextera XT (cDNA to library) kits. Libraries were then single-end sequenced on an Illumina HiSeq 4000, using Sequencer Software Real-Time Analysis (RTA) 2.7.7 and Illumina HiSeq Control Software (HCS) HD 3.4.0.38. Reads were aligned to remove repeat sequences and ribosomal RNA reads and then processed using RNA-seq unified mapper (RUM) package  mice were isolated via the same protocol as for RNA-seq analysis. RNA was purified using PicoPure RNA isolation kit . cDNA was made by using First-strand cDNA synthesis using superscript II RT kit  with Oligo(dT)12-18 primer . Quantitative PCR (qPCR) was performed using SYBR Green qPCR mixture . All primers have been previously described and listed in Gapdh mRNA.E11.5 stomach ENCDC of Z-stacks were created with ImageJ/FIJI. Increments between each slice were 1 μm, except for scanning the whole E10.5 embryo, where increments between slices were 3 μm. Videos of Z-stacks were created with the animation function of Imaris 9.02.High-resolution 3-dimensional images were acquired with a Zeiss LSM 710 confocal microscope using Zeiss ZEN 2.3 SP1 FP3 (black)  software and a 20×/0.8 air or 63×/1.4 oil DIC M27 Plan-Apochromat objective, except for whole bowel E12.5 and E13.5 imaging, which employed a 5× objective and the ZEN Tile Scan function. Image stitching employed ZEN software. RarαDN mutant and control mice.Early embryonic, perinatal, and adult mouse bowel cell counting used > 5 randomly selected ×20 fields per animal in each region and FIJI CellCounter module (NIH). Investigators were blinded to genotype when comparing t tests. P < 0.05 is considered statistically significant. Data show mean ± SEM.All experiments include at least 3 replicates. Statistical analyses employed GraphPad Prism 7 software. Data normality analyses employed the Shapiro-Wilk test. Comparisons between groups used 2-tailed unpaired Student’s Mouse studies were performed with approval from the Institutional Use and Care Committee at The Children’s Hospital of Philadelphia (CHOP) Research Institute (IAC 19-001041) and the Animal Studies Committee of Washington University School of Medicine in St. Louis (no. 20120130).TG and ROH led this study and wrote the manuscript. TG, ECWJ, RS, JBA, and ROH designed research studies, conducted experiments, acquired data, analyzed data, and edited the manuscript.


Herein, we report the first decarboxylative hydroxylation to synthesize phenols from benzoic acids at 35 °C via photoinduced ligand‐to‐metal charge transfer (LMCT)‐enabled radical decarboxylative carbometalation. The aromatic decarboxylative hydroxylation is synthetically promising due to its mild conditions, broad substrate scope, and late‐stage applications. Herein, the first decarboxylative hydroxylation reaction to synthesize phenols from benzoic acids is reported. The method overcomes the challenges associated with conventional decarboxylation of benzoic acids and can be applied even for the late‐stage functionalization. Conventional polar or radical decarboxylation have failed to provide a solution to phenol synthesis. The high activation barrier for polar decarboxylation often requires reaction temperatures of 140 °C or more and −1)ortho‐substitutents that can decreased the barriers by 3–5 kcal mol−1 due to their destabilization effect.−1  Sus Figure .[27] Tt Figure .[5] ThRadical decarboxylative carbometalation enabled by LMCT in copper benzoates provides the first decarboxylative hydroxylation of benzoic acids at 35 °C, a temperature about 100 °C below conventional decarboxylation of aryl carboxylic acids. Expansion of LMCT‐based decarboxylative carbometalation to enable C−O bond formation beyond the initially discovered C‐F bond formation establishes the utility and power of the new concept for previously inaccessible decarboxylative functionalizations.The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.Supporting InformationClick here for additional data file.


Moorena bouillonii, a chemically rich filamentous cyanobacterium, has been characterized as a pathogenic organism with an unusual ability to overgrow gorgonian corals, but little has been done to study its general growth habits or its unique association with the snapping shrimp Alpheus frontalis. Quantitative benthic surveys, and field and photographic observations were utilized to develop a better understanding of the ecology of these species, while growth experiments and nutrient analysis were performed to examine how this cyanobacterium may be benefiting from its shrimp symbiont. Colonies of M. bouillonii and A. frontalis displayed considerable habitat specificity in terms of occupied substrate. Although found to vary in abundance and density across survey sites and transects, M. bouillonii was consistently found to be thriving with A. frontalis within interstitial spaces on the reef. Removal of A. frontalis from cyanobacterial colonies in a laboratory experiment altered M. bouillonii pigmentation, whereas cyanobacteria-shrimp colonies in the field exhibited elevated nutrient levels compared to the surrounding seawater.Cyanobacteria have multifaceted ecological roles on coral reefs. The online version of this article (10.1007/s10452-021-09837-6) contains supplementary material, which is available to authorized users. Cyanobacteria, also known as blue-green algae, represent a wide array of organisms that operate in a diversity of roles in marine ecosystems. In addition to producing oxygen via photosynthesis, some cyanobacteria can also fix nitrogen; these capabilities make cyanobacteria important primary producers in many marine systems, especially in oligotrophic open ocean environments   . Alpheus frontalis (often collected with M. bouillonii) has been documented in various publications, catalogs of crustacean taxonomy, and collection records, suggesting that this partnership extends from the eastern African coast and the Red Sea, across the Indian Ocean, and throughout the Tropical Western Pacific, bounded by Australia, Southern Japan, and French Polynesia  has been identified as a prolific source of biologically active compounds , including the apratoxins, a family of exquisitely potent cancer cell cytotoxins  have been shown to be strong herbivore feeding deterrents  were recorded (100 m2 per transect). Shrimp were not often visible during surveys but could reliably be detected by coming into gentle contact with surfaces of M. bouillonii that displayed shrimp-associated morphologies and waiting for the distinct, and often startling “snap” of a defending shrimp. Colonies were also photographed. M. bouillonii growing without shrimp, which has previously been reported on Guam  in order to capture the local variability and patchiness in colony distribution at these smaller scales. Surveys of each transect were conducted simultaneously by two different SCUBA divers experienced in recognizing M. bouillonii—A. frontalis colonies, after which the midpoints between the counts of both divers were calculated per 20-m2 segment. The Laulau Bay abundance surveys were conducted along the 10- and 12-m depth contours, while the Finger Reef abundance surveys were conducted along the 7- and 12-m depth contours on the reef slope. Means and sample standard deviations for colonies/m2 were calculated using each 10-m segment along the 50-m transects as replicates and computed using Microsoft Excel. The purpose of the abundance surveys was to quantitatively characterize the concentration and distribution of M. bouillonii colonies across different reefs. They also allowed for the photographic documentation of the varied growth morphologies presented by colonies.Abundance surveys were conducted in the Mariana Islands Fig. a at LaulM. bouillonii—A. frontalis colony abundance. One survey at Finger Reef was conducted on the reef flat, between 1 and 2 m depth, and the other was conducted on the reef slope around the 9-m depth contour. Surveys at Piti Bomb Holes were conducted between less than 1 and 2 m on the reef flat near its boundary with the reef slope. The surveys on the Merizo fringing reef were conducted between 1 and 3 m at the boundary between the reef flat and reef slope. All M. bouillonii colonies within 1 m of either side of the transect that displayed morphologies consistent with shrimp association were photographed, providing a record of the substrate occupied. Colonies were attributed substrate types based upon the structure they were contacting; in cases where colonies were contacting multiple substrates, substrate was assigned based on the majority of contacts. Videos were also recorded along each transect to document surrounding benthic habitat and provide additional context for determining substrate occupied by colonies with multiple contacts. Both photographs and videos were captured using a Canon PowerShot® ELPH 100 HS set for underwater macro-photography or video mode, respectively. Additional observational photographs were made via SCUBA from various locations on Guam, and via snorkeling in Laulau Bay, Saipan. The purpose of the substrate surveys was to ascertain the frequencies and site specificity of substrate occupation, thereby providing additional information about how M. bouillonii colonies grow on Marianas’ reefs.Substrate surveys were conducted at Finger Reef, Apra Harbor, Guam, USA , Piti Bomb Holes, Guam, USA , and a fringing reef shoreward of Cocos Lagoon in Merizo, Guam, USA   of colonies were collected and examined via compound light microscopy to inspect the woven M. bouillonii tubes for other species of cyanobacterial filaments. In addition, partial 16S rRNA gene sequences were obtained from both shrimp-associated and non-shrimp-associated M. bouillonii for final confirmation of field identifications .lor Fig. a. MoreovAlpheus frontalis and M. bouillonii growing with (shrimp-associated cyanobacterium) and without A. frontalis (non-shrimp-associated cyanobacterium) were collected from Apra Harbor, Guam  and used to assemble four different growth conditions in reusable plastic food storage containers: shrimp-associated cyanobacterium with a shrimp, shrimp-associated cyanobacterium without a shrimp, non-shrimp-associated cyanobacterium with a shrimp, and non-shrimp-associated cyanobacterium without a shrimp, each with five replicates. Weighted containers were covered with window screen, placed in a blocked arrangement, and submerged in a flow-through raw seawater table. Submersion allowed for water exchange and the deposition of materials for shrimp to scavenge in each container, while the mesh was intended to prevent shrimp escape. The water table was covered with 50% shade cloth to limit light stress. The experiment was conducted over 15 days , with photographs taken each day to document changes in M. bouillonii pigmentation and wet weights of cyanobacterial biomass recorded on the first and last days. Excess water was removed from cyanobacterial biomass prior to wet weight measurements using a salad spinner. For each sample, the salad spinner was pumped 20 times at a rate of 1 pump per second, followed by a 10-s period of unconstrained spinning. Statistical analysis of changes in wet weight were conducted using the statsmodel package for Python 3 to build a two-way ANOVA model. Type of cyanobacteria (shrimp versus non-shrimp) and presence or absence of shrimp were set as independent variables for the two-way ANOVA, and an additional term was added to account for the blocked experimental design.M. bouillonii colonies, and from approximately one meter above each colony in the water column. Samples were flash frozen in dry ice and kept frozen until they were submitted for nutrient analyses at the Oceanographic Data Facility, Scripps Institution of Oceanography, San Diego, California. Statistical significance was determined for each nutrient using Student’s t-test , administered in Microsoft Excel.Five paired water samples were collected from Finger Reef, Apra Harbor, Guam on July 3, 2017. Syringes (50 mL) with blunt-tip needles were used to collect water from inside the woven tubes and chambers of five individual shrimp-associated M. bouillonii—A. frontalis colony growth in Laulau Bay, Saipan  = 0.79, 0.49; n = 5) than the two transects on the reef slope of Finger Reef  ; however, the sixth most densely populated 10-m segment of reef was surveyed on Finger Reef, with 3.8 colonies per m2. Every 10-m interval along the Saipan transects recorded densities greater than two M. bouillonii—A. frontalis colonies per square meter, and the most densely populated Saipan transect in particular represents an area of consistent high-density M. bouillonii—A. frontalis colony growth . The two transects in Guam, however, ranged from 3.75 to 0.05 colonies per m2 and from 1.5 to 0.08 colonies per m2, with the former illustrating wide variation in colony density even within small spatial scales (sd = 1.50). Of particular note, while many colonies were observed to be in contact with stony coral, none of the colonies observed during the abundance surveys were found to display morphologies that would suggest coral overgrowth .Field abundance surveys of pan Fig. b and at pan Fig. c, indicaM. bouillonii colonies growing mainly in coral-associated interstitial spaces. Colonies were documented to predominantly grow in association with Porites rus and Porites cylindrica, with a much smaller number of colonies growing on bare reef substrate and only two colonies found growing with Porites cf. deformis  , while both types of cyanobacteria grown with a shrimp decreased in wet weight on average . Two-way ANOVA accounting for the blocked experimental design was administered to test the statistical significance of the effects that cyanobacterial type and shrimp presence had on changes in wet weight. The blocked experimental design , type of cyanobacteria , and presence of shrimp  were all found to have statistically significant effects on cyanobacterial growth, as measured by changes in wet weight, while the interaction between cyanobacterial type and shrimp presence was not found to be significant  (Table S4). The significant effect of the blocked experimental design is attributable to the heterogeneity of flow environments in the water table in which the experiment took place; blocks differed in their proximity to inflow and outflow of seawater in the table.Out of the four growth conditions, non-shrimp associated cyanobacteria grown without a shrimp was the only condition that averaged net positive growth, as measured by change in wet weight , are combined with the gathered field data, a picture of h Cowles  and Fishh Cowles  describeh Cowles  assert tM. bouillonii and A. frontalis encountered thus far within the Mariana Islands displayed variable, substrate-dependent growth forms that favor occupation of interstitial spaces on coral reefs. This suggests that M. bouillonii—A. frontalis colonies may not exhibit a similar propensity to overgrow habitats dominated by hard coral as to that reported for habitats dominated by octocoral . In contrast, shrimp associated cyanobacteria that were denied access to shrimp were negatively impacted by their new nutrient-poor environment and required approximately two weeks to acclimate to the new conditions. This interpretation is further supported by the three replicates of shrimp associated cyanobacteria growing with shrimp whose shrimp-driven nutrient influence was removed during the course of the experiment via death or escape; in all three of these cases, cyanobacterial pigmentation deteriorated as the nutrient-supplying shrimp was no longer actively excreting among the filaments. Further support can be found in the results of the nutrient analyses, which found statistically significantly higher levels of phosphate, nitrate, nitrite, and ammonia within the tubes of M. bouillonii—A. frontalis colonies, as compared to ambient seawater. This suggests that enclosed tubes and chambers of M. bouillonii limit water exchange, allowing for shrimp excrements to accumulate and create a beneficial nutrient-enriched environment within their confines.Furthermore, the observed patterns in pigmentation change, considered alongside the nutrient analyses, help to better characterize the commensal interactions occurring between o et al.  in that A. frontalis has been described as an obligate tube dweller that benefits from its association with M. bouillonii by deriving chemically defended shelter . Another reported benefit is use of M. bouillonii by A. frontalis as a food source; Fishelson Supplementary material 2 (mov 38097 kb)Below is the link to the electronic supplementary material.


As the first intracellular host factors that directly interact with the genomes of RNA viruses, RNA binding proteins (RBPs) have a profound impact on the outcome of an infection. Recent discoveries brought about by new methodologies have led to an unprecedented ability to peer into the earliest events between viral RNA and the RBPs that act upon them. These discoveries have sparked a re-evaluation of current paradigms surrounding RBPs and post-transcriptional gene regulation. Here, we highlight questions that have bloomed from the implementation of these novel approaches. Canonical RBPs can impact the fates of both cellular and viral RNA during infection, sometimes in conflicting ways. Noncanonical RBPs, some of which were first characterized via interactions with viral RNA, may encompass physiological roles beyond viral pathogenesis. We discuss how these RBPs might discriminate between an RNA of either cellular or viral origin and thus exert either pro- or antiviral effects—which is a particular challenge as viruses contain mechanisms to mimic molecular features of cellular RNA. In the simplest terms, an RNA binding protein is a protein that can directly associate with RNA. However, upon recognition of a cognate RNA target, these proteins are responsible for a wide array of essential biological functions; from the post-transcriptional gene regulation (PTGR) of cellular transcripts to acting as sentinels for the detection of aberrant RNA, a potential sign of cellular damage or pathogen invasion. With respect to the innate immune system, one can broadly consider proteins that bind to RNA as belonging to two major categories: 1) The RNA-specific subset of pattern recognition receptors (PRRs) responsible for sensing pathogen-associated or damage-associated molecular patterns  and eliciting immune signaling pathways, or 2) as the more classically defined RNA binding proteins (RBPs) that manage gene expression at the level of RNA. While there exist many immune potent ligands that are of a firm pathogenic origin , appropriate detection of an RNA as foreign is less straightforward. As an RNA virus infects a cell, viral RNA (vRNA) is released into a milieu full of cellular RNA. Consequently, specialized RBPs must be able to discriminate whether an RNA is truly host derived. Although significant efforts have been made into defining features of “self” vs. “foreign” RNA, how—and whether—these marks can strictly distinguish the identity of an RNA is less clear ..IFNB1)  cap occurs in three steps: (1) cleavage of the PPP of the first transcribed base to PP by a triphosphatase (TPase), (2) addition of a guanosine monophosphate by a guanylyltransferase (GTase), and 3) methylation at the N7 residue of the guanosine by RNA Methyltransferase (RNMT) b 22]. T. T22]. T methylatMore details on the cap structure and the role of IFIT1 in the context of a viral infection will be discussed later in this review. Polyadenylation is achieved primarily by the activities of nuclear Poly(A) binding protein (PABPN1), Cleavage and polyadenylation specificity factor (CPSF), and Poly(A) polymerase (PAP) ,33. The The nuclear export of a cellular mRNA is mediated in part by an orchestration of multiple RBPs to form heterogeneous nuclear RNP C (hnRNPC) . Early iThe control of mRNA stability or degradation is facilitated by several functional classes of RBPs. Most simply, mRNA can be digested by ribonucleases (RNases) belonging to two different classes: exonucleases and endonucleases . These RComplete small RNA complementarity by an AGO2-containing RISC can lead to AGO2 dependent cleavage of transcripts, whereas RISC complexes  that bear seed sequence-dominant complementarity leads to the recruitment of the CCR4-NOT and DCP1-DCP2 complexes, thus promoting transcript decay .While mimicry of cellular molecules is a mechanism of survival for viruses, so is the recruitment or molecular hijacking of cellular proteins. Notably for RNA viruses, their RNA genomes (or antigenomes) must be translated by cellular machinery . Thus, cTo date, the identification and characterization of RBP targets are predominantly achieved as single-RBP-centric endeavors via the capture of an RBP, usually by antibodies, followed by high-throughput sequencing of its co-enriched RNAs ,57,58,59Even prior to the development of crosslinking approaches were strategies in which the goal was to identify the proteins that were bound to subsets of RNAs or single RNAs ,77,78,79Infection by RNA viruses presents a unique biological context in which the viral genomes themselves are the RNAs of interest, particularly given their impact to human health and disease. Since vRNAs must interact with cellular proteins for infection to progress, several methods were developed to probe RBPs specifically interacting with vRNAs in live cells infected with replication-competent virions  93,94].,94.93,94Notably, thiouracil cross-linking mass spectrometry (TUX-MS) utilizes the photoreactive ribonucleotide analogue, 4-thiouracil (4TU), along with a cellular transcription inhibitor to label only vRNAs during infection . While aTogaviridae, Orthomyxoviridae, Picornaviridae, Coronaviridae, Flaviviridae, Rhabdoviridae, and Phenuiviridae . . 128]. OThe intersection between the m6Am modification, a cap 0 structure with m6A as the first transcribed base, and the IFITs is still a mystery. It is known that DENV (a virus that is not restricted by IFIT1) contains m6Am . VSV, raFlaviviridae family but has a proviral effect on IAV [While more well-studied than m6Am, the roles of internal m6A modifications in viral infection and innate immunity are complex. We will provide a brief commentary on m6A in viral infection but direct the reader to recent reviews for a more in-depth discussion on the subject ,139. Orit on IAV .METTL3 and METTL14 typically deposit m6A cotranscriptionally on adenosine residues within the “DRACH” motif  . HoweverIFNB1, contain m6A in their 3′UTR [IFNB1 has been shown to destabilize the transcript, thus ensuring a punctuated immune response [Adenosine deaminase acting on RNA 1 (ADAR1) gene is m6A modified [Many immune signaling transcripts, including ir 3′UTR ,157,158.ir 3′UTR ,157,158.response . Additioresponse . While Ymodified . The ADAmodified . ADAR1 amodified ,162. Themodified ,164,165.modified . This enmodified . The YTHDF proteins also have potential effects on viral replication independent of innate immune PTGR. For example, YTHDF1-3 bind the HIV-1 genome and play an antiviral role in HIV-1 infection ,168. FurIt has recently been shown that the demethylase activity of ALKBH5 is impaired during VSV infection and that the pathways most affected by the impaired demethylase activity are metabolic pathways . This stWhile noncanonical RBPs reveal exciting new horizons for investigation, canonical RBPs and their differential impacts during immune activation are not to be overlooked. Indeed, many canonical RBPs have been characterized based on their abilities to modulate the stabilities of target transcripts. For example, Embryonic lethal abnormal vision-like protein 1 (ELAVL1), which has been shown to bind m6A, typically binds to AREs within the 3′ UTRs and introns of target transcripts ,179,180.It is likely that ISG RBPs did not evolve strictly to manage pathogen invasion—but to address cellular stressors more broadly. Discovering the role of their cellular target spectra is a challenge invigorated by the methodologies and reports discussed in this review. Areas of renewed interest highlighted here include RBPs that bind 5′ cap structures and m6A modifications. Both noncanonical RBPs and those involved in RNA stability are a subset of RBPs whose roles are being uncovered by studies on viral infection and immune activation. While an in-depth characterization of RBPs during viral infection can reveal novel functionalities restricted to the metabolism of vRNA, it can also give insight into an RBP’s native physiologic function in the absence of infection. As the distinction between hallmarks of “host” vs. “viral” RNA are becoming muddled, the lines between what have classically been defined as “host-specific RBPs” and “viral specific RBPs” are also blurred. In turn, strictly defining an RBP as either a pro- or anti-viral factor has become an increasingly difficult task. Perhaps these distinctions have been assigned through observed opportunity costs regulated by the stoichiometry of an RBP relative to available RNA substrates and the simplest explanation is true—RBPs cannot be neatly categorized into two groups.


Refining modelled structures to approach experimental accuracy is one of the most challenging problems in molecular biology. Despite many years’ efforts, the progress in protein or RNA structure refinement has been slow because the global minimum given by the energy scores is not at the experimentally determined “native” structure. Here, we propose a fully knowledge-based energy function that captures the full orientation dependence of base–base, base–oxygen and oxygen–oxygen interactions with the RNA backbone modelled by rotameric states and internal energies. A total of 4000 quantum-mechanical calculations were performed to reweight base–base statistical potentials for minimizing possible effects of indirect interactions. The resulting BRiQ knowledge-based potential, equipped with a nucleobase-centric sampling algorithm, provides a robust improvement in refining near-native RNA models generated by a wide variety of modelling techniques. Predicting RNA structure from sequence is challenging due to the relative sparsity of experimentally-determined RNA 3D structures for model training. Here, the authors propose a way to incorporate knowledge on interactions at the atomic and base–base level to refine the prediction of RNA structures. This huge gap continues to expand exponentially because sequencing entire transcriptomes is only a fraction of the cost and time required for structure determination of a single RNA by the techniques such as X-ray crystallography, nucleic magnetic resonance, and cryo-electron microscopy.Three-dimensional structures of RNAs offer the best clues for their functions and yet only a few thousand structures have been determined4. Homology modeling such as RNABuilder5 and ModeRNA6 attempts to predict the structure of a target RNA by mapping the query sequence onto the template structure(s) of its homologs. However, most RNAs do not have their respective homologous templates. Thus, method development was mainly devoted to predicting structures by de novo folding from sequences through fragment or motif assembling. Examples are FARNA7, MC-fold/MC-Sym8, Rosetta-SWM9, ifoldRNA11, Vfold13, 3dRNA15, and SimRNA16. Evaluation of these methods by RNApuzzles19 indicates that reasonable predictions are possible only for RNAs with either simple topology or existing homologous templates. Moreover, reasonable near-native predictions were often ranked poorly by the predictors. Clearly both homology and de novo models would greatly benefit from a structure-refinement technique that can bring the models closer to native structures locally and globally and improve near-native ranking.One possible solution to this growing problem is to predict RNA structures from their sequences by computational template-based homology modeling and de novo folding21. For RNAs, most all-atom refinements were performed after coarse-grained sampling for removing steric clashes and non-ideal bond lengths, bond angles, or torsional angles. For example, the QRNAS refinement22 utilizes a modified AMBER force field with enforced base-pair planarity, explicit hydrogen bonds, and backbone regularization. FARFAR24 improves model accuracy through all-atom refinement after FARNA coarse-grained sampling. It employed a Rosetta all-atom force field that mixes physical and knowledge-based scores with RNA-specific terms for Watson–Crick (WC) base pairing, base stacking, and torsional potentials25.Structure refinement, however, is challenging with few methods available for RNAs. Even for protein structure refinement, despite a long history of method development, only a moderate local improvement can be made by combining molecular mechanics force fields and knowledge-based potentials with pre-defined restraints to prevent large deviation away from the native basin26 with similar statistical potentials developed for RNA29. Unlike these atomic knowledge-based scores and previous all-atom RNA refinement energy scores23, the statistical potential in this work is tailored specifically to RNA interactions that were dominant by orientation-dependent base-pairing and stacking interactions30 with rotameric backbone31, in contrast to dominant distance-dependent hydrophobic interactions32 with rotameric sidechains33 in protein folding. Base–base interactions were described by fine grids in a six-dimensional orientational space and scaled by quantum mechanical calculations allowing better capture of both local and global interactions. This Backbone Rotameric and Quantum-mechanical-energy-scaled base–base knowledge-based potential (BRiQ) is integrated with a new nucleobase-centric tree algorithm that samples backbone conformations around predicted or known base pairs. The resulting refinement technique can consistently improve model structures for the majority of near-native structural models as demonstrated by refining Rosetta-SWM motif34, RNA puzzles19, and FARFAR224 models.Here, we build a knowledge-centric refinement energy score. Atomic-level knowledge-based energy functions, derived from known three-dimensional structures, have traditionally focused on distance dependence in protein structuresThe BRiQ refinement energy score is designed to capture stably stacked bases linked by a more flexible ribose and phosphate backbone. In particular, the interactions associated with bases and oxygen atoms in backbones are strongly orientation-dependent. To capture this unique structural property of RNA chains, the energy score (E) is separated into six terms: orientation-dependent interactions between two bases  algorithm for conformational sampling, in which each node is a base and each edge represents the relative position between two bases Fig. . Two con34 was developed to examine the ability of Rosetta-SWM to recover the loop region given some of the base pairs and environment contacts fixed  to optimize the BRiQ energy score. The lowest RMSD of top 1% predicted models by the BRiQ score are compared in Fig. 35, one example of refinement leading to worse structures  proposed by Parisien et al.37 is a tool for checking the quality of RNA structures. We calculated MolProbity scores for all RNApuzzle models. Supplementary Fig. MolProbilityIn addition to the whole motif, we further analyzed the refinement results of RNA puzzles at the base-pair level. Here, we employed DDM to measure the relative orientational difference between predicted and native base pairing structures according to four pseudo atoms employed for representing each base (see Methods). As Supplementary Data It is interesting to know what happens if BRiQ is applied to refining native structures. We examined the deviation from the native structure at the base-pair structural level. Supplementary Fig. 24 but only for those 12 RNA puzzles with RMSD of best FARFAR2-predicted models less than 6 Å as shown in Fig. We also test our refinement method starting from the recently developed FARFAR2 predicted modelsAs illustrative examples, Fig. This work presents a protocol for refining RNA model structures produced by other modeling tools. The protocol is nucleobase-centric in its knowledge-based energy score as well as the sampling algorithm. The fully knowledge-based score (BRiQ) captures the orientation dependence of base–base and base–oxygen, and oxygen–oxygen interactions by using a local coordinate system for each nucleobase, whereas the structures of ribose and phosphate backbones were governed by rotameric statistical potentials and empirical internal energies. Moreover, the dominant base–base interaction was scaled by quantum mechanical calculations for removing possible indirect interactions contained in structural statistics. This statistical potential was coupled with the NuTree algorithm that samples the conformations around pre-determined base pairs. This refinement protocol improves 81% Rosetta-SWM models with less than 2 Å RMSD, 100% RNA puzzle models with RMSD < 4 Å, and 83% FARFAR2 models with RMSD < 6 Å. The CPU time cost of refining a single RNA puzzle model is around 40 min for a model of 50 nt, and 2 h for a model of 100 nt on Intel Xeon Gold 6242 CPU 2.80 GHz, and the RAM requirement is about 2.9 G.6, FARNA7, MC-Fold/MC-Sym8, ifoldRNA11, Vfold13, 3dRNA15, and SimRNA16.The BRiQ statistical energy score is built on the knowledge that backbone conformations are determined by more stable stacked base pairs. This was done by focusing on base–base, base–oxygen, and oxygen–oxygen interactions while treating the backbone as rotameric states around the bases. The robustness of the scoring function is illustrated by the consistent improvement in refining a diverse set of RNA models produced from different methods participated in RNA puzzles including ModeRNA26.In order to secure as many RNA structures as possible, we included all RNA structures with resolution higher than 3.0 Å to collect base and backbone statistics for our BRiQ knowledge-based score. Although X-ray refinement at resolutions between 2.5 and 3.0 Å may contain errors and cryo EM structures may have poor density fitting, using 2.5 Å cut off and a limitation to X-ray structures will lead to 1225 structures with only 10.5% bases, a dataset that is too small to capture useful all-atom statistics. It is also noted that even for high-resolution structures, there are regions which have poor R-factor values or high clash scores. Structural regions with clashed atoms were automatically excluded from our statistics. Structural regions with poor R-factors simply reflect the regions that are more dynamic and the conformations of the regions are probable conformations among many. Thus, it is reasonable to incorporate these conformations as a part of statistics as commonly done for statistical potentials of proteinsAnother issue of the structural database is that the RNA structures included were dominated by rRNA and tRNA with about 89% base pairs from rRNAs. This raises the question whether or not our BRiQ energy function is biased toward rRNA and tRNA so that it would not be generalizable to other RNAs. In fact, RNA puzzles all are made of noncoding RNAs that are not involved in protein synthesis. Despite of this, BRiQ can consistently refine these noncoding RNAs, indicating that BRiQ is not an energy function limited to rRNA or tRNA.38. We found that the homologous structures from RNA puzzles contributed only to 0.1% of all structural data. The changes to the BRiQ energy score are negligible. Refinement results with the new BRiQ score are essentially the same (except those caused by stochastic nature of Monte Carlo sampling). For example, the difference before and after removing homologs for the refined FARFAR2 structures is only 0.02 Å for rp04 and 0.2 Å for rp18 for the best in top 1%, respectively. We will update the BRiQ energy score when more non-redundant RNA structures become available.The above demonstrated transferability is achieved because the statistics was made at base and atom levels, not at the structural motif level. Moreover, we are only interested in detailed energy surfaces around the local minima. That is, subtle or large conformational changes due to different ligands and crystallization conditions are useful to increase the resolution of the energy surface. In Supplementary Fig. 42 and deep learning techniques44.The NuTree algorithm samples the RNA conformations around predetermined base pairs from a given model. These preformed base pairs serve as the nucleus to speed up the folding of the rest of an RNA chain. However, if a base pair was incorrectly modeled, it will be energetically difficult to correct the mistake. This is the main reason why the current refinement protocol works best for those near-native structures, in which the majority of base pairs were correctly identified. Unlike Rosetta, the NuTree algorithm can fix both nested and non-nested (pseudoknot) base pairs. In other words, this algorithm will become more useful as secondary and tertiary base pairs are increasingly more accurately predicted by employing co-variational analysis of RNA homologous sequencesThis work is limited to RNA structural refinement. Another question is whether or not the RBiQ statistical energy function coupling with the NuTree algorithm can serve as an effective method for ab initio structure prediction. Initial studies suggest that a new sampling algorithm is likely required because ab initio folding requires more frequent breaking and forming of base pairs than what are typically involved in the NuTree algorithm. The work in this area is in progress.Relative orientation between two bases. The planar shaped bases make the orientation dependence a must for base–base interactions. Here, we consider each base i as a rigid body with a local coordinate system CSi whose origin is located at atom C1’. The x-axis is in the direction of C1’N9 and the z-axis is in the direction of C1’N9×N9C4 for A and G whereas the x-axis is in the direction of C1’N1 and the z-axis is in the direction of C1’N1×N1C2 for U and C. As shown in Fig. i and j can be described by (1) the distance rij between the origins of CSi and CSj, (2) the rotational angle ωij around rij, (3) the directional vector rij in CSi, and (4) the directional vector rji in CSj . The distance rij has a range of 0–15 Å with a uniform grid space of 0.3 Å. The rotational angle ωij varied from −180° to 180° with a uniform grid of 8°. We represent the orientation of the directional vector rji by 2000 uniformly distributed points on a sphere from Monte Carlo simulated annealing of points with repulsive interactions proportional to the inverse of squared distances between the points. Thus, the space between the two bases is separated into 50 × 45 × 2000 × 2000 discrete regions. Once the energy values for all these regions are known, the energy value at a given distance and orientation can be linearly interpolated. The above coordinate system for representing the base–base relative orientation was similar to but is more sophisticated than what was proposed for a coarse-grained two-particle representation of RNA chain45. The 6-dimensional base–base interactions were pre-calculated and stored in a table and only negative values were loaded into computer memory for quick access.Base-orientation representation. To define an orientation-dependent density, it is necessary to measure the structural similarity first. One common measure is the root-mean-squared distance (RMSD) between two base pairs. However, it is too time-consuming to calculate RMSD. To speed up the calculation, we assumed that four fixed points  in the local coordinate system of a base , T2 = , T3 =  and T4 = . The resulting correlation coefficient is 0.974  to calculate the orientation distribution of one base around another base f(x) by the following equations:N is the number of base pairs in the database of RNA structures and DDM is the distance between a target base pair and a base pair in the database. If The orientation distribution densities of base pairs with different sequence-separation distances  were calculated separately. For non-local base pairs (separation >2), the density was re-weighted by quantum mechanical energy.Quantum-mechanical-energy-weighted orientation distribution density. One issue associated with a statistical energy function is that a high population of specific orientation between two bases may not be due to a strong direct interaction at this specific orientation but due to the orientation preference of the interaction with another base. Here, we minimize the possibility of this type of indirect interactions by a weighting factor w(xi) according to quantum calculations. The assumption is that the strength of the directional interaction is related to the strength of the quantum interaction energy. Here,46. We investigated all possible ten pairs of bases . That is, both canonical  and all other possible noncanonical pairs were included in the statistics. Moreover, for each base-pair type, we generated 80 orientation cluster centers by minimizing the root mean square distance between all data points to the nearest cluster center. That is, not only base pairs, but base–base stacking and other base–base polar interactions were included in the statistics. For each representative configuration in a structural cluster, we employed 5 nearest base-pairing conformations with the highest-resolution PDB structures as the initial configurations for QM calculations. The initial structure from the PDB was further optimized quantum mechanically so as to minimize the effect of potentially inaccurate conformations. The steps for calculating QM base–base interactions are as follows: (1) remove all backbone atoms, (2) replace the C1’ atom with a H atom, (3) optimize the position of the H atom by ab initio Hartree–Fock calculations with the basis set 6-31 G* and (4) calculate the base–base interaction energy by E(AB) = H(AB) – H(A) – H(B) with the density-functional theory method M06-2X47 and the basis set 6-31+G. The average value of five QM calculations is considered as the QM energy for the representative configuration. A total of 4000 QM calculations for 10 possible base pairs (80 × 10 × 5) were performed. Figure Here, quantum mechanical (QM) calculations were performed by using Guassian 09Base–base interaction energy. The final energy function for base–base interactions is calculated by the following equations:fref(2+) was employed to scale the lowest energy value of non-local base pair to ‒8.0 based on the lowest energy from QM calculations and the scaling between statistical and QM calculations  and fref(2) were employed to scale the lowest energy value of local base pair to ‒4.0. Here, we also set the maximum base–base interaction energy to zero because repulsive interactions from the above six-dimensional statistical analysis are not reliable. Figure ons Fig. . fref(1)x and xi represent the target base–oxygen and a base–oxygen pair in the database, respectively, and θ angle between the base atom N or O, the main chain atom O  and the main chain atom C or P bonding to O. This was done by locating the angle range in RNA structures after removing top 3% angles from each side and defining s(θ)=0 for outside the angle range, s(θ)=1 when θ=θM, the median value of θ, andθu and θL are the upper and lower bounds for θ, respectively. The final expression for The interaction energy between a base and a main-chain oxygen atom is the interaction between a polar group and a polar atom. The orientation of a base is still represented by 4 points obtained previously. The relative orientation and position between an oxygen atom and a base can be described by the distance between the oxygen atom and the four points representing the base. The kernel function d between two hydrogen-bonding configurations is calculated by RMSD between the four atoms. Similar to We consider hydrogen bonding interactions between O2’-O2’, O2’-OP and OP-OP. The configuration of hydrogen bonds is determined by four atoms (two oxygen atoms and their connecting C or P atoms). The distance d is the atomic distance between two atoms, r0 is the shortest statistical distance from RNA structures between the two atoms and r0 is orientation independent for atoms in SP3 hybridization but is orientation-dependent for atoms in SP2 hybridization. That is, r0 is orientation-dependent between any atom with an atom in a base but is orientation-independent between two main-chain atoms. For orientation-independent r0, it is set as the top 5% shortest distance between two atoms found in RNA structures. For orientation-dependent r0 between a given atom a and an atom b in base B, the atomic position of a is transformed to the local coordinate system of the base B and clustered around the atom b. The top 5% shortest distance in different orientations is set as r0 in different orientations. The functional form of the clash energy is shown in Supplementary Fig. Due to lack of adequate statistics for hard-core exclusion in all statistical energy functions, we have set repulsive terms to 0 in χ rotating around the chemical bond connecting the base and ribose (N9-C1’ for bases A and G and N1-C1’ for bases U and C). The second is the improper angle ν between the C2’-C4’-O4’ plane and the C2’-C4’-C3’ plane within the connected ribose. Each dihedral angle can be separated into two regions, and each region into 300 to 500 rotamers, and a total of 1500 rotamers is employed to describe a ribose rotameric state.For protein structure prediction, the sidechain conformational space is discretized into rotamers. Here, we introduce main-chain rotameric states because stacked bases are more rigid than the main chains. The conformational space of a ribose rotamer is mainly determined by two dihedral angles Fig. . The firxi denotes a rotamer in the database and the summation is over all the rotamers in the database.The ribose rotamers are clustered according to RMSD. This is done by transforming atomic coordinates of eight ribose atoms to the local coordinate system for the base. RMSD is the average distance of ribose atoms in the local coordinate system. Once RMSD is known, we can use the kernel density estimation method to calculate the density of each rotamer, and then take the negative logarithm and convert it to the rotamer energy bl denotes the bond length between O5’i+1 and C5’i+1. We made this bond slightly flexible so that we can add a phosphate group  between two fixed riboses at fixed dihedral angles, other bond lengths and bond angles. The average bond length found in the RNA structures (1.422 Å) is employed here. Parameter Pi+1-O5’i+1-C5’i+1 and i+1-C5’i+1-C4’i+1. Similar to the bond length, we made these two angles flexible so that we can add a phosphate group between two fixed riboses at fixed dihedral angles, other bond lengths and bond angles. The average values for these two angles in RNA structures are 120.7° and 111.1°, respectively. Parameter We introduce the following internal energies i-C3’i-O3’i-Pi+1 and i-O3’i-Pi+1-O5’i+1) determines the position of the phosphate group. In addition, i-1- Pi+1-O5’i+1-C5’i+1, Pi+1-O5’i+1-C5’i+1-C4’i+1 and O5’i+1-C5’i+1-C4’i+1-O4’i+1, respectively48. Here, the coupling between neighboring three dihedral angles, rather than the statistics of a single torsion angle was considered to improve the accuracy of main-chain modeling.1. This set contains 272 ribosome, 221 riboswitches, 138 tRNA, 106 ribozymes, 42 aptamers, 121 virus RNA, 17 introns, 3 spliceosomes, and 1327 others. Among them, there are 1459 protein–RNA complex structures and 788 RNA-only structures. The base pairing information is dominated by ribosome (about 89%). We did not use the Cambridge database because it contains simple structures only. We did not remove any redundant sequences because we need a database as large as possible. Moreover, RNAs can have different structures in different complexes and we want to capture all possible conformations. We can do this because statistics are collected at the atomic or base level. See the discussion in more details.All statistical energy terms in Eq.  were der0C1G2C3A4A5G6C7 where the edge 0–1 is sequence-neighboring base pairs along the 5′ to 3′ direction in the Watson–Crick pairing region, edges 1–2 is sequence-neighboring base pairs along the 5′ to 3′ direction in non-Watson–Crick pairing regions, 2–3, and 3–4 are sequence-neighboring base pairs along the 5′ to 3′ direction in the loop regions, edges 0–7, 1–6 are Watson–Crick pairs and 2–5 is non-Watson–Crick pair. Although there is no edge between 4 and 5, we need to build the phosphate connection and calculate the internal energy between them.For conformational sampling, each RNA structure is represented by the NuTree. Each node in the NuTree denotes an RNA residue including its base position , the ribose rotameric state and the phosphate position attached to the 3′ position of the ribose . Both moves are local. They do not change the positions of other nodes and riboses. Edge sampling makes both local and global moves. The local move makes minor adjustment . The global move randomly selects a move from the set according to the edge type. Edge sampling moves will change the positions of all downstream nodes but not the relative positions between the nodes.The above sampling may change the relative positions of some neighboring riboses and make it necessary to rebuild the phosphate connection. Such connection can be rapidly built by the grid search from representative points for the lowest dE(AB), dE(AC), dE(BC), and dE(CC) denote the changes in interaction energies with the AB, AC, BC, and CC regions, respectively, and Nstep is proportional to the edge number in the NuTree.All nodes in a NuTree are divided into three types: those with positions fixed (A), those with relative internal positions fixed but not absolute positions (B), and those with both relative and absolute positions changed (C). The energy change after each move is calculated byn(wc edge) is the edge number of Watson–Crick pair or helix neighbor, n(nwc edge) is the edge number of Non-Watson–Crick pair or NWC neighbor, n(other edge) is the number of other edges.where Eclash and Einternal a low weight of 0.05 at the initial temperature and gradually increase the weight to 1.To increase the efficiency of conformational sampling, we give 34, downloaded from https://purl.stanford.edu/fq893cm4516. After removing redundant RNAs and those with more than 50 bases and non-standard RNA bases, we have 48 motifs listed in Supplementary Data The test set is obtained from the benchmark of Rosetta-SWMhttps://github.com/RNA-Puzzles/raw-dataset-and-for-assessment. The prediction results of FARFAR224 are downloaded from https://purl.stanford.edu/wn364wz7925.RNA Puzzle data set is downloaded from Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Reporting Summary


FUS mutants within multidomain structures and purified these proteins. The regulation of G4-RNA-dependent LLPS and LST pathways was lost for all ALS-linked FUS mutants defective in G4-RNA recognition tested, supporting the essential role of G4-RNA in this process. Noteworthy, the P525L mutation that causes juvenile ALS exhibited the largest effect on both G4-RNA binding and FUS aggregation. The findings described herein could provide a clue to the hitherto undefined connection between protein aggregation and dysfunction of RBPs in the complex pathway of ALS pathogenesis.Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the accumulation of protein aggregates in motor neurons. Recent discoveries of genetic mutations in ALS patients promoted research into the complex molecular mechanisms underlying ALS. FUS (fused in sarcoma) is a representative ALS-linked RNA-binding protein (RBP) that specifically recognizes G-quadruplex (G4)-DNA/RNAs. However, the effects of ALS-linked FUS mutations on the G4-RNA-binding activity and the phase behavior have never been investigated. Using the purified full-length FUS, we analyzed the molecular mechanisms of multidomain structures consisting of multiple functional modules that bind to G4. Here we succeeded to observe the liquid–liquid phase separation (LLPS) of FUS condensate formation and subsequent liquid-to-solid transition (LST) leading to the formation of FUS aggregates. This process was markedly promoted through FUS interaction with G4-RNA. To further investigate, we selected a total of eight representative ALS-linked  Neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTLD) are characterized by the progressive degeneration of nerve cells in the brain and spinal cord , 2. In tin vitro, but in the presence of RBPs, the functional structures are considered to be composed of ribonucleoprotein (RNP) complexes in cells . The N-terminal QGSY-rich region, one RGG region, and the C-terminal two RGG regions are the disordered low-complexity (LC) regions in the FUS protein sequence and are considered to mediate LLPS   . At the B) .C and Fig. 1D)  , 33. FusFig. 1D) C and S2.A and B).The purified FUS protein binds to G4-containing DNA/RNAs under structure-dependent manner and induces the deformation of G4 structure using its multiple RNA-binding modules for stable binding . First wC). Both G4 RNAs significantly promoted the formation of condensates compared with random RNA controls at concentrations 2 μM and above. No condensate formation was observed without FUS even at the highest concentrations of RNAs. Randomized RNA had a slight effect on condensate formation, supposedly due to nonspecific interactions with phosphate backbone, RNA bases of unstructured RNA, and/or G4-like sequence (D). These results strongly endorse the prediction that the FUS condensates are formed through the LLPS process, and this process is promoted in the presence of G4-RNA.We then analyzed the formation of FUS condensates in the presence of various concentrations of these model G4-RNAs. As a control, a randomized RNA (synthesized oligomer of mixed sequence composition) was used to compare and measure the formation of FUS condensates C. Both Gsequence . Fluoressequence D. These A; . Thus we concluded that the FUS condensation is promoted when FUS binds to G4-forming DNA/RNA.To get insights into the molecular basis of LLPS promotion, we next examined whether this effect depends on either G4 structure or G-rich sequence. To discriminate these two possibilities, we performed the condensate formation assay using two well-analyzed sequences, whose structures change depending on monovalent salt species. Recently, we identified that FUS binds to human telomere DNA forming hybrid type G4 in the presence of KCl, but not to the unfolded DNA by replacing to NaCl Fig. 3A; ). FUS alA; . FUS gene . AlthouFig. 3B; , 37). NeFUS have been identified in ALS patients (A). With use of full-length tag-free FUS protein, we recently found the involvement of multiple structural domains of FUS other than the RRM and Zn finger domains in specific and stable interaction with highly structured DNA/RNA probes . To get insight into possible participation of this region in the recognition and binding of G4 structure, we examined the direct binding activity of G4-RNA for the purified FUS proteins with these ALS-linked mutations. For this purpose, we selected a total of eight representative ALS-linked FUS mutants: two (P18S and A115N) within the N-terminal proximal QGSY-rich region; two (Δ173–174 and G206S) within the Gly-rich domain; two (R383C and M464I) within the split RGG domain; and two (R521C and P525L) within the C-terminal PY region containing nuclear localization signal (B). The full-length tag-free proteins were purified for all these eight FUS mutants as in the case of wild-type FUS protein (C).Up to the present time, more than 50 missense and internal deletion/insertion mutations of patients , 39, 40.patients A. With uA probes . Interesd region A. To getn signal B. The fu protein C.Figure D) to PSD-95 G4-RNA increased for four mutant FUS proteins, P18S, R383C, M464I, and P525L  assay system, the kinetic parameters were measured for molecular interactions between two representative model G4-RNAs (PSD-95 and CaMKIIα) and increasing concentrations of wild-type or mutant FUS proteins. The dissociation constant , its G4 structure appears to be loosened or melted even at lower temperatures as detected by UV melting studies , all eight residues tested might be involved in tight G4 binding. The maximum decrease in the binding affinity to CaMKIIα G4-RNA was observed for P525L mutant at the extreme end of PY-CTR containing NLS . Noteworthy is that these six positions of FUS are important for not only the condensate formation but also G4-RNA binding (see above).To get insight into the influence of ALS-linked mutations on the formation of FUS condensates through LLPS pathway, we next analyzed the effect of these mutations on the formation in the presence and absence of G4-RNAs. In the absence of RNA, wild-type FUS formed condensates as detected by the turbidity as well as the droplet level by microscopic observation A and S6.B and C, and B and C, and Figs. 5B and in vitro but its molecular basis might be due to either the alteration in binding to G4-RNA or as yet unidentified alteration in protein conformation.In the presence of G4-RNA, however, the level of condensate formation decreased for all eight mutant proteins (via liquid condensates (or condensate droplets). To block nonspecific protein interactions, plasmid DNA was added into the assay of condensate formation at relevant concentrations . This LST process is very rapid as observed by moving image (A and B) similar to the previous observations in vitro and in vivo .We then followed the fate of FUS condensates in the presence of RNAs. The FUS condensates remained stable for a while after addition of G4-RNA, but the condensates were gradually broken and instead protein aggregates appeared after 4 h . Since t pathway A. This Lng image , finallyng image , A and B in vivo , 42, 43.D and E, and B and C). The six group-1 mutants  showed a decrease in the transition level compared with wild-type FUS, while the level of two group-2 mutations [R383C and P525L] was as high as that of wild-type FUS. This finding was unexpected because these two mutations reduced the formation of condensate droplets in the presence of G4-RNAs (see B and D). The group-2 P525L mutation affects the structure of PY-NLS  . In comparison with FUS, human full-length TDP-43 possessed a typical α-helix confirmation with a negative peak at 206 nm under the same buffer condition as previously identified . These mutations might cause changes in β-sheet and β-turn occupancy  . In the entified . The posccupancy , 51.FiguB). These two mutant proteins [R383C and P525L] appear to have anomalous structures and different properties from group-1 mutations. Besides, we confirmed conformational alteration of FUS by G4-RNA binding. FUS-G4-RNA complexes exhibited more emphasized positive peak around at same wavelength , suggesting that the binding enhanced the stability of the β-sheet state involved in the FUS-FUS association essential for LLPS. In contrast, randomized RNA had no apparent effect on the FUS conformation. The scatter plot graph indicates the peak values of each CD spectrum of FUS mutants and their exponential approximation curve (see D). Group-2 peaks are significantly separated from the fitting curve, and structural changes closer to the enhanced β-sheet state may facilitate the FUS-FUS association. This hypothesis is supported by the results that group-2 mutant proteins avoided reduction at LLPS levels (see A) and LST levels (see D and E). However, in the absence of G4-RNA, the mimetic structure state is expected to be less stable, due to the lack of enhancing effect by G4.The two group-2 mutants, R383C and P525L, that did not affect the LST in the presence of G4-RNA see , showed Fig. 7urve see D. Group-vels see A and LSTvels see , D and EE and F). These anomalous molecular characteristics of group-2 might be related to the functional toxicity of these mutants. Moreover, the P525L mutation is also associated with altered nuclear localization, which may have a serious impact on the pathogenesis of ALS. In fact, this P525L mutation is known to be associated with severe clinical course in juvenile ALS patients (E). Similarly, in vivo analysis of humanized mutant FUS mice with the group-1 mutation (R521C) revealed a decrease in local protein synthesis without cytoplasmic aggregation , hnRNP A2/B1, hnRNPA3, EWSR1 (Ewing's sarcoma RNA binding protein 1), and TIA1 (T cell-restricted intracellular antigen-1) have been suggested to exhibit RNP granules mediated by LLPS  are rich in LC sequences . The speC). Our results appear to be consistent with recent report that RNAs of 30 nucleotides or less do not bind to multiple FUS proteins , was observed with the long randomized RNA (68 nt)  . Therefo (68 nt) , which c (68 nt) . The dis (68 nt) , 77, 78. (68 nt) . We have (68 nt) . It is p (68 nt) , (74) bu (68 nt) . Many RBE. coli expression plasmid pET-FUS was described previously  were all constructed in this study by PCR-based site-directed mutagenesis method using a set of primer pairs , to which 0.1 mg/ml plasmid DNA  or various concentrations of RNAs were added to make in a final volume of 80 μl . Turbidity measurements and photographs were taken after incubating for 30 min at 25 °C using a spectrophotometer (Eppendorf BioPhotomelor pus), a phase-contrast microscopy (Olympus CX42), and a fluorescent microscopy (Olympus BX53). Particle size was counted using ImageJ software  as described previously 20 as des. Under t2, 0.05% Tween-20, and 150 mM NaCl) using a BIAcoreJ instrument (Sytiva) at 25 °C. Kinetic constants of RNA–protein interaction were calculated using BIAEVALUATION software version 3.0 (Sytiva) according to the manufacturer's instructions. The poly-dA16 tailed oligonucleotides used in this study were obtained from GeneDesign, Inc. (RNAs) and Fasmac Co., Ltd (Random RNA and DNAs).SPR analysis was performed as described previously  in the STEM analysis was performed as described previously . Ten micakira.ishiguro.iu@hosei.ac.jp) at Hosei University.All data are in the manuscript and This article contains The authors declare that they have no conflict of interests with the content of this article.


Previous studies found that RNA editing may be critically involved in cancer and aging. However, the function of RNA editing in human early embryo development is still unclear. In this study, through analyzing single cell RNA sequencing data, 36.7% RNA editing sites were found to have a have differential editing ratio among early embryo developmental stages, and there was a great reprogramming of RNA editing rates at the 8-cell stage, at which most of the differentially edited RNA editing sites (99.2%) had a decreased RNA editing rate. In addition, RNA editing was more likely to occur on RNA splicing sites during human early embryo development. Furthermore, long non-coding RNA (lncRNA) editing sites were found more likely to be on RNA splicing sites , while mRNA editing sites were less likely . Besides, we found that the RNA editing rate on lncRNA had a significantly higher correlation coefficient with the percentage spliced index (PSI) of lncRNA exons , which indicated that RNA editing may regulate lncRNA splicing during human early embryo development. Finally, functional analysis revealed that those RNA editing-regulated lncRNAs were enriched in signal transduction, the regulation of transcript expression, and the transmembrane transport of mitochondrial calcium ion. Overall, our study might provide a new insight into the mechanism of RNA editing on lncRNAs in human developmental biology and common birth defects.RNA editing is a co- or post-transcriptional modification through which some cells can make discrete changes to specific  RNA editing is a process by which selected nucleotides on RNA molecules are changed post transcription of RNA. This results in changes RNA sequences that are different from the corresponding DNA sequence at modified positions. However, this slight difference has a great impact on various biological processes. In this manuscript, we investigated the function of RNA editing during human early embryo development. Through analyzing the sequencing data and computational biology, we found that RNA editing could regulate the alternative splicing of long-noncoding RNAs (lncRNAs) in human early embryo development. Here, lncRNAs are special kind of RNAs that do not translate into proteins, but have functions in the regulation of other genes. Alternative splicing is a process through which RNA molecules can be spliced into different transcripts after transcription. Thus, our results indicated that RNA editing could regulate the alternative splicing of lncRNAs and further affect the expression of downstream genes in human early embryo development. RNA editing could be the reason for the tremendous change of gene expression profile in human early embryo development which was found the previous researches. Early embryo development is a complicated biological process in which a large number of genes and factors are involved. Dynamic changes in gene expression were found during human early embryo development . Each deLong non-coding RNAs (lncRNAs), which are typically >200 nucleotides in length, are involved in human early embryo development . LncRNAsHomo sapiens, the most prevalent form of RNA editing is the single nucleotide change of adenosine (A) to inosine (I) by double-stranded RNA-specific adenosine deaminase enzymes ..43].Thirdly, those RNA editing regulated lncRNAs were found co-expressed with the mRNAs which have a function in the transmembrane transport of mitochondrial calcium ion . The pre-16)  .All these results indicated that RNA editing can affect distinct key events during human early embryo development by regulating relative lncRNAs.Previous studies found there was a great change in gene expression profile during human embryo development. LncRNAs were found to play roles in mammal early embryo development, including human and mouse . LncRNAsIn our study, through analyzing single cell data, it was found that RNA editing could affect the transcript splicing of lncRNAs during human early embryo development. Firstly, it was found that, during human early embryo development, the differentially edited RNA editing sites were over-represented on splicing sites. LncRNA editing sites were found more likely to be on the RNA splicing sites, while the mRNA editing sites were found less likely to be on the RNA splicing sites. Furthermore, the RNA editing rate of lncRNA exons had a significantly higher correlation coefficient with the expression level and PSI index of the lncRNA exon. It means that highly edited exons were more likely to remain in the final transcripts while lowly edited exons were not. Thus, regulation of RNA splicing might be the mechanism by which RNA editing affects lncRNA expression in human early embryo development.The differentially edited RNA editing sites in human early embryo development could be grouped into 3 clusters. The majority (cluster1 and cluster2) shared a special pattern: Both decease to minimum levels at the 8-cell stage. Overall, all three clusters of RNA editing sites had great changes in editing rate at the 8-cell stage. They either increased to the highest level or decreased to the lowest level at the 8-cell stage, which indicated that the 8-cell stage is an important stage for human early embryo development, during which ZGA happens. During ZGA, maternal transcripts are degraded and zygotic transcription begins, which is coordinated with other embryonic events, including changes in cell cycle, chromatin state, and nuclear-to-cytoplasmic component ratios . A previThe decreased editing ratio in the 8-cell stage observed in our study is in agreement with a previous study that reported that RNA editing was significantly lower in embryonic tissue than in adult tissue. Meanwhile, A-to-I editing levels in various human cancer types revealed general hypo-editing in cancer tissues . A possiOur study also found that RNA editing may participate in numerous functions during human early embryo development by regulating the splicing of related lncRNAs. First, our study found that RNA editing can regulate lncRNAs that are related to the regulation of transcript expression including RNA splicing, via transesterification reactions and gene silencing by RNA. As we all know that there is a great change of expression profile happening at the 8-cell stage during ZGA , our resRNA editing can also affect signal transduction through lncRNAs that participate in the activation of MAPKKK (MAP3K) activity and Rac protein signal transduction. Both of them are related to MAPK pathways : 1) MAPKPrevious studies also found that RNA editing could affect signaling pathways, which partly supported our results ,50. RNA In summary, our study found that RNA editing can regulate lncRNA splicing during development and play further roles in human early embryo development. Since RNA editing and lncRNAs are both important in multiple diseases and development processes, understanding the association between RNA editing and lncRNAs may provide a new insight into human developmental biology and common birth defects, as well as potential benefits for reproductive health and improvements in medicine.S1 Fig(A) DEsingle vs DEseq. The y-axis shows the overlapping ratio between the outputs of the two methods. (B) MAST vs DEseq. (C) MAST vs DEsingle.(TIF)Click here for additional data file.S2 FigX-axis means the different categories of RNA editing sites, and the y-axis is the FDR rate.(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(A) Distribution of the correlation coefficient between the RNA editing ratio and the expression level of each exon. The dash lines are the mean value of each type. The random is based on 10,000 random pairs of RNA editing ratio and exon expression level. (B) Distribution of correlation coefficient between RNA editing ratio and PSI of each exon. The dash lines are the mean value of each type. The random is based on 10,000 random pairs of RNA editing ratio and PSI level. PSI, percentage spliced index.(TIF)Click here for additional data file.S5 Fig(A) Distribution of the correlation coefficient between the RNA editing ratio and the expression level of each exon. The dash lines are the mean value of each type. The random is based on 10,000 random pairs of RNA editing ratio and exon expression level. (B) Distribution of correlation coefficient between RNA editing ratio and PSI of each exon. The dash lines are the mean value of each type. The random is based on 10,000 random pairs of RNA editing ratio and PSI level. PSI, percentage spliced index.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.


By using genetic circuits in bacteria to control these RNA expressions, we are able to program the physiology of the model animal Caenorhabditis elegans with logic gates. We anticipate that engineered bacteria can be used more extensively to program animal physiology for agricultural, therapeutic, and basic science applications.A central goal of synthetic biology is to predictably and efficiently reprogram living systems to perform computations and carry out specific biological tasks. Although there have been many advances in the bio-computational design of living systems, these advances have mainly been applied to microorganisms or cell lines; programming animal physiology remains challenging for synthetic biology because of the system complexity. Here, we present a bacteria-animal symbiont system in which engineered bacteria recognize external signals and modulate animal gene expression, twitching phenotype, and fat metabolism through RNA interference toward  Caenorhabditis elegans gene expression through genetic circuit controlled RNAi.Manipulating animal physiology can be difficult because of the complexity of multicellular systems. Here the authors use engineered bacteria to modulate  At the bacterial level, researchers have created logic functions and memory in living bacteria for bacteria and community oscillation, programmed bacteria to respond to signals, automated the circuit design process, and expanded the repertoire of logic gates so that they can recognize a variety of signals, including light and nutrients3. At the level of mammalian cells, researchers have engineered CAR-T cells and other mammalian cell types with switching logic circuits to program cell performance8. Despite rapid advances and promising results in bio-computational design, the programming of higher organisms remains largely unexplored because the engineering of an entire organism requires highly complex and sometimes impossible tasks, such as the delivery of gene editing cascades11 or the engineering of whole embryos13. Nevertheless, such programming is worthwhile because it will yield a greater understanding than we have so far of how biological phenomena emerge from genetic and cellular events. The ability to change animal including insects and worm physiology would open up possibilities for agriculture, biomedical, and bioenvironmental applications.Recent advances in synthetic biology have resulted in highly programmable bacteria and mammalian cells that perform specific functions17. Engineering bacteria have demonstrated their potentials with pest control19, plant growth promotion20, human disease diagnosis23, and therapeutics24. However, little has been done in terms of transferring the decision-making logics from bacteria to the animals to program animal physiology. Here, using bacteria as an engineering platform, we present an efficient strategy to program Caenorhabditis elegans GFP expression, twitching phenotype, and fat metabolism by building synthetic logic circuits in bacteria. We used the nematode C. elegans as the animal host as it has been extensively used as a model system to elucidate mechanisms of interaction between prokaryotes and their hosts26. The information from bacteria was transferred to C. elegans through an orthogonal gene transfer called RNA interference (RNAi), a widely used technique in which double-stranded RNA is exogenously introduced into an organism, causing knockdown of a target gene27. RNA interference is a regulatory mechanism conserved in eukaryotes that plays key roles in numerous biological processes, including RNA stability and processing, biotic and abiotic stress response, and the regulation of morphological and developmental events28. Horizontal transfer of mobile RNAs between different species has been observed between pathogens/parasites and host animals, pathogens/parasites and host plants, and plants and animals28. In C. elegans, RNAi is easy to implement because RNA can be delivered by feeding the worms bacteria that express double-stranded RNA complementary to a C. elegans gene of interest29. The inhibitory RNA is delivered from the bacterial cell to C. elegans when C. elegans grind on the bacteria by pharynx and absorb the bacterial contents29. The absorbed inhibitory RNA will then be distributed throughout the worm body leading to RNA interference30.Animals share a life-long relationship with microbes, which play significant roles in animal nutrition, immunity, behavior, and metabolismC. elegans green fluorescence protein (GFP) expression profiles, twitching phenotype, and worm fat storage with “AND” and “OR” logic gates. This study presents a platform for the programming of higher organisms by harnessing engineered prokaryotic species to exert effects on eukaryotic organisms , BL21(DE3), and OP50(xu363) have been utilized to produce RNA for RNAi in worm33, within which HT115(rnc-) lacks RNase III to produce most dsRNA32 and BL21 has the lowest RNA producing levels32. We started with BL21 because the relatively lower dsRNA production is correlated with less RNAi efficiency in C. elegans and thus can potentially provide a dynamic range of RNAi for a complete “ON” and OFF” state32. We used BL21(DE3) throughout this work. By feeding these induced plasmid carrying BL21(DE3) to C. elegans, we aimed to trigger gfp RNAi, which silences gfp in C. elegans31  and the uninduced  conditions  protein for the transport of RNA35. SID-1 has been shown to have a lower binding affinity for shorter RNA sequences34 leading to lower RNAi efficiency. We hypothesized that we could achieve the right dynamic range of RNAi by reducing the length of the RNA and thus transporting less RNA into the host with less RNAi efficiency. We constructed plasmids for RNA synthesis with lengths ranging from 100 bp to 400 bp in double-stranded as well as single-stranded format  protein co-overexpressed31. Since neuronal genes are more difficult to target with RNAi36, we usually see ~20% residual fluorescence on our “OFF” stage, which should come from the neuronal GFP that were not RNAi silenced.The efficiency of RNAi can be manipulated by changing the RNA concentrationate Fig. . To furtate Fig. . What is1. Here we put gfp RNA synthesis under the control of an “AND” or an “OR” gate and transferred the programming languages to C. elegans through RNAi.Logic gates, the building blocks of genetic circuits, can process complex signals and trigger downstream reactions37. T7 RNAP is a single-subunit RNA polymerase that drives transcription by acting on its cognate promoter, the T7 promoter PT7. By splitting T7 RNAP between amino acids 179 and 180, a transcriptional AND gate is created whereby both fragments of the split protein are needed to drive transcription from PT737. We used two orthogonal promoters, pBAD and pTet, to control T7 RNAP N-terminal and C-terminal expression, respectively. Both l-arabinose (Ara) and anhydrotetracycline (aTc), the respective inducers of these promoters, are needed to induce synthesis of gfp ssRNA coding strand fragment in the bacteria. The gfp RNA fragment will then lead to RNAi in the worm after the worm grinds the bacteria open and absorb the RNA fragment. With this system, worm GFP expression was silenced by >70% in the presence of both signals (Ara and aTc) whereas GFP was still expressed (>95%) with either no signal or only one of the two signals als Fig. . The RNAals Fig. .Fig. 3GFC. elegans. We observed that the addition of one or both of the inducers led to >90% reduction in C. elegans GFP intensity, while the no-inducer control group only had a 24% reduction  showed twitching phenotype. Using the same OR gate but replacing the GFP in the OR genetic circuits with unc-22, 100% of the worms grown on plates with one or both inducers showed twitching phenotype  demonstrated that C. elegans underwent real programming by the engineered bacteria. The most striking advantage of this system is the ease with which system complexity can be extended by applying more complicated logic gates. Since bacteria have already been engineered with more complicated logic gates, memory, and sensing more signals, we should be able to program the animals with three or four input logic gates3 or extend the capability of animals to recognize external signals41 transmitted by these engineered bacteria.Using 44, butyrate47, colonic acid48, and nitrite51) to communicate between bacteria and higher organisms. Metabolites as intermediaries have broad applications for studying microbe-host symbiosis in situ; for example, they can be used as a drug delivery system. Indeed, bacterial metabolites operate as signaling molecules in the microbiome. Secondly, in this study we used synthetic inducers that were manually introduced into the host environment.RNAi is especially useful in the area of pests control and worm physiology interference as a gene silencing tool. However, for higher organisms in which RNAi would not be sufficient to alter physiology, bacterial metabolites could be exploited to serve as mediators between bacterial and animal  was used for testing of constructed plasmids encoding the RNA production without logic gate control. E. coli BL21 was transformed with both the constructed logic gate and RNA production plasmids. E. coli OP50 was used for maintenance of C. elegans. Bacterial cultures were grown in LB media with appropriate antibiotics in a 37 °C shaking incubator overnight before use.Bacterial strain Caenorhabditis elegans Bristol N2 and SD1084  were cultured at room temperature on NGM (Nematode Growth Medium) agar plated with E. coli OP50 as described in WormBook52. Synchronized L1 worms were used in all RNAi treatment experiments. Synchronization was performed following the standard protocol52. Briefly, worms were allowed to grow on NGM plates with E. coli OP50 for 2–4 days then washed off the plates to collect a large number of gravid hermaphrodites, and eggs were isolated by bleach-NaOH lysis of the gravid worms. After 4X wash in M9 buffer to remove the residual bleach and NaOH, eggs were allowed to hatch in 10 mL of M9 buffer overnight and develop into starved L1, which were then used for the RNAi experiments.Wild-type gfp and unc-22 RNA synthesis, the RNA sequence of interest was inserted between two T7/LacO for the synthesis of both complementary strands on a pET24a vector.  Additional T7 lysozyme and LacI were constitutively expressed under the control of the proD promoter for double-stranded sbp-1 RNAi to reduce background expression level for the construction of the plasmid pET24a-dssbp-1-proD-lysS-proD-lacI. We have added ProD promoter into the plasmid to overexpress promoter repressors, which improves the tightness of the pTet and pBAD promoters in the genetic circuit. We also used ProD to produce T7 lysozyme to reduce the background synthesis of RNA from lac promoter by binding with the T7 lysozyme to reduce the background transcription of RNA product54.All plasmids were constructed using PCR and Gibson assembly and primers used are listed in Supplementary Table Plasmids for single-stranded RNA synthesis were constructed by putting the RNA sequence of interest downstream of one T7/Lac on a pET24a vector. (Plasmids pET24a-ssgfp-100bp and pET24a-ssgfp-400bp were synthesized this way)The AND gate plasmid (pTet-Ara-Split-T7-AND-Gate-proD) was modified from plasmid pTSlb-wt by constitutively expressing the TetR and araC repressors under proD promoters. The C-terminus of T7 RNA polymerase was regulated by pBAD, and the N-terminus of the T7 RNA polymerase was regulated by pTet. The OR gate (pTet-Ara-OR-Gate-proD) was constructed similarly by putting the full-length T7 RNA polymerase under the control of the two promoters arranged (pBAD and pTet) in tandem.E. coli BL21 bacteria transformed with plasmids for RNA synthesis and logic gates were grown at 37 °C in LB with 50 μg/ml kanamycin and 25 μg/ml chloramphenicol, then seeded onto NGM plates supplemented with appropriate inducers as indicated in the supplementary information. After overnight growth of the seeded bacteria on NGM plates, synchronized L1 larvae were added to the plates and day-1 adult worms were used for phenotype assays after 2 days.C. elegans GFP expression using IPTG, plasmids pET24a-T7-dsGFP, pET24a -dsgfp-100bp to pET24a-dsgfp-400bp were transformed into E. coli BL21(DE3) separately and fed to the worms. To induce the RNA synthesis in E. coli, 1 mM of IPTG was added into the NGM plates. To program GFP expression with an AND gate, E. coli BL21 was co- transformed with pTet-Ara-Split-T7-AND-Gate-proD and pET24a-ssgfp-200bp, and pTet-Ara-OR-Gate-proD and pET24a-ssgfp-200bp were co-transformed for OR gate programming. l-arabinose and anhydrotetracycline at 0.2 mg/mL and 0.1 μg/mL, respectively, were used as the external signal for both AND and OR gates.To program E. coli BL21 was co-transformed with pTet-Ara-Split-T7-AND-Gate-proD and pET24a-dsunc-22-400 bp. RNA synthesis was induced by 2 μg/mL l-arabinose and/or 0.1 μg/mL anhydrotetracycline. To program twitching phenotype using an OR gate, E. coli BL21 was co-transformed with pTet-Ara-OR-Gate-proD and pET24a-dsunc-22-200bp. RNA expression was induced by 0.2 mg/mL l-arabinose and/or 0.1 μg/mL anhydrotetracycline.For twitching phenotype AND gate programming, C. elegans was achieved by co-transforming E. coli BL21 with pTet-Ara-Split-T7-AND-Gate-proD and pET24a-dssbp-1-proD-lysS-proD-lacI, and expression was induced by 2 μg/mL l-arabinose and 0.1 μg/mL anhydrotetracycline. OR gate programming of fat storage was done by feeding the worms with E. coli BL21 co-transformed with pTet-Ara-OR-Gate-proD and pET24a-dssbp-1-proD-lysS-proD-lacI, and induced by 0.2 mg/mL l-arabinose and 0.1 μg/mL anhydrotetracycline.AND gate programming of fat storage in gfp and sbp-1 RNAi experiments, five worms from each condition were analyzed in one experiment, and each experiment was repeated three times 39. For the twitching phenotype, over 200 worms were analyzed from each condition. In each condition, we observed either twitching in all worms or no twitching at all. RNAi triggered 100% twitching phenotype was also observed in other study32. The number of worms we used for calculation is in the same range as other paper when dealing with GFP quantification, fat storage, and twitching phenotype39.For C. elegans was imaged on an Axiovert 200 M fluorescence microscope using the FTIC filter.Day-1 adult worms were rinsed off the NGM plates using PBS + 0.01% Triton X-100 (PBST) and washed twice with PBST to remove the bacteria. The worms were then transferred onto glass slides with 2% agarose containing 1 mM levamisole. Fluorescence in C. elegans fat storage was characterized by Nile Red staining55. Synchronized L1 stage N2 worms were put onto the bacteria lawns, and allowed to grow for 2 days at room temperature. Young adult worms were washed off the plates by PBST and then fixed by 40% isopropanol (v/v) for 3 min. For lipid staining, a Nile Red stock solution of 0.5 mg/ml in acetone was first prepared. The working solution was then made by adding 6 μL of the stock solution into 1 mL of 40% isopropanol. After the worms were fixed and the supernatant removed, the working solution was added, and the worms were incubated in the working solution in the dark for 2 h. Following the 2-h incubation, the supernatant was again removed, 150 μL PBST was added, and the worms were incubated for another 30 min. The worms were then imaged using an Axiovert 200 M fluorescence microscope, and fluorescence intensity was quantified in ImageJ.C. elegans were fed with the control and engineered E. coli for 2 days, the worms were washed off the plates using PBST. The worms were then washed once with PBST and resuspended with 20 μL 3 mM levamisole (in water)38. Resuspended worms were immediately transferred onto glass slides for recording under a microscope38.After gfp RNA was also evaluated by RT-qPCR. Indicated bacteria was first allowed to grow overnight at 37 °C with 250 rpm shaking in LB medium with appropriate antibiotics. The overnight culture was then diluted 100 times the next day with supplementation of the appropriate inducers and antibiotics. The total RNA was extracted with RNeasy Mini Kit (Qiagen) when the O.D. reached 0.6–0.8. A total amount of 20 ng of the extracted RNA was then used for RT-qPCR by qScript 1-Step SYBR Green qRT-PCR Kit (Quantabio) on a LightCycler 96 (Roche). Each sample was measured with three biological replicates, and each biological replicate had three technical replicates on the 96-well plate. The primers used to quantify gfp RNA synthesis levels were gfp-F: 5′-CGGAGAAGAACTTTTCACTGG-3′, and gfp-R: 5′-GGTAAGTTTTCCGTATGTTGC-3′. Synthesized gfp RNA levels were normalized to the expression of cysG56 with the primers cysG-F: 5′-GATCGCGACTGTCTGATTG-3′, and cysG-R: 5′-CGGTGAACTGTGGAATAAACG-3′.Synthesized single-stranded Further information on research design is available in the Supplementary InformationReporting SummaryPeer Review File