Background: Artemisinin-based combination therapies (ACTs) are the global mainstay treatment of uncomplicated Plasmodium falciparum infections. PfMDR1 and PfCRT are two transmembrane transporters, associated with sensitivity to several antimalarials, found in the parasite food vacuole. Herein, we explore if their relatedness extends to overlapping patterns of gene transcriptional activity before and during ACT administration.Methods: In a clinical trial performed in Tanzania, we explored the pfmdr1 and pfcrt transcription levels from 48 patients with uncomplicated P. falciparum malaria infections who underwent treatment with artemether-lumefantrine (AL). Samples analyzed were collected before treatment initiation and during the first 24 h of treatment. The frequency of PfMDR1 N86Y and PfCRT K76T was determined through PCR-RFLP or direct amplicon sequencing. Gene expression was analyzed by real-time quantitative PCR.Results: A wide range of pre-treatment expression levels was observed for both genes, approximately 10-fold for pfcrt and 50-fold for pfmdr1. In addition, a significant positive correlation demonstrates pfmdr1 and pfcrt co-expression. After AL treatment initiation, pfmdr1 and pfcrt maintained the positive co-expression correlation, with mild downregulation throughout the 24 h post-treatment. Additionally, a trend was observed for PfMDR1 N86 alleles and higher expression before treatment initiation.Conclusion:pfmdr1 and pfcrt showed significant co-expression patterns in vivo, which were generally maintained during ACT treatment. This observation points to relevant related roles in the normal parasite physiology, which seem essential to be maintained when the parasite is exposed to drug stress. In addition, keeping the simultaneous expression of both transporters might be advantageous for responding to the drug action. Plasmodium falciparum malaria is responsible for nearly half a million deaths every year, despite the recent worldwide reduction in the incidence of the disease. Worryingly, the trend of decreasing malaria mortality has stagnated since 2015, followed recently by a steep increase due to the impact of the Covid-19 pandemic   and P. falciparum chloroquine resistance transporter (PfCRT), have been shown to be pivotal in the parasite response to ACT partner drugs.The main culprit of artemisinin reduced susceptibility, clinically characterized as an infection with a clearance half-time of >5 h , has bee (PfK13) , with ot (PfK13) . HoweverPfMDR1  is a ca. 1419 amino acid protein comprising 12 trans-membrane domains, and a member of the ATP-binding cassette (ABC) superfamily (PfMDR1 is oriented toward the digestive vacuole (DV) lumen and works as an importer of solutes toward this organelle, including antimalarials (PfMDR1 single nucleotide polymorphisms (SNPs), and of particular importance, the N86Y allele, have been associated with in vivo and in vitro sensitivity to a range of quinoline antimalarials  and artemisinin derivatives , significant malnutrition, signs of severe malaria, or any other danger signs.Briefly, 50 patients between 1 and 10 years old, 31 girls and 19 boys, were included with microscopically confirmed acute uncomplicated All patients were hospitalized and followed up for 72 h during the AL treatment course. Six weight-adjusted doses of Coartem®  were administered under supervision at 0, 8, 24, 36, 48, and 60 h. Patients weighing 5–14 kg received one tablet/dose, patients weighing 15–24 kg received two tablets/dose, and patients weighing 25–34 kg received three tablets/dose. A clinical assessment was performed at 0, 2, 4, 8, 16, 24, 36, 48, 60, and 72 h. Parasite densities were determined in Giemsa-stained and parasite clearance (PC) was evaluated as a proportion of patients with positive microscopy . Venous The study was compliant with the ethical principles of the Declaration of Helsinki and ethically cleared by the National Institute for Medical Research, Dar-es-Salam, Tanzania, and the Regional Ethics Committee, Stockholm, Sweden, for the downstream molecular analysis of bio-samples.RNA was extracted using an ABIPRISM H6100 Nucleic Acid PrepStation , with the total RNA quality and quantity measured with an Agilent RNA 6000 Pico total RNA assay in an Agilent 2100 Bioanalyser™ . cDNA synthesis was performed with a High-Capacity cDNA Reverse Transcription Kit  for the first six time points . Two out of 50 patient sample sets were excluded from this study due to low total RNA quality.pfcrt and pfmdr1 transcripts were performed with an ABIPRISM® 7900HT Sequence Detection System . TaqMan® probes and primer sequences for target genes, pfmdr1 (PF3D7_0523000), pfcrt (PF3D7_0709000), and the endogenous control gene seryl-tRNA synthetase (PF3D7_0717700) that was shown to be transcribed stably throughout different intraerythrocytic stages ; pfcrt, ; seryl-tRNA synthetase . Real-time analyses of the c stages . Oligonud clones , pfmdr1,PfCRT K76T and PfMDR1 N86Y, Y184F and D1246Y alleles were analyzed for all samples before treatment initiation. PfCRT K76T and PfMDR1 N86Y were further analyzed during the treatment period for the time points 2 h (T2), 4 h (T4), 8 h (T8), 16 h (T16), and 24 h (T24) hours.PfMDR1 N86Y, Y184F, and D1246Y SNPs were analyzed through direct PCR amplicon sequencing, as previously described  with probes and primers, as described by P. falciparum DNA from 3D7 and FCB reference strains were used as the calibrator and the positive control, respectively (known copy number variation). All reactions were performed in an ABI PRISMH 7000 Sequence Detection System .PfCRT K76T SNP was evaluated through published PCR-RFLP methods . PfMDR1 escribed . The pfmhttp://www.integromics.com/StatMiner) was used to obtain the ΔCt  for each patient. The amplification efficiency was used as the correction factor as previously defined (-ΔCt was used to obtain the fold change expression pre-treatment (T0). For each patient, the pfmdr1 and pfcrt transcript fold change relative expression was calculated by the 2-ΔΔCt method  was observed for both genes. pfcrt transcripts span from 1.45 to 13.80-fold as compared with the internal control gene (PF3D7_0717700), whereas pfmdr1 showed a larger window from 2.19 to 56.79-fold  supporting the possibility of these genes being functionally associated with the normal physiology of the parasite  in T2 to 0.70 ± 0.79-fold (±SD) in T24, decreasing at all time points. The pfcrt relative expression decreased from 0.90 ± 0.42-fold (±SD) in T2 to 0.37 ± 0.30-fold (±SD) in T24, decreasing at all time points, except from T16 to T24 which remained constant. Of note, a subset of infections  showed a clear rise in pfmdr1 mRNA levels during the treatment period (threshold ≥1.5 fold).The most frequent individual occurrence for both genes was a decrease in the pfmdr1 increased copy number, with no such events having been detected.All enrolled patients were tested for infections carrying the PfMDR1 N86Y and PfCRT K76T mutations are considered risk factors for AL treatment failure (PfMDR1 N86 having a fold change of 19.7 ± 17.4 (±SD) versus the Y86 11.3 ± 10.2 fold change and PfCRT K76 having a fold change of 4.9 ± 0.5 vs the T76 3.8 ± 0.5  on T2 to 0.83 ± 0.91-fold (±SD) on T24 compared with PfMDR1 N86 allele, which decreased from 0.79 ± 0.29-fold (±SD) on T2 to 0.35 ± 0.20-fold (±SD) on T24.Concerning post-treatment expression levels, we did not detect significant differences between the me point ; Table 1PfCRT K76 and T76 alleles decreased in relative expression during the treatment from 0.80 ± 0.20-fold (±SD) and 1.10 ± 0.75-fold (±SD) on T0 to 0.34 ± 0.15-fold (±SD) and 0.37 ± 0.25-fold (±SD) on T24, respectively. T2 was the only time point at which a PfCRT allele (T76) had an increase in the mean of relative expression compared with pre-treatment.The PfMDR1 N86Y and PfCRT K76T SNP distribution (50 for PfMDR1 N86 was 6.48 ± 2.57 h (±SD) compared with 86Y of 6.09 ± 3.31 h (±SD). PC50 for PfCRT K76 was 5.67 ± 2.46 h (±SD) compared with 76T of 5.69 ± 3.63 h (±SD).Parasite treatment response, measured by parasite clearance (PC) time did not differ between ribution . PC50 foP. falciparum   . These o our own —drug exprocesses . Such prpression . From thulations .pfmdr1/pfcrt co-expression is not known. Of note, both genes harbor common putative xenobiotic response DNA elements in their 5’ promoter regions (up to 5 Kb upstream of the transcription initiation), especially nuclear receptor-recognition sequences  boxes, retinoic acid receptor (RAR) and retinoic acid receptor half-site (RXR) elements, and progesterone receptor-binding sites (PRE). It is conceivable that, similar to other eukaryotic systems (pfmdr1 product. Moreover, investigations using luciferase expression constructs to probe pfmdr1 promoter regions have shown these as significantly responsive (PfMDR1 protein content were also reported, albeit PfCRT stayed non-responsive, suggesting no significant involvement in our observations of the BARBIE Box (pfmdr1 to be induced might be ruled by the epigenetic status of the proximal 5′ promoter, with favorable conditions only statistically happening in a fraction of the parasite population at any time point. Further studies will be required to investigate this possibility.The molecular basis of the observed equences . Of inte systems , some of systems . This insponsive . FurtherRBIE Box . MoreovePfMDR1, which leads to significant increases in this gene transcript and associated protein, is a well-documented factor in the in vivo and in vitro parasite susceptibility to aminoalcohol quinolines and artemisinins (PfMDR1 N86 allele modulating AL therapy in Africa (pfmdr1 expression compared with the Y86 ones. In fact, if we consider any trend, it is for a higher expression among the 86Y carriers. The consistently observed selection of 86N alleles during AL treatment is more likely to be associated with SNP-driven changes in the structure of the protein, as predicted from in silico studies (An increased copy number of N86 carrying misinins . Linkingn Africa , we init studies  and not in vivo gene expression of two key markers of antimalarial resistance.A major limitation of the present study is the participant numbers, a factor related to the outstanding demands of these types of field studies. In addition, the nature of the study limited the availability of biological material for analysis, precluding the consistent use of multiple internal qPCR controls. However, the project’s exploratory nature did not prevent us from providing new insights into pfcrt and pfmdr1 genes during an ACT clinical exposure. By challenging the general view of a rather inert parasite transcriptome, at least concerning the genes under focus, we highlight the need to link valuable in vitro data on the parasite drug response mechanisms to the in vivo therapy context.In conclusion, this work represents, to our knowledge, the first detailed qPCR-based analysis of the parasite


The authors wish to make the following corrections to this paper :A: 5.89 ± 0.55 and 5.74 ± 0.38, respectively)”. The Log KA values were inconsistent with Figure 2 and the SI7.2.There was an error in the original publication. In Section 4.2, the article stated, “HSA binding affinities of perfluorohexanesulfonic acid (PFHxS) and perfluoroheptanoic acid (PFHpA) were exceptionally high ”.“The HSA binding affinities of perfluorohexanesulfonic acid (PFHxS) and perfluoroheptanoic acid (PFHpA) were exceptionally high (Log KThe authors apologize for any inconvenience caused and state that the scientific conclusions are unaffected. The original publication has also been updated.


Western blot, immunofluorescence, and quantitative real-time polymerase chain reaction were conducted to quantify SIRT1, APE1, p53, solute carrier family 7 member 11 (SLC7A11), and glutathione peroxidase 4 (GPX4) levels in GC cells. Silencing of SIRT1 decreased viability, GSH content, and expressions of GPX4 and SLC7A11, while increased Fe2+, MDA content, and p53 expression in GC cells. Such aforementioned effects were reversed by APE1 overexpression. Also, SiAPE1 generated the same effects as SiSIRT1 on the above aspects, which was offset by p53 silencing. In short, SIRT1/APE1 promotes the growth of GC cells by targeting p53 to inhibit ferroptosis.Gastric cancer (GC) is a common cancer worldwide with high mortality. Sirtuin 1 (SIRT1) and apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1) are abnormally expressed in GC cells and related to p53, which is involved in ferroptosis. Thus, we explore the mechanism via which SIRT1, APE1, and p53 impact ferroptosis in GC cells. Specifically, GC cells were transfected with small-interfering RNA for SIRT1 (SiSIRT1) or small-interfering RNA for APE1 (SiAPE1) or with short-hairpin RNA for p53, and the cell viability, Fe In the Ferroptosis is a form of non-apoptotic cell death characterized by iron-dependent lipid peroxidation . AccumulSirtuin 1 (SIRT1) is the founding member of class III histone deacetylases. In GC, SIRT1 is identified as a marker for prognosis and is involved in cell proliferation, cell cycle, autophagy, and drug resistance ,15,16. AMoreover, it has been reported that SIRT1 silencing leads to the death of human embryonic stem cells via suppressing apurinic/apyrimidinic endodeoxyribonuclease 1 (APE1), a member of DNA repair enzymes . APE1 isin vitro GC model was established using two GC cell lines in this study aiming to explore the regulatory network of SIRT1, APE1, and p53 in ferroptosis of GC cells.In light of this, an 22.12. According to the cell growth conditions, the medium was changed every 2 days.Human GC cell lines including SNU-1 (CRL-5971) and AGS (CRL-1739) were acquired from the American Type Culture Collection . These GC cells, accordingly, were grown in the Rosewell Park Memorial Institute-1640 medium  supplemented with 10% fetal bovine serum  and 1% antibiotic/antifungal reagent  at 37°C with 5% CO2.2® 3000  according to the manufacturer’s instructions. The plasmid overexpressing APE1 was constructed with pcDNA vector . The empty pcDNA vector was used as a negative control (NC). Besides, the small-interfering RNA for SIRT1 , small-interfering RNA for APE1 , short-hairpin RNA for p53 , small-interfering RNA for NC , and short-hairpin RNA for NC  were designed and constructed by GenePharma .The transfection was conducted using LipofectamineSubsequently, SiNC, NC, SiSIRT1, SiAPE1, Shp53, and plasmid overexpressing APE1 were separately transfected into GC cells, while SiNC and NC, SiSIRT1 and plasmid overexpressing APE1, SiNC and ShNC, or SiAPE1 and Shp53 were co-transfected into other GC cells. After incubation for 48 h, these GC cells were collected for later experiments.2.3−ΔΔCt method with glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as the normalization control [Total RNA was extracted from GC cells with Total RNA Extractor  and the purity was determined by NanoDrop™ One/OneC UV-Vis Spectrophotometer . Then, 1 µg RNA was reversely transcribed into complementary DNA (cDNA) with Thermo Scientific RevertAid RT kit . Subsequently, the cDNAs were subjected to qRT-PCR with the specific primers of SIRT1, APE1, or p53 using Maxima SYBR Green/ROX qPCR  in the Mx3005P system . The expressions of SIRT1, APE1, and p53 were calculated and determined using the 2 control . The rea2.43 cells per well. Next, GC cells were transfected as aforementioned and cultured in a cell incubator  at 37°C with 5% CO2 for 48 h. After that, 10 µL CCK-8 solution  was added to every well and incubated at 37°C for 4 h. The optical density (OD) value of every well was measured by an ELISA microplate reader  at the wavelength of 450 nm.GC cells were collected at the logarithmic phase and resuspended in phosphate-buffered solution . Subsequently, 100 µL of cell resuspension was added to 96-well plates and the density was adjusted to 1 × 102.52+ content in GC cells, AGS and SNU-1. In detail, GC cells (5 × 105) were homogenized in Iron Assay Buffer and centrifuged  at 4°C for 10 min, followed by the collection of supernatant. Then, 50 µL of supernatant was mixed with 5 µL of Iron Assay Buffer in 96-well plates and incubated at 25°C for 30 min in the dark. Thereafter, 100 µL of Iron Probe was added to every well and cultured at 25°C for 60 min avoiding light. Finally, the OD value was measured at the wavelength of 593 nm with an ELISA microplate reader.The Iron Assay Kit  was applied to determine the Fe2.6g) at 4°C for 5 min, the supernatant was collected. Next, the supernatant and the standard were chilled in ice bath for 10 min. The samples were finally transferred to a new 96-well microplate and the OD value was calculated using an microplate ELISA reader at the wavelength of 532 nm.The concentration of malondialdehyde (MDA) in GC cells was detected by Lipid Peroxidation (MDA) Assay Kit . GC cells were first cultivated with thiobarbituric acid (TBA) solution at 95°C for 1 h and then lysed with RIPA lysis buffer . After the mixture was centrifuged  created by the mixture of standard and standard/sample diluent 1, followed by incubation for 45 min. After the incubation, the working fluid was removed, and 350 µL of washing solution was added to every well for 2 min. Thereafter, the samples were incubated with 100 µL of horseradish peroxidase (HRP)-conjugated streptavidin working solution at 37°C for 30 min. Next, 300 µL of washing solution was used to wash each well four times. After staining with 90 µL of chromogenic agent at 37°C for 15 min without light, 50 µL of stop solution was used to terminate the reaction and the OD value was measured by an ELISA microplate reader at the wavelength of 450 nm.The glutathione (GSH) level in GC cells was determined by GSH ELISA Kit . The GC cells were washed with cold PBS and digested with trypsin . Post centrifugation at 1,000 × 2.7® 647)  at room temperature for 1 h in the dark. Subsequently, cells were washed with PBS thrice for 5 min and stained with 4′,6-diamidino-2-phenylindole , followed by being sealed with cover-glass  away from light. The image was observed under a fluorescence microscope  at the magnification of ×200.GC cells at the logarithmic phase were collected and washed twice with PBS for 5 min. Then, cells were fixed with 4% paraformaldehyde  and washed three times with PBS for 5 min. Next, these cells were transparentized with Triton X-100  at room temperature for 10 min, rinsed with PBS, and incubated in an immunofluorescence blocking buffer  at room temperature for 30 min. Thereafter, cells were incubated with anti-GPX4 antibody  at 4°C overnight, followed by being washed three times with PBS for 5 min. Afterwards, cells were cultured with Goat Anti-Rabbit IgG H&L  Protein Assay Kit . Following this, they were added to the protein loading buffer  and heated at 95°C for 5 min to ensure the denaturation. Then, the proteins were separated by 12% sodium dodecyl sulfate polyacrylamide gel electrophoresis  and transferred onto polyvinylidene difluoride membranes . Subsequently, the membranes were blocked with bovine serum albumin  for 1 h and incubated with the primary antibodies against p53, SLC7A11, GPX4, or GAPDH (a loading control) at 4°C overnight. The expressions of proteins were normalized to that of GAPDH. After washing three times with tris-buffered saline wash buffer with Tween 20  for 5 min, the proteins were incubated with secondary antibodies including goat anti-rabbit IgG H&L (HRP) and rabbit anti-mouse IgG H&L (HRP) for 1 h and washed three times with TBST for 5 min. The protein bands were visualized with High Sensitivity ECL Substrate Kit  and analyzed with an iBright™ CL1500 Imaging System . The information about all antibodies in this experiment is exhibited in 2.9P < 0.05 were defined to be statistically significant.All experiments in this study were repeated three times. All data were analyzed using GraphPad Prism 8.0 software  and presented as mean ± standard deviation. The comparison among multiple groups was conducted by one-way analysis of variance. The data with 33.1P < 0.001) and AGS (P < 0.001) cells. The expression of APE1, contrarily, was clearly upregulated after the transfection of plasmid overexpressing APE1 in SNU-1 (P < 0.001) and AGS (P < 0.001) cells. These results indicated the successful transfection of SiSIRT1 and plasmid overexpressing APE1.The relevant results of qRT-PCR are shown in P < 0.001) and AGS (P < 0.001) cells, the change of which was obviously offset by APE1 overexpression (P < 0.01). Compared with the cells transfected with SiNC and NC, the memorably increased Fe2+ content was evident in SNU-1 (P < 0.001) and AGS (P < 0.001) cells transfected with SiSIRT1. Likewise, this impact of SiSIRT1 was neutralized by APE1 overexpression (P < 0.001).According to P < 0.001) and AGS (P < 0.001) cells, the trend of which was offset by overexpressed APE1 (P < 0.001). Collectively speaking, SIRT1 silencing decreased cell viability and GSH content but increased Fe2+ and MDA contents in SNU-1 and AGS cells. Such impacts, however, were reversed by APE1 overexpression.The graphical representation of GSH and MDA contents is shown in 3.2The results of immunofluorescence assay are presented in P < 0.001) and AGS cells (P < 0.001). Besides, in SNU-1 (P < 0.001) and AGS (P < 0.001) cells with the transfection of SiSIRT1, APE1 overexpression decreased p53 level but increased SLC7A11 and GPX4 levels. Taken together, the overexpression of APE1 reversed the effects of SIRT1 silencing on GPX4, SLC7A11, and p53 levels in SNU-1 and AGS cells.Based on the assay of Western blot, we found that following the transfection of SiSIRT1, the p53 protein level was remarkably augmented but SLC7A11 and GPX4 levels were signally lessened in SNU-1 (3.3P < 0.001) and AGS cells (P < 0.001). Likewise, the expression of p53 was also markedly downregulated after transfection with Shp53 in SNU-1 (P < 0.001) and AGS (P < 0.001) cells.The results of qRT-PCR  demonstrP < 0.001) and AGS (P < 0.001) cells, but p53 silencing abrogated APE1 silencing-induced suppression of cell viability (P < 0.01). Moreover, the transfection of SiAPE1 observably increased the Fe2+ content in SNU-1 (P < 0.001) and AGS (P < 0.001) cells; however, such effect was evidently offset after the transfection of Shp53 (P < 0.001).In line with the data presented in P < 0.001) and AGS (P < 0.001) cells. Such levels of MDA and GSH were pronouncedly reversed following the knockdown of p53 (P < 0.001). In short, APE1 silencing decreased cell viability and GSH content yet increased Fe2+ and MDA contents in SNU-1 and AGS cells, while these effects were reversed by p53 silencing.In addition, it was demonstrated that after APE1 silencing, the MDA content was memorably increased, while the GSH content was prominently decreased in SNU-1 (3.4As depicted in P < 0.001) and AGS (P < 0.001) cells. The changes in the expressions of these aforementioned proteins were significantly reversed by p53 silencing (P < 0.01). In conclusion, APE1 silencing downregulated GPX4 and SLC7A11 levels yet upregulated p53 level in SNU-1 and AGS cells, the effects of which were offset by p53 silencing.Based on the data , after t4In the present study, we found that SIRT1 silencing promoted p53 expression and ferroptosis yet inhibited the viability of GC cells, and APE1 overexpression reversed these effects of SIRT1 silencing on GC cells. Moreover, the depletion of APE1 had the similar effect to SIRT1 silencing in the GC cells, and the knockdown of p53 reversed the effects of APE1 silencing on GC cells. In conclusion, SIRT1/APE1 participates in the development of GC by targeting p53 to regulate ferroptosis.Existing study has already demonstrated that SIRT1 is involved in the cell proliferation, apoptosis, and survival through regulating target gene expression and protein activities . However2+ content and the level of MDA, a marker of oxidative stress-induced lipid peroxidation [Besides, some other studies pointed out that SIRT1 is implicated in the inhibition of ferroptosis ,40,41. Sxidation , were alxidation . In thisxidation  (a posit2+ content, and MDA content were decreased in cells co-transfected with SiSIRT1 and APE1 overexpression plasmid. It could be then concluded that APE1 silencing inhibited cell viability and promoted ferroptosis, while SIRT1 silencing promoted ferroptosis by inhibiting APE1 expression.A previous study recognized that SIRT1 silencing decreases the expression of APE1 and causes the death of human embryonic stem cell . Our res2+ content, MDA content, and p53 expression in GC cells transfected with ShAPE1. These discoveries indicated that APE1 might inhibit ferroptosis via inactivating p53-mediated signal pathway.APE1 is confirmed to be lowly expressed in GC cells  and be c5Collectively speaking, we identify that SIRT1 and APE1 can regulate ferroptosis in GC cells and affect the development of GC cells by targeting p53. However, it should be noticed that there may exist some other signal pathways and factors of ferroptosis involved. Accordingly, the regulatory network targeting ferroptosis in GC cells needs to be validated in further experiments.


Only the groove’s intracellular vestibule contacts lipids, and mutagenesis suggests scrambling does not require specific protein-lipid interactions with the extracellular vestibule. We find scrambling can occur outside a closed groove in thinner membranes and is inhibited in thicker membranes, despite an open pathway. Our results show afTMEM16 thins the membrane to enable scrambling and that an open hydrophilic pathway is not a structural requirement to allow rapid transbilayer movement of lipids. This mechanism could be extended to other scramblases lacking a hydrophilic groove.TMEM16 scramblases dissipate the plasma membrane lipid asymmetry to activate multiple eukaryotic cellular pathways. Scrambling was proposed to occur with lipid headgroups moving between leaflets through a membrane-spanning hydrophilic groove. Direct information on lipid-groove interactions is lacking. We report the 2.3 Å resolution cryogenic electron microscopy structure of the nanodisc-reconstituted Ca Lipid scrambling is required for many cellular processes but the lipid-protein interactions that occur during transport are unknown. Using cryoEM and biochemical assays the authors show that membrane thinning is critical for lipid flipping. The headgroup asymmetry of the plasma membrane is established by the action of ATP-driven pumps which distribute phosphatidylethanolamine (PE) and phosphatidylserine (PS) to the inner leaflet and phosphatidylcholine (PC) to the outer leaflet1. Activated phospholipid scramblases dissipate this asymmetry and expose PS on the extracellular leaflet. This is critical for multiple signaling pathways, ranging from apoptosis to blood coagulation, autophagy and cell-cell fusion2. There are five known families of scramblases, the Ca2+-activated TMEM165, the caspase-activated Xk-related (Xkr) proteins6, and the more recently identified ATG9, TMEM41B and VMP19. Additionally, several GPCR’s scramble lipids when reconstituted in liposomes11.Biological membranes play a fundamental role in many cellular signaling pathways as they define the physical boundaries of cellular compartments and actively modulate the function of integral and membrane-associated proteins. In eukaryotic cells, the composition and distribution of the phospholipid constituents of the membrane is tightly regulated by the activity of a variety of dedicated enzymes, flipases, flopases and scramblases12. Dysregulation of TMEM16 scramblase activity can have disastrous consequences, as both gain- and loss- of function mutations have been associated with disorders of blood, brain, bone and muscle16. The TMEM16 superfamily is comprised of Cl- channels and dual function scramblases/non-selective ion channels4. Both subtypes share a common homodimeric architecture where each protomer is comprised of 10 transmembrane (TM) helices23  to determine the 2.3 Å resolution structure of the afTMEM16 scramblase from 2+-bound conformation in nanodiscs at 2.3 Å  and 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-(1′-rac-glycerol)  nanodiscs20, with Cα rmsd ~0.8 Å. Thus, headgroup choice and acyl-chain saturation do not influence the conformation of the protein. The significantly improved resolution of the C18/Ca2+ map allowed us to resolve 4 water molecules in the Ca2+ binding sites which coordinate two bound ions , four above and four below the interacting residues  and ~3.9 nm (C20 lipids)  Fig. . In contds) Fig. 20. Thus,ess Fig. . Remarka3.2 nm C1 lipids a2+ by occluding the pathway40 we measured scrambling in membranes formed by 70% C22 lipids and 30% C18 lipids, which are ~4 nm thick  and to D2 and D6 (in MSP2N2 map) seen in the C18/Ca2+ map  we could resolve 4–9 lipids per monomer, all localized near the dimer interface in positions closely resembling those seen in C18/Ca2+ structure  and expression was induced with 2% (w/v) galactose at 30 °C for 22 h. Cells were collected, snap frozen in liquid nitrogen, lysed by cryomilling (Retsch model MM400) in liquid nitrogen , and resuspended 150 mM KCl, 10% (w/v) glycerol, 50 mM Tris-HCl, pH8 supplemented with 1 mM EDTA, 5 μg ml−1 leupeptin, 2 μg ml−1 pepstatin, 100 μM phenylmethane sulphonylfluoride and protease inhibitor cocktail tablets (Roche). Protein was extracted using 1% (w/v) digitonin (EMD biosciences) at 4 °C for 2 h and the lysate was cleared by centrifugation at 40,000 × g for 45 min. The supernatant was supplemented with 1 mM MgCl2 and 10 mM Imidazole, loaded onto a column of Ni-NTA agarose resin (Qiagen), washed with 150 mM KCl, 10% (w/v) glycerol, 50 mM Tris-HCl, pH 8 + 30 mM Imidazole and 0.12% digitonin, and eluted with 150 mM KCl, 10% (w/v) glycerol, 50 mM Tris-HCl, pH8 + 300 mM Imidazole and 0.12% digitonin. The elution was treated with Tobacco Etch Virus protease overnight to remove the His tag and then further purified on a Superdex 200 10/300 GL column equilibrated with buffer A supplemented with 0.12% digitonin (GE Lifesciences). The afTMEM16 protein peak was collected and concentrated using a 50 kDa molecular weight cut off concentrator .afTMEM16 was expressed and purified following established protocols30. Briefly lipids in chloroform (Avanti), including 0.4% w/w tail labeled NBD-PE, were dried under N2, washed with pentane and resuspended at 20 mg ml−1 in 150 mM KCl, 50 mM HEPES pH 7.4 with 35 mM 3-[(3-cholamidopropyl)dimethylammonio]-1- propanesulfonate (CHAPS). All proteins were added at 5 µg protein/mg lipids and detergent was removed using five changes of 150 mg ml−1 Bio-Beads SM-2 (Bio-Rad) with rotation at 4 °C. Calcium or EGTA were introduced using sonicate, freeze-thaw cycles. Chain length experiments were done in the background of 7PC:3PG due to the availability of the long chain lipids. Lipids used were: 1,2-dimyristoyl-sn-glycero-3-phosphocholine , 1,2-dimyristoyl-sn-glycero-3-phospho-(1′-rac-glycerol) , 1,2-dipalmitoleoyl-sn-glycero-3-phosphocholine (16:1), 1,2-dipalmitoleoyl-sn-glycero-3-(1′-rac-glycerol) (16:1) 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine , POPG (16:0-18:1), 1,2-dioleoyl-sn-glycero-3-phosphocholine , 1,2-dioleoyl-sn-glycero-3-phospho-(1′-rac-glycerol) , 1,2-dieicosenoyl-sn-glycero-3-phosphocholine (20:1), 1,2-dieicosenoyl-sn-glycero-3-(1′-rac-glycerol) (20:1), 1,2-dierucoyl-sn-glycero-3-phosphocholine  and 1,2- dierucoyl-phosphatidylglycerol .Liposomes were prepared as described29.Reconstitution efficiency of WT and mutant afTMEM16 in each condition was comparable, as estimated by SDS-PAGE electrophoresis of samples collected before and at the end of reconstitution procedure11. Briefly, in protein free liposomes, fluorescently labeled lipids evenly distribute between leaflets and addition of dithionite causes a ~50% decrease of fluorescence. In liposomes containing an active afTMEM16 scramblase, dithionite addition results in a nearly complete loss in fluorescence. Since not all liposomes contain an active scramblase and due to the presence of multilamellar liposomes, a fraction of NBD fluorophores is shielded from the dithionite, resulting in incomplete fluorescence loss. Liposomes were extruded through a 400 nm membrane and 20 µl were added to a final volume of 2 mL of 50 mM HEPES pH 7.4, 300 mL KCl + 0.5 mM Ca(NO3)2 or 2 mM EGTA. The fluorescence intensity of the NBD (excitation-470 nm emission-530 nm) was monitored over time with mixing in a PTI spectrophotometer and after 100 s sodium dithionite was added at a final concentration of 40 mM. Data was collected using the FelixGX 4.1.0 software at a sampling rate of 3 Hz.The scrambling assay was carried out as described29. Briefly, the fluorescence time course was fit to the following equationFtot(t) is the total fluorescence at time t, LiPF is the fraction of NBD-labeled lipids in the inner leaflet of protein free liposomes, where γ is the rate constant of dithionite reduction, f0 is the fraction of protein-free liposomes in the sample, α and β are respectively the forward and backward scrambling rate constants andf0, α and β while LiPF and γ are determined from experiments on protein-free liposomes. In protein-free vesicles a very slow fluorescence decay is visible, likely reflecting a slow leakage of dithionite into the vesicles or the spontaneous flipping of the NBD-labeled lipids. A linear fit was used to estimate that the rate of this process is L = (5.4 ± 1.6)10−5 s−1 (n > 160)29. For wiltype and mutant afTMEM16 the leak is >2 orders of magnitude smaller than the rate constant of protein-mediated scrambling and therefore is negligible. All conditions were tested side by side with a control preparation of WT afTMEM16 reconstituted in C18 lipids. In some rare cases this control sample behaved anomalously, judged by scrambling fit parameters outside 3 times the standard deviation of the mean for the wildtype. In these cases, the whole batch of experiments was disregarded.Quantification of the scrambling rate constants by afTMEM16 was determined as described52. Briefly, MSP1E3 in a pET vector (Addgene #20064) was transformed into the BL21-Gold (DE3) strain (Stratagene). Transformed cells were grown in LB media supplemented with Kanamycin (50 mg l−1) to an OD600 of 0.8 and expression was induced with 1 mM IPTG for 3 h. Cells were harvested and resuspended in 40 mM Tris-HCl pH 78.0, 300 mM NaCl supplemented with 1% Triton X-100, 5 μg ml−1 leupeptin, 2 μg ml−1 pepstatin, 100 μM phenylmethane sulphonylfluoride and protease inhibitor cocktail tablets (Roche). Cells were lysed by sonication and the lysate was cleared by centrifugation at 30,000 × g for 45 min at 4 °C. The lysate was incubated with Ni-NTA agarose resin for 1 h at 4 °C followed by sequential washes with: 40 mM Tris-HCl pH 78.0, 300 mM NaCl +1% triton-100, +50 mM sodium cholate, +20 mM imidazole and +50 mM imidazole. The protein was eluted with buffer C +400 mM imidazole, desalted using a PD-10 desalting column (GE life science) equilibrated with 150 mM KCl, 50 mM Tris pH 8.0 supplemented with 0.5 mM EDTA. The final protein was concentrated to ~8 mg ml−1 using a 30 kDa molecular weight cut off concentrator , flash frozen and stored at −80 °C.MSP1E3 and MSP2N2 was expressed and purified as described20. Lipids in chloroform (Avanti) were dried under N2, washed with pentane and resuspended in buffer D and 40 mM sodium cholate (Anatrace) at a final concentration of 20 mM. Molar ratios 1:0.8:50 MSP1E3:afTMEM16:lipids and 1:0.8:140 for MSP2N2:afTMEM16:lipids were mixed at a final lipid concentration of 7 mM and incubated at room temperature for 20 min. Detergent removal was carried out at 4 °C via incubation with Bio-Beads SM-2 (Bio-Rad) with agitation for 2 h and then overnight with fresh Bio-Beads SM2 at a concentration of 200 mg ml−1. The reconstitution mixture was purified using a Superose6 Increase 10/300 GL column (GE Lifesciences) pre-equilibrated with 50 mM HEPES pH 8.0 150 mM KCl plus 5 mM EDTA or 0.5 mM CaCl2 and the peak corresponding to afTMEM16-containing nanodiscs was collected for cryo electron microscopy analysis.Reconstitution of afTMEM16 in nanodiscs was carried out as describedLarge unilamellar vesicles (LUVs) with concentration 0.01 mg/ml were deposited on freshly cleaved mica for 20 min, followed by careful rinsing with imaging buffer . The sample was kept in imaging buffer for all AFM measurements.53. Force-distance curves were acquired at 500 nm/s.All AFM measurements were performed using a JPK Nanowizard 4 AFM . Images were acquired in tapping mode by using cantilevers  with a normal spring constant of ~0.25 N/m, and a resonant frequency of ~110 kHZ in liquid. The sensitivity of cantilevers was measured on mica and the spring constant were calibrated by using thermal tune methods55,n is the Poisson’s ratio, d the indentation , and α is the half cone angle or half face angle of the pyramidal tip shape.All the data analysis were performed using JPK data processing software. The Young’s modulus of the supported lipid bilayers was derived by fitting the force distance curves with the following modelh) using the following equations (the Poisson’s ratio n is assumed 0.556):The area stretch modulus (−1) supplemented with 1.5 or 3 mM Fos-Choline-8-Fluorinated (Anatrace) was applied to a glow-discharged UltrAuFoil R1.2/1.3 300-mesh gold grid (Quantifoil) and incubated for one minute under 100% humidity at 15 °C. Following incubation, grids were blotted for 2 s and plunge frozen in liquid ethane using a Vitrobot Mark IV (FEI).3.5–5 μL of afTMEM16-containing nanodiscs (2–7 mg mL57. Motion correction was carried out using the Relion implementation of MotionCorr257 and contrast transfer function (CTF) estimation was performed using CTFFIND458 via Relion. For all datasets except the wildtype afTMEM16 in 50% 14:0 lipids, 2× binning was used for motion correction. All refinements were run in Relion initially without a mask, and the converged refinement was continued with a mask excluding the nanodisc or micelle. The final resolution of all maps was determined by applying a soft mask around the protein and the gold-standard Fourier shell correlation (FSC) = 0.143 criterion using Relion PostProccessing. Relion was used to estimate the local resolution for all final maps. Processing strategy for the various datasets differed; details are described below.Data collection was carried out for each structure using the parameters specified in Table 57 and Bayesian polishing59 followed by refinement and 3D classification with and without particle alignment were carried out, in which particles not resembling afTMEM16/nanodisc complex were discarded, resulting in a subset of 334,014 particles. This subset was used for extensive classification to identify alternate conformations described below. After no different conformations were identified, additional rounds of 3D classification without alignment and a mask excluding the nanodisc were used to improve the resolution, resulting in a final subset of 244,507 particles. Additional rounds of CTF refinement and Bayesian polishing led to a masked reconstruction with a resolution 3.4 Å, which was C2 symmetric. This initial map (without application of C2 symmetry) was used to analyze the effects on the membrane. Additional rounds of refinement were carried out with C2 symmetry applied resulting in a final reconstruction of 3.3 Å which was used for model building.7432 micrographs were collected and following manual inspection, 5253 were included for analysis with Relion 3.0. Following motion correction and CTF estimation, auto-picking was carried out using a 3D volume of the afTMEM16/nanodisc complex low pass filtered to 30 Å and yielded 1,728,771 particles. Particles were extracted using a box size of 271 Å with 2× binning and subjected to two rounds of 2D classification. 739,898 particles with structural features resembling the afTMEM16-nanodisc complex were selected and subjected to 3D classification without symmetry. 567,785 particles from well-defined 3D classes were extracted without binning and subjected 3D auto-refinement, which resulted in a 3.7 Å reconstruction. Several rounds of CTF refinement57 and Bayesian polishing59 followed by refinement and 3D classification with and without particle alignment, in which particles not resembling afTMEM16/nanodisc complex were discarded, resulting in a subset of 538,468 particles. This subset was used for extensive classification to identify alternate conformations described below, with the exception that classification on monomers was carried out on the reduced subset of 245,824 particles (see below). No attempts to find alternate conformations were successful. To improve the resolution, several additional rounds of 3D classification without alignment and CTF refinement and Bayesian polishing were carried out, leading to a subset of 245,824 particles. Using the Bayesian polishing job in Relion 3.1, these particles were further un-binned to a pixel size of 0.7066 (~1.3× binning from the micrographs). After additional rounds of 3D classification without alignment and CTF refinement and Bayesian polishing, a final subset of 132,332 particles were selected which resulted in a 2.76 Å reconstruction. The protein was not centered in the nanodisc and density for TM6 was missing from one monomer as previously described20. Signal subtraction of the nanodisc density resulted a symmetric reconstruction and additional rounds of refinements with imposed C2 symmetry were carried out, resulting in a final 2.7 Å reconstruction, which was used for model building. The C1 reconstruction prior to signal subtraction was used to analyze the effects on the membrane. Signal subtraction and symmetry expansion were also carried out on the 2× binned particles (pixel size 1.06) and the described classification of the monomers (below) was carried out on these particles.9395 micrographs were collected and after manual inspection 7157 were included for analysis in Relion 3.0 and 3.1 beta. Following motion correction with 2× binning (pixel size 1.06) and CTF estimation, auto-picking was carried out using a 3D volume of TMEM16/nanodisc complex low pass filtered to 30 Å and picked 2,643,590 particles. These particles were extracted using a box size of 271 Å with 4× binning (pixel size 2.12) and subjected to two rounds of 2D classification. 1,923,936 particles with structural features resembling the afTMEM16-nanodisc complex were selected and subjected to 3D classification without symmetry. 1,094,546 particles from well-defined 3D classes were extracted with 2× binning (pixel size 1.06) and subjected to several rounds of CTF refinement60. Following motion correction with 2× binning (pixel size 1.06) and CTF estimation, Laplacian of gaussian auto-picking with conservative parameters was carried out57. 447,947 particles were extracted with a box size of 271 Å with 2× binning (pixel size 1.06) and subjected to two rounds of 2D classification to generate templates for auto-picking. 1,641,081 particles picked with Template-based auto-picking were extracted with a box size of 305 Å and 4× binning (pixel size 2.12) and subjected to three rounds of 2D classification. 1,168,291 particles with features of the afTMME16/nanodisc complex were extracted with a box size of 305 Å and 2× binning (pixel size 1.06) and were imported into cryoSPARC and subjected to heterogenous refinement with forced classification with one model resembling the afTMEM16/nanodisc complex and two models resembling empty nanodiscs60. 208,670 particles from the class resembling the afTMEM16/nanodisc complex were subjected to several rounds of Ab-initio reconstruction with two classes in which particles that did not lead to models resembling the afTMEM16/nanodisc complex were discarded. A final subset of 31,890 particles resulted in a 4 Å reconstruction via non-uniform refinement. These particles were brought to Relion 3.0 using the csparc2star.py script61 and subjected to auto-refinement with local angular searches without symmetry. After three rounds of CTF refinement57 and Bayesian polishing59, these particles refined to 3.7 Å. As in the structure of afTMEM16 in 22:1 lipids in MSP1E3 nanodiscs, the protein was not centered in the nanodisc and density for TM6 was missing from one monomer. Signal subtraction of the nanodisc density was performed in Relion and the subtracted particles were imported into cryoSPARC and subjected to non-uniform refinement with C2 symmetry enforced, resulting in a 3.5 Å reconstruction, which was used for model building. The C1 reconstruction prior to signal subtraction was used to analyze the effects on the membrane.7915 micrographs were collected and after manual inspection 5805 were included for analysis in Relion3.0 and cryoSPARC57 and Bayesian polishing59, these particles were subjected to extensive classification to identify other conformations (see below). No attempts to identify alternate protein conformations were successful. Using the Bayesian polishing job in Relion 3.1, these particles were further un-binned to a pixel size of 0.7066 (*~1.3× binning from the micrographs). After three rounds of CTF refinement57 and Bayesian polishing59, these particles yielded a C2 symmetric 2.5 Å reconstruction. Additional rounds of refinement and classification without alignment were performed with C2 symmetry enforced, resulting in a final subset of 994,187 particles and a Å 2.28 reconstruction. To identify additional associated lipids, the 1,335,468 particles unbinned to 0.7066 Å/pix were symmetry expanded and further classified with a mask on one monomer, with and without local alignment with varying T values. One class with 1,058,829 monomers showed strong density for several additional lipids surrounding the permeation pathway. Local refinement of these particles yielded a Å 2.28 reconstruction which was used to build additional lipids  and CTF estimation, auto-picking was carried out using a 3D volume of the afTMEM16/nanodisc complex low pass filtered to 30 Å and picked 4,551,732 particles. These particles were extracted with a box size of 271 Å with 4× binning (pixel size 2.12) and subjected to two rounds of 2D classification from which 2,645,466 particles displayed afTMEM16/nanodisc complex features and were subjected to two rounds of 3D classification. 1,601,172 particles from classes resembling the afTMEM16/nanodisc complex were extracted with 2× binning (pixel size 1.06) and subjected an additional round of 3D classification. 1,335,468 particles were subjected to auto-refinement and yielded a 2.8 Å reconstruction. Following three rounds of CTF refinement57 and Bayesian polishing59 followed by refinement and 3D classification with and without particle alignment were carried out, in which particles not resembling afTMEM16/nanodisc complex were discarded, resulting in a subset of 483,023 particles. This subset was used for extensive classification to identify alternate conformations described below. After no alternate conformations were identified, additional rounds of 3D classification without alignment and a mask excluding the nanodisc were used to improve the resolution, resulting in a final subset of 155,902 particles. Additional rounds of CTF refinement57 and Bayesian polishing59 led to a masked reconstruction with a resolution 3.3 Å, which was C2 symmetric. This initial map (without application of C2 symmetry) was used to analyze the effects on the membrane. Additional rounds of refinement were carried out with C2 symmetry applied resulting in a final reconstruction of 3.08 Å which was used for model building.9853 micrographs were collected and following manual inspection, 7693 were included for analysis with Relion 3.0. Following motion correction and CTF estimation, 3136 particles were manually picked to generate 2D class averages that were subsequently used as templates to pick a total of 3,820,960 particles. Particles were extracted using a box size of 271 Å with 4× (pixel size 2.12) binning and subjected to two rounds of 2D classification. 2,151,574 particles with structural features resembling the afTMEM16-nanodisc complex were selected and subjected to 3D classification without symmetry. 886,967 particles from well-defined 3D classes were extracted with 2× binning (pixel size 1.06) and subjected 3D auto-refinement, which resulted in a 3.56 Å reconstruction. CTF refinement57 and Bayesian polishing59 followed by refinement and 3D classification with and without particle alignment were carried out, in which particles not resembling afTMEM16/nanodisc complex were discarded, resulting in a subset of 461,108 particles. This subset was used for extensive classification to identify alternate conformations described below. After no alternate conformations were identified, additional rounds of 3D classification without alignment and a mask excluding the nanodisc were used to improve the resolution, resulting in a final subset of 151,197 particles. Additional rounds of CTF refinement57 and Bayesian59 polishing led to a masked reconstruction with a resolution 3.25 Å, which was C2 symmetric. This initial map (without application of C2 symmetry) was used to analyze the effects on the membrane. Additional rounds of refinement were carried out with C2 symmetry applied resulting in a final reconstruction of 3.07 Å which was used for model building.6400 micrographs were collected and included for analysis with Relion 3.0. Following motion correction and CTF estimation, 5315 particles were manually picked to generate 2D class averages that were subsequently used as templates to automatically pick a total of 2,411,226 particles. Particles were extracted using a box size of 218 Å with 4× binning (pixel size 2.12) and subjected to 2D classification. 2,142,631 particles were selected and extracted with 2× binning (pixel size 0.85) for another round of 2D classification. 1,931,850 particles with structural features resembling the afTMEM16-nanodisc complex were selected and subjected to 3D classification without symmetry. 525,407 particles from well-defined classes were subjected 3D auto-refinement, which resulted in a 3.46 Å reconstruction. CTF refinement2+ in MSP2N2 nanodiscs, we carried out extensive 3D classification to identify potential alternate conformations. We tried classification with alignment with global searches and with local searches both with a without a mask excluding the nanodisc. The following approaches were tried: (i) varying the starting model (open and closed permeation pathways), the low pass filter (10–20 Å), the T parameter (4–10), and number of classes (6–20); (ii) 3D classification without alignment (using the angles from refinement) with masks excluding the nanodisc and varying the same parameters as classification with alignment; (iii) symmetry expansion and signal subtraction to isolate the monomers followed by subsequent focused classification/refinement on the permeation pathway (TM3–7)62 to account for potential alternate conformations between two protomers; (iv) focused classification on the monomers on the expanded but not subtracted particles; (v) refinement using cryoSPARC60 for all described structures and cisTEM63) for the C14/0 Ca2+ dataset using particles picked in Relion. In cryoSPARC, we sorted using one round of 2D classification followed by several rounds of ab initio model generation using 3–5 classes, discarding particles from each round that did not result in a reasonable reconstruction. Heterogeneous refinement with 3–5 classes was carried out following each round of ab initio model generation. We also tried 3D variability analysis on all particles selected from 2D classification and on a reduced set of particles sorted using ab initio and heterogenous refinement, both of which revealed a single population and no movements of the protein. In cisTEM we imported particles picked in Relion and sorted using 2D classification and tried global and local refinement using 5–10 classes. None of these approaches led to the observation of a class with an open permeation pathway.On all datasets except for afTMEM16 in C22/Ca2+-bound afTMEM16 structures were used as a starting model and fit into the new maps using several rounds of PHENIX real space refinement64 including morphing and simulated annealing every macrocycle. The improved resolution in these structures, particularly the C18/Ca2+ allowed us to correct errors in the earlier models and to build additional parts of the protein including the hairpin preceding TM3 and the extracellular region between TM5 and TM6  was determined and compared to the FSC between the modified-refined model and the other half map (FSCfree) which was not used in validation or refinement. The similarity in these curves indicates that the model was not over-fit. The quality of all three models was assessed using MolProbity65 which indicates that the models are of high quality. Statistics are found in Supplementary Table To validate the refinement, the FSC between the refined model and the final map was calculated (FSCsum). To evaluate for over-fitting, random shifts of up to 0.3 Å were introduced in the final model and the modified model was refined using PHENIXFor each cryoEM reconstruction, at least 1,000,000 individual particles were picked from cryoEM images and used for structural analysis following standard protocols. Data statistics and evaluation of resolution are documented in Supplementary Table Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Background and aim: Physicians need to be aware of the difficulties that SARS-CoV-2 infection brings to other regions of the body, such as the kidneys, even though the key emphasis is on pulmonary characteristics. The most frequent kidney complication among COVID-19 hospitalized patients is considered acute kidney injury (AKI). This study aimed to describe overall different aspects of acute kidney injury (AKI) in COVID-19 patients admitted to JLNMCH during the COVID-19 pandemic and to determine the prevalence of AKI among COVID-19 hospitalized patients.Methods and materials: All adult patients (over the age of 18 years) who screened positive for COVID-19 in a swab specimen from areas of nasopharyngeal by reverse transcriptase polymerase chain reaction and then hospitalized were included in the study. Information was gathered on the patient's demographics, general medical history, and drugs prescribed. From past medical information, associated comorbidities and home pharmaceuticals were identified. We gathered hospitalization information, such as duration of stay in ICU, details about the application of mechanical ventilation, information regarding extracorporeal membrane aeration, details of the use of vasopressor administration, and baseline results of laboratory test along with baseline clinical information during 48 hours of hospitalization.Results: The percentage of patients with no history of AKI requiring traumatic mechanical ventilation was 79.4%, while the percentage of patients with no history of AKI not requiring traumatic mechanical ventilation was 11.5%. The difference was relevant statistically (p<0.001). The percentage of patients with AKI of any stage requiring traumatic mechanical ventilation was 22.8%, while the percentage of patients with no history of AKI not requiring traumatic mechanical ventilation was 76.8%. The difference was relevant statistically (p<0.022).Conclusion: We discovered that AKI was a rather typical finding among hospitalized COVID-19 patients. Patients hospitalized for COVID-19 had a poor prognosis if they developed AKI. The COVID-19 virus has spread globally. The reason there have been so many confirmed cases is that the virus spreads through close contact between people who cough and sneeze while exchanging droplets or aerosols. Engaging on infected surfaces and subsequently touching transmission points like the areas of mouth, areas of eyes, and areas of nose can also result in infection . The comSARS-CoV-2 is usually an RNA virus that spreads through mouth and nasal secretions, as well as through tiny droplets created by coughing. The reverse transcriptase-polymerase chain reaction (RT-PCR) conducted on respiratory samples acquired by making a swab of the areas of nasopharynx is the usual method of diagnosis. Diverse clinical presentations are possible, including multi-organ failure, mortality, moderate upper respiratory tract disease symptoms, and catastrophic acute breathing distress condition ,6. SARS-We saw an incredible amount of patients who experienced AKI, at a significantly higher frequency than those reported from China, when we managed patients suffering from COVID-19. According to early data from China and Italy, the rate of AKI can range between 0.5% and 29%, with many of these estimates falling between these two extremes ,11.Clinical data include only critically ill ICU patients showing an overall 19% risk of AKI. Variations could be a consequence of the different populations investigated and the criteria of AKI used. Beyond the rate, there hasn't been much written about AKI in COVID-19; for instance, there aren't many details of the chronology, urine tests, connection to failure of respiratory system, in-depth analyses of the needs for renal replacement therapy for renal replacement, risk variables, or consequences after AKI ,13.In this research, we wanted to describe overall different aspects of the experience of AKI in this population of COVID-19 patients and to determine the prevalence of AKI among COVID-19 hospitalized patients.This was a retrospective study conducted at Jawaharlal Nehru Medical College and Hospital during the COVID-19 epidemic, where Jawaharlal Nehru Medical College and Hospital Review Board ethical approval with IRB number JLNMCH/2021/134 was taken. The hospital health record provided the information for this investigation. All adult patients (over the age of 18 years) who screened positive for COVID-19 in a swab specimen from areas of nasopharyngeal by RT-PCR and then hospitalized were included in the study. Transfers of patients among institutions within the same health system were handled as a single hospital engagement.Patients were disqualified if they had undergone a kidney transplant in the past, had suffered from end-stage renal failure, were relocated to hospitals outside the healthcare system, or had serum levels of serum creatinine that were below 2 at the time of admission. Before the study began, the study protocol was approved by the institutional review board. We obtained preliminary data on AKI as well as renal replacement therapy (RRT) from 8000 COVID-19 patients who died or were discharged from our hospital.KDIGO guidelines were used to define AKI in the following manner: stage 1 involves an elevation in creatinine level in serum of 0.3 mg/dl during 48 h or even a 1.5-fold to 1.9-fold elevation from values at baseline under seven days, stage 2 involves a 2.9-fold increase under seven days, and stage 3 involves an increment of three-fold or greater within seven days or the start of RRT. Participants were divided into groups based on the maximum AKI stage they had reached while in the hospital.By using creatinine equation developed by the Chronic Disease of Kidney Epidemiology Consortium, the estimated rate of glomerular filtration was determined. Additionally, we gathered the findings of computerized microscopy urine analysis and urine electrolytes tests that were performed within 24 h or 48 h following the onset of AKI. There was no way for this investigation to determine if a urethral catheter was in place.OutcomesThe emergence of AKI was the main outcome. Hospital disposition including discharge of the patient from hospital or death and the requirement for RRT were secondary results. In our healthcare system, individuals with AKI could choose between interrupted hemodialysis and continuous CRRT as their RRT options.CovariatesWe gathered information on the patient's demographics, general medical history, and prescribed drugs. From past medical information, associated comorbidities and home pharmaceuticals were identified. We gathered hospitalization information, such as duration of stay in ICU, details about the application of mechanical ventilation, information regarding extracorporeal membrane aeration, details of the use of vasopressor administration, and baseline results of laboratory test along with baseline clinical information during 48 h of hospitalization. Numerous extra ICUs were built in unconventional hospital sections and units as a result of the COVID-19 epidemic. Therefore, an ICU stay was determined as the requirement for intrusive mechanical breathing, the requirement for vasopressor support, the requirement for assistance from an ICU provider, or the requirement for treatment in a designated ICU location.Statistic evaluationFor continuous variables with a normally distributed distribution, we calculated means and standard deviations (SD) for without normal distribution variables, medians and interquartile ranges (IQR), and for explanatory data, the percentage was used. Fisher's exact tests were used to compare categorical parameters between individuals with AKI and without AKI, and the nonparametric Kruskal-Wallis test was used to evaluate continuous variables. We used the Kruskal-Wallis test to evaluate the clinical traits of individuals with various stages of AKI.We used logistic regression analysis with accounting for risk factors that were different between individuals who got AKI compared to those who did not in order to determine risk factors related to the onset of AKI. Whenever the level of the risk factor was less than 0.15, variables were added to the models utilizing a stepwise construction process. A p-value of 0.05 or less was regarded as statistically significant for all two-sided statistical tests.The mean age of study participants having no AKI was 60.3 ± 0.05 years. The mean age of study participants having AKI of any stage was 70.1 ± 0.13 years. Study participants having AKI stage 1 was 70.8 ± 0.20 years, AKI stage 2 was 71.2 ± 0.31, and AKI stage 3 was 68.1 ± 0.24 years. The percentage of male in study participants having no AKI was 61.3%, having AKI any stage was 64.8%, having AKI stage 1 was 61.1%, having AKI stage 2 was 61.7%, and having AKI stage 3 was 71.5%. The percentage of female in study participants having no AKI was 41.9%, having AKI any stage was 37.4%, having AKI stage 1 was 41.1%, having AKI stage 2 was 42.6% and having AKI stage 3 was 26.8% (Table The percentage of COVID-19 study participants with history of hypertension was as follows: no AKI (51.5%), AKI any stage (65.8%), AKI stage 1 (68.3%), AKI stage 2 (65.2%), and AKI stage 3 (62.6%). The percentage of COVID-19 study participants with history of coronary artery disease was as follows: no AKI (10.0%), AKI any stage (15.5%), AKI stage 1 (15.7%), AKI stage 2 (17.1%), and AKI stage 3 (14.1%). The percentage of COVID-19 study participants with history of heart failure was as follows: no AKI (5.1%), AKI any stage (11.4%), AKI stage 1 (13.1%), AKI stage 2 (11.5%), and AKI stage 3 (8.9%). The percentage of COVID-19 study participants with history of peripheral, vascular disease was as follows: no AKI (2.1%), AKI any stage (4.1%), AKI stage 1 (3.4%), AKI stage 2 (5.0%), and AKI stage 3 (4.4%). The percentage of COVID-19 study participants with history of diabetes was as follows: no AKI (31.2%), AKI any stage (43.7%), AKI stage 1 (41.8%), AKI stage 2 (42.3%), and AKI stage 3 (45.6%). The percentage of COVID-19 study participants with history of HIV was as follows: no AKI (0.9%), AKI any stage (0.7%), AKI stage 1 (0.8%), AKI stage 2 (1.1%), and AKI stage 3 (0.4%). The percentage of COVID-19 study participants with history of chronic obstructive pulmonary disease was as follows: no AKI (4.5%), AKI any stage (8.7%), AKI stage 1 (10.2%), AKI stage 2 (9.3%), and AKI stage 3 (8.2%) (Table 2 in different categories of COVID-19 patients were as follows: no AKI (83.6), AKI any stage (57.1), AKI stage 1 (55.3), AKI stage 2 (54.2), and AKI stage 3 (63.1). The values of eGFR at discharge was expressed in ml/min/1.73 m2 in different categories of COVID-19 patients were as follows: no AKI (95.1), AKI any stage (46.1), AKI stage 1 (70.1), AKI stage 2 (51.0), and AKI stage 3 (15.1) , AKI any stage 1.36), AKI stage 1 (1.38), AKI stage 2 (1.33), and AKI stage 3 (1.32). The values of SCr in mg/dl at the time of discharge in different categories of COVID-19 patients were as follows: no AKI (0.92), AKI any stage (1.52), AKI stage 1 (1.23), AKI stage 2 (1.59), and AKI stage 3 (4.46). The values of peak SCr in mg/dl in different categories of COVID-19 patients were as follows: no AKI (1.20), AKI any stage (2.61), AKI stage 1 (1.72), AKI stage 2 (2.35), and AKI stage 3 (5.42). The values of median SCr in mg/dl in different categories of COVID-19 patients were as follows: no AKI (0.97), AKI any stage (1.31), AKI stage 1 (1.15), AKI stage 2 (1.21), and AKI stage 3 (2.16). The values of eGFR at admission expressed in ml/min/1.73 m6, AKI stThere was an analysis regarding the need for invasive mechanical ventilation in different categories of COVID -19 patients. The percentage of patients with no history of AKI requiring traumatic mechanical ventilation was 79.4%, while the percentage of patients with no history of AKI not requiring traumatic mechanical ventilation was 11.5%. The difference was statistically relevant (p<0.001). The percentage of patients with AKI of any stage requiring traumatic mechanical ventilation was 22.8%, while the percentage of patients with no history of AKI not requiring traumatic mechanical ventilation was 76.8%. The difference was statistically relevant (p<0.022). The percentage of patients with AKI stage 1 requiring traumatic mechanical ventilation was 16.4%, while the percentage of patients with no history of AKI not requiring traumatic mechanical ventilation was 24.4%. The difference was not statistically relevant (p<0.062). The percentage of patients with AKI stage 2 requiring traumatic mechanical ventilation was 4.3%, while the percentage of patients with no history of AKI not requiring traumatic mechanical ventilation was 22.0%. The difference was statistically relevant (p<0.052). The percentage of patients with AKI stage 3 requiring traumatic mechanical ventilation was 2.7%, while the percentage of patients with no history of AKI not requiring traumatic mechanical ventilation was 44.6%. The difference was statistically relevant (p<0.001). The percentage of patients with renal replacement therapy requiring traumatic mechanical ventilation was 0.3%, while the percentage of patients with no history of AKI not requiring traumatic mechanical ventilation was 24.3%. The difference was statistically relevant (p<0.001)  or angiotensin receptor blocker (ARB) were associated with AKI significantly Table .When we treated COVID-19 patients, we observed a staggering number of patients who had AKI, at a frequency that was noticeably higher than those reported from China. The rate of AKI can range from 0.5% to 29%, with many of these estimates falling between these two extremes, according to early data from China and Italy. Only critically sick ICU patients are included in clinical data, and the total risk of AKI is 19%. Variations may result from the many populations examined and the various AKI criteria applied ,15. ExceWe aimed to find out the prevalence of AKI among hospitalized COVID-19 patients and describe the various general characteristics of AKI experience in this cohort of patients. KDIGO guidelines were used to define AKI in the following manner: stage 1 involves an elevation in creatinine level in serum of 0.3 mg/dl during 48 h or even a 1.5-fold to 1.9-fold elevation from values at baseline under seven days, stage 2 involves a 2.9-fold increase under seven days, and stage 3 involves an increment of three-fold or greater within seven days or the start of RRT. Participants were divided into groups based on the maximum AKI stage they had reached while in the hospital.Thirty-four patients with AKI/CKD were admitted to JLNMCH, Bhagalpur, during the COVID-19 pandemic. Out of 8000 patients with COVID-19 admitted in JLNMCH, 460 patients were admitted to ICU of which we had 34 patients who went for renal dialysis. Seven patients were of AKI and the rest was CKD. In patients with AKI, serum creatinine levels were found between seven and 16, while in patients with CKD, the level was between five and 12. Patients usually had a history of diabetes and hypertension in CKD while anuria was the major complaint of AKI. Hemoglobin level was markedly reduced in patients with CKD.When there was analysis regarding factors associated with AKI in patients with COVID-19 then it was found that factors like old age, male gender, history of diabetes, hypertension, cardiovascular disease, mechanical ventilation, use of vasoactive medication and use of ACE-1 or ARB were associated with AKI significantly.In this study, the mean age of participants having no AKI was 60.3 ± 0.05 years, participants having AKI of any stage was 70.1 ± 0.13 years, participants having AKI stage 1 was 70.8 ± 0.20 years, participants having AKI stage 2 was 71.2± 0.31, and participants having AKI stage 3 was 68.1 ± 0.24 years. The percentage of male study participants having no AKI was 61.3%, having AKI of any stage was 64.8%, having AKI stage 1 was 61.1%, having AKI stage 2 was 61.7%, and having AKI stage 3 was 71.5%. The percentage of female study participants having no AKI was 41.9%, having AKI any stage was 37.4%, having AKI stage 1 was 41.1%, having AKI stage 2 was 42.6%, and having AKI stage 3 was 26.8% . The percentage of patients with AKI of any stage requiring traumatic mechanical ventilation was 22.8%, while the percentage of patients with no history of AKI not requiring traumatic mechanical ventilation was 76.8%. The difference was relevant statistically (p<0.022).SARS-CoV-2 is typically an RNA virus that spreads by secretions from the mouth and nose as well as via microscopic droplets produced while coughing. The standard procedure for diagnosis uses RT-PCR on respiratory samples obtained by taking a swab from the nasopharynx. Multiple organ failure, death, mild upper respiratory tract disease symptoms, and severe acute breathing distress are just a few examples of the various clinical presentations that could occur ,19. SARSParticipants in the COVID-19 research who had a history of hypertension were more likely to have no AKI (51.5%), any stage of AKI (65.8%), stage 1 AKI (68.3%), stage 2 AKI (65.2%), and stage 3 AKI (62.6%). The percentage of COVID-19 study participants with history of coronary artery disease was as follows: no AKI (10.0%), AKI any stage (15.5%), AKI stage 1 (15.7%), AKI stage 2 (17.1%), and AKI stage 3 (14.1%). The percentage of COVID-19 study participants with history of heart failure was as follows: no AKI (5.1%), AKI any stage (11.4%), AKI stage 1 (13.1%), AKI stage 2 (11.5%), and AKI stage 3 (8.9%).Every nation now has the COVID-19 virus. The virus spreads through close contact between people who cough, sneeze, or speak while exchanging droplets or aerosols, which is why there have been so many confirmed cases. Infection can also occur by interacting with infected surfaces and then touching transmission points like the mouth, eyes, and nose. The primary organ system impacted by COVID-19 infection is the respiratory system as a whole. In severe cases, this infection can cause pneumonia, hypoxemia, and acute respiratory distress syndrome ,25. EvenOur study offers some advantages. This cohort of COVID-19 patients that are hospitalized and have an emphasis on AKI is now by far the largest. There are restrictions on this study. First, because it is observational research, we are unable to draw conclusions about the causal links between various levels of exposure to AKI. Second, despite the fact that we have made adjustments for possible confounders, there can still be unmeasured confounders.In conclusion, we discovered that AKI was a rather typical finding among hospitalized COVID-19 patients. It was a rare condition when COVID-19 individuals did not need ventilation and were closely related to the incidence of respiratory failure. Hospitalized COVID-19 patients had a poor prognosis if they developed AKI. To further comprehend the causes of AKI including patient outcomes, more research will be required.


Glycogen-Interacting Protein 1 (GNIP1), an E3 ligase, is a member of the tripartite motif (TRIM) family proteins. Current studies on GNIP1 mainly focus on glycogen metabolism. However, the function and molecular mechanisms of GNIP1 in regulating autophagy still remains unclear. This study aimed to investigate the regulatory mechanism of GNIP1 in regulating autophagy in non-small cell lung cancer (NSCLC).Crystal violet staining assays were used to evaluate the ability of cell growth and proliferation. Transwell and scratch wound healing assays were used to evaluate the cell migration ability. The protein expressions were measured by western blot and immunohistochemistry. Co-immunoprecipitation assays determined the protein–protein interactions. The in vivo effect of GNIP1 on tumor growth was determined by xenograft assay.We found that GNIP1 was overexpressed in tumor tissues and the expression level of GNIP1 was related to the poor prognosis and the survival time of NSCLC patients. In non-small cell lung cancer (NSCLC), GNIP1 increased proliferation and migration of cancer cells by promoting autophagy. Mechanistic studies indicated that GNIP1, as a scaffold protein, recruited BECN1 and LC3B to promote the formation of autophagosomes. Besides, GNIP1 mediated the degradation of 14-3-3ζ, the negative regulator of VPS34 complex, thus promoting autophagy. Overexpressing GNIP1 promoted tumorigenesis and enhanced autophagy in xenograft models.GNIP1 promotes proliferation and migration of NSCLC cells through mediating autophagy, which provides theoretical basis for targeting GNIP1 as anti-cancer drugs.Video AbstractThe online version contains supplementary material available at 10.1186/s12964-022-00936-x. Autophagy is a conserved biological process associated with many cell functions , 2. AutoAutophagy is a complex process involving three major steps: formation of the autophagosomes; fusion of lysosomes with autophagosomes; degradation of engulfed material by lysosomes. More than 30 autophagy related genes (ATGs) have been found to be involved in this process. Briefly, BECN1 is crucial in the initial autophagosome formation. It forms a complex with hVps34 (Human vacuolar protein sorting 34), also known as PIK3C3 (Phosphatidylinositol-3-kinase class III), to regulate localization of other autophagic proteins . LC3B isTRIM7 gene: GNIP1, GNIP2, GNIP3 and TRIM7. Among them, GNIP1 is the longest subtype while TRIM7 is the shortest [GNIP1 (Glycogenin Interacting Protein 1), commonly known as TRIM7, is a member of the GNIP1 and TRIM protein family. There are four subtypes of shortest . GNIP1 wshortest . Compareshortest –16. So fshortest . GNIP1, shortest . And in shortest . Our preshortest . Studiesshortest –31. HoweHEK293T, Human lung epithelial cells (BEAS-2B) and the non-small cell lung cancer cell lines  were purchased from the American Type Culture Collection . SPC-A1 was obtained from the National Infrastructure of Cell Line Resource . The HEK293T cells were cultured in Dulbecco modified Eagle medium (DMEM) . BEAS-2B, A549, H292, PC9, SPCA1 were cultured with RPMI 1640 .Fresh non-small cell lung cancer tissues were obtained from the First Affiliated Hospital of Nanchang University. All patients had not received preoperative chemotherapy, radiotherapy, or immunotherapy based on histopathological criteria. The samples were immediately frozen and stored at − 80 °C for subsequent experimental analysis.GNIP1/RNF90 , β-Actin , GAPDH , DDDDK Tag , LC3B , SQSTM1/p62 , BECN1 , ULK1 , HA-tag , GFP , UB antibody , K48 , K63 , PIK3C3/VPS34 , 14–3-3ζ , anti-His , anti-V5 . Cycloheximide , DMSO , MG132 . Myc-DDDK-Tag-GNIP1, pcDNA3.1-Flag-GNIP1-W57A, PCDH-Flag-GNIP1, pEGFP-C2-LC3B, pCMV-HA-BECN1, pCMV-HA, pcDNA3.1-Flag-PIK3C3, pCMV-Flag-14-3-3ζ, pcDNA3.1-V5-BBCC, pcDNA3.1-V5-His-RBB and pcDNA3.1-V5-His-CCB plasmids were constructed by ourselves.For siRNAs knockdown, when the cell density was about 50%, we carried out the experiment according to the instructions of Pufei siRNA transfection reagent kit . After 48 h, the interference effect was detected by western blot using corresponding antibody. The BECN1 siRNAs were ordered from Thermo Fisher Scientific .For plasmids transfection, when the cell density was about 70–80%, we carried out the experiment according to the instructions of Pufei plasmids transfection reagent kit . After 48 h, the expression effect was detected by western blot using corresponding antibody.The cells were lysed in NP-40 buffer containing PMSF. The cell lysate was centrifuged at 12,000 rpm at 4 °C for 15 min. Then cell supernatant was collected. For western blotting, an appropriate amount of 2 × loading buffer was added to denaturate the protein. SDS-PAGE gel electrophoresis was performed. After electrophoresis, the membrane  was transformed, incubate with 5% milk , subsequently, the protein was combined with primary antibody and incubated overnight. The next day, washing the membrane with 1 × TBST in a room temperature shaker three times for 10 min each time and the secondary antibodies were incubated at room temperature for 1 h. Washing three times and developing imaging .For immunoprecipitation, protein G agarose beads  were added to the cell supernatant and incubate at 4 °C for 1 h. The corresponding antibodies were then added to the above supernatant containing protein G agarose beads and incubated overnight at 4 °C. Next, the immune complex was washed 3 times. Mix beads with 2 × loading buffer and boil to denaturate and carried out western blot. For the ubiquitylation assays, SDS was added in the cell lysis buffer at a final concentration of 1%, then the lysate was heated for 10 min to disrupt non-covalent interactions, and the cell extract was diluted down to 0.1% SDS before immunoprecipitating the target protein, followed by the above immunoprecipitation procedures.The tissues were fixed in neutral formalin and buried in wax. The tissue of the mouse xenograft specimen was sectioned and placed on a slide for histopathological evaluation. Briefly, tissue was dewaxed, and heated with EDTA buffer for 40 min. The slides were incubated with appropriate primary antibodies in accordance with the standard antigen retrieval protocol. The primary antibodies Ki67 , TTF1, LC3B , SQSMT1  were incubated overnight at 4 °C. Washing three times, the second antibody was incubated for 1 h. Add color solution to develop. ImageScope software was used to analyze tissue microarray images.3000 or 5000 cells were cultured in plates. A 24-well plate was collected every 48 h for 6 consecutive days, and the medium was replaced every 24 h. At the specified time, 4% formaldehyde was fixed at room temperature for 30 min, and 0.1% crystal violet was stained. Then, remove the crystal violet and add 10% acetic acid to dissolve. Measuring the absorbance at 595 nm and the relative growth curve was plotted.5 cells to a chamber containing 200 ul 1% FBS medium, add 500 ul 10% FBS medium to the 24-well plate. After 12 h in an incubator at 37 °C, the chambers were taken out, then soaked in 95% ethanol for 20 min. Washing with PBS and staining with crystal violet. Finally, the cells were photographed under a microscope .Add 10For the scratch wound healing assay, the cells were seeded in a 6-well plate and scratched with a 200 ul pipet tips. when the density reached about 90%. Washing with PBS until no suspended cells. Cells were cultured with 1% FBS medium and photographed under a microscope  every 12 h until the scratches healed.7 cells were re-suspended in serum-free medium 1640 (100 μL), mixed with an appropriate amount of matrigel, and then inoculated subcutaneously into 4 week-old male BALB/ c-nu nude mice  with 1 ml syringe. The mice were fed in the animal center of SPF class. The mice and tumor formation were observed every day. After one month, the mice were anesthetized and sacrificed, and the tumor volume was weighed and measured.The 10p < 0.05 (*), p < 0.01 (**), p < 0.001 (***) and p < 0.0001 (****) indicated statistically significant. Statistical analysis was performed using SPSS software version 21.0 .Three independent replicates were performed for each experiment. All data are represented as mean ± SD. Differences in quantitative data between the two groups were analyzed using the t-test. The Kaplan–Meier method and the log-rank test were used for survival analysis. http://cancergenome.nih.gov/). First, the relationship between GNIP1 mRNA expression level and clinicopathological features of patients with NSCLC was analyzed. The mRNA expression of GNIP1 in N1-N3 stage was higher than that in N0 stage . These results suggested that GNIP1 was related to the progression of NSCLC.To identify whether GNIP1 is related to progression of NSCLC, we performed a series of bioinformatics analyses using the TCGA database , the protein level of GNIP1 was obviously upregulated in NSCLC cells, and the expression of GNIP1 in non-small cell lung cancer tissues was increased  assays were conducted to detect the interaction of GNIP1 with LC3B and BECN1. Figure To further clarify the exact binding sites of GNIP1 with LC3B and BECN1, we conducted a series of truncated plasmids of GNIP1. GNIP1 contains four domains: the RING domain, B-box domain, Coiled-coil domain and B30.2 domain . TherefoNext, we further explored the regulatory mechanism of GNIP1 involved in autophagy. 14-3-3ζ, also known as YWHAZ, is an important member of the highly conserved 14-3-3 family and plays a key role in many biological processes in eukaryotic cells –36. 14-3We next detected whether GNIP1 affected the ubiquitination of 14-3-3ζ. As expected, the ubiquitination level of 14-3-3ζ was increased when overexpressing GNIP1 , which blocks the binding of RING domain to ubiquitin- conjugating enzymes . The eff7A, whichOur previous data has shown that GNIP1 contributed to the proliferation and migration of NSCLC cells Fig. . We hypoIn order to further prove that GNIP1 promotes the proliferation and migration of NSCLC cells via autophagy, BECN1 was knocked down using siRNAs. Knocking down BECN1 significantly decreased the tumor promoting effects of GNIP1 no matter on cell proliferation or migration Fig. G–L. The To further verify the function of GNIP1 in NSCLC cells, we used A549 cells with stable expression of GNIP1 to construct a xenograft model to determine whether GNIP1 has an effect on the proliferation of NSCLC cells in vivo. The results showed that GNIP1 expression led to rapid tumour growth Fig. A, B, botAutophagy is a homeostasis mechanism in eukaryotes, cells using this biological process to meet their metabolic needs, or remove toxic aggregates from the cytoplasm to maintain homeostasis. Autophagy involves many proteins. ULK1 regulates the initiation of autophagy, and BECN1 plays a key role in the formation of autophagosome membranes . The subGNIP1 is a member of the TRIM family proteins, the TRIM protein usually consists of RING domain, B-box domain, and coiled-coil domain. A lot of TRIMs have variable C-terminal domains for binding substrates. TRIM family proteins are involved in a wide range of biological processes, and their alterations are associated with a variety of pathological conditions, such as neurodegenerative diseases, cancer, developmental disorders, and viral infections –47. Stud14-3-3 family proteins, a family of phosphoserine/threonine proteins, are involved in a variety of cellular processes, including migration, cell cycle progression, differentiation and apoptosis . Seven mIn conclusion, our studies reveal a novel autophagy regulator-GNIP1 Fig. G. BiocheTogether, these findings demonstrated that GNIP1 is a novel regulatory factor of autophagy and promotes the proliferation and migration of NSCLC cells via autophagy. Our study for the first time revealed the role of GNIP1 as a tumor promoting factor in NSCLC, suggesting that GNIP1 could be a promising anti-tumor target in NSCLC.Additional file 1: Supplementary Figures 1-3.Additional file 2: Supplementary Figure legends.Additional file 3: Raw western blot data.