Anion exchanger 1 (AE1) is responsible for the exchange of bicarbonate and chloride across the erythrocyte plasma membrane. Human AE1 consists of a cytoplasmic and a membrane domain joined by a 33-residue flexible linker. Crystal structures of the individual domains have been determined, but the intact AE1 structure remains elusive. In this study, we use molecular dynamics simulations and modeling to build intact AE1 structures in a complex lipid bilayer that resembles the native erythrocyte plasma membrane. AE1 models were evaluated using available experimental data to provide an atomistic view of the interaction and dynamics of the cytoplasmic domain, the membrane domain, and the connecting linker in a complete model of AE1 in a lipid bilayer. Anionic lipids were found to interact strongly with AE1 at specific amino acid residues that are linked to diseases and blood group antigens. Cholesterol was found in the dimeric interface of AE1, suggesting that it may regulate subunit interactions and anion transport. Changes in proteins that are found in red blood cells may cause inherited diseases . For this reason, it is critical to understand how such proteins function at the molecular level. One such protein is anion exchanger 1 (AE1), which is responsible for the exchange of bicarbonate and chloride across the erythrocyte plasma membrane. In this study, we used computational modeling and simulations to build models of the intact AE1 in a model bilayer that mimics the native erythrocyte plasma membrane and discuss how mutations alter their function. Our results suggest that AE1 strongly interacts with specific lipids in the red blood cell membrane and highlight residues that, when mutated, may result in diseases.β-strand plays a critical role  is responsible for the rapid exchange of bicarbonate and chloride across the red blood cell plasma membrane, a process necessary for efficient respiration . Human Acal role , 10. Mutcal role . TherefoAlthough crystal structures of the individual cdAE1 and the mdAE1 domains have been obtained in different crystallization conditions , 9, 10, Recently, a negative stain electron microscopy (EM) reconstruction using the bovine homolog revealed that the cdAE1 and mdAE1 are connected by a pillar-shape linker region . This stIn this study, we examined the interactions and dynamics of the cdAE1 and mdAE1 domains by developing a near full-length model of the red cell anion exchanger AE1 in the outward-facing conformation. The N- and C-terminal regions of AE1  that are predicted to be disordered are omitted from the models as was the inhibitor DIDS located at the interface of the core and gate domains in the crystal structure. Our results demonstrate the role of the linker region in mediating the interactions between the two AE1 domains and propose possible orientations of the cytoplasmic domain with respect to the membrane domain. Additionally, the models of the intact AE1 were simulated in a native-like model red blood cell plasma membrane, containing a full complement of phospholipids as well as cholesterol and sphingomyelin (SM) , 27. OurHomo sapiens was obtained from the Universal Protein Resource Knowledgebase database ((https://www.ncbi.nlm.nih.gov/homologene accessed 26-11-2018) was used to select three sets of homologous members from different species of AE1, AE2, and AE3 belonging to the SLC4 family . They have been selected because they all mediate anion (Cl−/HCO3−) exchange  protein sequence from atabase  generated during the modeling stage for each AE1 conformer is shown in A. The resulting final distance between the dimeric cytoplasmic and membrane domains is ∼24 and ∼20 Å for the rev-V and the V conformers, respectively. This is approximately the distance detected in the three-dimensional map of the bovine AE1  (The coordinates of the mdAE1 residues 382–887) were obtained from the crystal structure Protein Data Bank (PDB): 4YZF . The mod2–887 wer homolog . The dim homolog . Ten mod homolog . Note th., 30 Å) . All the., 30 Å) .Figure 1rev-V and the V conformers, respectively). Therefore, no elastic bonds are present between the TM and the cytoplasmic regions, but elastic bonds were present within the TM or the cytoplasmic region. Regarding the linker region, out of the 33 residues that comprise each linker region, 10 and 15 residues from each linker in the rev-V parallel, 9 and 6 residues from each linker in the rev-V twisted, 7 and 12 residues from each linker in the V parallel, 6 and 7 residues from each linker in the V twisted participate in elastic bonds. Most of these residues are in the N- and C-terminal that connects the linker with the TM or the cytoplasmic domains. It should be noted that our analysis also demonstrates the flexibility of the linker regions during the simulations  to allow the membrane bilayer to equilibrate around the AE1 models. Five repeat simulations starting from different initial velocities were simulated for a total of 5 μs with an integration step of 20 fs. All simulations were performed at 323 K, with protein, lipids, and solvent separately coupled to an external bath using the V-rescale thermostat (−6 bar−1 using the Berendsen barostat (The coarse-grained molecular dynamics (CG-MD) simulations were performed using the Martini 2.2 force field , 43 and s see C; , and S4.s see C; . The bilermostat   was removed from the centroid of the first cluster of each system. These simulations were run using the same parameters as above. For each model, five repeat simulations were run for 5 −1 × nm−2). Three repeat simulations of the same system were simulated starting from different initial velocities. Simulations were run using the CHARMM36 force field (−5 bar−1). Verlet was used as the cut-off scheme, with the neighboring list updated every 20 steps. The cut-off distance for Lennard-Jones was 12 Å. Long-range electrostatics were managed using the particle-mesh Ewald method, with a cut-off distance of 12 Å , respectively. H-bonds on the crystal structures were identified with UCSF Chimera 1.12 (μs (5 × 5 μs) trajectory for each conformer. Because of the differences between conformers , we have used a RMSD cut-off distance of 2.5 Å to be able to pick more subtle differences between the conformations of the linker region. Calculation of the volume of cavities was done using the trj_cavity tool (https://sourceforge.net/projects/trjcavity/) (http://www.ks.uiuc.edu/Research/vmd/). Data were plotted using Grace (http://plasma-gate.weiz-mann.ac.il/Grace/).Root mean-square fluctuation (RMSF) analysis was performed using the tool g_rmsf from the GROMACS package. Radius of gyration, distances, buried surfaces, and contact analyses were also performed using tools from the GROMACS package . Hydrogen bonds (within a distance cut-off of 3.5 Å and up to 30° off-axis angle) and salt bridges (within a cut-off distance of 5.5 Å) were identified using g_hbond from GROMACS and VMD 1.9.3  (http://era 1.12 . Other aera 1.12 . For thicavity/) . Note thcavity/) . Moleculcavity/)  (http://reversed-V (rev-V) and V conformation, respectively, because of the characteristic V-shape groove formed by the cdAE1 helices (purple arrow in A).The starting point for our study was to create a model of the intact AE1 using the available structures of cdAE1 (residues 55–348) and mdAE1 (residues 382–887) connected by a selected model of the linker region (residues 349–381). Using the orientations proposed for the cdAE1 relative to the mdAE1 from previous studies , 23, tworev-V and V orientations. Therefore, we simulated four different starting configurations (A). This does not preclude that there is a dynamic interconversion of these states that changes the orientation of the cdAE1 and mdAE1. The N- and C-terminal regions of AE1  that are predicted to be disordered are omitted from the model (see The length of the linker region connecting the mdAE1 with the cdAE1 (33 residues) also allowed positioning of the cdAE1 in a parallel or a twisted orientation as suggested by a previous EM study  in both urations A. This odel see . The forodel see . The purB). Calculation of the distances between the centers of mass of the cdAE1 and the mdAE1 showed that the cdAE1 interacts with the mdAE1 domain at the early stages of the simulations. The cdAE1/mdAE1 distance for the twisted (rev-V and V) conformers was ∼0.25 nm smaller compared to the parallel (rev-V and V) conformers (B). This is probably because a parallel orientation of the linker region provides more flexibility to the cdAE1, whereas the twisted orientation allows tighter packing of the cdAE1 to the mdAE1. Cluster analysis and calculation of the radius of gyration across all simulations of each conformer confirms that the structures increased their compactness with respect to the initial coordinates shown in A, with the V parallel being the least compact conformer (C).Despite the differences in the initial orientation of the cdAE1 relative to the mdAE1, in all cases, the cdAE1 moves closer to the membrane and forms a complex with the cytoplasmic side of the mdAE1 domain, resulting in a more compact structure B. Calcunformers B. This onformer C.FigureC). The first cluster contained the majority of structures in all systems, showing that these protein conformations were the most observed within the simulation ensemble . For the rev-V twisted, we found 229 clusters with the first cluster containing 6473 structures (13.0%). For the V parallel, we found 267 clusters with the first cluster containing 5571 structures (11.1%). For the V twisted, we found 171 clusters with the first cluster containing 5652 structures (11.3%). Moreover, a more detailed analysis of the occurrence of each cluster in the repeat simulations within the simulation ensemble suggests that the structure from the top cluster from each individual repeat simulation is very similar to the structure used to initiate our atomistic simulations  were previously reported to be involved in red cell membrane integrity via interactions with ankyrin  is only exposed to the cytoplasm in the rev-V conformers in our models (B). Additionally, the dimeric form of AE1 has been found to associate with the actin junctional complexes (A and B). This groove in our models faces the cytoplasmic side in the rev-V conformers, and it is rather hidden in the V conformers.The recently discovered ankyrin cross-linking site (residues 142–143)  that is r models  B. Additomplexes , with reomplexes . Residueomplexes , A and Brev-V conformers, whereas it is rather distant in the V conformers . The Tuscaloosa variant has been shown to decrease the interaction with protein 4.2 by 29% by introducing an arginine in cdAE1 . This variant exhibits a 20% decrease in anion transport  , 64, 65,ransport . The negransport . These frev-V conformers in which the increase in compactness brings the linker very close to the mdAE1 domain (B). Hence, the ready exposure of this residue to trypsin cleavage suggests that although there may be one energetically preferred conformation, the interaction of cdAE1 and mdAE1 might be rather dynamic, exposing Lys360 to solvent.Finally, the AE1 linker region can be readily cleaved by mild trypsin treatment at Lys360 , 67. The1 domain . The Lys1 domain B. Henceμs each were run under the same conditions as our previous CG simulations  from the first centroid of each conformer from all our models was removed see . Five reions see . Our anaions see  indicaterev-V conformation is the most consistent with available biochemical and functional data. Moreover, although it does not occur during our simulations, it is possible that because of the long linker, the cdAE1 is dynamic, and there is an interconversion of the parallel and twisted conformations in the native protein regulating the interaction of AE1 with partner proteins. The dynamic nature of the linker supports the view that it is not a major mediator of cdAE1-mdAE1 interactions.Overall, our comparisons above suggest that the A shows the most likely conformations adopted by the linker region during the simulations. Our analysis for the likely conformations adopted by the linker region reveals a rather flexible behavior in the V conformations but less flexibility of the linker in the rev-V conformations (see A). Note that the flexibility of the linker region is observed both in the coarse-grained and in the atomistic simulations (A and B). Additionally, our analysis suggests that the linker interacts with both the cdAE1 and the mdAE1 after the formation of the cdAE1/mdAE1 complex . In the rev-V twisted conformer, the linker interacts strongly with residues 194, 198, 341, 344, 345, and 348. Our contact analysis, using a cut-off distance of 0.55 nm, suggests that the majority of the linker residues interact with the rest of AE1 in all conformers (B). Interestingly, the natural variant Tuscaloosa  is in ctructure . TherefoV and rev-V conformations. Within this latter region, the Gly701 when mutated in Asp (G701D) is reported to impair AE1 expression at the cell membrane and cause the autosomal dominant distal renal tubular acidosis (AD-dRTA)  , 70, 71.rev-V twisted conformers, residues belonging to the cdAE1 dimerization arms form the largest number of interactions between the AE1 monomers . This suggests that in AE1, the largest dimerization interface is found in the cdAE1 and not in the mdAE1. The cdAE1/cdAE1 interactions are formed mainly via residues 98–110, nearby residue 200, and residues 320–340 (A). Most of the interactions between the mdAE1 monomers are formed between residues 593–600, 623–628, 690–705, and 813–824. For the residues that are embedded within the bilayer, Phe583, which is located in the middle of the TM 6, forms the highest number of mdAE1/mdAE1 interactions. Interestingly, in our simulations, even though predicted to be disordered, the linker region is part of the AE1/AE1 dimer interface in all conformers except for the V parallel .Analysis of the interactions between the two monomers of the intact AE1 dimer suggests that in both the parallel and the  320–340 A. Most B). Our simulations suggest that the mdAE1 cytoplasmic loops that include residues 690–705 , 736–760 , 801–830 , and 873–887  form the main direct interactions between the mdAE1 and the cdAE1 in both the rev-V and V orientations of the cdAE1 (B). The interacting cdAE1 residues are different between the rev-V and V conformers and are shown in B. Interestingly, in the rev-V conformers, the cdAE1 N-terminal residues 55–56 interact strongly with the mdAE1 , but these interactions may be not present in the AE1 kidney isoform, which lacks the first 65 amino acids .Our intact AE1 models allow, for the first time to our knowledge, to identify which are the interacting residues between the cdAE1 and mdAE1 B. Our she cdAE1 B. The ino acids . MoreoveTo characterize the interactions between the two cdAE1 and mdAE1 domains in more detail, the first centroid from the cluster analysis shown in B and C; 2 for PDB: 1HYN   . Both paA and D). This cavity is almost three times larger in the V conformers compared to the rev-V models in which the values are also more comparable between the parallel and twisted conformers. This may be because of the fact that in the V conformers, the V-shape groove in the cytoplasmic domains faces the mdAE1, creating a cavity between the mdAE1 and the cdAE1 (A). This may suggest that the cdAE1/mdAE1 complex dimer is likely to be less stable in the V conformer compared to the rev-V. In line with this, the V conformers are also less compact compared to the rev-V as suggested by the radius of gyration calculated in the atomistic simulations (E).To measure the compactness of each conformer, we measured the volume of the cavity that is formed between the membrane and the cytosolic domains of the AE1 after they form a complex , A and Dhe cdAE1 A. This ulations E.rev-V parallel, rev-V twisted, V parallel, and V twisted, respectively . This is because in the rev-V configuration, the linker is positioned between the mdAE1 and the cdAE1 (A). This may also explain why the V structures are less compact. Our analysis also shows that the extracellular loop between TM5 and TM6, where most of the blood group antigens reside  , 73, 74.2)  . The resA). Importantly, the TM6 has a sequence—VL577MAGTFF583FAMMLR589K—that resembles a CRAC motif  to an atomistic representation allowed us to study in more detail the volume of the cavity in the dimer interface and its evolution (A). The volume of this cavity in the rev-V conformers is somewhat increased with respect to the value calculated from the crystal structure . Interestingly, the lipids that were already present in the TM cavity stably reside within this cavity during the simulations (A). In particular, one SM lipid, one POPC lipid, and one POPC lipid were found in the atomistic simulations pa_rev-V_AE1_AT_native, tw_rev-V_AE1_AT_native and pa_V_AE1_AT_native, respectively .Conversion of the first centroids of each CG-MD repeat simulations A to an volution A. The vulations A. In paively A; . Noticeaively A; , B and C2 in the CG-MD simulations of all conformers reveals specific sites of interactions with anionic lipids. These are located in the H1 helix (residues 383–401), cytoplasmic loops between TM4, 5 (residues 506–518), TM6, 7 (residues 593–600), TM12, and 13 (residues 801–830), and the C-terminal region (residues 873–887) (C and D).Contact analysis between AE1 and the headgroups of anionic lipids POPS and PIP873–887) , C and Drev-V conformers. Noticeably, residue Arg602   , G714 (ODresden) , Trp831,Dresden) , 82, ArgDresden) )  .rev-V configuration is more likely, although a V arrangement may also be possible because of the long linker between the mdAE1 and the cdAE1 , suggesting that this portion of the cdAE1 should be more exposed and accessible from the cytoplasmic side. This only occurs in our models in which the cdAE1 is in a rev-V conformer (A) with the exception of a positively charged region in the V-shaped groove that may be important in the recognition of cytoskeletal proteins. Accordingly, previous data showed an in vitro pH dependence for the AE1-ankyrin association , but this V model is not as compact and stable as the rev-V conformers  is 19.5 Å.The model proposed by Rivera-Santiago et al.  is compais study A, but tnformers . Additionformers  and the onformer A is 19.C and D). Moreover, in our CG-MD simulations, linker residues are involved in the interface between cdAE1 and mdAE1  that connects the two AE1 main domains and possibly facilitates their interaction. Although the linker is predicted to be disordered and unstructured (residues 332–376), it has been suggested to introduce flexibility to the cytoplasmic domain . Our simnd mdAE1  and resind mdAE1 .2 lipids  in good agreement with a previous study of the isolated mdAE1  (A). Both of these lipids were in the outer leaflet before entering the AE1 cavity. Interestingly, SM and POPC were shown to compete with cholesterol to negatively (SM) or positively (POPC) alter the anion transport in AE1  A. Both t in AE1 . The POPt in AE1 . Higher t in AE1 , 93, andt in AE1 , preventt in AE1 .2 molecules were found to interact strongly with AE1 in our simulations  the helical segment H1, 2) the region between TM6 and TM7, and 3) the C-terminal tail . Noticeably, many mutations result in the change of the net charge of the mutated residue, usually the loss of a positive charge , R8rmingham ), G714R rmingham ), R518C rmingham ) and R60rmingham ). Therefrmingham , 102. Oulesterol . These rlesterol , 104. ThA and B). This region is comprised of a short segment that connects the TM6 and TM7 (residues 593–600). This segment interacts mainly with PIP2 (C and D) and with some other lipid species present in our native lipid mixture (A and B) are involved in H-bonds with anionic lipids in both chains (Xenopus oocytes, the mutant protein showed a significant chloride transport activity, indicating that the disease is not related simply to the anion transport activity of the mutant protein (Calculation of the electrostatic potential of the intact AE1 revealed a positively charged patch that is located at the interface between the two mdAE1 chains , A and Bith PIP2 , C and D mixture . A serie mixture , A and Bh chains , suggesth chains , 106, suh chains , G609R (h chains , S613F (h chains , and R58h chains  in the Ah chains , therefo protein .Additionally, in our CG-MD and atomistic simulations, we found that anionic lipids and POPE are strongly associated with AE1 C-terminal residues. Interestingly, the AE1 C-terminal segment 812–830 has been defined as the red cell senescent antigen , 110. AcIn summary, this study proposes possible models for the near full-length AE1 and reveals critical regions of interactions between cdAE1 and mdAE1 and the role of the linker region. The validity of different conformers has been assessed using available biochemical, functional, and structural data. Our findings suggest that when the cdAE1 is in complex with mdAE1, it is more likely that its V-shaped groove faces the cytoplasm but does not rule out a dynamic interaction that may change the orientation of the two domains. Our results also provide new insights into how the intact AE1 interacts with lipids in its membrane environment. Interestingly, several residues that are linked to diseases and antigens interact strongly with lipids, suggesting that these mutations may change such interactions, resulting in a deficient AE1 folding, stability, and function.The research was designed by D.D.V., R.A.F.R., and A.C.K.; and D.D.V. performed the research and acquired and analyzed all the data. A.C.K. and R.A.F.R. supervised the project. D.D.V., R.A.F.R., and A.C.K. wrote the article.


This dataset presents 159 players' computer log data when they play a Serious Game along with their characteristics data including problem-solving performance scores, demographic information, metacognition, and goal orientation measurements. A total of 85,194 log files were recorded during one-hour game play. The data here are related to the research paper entitled “The Impact of Learner Metacognition and Goal Orientation on Problem-Solving in a Serious Game Environment” by Liu and Liu [1]. The data collected would provide insights on the relationship among individual player characteristics, in-game behavior and performance in the game. Four csDuration_Charateristics.csv file contains filtered data for 159 players, including the player time spent duration on each of the 10 tools, Gender, player metacognition score , game performance score , and six goal orientation scores .Gates.csv file presents raw data regarding player movement in the game environment. It contains four columns including player gate access , timestamps, and player ID. A total of 4460 gate access actions were recorded.Consoles.csv file contains raw data on player actions regarding console control in the game environment. There are four columns in the file, including player actions , timestamps, console  and player ID. A total of 5916 console actions were recorded.2http://alienrescue.edb.utexas.edu) game environment [This data collection utilized Alien Rescue   and goalCosine (Cos), Jaccard (Jac), and Longest Common Substring (LCS) coefficients were used [K-means cluster analysis was used in SPSS to determine player metacognitive levels and goal orientation groups. Tableau and R were used to visualize player log data. These log data were grouped into two types of actions during problem-solving in the game including room visit action (sequences) and tool use action . Chord Diagrams  and the ere used  to compa


Here we used a genetic mouse model of hyperglycemia (Ins2AKITA) to address whether prolonged systemic hyperglycemia induces BBB dysfunction and leakage. We applied a variety of methodologies to carefully evaluate BBB function and cellular integrity in vivo, including the quantification and visualization of specific tracers and evaluation of transcriptional and morphological changes in the BBB and its supporting cellular components. These experiments did neither reveal altered BBB permeability nor morphological changes of the brain vasculature in hyperglycemic mice. We conclude that prolonged hyperglycemia does not lead to BBB dysfunction, and thus the cognitive impairment observed in diabetes may have other causes.Diabetes mellitus is associated with cognitive impairment and various central nervous system pathologies such as stroke, vascular dementia, or Alzheimer’s disease. The exact pathophysiology of these conditions is poorly understood. Recent reports suggest that hyperglycemia causes cerebral microcirculation pathology and blood-brain barrier (BBB) dysfunction and leakage. The majority of these reports, however, are based on methods including  Recent evidence also links hyperglycemia with cognitive deficits, as well as with central nervous system (CNS) pathologies including vascular dementia, stroke, and Alzheimer’s disease4. The underlying causes of hyperglycemia-induced CNS complications are poorly understood and likely multifactorial. It has been suggested that blood-brain barrier (BBB) damage may play a role5, a hypothesis supported by data from in vitro models of the BBB, as well as animal studies . However, modeling the full complexity of the BBB in vitro is currently not possible, weakening any extrapolation of the results to the in vivo situation. Also the animal studies, which were based on hyperglycemia induced by the beta cell toxin streptozotocin8, have caveats. Firstly, streptozotocin may not be entirely selective in its toxic activity; its effect could be broader9 and even directly target the brain vasculature. Secondly, hyperglycemia elicited by streptozotocin and similar drugs shows extensive inter-individual variability and depends on administration method, dosage, genetic background and other factors10. Finally, visualization and quantification of BBB permeability is still challenging, and different protocols may lead to different results. Therefore, whether hyperglycemia causes BBB dysfunction remains an open question.Chronic hyperglycemia constitutes the major systemic risk factor for microvascular complications in diabetes mellitus (DM), such as retinopathyIns2AKITA mouse, constitutes a novel approach for studying hyperglycemia complications in vivo. The Ins2AKITA mouse has a point mutation in the Ins2 gene resulting in a conformational change in the protein that leads to its accumulation in pancreatic beta cells causing cell death11. This toxicity is completely specific to the Ins2-expressing beta cells, thus mimicking human type 1 DM. Heterozygous male Ins2AKITA mice display a non-obese phenotype and develop consistent hyperglycemia, hypoinsulinemia, polydipsia, and polyuria around the age of 4 weeks11. Thus, the Ins2AKITA mouse constitutes a clear advance over chemically-induced DM, as the primary insult is beta cell-specific. Additionally, this mouse model is stable and reproducible and hence allows longitudinal studies of hyperglycemia complications.The usage of genetic models of DM, such as the Ins2AKITA heterozygous males and littermate wild-type controls. The Ins2AKITA mice yielded consistent, prolonged hyperglycemia throughout the study. BBB leakage was studied in long-term hyperglycemic animals by injecting different exogenous tracers intravenously12. BBB integrity was studied by quantifying and visualizing these injected tracers in the CNS13. Complementarily, we analyzed whether hyperglycemia leads to transcriptional or morphological changes in the mouse brain microvasculature. In sum, these methods failed to demonstrate increased BBB permeability in Ins2AKITA mice.In our study, we sought to determine whether prolonged hyperglycemia causes BBB dysfunction and leakage in the mouse brain by using several state-of-the-art methodologies to characterize BBB dysfunction. We conducted our studies in per se does not cause BBB permeability in the mouse brain. In the light of novel studies in human DM patients showing an association with BBB leakage and dementia14, our study suggests that factors other than hyperglycemia contribute to BBB dysfunction.Collectively, our results – along with the study by Corem and Ben-Zvi (submitted) - lead us to conclude that persistent systemic hyperglycemia Ins2AKITA mice11 (referred here as Ins2AKITA) and littermate wild-type controls (referred here as WT) were used for this study. Mice were purchased from Jackson Labs  on a C57Bl/6-J background and bred in house at the Scheele Animal House, Karolinska Institute. Animals were housed in Allentown XJ type II long cages , with 12/12 h light-dark cycle and access to water and standard chow ad libitum. Cage bedding was changed 2–3 times per week due to polyuria. Glycemia  and body weight were monitored weekly from 6 weeks of age. Mice were sacrificed between 26 to 38 weeks of age, and samples collected. All animals were sacrificed between 10–11 AM. The procedures were carried out in accordance with institutional and national Swedish policies following approval from the Animal Ethical Board of Northern Stockholm.Male heterozygous Mice were perfused transcardially under full anesthesia (hypnorm-midazolam cocktail) with Hank’s Buffered Salt Solution  followed by 4% paraformaldehyde (PFA) in Phosphate Buffered Saline (PBS). Tissues were removed and postfixed in 4% PFA in PBS for 4 h at 4 °C. Fifty μm brain sagittal vibratome sections were incubated in blocking/permeabilization solution , 0.5% TritonX-100  in PBS overnight at 4 °C, followed by incubation in primary antibody solution for 48 h at 4 °C, and subsequently in secondary antibody solution, overnight at 4 °C. Sections were mounted in Prolong Gold anti-fade reagent with DAPI . Primary antibodies used: goat anti-mouse PECAM1 , rat anti-mouse Laminin α-2 chain (LAMA2) , rat anti-mouse ANPEP ; rabbit anti-mouse Collagen IV ; rabbit anti-GLUT1 ; rat anti-mouse PDGFR-β , rabbit anti-Vitronectin (VTN) , rabbit anti-Desmin (DES) , rat anti-GFAP , rabbit anti-AIF1 . Secondary antibodies for multiple labelling  conjugated with the appropriate fluorescent dyes were from Thermo Fischer Scientific or Jackson ImmunoResearch . As a negative control the sections were incubated with secondary antibodies only. Image processing was done using Volocity 64 , Fiji, Adobe Photoshop CS6, and Adobe Illustrator CS6 . All immunohistochemistry images were taken with Leica TCS SP8 confocal microscope  and presented as maximum intensity reconstructions of confocal z-stacks.15. Briefly, 3D confocal images were projected with maximum intensity projection from 10 μm thick z-stacks, contrast enhanced, de-speckled, smoothened, and a threshold was set to generate a binary image. These images were then subjected to ‘dilate’ and ‘close’ commands prior to running the AnalyzeSkeleton plug-in in Fiji (https://fiji.sc/). Total skeletal length was calculated for both PECAM1 stained images  and ANPEP stained images (pericyte-covered vessels), and the ratio of the vessel lengths was used as a measurement of coverage. One to three fields per mouse were acquired with 20× objective from cerebral cortex of seven WT and six Ins2AKITA mice. All image processing was done automatically with a custom macro designed in Fiji.Pericyte coverage was quantified as previously reportedIns2AKITA mice. Ten μm thick confocal stacks of AIF1 stained brain sections were analyzed with Imaris ×64 8.3.1 software . Microglia filament length in μm was measured in 3D-mode by filament module.Three fields per mouse were acquired with 40× objective from cerebral cortex of six WT and seven pdgfbret/ret mice12. Animals were subsequently perfused transcardially with PBS for 5′ and brains removed. Successful cadaverine injections were verified by examining kidneys of injected animals under a fluorescence stereomicroscope (Leica Microsystems). Quantification of extravasated EB or cadaverine in the brain parenchyma was performed as published16.To address BBB permeability, we injected intravenously either Evans Blue dye  or lysine-fixable cadaverine conjugated to Alexa Fluor 488 or 555 . Circulation time was overnight for EB and 2 h for cadaverine. As a positive control for EB leakage we used 17 with modifications. Briefly, after collagenase A treatment, brain tissue was first gently pressed through a 100 μm and then through a 40 μm cell strainer. The tissue homogenate was incubated with rat anti-PECAM1 antibody-coated magnetic beads  at 4 °C for 1 h. Microvascular fragments adherent to magnetic beads were washed 6 times with HBSS containing 1% BSA followed by 2 times with HBSS alone. Microvasculature fragments were lysed in 350 μl RLT buffer .Purification of microvasculature was performed as describedPdgfrb (Mm00435546_m1_FAM), Rgs5 (Mm00654112_m1), Gfap (Mm01253033_FAM) and mouse Gapdh (Mm99999915_g1_FAM) in the purified brain microvasculature samples and for mouse Gfap (Mm01253033_FAM) and mouse Hprt (Mm03024075_m1_FAM) in the cerebral cortex samples. Reference genes Gapdh and Hprt were used for ΔΔCt calculations. Gene of interest expression was presented as fold change over reference gene.Total RNA was isolated from whole cerebral cortex or purified brain microvasculature using RNeasy Mini kit (Qiagen). RNA was DNase-treated (Invitrogen) and samples were cleaned using RNeasy Mini-elute Cleanup Kit (Qiagen). cDNA was synthesized from 1 ug of total RNA using Superscript III (Invitrogen) and mRNA expression quantified by Taqman assays (Applied Biosystems) for mouse RNA integrity and concentration of the isolated brain microvasculature was checked with the Agilent RNA 6000 pico kit and the Agilent 2100 Bioanalyzer . For conversion of the RNA to a cDNA library for Illumina sequencing, the SMARTer® Stranded Total RNA Sample Prep Kit - Low Input Mammalian kit was used according to the manufacturer’s protocol . The total RNA was depleted from rRNA and tRNA’s with the RiboGone- Mammalian kit (Clontech) prior to library construction.18. The samples were sequenced on an Illumina HiSeq. 2500 sequencer at Science for Life laboratory (SciLife Lab), Uppsala sequencing node. The sequence reads were mapped to the Ensembl mouse gene assembly (version: NCBIM37) using TopHat2 software (version 2.0.4)19, and the duplicated reads were filtered using the Picard tool . Statistical tests were performed using the Cufflinks tool (version 2.2.1)20. Gene expression levels in brain microvasculature between the Ins2AKITA and WT mice were compared, and the genes with multiple-test corrected p < 0.05 were identified as significantly differentially expressed genes. The sequence data is deposited in NCBI GEO, accession number GSE113919.The RNA-seq libraries were prepared as previously describedt test was performed . A p-value of ≤0.05 was considered statistically significant.Data are presented as mean ± SEM. For two group comparisons two-tailed, unpaired student’s Ins2AKITA mice. As reported elsewhere23, Ins2AKITA animals had elevated glycemia at 6 weeks of age as compared to WT mice , whereas WT mice remained normoglycemic (<10 mmol/l) throughout the experiment , while WT animals continued to gain weight normally  intravenously and perfused the animals after the dye had been circulating for 2 h. We could not detect a significant difference in tracer accumulation in brains between Ins2AKITA and WT mice, either by microscopical visualization  by quantitative, real-time PCR  was 1.8-fold downregulated in Ins2AKITA , makes it impossible to extrapolate in vitro results to the in vivo scenario. Previous animal studies are also problematic, since they applied chemically induced hyperglycemia8. Although popular, this technique has problems with specificity29 and variability as it depends on administration route, dosage, and genetic background10.The question whether hyperglycemia causes BBB permeability has been addressed in numerous reportsIns2AKITA mouse11, offers the opportunity of studying the effect of hyperglycemia without many of the above-mentioned confounding factors. To date, a few reports have addressed the role of hyperglycemia on the blood-retinal barrier (BRB) in Ins2AKITA mouse. Two studies found early signs of retinopathy including pericyte loss and increased BRB permeability30. Subsequently, it was shown that the presence of the rd8 mutation in the Crb1 gene – causing retinal degeneration31 - might account for the previous observations of advanced retinal pathology23. To the best of our knowledge, no studies of the BBB structure and function in Ins2AKITA mice have previously been reported.The advent of genetic mouse models, such as the 13, and secondly, a robust mouse model of hyperglycemia, the Ins2AKITA, were used. Our data clearly show that prolonged systemic hyperglycemia does not elicit BBB dysfunction and leakage in the mouse brain. Moreover, different cellular and molecular components at the NVU appear intact in Ins2AKITA mice, such as endothelial cells, pericytes, astrocytes, and basement membrane. Based on our results and the Corem and Ben-Zvi (submitted), we propose that hyperglycemia-induced BBB leakage is likely negligible.In the present study, we aimed at evaluating hyperglycemia-induced BBB permeability by employing highly specific tools. Firstly, a range of well-established techniques for the study of BBB and neurovascular unit (NVU) integritySlc2a1 to be 1.8-fold downregulated in brain vessel-fragments from Ins2AKITA animals (Table 32 to transient hyperglycemia conditions in healthy human beings33. Others have shown a decrease in glucose uptake in experimental non-obese DM animals34. Our study shows that Slc2a1 is downregulated at the transcriptional level specifically in the hyperglycemic mouse brain vasculature. Of note, allelic disorders associated with SLC2A1 mutations, glucose-deficiency syndrome, epilepsy, and dystonia, are associated with cognitive disability35. Thus, downregulation of SLC2A1 on brain blood vessels due to hyperglycemia could lead to metabolic changes in neural tissue leading to cognitive dysfunction especially after repeated hypoglycemic episodes.Transcriptional analysis of microvascular fragments comparing hyperglycemic animals and WT controls revealed Ins2AKITA mice was Pin1, a phospho-serine/threonine-proline isomerase that has been implicated in cellular proliferation. Pin1 has previously been reported to be upregulated in an in vitro model of hyperglycemia and in the aorta of streptozotocin-induced diabetic mice, resulting in mitochondrial oxidative stress, vascular relaxation dysfunction and vascular inflammation36. Thus, our results imply that even if hyperglycemia-induced upregulation of Pin1 might cause similar stresses to the brain vasculature, these do not induce significant BBB disruption.Interestingly, the only gene that was found to be upregulated in Ins2AKITA mice into a more amoeboid-like shape with shorter and thicker processes compared to the highly ramified process morphology seen in WT brains. Our findings are consistent with an “activated” microglial response. How the microglia is activated is unknown, but it has been suggested that hyperglycemia can potentially activate retinal microglia through a variety of molecular mechanisms37. Furthermore, it is hypothesized that permeability at the BRB contributes to the microglia activation37. Our results reveal the same morphological changes in the hyperglycemic mouse brain than these already reported in retinas23. Because the integrity of the BBB and NVU are preserved in the Ins2AKITA mice, other microglial activating factors than BBB permeability should be considered.Our analysis of microglia showed that these cells change their morphology in 14. However, even the most prevalent microvascular DM complications, such as retinopathy or nephropathy, do not affect all DM patients38. Beyond hyperglycemia, other DM features, such as hyperglycemia/hypoglycemia cycles, dyslipidemia, or genetic susceptibility should be considered as possible causal factors for these pathologies. Our results presented herein show that hyperglycemia has minor, if any, effect on BBB permeability, and therefore fails to support increased BBB leakage as an underlying cause of DM-related brain pathologies.The association between DM and different brain pathologies is an underexplored but emerging research area. BBB dysfunction is commonly found in conditions such as Alzheimer’s disease, and it is therefore tempting to speculate that hyperglycemia-induced BBB permeability is the underlying mechanism of brain pathology in human DM patients


Rela transcription. Our report provides new insights into the previously unrecognized immune gene regulatory mechanism of ERV-derived lncRNAs.Endogenous retroviruses are transposable genetic elements comprising 8% to 10% of the human and mouse genomes. Although most ERVs have been inactivated due to deleterious mutations, some are still transcribed. However, the biological functions of transcribed ERVs are largely unknown. Here, we identified a full-length ERV-derived lncRNA, designated lnc-EPAV, as a positive regulator of host innate immune responses. We found that silencing lnc-EPAV impaired virus-induced cytokine production, resulting in increased viral replication in cells. The lnc-EPAV-deficient mice exhibited enhanced susceptibility to viral challenge. We also found that lnc-EPAV regulated expression of RELA, an NF-κB subunit that plays a critical role in antiviral responses. ERV-derived lncRNA coordinated with a transcription repressor, SFPQ, to control  Rela. Altogether, our work demonstrates an alternative mechanism by which ERVs regulate antiviral immune responses.Endogenous retroviruses (ERVs) are transposable elements that cause host genome instability and usually play deleterious roles in disease such as tumorigenesis. Recent advances also suggest that this “enemy within” may encode a viral mimic to induce antiviral immune responses through viral sensors. Here, through whole-genome transcriptome analysis with RNA sequencing (RNA-Seq), we discovered that a full-length ERV-derived long noncoding RNA (lncRNA), designated lnc-EPAV , was a positive regulator of NF-κB signaling. lnc-EPAV expression was rapidly upregulated by viral RNA mimics or RNA viruses to facilitate the expression of RELA, an NF-κB subunit that plays a crucial role in antiviral responses. Transcriptome analysis of lnc-EPAV-silenced macrophages showed that lnc-EPAV was critical for RELA target gene expression and innate immune responses. Consistently, lnc-EPAV-deficient mice exhibited reduced expression of type I interferons (IFNs) and, consequently, increased viral loads and mortality following lethal RNA virus infection. Mechanistically, lnc-EPAV promoted expression of RELA by competitively binding to and displacing SFPQ, a transcriptional repressor of  TNF-α), interferon (IFN)-stimulated genes (ISGs) , and the Ifng gene (–Endogenous retroviruses (ERVs) are the result of successful retroviral insertions of ancient and modern retroviruses, which have successfully transferred from parent to progeny by colonizing in the germ line of their hosts , 2. Provfng gene –20. HoweHost antiviral immunity begins with viral recognition  by several families of pathogen recognition receptors (PRRs), such as RIG-I-like receptor (RLR), Toll-like receptor (TLR), and NOD-like receptor (NLR) . PRRs inIn vitro and in vivo studies revealed that lnc-EPAV acted as a positive regulator of host antiviral responses through controlling the transcription of Rela. It bound and sequestered SFPQ, a transcriptional repressor of Rela. In addition, RELA promoted expression of lnc-EPAV, which formed a positive-feedback loop to facilitate antiviral immune responses.In this study, we characterized the physiological function of one of the most dramatically upregulated ERV-derived lncRNAs, designated lnc-EPAV , which was identified by genome-wide profiling of ERV-derived lncRNA expression in mouse genome. To investigate the dynamic transcription of ERV-derived transcripts induced by pathogens, genome-wide transcriptome analysis with RNA sequencing (RNA-seq) was performed for bone marrow-derived macrophages (BMDMs) from C57BL/6 mice stimulated with or not stimulated with viral mimic poly(I·C). Significant increases in the levels of expression of inflammatory cytokines, interferons, and ISGs confirmed activation of the immune responses see , consist10.1128/mBio.00937-19.1FIG S1Il6, Il1b, Ifnb1, and Ccl5 mRNA levels by qPCR in BMDMs stimulated with poly(I·C) (10 μg/ml) for 2 h. (B) Quantification of lnc-EPAV expression levels by qPCR in BMDMs from BALB/c and 129/Sv mice after VSV infection (MOI = 0.1) for 6 h. Three biological replicates were pooled for qPCR. Data shown represent means ± SEM of results from three technical replicates. ***, P < 0.001 (Student′s t test). Download FIG S1, PDF file, 0.2 MB.Poly(I:C)-induced or virus-induced immune responses in BMDMs. (A) Quantification of Copyright © 2019 Zhou et al.2019Zhou et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Fibroblast growth factor receptor 4 (Fgfr4) and Nuclear receptor-binding SET-domain protein 1 (Nsd1) in the 13qB1 chromosome  treatment in mouse macrophages. We identified 5,322 FL-ERVs among a total of 896,922 ERV elements from the mouse genome by the use of LTR_FINDER . To furtromosome .We next verified whether lnc-EPAV was upregulated by RNA viruses. Northern blotting detected an ∼4.7-kb lnc-EPAV transcript, in line with the full-length signal identified by RNA-seq  and D. IWe hypothesized that the rapid upregulation of lnc-EPAV after pathogenic stimulation was mediated by immune-related transcription factors (TFs). To identify such TFs, the TRANSFAC database was used to analyze the TF binding sites of promoter region  of lnc-EPAV. Gene Ontology (GO) annotation enrichment analysis was performed , and 10 10.1128/mBio.00937-19.2FIG S2Rela. (D) Analysis of the distribution of FL-ERV-derived lncRNAs from four ERV families . The number and percentage of lncRNAs of four ERV families are shown in parentheses. (E) Analysis of the RELA binding motif numbers per FL-ERV-derived lncRNA in four ERV families. Download FIG S2, PDF file, 2.3 MB.lnc-EPAV is activated by NF-κB subunit RELA. (A) GO term enrichment analysis was performed for potential transcription factors involved in regulating lnc-EPAV expression. (B) Confocal immunofluorescence microscopy of RELA in BMDMs pretreated with PDTC (10 μM) for 6 h and then infected with VSV (MOI = 0.1) for 12 h. RELA was stained with a mouse anti-RELA antibody followed by a secondary antibody conjugated with Alexa Fluor 546. Images were acquired using a Carl Zeiss LSM710 microscope . (C) Immunoblotting analysis of RELA expression in J774A.1 macrophages expressing shRNA against Copyright © 2019 Zhou et al.2019Zhou et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the Rela-silenced cells than in control cells upon VSV infection , which prevented RELA from transferring to the nucleus and accumulating in the cytoplasm . This trnfection . Taken tWe next asked whether the RELA motif also existed in other ERV families. Four ERV family-derived lncRNAs were identified in this study, namely, ERV1, ERVL, ERVL-MaLR, and ERVK . By scan+) cell numbers  targeting two different sites of lnc-EPAV and generated lnc-EPAV-silenced mouse J774A.1 macrophages. Endogenous lnc-EPAV was silenced efficiently as quantified by qPCR . We did  numbers . Consist numbers  and C. I numbers . These d10.1128/mBio.00937-19.3FIG S3P < 0.001 (Student′s t test). Data are representative of results from at least three independent experiments. Download FIG S3, PDF file, 0.5 MB.lnc-EPAV enhances cellular antiviral responses. (A) Quantification of endogenous lnc-EPAV expression by qPCR from J774A.1 macrophages stably expressing either scrambled shRNA or lnc-EPAV-targeting shRNA. (B to I) Quantification of putative shRNA off-target ERV transcript levels by qPCR from J774A.1 macrophages stably expressing either scrambled shRNA or lnc-EPAV-targeting shRNA. (J) Quantification of intracellular VSV loads by qPCR from J774A.1 macrophages overexpressing lnc-EPAV after VSV infection (MOI = 0.1) for 12 h. Data shown represent means ± SEM. ***, Copyright © 2019 Zhou et al.2019Zhou et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the q] value, ≤0.05). Most enriched KEGG pathways were involved in pathogen infection and immune responses  performed with KEGG gene sets  combined with immunofluorescence further demonstrated the colocalization of lnc-EPAV and SFPQ in the nucleus of BMDMs  to identify the interacting proteins of lnc-EPAV. Biotinylated full-length lnc-EPAV and lnc-EPAV E2 truncation mutants were incubated with nuclear extracts and pulled down with streptavidin magnetic beads. The associated proteins were analyzed by SDS-PAGE with silver staining  followedof BMDMs .10.1128/mBio.00937-19.4FIG S4Sfpq. Download FIG S4, PDF file, 2.8 MB.SFPQ is a binding partner of lnc-EPAV in the nucleus. (A) Silver staining of biotinylated lnc-EPAV-E2-associated proteins. The lnc-EPAV-E2 specific bands (highlighted bands) were excised and analyzed by mass spectrometry. (B) DDX21 RIP followed by RT-PCR analysis of copurified RNAs from non-cross-linked BMDMs. (C) Immunoblotting analysis of RELA expression in J774A.1 macrophages stably expressing shRNA against Copyright © 2019 Zhou et al.2019Zhou et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mBio.00937-19.6TABLE S1Table S1, DOCX file, 0.02 MB.Mass spectrometry analysis of SFPQ. Download Copyright © 2019 Zhou et al.2019Zhou et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the CYP17  to regulate gene expression , 30. SFPCYP17  and IL-817  was performed in BMDMs. Model-based ChIP-seq analysis (MACS)   was useds (MACS) . We thenS) /Cas9 genome-editing technology  functioned as a positive regulator of virus-induced host antiviral immune responses. lnc-EPAV expression was rapidly upregulated by viral RNA mimics or RNA viruses. Transcriptome analysis of lnc-EPAV-silenced macrophages showed that lnc-EPAV was critical for induction of NF-κB/RELA target genes during viral infection. lnc-EPAV deficiency led to reduced interferon production, resulting in enhanced susceptibility to VSV infection in mice. Mechanically, the expression of lnc-EPAV competitively displaced SFPQ from the trans-acting factors  and cis-regulatory elements in promoter or LTR regions . SFPQ protein belongs to a conserved family of Drosophila behavior human splicing (DBHS) proteins   , we obse3T cells . Consist3T cells . We hypo3T cells . We used3T cells  and E. T58444 , w10.1128/mBio.00937-19.5FIG S5GSE58444). The distribution of putative SFPQ binding sites was enriched around the TSS gene (−2.5 kb to +2.5 kb around the TSS) in MCF cells (left). Gene tracks software (Integrated Genome Browser) was used for analysis of SFPQ enrichment at the promoter region of RELA in human MCF cells (right). (B) ChIP-qPCR analysis (left) and ChIP-PCR analysis (right) of SFPQ in the RELA promoter in HEK293T cells infected with VSV (MOI = 0.1) for 12 h. (C) qPCR analysis (left) and immunoblotting analysis (right) of RELA expression levels in SFPQ-knockdown HEK293T cells. (D) RIP-seq analysis of putative SFPQ binding HERV-derived transcripts in HEK293T cells . IgG was used as a negative control. (E) SFPQ RIP analysis followed by RT-PCR analysis of copurified RNAs from non-cross-linked HEK293T. Download FIG S5, PDF file, 1.5 MB.Human SFPQ regulates RELA expression. (A) ChIP-seq analysis of SFPQ in human MCF cells by using public data , rabbit anti-RELA , and rabbit anti-SFPQ  were purchased from Sigma-Aldrich . Mouse anti-RELA , rabbit anti-SIRT6 , and rabbit anti-GAPDH   were purchased from Cell Signaling Technology . Mouse anti-DDX21  was purchased from Santa Cruz Biotechnology . Alexa Fluor 546-conjugated goat anti-mouse secondary antibody  was obtained from Thermo Fisher . The antibodies were diluted 1,000 times for immunoblots and 200 times for immunofluorescence and immunoprecipitation. Lipofectamine 2000  was used for transfection of nucleic acids. PDTC  was purchased from Sigma-Aldrich . Alexa Fluor 488-conjugated Avidin  was purchased from Thermo Fisher . High-molecular-weight poly(I·C)  and puromycin  were obtained from InvivoGen (USA). Sendai virus (SeV) was a kind gift from Bo Zhong . Green fluorescent protein-tagged vesicular stomatitis virus (VSV-GFP) and VSV provided by Guang Yang  were passaged once in Vero cells, and viral PFU levels were quantified by plaque assay .Sfpq, and Rela were inserted into pLKO.1 vector . For overexpression of SFPQ, mouse Sfpq was amplified by PCR, cut by restriction enzymes, and inserted into pcDNA3.1-Flag vector. pRL-TK plasmid and pGL3-Basic vector were purchased from Promega for reporter assays. The lnc-EPAV promoter  and Rela promoter were cloned into pGL3-Basic vector. All constructs were verified by sequencing the relevant regions. The PCR primers are listed in For nuclear expression of lnc-EPAV, pZW1-snoVector  was modified into pCDH-puro . The full-length or truncated forms of lnc-EPAV were cloned into modified pCDH-pZW1-snoVector. For gene knockdown, the annealed shRNA oligonucleotides for lnc-EPAV, 10.1128/mBio.00937-19.9TABLE S4Table S4, DOCX file, 0.02 MB.Primer information. Download Copyright © 2019 Zhou et al.2019Zhou et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the http://www.rgenome.net/cas-offinder/) with a cutoff mismatch value of ≤3. The putative off-target sequences are listed in 7 PFU of VSV through intraperitoneal injection. Morbidity and mortality were monitored twice a day. All mouse experiments were conducted in accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of Fudan University, with the approval of the Fudan University Laboratory Animal Center (201802148S).lnc-EPAV knockout (KO) mice were generated with CRISPR/Cas9 technology on a C57BL/6J background by Biocytogen . Putative single guide RNA (sgRNA) off-target sequences in mice genome were predicted by the use of Cas-OFFinder tools   or RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) and antibiotics .HEK293T cells and Vero cells were obtained from the Type Culture Collection of the Chinese Academy of Science. The J774A.1 macrophages and the L929-ISRE cell line were kind gifts from Guang Yang . BMDMs were differentiated using a previously published method . The celThe UCSC  genome browser bioinformatic RepeatMasker  and BLASRNA-seq libraries were prepared according to the instructions provided with KAPA stranded RNA-Seq kits . The libraries were sequenced using an Illumina HiSeq X Ten platform in a paired-end 2 × 150-bp manner. Processed raw data were aligned to the mouse genome (version Mm9) or human genome (version Hg38) using STAR . To analTotal RNA was extracted using TRI reagent , and cDNA was synthesized using a reverse transcription reagent kit . Amplification was performed using SYBR green qPCR master mix  and gene-specific primers in a CFX-96 system , and values were normalized to those of a housekeeping gene. The qPCR primers are listed in in vitro using a HiScribe T7 Quick high-yield RNA synthesis kit  and biotin RNA labeling mix . The assay was carried out according to the manufacturer’s protocol .For immunoblotting, cells were harvested using radioimmunoprecipitation assay (RIPA) lysis buffer containing protease inhibitor cocktail . Equal amounts of proteins were separated by SDS-PAGE and then transferred onto polyvinylidene difluoride (PVDF) membranes . Immunoblots were probed with the indicated antibodies developed by the use of NcmECL Ultra reagent . For Northern blotting, total RNA of BMDMs was extracted using TRI reagent . Biotin-labeled antisense and sense RNA probes  were made Rela-Luc), pRL-TK plasmid, or the indicated TF expression plasmid for 24 h. For experiments examining viral infection, cells were infected with VSV (multiplicity of infection [MOI] = 0.1) for 12 h. Cells were lysed, and luciferase activities were measured by the use of a TransDetect dual-luciferase reporter assay kit  according to the manufacturer’s instructions.HEK293T cells were transfected with luciferase reporter plasmids  kits were used to measure the levels of IL-6 protein  and TNF-α protein  in cell culture supernatants. The level of type I interferon was measured as described previously  with refin vitro using a HiScribe T7 Quick high-yield RNA synthesis kit  and biotin RNA labeling mix . Biotinylated probes were incubated with BMDM nuclear extracts  for 4 h at room temperature followed by incubation with Dynabeads MyOne Streptavidin C1 beads  at 4°C for 4 h. The proteins on the beads were separated by SDS-PAGE followed by silver staining and mass spectrometry (MS) identification.Biotin-labeled RNA probes lnc-EPAV and lnc-EPAV-E2  were made Mouse anti-SFPQ antibody, mouse anti-DDX21 antibody, or IgG control was added to the BMDM nuclear extracts and incubated at 4°C for 4 h followed by incubation with SureBeads protein G magnetic beads  at 4°C for 2 h. RNA/protein complexes were immunoprecipitated, and RNA was extracted and quantified with RT-PCR.l-lysine-coated coverslips were fixed with 4% paraformaldehyde, permeabilized with 0.5% Triton X-100, washed, and stored with 70% ethanol at −20°C. Endogenous biotin signal was blocked by using an endogenous biotin blocking kit . BMDMs were incubated with biotin-labeled lnc-EPAV probe at 50°C overnight. The cells were then incubated with Alexa Fluor 488-conjugated avidin  at room temperature for 4 h. For immunofluorescence analysis, cells were sequentially fixed with 4% paraformaldehyde, permeabilized with 0.5% Triton X-100, blocked with 5% bovine serum albumin (BSA), and incubated with primary antibody  followed by Alexa Fluor 546-conjugated goat anti-mouse secondary antibody . Nuclei were counterstained with DAPI . Images were acquired using a Carl Zeiss LSM710 microscope .For lnc-EPAV RNA FISH assay, BMDMs cultured on poly-BMDMs were fixed with 1% formaldehyde and quenched with glycine. Purified chromatin was sonicated to a level of 300 to 500 bp using ultrasonic processing  and incubated with mouse anti-RELA antibody , mouse anti-SFPQ antibody , or mouse IgG control . DNA-protein complexes were immunoprecipitated by the use of salmon sperm DNA-blocked SureBeads protein G magnetic beads , followed by reverse cross-linking processes. The DNA was then purified and quantified by qPCR and PCR.For ChIP-seq library construction, purified DNA was processed for end repair, followed by a 3′-end dA-adding reaction and Y-shape adaptor ligation. The final sequencing library was obtained by PCR amplification, and sequencing was then performed via the use of an Illumina HiSeq X Ten platform. For data analysis, model-based analysis of ChIP-seq (MACS) was used to identify enrichment regions . The ChIt test. Survival curves were analyzed using a log rank (Mantel-Cox) test. Data are presented as means ± standard errors of the means (SEM). Data are representative of results from at least three independent experiments. P values of <0.05 were considered statistically significant.GraphPad Prism software was used for statistical analyses. The analyses of results were performed with a two-tailed unpaired Student′s PRJNA503657. Data from the proteomics studies are available via ProteomeXchange with identifier PXD011577.All sequencing data have been deposited in the NCBI SRA database under accession number


Microsatellites, or Simple Sequence Repeats (SSRs), are short tandem repeats of 1–6 nt motifs present in all genomes. Emerging evidence points to their role in cellular processes and gene regulation. Despite the huge resource of genomic information currently available, SSRs have been studied in a limited context and compared across relatively few species.Leishmania as well as in cereal crops, Drosophila, birds and primates. We also find that distinct SSRs preferentially exist as long repeating units in different subgroups; most unicellular organisms show no length preference for any SSR class, while many SSR motifs accumulate as long repeats in complex organisms, especially in mammals.We have identified ~ 685 million eukaryotic microsatellites and analyzed their genomic trends across 15 taxonomic subgroups from protists to mammals. The distribution of SSRs reveals taxon-specific variations in their exonic, intronic and intergenic densities. Our analysis reveals the differences among non-related species and novel patterns uniquely demarcating closely related species. We document several repeats common across subgroups as well as rare SSRs that are excluded almost throughout evolution. We further identify species-specific signatures in pathogens like We present a comprehensive analysis of SSRs across taxa at an unprecedented scale. Our analysis indicates that the SSR composition of organisms with heterogeneous cell types is highly constrained, while simpler organisms such as protists, green algae and fungi show greater diversity in motif abundance, density and GC content. The microsatellite dataset generated in this work provides a large number of candidates for functional analysis and for studying their roles across the evolutionary landscape.The online version of this article (10.1186/s12864-019-5516-5) contains supplementary material, which is available to authorized users. Repetitive DNA in eukaryotic genomes can be broadly classified into interspersed and tandem repeats. Microsatellites, also known as Simple Sequence Repeats or SSRs, are short tandem repeats of 1–6 nucleotide DNA motifs. They comprise a significant portion of the genome in complex organisms, often surpassing the proportion of coding sequences , and disRecent studies have focused on the role of SSRs in cellular processes such as the epigenetic regulation of gene expression –13 and gHere, we have identified microsatellites from 719 eukaryotic species and studied their abundance across 15 taxonomic subgroups from protists to mammals, spanning 1.7 billion years of evolution. Our analysis reveals a large number of novel taxon-specific SSR signatures, and evolutionary differences in the context of SSR length, GC content and size of the repeat motifs. Interestingly, the microsatellite trends accurately reflect phylogeny and we posit that they can be useful in understanding evolutionary relationships. Finally, we have used the available genome annotation data from 334 species to understand the distribution of SSRs in coding and non-coding regions and report several novel observations that open up avenues for further experimental scrutiny.Aegilops tauschii) and some fish , each containing 4–5 million SSRs and with genome sizes > 2.2 Gb. Of note, the highest number of SSRs are found in the octopus , the only non-vertebrate animal in the top 50 of the SSR abundance table  has the lowest number of SSRs, which correlates well with the fact that this pathogen has one of the smallest genomes studied (2.2 Mb). In order to normalize their occurrence to the genome size we looked at the density of SSRs (i.e. bp covered by SSRs per Mb of the genome). We found that unlike SSR abundance, there is no correlation between the SSR density and the genome size  is the top ranked organism in terms of SSR abundance per Mb of the genome, i.e. 15.5% of its genome is covered by SSRs. A recent analysis also made a similar observation, albiet from a comparison of only insect genomes [Dipodomys ordii) has the highest density (34 Kb/Mb) followed closely by the house mouse . As a subgroup, mammals show little variance in SSR densities   and the Plasmodium species of the Apicomplexans subgroup (SSR density 124 Kb/Mb) have the highest SSR densities among all groups examined while those belonging to the Oomycetes class and the Entamoeba species have significantly lower SSR densities such as Giardia lamblia (SSR density 5228 bp/Mb) and Aphanomyces invadans ; in fact they occupy the lowest end of the density spectrum among all 719 species  and these SSRs are notably GC-rich (SSR GC range of around 60%). It is interesting to note that these protists have such a high SSR density despite their small genome size. Fungi have the lowest average SSR density among all the subgroups while among vertebrates, birds have the lowest average, again with very little variance  that has a very high SSR density of 61 Kb/Mb . Further analysis of SSR content in this genome revealed multiple instances of long C/G stretches (upto 4 kb) which are likely to be assembly errors creating an artifact in the SSR density calculations.Protists, on the other hand, occupy the highest density ranges and show the greatest variance among individual organismal SSR densities - upto 27 fold difference between the highest and lowest SSR densities among all protists  with GC-rich genomes (genomic GC 55–65%) have an abundance of GC-rich SSRs (SSR GC range 75–100%). SSRs of intermediate GC content (SSR GC range 50%) are abundant in fungi. Protostomes and deuterostomes, however, have uniformly AT-rich SSRs (SSR GC range < 25%). We note that there is no correlation between the overall SSR abundance of an organism and its genomic GC content  that are not very abundant in multicellular organisms. Green algae show a high abundance of some GC-rich repeats  correlating with their high average genomic GC content of 61.3%. Interestingly, in many fungi, especially from the Ascomycetes and Microsporidia subgroups, upto 95% of the repeat classes appear to be missing  though this trend shows a sharp change in the Basidiomycetes subgroup where only a few SSR classes are absent. Some of the Protists  and green algae  too show a similar trend. Most other organisms (486/719 organisms) have some representation of all 501 classes of SSRs. Notably, the ACGCGT repeat is absent in about 64% of the organisms across evolution , except for bees, ants, wasps, and some fungi. Similarly, AGCGCT (red arrow) also appears to be unpopular, missing from about 51% of the genomes, including all deuterostomes. Both these repeats in fact have the smallest lengths of occurrence as well (see below) suggesting that they are not well tolerated in genomes.Leishmania (F7) and Drosophila  species as well as in higher organisms such as birds (I4), ruminants , and in primates  (Table p > 0.05) and are hence not analyzed further. Notably, the enrichment trend for specific SSRs is sharply contained within each of these groups of organisms . The estimated divergence time point from the common ancestor is retrieved from Timetree (http://www.timetree.org), Ma = Million Years Ago.SSR rank is calculated based on relative density compared to other SSRs within the organism as described in Fig. Rhincodon typus . We next looked at other longest repeats apart from the telomeric repeat and found that AT and AAT also frequently appear as the longest SSRs . The longest SSR seen among all organisms is almost 52 kb long – 12,980 perfectly repeating units of AAAT in the mammalian Cercocebus atys mapping to an intergenic region, at a distance of 23.5 kb from the nearest gene, LOC105598351.We looked at the length of each SSR across all occurrences in the 719 organisms. As expected, longer SSRs were found to be present in the larger genomes  and mammals , their SSR length range is higher than reptiles and mammals .We then analyzed the length distribution for each SSR in a subgroup-specific manner Fig. a. As a sp < 10e-5, Fisher’s exact test). Instead, AGAT is the predominantly represented long SSR in fishes and reptiles, and AAAG in mammals. Dimers of AC, AG and AT vary in their representation as long stretches in different subgroups; plants have maximal representation of AG and AT as longest SSRs while animals generally have longer AC stretches than AG or AT stretches . We next calculated the median from the 1000 longest instances of each SSR class to check for the preference of various SSR classes to exist as long repeats . This distribution remains the same across all the 7 subgroups, correlating with the respective genomic percentages of intergenic regions, introns and exons. We checked for specific differences in the genomic distribution of SSRs with respect to the repeat motif size. Introns and intergenic regions mostly show a similar distribution except in monomers which are slightly enriched in introns G tend to have the longest lengths of occurrence. Earlier studies  have shoan cells . In thisThe sharp boundaries of change in SSR abundance coincide perfectly with the phylogeny of groups and subgroups. We were able to identify many taxon-specific microsatellite patterns within closely related species. In the light of the high polymorphism of microsatellites normally seen among individuals of the same species, identification of tightly conserved species-specific enrichment is useful in the context of microsatellite markers. Whether specific enrichment of a particular SSR indicates functional significance or mirrors close evolutionary relationships remains to be verified experimentally. Repeat elements have long been thought to have functional, mutational and evolutionary significance , 26 and Dictyostelium and Plasmodium species harbor AT-rich SSRs while Eimeria and Micromonas species have GC-rich repeats. Green algae have a preponderance of GC-rich SSRs while most fungal SSRs are of intermediate GC content. On the other hand, complex genomes, including in land plants, carry only AT-rich SSRs with most GC-rich repeats having been filtered out  of specific SSRs in the context of their genomic locations. However, the current level of genome annotation does not allow such an analysis at this scale, limiting this study to the analysis of SSR distribution trends in exonic, intronic and intergenic regions for 334 genomes where such annotations are available. The observations presented in this work serve as a snapshot of eukaryotic genomes in the context of perfect SSRs – instances of repeats that were preserved without even a single nucleotide change. We have not considered imperfect SSRs in our study to avoid limitations posed by currently available imperfect SSR identification tools. Hence, this work cannot address whether these elements are a consequence of DNA repair / replication errors or mutational mechanisms. Different taxonomic groups have distinct patterns of microsatellite presence and abundance, representing their establishment in a common ancient founding member of all organisms in the group. Evolutionary trends thus correlate with the preferential and selective inclusion of SSRs that may have been retained for the advantage they confer via novel regulatory mechanisms. These are testable hypotheses and the SSR signatures identified in this work can serve as a starting point for understanding this paradigm.We have identified nearly 685 million perfect SSRs across 719 eukaryotes to reveal constraints in SSR distribution across evolutionarily related species. Our results provide a comprehensive framework to understand microsatellite features in genomes. We have documented novel species- and subgroup-specific patterns of SSR enrichment that offer insights into microsatellite conservation among taxonomic groups. Interestingly, some defined patterns seem to persist in genomes, especially across multicellular and complex organisms, suggesting a role for SSRs in the genome regulatory toolkit.ftp://ftp.ncbi.nlm.nih.gov/genome). The primary classification of each organism was adopted from NCBI using the hierarchical classification of kingdom, group, and subgroup. As our study was limited to eukaryotic species, all organisms belonged to the kingdom “Eukaryota”; the group Animalia was subdivided into Protostomes and Deuterostomes. In order to investigate globally conserved evolutionary trends, we utilized the subgroup information of NCBI to categorize all the species. Poorly represented subgroups (< 5 species) were removed from the analysis to avoid sampling error; these included 2 poriferans, 3 cnidarians, 3 flatworms, 4 arachnids and a few singleton species belonging to distant taxa. We preferred not to merge clades containing few representatives as this could confound the trends in favor of the most represented sub-clade. The final list consisted of 719 species spanning 15 subgroups , an interactive tool for generation and exploratory analysis. The color scale on the heatmap ranged from a high score of 3 (black) to a lowest of − 2 (red) as described above and in Fig. SSR frequency, base coverage, and density for each of the 501 repeat classes were calculated in each organism using in-house Python scripts. To identify repeats that are enriched/absent in various sets of organisms independent of their taxa, we ranked all the repeat classes based on their density in each organism. Briefly, we first gave the lowest score of − 2 to those repeats which had a frequency of < 10 in a given organism, to reduce sampling bias. Further, we assigned scores 3, 2, and 1 to repeats with the top 10, 25 and 100 ranks in the genome, respectively. Repeats in the bottom 100 ranks and frequency of at least 10 were given a score of − 1. All other repeats were assigned a score of 0. A matrix was built using the score information, where each row represents an organism and columns represent the repeat classes. Hierarchical clustering of the repeat classes was done using the Euclidean distance between columns of the matrix. This scoring system was used to maximize the clustering of repeats based on similarities in enrichment/absence, as Euclidean distance is sensitive to variance in the data. The clustered matrix was visualized as a heatmap using Morpheus  formed by repeating the base motif in tandem. The 5 groups of SSR GC content are <=25%, 26–49, 50%, 51–74%, > = 75%, which encompass 70, 120, 133, 108, and 170 motifs respectively.ftp://ftp.ncbi.nlm.nih.gov/genome). Annotation for SSRs was done based on the GFF files using an in-house Python script  followed by Tukey’s post-hoc tests were done using aov and TukeyHSD functions of R respectively, using a confidence interval of 0.99. Fisher’s exact test was done using fisher.test function of R. Plots were made using ggplot2 and the Plotly API of R and Python unless specified otherwise.Two sample t-test was done using t.test function in R. Pairwise calculations were done using pairwise.t.test in R, and Additional file 1:Table S1. Mastersheet of SSR attributes and genomic information for 719 organisms arranged in evolutionary order. (XLSX 129 kb)Additional file 2:Figures. S1-S9. Along with their legends. (PDF 1221 kb)Additional file 3:Table S2. Table of SSR- and species-wise median lengths of longest repeat instances (XLSX 101 kb)Additional file 4:Table S3. Table of length preferences (DOCX 13 kb)Additional file 5:Table S4. Table explaining the classification of SSR motifs. Repeat classification is shown for a normal motif (AAG), a palindrome (ACGT) and a cyclical variation of a palindrome  (XLSX 8 kb)Additional file 6:https://software.broadinstitute.org/morpheus/) (JSON 868 kb)Heatmap.json. Organisms and their SSR information, to be viewed as a heatmap in Morpheus Additional file 8:GenomeAnnotation.py. Python script for genome annotation (PY 13 kb)


MNAT1 , a cyclin-dependent kinase-activating kinase (CAK) complex, high expresses in various cancers and is involved in cancer pathogenesis. However, mechanisms underlying its regulation in carcinogenesis are unclear..The tissue microarray of colorectal cancer (CRC) was used to evaluate MNAT1 expressions in CRC tissues using immunohistochemistry, CRC cell lines were also detected MNAT1 expression using Western-blotting. MNAT1 and shMNAT1 vectors were constructed, and transfected into CRC cells. Cell growths of the transfected cells were observed using MTT and colony formation. The affects of MNAT1 on p53 expression were analyzed using Western-blotting and Real-time PCR. Immunoprecipitation assay was used to analyze the interaction p53 and MNAT1, and Western-blotting was used to test the effects of MNAT1 on p53 downstream molecules. The apoptosis of CRC cells with MNAT1 or shMNAT1 were analyzed using flow cytometry. BABL/c athymic nude mice were used to observe the effect of MNAT1 on CRC cell growth in vivoMNAT1-knockin increased CRC cell growth and colony formation, and MNAT1-knockdown dramatically decreased cell motility and invasion. MNAT1 physically interacted with p53, MNAT1 also increased the interaction of MDM2 with p53. Strikingly, MNAT1 mediated p53 ubiquitin-degradation. MNAT1 shortened p53 half-life, and ectopic MNAT1 expression decreased p53 protein stability. Moreover, MNAT1 induced RAD51 and reduced p21, cleaved-caspase3, cleaved-PARP and BAX expression. MNAT1 inhibited CRC cell apoptosis. shMANT1 decreased tumor growths in nude mice following p53 increase.MNAT1 was found to be overexpressed in CRC tissues and cells, and MNAT1 expressions in CRC tissue samples were associated with CRC carcinogenesis and poor patient outcomes. MNAT1 binds to p53, mediates p53 ubiquitin-degradation through MDM2, increases cell growth and decreases cell apoptosis, and finally promotes CRC malignance. MNAT1 binding to p53 and mediating p53 ubiquitin-degradation axis represents a novel molecular joint in the p53 pathway.The online version of this article (10.1186/s13046-018-0956-3) contains supplementary material, which is available to authorized users. Colorectal cancer (CRC) is one of the most common malignancies worldwide, with approximately 1.2 million new cases and 608,700 deaths every year . This prMNAT1  was initially identified as the third subunit besides CDK7 and Cyclin H in cyclin-dependent kinase-activating kinase (CAK) complex –12. MNATChemical reagents for molecular biology were purchased from Sigma-Aldrich . Dulbecco’s modified Eagle medium (DMEM) and other supplements were obtained from Life Technologies . Antibodies against p53, p21, PARP, cleaved-PARP, RAD51, caspase3, cleaved-caspase3, BAX, MDM2, and BCL-2 were purchased from Abnova Company . Antibodies against MNAT1, HSP70, GAPDH, HA and Flag were purchased from Santa Cruz Biotechnology, Inc.  and Cell Signal Technology, Inc. .Human tissue microarrays containing 80 pairs of CRC tissues and corresponding adjacent non-tumor tissues, and 20 cases of CRC cancers at various stages were purchased from Outdo Biotech Company . One hundred patients enrolled into this study contained 57 males and 43 females. The median age of the patients was 46.5 age years (range 35–76), < 50 age year patients were 34 cases, > 50 age years patients were 66 cases. The tumor histology and stages were classified according to the WHO classification and the TNM staging system of the UICC, respectively. Patients in T1-T2 stages were 37 cases, and T3-T4 patients were 63 cases. Patients in N0 stage were 39 cases, and N1-N3 patients were 61 cases. Patients in M0 stage were 34 cases, and M1 patients were 66 cases. These tissue microarrays (HcolA180su10) were stained with MNAT1 antibody (dilution 1:5000) as described previously . The sta+/+, HCT116 p53−/−, and HEK293T (an embryonic kidney cell line 293 T) were obtained from American Type Culture Collection (Maryland). All the cell lines were grown in DMEM supplemented with 10% fetal bovine serum (FBS) at 37 °C and in 5% CO2.CRC cell lines, SW480, HT-29, SW620, DLD1, HCT116, loVo, RK0, HCT116 p53MNAT1 DNA fragment was generated by polymerase chain reaction (PCR) and cloned into pSIN-vector containing a FLAG, HA or V5 tag sequence. PT53 was generated using PCR and cloned into vector containing HA or FLAG. Short hairpin RNAs (sh) target MNAT1, and shMDM2 targets MDM2. shMNAT1#1 and shMNAT1#2 were designed, and shMNAT1 and shMNAT1#2 sequences are shown in Additional file PT53 and MNAT1 domains were generated using Quick-Change Site-Directed Mutagenesis Kit . PCR primers used are listed in Additional file 4 of HCT116 and DLD1 cells were transfected with 2 μg DNA of pSIN, pSIN-MNAT1, pLVX-shMNAT#1, pLVX-shMNAT1#2 or pLVX-shscramble following the manufacture’s suggested protocol. HEK293T cells were transfected with pSIN or pSIN-MNAT1. The stably transfected cell lines, pSIN-HCT116, MNAT1-HCT116, pSIN-DLD1, MNAT1-DLD1, shscramble-HCT116, shMNAT1#1-HCT116, shMNAT1#2-HCT116, shscramble-DLD1, shMNAT1#1-DLD1, shMNAT1#2-DLD1, pSIN-HEK293T, and pSIN-MNAT1-HEK293T were obtained by selection and further confirmed by assessing MNAT1 expression.Gene transfection and stable cell line establishment were performed as described previously . Briefly6 cells were lysed with lysis buffer . Cell lysates obtained were centrifuged, and protein concentration of the clarified lysates was measured using Easy II Protein Quantitative Kit (BCA). 40 μg of the supernatant protein was separated by 10% SDS-PAGE and transferred onto a nitrocellulose membrane. The blot was blocked with 5% non fat milk, incubated with the indicated antibody, and then incubated with an appropriate peroxidase conjugated secondary antibody. The signal was developed using 4-chloro-1-napthol/3,3-o-diaminobenzidine, and relative photographic density was quantified by a gel documentation and analysis system. GAPDH or HSP70 was used as an internal control to verify basal expression levels and equal protein loading. The ratio of the specific proteins to GAPDH orHSP70 was calculated. 100 μg of the clarified supernatants were immunoprecipitated using anti-FLAG-agarose or anti-HA-agarose antibody . MNAT1 or p53 in the immunoprecipitated complexes was respectively determined by Western-blotting with anti-MNAT1 or anti-p53 antibody.Western-blotting and immunoprecipitation were performed as described previously . Briefly4 cells of shscramble-HCT116, shMNAT1#1-HCT116, shMNAT1#2-HCT116, pSIN-HEK293T, and pSIN-MNAT1-HEK293T were seeded on six-well plates and cultured to reach 70% confluence, and were treated with 10 or 80 μg/ml 5-fluorouracil (5-FU). After 24 h treatment, the cells were collected by 0.02% trypsin without eathylene diamine tetra acetic acid (EDTA), and stained with annexin V-EGFP (Enhanced Green Fluorescent Protein) and propidium iodide (KeyGen Biotec) according to the manufacturer’s recommendations, and analyzed by flow cytometry.Apoptosis analysis was performed as described previously . Briefly#1-HCT116, shMNAT1#2-HCT116, shscramble-DLD1, shMNAT1#1-DLD1, and shMNAT1#2-DLD1 cells (1 × 103) were seeded in 96-well microplates. The cells were cultured for the indicated time, followed by incubation with MTT for 4 h. Optical density (OD) was determined at 450 nm using a microplate reader. Measurements were acquired once per day for 5 d. For the colony-formation assay, the cells were plated at a density of 500 cells/well in six-well plates and were cultured for 12 d. Colonies were fixed in methanol, stained with 0.5% gentian violet, and counted  assays as described previously . Briefly counted . ResultsReal-time PCR was performed as described previously . Briefly6 cells shscramble-HCT116, shMNAT1#1-HCT116p53+/+ or shMNAT1#1-HCT116p53−/−, 6 mice per group. Tumor sizes of nude mice were measured every 2 or 3 d, and tumor volume was estimated. After 17 days, the mice were euthanized, and the tumors were removed and weighed.In vivo tumor growth assays were performed as described previously . Briefly#1-HCT116, shscramble-DLD1, and shMNAT1#1- DLD1 cells were detected using Boyden chamber invasion assay in vitro. Briefly, for invasion assay, matrigel  was added into the chamber to be 8 mm pore size polycarbonate membrane filters. The cells were trypsinized to be suspension cells, and were seeded into the Boyden chamber  at the upper part at a density of 1.5 × 104 cells/well in 50 μl of serum-free medium, and then incubated for 12 h at 37 °C. The bottom chamber also contained standard medium with 20% FBS. The cells invaded to the lower surface of membrane were fixed with methanol and stained with hematoxylin and eosin. Invaded cell numbers were counted under a light microscope. The motility assay was carried out as described in the invasion assay with no coating of matrigel.Cell invasion and motility were assayed according to the methods described previously with minor modifications . Cell in#1- LoVo cells were treated with 10 mg/mL cycloheximide (CHX), and the treated cells were collected at indicated time points after CHX treatment for 0, 20, 40, 60, 90 and 120 min. Protein of the collected cells was extracted for performing Western-blotting with anti-p53 or anti-MNAT1 antibody. GAPDH was used as an internal control to verify basal level expression and equal protein loading. The abundance ratio to HSP70 was counted, and half-life time of the proteins was calculated.Protein half-life was determined as described previously . Briefly#1 or shMNAT1#2. The stable cell lines were cotransfected with plasmids expressing 3Flag-p53 and HA-ubiquitin. The cells were lysed in lysis buffer. The cell lysates were centrifuged. The supernatants were immunoprecipitated with anti-Flag agarose, and the immunocomplexes were immunoblotted using anti-HA antibody.In vivo ubiquitination assay was performed as described previously , 38. Brip = 0.042). The positive rates of MNAT1 expression were compared in normal colorectal, primary CRC, and metastatic CRC tissues. The positive rates of MNAT1 were 11.3% in normal tissues, 55.9% in primary CRC and 56.1% in metastatic CRC tissues, respectively (Table p = 0.031) and metastatic CRC tissues (Table p = 0.033), whereas no difference of MICAL2 between primary CRC and metastatic CRC tissues. In addition, MNAT1 level may be used for the CRC diagnosis, and the area under the curve (AUC) was calculated. Cut off of MNAT1 was 4.6 . Sensitivity was 87.6%, and specificity was 67.7%. The association of MNAT1 expression with CRC stages was analyzed. MNAT1 expression was not correlated with T stage  (Table p = 0.882), N stage (lymph node metastasis) (Table p = 0.891), nor M stage (distant metastasis) (Table p = 0.897). The patients with high MNAT1 displayed shorter overall survival than low MNAT1 expression . These data strongly suggest that high MNAT1 has oncogenic potency and is associated with CRC poor outcomes.To clarify MNAT1 expression in CRC cells, CRC cell lines, SW480, HT-29, SW620, DLD1, RK0, LoVo, and HCT116 cells were detected MNAT1 expression using Western-blotting. Compared with HEK293T, MNAT1 protein levels were mostly elevated in SW480, HT-29, SW620, DLD1, RK0, LoVo, and HCT116  and DLD1 cells  increased when being transfected with MNAT1. Further, cell colony formation of the transfected cells was detected. MNAT1 dramatically increased colony formation of HCT116  and DLD1 cells . Next, shMNAT1#1 and shMNAT1#2 were designed to target MNAT1, and pLVX-shMNAT1#1 and pLVX-shMNAT1#2 were constructed. HCT116 and DLD1 were infected with pLVX-shMNAT1#1 and pLVX-shMNAT1#2. Western-blotting and real-time PCR were performed to evaluate the efficiency of shMNAT1#1, 2. The results showed that the shMNAT1#1, 2 effectively blocked MNAT1 protein  and DLD1  growths were decreased when compared with the scramble control. Simultaneously, motility and invasion of the transfected cells were also observed, 10 fields were randomly selected and counted the invaded cells per cell well. The results showed that the motility and invasion of MNAT1-knockdown cells were dramatically decreased when compared with the scramble group .HCT116 and DLD1 cells with low MNAT1 expressions were used to investigate MNAT1 function in CRC cell growth. We constructed MNAT1 expression vector, pSIN-MNAT1. HCT116 and DLD1 cells were transfected with pSIN-MNAT1. MNAT1 was detected in the transfected cells using Western-blotting, and the results displayed that MNAT1 was overexpressed in the transfected cells Fig. A. ViabilPT53 mRNA expressions were detected. The results showed that p53 expressions significantly decreased after being transfected with MNAT1 . To further confirm whether MNAT1-mediated p53 decrease is through MDM2, stable cell line, MANT1-HEK293 cells were knockdown MDM2 with shMDM2 to observe p53 expression. The results showed that MNAT1 significantly decreased p53 expression, but p53 decrease was not significant when MDM2 knockdown , and pSIN-MNAT1 reduced 5-FU-induced apoptosis . Collectively, MNAT1 decreases p53 expression, and regulates p53 downstream molecules, reduced cell apoptosis.The above findings showed that MNAT1 decreases p53 through ubiquitin-degradation. In the next step, we investigated whether MNAT1-decreased p53 affects p53 downstream genes. HEK293T cells were transfected with pSIN-MNAT1, and then p53, p21, caspase, PARP, RAD51, BAX, and Bcl2 were tested. p53, p21, cleaved- caspase 3, cleaved-PARP and BAX were decreased in the transfected cells, and RAD51 was increased Fig. A. To furlls Fig. B. DOX islls Fig. C. Whethells Fig. D. Simult#1-HCT116 cells were subcutaneously injected into the dorsal flanks of mice. The tumors of mice were measured per 2 d. After 17 days, the mice were euthanized, and tumor weights were measured. Data showed that the tumors of mice injected with shMNAT1#1-HCT116 were smaller than that of the shscramble mice . MNAT1 and p53 expression was detected in these tumor tissues using immunohistochemistry, and positive cells were counted in 10 fields of the IHC stained section under microscopy. Immunohistochemical results showed that MNAT1 expression was low, and p53 was high in the tumor tissues of shMNAT1#1-HCT116 when compared with the shscramble group . These results indicated that MNAT1 promoted CRC growth through down-regulating p53 in vivo. Summarily, MNAT1 binds to p53, and mediates p53 ubiquitin-degradation through MDM2, decreased p53 functions, and finally promotes CRC growth  In vivo, MNAT1-knockdown decreases tumor growth. These results suggested that MNAT1 promotes CRC development. Determination of the underlying mechanism indicated that MNAT1 promotes CRC development through downregulating p53.The adenoma–carcinoma multistage theory has been documented for CRC carcinogenesis. In this carcinogenesis process, mutation activating multiple oncogenes and inactivating tumor-suppressor genes accumulate in normal colonic epithelial cells and cause adenomas . Our resTP53 gene, is a central player in cellular DNA damage responses and is mutated in 50 to 55% of human cancers, whose primary function is to promote cell-cycle arrest and induce apoptosis when necessary [Tumor suppressor p53, encoded by ecessary . p53, whecessary , plays aecessary . p53 is ecessary . Of the ecessary . Loss ofecessary –58. In tecessary . p53 medecessary , 61. In ecessary , 63. MANMNAT1 knock-in increased MDM2 interaction with p53, and this interaction was decreased when MNAT1 knockdown. MDM2-knockdown decreased MNAT1-reduced p53. These data suggest that MNAT1-mediated p53 ubiquitin-degradation is through increasing the interaction of MDM2 with p53. MDM2 may be a critical factor in MNAT1 mediated-p53 ubiquitination.Interestingly, MNAT1 interacted with p53 and promoted p53 ubiquitination and degradation. MNAT1-mediated p53 degradation may be critical for CRC initiation and progression. p53 activity can be regulated through ubiquitination, oxidation, phosphorylation, acetylation and methylation –66. The MNAT1 binds to p53, promotes p53 ubiquitin-degradation, and decreases its function. MNAT1-reduced p53 decreases CRC cell apoptosis and increases CRC cell growth both in vitro and in vivo, thus promoting CRC malignance Fig. D. MNAT1 Additional file 1:Table S1. MNAT1 shRNA sequence. (DOCX 15 kb)Additional file 2:Table S2. PCR primers of plasmids construct. (DOCX 16 kb)Additional file 3:Table S3. Primers of Real-time PCR. (DOCX 15 kb)