In hypoxic stress conditions, glycolysis enzymes assemble into singular cytoplasmic granules called glycolytic (G) bodies. G body formation in yeast correlates with increased glucose consumption and cell survival. However, the physical properties and organizing principles that define G body formation are unclear. We demonstrate that glycolysis enzymes are non-canonical RNA binding proteins, sharing many common mRNA substrates that are also integral constituents of G bodies. Targeting nonspecific endoribonucleases to G bodies reveals that RNA nucleates G body formation and maintains its structural integrity. Consistent with a phase separation mechanism of biogenesis, recruitment of glycolysis enzymes to G bodies relies on multivalent homotypic and heterotypic interactions. Furthermore, G bodies fuse in vivo and are largely insensitive to 1,6-hexanediol, consistent with a hydrogel-like composition. Taken together, our results elucidate the biophysical nature of G bodies and demonstrate that RNA nucleates phase separation of the glycolysis machinery in response to hypoxic stress. Cells perform many diverse activities that are spatially and temporally organized into non-membrane-bound compartments that often form transiently and can display solid, gel, or liquid-like properties. Liquid-like structures formed through phase separation of protein polymers display fast internal rearrangements, undergo fusion and fission, and exchange components with the surrounding solvent .C. elegans, and mammalian cells  in hypoxic stress in yeast, an cells . In yeasan cells . These d neurons . A phosp neurons . HoweverG bodies have a number of features common to known phase-separated bodies. In addition to being non-membrane bound, some G body components, including phosphofructokinase 2 (Pfk2), contain intrinsically disordered regions (IDRs), a feature of proteins that undergo phase transitions . The IDRCLN3, but not in the presence of total RNA  into RNA in log phase yeast cells grown under normoxic conditions and combined photoactivatable-ribonucleoside-enhanced cross-linking (PAR-CL) with oligo(dT) affinity purification and tandem mass spectrometry  thresholds  for each library  than overlapping sites and identical sites in multiple datasets, suggesting that these sites were more tightly bound  and coding sequences, followed by non-coding RNAs and 5’ UTRs . We usedly bound . Thus, tWe previously developed a method using differential centrifugation and affinity capture to isolate G bodies and identify their resident proteins by mass spectrometry, followed by validation using colocalization to G bodies . Of the p-value of less than 0.05, we identified 297 transcripts enriched in purified G bodies over the flow-through RNA , as well as the RNA in the flow-through and total RNA. Gene RPMs within individual replicates were highly correlated , thus weough RNA , most ofough RNA . Applyinough RNA , indicatOLE1 mRNA was not enriched in G body RIP-seq, we used it as a negative control when analyzing distance from G body centers. Normalizing the cumulative distribution of distances from G body centers for each mRNA to the OLE1 distribution revealed that G body mRNAs were most enriched near G body centers, becoming more randomly distributed as the distance from G bodies increased . In each example, we detected cases of overlap and peripheral localization of RNA to G bodies . The numncreased . We alsoncreased . This peE. coli MqsR, an RNase that preferentially cleaves RNA at GCU sequences, followed by a C-terminal Flag tag (4) inducible CUP1 promoter on a centromeric plasmid, and introduced this plasmid, or a control vector, into cells expressing the G body reporter Pfk2-GFP  in hypoxia, the Pfk2-MqsR-Flag fusion protein was targeted to G bodies and colocalized with Pfk2-GFP  with Pfk2-MqsR-Flag showed multiple puncta per cell  Nascent G body formation was inhibited by RNases in a concentration-dependent manner ; 2) Inhi∆140-165-GFP) increased the number of cells forming multiple foci and decreased overall G body formation (S. cerevisiae (28.7% identity) and other yeast species. The crystal structure of S. cerevisiae heterooctameric phosphofructokinase (with four copies each of Pfk1 and Pfk2) lacks this region for both Pfk1 and Pfk2, due to proteolytic cleavage in sample preparation  . This red puncta , suggest1-202-GFP, 1-202-gfpPFK2 was integrated and replaced full-length endogenous PFK2, Pfk21-202-GFP did not form puncta  and σY (0.74 +/– 0.060 µm) values were lower than untreated cells , indicating a smaller granule radius  and performed FRAP. Surprisingly, we detected slightly greater overall percentage of recovery (13% vs. 4.1%) for cells expressing Pfk2D348S-MqsR-Flag, although the weak recovery was more rapid (5.7 min vs. 10.6 min)  . Taken ta and α yeast cells, each expressing Pfk2-GFP, and imaged the mating cells over time following hypoxic incubation. When cells were cultured in hypoxia for 18 hr and subsequently placed under a coverslip, we could track individual G bodies over several hours , we measured the fraction of foci with each label that were overlapping, adjacent to, or distinct from puncta with the other label . Conversely, a decrease would indicate a competitive advantage for cells expressing the Pfk2-MqsR-Flag plasmid (which do not form G bodies) . By comp fitness . To dete fitness , consistC. elegans germline and stress granules can undergo fission and fusion , were shown to form via phase separation . For larger-scale biochemical experiments, cells were grown in 125 ml Erlenmeyer flasks in 10–25 ml of media in 7 L boxes. For analysis of copper inducible proteins, cells were grown in SMD lacking uracil and supplemented with CuSO4 at varying concentrations.Cells were grown as indicated either in YPD  or SMD . For hypoxic growth of yeast for imaging, cells were reinoculated from stationary phase starter cultures in 0.5–1 ml of the indicated media in 24-well plates at an OD600 of 0.15. Cells were grown for 6 hr shaking at 30°C to log phase and pelleted for 2 min at 500 x g. Cells were washed and resuspended in SM media lacking glucose with or without 5% 1,6-hexanediol for 30 min at room temperature. Cells were either immediately imaged or had 1,6-hexanediol in PBST directly added to the media, incubated for 1 hr at room temperature, and then imaged.For glucose starvation, cells were reinoculated from YPD starter cultures into SMD at an OD600 of 0.05 and grown 20 hr in hypoxia and imaged. For treatment with 1,6-hexanediol after hypoxia, cells were cultured in the same way and had 1,6-hexanediol in PBST directly added to the media, incubated for 1 hr at room temperature, and then imaged.For hypoxic treatment with 1,6-hexanediol, cells were grown in YPD and reinoculated in SMD with 0%, 1%, or 2% 1,6-hexanediol at an ODH12A was introduced with site-directed mutagenesis and verified by sequencing. Pfk2D348S was introduced using site directed mutagenesis and verified by sequencing.Plasmids were generated by overlap extension PCR fusing fragments amplified from genomic DNA to fragments amplified from plasmid sources. Pfk2-Rnase A was generated by fusing the Pfk2-linker sequence from THY62 to RNase A, omitting the signal peptide from the pET22B RNase A plasmid, which was a gift from Ronald Raines (Addgene plasmid #58903). Pfk2-MqsR was generated by fusing the Pfk2-linker sequence from THY62 to MqsR amplified with a 3’ 1X Flag peptide sequence from the pSLC-241 plasmid, which was a gift from Swaine Chen (Addgene plasmid #73194). Pfk2-MqsR-MqsA was generated by fusing Pfk2-MqsR-Fl to MqsA and subsequently fusing the Pfk2 3’ UTR. Cytoplasmic MqsR was generated by amplifying MqsR from pSLC-241. Each construct was subsequently fused to the Pfk2 3’ UTR amplified from genomic DNA and subcloned into pCu416CUP1 (ATCC 87729) using Spe1 and Xho1 sites. Pfk2-Rnase A600 of ~2 in 1 ml volumes. Formaldehyde was added to culture media to 1% for 10 min and then washed away. Cells were resuspended in KM  with 4% formaldehyde for 1 hr at 30°C and washed twice with KM and once with KM with 50% sorbitol (KMS). Cells were resuspended in 50 µL KMS with 5 U Zymolyase (Zymo E1004) for 20 min at 37°C. Cells were washed and resuspended in KMS and adhered to Superfrost Plus (Fisher 12-550-15) slides. For probing Pfk2-RNase A, cells were dipped in ice-cold methanol for 6 min followed by acetone for 30 s and dried at 50°C. For Pfk2-MqsR, to preserve GFP fluorescence, this step was omitted. Cells were blocked in 1% BSA in PBST 2 hr at room temperature. For Pfk2-RNase A, cells were incubated with 1:200 rabbit anti-S tag antibody (Genscript A00625) overnight and 1:1000 mouse anti-GFP antibody (Thermo Fisher A-11120) for Pfk2-RNase A and 1:200 mouse anti-Flag antibody in PBST overnight at 4°C. Cells were washed three times in PBST and 1:500 donkey anti-mouse Alexa Fluor 488 antibody (A-21202), and 1:500 goat anti-rabbit Alexa Fluor 647 antibody (A-21245) for Pfk2-RNase A in PBST was added for 1 hr at room temperature. For Pfk2-MqsR-Fl experiments, 1:500 donkey anti-mouse Alexa Fluor 647 antibody (A-31571) was added in PBST for 1 hr at room temperature. Cells were mounted in Vectashield plus DAPI  and imaged.Yeast immunofluorescence protocol was adapted from All cells were imaged using a Zeiss AxioImager M2 with an ORCA-Flash 4.0 LT camera with band pass GFP filter (Zeiss 38 HE eGFP) illuminated with a 488 nm LED with either 40X or 100X objective taking Z stacks to cover the entire cell. Immunofluorescence images were taken with illumination from a mercury halide arc lamp and band pass GFP filter and CY5 filter (Zeiss 50).For assaying G body formation, cells were manually counted and classified into one of three categories: cells with single puncta, cells with multiple puncta, and cells with no puncta. At least 100 cells were considered for each replicate and condition.600 of 0.05 and grown 18 hr -O2. Cells were allowed to settle in 24-well plates for mating to proceed and sampled after 3, 5, 7, and 24 hr. For mating cells with Pfk2-Azurite and Pfk2-GFP, Azurite was imaged with a BFP band pass filterset (Zeiss 96 HE) illuminated with a 365 nm LED, and GFP was imaged as above. To compensate for high background, mating cells were resuspended in PBS before imaging. Z stacks were taken in each channel and brightfield. Overlapping puncta were manually counted in all Z planes, and puncta were classed into four categories: overlapping puncta in cells with one focus, overlapping puncta in cells with multiple foci, adjacent puncta, and puncta not associated with other puncta. For mating cells with Pfk2-GFP only to observe kinetics, cells were mixed and grown 18 hr -O2 and placed on a slide. Fields of cells were imaged taking Z stacks every 2 min through a GFP filter.For mating experiments, cells of each mating type were mixed at an OD0 and y0 define the center coordinate, σx and σy represent the standard deviation along each axis and θ defines the rotation of the punctum.For size distributions of G bodies, local maxima were identified in maximum intensity projections in FIJI  using th600 of 0.003 and grown until OD600 of 0.7–0.8. Cycloheximide was added to a final concentration of 0.1 mg/ml and incubated at 30°C for 5 min. Cells were pelleted by centrifugation for 5 min at 4,000 rpm at 18°C using a JLA-10.5 rotor in an Avanti J-26XP centrifuge, resuspended in 10 ml 1X PBS (with 0.1 mg/ml cycloheximide), transferred to a 150 mm glass Petri dish, placed on ice, and irradiated 4 times with 365 nm UV light at 150 mJ/cm2 using a UVP CL-1000L UV crosslinker. The cells were then transferred to a 15 ml conical tube and pelleted for 3 min at 3000 x g at room temperature. After removing the PBS, the cells were frozen in liquid nitrogen. For the negative control, cells were frozen without UV irradiation. Frozen cell pellets were pulverized for two cycles, each for 1 min, at 30 Hz on a Retsch MM 4000 ball mill homogenizer. Sample chambers were pre-chilled in liquid nitrogen and re-chilled between cycles. The resulting frozen powdered homogenate was resuspended in 3 ml of polysome lysis buffer  and incubated on ice for 10 min. Cell debris was removed by centrifugation for 2 min at 3000 x g at 4°C. The supernatant fraction was clarified by a 20,000 x g spin for 10 min at 4°C and supplemented with 12 μl SUPERase·In (20 U/μl). 0.3 ml of 1 M (34.2% w/v) sucrose cushion solution, prepared in polysome lysis buffer, was layered on the bottom of each 11 × 34 mm polycarbonate centrifugation tube. 3 ml of clarified lysate was loaded onto three sucrose cushions (1 ml per cushion) and spun for 80 min at 54,000 rpm at 4°C in a TLS-55 rotor using an Optima Ultracentrifuge MAX-E. After centrifugation, the top 1 ml of solution was recovered from each tube , mixed with 1.5 g of guanidine thiocyanate , vortexed to dissolve GuSCN, and heated for 5 min at 65°C. A Zeba desalting column  was used to remove GuSCN and to exchange buffer to 50 mM NaCl buffer . Buffer-exchanged lysate was combined with 0.1 vol of 5 M NaCl to adjust the salt concentration to 0.5 M and supplemented with 6 μl SUPERase·In (20 U/μl). The lysate was incubated with 37.5 mg of oligo(dT)25 beads (NEB S1419S) for 30 min at 4°C on a Nutator. Beads were washed four times with ice-cold low-salt wash buffer . RNAs were eluted in 1 ml of elution buffer  by heating for 3 min at 65°C. Eluted RNAs were concentrated to 80 μl with Amicon spin filters . Concentrated RNAs were mixed with 40 μl of 3X SDS sample buffer  and heated for 5 min at 65°C. 20 μl of sample was loaded per lane  of a 4–12% NU-PAGE Bis-Tris gel and run for 10 min at 100 V, followed by 70 min at 150 V. The gel was stained with Colloidal Blue (Invitrogen LC6025), and a gel piece, 0.1 cm-1.0 cm below the well, was excised and stored at −80°C before mass spectrometry analysis.3 L of BY4742 were cultured from an initial OD2, containing 12.5 ng/μl trypsin. The gel pieces were rehydrated at room temperature for 30–45 min. The enzyme supernatant fraction was not removed, and 50 μl digestion buffer, without enzyme, was added before overnight digestion. A MudPIT microcolumn (http://www.yeastgenome.org) with reversed sequences using ProLuCID . Deionized water  was used for all preparations. Buffer A consists of 5% acetonitrile, 0.1% formic acid; buffer B consists of 80% acetonitrile, 0.1% formic acid; and buffer C consists of 500 mM ammonium acetate. All buffers were filtered through 0.2 mm membrane filters . In-gel digestion was performed as in rocolumn  was preprocolumn  and wereProLuCID . The sea –dm –in .RBP validation was performed similar to the PAR-CLIP protocol but omitting the linker ligation steps. Cells collected from 50 ml of log-phase culture were used in each IP. After autoradiography, the same membrane was blotted with a peroxidase anti-peroxidase soluble complex antibody (Sigma-Aldrich P2416) at 1:10,000 dilution and developed with ECL substrates (Pierce 32209).PAR-CLIP was performed as described previously . BrieflyS. cerevisiae genome  with Bowtie  mapped reads threshold for each library was empirically determined by simulating replicate data from each PAR-CLIP-seq dataset. Two sets of binding site RPM values were randomly sampled from all binding sites passing a minimum RPM threshold in a single dataset such that each sample contained 20% of the binding sites. A non-parametric two-sample Kolmogorov-Smirnov (K-S) test was performed on the two sets of RPM values, and the resulting K-S test statistic was recorded. This test was repeated 10,000 times for each of 36 RPM threshold values ranging from 0 to 25. Mean K-S test statistic values were plotted for each RPM threshold value, and a final binding site RPM threshold value for the library was chosen when the K-S test statistic stabilized . For Pfk600 of 0.005 split into five 125 ml Erlenmeyer flasks. Cells were grown in hypoxia for 18 hr. Cells were imaged to ensure normal G body formation. 100 OD600 of cells were spun down at 3000 x g for 10 min and decanted with remaining media being aspirated. Cells were resuspended in 1.5 ml of lysis buffer and lysed by glass bead lysis for 10 min alternating vortexing and ice every 30 s. Large cell debris were removed from the supernatant by centrifugation at 500 x g for 5 min at 4°C. The supernatant was precleared once with 25 µl of Protein G Dynabeads nutating for 30 min at 4°C. G bodies were then pelleted by centrifugation at 5000 x g for 10 min and resuspended in 1 ml of lysis buffer and incubated with Dynabeads for 2 hr at 4°C. After 1 hr, Dynabeads were imaged to ensure capture of G bodies. 250 µl of the flow through was saved for RNA extraction. Beads were washed 3X with 1 ml of lysis buffer and once with 1 ml of Proteinase K buffer . Beads were resuspended in 120 µl 2 mg/ml 3X Flag peptide (Millipore F4799) in Proteinase K buffer and eluted for 30 min at 37°C on a thermomixer at 850 rpm. After 15 min, 1 µl of the eluate was imaged to validate elution of G bodies from Dynabeads. The eluate was mixed 1:1 with 8 mg/ml Proteinase K (Thermo Fisher 25530015) in proteinase K buffer for 1 hr in a thermomixer at 37°C at 850 rpm. Eluates and flow-through RNA were extracted with Tri Reagent (Sigma-Aldrich T9424) according to the manufacturer instructions. The aqueous phase was mixed 1:1 with isopropanol with 1 µl of glycogen (Thermo Fisher R0561) and precipitated for 4 hr at −80°C. RNA was pelleted by centrifugation at 4°C at 21,000 x g for 30 min. RNA was washed 3 times with 80% ethanol and re-precipitated in 80% ethanol with sodium acetate overnight at −80° C. RNA was pelleted again and washed again and dissolved in 30 µl of H2O. For each sample, 10 µl of RNA was used to generate two technical replicates of cDNA with the High-Capacity cDNA reverse transcription Kit (Thermo Fisher 4368813) per the manufacturer’s instructions. Individual probes  were conjugated to mouse anti-Flag M2 antibody (Sigma Aldrich F1804) as previously described . Beads wl probes  were useS. cerevisiae genome (R64-1-1.93) using Bowtie  with between 5 and 25 ng of RNA. Libraries were analyzed on an Agilent 4200 TapeStation. Libraries were sequenced using a HiSeq2500 in rapid run mode. Data were trimmed and analyzed using Galaxy . Librarig Bowtie . Reads wCustom Stellaris FISH probes labeled with Quasar 670 dye were obtained from LGC, BioSearch Technologies . Cells were inoculated from overnight cultures and grown in hypoxia for 16 hr. Cells were fixed and cell walls were digested using Zymolyase. Cells were then washed and incubated with custom Stellaris probes in FISH buffer  at 30°C overnight. Cells were then washed in SSC + 10% formamide prepared fresh twice for 30 min at 30°C. Cells were mounted in Vectashield and imaged with a Cy5 filter (Zeiss 50).Data were analyzed by first removing background fluorescence. Cells were manually outlined and GFP signal was segmented from a maximum intensity projection using Otsu thresholding following subtraction of background signal in FIJI. The most in-focus slice was identified. Next, mRNAs were identified using the Find Maxima tool and each was outlined. mRNAs in the same slice as the G body or one slice away were recorded. The Euclidean distance between the G body center and each point in each of the mRNAs within the same plane as the G body was measured with Python scripts. Distance distributions were pooled for all experiments.Cells were reinocculated from stationary cultures into the indicated media and grown for 18 hr in hypoxia. Cells were imaged on a Nikon Ti Eclipse microscope with a 100X objective using a FITC filter. After a Z stack was captured, individual pixels were chosen to bleach. G bodies were bleached with a 50 mW 405 nm laser at 40% laser power for 50 ms. Z stacks were then captured every minute for 40 min. Background signal was subtracted and data were normalized for photobleaching from the average of three unbleached control cells in each image. G bodies were tracked by measuring a circle around the brightest focus in each cell with a radius determined by maximizing the percent of signal bleached. Only traces with <40% of fluorescence remaining in the G body were considered. Data were then normalized from 0 (post-bleach) to 1 (pre-bleach) and averaged across replicates. Recovery rates were determined by fitting to an exponential decay function:4 and those carrying plasmids that do not prevent G body formation (GB+) were reinocculated from overnight cultures in SMD(-Ura) and grown to log phase in SMD(-Ura). Cells were then mixed in SMD(-Ura) + 50 µM CuSO4 either singly at an OD600 of 0.05 or together at an OD600 of 0.025 each. Cells were grown in hypoxia for 20 hr. For multiple generation assays, serial dilutions of cultures were made and imaged after 24 and 48 hr in hypoxia. ODs were measured and cells were imaged. The fraction of cells from the GB+ and GB– cells in the mixed culture was calculated as:mix is the % of cells from the mixed cultures with G bodies, %GB+ is the % of cells from the GB+ strain alone with G bodies and %GB– is the percent of cells from the GB– strain alone with G bodies. Relative proportions of cells at 0 generations were determined by spotting 10 µl of a 1:103 dilution of each population at OD600 = 0.1 to YPD and counting colony forming units (CFU).Cells carrying plasmids that prevent G body formation (GB–) in the presence of CuSO In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:eLife.This paper implicates RNA and the ability to bind RNA in the formation of assemblies of glycolytic enzymes, so called G-bodies observed in yeast under hypoxic conditions. Though the precise chain of causality, linking hypoxia-induced changes in RNA binding properties of the glycolytic enzymes, to the dynamics of G-bodies remain unresolved, the evidence for RNA's role in the process is deemed of sufficient value to merit publication in Decision letter after peer review:eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by David Ron as the Senior and Reviewing Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "RNA promotes phase separation of glycolysis enzymes into yeast G bodies in hypoxia" for consideration by The reviewers have discussed the reviews with one another and the Editor has drafted this decision to help you prepare a revised submission.Summary:eLife. However, the expert reviewers  have identified several limitations to the work, as it currently stands, limitations that undermine the strength of the conclusions drawn.This manuscript builds on a previous work establishing that glycolytic enzymes coalesce in G bodies under hypoxic stress conditions in yeast. The authors describe for the first time the presence of RNA in yeast G bodies, membraneless cytoplasmic granules where the glycolytic machinery concentrates under hypoxic stress conditions, and explore the role that RNA may play in the formation/maintenance of these structures. The main message of the manuscript is that RNA binding conveys specific properties to G bodies. This message, if adequately supported by the experimental data, is deemed sufficiently important to merit publication in Essential revisions:1) The role of the RNA component in supporting the key features of the G-bodies: namely their ability to fuse and display properties that makes them similar to stress granules, needs to be strengthened. This after all is the main conclusion of the study.2) The key features of the glycolytic enzymes as RNA binding proteins have been established experimentally under normoxic conditions, yet the premise of the paper is that such interactions are required for formation of G-bodies under hypoxic conditions. This limitation needs to be addressed.3) The functional role of the association between enzymes and RNAs as defined by the discovery of binding sites on the various mRNAs, needs to be addressed.Reviewer #1:In the manuscript "RNA promotes phase separation of glycolysis enzymes into yeast G bodies in hypoxia", Gregory G. Fuller and co-authors describe for the first time the presence of RNA in yeast G bodies, membraneless cytoplasmic granules where the glycolytic machinery concentrates under hypoxic stress conditions, and explore the role that RNA may play in the formation/maintenance of these structures. The starting point of this study is the discovery of the yeast mRNA-bound proteome, where most of the glycolysis enzymes were identified as RNA binding proteins. Indeed, the capacity of many of these enzymes to bind RNA had been described in earlier studies. The selective degradation of RNAs at G bodies affects the integrity/dynamics of these structures, demonstrating that RNA could convey at least some of the phase separation properties of these G bodies which are somewhat similar to stress granules (but not P bodies).As a nice follow up of the important previous work from Dr. Kim's group, this work brings new insights into the structure and regulation of G bodies, which is of course of great interest for the RNA and cell biology communities.The main message of the manuscript is that RNA binding conveys specific properties to G bodies. However, the experimental path followed is sometimes not straight enough, or insufficient to support the author's claims. These are some of the main points to address:1) The authors establish the capacity of glycolysis enzymes to bind RNA under normoxic conditions, but not under hypoxic stress when G bodies are assembled. Could stress modulate the capacity of G body components to bind RNA?2) Similarly, the RNA targets of some of the main G body components are only defined under normoxic conditions. Thus, the fact that glycolysis enzymes bind to specific sites in the yeast transcriptome under non stress conditions may not be related to the formation or dynamics of G bodies.3) Again, the role that the association between glycolysis enzymes and their target mRNAs is not addressed in a straightforward manner. For instance, the authors identify putative elements to which Eno1/Eno2/Pfk2/Fba1 may bind, but then they do not demonstrated whether these binding sites serve to target mRNAs into G bodies. Are these mRNAs translated in the vicinity/surface of G bodies? Are G bodies concentrating the majority of these mRNAs in foci, or just a small fraction?4) The authors demonstrate that G bodies can fuse and display properties that makes them similar to stress granules. While it is tempting to speculate that these properties are at least in part due to the RNA component of G bodies, this role is not established in a direct, convincing manner. Alongside this observation, it may be convenient to consider if the experimental evidence meets the main statement of the work represented in its title.These caveats should be addressed properly. Nevertheless, it is important to remark the novelty and relevance of this work, and the overall quality of the experimental evidence presented.Reviewer #2:This manuscript builds on a previous work establishing that glycolytic enzymes coalesce in G bodies under hypoxic stress conditions in yeast, and it aims to examine the physical properties and properties of G body formation. The authors elegantly demonstrate that glycolysis enzymes can bind a specific set of mRNA that are required for G body formation, maintenance and structural integrity. These structures are suggested to be formed via phase separation mechanism in vivo. Finally, authors suggest a model in which G bodies enhance the rate of glycolysis when cellular demand for energy rely on it. In this respect, it would be effective to show that disruption of G bodies in hypoxic conditions, by targeting non-specific RNAse impact glycolysis output  or cell survival. Although not required for the main message of the work (e.g. that RNA promotes phase separation of glycolysis enzymes into yeast G bodies in hypoxia) but it will enhance the biological relevance of RNA-mediated G bodies formation. Overall, the work is carefully done.eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by David Ron as the Senior and Reviewing Editor. The reviewers have opted to remain anonymous.Thank you for re-submitting your article "RNA promotes phase separation of glycolysis enzymes into yeast G bodies in hypoxia" for consideration by Summary:This a revised version of a paper initially reviewed in August 2019. As noted then, the authors describe for the first time the presence of RNA in yeast G bodies, membraneless cytoplasmic granules where the glycolytic machinery concentrates under hypoxic stress conditions, and explore the role that RNA may play in the formation/maintenance of these structures. The main message of the manuscript is that RNA binding conveys specific properties to G bodies.Certain limitation were noted at the time and these have been largely addressed in the revised version. While the manuscript has been improved, two important issues remain.Essential revisions:The chain of causality implicating mRNA binding by glycolytic enzymes in the hypoxia-dependent formation of G-bodies may in fact be inverted. This is not detrimental to your conclusions – mRNAs are still drivers of G-bodies – but it raises the possibility that the detailed mechanism you propose may not be correct.We also note a limitation in the interpretation of the effect of targeting nucleases to the G-bodies, as such crude tools may have all sorts of pleiotropic consequences.Ordinarily, we would have set as conditions for acceptance of the manuscript that you address point 1 of reviewer #1's comments experimentally; namely that you devise experiment to compare the association of glycolytic enzymes with their target RNAs under normoxic and hypoxic conditions, and that you address editorially the limitations in interpreting the consequences of unleashing upon the cells an endonuclease – even one ostensibly localised to a specific site.eLife shall provide you with an effectively indefinite extension to do so.However, given the current pandemic, we appreciate that it may be a while before any experiment gets done anywhere. Therefore we would like to offer you the choice of (a) responding editorially to both critiques and we will consider a revised manuscript along these lines or (b) addressing the issue experimentally, and Reviewer #1:The updated version of Gregory Fuller et al. has been improved and is now a better, more complete contribution. As a general criticism, the experimental evidence is in agreement with the main conclusions of this work, but in many instances it does not provide sufficient support to the authors' model.1) smFISH and G body purification experiments show that only a minor fraction of G body component-interacting mRNAs are recruited to G bodies under hypoxia. Most likely, the recognition of specific cis acting elements by glycolytic enzymes should not serve to regulate the expression of target mRNAs at G bodies under hypoxic conditions. Instead, the authors propose that RNA molecules work as a co-factor for the assembly/maintenance of G bodies.Alternatively, the well-characterized binding of glycolytic enzymes to specific 3'UTR elements under normoxic conditions could be relevant precisely under normoxic conditions, where these enzymes would contribute to regulate mRNA translation or stability of their target mRNAs. Hypoxia may alter these interactions, such that the bulk of glycolytic enzyme-bound mRNAs would be released from these mRNAs and concentrate in G bodies (in a process that could still be facilitated by RNA). To distinguish between these two hypotheses, it would have been interesting to address if the association between glycolytic enzymes and RNA is quantitatively altered by hypoxic conditions.Unfortunately, the methodological approaches conducted to examine the association of glycolytic enzymes to RNA in normoxic and hypoxic conditions are different , and for this reason it is hard to tell if hypoxia enhances or reduces the association of glycolytic enzymes to RNA. While the model proposed in this manuscript is plausible and attractive, the authors should either test directly if hypoxic conditions reduce/increase RNA binding capacity of G body components or, at least, mention alternative explanations to their observations in the Discussion.2) Many of the conclusions of this work are supported by the artificial targeting of RNAses to G bodies, which is achieved by the fusion of Pfk2 to RNAse A or Mqs-R, expressed from the copper-inducible, CUP1 promoter. This tool is used to establish the functional importance of RNA in the assembly and function of G bodies, as well as in the dynamic properties of these foci. Since these experiments provide essential support for the model proposed, the authors should evaluate if the main effects derived from the expression of these RNAse fusion proteins result from the disruption of G bodies, or from the degradation of mRNAs that bound by Pfk2. In other words, they should test if these RNAse fusion proteins reduce the levels of G body components , which could affect cell fitness and G body integrity in a way that is not considered in the current manuscript.Reviewer #2:eLife.The authors have responded to all the reviewers' critiques either by additional experiments, including, Figure 3, Figure 7 and Figure 2—figure supplement 1D, Figure 3—figure supplement 1, Figure 7—figure supplements 2 and 4), or by modulating the text in the Discussion. Overall, the work is carefully done and I have no more comments. I recommend the revised manuscript for publication in  Essential revisions:1) The role of the RNA component in supporting the key features of the G-bodies: namely their ability to fuse and display properties that makes them similar to stress granules, needs to be strengthened. This after all is the main conclusion of the study.We agree that it is important to demonstrate a relationship between RNA binding and the physical properties of G bodies. Several lines of evidence suggest that RNA is important for G body physical properties. First, we demonstrate that RNA is required for G body formation by targeting RNases to nascent G bodies. Second, when RNase is directed to G bodies that already exist, we observe multiple foci. If these foci, like nascent G bodies, can fuse, then we would observe large single foci. However, this is not the case. This point is now highlighted in the Discussion. Third, we have now performed FRAP experiments in vivo. In cells in which we have weakly induced Pfk2-MqsR-Flag, we observe slightly greater recovery rates than cells with a vector control suggesting that loss of RNA enhances Pfk2-GFP dynamics in G bodies . This is consistent with weaker association of Pfk2-GFP in G bodies as it is now more able to exchange with the cytoplasmic fraction. Thus, RNA may promote fusion by stabilizing binding of granule components.2) The key features of the glycolytic enzymes as RNA binding proteins have been established experimentally under normoxic conditions, yet the premise of the paper is that such interactions are required for formation of G-bodies under hypoxic conditions. This limitation needs to be addressed.We agree that identifying the RNAs in G bodies in hypoxia is important. We have done substantial additional experiments to identify and validate these RNA interactions with G bodies. We have applied two orthogonal methods of analyzing G body localization of RNA. In particular, we have sequenced RNAs from purified G bodies as well as total RNA and flow through RNA to identify transcripts enriched in G bodies . The overlap of RNAs bound by glycolysis enzymes in normoxia and the most enriched RNAs in purified G bodies is significantly nearly 2-fold greater than expected by chance . We have validated that RNAs localize to G bodies by smFISH with varying levels of G body enrichment . These two methods agree in that the most enriched RNAs in G body RIP-seq are also the most enriched by smFISH . However, surprisingly, while some RNAs are enriched in G bodies, the majority of any given RNA species is localized outside of G bodies. Thus, while there is some degree of specificity in the RNAs recruited to G bodies, G bodies are not highly concentrating particular RNAs.3) The functional role of the association between enzymes and RNAs as defined by the discovery of binding sites on the various mRNAs, needs to be addressed.The observation that RNAs bound to glycolysis enzymes in normoxia are overrepresented in G body associated RNAs  suggests that binding of glycolysis enzymes to RNA is important for the localization of RNAs to G bodies. However, only a small fraction of even the most enriched mRNAs localizes to G bodies . Thus, G bodies likely have a limited, if any, role in regulating the translation or stability of constituent RNAs. Thus, while binding of RNA to G body proteins is important for the recruitment of each to G bodies, the formation of G bodies appears to be the function of this association. To test this point, we used a growth competition assay to determine the relative fitness of cells with and without G bodies . Cells able to form G bodies consistently outcompeted cells unable to form G bodies, suggesting that RNA nucleation of G bodies has a functional output in cells.Reviewer #1:[…] The main message of the manuscript is that RNA binding conveys specific properties to G bodies. However, the experimental path followed is sometimes not straight enough, or insufficient to support the author's claims. These are some of the main points to address:1) The authors establish the capacity of glycolysis enzymes to bind RNA under normoxic conditions, but not under hypoxic stress when G bodies are assembled. Could stress modulate the capacity of G body components to bind RNA?We agree that this was a weakness of our earlier submission. We have addressed this concern through RNA sequencing of G body-associated RNAs and smFISH in hypoxia cells .2) Similarly, the RNA targets of some of the main G body components are only defined under normoxic conditions. Thus, the fact that glycolysis enzymes bind to specific sites in the yeast transcriptome under non stress conditions may not be related to the formation or dynamics of G bodies.In our analysis of G body RNA sequencing data, we identify significant overlap between those RNAs bound by glycolysis enzymes and the most enriched RNAs in G bodies suggesting that this normoxic RNA binding is relevant to RNA recruitment to G bodies .3) Again, the role that the association between glycolysis enzymes and their target mRNAs is not addressed in a straightforward manner. For instance, the authors identify putative elements to which Eno1/Eno2/Pfk2/Fba1 may bind, but then they do not demonstrated whether these binding sites serve to target mRNAs into G bodies. Are these mRNAs translated in the vicinity/surface of G bodies? Are G bodies concentrating the majority of these mRNAs in foci, or just a small fraction?Our findings indicate that many of the RNAs bound by glycolysis enzymes in normoxia bind to are present in G bodies. However, there are many other RNAs as well amounting to hundreds of RNAs in total . Furthermore, using smFISH, we found that even the most enriched G body transcripts are only modestly concentrated in G bodies and that only a small fraction of any mRNA will localize to G bodies . Taken together with our nuclease treatments demonstrating the necessity of RNA for G body formation, these observations suggest that while there must be RNA present for G body formation, the identity of the RNA may not be important for G body formation. The binding sites of the glycolysis enzymes on various mRNAs, then, influence the likelihood of that RNA being recruited to G bodies and allow these mRNAs to seed G body formation. It is unlikely, however, given the small fraction of any mRNA in G bodies, that G body localization plays a substantial role in affecting overall translation rates, and any local translation near G bodies will therefore be difficult to determine. Future studies may be able to address this possibility and determine if there are specific mRNAs required for G body formation.4) The authors demonstrate that G bodies can fuse and display properties that makes them similar to stress granules. While it is tempting to speculate that these properties are at least in part due to the RNA component of G bodies, this role is not established in a direct, convincing manner. Along this this observation, it may be convenient to consider if the experimental evidence meets the main statement of the work represented in its title.Please see our response to Essential Revision #3 above.Reviewer #2:This manuscript builds on a previous work establishing that glycolytic enzymes coalesce in G bodies under hypoxic stress conditions in yeast, and it aims to examine the physical properties and properties of G body formation. The authors elegantly demonstrate that glycolysis enzymes can bind a specific set of mRNA that are required for G body formation, maintenance and structural integrity. These structures are suggested to be formed via phase separation mechanism in vivo. Finally, authors suggest a model in which G bodies enhance the rate of glycolysis when cellular demand for energy rely on it. In this respect, it would be effective to show that disruption of G bodies in hypoxic conditions, by targeting non-specific RNAse impact glycolysis output  or cell survival. Although not required for the main message of the work (e.g. that RNA promotes phase separation of glycolysis enzymes into yeast G bodies in hypoxia) but it will enhance the biological relevance of RNA-mediated G bodies formation. Overall, the work is carefully done.Degrading G bodies using Pfk2-MqsR-Flag and Pfk2-RNase A requires us to express each protein in addition to the endogenous protein. Thus, this may influence glucose flux rates. However, we have included a growth competition assay that demonstrates a distinct advantage for cells that are able to form G bodies relative cells that cannot form G bodies.Essential revisions:The chain of causality implicating mRNA binding by glycolytic enzymes in the hypoxia-dependent formation of G-bodies may in fact be inverted. This is not detrimental to your conclusions – mRNAs are still drivers of G-bodies – but it raises the possibility that the detailed mechanism you propose may not be correct.We thank reviewer 1 for bringing up this issue and have now added text to further clarify our conclusions in the consider the alternative model in the Discussion section:“Alternatively, hypoxia may weaken interactions of glycolysis enzymes with RNAs allowing for G bodies to form via protein-protein interactions. Many RNAs are localized peripherally to G bodies . These RNAs may then allow G bodies to fuse by binding the glycolysis enzymes in other G bodies, explaining the loss of foci with loss of RNA. Experiments measuring binding affinity of glycolysis enzymes to mRNA in hypoxia and normoxia will be required to differentiate these possibilities.”We also note a limitation in the interpretation of the effect of targeting nucleases to the G-bodies, as such crude tools may have all sorts of pleiotropic consequences.We have added text to the results to clarify the logic of our interpretation as well as acknowledge this caveat in the Results section:“Taken together, these data indicate that 1) Nascent G body formation was inhibited by RNases in a concentration-dependent manner ; 2) Inhibition of de novo G body formation required both RNase activity and targeting to the site of G body formation ; 3) Once formed, targeting an RNase to G bodies led to cells with multiple foci, suggesting fragmentation but not dissolution of G bodies ; and 4) Once formed in hypoxia, G bodies could persist for tens of hours in normoxia . […] Loss of G body components is unlikely to explain the fracturing of extant G bodies following induction after shifting from hypoxia to normoxia.”


The RNA editing core complex (RECC) catalyzes mitochondrial U-insertion/deletion mRNA editing in trypanosomatid flagellates. Some naphthalene-based sulfonated compounds, such as C35 and MrB, competitively inhibit the auto-adenylylation activity of an essential RECC enzyme, kinetoplastid RNA editing ligase 1 (KREL1), required for the final step in editing. Previous studies revealed the ability of these compounds to interfere with the interaction between the editosome and its RNA substrates, consequently affecting all catalytic activities that comprise RNA editing. This observation implicates a critical function for the affected RNA binding proteins in RNA editing. In this study, using the inhibitory compounds, we analyzed the composition and editing activities of functional editosomes and identified the mitochondrial RNA binding proteins 1 and 2 (MRP1/2) as their preferred targets. While the MRP1/2 heterotetramer complex is known to bind guide RNA and promote annealing to its cognate pre-edited mRNA, its role in RNA editing remained enigmatic. We show that the compounds affect the association between the RECC and MRP1/2 heterotetramer. Furthermore, RECC purified post-treatment with these compounds exhibit compromised in vitro RNA editing activity that, remarkably, recovers upon the addition of recombinant MRP1/2 proteins. This work provides experimental evidence that the MRP1/2 heterotetramer is required for in vitro RNA editing activity and substantiates the hypothesized role of these proteins in presenting the RNA duplex to the catalytic complex in the initial steps of RNA editing. Most mitochondrial transcripts in kinetoplastids undergo a unique post-transcriptional maturation process known as RNA editing, which is essential for parasite energy generation and viability of both insect procyclic form (PF) and mammalian bloodstream form (BF), and is thus considered a suitable drug target . A multiT. brucei; the enzyme responsible for the last step in RNA editing , compet editing . C35 als editing , as both editing . The cat editing . Catalys editing . KREL1 a editing . However editing . In the  editing , 2015. M editing . HoweverThe RECC works with other multiprotein complexes including the RNA Editing Substrate binding Complex , constituting the RNA editing holoenzyme . The RESFormerly known as the gRNA binding proteins gBP21 and gBP25, the mitochondrial RNA binding proteins 1 and 2 (MRP1/2) form a ∼100 kDa stable heterotetrameric complex  that assHere, we identify the MRP1/2 proteins as targets of C35 and with its help we show that MRP1/2 inhibition abrogates in vitro RNA editing activity, implicating the MRP1/2 accessory complex in RNA editing initiation as proposed earlier.via a N-hydroxysuccinimide linker that binds to the primary amine group on the compound. These beads were used as bait to precipitate the potential target proteins from the T. brucei mitochondrial lysate in the presence or absence of RNase A, since RECC interaction with MRP1/2 heterotetramer is RNase sensitive  complexes on an electro-mobility shift assay  . We analensitive . Westernensitive D. A majoensitive . On the ensitive , suggestUsing EMSAs, we next validated C35's effect on the gRNA binding activities of coexpressed recombinant MRP1/2 and KREPA4 ; KREPA4 To assess the effect of MRP1/2 inhibition in the context of in vitro RNA editing activities, functional editosomes with differing abundances of MRP1/2 were prepared using tandem affinity purification (TAP) of tagged KREL1. The TAP-tag fused to KREL1 contains two affinity tags, a protein A domain and a calmodulin-binding peptide, separated by a TEV protease cleavage site. This system allows for sequential purification of the tagged RECC through IgG agarose  and calmodulin affinity resins . The resulting eluates were analyzed against the KREPA1, KREPA2, KREPA3, and KREL1 antibodies on a western blot, and eluate volumes corresponding to equal intensities were used in further experiments (data not shown). Equal band intensities on this western blot correlated to 90 ng/µL of total protein concentration in the TEV eluate versus 35 ng/µL of total protein concentration in the Calmodulin eluate, as estimated through Bradford quantification.Ligase auto-adenylylation in the TEV eluate was only affected at a lower protein amount tested (90 ng) and enhanced at higher amounts (450 and 900 ng) A, as obsSupplemental Fig. S1 in Supplemental Information).To determine if inhibition of the MRP1/2 complex by the compounds displaces the heterotetramer off the RECC, KREL1 TEV eluate was bound to the Calmodulin Affinity resin in the presence of C35, MrB (20 µM each), or RNase A (0.1 mg/mL) as a control. The EGTA elutions were performed after adequate washes to eliminate contaminating inhibitor or RNase A in the eluates. While western blots revealed a considerable loss in associated MRP2 in the eluates pretreated with the compounds and RNase A A, these 1280, Sigma), namely aurintricarboxylic acid (ATA), PPNDS and NF449, that not only affect RNA–protein interaction but also all catalytic activities required in editing in vitro  at 4°C overnight, in a total reaction volume of 20 µL, containing 50 mM Tris-HCl [pH 7.5], 10 mM MgCl2, 1 mM ATP, 1 mM DTT, 15% glycerol, 10% DMSO, and 10 units of T4 RNA ligase 1 (New England Biolabs). RNA substrates required for the in vitro precleaved deletion editing assay  were prepared as described previously  at 37°C for 1 h, in a 10 µL reaction, containing 70 mM Tris-HCl [pH 7.6], 10 mM MgCl2, 5 mM DTT, and 10 units of T4 polynucleotide kinase (New England Biolabs). The radiolabeled RNA was PAGE-purified on a 9% (w/v) 7 M urea denaturing gel and quantified by scintillation counting. RNA substrates required in the in vitro FRET-based editing assay  were prepared as described previously (Electrophoretic mobility shift assays were performed with synthetic gA6[14] gRNA that specifies editing of the first editing site of the ATPase subunit 6 (A6) pre-mRNA. Preparation of gA6[14] gRNA was carried out via in vitro transcription of PCR amplified DNA template as described previously , and radeviously . The 5′ eviously .T. brucei (strain IsTaR 1.7A) cells grown in SDM-79 media to late log phase (∼2 × 107 cells/mL). Following glycerol gradient sedimentation (10%–30%) of the crude mitochondrial lysate, 500 µL fractions containing active in vitro RNA editing activity (F10–F12) were isolated  of the 3′ end-labeled gA6 [14] RNA in a total 20 µL of a mixture containing 20 mM Tris-HCl [pH 7.6], 150 mM KCl, 5 mM MgCl2, 100 µg/mL BSA, 10% glycerol, 1 mM DTT, and 20 units of RNasin ribonuclease inhibitor (Promega) for 30 min at room temperature. The RNA substrates were heated at 95°C for 5 min and cooled at room temperature prior to assembling the reactions. Samples were mixed with gel loading solution  and run on 4% (w/v) native TBE gels as per the protocol  were used. An amount of 15 µL of this preparation (10 µg/µL) was incubated with 5 pmol of unlabeled gA6[14] RNA and 0.5 pmol  of purified rMRP1/2 and rKREPA4 were first incubated in the presence and absence of 10 µM C35 on ice for 10 min. These mixtures were then incubated with 0.5 pmol labeled gA6[14] for 30 min at room temperature as done above, run on 10% (w/v) native TBE gels (BioRad) and visualized by phosphorimaging.m HEPES (pH 7.9), 25 mm KCl, 5 mm magnesium acetate, 0.25 mm DTT, 40 nm [α-32P] ATP , 0.1% (w/v) Triton X-100, and 5% (v/v) of DMSO (control) or 1 µL of 200 µM C35 or MrB , as described elsewhere  or 5 µL of calmodulin eluate  with the RNA mixture, in a 20 µL solution containing 25 mM HEPES [pH 7.9], 10 mM Mg(OAc)2, 1 mM EDTA, 5 mM CaCl2, 10 µM ATP, 0.1% Triton X-100 (w/v), 16.7 ng/µL Torula yeast RNA, 15 units of RNasin plus ribonuclease inhibitor, and 0.5 mM DTT. rMRP1/2 was added in the reactions as indicated. The editing reactions were allowed to proceed for 3 h at 28°C and stopped by the addition of 2 µl of 260 mM EDTA-2.5% sodium dodecyl sulfate mixture. RNA was then extracted using phenol:chloroform:isoamyl alcohol (25:24:1), precipitated and resuspended in 10 M urea dye before running onto 15% (w/v) 7 M urea denaturing polyacrylamide gel and visualized by phosphorimaging. Full-round FRET-based RNA editing assay was performed identically as described previously (In vitro precleaved RNA editing assays were performed following procedures from literature . RNA subeviously . rMRP1/2Supplemental material is available for this article.


Long non-coding RNAs (lncRNAs) are a rapidly expanding field of research, with many new transcripts identified each year. However, only a small subset of lncRNAs has been characterized functionally thus far. To aid investigating the mechanisms of action by which new lncRNAs act, bioinformatic tools and databases are invaluable. Here, we review a selection of computational tools and databases for the in silico analysis of lncRNAs, including tissue-specific expression, protein coding potential, subcellular localization, structural conformation, and interaction partners. The assembled lncRNA toolkit is aimed primarily at experimental researchers as a useful starting point to guide wet-lab experiments, mainly containing multi-functional, user-friendly interfaces. With more and more new lncRNA analysis tools available, it will be essential to provide continuous updates and maintain the availability of key software in the future. For decades, the human genome was thought to be a desert of ‘junk DNA’ with sporadic oases of transcriptionally active genes, most of them coding for proteins. This theory has since fallen out of favor with the growing support of pervasive transcription . The humIn this review, we detail a selection of lncRNA predictive tools and databases with the aim of providing a toolkit for experimental scientists. Although numerous tools and databases are available, with new ones being added frequently, we chose the tools discussed here based on accuracy, functionality, frequency of updates, and ease of use for biologists. Links to all included tools and databases are included in In recent years, there has been an exponential increase in lncRNA research  and the Originally released in 2013 , the latIn contrast to LNCipedia, LNCBook collates lncRNAs from both pre-existing databases and experimentally verified community-curated transcripts . The preBoth general lncRNA databases covered here offer an extensive list of long non-coding transcripts, in addition to intuitive interfaces, integrated literature searches and conservation across species. Future improvements for these and other databases could include further integration of predictive tools and additional experimental data, such as subcellular localization, and interaction partners of lncRNAs. Although no comprehensive review of lncRNA databases has been published recently, there are a multitude of different databases available, ranging from general to highly specific. These include but are not limited to: MONOCLdb for mouse lung lncRNAs , the CanLncRNAs are usually expressed in a cell-type and tissue-specific manner , indicatThe Genotype-Tissue Expression database (GTEx) is a project first described in 2013 by the Broad Institute . It is bThe Atlas of Non-coding RNAs in Cancer (TANRIC) was first compiled in 2015 . The datA. thaliana, B. rapa, G. max and M. domestica. This has since been updated to CANTATAdb v2.0 in 2019, increasing the database content to 239,631 lncRNAs across 39 different plant species  [CANTATAdb is a lncRNA database specifically for plants . It was species) . LncRNAsspecies) . CANTATAspecies)  and NONCspecies)  databaseAn emerging field in lncRNA expression databases is the collation of single-cell expression profiles. This allows researchers to understand the transcriptomes of single cells, as opposed to the average expression across a tissue as a whole . What liIdentifying the coding potential of a novel transcript is the vital first step in accurate annotation and downstream analysis of a lncRNA . DetermiH. sapiens, D. melanogaster, D. rerio, and S. cerevisiae. CPPred outperformed the other tools in the non-human tests, but was less accurate than PLEK in the human set. Although most tools available for coding potential prediction are implemented in a web-server-based interface, CPPred is only available as a Python package at present, potentially making it difficult to use for those unfamiliar with programming languages.Recent studies revealed that some transcripts previously classified as lncRNAs encode micropeptides ,56,57. MH. sapiens, A. thaliana, M. musculus, D. melanogaster, D. rerio and C. elegans. CNIT performed better than the other programs in H. sapiens, A. thaliana, and D. rerio [CNIT (Coding-Non-coding Identifying Tool)  was builD. rerio . CNIT waD. rerio . In addiD. rerio .An important consideration in the development of new tools for coding potential prediction is training them to work across species, and ensure that micropeptide producing transcripts are not misclassified. The tools covered here are by no means an exhaustive list of options, with other commonly used tools including PhyloCSF  and COMEThe subcellular localization of lncRNAs is an important factor in understanding their potential function. LncRNAs localizing to the nucleus are often involved in regulating gene expression and/or splicing , imprintPublished in 2018, LncSLdb  is a database driven application detailing the subcellular localization data for >11,000 non-coding transcripts . The datMALAT1 which is nuclear-retained, or DANCR which predominantly localizes to the cytoplasm) can be added to the query. The localization of each lncRNA is quantified using their Relative Concentration Index (RCI), a log2-transformed ratio of respective FPKMs in the nucleus and cytoplasm. The RCI is proposed to simplify assessment of localization of a lncRNA, with a positive value indicating cytoplasmic and a negative value indicating nuclear localization.The LncATLAS database of lncRNA localization was developed from subcellular RNA-Seq data published by the ENCODE Consortium . Data waAlthough databases of lncRNA localization provide accurate data across several cell lines, they are limited by the need for pre-existing experimental data, and are not suitable for lncRNAs that have not been previously captured by high-throughput experiments. Recently, new computational tools to predict subcellular localization for novel transcripts have been developed, including LncLocator , iLoc-lnReleased in 2018, LncLocator was the first tool dedicated to predicting the subcellular localization of lncRNAs . The webMEG3), which comprises three predicted structural motifs which are conserved across its multiple isoforms [Braveheart, which changes its 3D structure upon binding to Cellular Nucleic acid Binding Protein (CNBP) [HOTAIR, the structure of which was recently determined using atomic force microscopy [SRA) [The secondary and tertiary structure of a lncRNA may reveal information about possible interaction partners and function of the transcript, such as for the lncRNA Maternally Expressed Gene 3 (isoforms . Two of isoforms . Howevern (CNBP) . Secondlcroscopy . It is ccroscopy . Finallypy [SRA) . The imppy [SRA) , and Zampy [SRA) . Here, wpy [SRA)  and two py [SRA) ,96 for tDetermining the structural conformation of RNAs is an emerging field, with many contributing factors remaining unknown thus far . As the If structural information for a lncRNA of interest is not part of a database or previously generated experimental datasets, RNA structure prediction tools can be used to predict secondary structure. These tools use a variety of methods, including Minimum Free Energy (MFE)-based approaches  and deepRNAfold is an RNA structure prediction tool from the Vienna Websuite . RNAfoldMEG3 required for p53 activation [DMfold is an alternative prediction tool that unlike RNAfold, can take pseudoknots into account using deep learning . DMfold tivation .Finally, RNA structure prediction in general is relatively error prone , emphasiXIST, which inactivates one of the two X chromosomes in females, requires a physical interaction with Polycomb repressive complex 2 (PRC2) to achieve its function [TINCR is known to control somatic tissue differentiation by interacting with mRNA [FENDRR binds directly to DNA to regulate gene expression [Identifying molecular lncRNA interaction partners is critical in the process of understanding function, and thereby their potential impact on cellular processes. For example, the lncRNA function . In addiith mRNA , while tpression . The toppression , and is pression . Here, wThere are several databases cataloging interactions, as well as numerous prediction tools, which use a variety of methods to predict RNA interactions. Although prediction tools are continuously improving in accuracy, it should be noted that databases containing experimental evidence will be more reliable. Importantly, interaction partners of a lncRNA may also vary in different cell types or in a disease context, depending on the respective transcriptome and proteome of the cell, neither of which are taken into account by prediction algorithms. Here, we discuss two databases of experimentally determined interactions: RNAInter (previously known as RNA Associated Interaction Database (RAID))  and RNA RNAInter is designed to be a “one-stop” RNA interactions database . It listH. sapiens, M. musculus and S. cerevisiae, as well as from 10 different cell lines across these species [RISE is a database published in 2018, and contains 328,811 RNA-RNA interactions from  species . These i species ,102,125  species ,127, RNA species  and RNA  species . The tar species , RAIDv2. species , and NPI species  database species . RISE co species , providiTools to predict RNA-RNA interactions have been available since the late 2000 s, with the number of tools released continuously increasing. Although these algorithms are useful in the absence of experimental evidence for a lncRNA of interest, they often result in low prediction accuracy when benchmarked ,108, simIntaRNA 2.0  is an opLncRRIsearch is a web server for rapid identification of lncRNA-mRNA and lncRNA-lncRNA interactions in human and mouse , and difHOTAIR, resulting in histone modifications [FENDRR, forming a lncRNA-DNA triplex resulting in recruitment of the PRC2 complex [Investigation of lncRNA-DNA interactions have the potential to elucidate lncRNA-mediated regulation of gene expression. This is exemplified by the recruitment of chromatin modifying enzymes by the lncRNA ications , and the complex . The fie complex , and dis complex . Here, w complex , an expe complex , a populAlthough the existence of lncRNA-chromatin interactions has been established for several years, development of databases and tools for investigation of such interactions is still an emerging field. LnChrom is a recently published, comprehensive database of experimentally validated lncRNA-chromatin interactions . AlthougH. sapiens, M. musculus, D. rerio, D. melanogaster and C. elegans [One method of predicting a direct lncRNA-DNA interaction is to estimate the likelihood of triplex formation, where a single stranded RNA undergoes Hoogsteen base-pairing with the double stranded DNA, forming a three stranded structure ,160. In  elegans . Brute f elegans . Triplex elegans . First,  elegans . The use elegans ), and th elegans , TriplexLncRNA interactions with proteins have been shown to drive important cellular processes such as recruitment of protein complexes to chromatin ,164 as wSFPEL-LPI is a prediction algorithm that finds known and predicted interactions between RNAs and proteins by using a feature projection ensemble learning frame to integrate sequence derived features and similarities . The SFPThe myriad of possible interaction partners makes lncRNA interaction databases and predictive tools a complex and emerging field of research. Understanding the interactions of lncRNAs is an important starting point towards elucidating their potential functions. Additional tools take it one step further, directly providing functional prediction of lncRNAs, as reviewed below.Predicting the function of a lncRNA from sequence alone has been a challenging task in the field of lncRNA biology ,169. HerXist and Kcnq1ot1i, which both regulate gene expression in cis through the PRC, often have little to no sequence similarity. Accordingly, discerning the function of one lncRNA experimentally likely does not yield insights into the functions of other lncRNAs. To overcome this, Kirk et al. developed SEEKR , which uses the relative frequencies of k-mers in lncRNA sequences to infer function based on similarity to other lncRNAs [LncRNAs which broadly serve similar functions, for example  lncRNAs . SEEKR i lncRNAs . The k-m lncRNAs . In pracSEEKR can be used to infer the function of a lncRNA of interest, providing researchers with a useful starting point for further functional characterization in the lab. Examples for experimental validation include the generation and in vitro characterization of knockout/knock-in/knockdown models coupled to RNA-Seq, mass spectrometry, and/or metabolic assays ,171. A pWith rapidly increasing interest in lncRNAs and their widespread roles across disease, genomic regulation and development, databases and prediction tools are essential as a first step in characterizing a new lncRNA. For example, if the LncATLAS database shows a lncRNA localizes to the nucleus, and Triplexator predicts that it can form a triplex with a DNA sequence, the next step of characterization could be a ChIRP experiment, to determine which particular regions of DNA the lncRNA is interacting with.Future directions in the field are wide-ranging. Subcellular localization prediction is still an emerging field with few tools at present. As more experimental localization data becomes available, prediction algorithms should improve in accuracy, due to larger and more accurate training and benchmarking datasets, in addition to more robust algorithm design platforms becoming available. Although there are many programs to predict RNA interactions and structures, these often struggle with longer or more complex structures, especially those containing pseudoknots. This may be a long-term weakness due to the complexity of pseudoknots; however, some newer tools are beginning to touch on the area . AlAs more tools and databases are released each year, keeping track of the most accurate and expansive of each will be important for lncRNA researchers. As such, annual benchmarks of tools and reviews of databases would be helpful. What could aid in the benchmarking of existing tools, and in the training of new tools, is the construction of a ‘master lncRNA training database’. This would consist of many well-characterized and thoroughly experimentally validated lncRNAs, which would include localization data, known interactions, disease associations, and expression levels across tissue types/species.


Penicillium digitatum after C12O3TR treatment was analyzed by RNA-Seq technology. A total of 2562 and 667 genes in P. digitatum were differentially expressed after 2 and 12 h treatment, respectively. These genes were respectively mapped to 91 and 79 KEGG pathways. The expression patterns of differentially expressed genes (DEGs) at 2 and 12 h were similar, mainly were the metabolic processes in cell wall, cell membrane, genetic information and energy. Particularly, the main metabolic process which was affected by C12O3TR stress for 2 and 12 h was cell integrity, including cell wall and cell membrane. The changes of chitin in cell wall was observed by Calcofluor White (CFW) staining assay. The weaker blue fluorescence in the cell wall septa, the decrease of β-1, 3-glucan synthase activity and the increase of chitinase and AKP activity showed that C12O3TR could damage the cell wall integrity. In conclusion, these results suggested that C12O3TR could inhibit the growth of P. digitatum through various mechanisms at transcriptional level, and could influence the cell wall permeability and integrity.In this study, the transcriptional profiling of  Penicillium digitatum  are one of the strong candidates. AMPs are gene-encoded, ribosomally synthesized polypeptides, which are cationic or anionic . AMPs ar12-OOWW-NH2 (C12O3TR) which was synthesized by combining peptide O3TR (H-OOWW-NH2) with saturated fatty acids, could effectively inhibit some clinically important bacteria  and postharvest pathogens  (12O3TR could significantly suppress the growth of P. digitatum with the minimum inhibitory concentration (MIC) of 6.25 μmol/L. But the mechanism of C12O3TR against P. digitatum was still unclear. The classic antifungal mechanism mainly focuses on the interaction between AMPs and cell membrane (12O3TR could inhibit the growth of fungi by changing the membrane permeabilization (Peptide Culmorum) . Our premembrane . While smembrane . Recent lization , but wheRNA-Seq is an innovative technology for the quantification and identification of gene expression. Due to its sensitivity, high resolution and comprehensive features, it has become increasingly popular in various studies which aim was to reveal the change of the organism gene expression in different environment . RNA-seq12O3TR against P. digitatum by using high-throughput RNA-Seq technique, and to confirm the effect of C12O3TR on cell wall at superficial level.The objective of this work was to reveal the molecular antifungal mechanism of CPenicillium digitatum was used in this work and cultured on potato dextrose agar (PDA) plates at 25°C -OOWW-NH2 (C12O3TR) was purchased from GenScript Corporation  with > 90% purity. The purity was selected based on other relevant studies and cost consideration (12O3TR was synthesized by the solid-phase methods using 9-fluorenylmethoxy carbonyl (Fmoc)-type chemistry. C12O3TR was amidated at the C terminus. Stock solutions of peptide was reconstituted at 1 mmol/L by using sterile distilled water (C12O3TR could completely dissolved by using sterile distilled) and then stored at −40°C. C12O3TR was not sensitive to oxidation.The peptide  were inoculated in 20 mL potato dextrose broth (PDB), and incubated at 25°C in thermostatic shaker at 160 rpm for 2 d. The P. digitatum mycelia were obtained by centrifuging at 4000 × g for 15 min. After washing with PBS (pH 7.0) 3 times, the mycelia were resuspended in 20 mL PBS (pH 7.0). Subsequently, the peptide C12O3TR was added into the suspensions to the final concentration of 6.25 μmol/L (MIC), and then incubated at 25°C for 0, 2, 4, 6, and 12 h. PBS (pH 7.0) was used as the control. Each treatment was repeated three times. Finally, the mycelia samples which removed PBS were immediately frozen in liquid nitrogen and stored at -80°C until use.P. digitatum mycelia samples after 2 and 12 h of C12O3TR and PBS treatment were used for this experiment. The four treatments were named C2, C12O3TR2, C12, C12O3TR12, respectively. Total RNA preparation, RNA quality detection, cDNA libraries construction and RNA-seq were carried out by using a service from Novogene Bioinformatics Technology Co., Ltd. . Total RNA was extracted by using TRIzol reagent  according to the manufacturer’s instructions. RNA contamination and degradation were monitored on 1% agarose gels. The NanoPhotometer® spectrophotometer  was used to check RNA purity. RNA integrity was assessed by using the RNA Nano 6000 Assay Kit of the Bioanalyzer 2100 system  (The  States) .® UltraTM RNA Library Prep Kit for Illumina®  following manufacturer recommendations was used to generate sequencing libraries and index codes were added to attribute sequences to each sample. Then the cDNA libraries were sequenced by using an Illumina HiSeq2000TM platform (Penicillium digitatum Pd1 (GCA_000315645)1 1 . Gene mo12O3TR2, or C12 and C12O3TR12, transcript abundance was estimated by using the method of expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced (FRKM)  between C2 and Cd (FRKM) . Differepression .P-value (padj) < 0.05 were considered significantly enriched  pathway database.3 The statistical enrichment of DEGs in KEGG pathways were tested by using cluster Profiler R package (Gene Ontology (GO) enrichment analysis of DEGs were implemented by using the cluster Profiler R package. GO terms with corrected enriched . In orde package . The two® RT Reagent Kit with gDNA Eraser . qRT-PCR analysis was performed as described by (–ΔΔ Ct) method  analysis, was extracted by using the same method of 2.3 and qualified by Nanodrop 2000 Spectrophotometer . cDNAs were constructed from 1 μg total RNA by reverse transcription using the PrimeScript) method . The aim) method . Each PCP. digitatum mycelia was determined by AKP kit . The P. digitatum mycelia samples after 0, 2, 4, 6, and 12 h of C12O3TR treatment were used for this experiment. PBS (pH 7.0) was used as the control. Each experiment was repeated three times. One unit of AKP activity was defined as the time (min) to produce 1 μmol phenol per 1 g P. digitatum mycelia sample at 37°C.The AKP activity of P. digitatum mycelia samples were homogenized with 0.1 mol/L citrate - 0.2 mol/L disodium phosphate buffer  in liquid nitrogen. The mixture was then centrifuged at 12000 × g for 10 min at 4°C, and the supernatant was collected as the enzyme extract liquid. After that, 1 mL enzyme extract liquid and 1 mL 0.1 mol/L citrate - 0.2 mol/L disodium phosphate buffer (pH 4.8) were mixed with fucoidan solution (1 mg of fucoidan dissolved in 1 mL distilled water), and then the solution was incubated at 30°C water bath for 1 h. Then, distilled water (1.5 mL), anthrone ethyl acetate (0.5 mL) and H2SO4 (3 mL) were respectively orderly added in 0.5 mL solution which was after 30°C water bath. The whole mixture was heated in boiling water bath for 1 min, and then cooled down to room temperature to measure the absorbance at 630 nm. One unit of β-1, 3-glucan synthase activity was defined as the time (hour) to produce 1 mg glucose per 1 g P. digitatum mycelia sample at 30°C. The glucose content standard curve was calculated based on glucose content (x axis) against absorbance value (y axis) .β-1, 3-glucan synthase activity was analyzed as described by 12O3TR treatment were used for this experiment. Two hundred mg P. digitatum mycelia samples were homogenized in liquid nitrogen with acetic acid buffer (1 mL), and then centrifuged at 12000 × g for 10 min at 4°C. The supernatant was collected as enzyme extract liquid. The enzyme extract liquid (0.4 mL) and acetic acid buffer (0.4 mL) were mixed with colloidal chitin solution, and then the mixture was incubated at 37°C water bath for 2 h. The reaction system was stopped by centrifuging at 4000 × g for 10 min. Afterward, 0.4 mL of the supernatant was mixed with saturated borax solution (0.2 mL), and then the mixture was heated in boiling water bath for 7 min. After cooling to room temperature, glacial acetic acid (2 mL) and 1% p-dimethylaminobenzaldehyde (DMAB) (1 mL) were added in the solution which was cooled to room temperature. The absorbance was measured at 585 nm, and the chitinase activity was calculated based on the standard curve. One unit of chitinase activity was defined as the time (hour) to produce 1 μg of N-acetylglucosamine (GlcNAc) per 1 g of P. digitatum mycelia at 37°C. The standard curve of GlcNAc content was established according to GlcNAc content (x axis) against the absorbance value (y axis) .Chitinase activity was analyzed as described by 12O3TR on P. digitatum cell wall integrity was determined as described by P. digitatum conidia  mixed with 5% PDB were cultured at 25°C for 48 h. C12O3TR with the final concentrations of 6.25 μmol/L (MIC) was added and then incubated for 0, 2, and 12 h. PBS (pH 7.0) was used as control. Each sample was then stained with 50 mg/L Calcofluor White (CFW) for 5 min in dark, and the fluorescence was examined and photographed by the Eclipse TS100 epifluorescence microscope  with DAPI filter sets. The reproducibility of experiment results was confirmed by three replicates.The effect of Cp < 0.05.All experiments included three parallel sets and were repeated three times. The data were processed with Microsoft Excel 2013, and analyzed by statistical software SPSS 21.0 . The variance of data was analyzed via one-way analysis of variance (ANOVA) with Duncan’s multiple range tests at 12O3TR2, C12, and C12O3TR12 samples, respectively. After filtering the adaptor sequences, the average clean reads were 62.50 ± 4.62 million, 64.98 ± 5.89 million, 57.76 ± 2.65 million and 56.47 ± 1.86 million for the four treatment groups, respectively. Among them, 94.87 ± 0.08%, 95.06 ± 0.22%, 95.23 ± 0.10% and 95.21 ± 0.16% of the total reads were mapped to the genome of P. digitatum in C2, C12O3TR2, C12, and C12O3TR12 samples, respectively. In P. digitatum were all more than 90%. And 94.39 ± 0.10%, 94.60 ± 0.22%, 94.66 ± 0.08% and 94.61 ± 0.14% of the reads were uniquely mapped. In addition, the mapped reads of 12 samples represented the filtered data were all less than 1%, in 12O3TR.3.2. Transcriptional Stress Response of P. digitatum to peptide CThe profile of transcriptome sequence average data was shown in 12O3TR2, C12 and C12O3TR12 samples. There were also differences in gene expression distribution among three biological replicates under the same treatment  when treated with C12O3TR for 12 h, while the activity was still 2.91 ± 0.01 mg/(g ⋅ h) in control group.The influence of C12O3TR treated group showed higher chitinase activity than control group (P < 0.05), and the chitinase activity also increased with the C12O3TR treatment time (12O3TR treatment was 37.74 ± 1.79 μg/(g h), which was significantly higher than that in control group [21.79 ± 1.93 μg/(g ⋅ h)].The chitinase is the vital enzyme which effectively catalytic hydrolysis of chitin in cell wall. For chitinase activity, Cent time . At 12 h12O3TR also increased the AKP activity in P. digitatum (12O3TR group was 0.08 ± 0.00 μmol/(g ⋅min), yet it was 0.05 ± 0.01 μmol/(g ⋅min) in control group. This change became more evident when treated with longer time (P < 0.05). At 12 h, the extracellular AKP activity in the C12O3TR treatment was 0.77 ± 0.07 μmol/(g ⋅min), which was significantly higher than that in control group [0.23 ± 0.02 μmol/(g min)].AKP is produced in the cytoplasm and leaked into the periplasmic space. Generally, AKP releases from fungal cells with impaired cell wall permeability. In our study, Cigitatum . After 212O3TR was effective to inhibit the growth of P. digitatum. Therefore C12O3TR was useful to control the green mold on citrus fruit (12O3TR against P. digitatum by transcriptomic profile determination through RNA-Seq analysis. Our results showed that C12O3TR significantly influenced a large number of gene expression and metabolic processes. The expression patterns of DEGs at 2 and 12 h were similar, which included the metabolic processes in cell wall, cell membrane, genetic information and energy. However, the expression of pathways, which were related to genetic information processing, were different in 2 and 12 h. The difference shown that the effect of C12O3TR on P. digitatum was different with the treat time owing to differential cellular statuses. This result was similar with the effect of essential oil decanal on the postharvest fungal pathogen Penicillium expansum in different time (Our previous study found that peptide Cus fruit . The preent time .12O3TR affected multiple cell membrane related metabolic processes, such as fatty acid biosynthesis, biosynthesis of unsaturated fatty acids, steroid biosynthesis, fatty acid metabolism and fatty acid degradation among others (12O3TR disrupted the normal metabolic processes of the cell membrane, and finally damaged the cell member integrity. In addition, we have demonstrated that C12O3TR could enhance the membrane permeabilization of P. digitatum by using fluorescence microscopy in our previous study (Candida albicans by changing the normal expression of genes which encoded ergosterol metabolism and fatty acid biosynthesis. And these pathways were related to the metabolic processes of cell membrane (Cell membrane plays an important role in maintaining the cell viability because it is a barrier that separates the cell from its surroundings, and is a channel for exchanging substances and energy between the cell and the surrounding environment . Its intg others . These rus study . These rmembrane .P. digitatum was affected by C12O3TR, due to the changes in fructose and mannose metabolism, amino sugar and nucleotide sugar metabolism (12O3TR on the cell wall (12O3TR stress (12O3TR treatment. This result was similar with the effect of antifungal proteins on cell wall chitin (12O3TR stress decreased β-1, 3-glucan synthase activity of P. digitatum but increased the chitinase activity (12O3TR (The cell wall is mainly composed by mannose glycoprotein, β-glucan and chitin. The RNA-Seq results also indicated that the cell wall of tabolism . This fitabolism . Similartabolism . Cell watabolism . Chitin tabolism . The CFWell wall . CFW wasell wall . In thisR stress , indicatl chitin . It was l chitin . The preactivity . The decactivity . The inc (12O3TR .12O3TR stress (P. digitatum key mechanisms.Meanwhile, the metabolic processes of genetic information and energy were also found to be affected by CR stress . But the12O3TR was effective to inhibit P. digitatum growth through complex influences on P. digitatum metabolisms. And this study also observed the impairment on cell wall formation at superficial level. Further studies are still required to investigate the key anti-fungal mechanisms of C12O3TR against P. digitatum especially the genetic information and energy related metabolic processes.Overall, the results from this study at transcriptional level revealed that Chttp://bigd.big.ac.cn/gsa/s/oUJ31ulZ.The original contributions presented in the study are publicly available. This data can be found here: KZ conceived and supervised the project. XL, GF, and WW designed the experiments and performed most of the experiments. XL analyzed the data and wrote the manuscript. LD and LY gave advises and edited the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.