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==== Front Cureus Cureus 2168-8184 Cureus 2168-8184 Cureus Palo Alto (CA) 30349765 10.7759/cureus.3158 Cardiology Endocrinology/Diabetes/Metabolism Internal Medicine Effects of Body Mass Index, Glycemic Control, and Hypoglycemic Drugs on Serum Uric Acid Levels in Type 2 Diabetic Patients Muacevic Alexander Adler John R Hussain Azhar 1 Latiwesh Omar B 2 Ali Farwa 3 Younis M.Y. G 4 Alammari Jamal A 5 1 Medicine, Xavier University School of Medicine, Oranjestad, ABW 2 Medical Laboratory, Higher Institute of Medical Professions, Benghazi, LBY 3 Medicine, American University of Antigua College of Medicine, New York, USA 4 Assistant Professor and Head of the Department of Biochemistry, University of Benghazi, Faculty of Medicine, Benghazi, LBY 5 Public Health, Higher Institute of Comprehensive Vocations, Gamins, LBY Azhar Hussain azharhu786@gmail.com 17 8 2018 8 2018 10 8 e31589 8 2018 17 8 2018 Copyright © 2018, Hussain et al. 2018 Hussain et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from https://www.cureus.com/articles/14197-effects-of-body-mass-index-glycemic-control-and-hypoglycemic-drugs-on-serum-uric-acid-levels-in-type-2-diabetic-patients Background Plasma uric acid has been shown to be associated with an increased risk of hypertension, cardiovascular disease, chronic kidney disease, insulin resistance, and metabolic syndrome. Conflicting data regarding plasma uric acid levels in type 2 diabetes mellitus and their role in the development and progression of diabetic complications have been observed by many studies. The present study aimed to evaluate plasma uric acid levels in type 2 diabetic patients and to determine the effects of hypoglycemic drugs and pharmacologic insulin on plasma uric acid concentration. Subjects and methods The study included 162 type 2 diabetic patients divided into three groups (insulin taking group (N=58), glibenclamide taking group (N=40), and metformin taking group (N=64), and 47 normal healthy controls. A questionnaire that included variables such as age, sex, duration of disease, and body mass index (BMI) were answered by all the participants. Blood samples were collected and estimated for serum uric acid (SUA), fasting blood sugar (FBS), and glycated hemoglobin (HbA1c) using standard methods and the data were statistically analyzed. Results Diabetic patients showed a significant increase in serum uric acid, fasting blood sugar, glycated hemoglobin, and body mass index when compared to control subjects. The serum uric acid levels of metformin and glibenclamide taking groups were significantly higher than the control group. The difference of serum uric concentration between the insulin taking group and both the control and metformin groups was statistically non-significant. On the other hand, obese diabetics showed a significantly higher serum uric acid than overweight and lean diabetics. Furthermore, serum uric acid had a significant strong positive correlation with body mass index. Conclusion Type 2 diabetes mellitus (T2DM) is associated with high serum uric acid levels. Hypoglycemic drugs and pharmacologic insulin do not have a large impact on SUA concentration, but obesity seems to be the primary determinant of SUA levels in T2DM patients. The condition of diabetes may have a direct effect on the oxidation of the purine nucleotides resulting in the increased uric acid (UA) levels. In addition, hyperinsulinemia could lead to hyperuricemia by increasing the rate of xanthine oxidase synthesis. There is a strong relationship between T2DM and obesity with high uric acid levels. serum uric acid type 2 diabetes mellitus cardiovascular diseases hypoglycemic drugs insulin ==== Body pmcIntroduction Uric acid (UA) is the final oxidation product of purine catabolism [1]. Uric acid can act as a pro-oxidant, particularly at increased concentrations and may thus be a marker of oxidative stress [2]. Thus, it is unclear whether increased concentrations of UA in diseases associated with oxidative stress, such as atherosclerotic coronary heart disease (CHD), stroke, and peripheral arterial occlusive disease, are a protective response or a primary cause [3]. Type 2 diabetes mellitus (T2DM) is a risk factor for nephrolithiasis and has been associated with UA stones [4]. It has been suggested that patients with UA stones, especially if overweight, should be screened for T2DM or metabolic syndrome [5]. The association between high uric acid levels and insulin resistance is not fully understood [3,4]. Hyperuricemia in T2DM is usually the result of underexcretion of urate. Reaven et al. attributed the presence of hyperuricemia in metabolic syndrome to a secondary response to hyperinsulinemia. The association has been attributed to the effects of insulin on proximal tubular urate transport of the kidney [6]. Insulin can also enhance renal tubular sodium reabsorption [7], which in turn can reduce renal excretion of UA. Hyperinsulinemia could lead to hyperuricemia by increasing the rate of xanthine oxidase synthesis, an enzyme involved in UA production [4]. Because endothelial nitric oxide synthase deficiency results in the features of insulin resistance and metabolic syndrome [8], and because UA has been shown to reduce nitric oxide bioavailability [9], consequently, hyperuricemia may have a key role in the pathogenesis of insulin resistance and thus of T2DM. Moreover, many studies have shown the implication of UA and its metabolites in the development and progression of diabetic complications such as peripheral neuropathy [10], retinopathy [11], nephropathy [12], and cardiovascular diseases [13]. T2DM is a major health problem in the Libyan population and accounts for a high mortality rate; hence this study was conducted to evaluate the association of serum UA levels with obesity and glycemic control in T2DM patients and to find out the effects of hypoglycemic drugs and pharmacologic insulin on serum UA levels. This study was presented at the 2nd Libyan Conference on Chemistry and Its Applications, LCCA-2 on September 9, 2017. Materials and methods A total of 162 patients with T2DM were recruited from the Benghazi Center for Diagnosis and Treatment of Diabetes. They are divided into the following three groups according to diabetes treatment: (insulin taking group (N=58), glibenclamide taking group (N=40), and metformin taking group (N=64). Forty-seven apparently healthy age and sex-matched individuals were selected from our family members and relatives to serve as controls. Informed consent was obtained from all the participants before the study, and approval was obtained from the Ethics Review Board of the University of Benghazi. All patients were diagnosed with T2DM based on the American Diabetes Association criteria 2006, i.e., A1c ≥ 6.5%, or fasting plasma glucose level ≥ 126 mg/dL, or 2-h plasma glucose ≥ 200 mg/dl during an oral glucose tolerance test. Clinical information and medical history were obtained through a questionnaire that included variables such as age, sex, date of the diagnosis, physical activity, following a diet, and any health problems, or prescribed drugs. The height and weight were measured, and obesity was defined as body mass index (BMI) of ≥ 30 kg/m2, where BMI was calculated by dividing the weight in kilograms over height in meters squared. All patients presented stable metabolic conditions. Patients presenting any disease that could affect their metabolic status and the parameters studied, such as nephrotic syndrome, acute or chronic renal failure, hepatitis or other liver diseases, cardiovascular diseases, arthritis, acute or chronic inflammatory conditions, gout, and cerebrovascular diseases, were excluded from the study. Patients with a history of smoking or alcohol intake were also excluded. Pregnant and lactating women were excluded. The history of medication was recorded and patients taking any drugs that could affect serum UA levels were also excluded. The control group consisted of healthy subjects who were not suffering from an acute infection or metabolic or psychological disorder. They were non-smokers and non-overweight. They had no history of acquired or inherited hyperuricemia or diabetes mellitus. Venous blood samples were drawn from all the participants after at least 10 hours of fasting. Blood was collected in ethylenediaminetetraacetic acid (EDTA) and plain tubes, and sera were separated from plain tubes and stored at – 20oC until the assays were performed. The whole blood was stored at 4 – 8oC and analyzed for HbA1c within a week using a fully automated Cobas Integra 400 plus (Roche, Germany). Sera were analyzed manually for fasting blood sugar, and UA were analyzed by commercial kits supplied by Linear Chemicals SL, Spain, using Photometer 4040v5+ Robert Riele GmbH & Co, Germany. The data were statistically analyzed using Statistical Package for the Social Sciences (SPSS 17, IBM Corporation). Analysis of variance test (ANOVA) and independent samples T-test were used to determine the variance between different subject groups. Pearson's correlation analysis was done to evaluate the degree of association between different clinical and biochemical parameters. Descriptive characteristics of the study participants were calculated as the mean ± standard deviation (SD), and a P value < 0.05 was considered as statistically significant. Results The mean age and standard deviation of the diabetic group was 51.2 ± 10.9, and the male:female ratio was 4:5. The age range was 18-80 years with the duration of disease ranging from 1-30 years. The mean age and SD of the healthy control subjects was 49.4 ± 12.6, and the male:female ratio was 12:13. The age range was 35-81 years. As shown in Table 1, body mass index (BMI), fasting blood sugar (FBS), glycosylated hemoglobin (HbA1c), and serum uric acid (SUA) levels were significantly higher in the diabetic group than in the normal control group (p< 0.05). Fasting blood sugar was significantly lower in the control group when compared to each of the diabetic groups (p< 0.05). The difference in FBS was also statistically significant between the metformin taking group and both the insulin and the glibenclamide taking groups (p< 0.05). No significant difference has been found between the insulin group and the glibenclamide group. HbA1c concentration was statistically higher in all diabetic groups when compared to that in the control group (p< 0.05). Moreover, the difference in mean HbA1c between different diabetic groups was statistically significant (p< 0.05). The body mass index was significantly higher in both metformin and glibenclamide groups when compared to either insulin or control groups. On the contrary, significant difference in BMI has not been observed between the metformin group and the glibenclamide group or between the insulin group and the control group. Furthermore, similar to BMI, SUA levels in the metformin and glibenclamide groups were significantly higher than in the insulin and control groups. Similarly, there was a non-significant difference in UA levels between the metformin and the glibenclamide groups, as well as between the insulin and the control groups. Table 1 Mean ± standard deviation (SD) of body mass index (BMI) and biochemical characteristics of type 2 diabetic patients and normal control groups. Multiple Comparisons  (Post Hoc analysis)   Diabetic and control groups   Parameters Metformin (N= 64 ) Glibenclamide (N= 40 ) Insulin (N= 58) Control (N= 47 ) P value   Fasting Blood Sugar (mg/dl) 161.9 ± 58 214.7 ± 91.3 193.6 ± 80 92.7 ± 12 0.00   Metformin - 0.001 0.007 0.00   Glibenclamide 0.001 - 0.17 0.00   Insulin 0.007 0.17 - 0.00   Control 0.00 0.00 0.00 -   Glycated Hemoglobin (HbA1c ) (%) 7.7 ± 1.8 9.5 ± 2.4 8.4 ± 2.2 5 ± 0.4 0.00   Metformin -   0.036 0.00   Glibenclamide 0.00 - 0.017 0.00   Insulin 0.036 0.017 - 0.00   Control 0.00 0.00 0.00 -   Body Mass Index (kg/ m2) 29 ± 6.1 29.4 ± 4.7 26 ± 5.6 24.4 ± 0.5 0.002 Metformin - 0.72 0.004 0.004   Glibenclamide 0.72 - 0.012 0.009   Insulin 0.004 0.012 - 0.82   Control 0.004 0.009 0.82 -   Serum Uric Acid (mg/dl) 5.8 ± 2.7 6.4 ± 2 4.9 ± 1.8 4.7 ± 0.9 0.001 Metformin - 0.19 0.02 0.007   Glibenclamide 0.19 - 0.003 0.001   Insulin 0.02 0.003 - 0.6   Control 0.007 0.001 0.6 -   Dividing diabetic patients according to the BMI cut-off value of 30 kg/m2 revealed a significantly higher SUA level in obese diabetics in comparison to lean and overweight diabetics (mean SUA of 6.52 ± 2.6 in obese diabetics versus mean SUA of 5.09 ± 2 in lean and overweight diabetics, p= 0.00). In addition, by dividing diabetic patients according to the HbA1c cut-off value of 7.5%, we found a significantly higher SUA concentration in patients with HbA1c > 7.5% when compared to those with HbA1c ≤ 7.5% (mean SUA of 6.05 ± 2.5 in diabetics with HbA1c > 7.5% versus mean SUA of 4.9 ± 2.1 in diabetics with HbA1c ≤ 7.5%, p= 0.006). In the type 2 diabetic group, Pearson’s correlation analysis revealed significantly positive correlations between SUA levels and both body mass index (p= 0.001, r= 0.279) (Figure 1), and HbA1c concentration (p= 0.017, r= 0.197) (Figure 2). On the other hand, no significant correlation has been found between SUA and FBS (p= 0.12, r= 0.126), age (p= 0.5, r= - 0.4), or duration of disease (p= 0.59, r= - 0.05). Figure 1 Correlation between serum uric acid (SUA) and body mass index (BMI). Figure 2 Correlation between serum uric acid (SUA) and glycated hemoglobin (HbA1c). Discussion In the present case-control study, SUA levels were significantly higher in diabetic patients than normal healthy controls. This finding is in line with data published in previous studies in which high SUA level has been associated with T2DM [14-17]. Persons diagnosed with T2DM have shown very high UA levels in their blood compared to people suffering from diseases such as gout. This indicates that the condition of diabetes may have effects on the oxidation of purine nucleotides. However, the actual relationship between the two is not fully understood due to the complications of metabolic syndrome [17]. Hyperuricemia in T2DM is usually the result of underexcretion of urate as a secondary response to hyperinsulinemia [6,7]. In addition, hyperinsulinemia could lead to hyperuricemia by increasing the rate of xanthine oxidase synthesis, an enzyme involved in UA production [4]. Some studies showed a non-significant difference in UA levels between diabetic patients and controls [18], while other studies showed a significant lower SUA concentration in T2DM patients when compared to normal controls [19]. In the present study, SUA showed significant positive correlation with HbA1c in diabetics. Choi et al. [20] in their study of hemoglobin A1c, fasting glucose, serum C-peptide, and insulin resistance in relation to SUA levels observed that SUA levels and the frequency of hyperuricemia increased with moderately increasing levels of HbA1c and fasting plasma glucose (FPG) and then decreased with further increasing levels of HbA1c (bell-shaped relation). A biological mechanism underlying the bell-shaped relation between blood glucose levels and SUA levels is thought to be due to the uricosuric effect of glycosuria, which occurs when the blood glucose level is greater than 180 mg/dl [21]. We found a significant positive correlation between SUA and BMI in the type 2 diabetic group, and this observation is in agreement with the results of other studies [20-21]. This might be explained with the presence of increased intracellular adenosine (uric acid precursor), a derivative of higher adenosine monophosphate (AMP) concentrations due to increased synthesis of fatty acyl-CoA in peripheral tissues [22], and experiments on mice showed a high xanthine oxidase activity in adipose tissues [23]. In the present study, body mass index was significantly higher in both the metformin and glibenclamide groups when compared to either insulin or control groups. In contrast to our findings, a study by Barskova VG et al., to evaluate results of metformin (MF) therapy during one year of UA metabolism and the clinical course of gout with insulin resistance, revealed the hypouricemic effect of MF and hypothesized that MF reduces the production of UA in patients with gout due to the inhibition of synthesis of free fatty acids [24]. Moreover, metformin use in T2DM improves the sensitivity of peripheral tissues to insulin, which results in a reduction of circulating insulin levels [25], thus decreasing the effect of hyperinsulinemia in reducing the excretion of uric acid. The effect of pharmacologic insulin on SUA has been studied by Lindsey A et al. and Ter Maaten JC et al, and they found a significant increase in serum uric acid levels in diabetic and healthy individuals treated with exogenous insulin, and they referred their finding to insulin’s effects on renal handling of urate [26, 27]. After the treatment with metformin, an increased level of SUA has been observed in type 2 diabetic patients. In comparison to patients treated with rosiglitazone, there is a non-significant difference in SUA levels [28]. A study conducted in Iraq by Ismail NS revealed a non-significant difference in SUA level between both glibenclamide and metformin groups and concluded that glibenclamide and/or metformin had no significant effect on the SUA level in patients with T2DM [29]. In a study by Luque-Ramírez M et al., SUA levels were measured in 40 polycystic ovary syndrome (PCOS) patients and 40 normal healthy women matched for BMI and obesity grade and were followed up for 24 weeks in 34 PCOS patients who were randomized to an oral contraceptive (Diane Diario) or metformin (850 mg twice daily). They found a non-significant difference in SUA between the PCOS group and normal women. When they divided PCOS and normal women as a whole according to BMI, it was revealed that obese women showed higher UA concentrations than lean and overweight women, and that BMI is the main determinant of SUA levels in PCOS patients [30]. This observation supports our finding that metformin and glibenclamide groups both had significantly higher BMI and SUA levels than either insulin group or control group and that SUA level was significantly higher in obese diabetics than lean and overweight diabetics. Conclusions T2DM is associated with high serum uric acid levels. Hypoglycemic drugs and pharmacologic insulin do not have a large impact on SUA concentration, but obesity seems to be the primary determinant of SUA levels in T2DM patients. The condition of diabetes may have a direct effect on the oxidation of the purine nucleotides resulting in increased UA levels. In addition, hyperinsulinemia could lead to hyperuricemia by increasing the rate of xanthine oxidase synthesis. There is a strong relationship between T2DM and obesity with high uric acid levels. Human Ethics Animal Ethics Consent was obtained or waived by all participants in this study. Ethics Review Board of the University of Benghazi issued approval N/A. Informed consent was obtained from all the participants before the study, and approval was obtained from the Ethics Review Board of the University of Benghazi. Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue. The authors have declared that no competing interests exist. ==== Refs References 1 Towards the physiological function of uric acid Free Radic Biol Med Becker BF 615 631 14 1993 8325534 2 Uric acid and oxidative stress: relative impact on cardiovascular risk Nutr Metab Cardiovasc Dis Strazzullo P Puig JG 409 414 17 2007 17643880 3 Uric acid, type 2 diabetes, and cardiovascular diseases: fueling the common soil hypothesis? Cli Chem Koenig W Meisinger C 231 223 54 2008 4 Urine composition in type 2 diabetes: predisposition to uric acid nephrolithiasis Clin J Am Soc Nephrol Cameron MA Maalouf MN Adams-Huet B Moe WO Sakhaee K 1422 1428 17 2006 5 Type 2 diabetes increases the risk for uric acid stones Clin J Am Soc Nephrol Daudon M Traxer O Conort P Lacour B Jungers P 2026 2033 17 2006 6 The kidney: an unwilling accomplice in syndrome X Am J Kid Dis Reaven GM 928 931 30 1997 9398143 7 Decreases in serum uric acid by amelioration of insulin resistance in overweight hypertensive patients: effect of a low-energy diet and an insulin-sensitizing agent Am J Hypertens Tsunoda S Kamide K Minami J Kawano Y 697 701 15 2002 12160192 8 Insulin stimulation of glucose uptake in skeletal muscles and adipose tissues in vivo is NO dependent Am J Physiol Endocrinol Metab Roy D Perreault M Marette A 692 699 274 199 9 Hyperuricemia induces endothelial dysfunction Kidney Int Khosla UM Zharikov S Finch JL 1739 1742 67 2005 15840020 10 Association between serum uric acid level and microalbuminuria to chronic vascular complications in Thai patients with type 2 diabetes J Diabetes Complications Chuengsamarn S Rattanamongkolgul S Jirawatnotai S 124 129 28 2014 24412514 11 Diabetic retinopathy is associated with visceral fat accumulation in Japanese type 2 diabetes mellitus patients Metab Clin Exp Anan F Masaki T Ito Y 314 319 59 2010 20004426 12 Diabetic hypouricemia as an indicator of clinical nephropathy Am J Nephrol Shichiri M Iwamoto H Marumo F 115 122 10 1990 2190467 13 The association of serum uric acid levels with outcomes following percutaneous coronary intervention J Interv Cardiol Spoon DB Lerman A Rule AD Prasad A Lennon RJ Holmes DR Rihal CS 277 283 23 2010 20636849 14 Correlation of the serum insulin and the serum uric acid levels with the glycated haemoglobin levels in the patients of type 2 diabetes mellitus J Clin Diagn Res Gill A Kukreja S Malhotra N 1295 1297 7 2013 23998049 15 To study serum uric acid in type 2 diabetes mellitus patient IOSR J Dent Med Sci 8 2018 Khare S Vishandasani JK Kansal A 5 14 2015 https://pdfs.semanticscholar.org/23d6/1f2f9b6a6e42c1047260d3037131734f9335.pdf 16 Relation between serum uric acid and non insulin dependent diabetes mellitus (NIDDM) Natl J Integr Res Med 8 2018 Nayak MS Shah RM 72 75 4 2013 http://www.scopemed.org/?mno=47448 17 To study serum uric acid and urine microalbumin in type-2 diabetes mellitus Int J Med Sci Suryawanshi KS Jagtap PE Belwalkar GJ Dhonde S Nagane NS Joshi VS 24 29 2 2015 http://www.internationaljournalssrg.org/IJMS/2015/Volume2-Issue3/IJMS-V2I3P104.pdf 18 A study of serum uric acid in diabetes mellitus and prediabetes in a South Indian tertiary care hospital Nitte Uni J Heal Sci 8 2018 Rao MS Sahayo BJ 18 23 2 2012 http://nitte.edu.in/journal/juneSplit/Nitte%20University%20Journal%20June%202012_18_23.pdf 19 Hypouricemia and hyperuricemia in type 2 diabetes: two different phenotypes Eur J Clin Invest Bo S Cavallo‐Perin P Gentile L Repetti E Pagano G 318 321 31 2001 11298778 20 Haemoglobin A1c, fasting glucose, serum C-peptide and insulin resistance in relation to serum uric acid levels—the Third National Health and Nutrition Examination Survey Rheumatology Choi HK Ford ES 713 717 47 2008 18390895 21 Serum uric acid, serum glucose and diabetes: relationships in a population study Post Grad Med J Cook DG Shaper AG Thelle DS Whitehead TP 1001 1006 62 1986 22 Uric acid is associated with features of insulin resistance syndrome in obese children at prepubertal stage Nutr Hosp 8 2018 Gil-Campos M Aguilera CM Cañete R Gil Ay 607 613 24 2009 http://scielo.isciii.es/scielo.php?script=sci_arttext&pid=S0212-16112009000500013&lng=en&nrm=iso&tlng=en#back 19893872 23 Uric acid secretion from adipose tissue and its increase in obesity J Biol Chem 8 2018 Tsushima Y Nishizawa H Tochino Y 27138 27149 288 2013 http://www.jbc.org/content/288/38/27138 23913681 24 Effect of metformin on the clinical course of gout and insulin resistance. [Article in Russian] Klin Med Barskova VG Eliseev MS Kudaeva FM Aleksandrova EN Volkov AV Nasonova VA Nasonov EL 41 46 87 2008 https://www.ncbi.nlm.nih.gov/pubmed/19705791 25 Metformin New Engl J Med Bailey CJ Turner RC 574 579 334 1996 8569826 26 The effect of initiating pharmacologic insulin on serum uric acid levels in patients with diabetes: a matched cohort analysis Semin Arthritis Rheum MacFarlane LA Liu CC Solomon DH 592 596 44 2015 25455681 27 Renal handling of urate and sodium during acute physiological hyperinsulinaemia in healthy subjects Cli Sci Ter Maaten JC Voorburg A Heine RJ Ter Wee PM Donker AJM Gans ROB 51 58 92 1997 28 Metabolic effects of rosiglitazone and metformin in Greek patients with recently diagnosed type 2 diabetes Int J Experi Cli Pathophys Drug Res 8 2018 Iliadis F Kadoglou NP Hatzitolios A Karamouzis M Alevizos M Karamitsos D 1107 1114 21 2007 http://iv.iiarjournals.org/content/21/6/1107.long 29 Effects of glibenclamide and metformin on serum uric acid level in patients with type 2 diabetes mellitus Iraq J Pharm 8 2018 Ismail NS 11 2011 https://www.iasj.net/iasj?func=fulltext&aId=49595 30 Serum uric acid concentration as non-classic cardiovascular risk factor in women with polycystic ovary syndrome: effect of treatment with ethinyl-estradiol plus cyproterone acetate versus metformin Hum Reprod Luque-Ramírez M Álvarez-Blasco F Uriol-Rivera MG Escobar-Morreale HF 1594 1601 23 2008 18375410

PMC007xxxxxx/PMC7012834.txt

==== Front Nat Commun Nat Commun Nature Communications 2041-1723 Nature Publishing Group UK London 32047164 14658 10.1038/s41467-020-14658-6 Article Atomic structures of anthrax toxin protective antigen channels bound to partially unfolded lethal and edema factors http://orcid.org/0000-0003-1539-6768 Hardenbrook Nathan J. 1 http://orcid.org/0000-0002-0561-1981 Liu Shiheng 23 http://orcid.org/0000-0002-3034-2808 Zhou Kang 23 http://orcid.org/0000-0002-2344-9631 Ghosal Koyel 1 http://orcid.org/0000-0002-8373-4717 Zhou Z. Hong Hong.Zhou@UCLA.edu 23 http://orcid.org/0000-0002-4911-5824 Krantz Bryan A. bkrantz@umaryland.edu 1 1 grid.411024.2 0000 0001 2175 4264 Department of Microbial Pathogenesis, University of Maryland, Baltimore, Baltimore, MD 21201 USA 2 grid.19006.3e 0000 0000 9632 6718 Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, CA 90095 USA 3 grid.509979.b 0000 0004 7666 6191 California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA 11 2 2020 11 2 2020 2020 11 8408 7 2019 15 1 2020 © The Author(s) 2020, corrected publication 2023 https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Following assembly, the anthrax protective antigen (PA) forms an oligomeric translocon that unfolds and translocates either its lethal factor (LF) or edema factor (EF) into the host cell. Here, we report the cryo-EM structures of heptameric PA channels with partially unfolded LF and EF at 4.6 and 3.1-Å resolution, respectively. The first α helix and β strand of LF and EF unfold and dock into a deep amphipathic cleft, called the α clamp, which resides at the interface of two PA monomers. The α-clamp-helix interactions exhibit structural plasticity when comparing the structures of lethal and edema toxins. EF undergoes a largescale conformational rearrangement when forming the complex with the channel. A critical loop in the PA binding interface is displaced for about 4 Å, leading to the weakening of the binding interface prior to translocation. These structures provide key insights into the molecular mechanisms of translocation-coupled protein unfolding and translocation. The bacterial translocase channel anthrax toxin is composed of the protective antigen (PA) that forms a translocase channel and two cytotoxic enzymes: lethal factor (LF) and edema factor (EF), which go through the PA channel to enter host cells. Here the authors provide mechanistic insights into LF and EF translocation by determining the cryo-EM structures of the anthrax toxin transmembrane channel protein in complex with LF and EF. Subject terms Cryoelectron microscopy Pathogens issue-copyright-statement© Springer Nature Limited 2020 ==== Body pmcIntroduction Protein translocation is an essential process in cells. Nearly one half of all proteins are translocated across a membrane to perform their respective functions1. This process often requires dedicated protein translocation machineries, generally referred to as translocons, to catalyze the unfolding and translocation of proteins1. In their native state, most proteins are thermodynamically stable. Therefore, translocons require energy in various forms, such as a proton gradient2, hydrolysis of ATP3, or membrane potential3,4, to drive the translocation of their substrates. This process utilizes polypeptide clamps, or catalytic active sites that are responsible for promoting translocation of the protein. In many types of unfoldases5, translocases6, and secretion channels7, these polypeptide clamps engage the polypeptide chain nonspecifically as it is unfolded and translocated. However, in the absence of high-resolution structures of translocons engaged in translocation of an unfolded protein substrate, the biophysical mechanisms involved in protein unfolding and translocation through translocons remain poorly understood. Anthrax toxin8 is well suited for the study of protein translocation. The toxin functions as a binary A2B toxin, with enzymatic A factors, lethal factor (LF, 91 kDa) and edema factor (EF, 89 kDa), and a cell binding B factor, protective antigen (PA, 83 kDa). Anthrax lethal factor (LF) is a 776-amino acid protein consisting of four protein domains; domain 1 is a PA-binding domain (PABD), domain 2 a VIP2-like domain, domain 3 is a helical bundle, and domain 4 is the catalytic center domain (CCD)9. LF has been shown to be a protease, which targets the mitogen activated protein kinase (MAPK) pathway, specifically by cleaving MAPK kinases10–12. Edema factor is also a four domain protein with a PA-binding domain (PABD), two adenylate cyclase domains (ACD), and a helical domain (HD)13. As an adenylate cyclase, EF requires calmodulin (CaM) for its activity upon translocation to the host cytosol14. EF has a catalytic rate of ~2000 molecules per second, resulting in high levels of cyclic adenosine monophosphate (cAMP) that activates protein kinase A (PKA) signaling pathways15,16. Currently, the only protein structure of EF containing all four domains is a CaM-bound crystal structure. Thus while these proteins are both translocated by PA into the cytosol, they perform very different functions. The PA undergoes furin cleavage to form a ring-shaped homooligomeric pre−channel, either a heptamer or an octamer17. The pre-channel PA can then bind to LF or EF, forming lethal toxin (LT) or edema toxin (ET), respectively. The toxin is then endocytosed into an acidic compartment inside the cell. Within the endosomal compartment, the acidic environment induces a conformational change in the PA, resulting in the formation of a β-barrel channel that can insert itself into the endosomal membrane18. A proton gradient forms between the endosome and the cytosol to drive the translocation process2. The enzymatic factors, LF and EF, bound to the PA channel are destabilized by the acidic environment within the endosome and then unfold and translocate through the channel19. Atomic structures of the anthrax toxin PA pre-channel and channel have been determined by X-ray crystallography17,20,21and cryo-EM18, respectively, revealing structural features supporting protein unfolding and translocation. The overall structure of the PA channel has a mushroom-shaped architecture, similar to bacterial α-hemolysin22. The PA channel contains three polypeptide clamp sites23: the α clamp20, the ϕ clamp24, and the charge clamp25. The α clamps, found on the topmost surface of PA, are clefts formed between two PA subunits that binds to α helices nonspecifically20. Through the α clamps and other more specific binding sites, the PA heptamers or octamers can bind three or four LF and/or EF, respectively. In addition to the α clamp, there is a binding interface formed between PA and the C-terminus of the PABD of LF (LFN) in this region. PA residues K213 and K214 have been shown previously to be important in the binding of LFN to PA26. PA K213 was shown to interacts with D187 in LFN in this binding interface26. This was later shown structurally, with K213 and K214 in PA forming salt bridges with LFN residues D187 and D184, respectively20. A charge reversal in either of these PA residues was shown to drastically inhibit binding of LFN26. Directly below the α clamp within the center of the channel is the ϕ clamp. The 2.9-Å resolution cryo-EM structure of the PA pore reveals that the ϕ clamp forms a constricted 6-Å bottleneck of Phe427 residues18. The α clamp and ϕ clamp appeared to behave in an allosteric manner that the peptide binding at the α-clamp site are required for allosteric gating of the ϕ clamp to a clamped state27. Below the ϕ clamp is a charge clamp formed by the transmembrane β-barrel25. As the partially protonated polypeptide chain moves from the lower pH in the endosome toward the higher pH within the cytosol, it passes the negatively charged acidic residues of the clamp. Here the chain becomes deprotonated, and thereafter cannot retro-translocate back through the channel. The inner diameter of the channel spans a range of diameters as low as 20 Å, wide enough to accommodate α helix in the translocating peptide, but not large enough to fit folded domains of the enzymatic factors. Many questions remain with respect to the translocation of substrates through the PA channel. How do the catalytic domains of the substrate proteins interact with the channel? Are there binding sites beyond the α clamp that stabilize partially unfolded substrate? Are there changes within the channel structure when bound to substrate? A lack of structural information on the PA channel bound to substrate has made it difficult to address these questions. Here we report the cryo-EM structures of the PA channel bound to LF and EF. These high-resolution structures of the PA with partially unfolded protein factors reveal conformational changes occurring within the enzymatic factors upon binding to the PA channel, providing key insight on the mechanism of proton-driven protein translocation. Results Overall structures of PA channel in complex with LF and EF Conversion of the PA pre-channel to the channel by in vitro acidification treatment leads to rapid and irreversible aggregation due to exposure of the hydrophobic transmembrane β-barrel structure. Attempts to prevent aggregation by screening detergents were mostly unsuccessful. We next tried to apply low-pH treatment of PA pre-channels directly on carbon-coated grids as done before17, but were only able to obtain limited number of dispersed particles of PA channel without aggregation. To overcome these issues, we used lipid nanodiscs28 to assemble water-soluble complexes containing the PA channel bound to LF and EF29,30. Each complex was assembled on nickel affinity resin using His-tags in the enzyme substrates, and eluted with imidazole. The resulting complexes of PA bound by its cytotoxic substrates inserted into lipid nanodiscs provide soluble samples that take random orientation allowing for single-particle cryo-EM analysis (Supplementary Fig. 1). The available space on the heptameric PA channel for EF binding can only accommodate up to three EF molecules due to steric hindrance. Indeed, 2D classification of cryo-EM images of PA channel bound with EF showed that the EF binding varies in different classes (Supplementary Fig. 1a, b), suggesting that the space is not fully occupied. Therefore, we used a symmetry expansion method in Relion for 3D classification and were able to resolve the asymmetrically attached EF (Methods, Supplementary Fig. 2). Remarkably, the same symmetry expansion method also worked for cryo-EM images of PA channel with LF bound even though the 2D classification failed to classify the asymmetrically bound LF in the PA-LF complex (Methods, Supplementary Figs. 1c, d and 3). In total, we determined four structures: one for the LF in complex with the heptameric PA channel and three for the EF(s) in complex with the heptameric PA channel, at an average resolution of 4.6 Å and 3.2–3.4 Å, respectively (Supplementary Figs. 2 and 3), based on the “gold-standard” Fourier shell correlation (FSC) 0.143 cutoff criterion31,32. The resulting maps revealed a “flower-on-a-stem” heptameric channel with 27-Å wide β barrel, consistent with the channel conformation. In all our structures, the conformation of the PA channel remains largely unchanged from the previously determined structure of the PA channel without substrate bound (PDB 3J9C18) (Figs. 1a and 2a). Atomic models of LF and EF were built into cryo-EM density maps. Only one LF is visible in the LF binding complex (refer to as PA7-LF), while there are three configurations of the EF-bound structures: one EF and two isoforms of two EF in the EF binding complexes (referred to as PA7-EF; PA7-1,3-EF; PA7-1,4-EF) (Supplementary Fig. 2). Regardless of this, we could clearly observe that an amino-terminal helix of both LF and EF binds to the α clamp of the heptameric PA channel in all complex structures; meanwhile, the rest of the amino-terminal domains of both LF and EF are well-ordered (Supplementary Fig. 4). These structures reveal how the enzymatic factors bind to the PA channel to form a complex and how the subunits in the complex interact with one another in preparation for the translocation process.Fig. 1 Structure of LF-bound PA7 channel (PA7-LF). a Two orthogonal views of the overall PA7-LF structure. b Structure comparison of substrate-binding α clamp between PA7-LF channel and PA8-(LFN)4 pre-channel. Fig. 2 Structure of EF-bound PA7 channel (PA7-EF). a Overall structure of PA7-EF shown as ribbon. b Zoom-in view (view 1) of the PA7 α-clamp site showing its detailed interactions with α1 of EF. The cryo-EM density is shown as semi-transparent gray. c Rotated view 1 showing structure comparison of the substrate-binding α-clamp between PA7-EF channel (color) and PA8-(LFN)4 pre-channel (gray), except that the density is not shown for clarity. d Zoom-in view (view 2) showing the details of the PABD domain of EF binding to PAN and PAC. e Rotated view 2 showing the superposition of PA-bound EF (purple for EF, orange for PA) and PA-bound LF (gray for both PA and LF), except that the density is not shown for clarity. Hydrogen bonds are shown as dashed lines. α-clamp site from pre-channel to channel complex In our PA7-LF channel structure, the LFN (the amino terminal domain of LF) binds two neighboring PA subunits, one denoted as PAN, which binds the N-terminus of LFN, and the other as PAC, which binds the C-terminus of LFN (Fig. 1a). The cryo-EM density reveals a helix of LFN bound in the α-clamp site (Supplementary Fig. 4), indicating that this site continues to engage the enzymatic factors in the PA channel. This helix of LFN in PA7 α-clamp appears to bind within this site in much the same manner as in the PA8-(LFN)4 pre-channel structure (PDB 3KWV20) (Fig. 1b). However, at 4.6-Å resolution, it is not possible to determine whether hydrogen bonds form within the α-clamp site between LFN β1 and PAN β13. Upon alignment with 3KWV20, nonetheless, β13 in the PA channel and in the PA8-(LFN)4 pre-channel has a highly similar conformation, with the first α-helix (LFN α1) aligning well between the two structures. This alignment indicates that LFN α1 binds within the α-clamp site similarly in both the PA channel and the pre-channel. The catalytic domain of LF is invisible in our EM structure, suggesting that it is flexible. The amino terminal domain of EF (EFN) and LFN share similar structures upon binding to the PA channel (Figs. 2a and 1a). In the crystal structures free of PA binding, LF α1 is an ordered α-helix9 but the homologous region in EF is flexible and disordered33. Upon PA binding, these disordered residues of EF (residues 20–30) refold into an α-helix (EFN α1) and bind within the α-clamp site (Fig. 2b). β1 (Leu33 to Lys35) of EFN forms parallel β-sheet with β13 (Leu203 to Pro205) of PAN (Fig. 2b). The hydrogen bonds between the two β-strands are analogous to those found in the PA8-(LFN)4 pre-channel structure (PDB 3KWV20), confirming predictions that the amino terminus of EF binds in a similar way as LF20. Plasticity of helix binding within the α-clamp site While α1 and β1 in LF and EF bind to the α clamp of PA analogously with β1 forming hydrogen bonds with PAN, their α1 helices dock within the α clamp differently, indicating that there is structural plasticity of α-helix binding within this α-clamp site (Fig. 2c). LF α1 is angled downward towards the pore in the α-clamp site, while the amino-terminal end of EF α1 is elevated ~2.9 Å as measured using the carbonyl groups on LF Glu34 and EF Glu24. This elevation in EF α1 appears to be caused by a change in the orientation of PAN Phe464. The phenyl ring in PAN Phe464 is positioned outwards toward the bound EF α1. This change in the orientation of Phe464 appears to restrict EF α1 in its elevated conformation in the α-clamp site. Overall, this structural plasticity makes sense, given previous work determining that the α clamp in PA binds α helices repeatedly and nonspecifically during translocation of its substrates27. Interface destabilization may play role in translocation A hydrophobic interface is formed in the carboxy-terminal subdomain of EFN with PA between EF residues Val223, Leu226, Tyr227 and PAC residues Phe202, Pro205, Ile207, Ile210 (Fig. 2d). Like those in the PA8-(LFN)4 pre-channel complex crystal structure previously determined20, PAC Ile210 and EF Tyr227 are well packed in this hydrophobic interface, allowing the phenol hydroxyl to form hydrogen bonds within the interface with PAC residues His211 and Asp195 (Fig. 2d). Despite the above similarity, it is worth noting that, upon undergoing the conformational change from pre-channel to channel, the substrate appears to have moved up (Fig. 2e), away from the binding interface with PAC compared to the pre-channel structure20. This conformational change has resulted in a loss of salt bridges that had previously formed upon binding of the LF to the pre-channel between residues PAC Lys213 and Lys214 and LF Asp187 and Asp184, respectively, where the distance between PAC Lys213 and LF Asp187 increases from 3.5 Å in the pre-channel structure to 4.6 Å in the PA7-EF channel structure; the distance between PAC Lys214 and LF Asp184 increases from 2.8 Å in the pre-channel structure to 4.3 Å in the PA7-EF channel structure (Fig. 2e). The loss of these salt bridges in the binding interface of PA7-LF should destabilize the binding interface, preparing the substrate for subsequent dissociation and unfolding prior to its translocation. Indeed, when we mutate EF residues Asp171 and Asp174 to alanine, we see no change in binding affinity compared to wild type using planar bilayer electrophysiology (Supplementary Fig. 6). This result indicates that the salt bridges are weakened significantly once PA reaches the channel state. Thus, the LF/EF binding interface with PA can be maintained in a higher affinity mode when PA is in the pre-channel conformation and complex assembly is more important; but when PA converts into the channel state, the affinity of the LF/EF binding interface is destabilized, allowing for more rapid dissociation and unfolding of LF/EF during translocation. EF domains reorganize upon binding the PA channel Unlike LF, all the domains of EF are well resolved in our channel complex structures (Fig. 3a, b). The corresponding amino acid sequence for the different domains with respective α helices and β sheets is shown in Fig. 3c. In all our structures of the EF-bound channel (PA7-EF, PA7-1,3-EF, and PA7-1,4-EF) presented here, EF undergoes a significant conformation change compared to its calmodulin (CaM)-bound structure33. In the previous CaM-bound EF structure (PDB: 1XFY33), CaM stabilizes the CA and CB ACD and the HD of EF, and there are no significant interactions among these domains of EF (Fig. 3e). While in the PA7-bound EF structures, the HD domain contributes to a new conformation by bridging the PABD and ACD. Further analysis indicated that the folding pattern within the three domains only changes slightly from CaM-bound EF to PA7-bound EF, but the three domains are reorganized in PA7-bound EF (Fig. 3e and Supplementary Movie 1).Fig. 3 Structural comparison of EF between its PA7-bound and CaM-bound forms. a Domain architecture of EF with individual domains colored and the boundary residues numbered. b Structure of PA7-EF with EF shown as ribbon and PA7 as surface colored by protomers. The three domains of EF—PABD, ACD, HD—are colored as in a. c Sequence and secondary structures of the PA7-bound EF. d Close-up view at the interactions among PABD, ACD and HD domains in PA7-bound EF. The structural elements involved in domain interactions are highlighted and hydrogen bonds are shown as dashed lines. e Superposition of EF structures in its PA7-bound and CaM-bound (PDB: 1XFY) forms. The two EFs are aligned by the PABD domain for clarity. Three domains of CaM-bound EF—PABD, ACD, HD—are colored in green, light blue and cyan, respectively. Domain reorganizations are marked by arrows. In more detail, on one side of the HD, residues near α29 and α30 of HD interact with those near α2 and β1 of the PABD. Hydrogen bonds are formed between inter-domain residues, one from HD and the other from PABD, such as Gln746-Asn40, Lys767-Gln50, Asn737-Ile71, Asn737-Phe73, and Glu739-Phe73 (Fig. 3d). On the other side of HD, a loop between α26 and α27 interacts with residues near α22 and α24 of ACD, mainly through inter-domain hydrogen bonds (Fig. 3d). With the extensive interactions mentioned above, HD moves toward and binds PABD eventually. Notably, the refolding of N-terminal residues (Lys20 to Thr42) of PABD (Fig. 3e), which is a consequence of PA7 binding, yields the space that enables the interactions between HD and PABD (Fig. 3d, pink arrow in Fig. 3e, and Supplementary Movie 1), leading to a 60° swing of ACD (yellow arrow in Fig. 3e), which mounts α22 and α24 of ACD on the loops near α25, α26, and α27 of HD (Fig. 3d). Discussion Here we report a total of four cryo-EM structures of heptameric PA channel bound with toxin substrates: three for the complex with EF at resolutions ranging from 3.2 to 3.4 Å and one for the complex with LF at 4.6 Å resolution. Our results reveal that upon the binding of the substrate to the PA channel, conformational changes occur in the enzymatic substrates LF and EF. When full-length LF binds to the PA channel, its catalytic domains exhibit significant flexibility, and thus only the PA binding domain, LFN, is visible in the cryo-EM density map; by contrast, the PA-binding and catalytic domains are visible in the crystal structure of LF9. In the case of EF, its domains reorganize, compared to the EF structure bound to CaM33. This CaM-bound structure is the only other full-length structure of EF available for comparison, but highlights the conformational changes EF undergoes during its lifetime. It is interesting that EF binds to PA in a different way than it binds to CaM, with the helical domain stabilizing its PABC and ACD. This reorganization involves refolding of PABD residues (Lys20 to Thr42), a 70° swing of HD toward PABD and mounting of ACD to HD. The reorganized conformation of EF is stabilized by the formation of hydrogen bonds. We suggest that this reorganization of the domains plays a role in the ordered translocation of the EF through the channel. Previously, Feld et al. showed in detail that different substrates could bind to the α clamp20, indicating nonspecific binding at the α-clamp site. Our results also show the α clamp engages different α helices, either from EF or LF. Interestingly, PA’s Phe464, a residue lining the α clamp, changes conformation to accommodate different residues in these helices. These results demonstrate plasticity within the α-clamp site, which allows for the binding of different helical substrates. When bound to the pre-channel, LFN forms numerous stabilizing interactions on its amino and carboxyl terminal subdomains. Upon conversion to the channel conformation, the carboxyl terminal subdomain of LFN destabilizes its interface with PA. This destabilization occurs while the complex is exposed to the acidic pH of the endosomal compartment. This interface destabilization, paired with the acidic environment, most likely plays an important role in allowing the bound substrate to unfold and translocate through the channel more efficiently. Our four high-resolution structures of PA channel with LF and EF—representing the structures of the complex in the channel conformation—provide further insights into the mechanism of how substrate proteins are translocated across membranes by the PA channel. In our current model (Fig. 4), the enzymatic factors bind to PA pre-channels, before the cell undergoes endocytosis. The PA prechannel undergoes a conformational change within the endosomal compartment, forming the channel state. This conformational change results in an alteration of the binding interaction between the channel and its substrate enzymes, thus destabilizing the interaction. This destabilization, accompanied by partial protonation of the polypeptides, allows the proton gradient to drive translocation of the bound substrate through the channel. As the polypeptide is translocated through the channel, it is engaged by the α-clamp repeatedly and non-specifically27. During much of the translocation process, the polypeptide is accommodated by the channel in its secondary structure. It is engaged as an α helix while binding within the α clamp. As it moves down and is bound in the ϕ-clamp site, the α-clamp engages the polypeptide again. When the α clamp re-engages with the polypeptide, it causes an allosteric change in the ϕ clamp24. This change in the ϕ clamp applies force to the α-helix, changing its conformation to extended chain and driving it past the charge clamp site. Once past the ϕ clamp, the polypeptide is deprotonated within the anionic charge clamp. This prevents retro-translocation of the polypeptide chain back toward the endosome. At this point the polypeptide can begin to reform its secondary structure. Once exiting the channel, the translocating polypeptide refolds into its tertiary structure and can perform its enzymatic effects on the host cytosol. In the case of EF, this involves binding CaM and taking on its CaM-bound domain organization33.Fig. 4 Mechanism of EF translocation. Illustration of the anthrax toxin channel translocation steps with EF. Initially, EF binds to the PA pre-channel, and the N-terminal α helix of the PABD of EF docks into the α clamp, yielding the space for domain reorganization of EF. After the PA pre-channel changes to the channel state at low pH, the destabilization of the interface between the PABD of EF and the PA channel allows the N-terminal α helix to translocate down to the ϕ-clamp site. In parallel, the α clamp engages the EF polypeptide again, causing an allosteric change in the ϕ clamp. The change in the ϕ clamp applies force to the α helix, changing its conformation to extended chain and driving it past the charge clamp site located near the top of the β barrel. The cycle repeats on the next section of EF polypeptide. Recently, structures of the substrate-engaged SecY protein translocon have been determined using X-ray crystallography and cryoEM34,35. The SecY system is one of the few other protein translocation systems where structural information is available. Like PA, within the SecY channel there is a hydrophobic pore ring that interacts with the translocating polypeptide. In addition, a polypeptide clamp has been identified in SecA which would position the translocating polypeptide right above the SecY pore36. The recent structure of the clamp bound to the translocating substrate indicates that it engages with the polypeptide in a sequence-independent manner by inducing short β strand conformations in the polypeptide35. This action would allow a broad range of polypeptides to be bound and translocated by the SecA. Hence this clamp is like the α clamp in PA, which also engages multiple sequences. This similarity suggests that perhaps there are universally shared phenomenon amongst different translocons, in which substrate is engaged sequence-independently based on secondary structure. In general, these two translocons allow different polypeptide segments to be engaged repeatedly and nonspecifically as they translocate through their respective channels. Methods Protein expression and purification Heptameric PA oligomer (PA7) was prepared as described17. Briefly, PA83 was expressed in Escherichia coli BL21(DE3) using a pET22b plasmid directing expression to the periplasm. PA83 was extracted from the periplasm and further purified using Q-Sepharose anion-exchange chromatography in 20 mM Tris-chloride, pH 8.0, and eluted with a gradient of 20 mM Tris-chloride, pH 8.0 with 1 M NaCl. PA83 was then treated with trypsin (1:1000 wt/wt trypsin:PA) for 30 min at room temperature to form PA63. The trypsin was inhibited with soybean trypsin inhibitor at 1:100 dilution (wt/wt soybean trypsin inhibitor:PA). The trysinized PA was subjected to anion-exchange chromatography to isolate the oligomerized PA7. The trypsinized PA was applied to the anion exchange column in 20 mM Tris-chloride, pH 8.0, and the oligomerized PA7 was eluted from the anion exchange column using a gradient of 20 mM Tris-chloride, 1 M sodium chloride, pH 8.0. Recombinant WT LF and WT EF and EF point mutants, containing an amino-terminal six-histidine His-tag (His6) were overexpressed in Escherichia coli BL21(DE3) from pET15b constructs and purified from the cytosol using His6 affinity chromatography. Cytoplasmic lysates of His6-LF and His6-EF were made by treatment with hen egg white lysozyme for 30 min at room temperature. The lysates were briefly sonicated at 4 °C (for 2 min) to shear genomic DNA and reduce sample turbidity. His6-LF and His6-EF lysates were applied to immobilized nickel affinity chromatography column in 20 mM Tris-chloride, 35 mM imidazole, 1 M sodium chloride pH 8.0, and His6-LF and His6-EF were eluted using a gradient of 20 mM Tris-chloride, 500 mM imidazole, 1 M sodium chloride pH 8.0. Affinity-purified His6-LF and His6-EF were then subjected to S200 gel filtration chromatography in 20 mM Tris-chloride, 150 mM sodium chloride, pH 8.0. EF point mutants were made using the Quik-Change mutagenesis kit (Stratagene) according to the manufacturers procedure with the primer designs listed in Supplementary Table 1. PA-LF and PA-EF complex assembly His6-LF or His6-EF were mixed with PA7 pre-channel at a ratio of 5:1 (LF/EF:PA7) and allowed to assemble on ice for 1 h. The PA7 pre-channel in complex with His6-LF and His6-EF was then purified over S400 gel filtration in 20 mM Tris-chloride pH 8.0, 150 mM sodium chloride. Nanodisc insertion The His6 tag was removed from membrane scaffold protein 1D1 (MSP1D1)28. pMSP1D1 was a gift from Stephen Sligar (Addgene plasmid #20061). In all, 300 µL wet volume Ni-NTA Superflow resin (Qiagen) was added to an 800-µL centrifuge column (Pierce) twice with 50 mM sodium chloride, 50 mM Tris-chloride pH 7.5 (Buffer A). In all, 300 µL of 1 µM of our PA complex and 300 µL of 2 M urea were added to the resin, for a final urea concentration of 1 M. This mix was collected and incubated at 37 °C for 5 min to induce conversion from the pre-channel to channel conformation29. The mix was then collected and added back into a centrifuge column, and the resin (now bound to complex) was washed twice with 500 µL Buffer A. A mixture containing MSP1D1 and palmitoyloleoyl phosphocholine (POPC) was made by first evaporating chloroform off of POPC, then adding MSP1D1 and sodium cholate in Buffer A. The final concentration contained 4 µM MSP1D1, 400 µM POPC, and 25 mM sodium cholate in Buffer A. In all, 500 µL of a MSP1D1-(POPC) mix was added to the dry resin bound with PA complex30. This resin slurry was then collected and dialyzed in Slide-A-Lyzer cassette (10 kDa molecular weight cut-off) (Thermo Scientific) in excess Buffer A for 8–12 h at a time, with two buffer changes. The Ni-NTA was then collected after dialysis. The resin was washed twice with 500 µL Buffer A. The resin was then washed with 500 µL of 50 mM NaCl, 50 mM imidazole, 50 mM Tris pH 7.5 to elute any remaining proteins bound non-specifically. The nanodisc complex was then eluted in 50 mM sodium chloride, 300 mM imidazole, 50 mM Tris-chloride pH 7.5. This eluted sample was then dialyzed into Buffer A and concentrated to 0.274 mg ml−1 (PA channel in complex with LF) and 0.498 mg ml−1 (PA channel in complex with EF) Concentration was estimated by a Nanodrop spectrophotometer. Cryo-EM sample preparation and imaging For cryo-EM sample optimization, an aliquot of 2.5 μl of sample was applied onto a glow-discharged holey carbon copper grid (300 mesh, QUANTIFOIL® R 2/1). The grid was blotted and flash-frozen in liquid ethane with an FEI Mark IV Vitrobot. An FEI TF20 cryo-EM instrument was used to screen grids. Cryo-EM grids with optimal particle distribution and ice thickness were obtained by varying the gas source (air or H2/O2), time for glow discharge, the volume of applied samples, chamber temperature/humidity, blotting time/force. For the PA channel in complex with LF, our best grids were obtained using H2/O2 for glow discharge and with the Vitrobot sample chamber set at 12 °C temperature and 100% humidity. For the PA channel in complex with EF, our best grids were obtained using air for glow discharge and with the Vitrobot sample chamber set at 16 °C temperature and 100% humidity. Optimized cryo-EM grids were loaded into an FEI Titan Krios electron microscope with a Gatan Imaging Filter (GIF) Quantum LS device and a post-GIF K2 Summit direct electron detector. The microscope was operated at 300 kV with the GIF energy-filtering slit width set at 20 eV. Movies were acquired with Leginon37 by electron counting in super-resolution mode at a pixel size of 0.535 Å per pixel. A total number of 45 frames were acquired in 9 s for each movie, giving a total dose of ~60 e−/Å2/movie. Drift correction for movie frames Frames in each movie were aligned for drift correction with the graphics processing unit (GPU)-accelerated program MotionCor238. The first frame was skipped during drift correction due to concern of more severe drift/charging of this frame. Two averaged micrographs, one with dose weighting and the other without dose weighting, were generated for each movie after drift correction. The averaged micrographs have a calibrated pixel size of 1.07 Å on the specimen scale. The averaged micrographs without dose weighting were used only for defocus determination and the averaged micrographs with dose weighting were used for all other steps of image processing. Structure determination for PA channel in complex with EF For the PA channel in complex with EF, the defocus value of each averaged micrograph was determined by CTFFIND439 generating values ranging from −1.5 to −3 μm. Initially, a total of 1,481,285 particles were automatically picked from 6811 averaged images without reference using Gautomatch (http://www.mrc-lmb.cam.ac.uk/kzhang). The particles were boxed out in dimensions of 256 × 256 square pixels square before further processing by the GPU accelerated RELION2.1. Several iterations of reference-free 2D classification were subsequently performed to remove bad particles (i.e., classes with fuzzy or un-interpretable features), yielding 725,251 good particles. The reported map of the heptameric anthrax toxin PA channel18 (EMD-6224) was low-pass filtered to 60 Å to serve as an initial model for 3D classification. After one round of 3D classification with C7 symmetry, only the classes showing feature corresponding to the intact PA7 channel were kept, which contained 486,169 particles. We re-centered those particles and removed duplications based on the unique index of each particle given by RELION32. The resulting 486,169 particles were applied one round of auto-refinement by RELION, yielding a map with an average resolution of 3.0 Å. Next, we expanded C7 symmetry to C1, yielding 3,403,183 (486,169 × 7) particles. These particles were submitted to further classification (skip align) with 29 classes. A cylinder mask was created only for the EF binding region (Supplementary Fig. 2) and applied for the focus classification. Among these 29 classes, four different types of density maps were identified. Four classes have no clear density of EF (PA7), 14 classes show clear density of only one EF binds to the PA7 channel (PA7-EF), six classes with density of two EF (PA7-1,3-EF), and four classes with density of two EF which were located further away from each other (PA7-1,4-EF) (Supplementary Fig. 2). Subsequently, we merged the particles from classes belonging to PA7-EF, PA7-1,3-EF, PA7-1,4-EF, respectively. After removing duplications based on the unique particle names given by RELION, we got 333,455 particles for PA7-EF (68.8% of all particles), 72,864 particles for PA7-1,3-EF (15.0% of all particles) and 73,784 particles for PA7-1,4-EF (15.1% of all particles). The unique particles of each dataset (PA7-EF, PA7-1,3-EF, PA7-1,4-EF) resulting from the focused classification were subjected to a final step of 3D auto-refinement with C1 symmetry. The two half maps of each dataset from this auto-refinement step were subjected to RELION’s standard post-processing procedure. The final maps of PA7-EF, PA7-1,3-EF, PA7-1,4-EF achieved an average resolution of 3.2, 3.4, and 3.4 Å, respectively, based on RELION’s gold-standard FSC (see below). Structure determination for PA channel in complex with LF For the PA channel in complex with LF, the defocus value of each averaged micrograph was determined by CTFFIND439 generating values ranging from −1.5 to −3 μm. Initially, a total of 616,153 particles were automatically picked from 2502 averaged images without reference using Gautomatch (http://www.mrc-lmb.cam.ac.uk/kzhang). The particles were boxed out in dimensions of 320 × 320 square pixels square and binned to 160 × 160 square pixels (pixel size of 2.14 Å) before further processing by the GPU accelerated RELION2.1. Several iterations of reference-free 2D classification were subsequently performed to remove bad particles (i.e., classes with fuzzy or un-interpretable features), yielding 204,395 good particles. The reported map of the heptameric anthrax toxin PA channel18 (EMD-6224) was low-pass filtered to 60 Å to serve as an initial model for 3D classification. After one round of 3D classification with C7 symmetry, only the classes showing feature corresponding to the intact PA7 channel were kept, which contained 194,849 particles. We re-centered those particles and removed duplications based on the unique index of each particle given by RELION. The resulting 194,775 particles were un-binned to 320 × 320 square pixels (pixel size of 1.07 Å) and applied one round of auto-refinement by RELION, yielding a map with an average resolution of 3.4 Å. The C7 symmetry was then expanded to C1, giving 1,363,425 (194,775 × 7) particles for further classification. A cylinder mask was created only for the LF-binding region (Supplementary Fig. 3) and applied for the focus classification with seven classes. Six of the seven classes show clear density for only one LF bound to the PA7 channel (PA7-LF) (Supplementary Fig. 3). We next merged the good particles from the six classes and removed duplications based on the unique particle names given by RELION. The 63,807 un-binned, unique particles (10.4% of all particles) resulting from the focused classification were subjected to a final step of 3D auto-refinement with C1 symmetry. The two half maps from this auto-refinement step were subjected to RELION’s standard post-processing procedure. The final map of the PA7-LF complex has an average resolution of 4.6 Å based on RELION’s gold-standard FSC. We also got a 3D auto-refinement result (3.6 Å) with C7 symmetry using this dataset, which helped the model building process (see model building below). Resolution assessment All resolutions reported above are based on the “gold-standard” FSC 0.143 criterion40. FSC curves were calculated using soft spherical masks and high-resolution noise substitution was used to correct for convolution effects of the masks on the FSC curves41. Prior to visualization, all maps were sharpened by applying a negative B-factor, which was estimated using automated procedures20. Local resolution was estimated using ResMap42. The overall quality of the maps for the PA channel in complex with EF and LF is presented in Supplementary Figs. 2 and 3, respectively. Data collection and reconstruction statistics are presented in Supplementary Table 2. Model building and refinement Atomic model building was accomplished in an iterative process involving Coot43, Chimera44, and Phenix45. For the PA7-LF complex, the structure of anthrax toxin PA channel heptamer18 (PDB ID: 3J9C) was fitted into cryo-EM map (4.6 Å, C1 symmetry) by using the ‘fit in map’ routine in Chimera. The atomic model building of PA7 channel was facilitated by using the 3.6 Å cryo-EM map in C7 symmetry (63,807 particles, Supplementary Fig. 2). Next, the crystal structure of LF9 (PDB ID: 1J7N) was fitted in to the cryo-EM map (4.6 Å, C1 symmetry) to create a full atomic model for PA7-LF. Finally, the structure was manually adjusted using Coot and refined using Phenix in real space with secondary structure and geometry restraints. For the PA channel in complex with EF, we have three different types of density maps—PA7-EF, PA7-1,3-EF, and PA7-1,4-EF. Owing to the higher resolution and single EF binding in PA7-EF, we firstly carried out model building on this density map. The structure of PA7 channel18 (PDB ID: 3J9C), was fitted into the cryo-EM map of PA7-EF as initial model by using the ‘fit in map’ routine in Chimera. This fit revealed the extra density corresponding to EF. However, further docking showed the density of EF in cryo-EM map has significant differences with respect to the crystal structure of EF33 (PDB ID: 1XFX). The full-length EF consists of four domains, the (PABD), two catalytic core domains CA and CB forming the ACD, and the HD. The domains in the cryo-EM map have a different arrangement, however. Thus, we fit the domains into the density separately to create an initial atomic model for PA7-EF, which was refined by “real-space refinement” in Phenix. We then manually adjusted the main chain and side chains to match the cryo-EM density map with Coot. This process of real space refinement and manual adjustment steps was repeated until the peptide backbone and side chain conformations were optimized. Secondary structure and geometry restraints were used during the refinement. Refinement statistics of the PA channel in complex with LF and EF are summarized in Supplementary Table 2. These models were also evaluated based on MolProbity scores46 and Ramachandran plots (Supplementary Table 2). Representative densities for the proteins are shown in Supplementary Fig. 4. Planar lipid bilayer electrophysiology apparatus Planar lipid bilayer currents were recorded using an Axopatch 200B amplifier and a Digidata 1440 A acquisition system (Molecular Devices Corp., Sunnyvale, CA)17,47. Ensemble recordings were recorded at 200 Hz and filtered at 100 Hz using PCLAMP10 software. The membrane potential difference is defined as Δψ ≡ ψcis − ψtrans (ψtrans ≡ 0 V). Ensemble binding analysis using electrophysiology A prior method20 was used to monitor EF binding to PA channels at symmetrical pH and a Δψ of 0 mV by means of an applied potassium chloride gradient. Membranes were painted on a 100 μm aperture of a 1-mL, white-Delrin cup with 3% (wt/vol) 1,2-diphytanoyl-sn-glycerol-3-phosphocholine (Avanti Polar Lipids, Alabaster, AL) in n-decane (Sigma-Aldrich, St. Louis, MO); and the cis chamber was bathed in 10 mM potassium phosphate, 100 mM potassium chloride, pH 7. During the setup, the trans chamber was bathed in 10 mM potassium phosphate, pH 7. PA channels were inserted by adding 20 nmol of PA7 to the cis chamber at pH 7. PA currents reached ~5 nA. Upon stabilization of the ensemble current, the cis chamber was perfused to exchange fresh 10 mM phosphate, 100 mM KCl at pH 7. EF and mutants thereof were added in small increments to the cis side of the membrane, allowing for binding to reach equilibrium as indicated by the observed decrease in current which reached a steady-state plateau. Fraction of closed channels (θobs) versus [P] plots (where P denotes free EF) were fit to a simple single-site model, θobs = 1/(1 + KD/[P]), to obtain an equilibrium dissociation constant, KD. Three to four independent measurements of KD for each EF mutant and wild type were made and averages and standard deviations were computed in Microcal ORIGIN9 software. Reporting summary Further information on research design is available in the Nature Research Reporting Summary linked to this article. Supplementary information Supplementary Information Peer Review File Description of Additional Supplementary Files Supplementary Movie 1 Reporting Summary Source data Source Data Supplementary information Supplementary information is available for this paper at 10.1038/s41467-020-14658-6. Acknowledgements We thank J. Jiang for their suggestions about sample preparation and data processing, Y. Cui for assistance in cryo-EM and suggestions about data processing, I. Atanasov and W. Hui for assistance in cryo-EM. This work was supported in part grants from the National Science Foundation (NSF, under grant no. DMR-1548924) and by grants from the National Institutes of Health (R01GM071940/AI094386/DE025567 to Z.H.Z. and R21AI124020 to B.K.) and the Training Program in Integrative Membrane Biology at the University of Maryland, Baltimore (T32GM008181). We acknowledge the use of resources in the Electron Imaging Center for Nanomachines supported by UCLA and grants from the NIH (S10RR23057, S10OD018111, and U24GM116792) and NSF (DBI-1338135). K.Z. acknowledges support from the China Scholarship Council. Author contributions Z.H.Z. and B.K. conceived the project; N.J.H. engineered and isolated samples; S.L. and K.Z. evaluated the samples, performed electron microscopy, processed the data, built atomic models, and prepared figures; N.J.H and K.G. performed equilibrium binding electrophysiology experiments; all authors wrote the paper. Data availability The cryo-EM maps have been deposited in the Electron Microscopy Data Bank under accession numbers EMD-20459, EMD-20955, EMD-20957, and EMD-20958. The atomic structure coordinates have been deposited in the Protein Data Bank under the accession numbers 6PSN [10.2210/pdb6PSN/pdb], 6UZB [10.2210/pdb6UZB/pdb], 6UZD [10.2210/pdb6UZD/pdb], and 6UZE [10.2210/pdb6UZE/pdb]. The source data underlying Supplementary Fig. 6 are provided as a Source Data file. Other data can be obtained from the corresponding authors upon reasonable request. Competing interests The authors declare no competing interests. Peer review information Nature Communications thanks Doryen Bubeck, Rodney Tweten and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. These authors contributed equally: Nathan J. Hardenbrook, Shiheng Liu, Kang Zhou. Change history 7/13/2023 In the original version of this article, the given and family names of Z. Hong Zhou were incorrectly structured. The name was displayed correctly in all versions at the time of publication. The original article has been corrected. ==== Refs References 1. 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==== Front Mol Cancer Mol Cancer Molecular Cancer 1476-4598 BioMed Central London 32487167 1215 10.1186/s12943-020-01215-4 Research Circular RNA circCORO1C promotes laryngeal squamous cell carcinoma progression by modulating the let-7c-5p/PBX3 axis Wu Yongyan 12345 Zhang Yuliang 12 Zheng Xiwang 12 Dai Fengsheng 13 Lu Yan 6 Dai Li 13 Niu Min 12 Guo Huina 12 Li Wenqi 13 Xue Xuting 12 Bo Yunfeng 7 Guo Yujia 12 Qin Jiangbo 8 Qin Yixiao 13 Liu Hongliang 129 Zhang Yu 410 Yang Tao 5 Li Li 9 Zhang Linshi 11 Hou Rui 12 Wen Shuxin 13 An Changming anchangming@cicams.ac.cn 14 Li Huizheng huizhengli2004@163.com 15 Xu Wei xuwhns@126.com 161718 http://orcid.org/0000-0001-7836-2851 Gao Wei gaoweisxent@sxent.org 12349 1 grid.263452.4 0000 0004 1798 4018 Shanxi Key Laboratory of Otorhinolaryngology Head and Neck Cancer, Shanxi Medical University, Taiyuan, 030001 Shanxi People’s Republic of China 2 grid.452461.0 0000 0004 1762 8478 Shanxi Province Clinical Medical Research Center for Precision Medicine of Head and Neck Cancer, The First Hospital of Shanxi Medical University, Taiyuan, 030001 Shanxi People’s Republic of China 3 grid.452461.0 0000 0004 1762 8478 Department of Otolaryngology Head & Neck Surgery, The First Hospital of Shanxi Medical University, Taiyuan, 030001 Shanxi People’s Republic of China 4 grid.263452.4 0000 0004 1798 4018 Key Laboratory of Cellular Physiology, Ministry of Education, Shanxi Medical University, Taiyuan, 030001 Shanxi People’s Republic of China 5 grid.263452.4 0000 0004 1798 4018 Department of Biochemistry & Molecular Biology, Shanxi Medical University, Taiyuan, 030001 Shanxi People’s Republic of China 6 grid.454145.5 0000 0000 9860 0426 Department of Otolaryngology Head & Neck Surgery, The First Hospital, Jinzhou Medical University, Jinzhou, 121001 Liaoning People’s Republic of China 7 grid.263452.4 0000 0004 1798 4018 Department of Pathology, Shanxi Cancer Hospital, Shanxi Medical University, Taiyuan, 030013 Shanxi People’s Republic of China 8 grid.254020.1 0000 0004 1798 4253 Department of Otolaryngology Head & Neck Surgery, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, 046000 Shanxi People’s Republic of China 9 grid.263452.4 0000 0004 1798 4018 Department of Cell Biology and Genetics, Basic Medical School of Shanxi Medical University, Taiyuan, 030001 Shanxi People’s Republic of China 10 grid.263452.4 0000 0004 1798 4018 Department of Physiology, Shanxi Medical University, Taiyuan, 030001 Shanxi People’s Republic of China 11 grid.13402.34 0000 0004 1759 700X Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310009 Zhejiang, People’s Republic of China 12 grid.1012.2 0000 0004 1936 7910 Harry Perkins Institute of Medical Research, QEII Medical Centre and Centre for Medical Research, the University of Western Australia, PO Box 7214, 6 Verdun Street, Nedlands, Perth, Western Australia 6009 Australia 13 grid.263488.3 0000 0001 0472 9649 General Hospital, Shenzhen University, Shenzhen, 518055 Guangdong People’s Republic of China 14 grid.506261.6 0000 0001 0706 7839 Department of Head and Neck Surgery, Cancer Hospital, National Cancer Center, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100021 People’s Republic of China 15 grid.411971.b 0000 0000 9558 1426 Department of Otolaryngology Head & Neck Surgery, Dalian Municipal Friendship Hospital, Dalian Medical University, Dalian, 116100 Liaoning People’s Republic of China 16 grid.27255.37 0000 0004 1761 1174 Shandong Provincial ENT Hospital Affiliated to Shandong University, Jinan, 250022 Shandong People’s Republic of China 17 Shandong Provincial Institute of Otolaryngology, Jinan, 250022 Shandong People’s Republic of China 18 grid.27255.37 0000 0004 1761 1174 Key Laboratory of Otolaryngology, Ministry of Health, Shandong University, Jinan, 250022 Shandong People’s Republic of China 2 6 2020 2 6 2020 2020 19 9920 1 2020 11 5 2020 © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/ Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Background Laryngeal squamous cell carcinoma (LSCC) is a common malignant tumor of the head and neck. LSCC patients have seriously impaired vocal, respiratory, and swallowing functions with poor prognosis. Circular RNA (circRNA) has attracted great attention in cancer research. However, the expression patterns and roles of circRNAs in LSCC remain largely unknown. Methods RNA sequencing was performed on 57 pairs of LSCC and matched adjacent normal mucosa tissues to construct circRNA, miRNA, and mRNA expression profiles. RT-PCR, qPCR, Sanger sequencing, and FISH were undertaken to study the expression, localization, and clinical significance of circCORO1C in LSCC tissues and cells. The functions of circCORO1C in LSCC were investigated by RNAi-mediated knockdown, proliferation analysis, EdU staining, colony formation assay, Transwell assay, and apoptosis analysis. The regulatory mechanisms among circCORO1C, let-7c-5p, and PBX3 were investigated by luciferase assay, RNA immunoprecipitation, western blotting, and immunohistochemistry. Results circCORO1C was highly expressed in LSCC tissues and cells, and this high expression was closely associated with the malignant progression and poor prognosis of LSCC. Knockdown of circCORO1C inhibited the proliferation, migration, invasion, and in vivo tumorigenesis of LSCC cells. Mechanistic studies revealed that circCORO1C competitively bound to let-7c-5p and prevented it from decreasing the level of PBX3, which promoted the epithelial–mesenchymal transition and finally facilitated the malignant progression of LSCC. Conclusions circCORO1C has an oncogenic role in LSCC progression and may serve as a novel target for LSCC therapy. circCORO1C expression has the potential to serve as a novel diagnostic and prognostic biomarker for LSCC detection. Keywords circCORO1C Let-7c-5p PBX3 Laryngeal squamous cell carcinoma Epithelial–mesenchymal transition Metastasis http://dx.doi.org/10.13039/501100001809 National Natural Science Foundation of China 81872210 81802793 81802948 Wu Yongyan Liu Hongliang Gao Wei http://dx.doi.org/10.13039/501100010031 Postdoctoral Research Foundation of China 2016M591412 2017M610174 Wu Yongyan Gao Wei The Excellent talent science and technology innovation project of Shanxi Province201605D211029 201705D211018 201805D211007 Wu Yongyan Wen Shuxin Gao Wei Youth Science and Technology Research Fund of Shanxi Province201901D211486 201901D211490 Wu Yongyan Guo Huina Shanxi Province Scientific and Technological Achievements Transformation Guidance Foundation201604D131002 201604D132040 201804D131043 Wen Shuxin Gao Wei Youth Foundation of The First Hospital Affiliated with Shanxi Medical UniversityYQ1503 Gao Wei Youth Top Talent Program Fund of Shanxi Province20180610 Wu Yongyan Fund of Shanxi “1331” Projectissue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2020 ==== Body pmcBackground Laryngeal squamous cell carcinoma (LSCC) is a common malignant tumor of the head and neck originating from the laryngeal mucosal epithelium. LSCC accounts for approximately 2.4% of systemic malignancies worldwide each year; in 2018, around 95,000 people died of laryngeal cancer [1]. The onset of LSCC is occult, and approximately 60% of patients are in the advanced stages when diagnosed (clinical stages III and IV) [2]. The proneness of LSCC to local invasion and cervical lymph node metastasis seriously interferes with patient survival rates [3]. Surgery remains the main treatment approach for LSCC [4]. Unfortunately, LSCC is one of the few tumors with a decreasing survival rate in recent years, and its 5-year survival rate has declined from 66 to 63% over the past 40 years [5], which is mainly attributed to its unclear mechanism of occurrence and progression. Therefore, it is urgent to reveal the pathogenesis of LSCC, identify biomarkers for its diagnosis, and investigate effective new therapeutic targets. Circular RNA (circRNAs) is a recently identified non-coding RNA that has become the latest hotspot in cancer research. The circRNA molecule has a closed loop structure that is not affected by exonucleases and is not easily degraded. circRNAs also have features of high conservation and abundance [6]. Hence, circRNAs have unique advantages as biomarkers for disease diagnosis and prognosis. Recent studies have shown that circRNA molecules are rich in miRNA binding sites and can specifically bind miRNAs, thereby eliminating the inhibitory effect of miRNAs on target genes and upregulating the expression level of target genes, that is, functioning as competing endogenous RNA (ceRNA) [7]. circRNAs also bind to RNA binding proteins and may translate proteins to exert their functions [8, 9]. circRNAs have critical regulatory effects in the occurrence and development of a variety of cancers, affecting cell cycle, apoptosis, metabolism, invasion, and metastasis [10]. circAGFG1 upregulates CCNE1 expression and promotes the proliferation, migration, and invasion of breast cancer cells [11]. circPPP1R12A-encoded protein circPPP1R12A-73aa promotes tumor growth and metastasis of colon cancer [12]. However, to date, little is known about the expression, functions, and regulatory mechanisms of circRNAs in LSCC. Pre-B-cell leukemia homeobox transcription factor 3 (PBX3) is a member of the evolutionarily conserved three-amino acid-loop-extension (TALE) homeodomain transcription factor family. A recent study revealed that PBX3 is a critical regulatory protein of the epithelial–mesenchymal transition (EMT) network in colorectal cancer [13]. Dysregulation of PBX3 expression has been observed in many cancer types, such as prostate, gastric, cervical, and liver cancer [14–17]. Nonetheless, the expression and function of PBX3 in LSCC are still unknown. In this study, we performed RNA sequencing of 57 pairs LSCC tissues and matched adjacent normal mucosa (ANM) tissues and identified abnormally upregulated expression of circCORO1C in LSCC tissues. Furthermore, the expression of circCORO1C was strongly associated with the clinical features and prognosis of LSCC patients. We found that circCORO1C could bind to let-7c-5p and prevent it from decreasing the level of PBX3, which promoted EMT and stimulated the proliferation, migration, and invasion of LSCC cells in vitro and in vivo. Methods LSCC patient tissue A total of 164 pairs of LSCC tissues and matched ANM tissues (taken 1–3 cm from the edge of cancer tissues) were obtained from patients undergoing surgery at the Department of Otolaryngology Head and Neck Surgery, The First Hospital of Shanxi Medical University, from January 2013 to January 2017. None of the patients received chemotherapy or radiotherapy before surgery. The tissue samples were diagnosed independently by two experienced clinical pathologists. The histological types of LSCC were determined according the World Health Organization (WHO) system, and TNM (Tumor, Node, Metastasis) stage was defined according to the criteria of the American Joint Committee on Cancer (AJCC, 8th edition). Fresh specimens were immediately frozen in liquid nitrogen. Among the 164 pairs of tissue samples, 57 paired LSCC (Additional file 1: Table S1) and ANM tissues were used for RNA sequencing, and 107 paired samples for qPCR analysis (Additional file 1: Table S2). Cell lines and cell culture Human LSCC cell line FD-LSC-1 (a gift from Professor Liang Zhou [18]) was cultured in BEGM™ Bronchial Epithelial Cell Growth Medium (Lonza, Walkersville, MD, USA) supplemented with 10% FBS (Biological Industries, CT, USA). Human LSCC cell line TU-177 purchased from Bioleaf Biotech Corporation (Shanghai, China) was maintained in DMEM supplemented with 10% FBS. Human HEK293T and MRC-5 cell lines were purchased from the China Center for Type Culture Collection (CCTCC). HEK293T cells were cultured in DMEM with 10% FBS. MRC-5 cells were cultured in MEM with 10% FBS. Human oral keratinocytes (HOK) purchased from ScienCell Research Laboratories (Carlsbad, CA) were cultured in DMEM with 10% FBS. All cells were cultured at 37 °C with 5% CO2. Cell lines were tested for mycoplasma contamination using the TransDetect PCR Mycoplasma Detection Kit (TransGen Biotech, Beijing, China). RNA and genomic DNA (gDNA) extraction Total RNA was extracted from tissues or cells using Trizol reagent (Invitrogen, Waltham, MA) following the manufacturer’s instructions. The nuclear and cytoplasmic fractions were extracted using a PARIS kit (ThermoFisher Scientific, Waltham, MA). gDNA was extracted using a genomic DNA isolation kit (TIANGEN Biotech (Beijing) Co., Ltd., Beijing, China). RNA sequencing analysis The RNA integrity of 57 pairs of LSCC/matched ANM tissues was examined with a Bioanalyzer 2100 (Agilent, Santa Clara, CA). High-quality RNA (RIN > 7) samples were subjected to library construction, and then each library was sequenced on an Illumina HiSeq 4000 (circRNA and mRNA) and Illumina HiSeq 2000 (miRNA) following the standard procedures by Novogene (Beijing, China). RNA sequencing data were deposited at GEO and are accessible via accession numbers GSE127165 and GSE133632. Differentially expressed circRNAs, miRNAs, and mRNAs were screened as reported [19] (Additional file 1: Table S3–5). RT-PCR and quantitative real-time PCR (qPCR) For PCR of mRNA and circRNA, RNA was reverse-transcribed using a HiScript II 1st Strand cDNA Synthesis Kit (Vazyme, Nanjing, China). For qPCR of miRNA, cDNA was synthesized using an All-in-One™ miRNA First-Strand cDNA Synthesis Kit (GeneCopoeia, Rockville, MD). qPCR was performed using ChamQ SYBR qPCR Master Mix (Vazyme, Nanjing, China) on an ABI Stepone Plus system. The relative expression levels were calculated using the 2 (−△△CT) method. The circRNA and mRNA levels were normalized by 18 s rRNA. The miRNA level was normalized against U6 small nuclear RNA. Primer sequences are listed in Additional file 1: Table S6. RNase R treatment Total RNA (2 μg) was incubated for 10 min at 37 °C with or without 3 U/μg RNase R (Geneseed Biotech Co., Ltd., Guangzhou, China), followed by RNA purification using a RNeasy MinElute Cleanup kit (Qiagen, Hilden, Germany) and analyzed by RT-PCR. Agarose gel electrophoresis PCR products were separated by 2% agarose gel electrophoresis with TAE buffer using a 100 bp DNA ladder (TransGen Biotech, Beijing, China). The bands were photographed under an Azure C600 imager (Azure Biosystems, Dublin, CA). Fluorescence in situ hybridization (FISH) Cy3-labeled circCORO1C probes (5′- AGAGCAATTGGTTCCTGCATATTTTTCTGGCAATCTCACATTTGTTAACATC -3′) were synthesized by Sangon Biotech (Shanghai, China). FISH was performed using a FISH kit (RiboBio, Guangzhou, China) according to the manufacturer’s instructions. Nuclei were stained with DAPI. Images were acquired on a Leica TCS SP8 confocal laser scanning microscope (Leica Microsystems Inc., Buffalo Grove, IL). Plasmid construction and cell transfection The PBX3 overexpression plasmid was generated by inserting PBX3 CDS sequence into the p3 × FLAG-CMV-10 vector (Sigma-Aldrich, St. Louis, MO). shRNA lentiviral plasmid targeting circCORO1C (psh-circCORO1C) was constructed by inserting annealed shRNA template DNA sequence into the pLKO.1 vector. For luciferase reporter plasmids, the sequences of circCORO1C, wild type, and let-7c-5p binding site mutant PBX3 3′ UTR were cloned into the psiCHECK-2 vector (Promega, Madison, WI). Cells were transfected using Lipofectamine 3000 (Invitrogen) according to the manufacturer’s instructions. siRNAs, miRNA mimics, and inhibitor siRNAs targeting circCORO1C (si-circCORO1C #1: 5′-AGAUUGCCAGAAAAAUAUGCA-3′; si-circCORO1C #2: 5′-UUGCCAGAAAAAUAUGCAGGA-3′), negative control siRNAs (si-NC), stable oligonucleotides (modified by cholesterol, 2′-OMe and phosphorothioate), let-7c-5p mimics, and NC were synthesized by GenePharma (Shanghai, China). miRNA inhibitor is small, chemically modified single-stranded RNA molecule that can competitively bind to and inhibit the function of specific endogenous mature miRNA. 2′-OMe-modified let-7c-5p inhibitor and NC inhibitor were synthesized by GenePharma. Generation of circCORO1C knockdown cells To generate FD-LSC-1 cells with stable knockdown of circCORO1C, lentiviruses were produced in HEK293T cells by cotransfection with psh-circCORO1C and packaging plasmids GAG and VSVG. Virus supernatant was harvested 48 h after transfection, mixed with polybrene (8 μg/ml), and added to FD-LSC-1 cells. After 48 h incubation, 2 μg/ml puromycin (Santa Cruz Biotechnology, Dallas, TX) was added for 1 week to screen for stable cell clones. CCK8 assay After 24 h transfection, cells were digested and seeded into 96-well plates (3 × 103/well). At 0, 24, 48, 72, and 96 h after seeding, each well was replaced with 100 μL fresh complete medium and 10 μL TransDetect CCK (TransGen Biotech, Beijing, China) followed by incubation at 37 °C with 5% CO2 for 1 h. The absorbance of the solution was measured at 450 nm using a Spectra Max i3x Multifunctional microplate detection system (Molecular Devices, San Jose, CA). 5-Ethynyl-2′-deoxyuridine (EdU) staining Cells were incubated with DMEM medium containing 50 μM EdU (RiboBio) at 37 °C with 5% CO2 for 2 h. Cells were washed twice with PBS, fixed with 50 μL 4% paraformaldehyde for 30 min, neutralized with 50 μL 2 mg/mL glycine solution and permeabilized by adding 100 μL 0.5% Triton X-100. After washing with PBS, 100 μL 1 × Apollo dye was added to each well, then cells were incubated at room temperature for 30 min. Next, 100 μL Hoechst 33342 was added and incubated for another 30 min. Images were captured and analyzed on an ImageXpress high-content screening system (Molecular Devices). Colony formation assay Transfected cells were seeded at a density of 600 cells/well into a 35-mm dish and then cultured for 10 days. Cells were washed with PBS once and colonies were fixed with 4% paraformaldehyde for 20 min and stained with 0.1% crystal violet solution for 10 min at room temperature, followed by image capture. Transwell migration and invasion assays After 24 h transfection, cells were digested, washed twice with PBS and resuspended in serum-free DMEM. Transwell chambers for invasion assay were precoated with Matrigel (BD Biosciences, San Jose, CA). Serum-free DMEM (200 μL) containing cells (4 × 104 cells/well for migration assay, 1 × 105 cells/well for invasion assay) was added to the upper chamber. Then 500 μL DMEM medium supplemented with 20% FBS was added to the lower chamber. After 24 h, cells in the upper chamber were removed with cotton swabs and the lower side of the chamber was gently washed twice with PBS, fixed with 4% paraformaldehyde for 20 min, and stained with 0.1% crystal violet for 10 min, and then images were captured by microscope. Apoptosis analysis Apoptosis was determined using a Dead Cell Apoptosis kit (ThermoFisher Scientific). Briefly, cells were digested with EDTA-free trypsin and washed with ice-cold PBS, followed by a 15-min incubation with Alexa Fluor 488 annexin V and PI, then cells were analyzed by a NovoCyte flow cytometer (ACEA Biosciences, Hangzhou, China). Prediction of RNA interaction Target gene prediction of let-7c-5p was performed using the ENCORI online program with strict stringency (http://starbase.sysu.edu.cn/index.php). The interaction between circCORO1C and miRNA was predicted by seedVicious v1.0 and RegRNA 2.0 (https://seedvicious.essex.ac.uk/predict.html, http://regrna2.mbc.nctu.edu.tw/index.html). TCGA data analysis Transcriptome sequencing data and clinical features of head and neck squamous cell carcinoma (HNSCC) were downloaded from The Cancer Genome Atlas (TCGA) HNSCC cohort (https://portal.gdc.cancer.gov/projects/TCGA-HNSC), followed by expression analysis of PBX3 and let-7c-5p with normalized FPKM and RPM values. RNA immunoprecipitation (RIP) RIP experiments were performed with a Magna RIP RNA-Binding Protein Immunoprecipitation Kit (Millipore, Billerica, MA) according to the manufacturer’s instructions. Briefly, 1 × 107 cells were collected and resuspended in 300 μL RIPA lysis buffer containing protease inhibitor cocktail and RNase inhibitors. The cell lysates (200 μL) were incubated with 5 μg AGO2 antibody (#2897; CST, Danvers, MA) or rabbit IgG and protein A/G magnetic beads at 4 °C overnight with rotation. Immunoprecipitated RNA was purified using a RNeasy MinElute Cleanup kit (Qiagen). The enrichment of circCORO1C was evaluated by qPCR. Luciferase reporter assay HEK293T cells were cotransfected with luciferase reporter plasmid and let-7c-5p mimics or NC mimics for 48 h. The luciferase activity was measured using a dual luciferase reporter assay system (Promega) on a Spectra Max i3x Multifunctional microplate detection system (Molecular Devices). The luciferase values were normalized and then the relative luciferase activity was calculated. Western blotting Total protein was extracted with RIPA buffer containing protease inhibitor cocktail (ThermoFisher Scientific). The protein concentration was determined using a Coomassie (Bradford) Protein Assay Kit (ThermoFisher Scientific). Equal amounts of total protein (30 μg) were separated by 12% SDS-PAGE and transferred onto PVDF membranes (Millipore, Billerica, MA), followed by blocking with 5% skim milk. The membranes were incubated with antibodies against PBX3 (#12571-1-AP; Proteintech, Wuhan, China), E-cadherin (#3195S; CST, Danvers, MA), N-cadherin (#13116S; CST), Vimentin (#5741S; CST), Slug (#sc-166476; Santa Cruz Biotechnology), or GAPDH (#HC301-02; TransGen Biotech) overnight at 4 °C. Then membranes were washed three times with TBST followed by secondary antibody incubation for 2 h at room temperature. Bands were detected by a chemiluminescence imaging system (SageCreation Science, Beijing, China) with Western Bright ECL HRP substrate (Advansta Inc., San Jose, CA). Xenograft tumorigenesis SPF-grade male BALB/C nude mice (6-8 weeks) were purchased from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China) and housed under SPF conditions (TECNIPLAST S.p.A., Italy). A total of 2 × 106 FD-LSC-1 cells were suspended in 200 μL serum-free DMEM and subcutaneously injected into the right flank of each mouse. The volumes of tumors were measured from 7 days after injection. Tumor volume was calculated as follows: V (volume) = (length × width2)/2. After 25 days, the mice were killed and the tumors were dissected, weighed, and processed for histological analysis. Immunohistochemical (IHC) staining IHC staining was performed as previously described [3]. In brief, tissues were fixed in 4% (v/v) formaldehyde in PBS, embedded in paraffin, and cut into 3-μm sections. Sections underwent dewaxing, re-hydration, antigen retrieval, and blocking, and then were incubated with antibodies against PBX3, Ki67, E-cadherin, N-cadherin, and Vimentin overnight at 4 °C in a moist chamber, and washed three times with PBST. Sections were incubated with HRP-conjugated secondary antibody (CST) for 15 min at room temperature, washed three times with PBST, and then stained with DAB and hematoxylin. Next, sections were dehydrated and mounted with coverslips. Statistical analysis Statistical analysis was performed using GraphPad Prism 7.0 software (La Jolla, USA). Comparisons between two groups were performed using the two-tailed Student’s t-test. Correlations were analyzed by Pearson’s correlation. Kaplan-Meier survival curve and log-rank test were employed to depict the overall survival probability of LSCC patients with different expression levels of circCORO1C. Results are presented as mean ± standard deviation (SD). P values of < 0.05 were considered statistically significant. Results circCORO1C is frequently upregulated in LSCC and is associated with malignant progression and poor prognosis We performed RNA sequencing in 57 pairs of LSCC and matched ANM tissues. Differential expression screening showed that 410 circRNAs were upregulated in LSCC tissues (Additional file 1: Table S3), in which 18 circRNAs were detected in all sequenced tissues (Fig. 1a). Expression of the 18 circRNAs was verified by RT-PCR and Sanger sequencing, and 12 circRNAs were validated successfully (Additional file 2: Figure S1a and b). Next, we screened circRNAs that affect LSCC proliferation by siRNA-mediated knockdown and high-content screening. We found that knockdown of circRNA hg19_circ_0008714 significantly inhibited LSCC cell proliferation (Additional file 2: Figure S1c). Hence, we focused on this circRNA in this study. Sequence analysis revealed that hg19_circ_0008714 was formed by back-splicing of exons 7 and 8 of the Coronin-like actin-binding protein 1C gene (CORO1C) and was therefore named circCORO1C (Fig. 1b). RT-PCR and Sanger sequencing were performed to verify the expression and head-to-tail splicing of circCORO1C in LSCC (Fig. 1b). Moreover, we compared the expression levels of circCORO1C in LSCC cells and normal control cell lines by qPCR. The results showed that the expression levels of circCORO1C in LSCC cell lines FD-LSC-1 and TU-177 were significantly higher than those in normal control cell lines HEK293T, HOK, and MRC-5 (Fig. 1c). Fig. 1 circCORO1C is upregulated in LSCC tissues and is associated with poor prognosis. a RNA sequencing of 57 pairs of LSCC and matched adjacent normal mucosal (ANM) tissues to screen differentially expressed circRNAs. Heatmap showing circRNAs expressed in all tissues and those upregulated in LSCC tissues. b Schematic illustration showed the circularization of CORO1C exons 7 and 8 to form circCORO1C. The back-splicing junction of circCORO1C was verified by RT-PCR and Sanger sequencing. c circCORO1C expression levels in human LSCC cell lines (FD-LSC-1, TU-177) and normal cell lines (HOK, HEK293T, MRC-5) were determined by qPCR. d circCORO1C expression in FD-LSC-1 and TU-177 cells verified by RT-PCR. Agarose gel electrophoresis showed that divergent primers amplified circCORO1C in cDNA but not genomic DNA (gDNA). GAPDH served as a negative control. e Validation of circCORO1C stability by RNase R treatment and RT-PCR analysis. f qPCR analysis of the abundance of circCORO1C and linear CORO1C in FD-LSC-1 and TU-177 cells treated with actinomycin D at the indicated times. g Expression levels of circCORO1C in an additional 107 paired LSCC tissues were determined by qPCR. h–j Correlation analysis between circCORO1C expression levels and clinicopathological parameters of LSCC. Expression level of circCORO1C was significantly associated with T stage (h), N stage (i), and clinical stage (j). k Kaplan-Meier analysis of the correlation between circCORO1C expression and overall survival of 96 patients with LSCC. l circCORO1C abundance in nuclear and cytoplasmic fractions of FD-LSC-1 and TU-177 cells was evaluated by qPCR. 18S RNA acted as a positive control of RNA distributed in the cytoplasm, and U6 RNA acted as a positive control of RNA distributed in the nucleus. m Localization of circCORO1C in FD-LSC-1 and TU-177 cells was detected by FISH. Nuclei were stained with DAPI (blue) and circCORO1C probes were labeled with Cy3 (red). The error bars (c, f and l) represent SD of three independent experiments. **P < 0.001 Head-to-tail splicing sequences may be produced by mechanisms other than the formation of circRNA, such as trans-splicing and genomic rearrangements [20]. To rule out the possibility of the latter two, convergent primers for linear and special divergent primers for circular RNA were designed. The convergent primers could be amplified when the template contained CORO1C linear mRNA or genomic DNA (gDNA), while the divergent primers could only be specifically amplified in the presence of circCORO1C. cDNA and gDNA from FD-LSC-1 and TU-177 cells, respectively, were used as PCR templates. Nucleic acid electrophoresis results indicated that the divergent primers could amplify circCORO1C only in cDNA, but no products were detected in the gDNA (Fig. 1d). High stability is a crucial feature of circRNA. To confirm the stability of circCORO1C, RNase R was used to pretreat the RNA; the results demonstrated that linear CORO1C mRNA was significantly reduced after RNase R treatment, while circCORO1C was resistant to RNase R (Fig. 1e). Moreover, we further compared the half-life of the circular form and linear CORO1C through treatment with RNA transcription inhibitor actinomycin D and qPCR assay, and found that circCORO1C had a significantly longer half-life than the linear CORO1C (Fig. 1f). These data confirmed essential features of circCORO1C. To investigate the correlation between circCORO1C levels and LSCC, we detected the expression of circCORO1C in 107 pairs of LSCC and ANM tissues by qPCR. The relative abundance of circCORO1C in LSCC tissues was significantly higher than that in ANM tissues (Fig. 1g). Moreover, the expression level of circCORO1C was significantly correlated with T stage, N stage, and clinical stage. Patients in the advanced stage and those with cervical lymph node metastasis had high expression levels of circCORO1C (Fig. 1h–j). Importantly, Kaplan-Meier analysis revealed that LSCC patients with high circCORO1C levels had poor overall survival (Fig. 1k). Next, we investigated the location of circCORO1C in cells by nuclear and cytoplasmic RNA extraction and qPCR. The results showed that circCORO1C was mainly localized to the cytoplasm (Fig. 1l). FISH further confirmed that circCORO1C was mainly localized to the cytoplasm (Fig. 1m). These results indicated that circCORO1C upregulation is common in LSCC and may have important functions in the progression of LSCC. circCORO1C promotes the proliferation, migration, and invasion of LSCC cells To investigate the functions of circCORO1C in LSCC cells, we designed and synthesized two siRNAs that specifically targeted the back-splicing region of circCORO1C. After LSCC cell lines FD-LSC-1 and TU-177 were transfected with siRNA, qPCR was performed to evaluate the knockdown efficiency. The results showed that both siRNAs could significantly reduce circCORO1C, while the level of linear CORO1C was not significantly changed, and siRNA #1 had the highest knockdown efficiency (Fig. 2a). We also tested cell viability by CCK-8 assay, and found that knockdown of circCORO1C significantly inhibited the viability of LSCC cells (Fig. 2b). EdU staining experiments confirmed that knockdown of circCORO1C inhibited the proliferation of LSCC cells (Fig. 2c). Colony formation experiments showed that knockdown of circCORO1C significantly inhibited the colony formation of FD-LSC-1 and TU-177 cells (Fig. 2d). Fig. 2 circCORO1C promotes the proliferation, migration, and invasion of LSCC cells. a circCORO1C siRNA was transfected into FD-LSC-1 and TU-177 cells, and the expression levels of cicCORO1C and CORO1C were detected by qPCR. b FD-LSC-1 and TU-177 cells were transfected with circCORO1C siRNAs. Cell proliferation capacity was detected at the indicated time points by CCK8 assays. c FD-LSC-1 and TU-177 cells were transfected with circCORO1C siRNAs, and the changes in cell proliferation were determined by EdU staining. d circCORO1C knockdown inhibited colony formation of both FD-LSC-1 and TU-177 cells. e & f Knockdown of circCORO1C inhibited the migration and invasion of FD-LSC-1 (e) and TU-177 (f) cells as determined by Transwell migration and invasion assays. g FD-LSC-1 and TU-177 cells were transfected with circCORO1C siRNAs. Cells were stained with Annexin V-FITC and PI, and the percentage of apoptotic cells was detected by flow cytometry. Data are presented as the mean ± SD of three independent experiments. *P < 0.05; **P < 0.001 Furthermore, we investigated the effects of circCORO1C on the migration and invasion of LSCC cells by Transwell assays. Knockdown of circCORO1C significantly decreased cell migration and invasion (Fig. 2e and f). We further investigated the effect of circCORO1C on apoptosis, and found that knockdown of circCORO1C promoted apoptosis in LSCC cells (Fig. 2g). Taken together, these findings suggested that circCORO1C has an oncogenic role in LSCC, and si-circCORO1C #1 had the strongest effect on the cell functions, which was consistent with the knockdown efficiency. Thus, in subsequent studies, we performed experiments using si-circCORO1C #1. circCORO1C acts as a miRNA sponge of let-7c-5p in LSCC cells Studies have showed that circRNAs can function as miRNA sponges to competitively bind to miRNA, thus abrogating the inhibitory effect of miRNA on downstream target genes. Since circCORO1C is distributed in the cytoplasm, we studied whether it could function as a miRNA sponge in LSCC cells. We used the online tools RegRNA and seedVicious to predict the circCORO1C-binding miRNA (Additional file 1: Table S7), and intersected the data with miRNAs that were found to be downregulated in RNA sequencing of our 57 pairs of LSCC and ANM tissues. Notably, let-7c-5p was the only one common miRNA in these three datasets (Fig. 3a). Fig. 3 circCORO1C acted as a sponge for miRNA let-7c-5p in LSCC cells. a Combined analysis of bioinformatics prediction and LSCC tissue RNA sequencing data to screen for circCORO1C-binding miRNAs. b RIP assays were performed using AGO2 antibody in FD-LSC-1 and HOK cells, then the enrichment of circCORO1C was detected by qPCR. c HEK293T cells were co-transfected with let-7c-5p mimics and wild-type or mutant circCORO1C luciferase reporter vector, and luciferase reporter activity was detected. d Correlation analysis of circCORO1C and let-7c-5p RNA levels in 20 pairs of LSCC tissues. Expression of circCORO1C and let-7c-5p in 20 cases of LSCC and matched ANM tissues was determined by qPCR, and the relative expression of circCORO1C and let-7c-5p was normalized to ANM. e Expression levels of circCORO1C and let-7c-5p in FD-LSC-1 and TU-177 cells transfected with circCORO1C siRNAs were evaluated by qPCR. Data are presented as the means ± SD of three independent experiments. *P < 0.05; **P < 0.001 As one of the critical components of RNA-induced silencing complex (RISC), Argonaute 2 (AGO2) is the major protein that mediates the interaction between circRNA and target miRNAs [21]. To demonstrate that circCORO1C functions as a miRNA sponge, we performed RIP assay using AGO2 antibody in FD-LSC-1 and HOK cells. The results showed that circCORO1C was pulled down with AGO2 and was less enriched in HOK cells compared with FD-LSC-1 cells, which consistent with the differential circCORO1C levels of these two cell lines (Fig. 3b). In addition, the results of RIP in let-7c-5p-transfected FD-LSC-1 cells indicated that circCORO1C was specifically enriched by AGO2 antibody (Additional file 2: Figure S2). Moreover, we constructed luciferase reporter vectors for wild-type circCORO1C and let-7c-5p binding site mutant circCORO1C and co-transfected HEK293T cells with let-7c-5p mimics or NC mimics. The results showed that the luciferase activity in the wild-type group co-transfected with let-7c-5p mimics was significantly reduced, while the luciferase activity in the binding site mutant group was not significantly changed (Fig. 3c). Furthermore, the expression correlation between circCORO1C and let-7c-5p was analyzed by qPCR in 20 pairs of LSCC tissues, revealing that let-7c-5p levels were negatively correlated with circCORO1C levels in LSCC tissues (Fig. 3d). In addition, we found that expression of let-7c-5p in FD-LSC-1 and TU-177 cells was increased significantly after circCORO1C knockdown (Fig. 3e). Collectively, these results indicated that circCORO1C functions as a miRNA sponge to directly interact with let-7c-5p in LSCC cells. let-7c-5p is downregulated in LSCC tissues and inhibits the malignant phenotype of LSCC cells RNA sequencing data indicated that let-7c-5p expression levels in LSCC tissues were significantly lower than those in ANM tissues (Fig. 4a). Analysis of TCGA data confirmed that let-7c-5p expression was downregulated in HNSCC and LSCC (Fig. 4b and c), indicating that let-7c-5p may have important roles in LSCC. Therefore, we investigated the functions of let-7c-5p in LSCC cells. FD-LSC-1 and TU-177 cells were transfected with let-7c-5p mimics or negative control mimics (NC mimics), then the transfection efficiency was verified by qPCR, which revealed that let-7c-5p expression was elevated (Fig. 4d). CCK8 assay and EdU staining indicated that overexpression of let-7c-5p inhibited LSCC cell proliferation (Fig. 4e and f). Colony formation experiments found that the colony formation ability of LSCC cells overexpressing let-7c-5p was significantly decreased (Fig. 4g), while Transwell assay showed that the migration and invasion of LSCC cells were significantly attenuated after overexpression of let-7c-5p (Fig. 4h and i). Apoptosis assay showed that let-7c-5p overexpression promoted the apoptosis of LSCC cells (Fig. 4j). Overall, these data demonstrated that let-7c-5p inhibits the proliferation, migration, and invasion of LSCC cells and promotes their apoptosis. Fig. 4 let-7c-5p inhibited the proliferation, migration, and invasion of LSCC cells. a Expression of let-7c-5p in 57 LSCC tissues and matched ANM tissues was analyzed using RNA sequencing data. b & c Analysis of let-7c-5p expression in HNSCC (b) and LSCC (c) tissues using transcriptomic sequencing data from TCGA database. d FD-LSC-1 and TU-177 cells were transfected with let-7c-5p mimics or NC mimics, then let-7c-5p expression was determined by qPCR. e FD-LSC-1 and TU-177 cells were transfected with let-7c-5p mimics or NC mimics. Cell proliferation was detected by CCK8 assay. f FD-LSC-1 and TU-177 cells were transfected with let-7c-5p mimics or NC mimics, and cell proliferation was assessed by EdU staining. g Proliferative capacity of FD-LSC-1 and TU-177 cells transfected with let-7c-5p mimics or NC mimics was evaluated by colony formation assay. h & i. Effect of let-7c-5p on the migration and invasion of FD-LSC-1 (h) and TU-177 (i) cells was assessed by Transwell migration and invasion assays. j FD-LSC-1 and TU-177 cells were transfected with let-7c-5p mimics or NC mimics. Cells were stained with Annexin V-FITC and PI, and the percentage of apoptotic cells was detected by flow cytometry. Data are presented as the mean ± SD of three independent experiments. *P < 0.05; **P < 0.001 let-7c-5p reversed the tumor-promoting effects of circCORO1C in LSCC cells To identify whether circCORO1C promoted LSCC cell proliferation, migration, and invasion by interacting with let-7c-5p, we conducted a rescue experiment. FD-LSC-1 and TU-177 cells were cotransfected with si-circCORO1C and let-7c-5p inhibitor (Fig. 5a), and then CCK-8, EdU staining, and colony formation assays were conducted. The results showed that when let-7c-5p function was inhibited with miRNA inhibitor, the proliferation and colony formation ability of LSCC cells was significantly enhanced (Fig. 5b–d). Notably, transfection with let-7c-5p inhibitor could reverse the decreased cell viability caused by si-circCORO1C (Fig. 5b–d). Transwell assays showed that let-7c-5p inhibition reversed the reduction in the migration and invasion of FD-LSC-1 and TU-177 cells caused by circCORO1C knockdown (Fig. 5e). Compared with cells transfected with si-circCORO1C alone, co-transfection of LSCC cells with si-circCORO1C and let-7c-5p inhibitor significantly reduced apoptosis (Fig. 5f). Taken together, these results indicated that circCORO1C promoted the malignant progression of LSCC cells mainly by abolishing the anti-tumor effect of let-7c-5p. Fig. 5 let-7c-5p reversed the tumor-promoting effect of circCORO1C in LSCC cells. a FD-LSC-1 and TU-177 cells were transfected with si-circCORO1C or co-transfected with si-circCORO1C and let-7c-5p inhibitor. CircCORO1C and let-7c-5p expression was detected by qPCR. b FD-LSC-1 and TU-177 cells were transfected with si-circCORO1C or co-transfected with si-circCORO1C and let-7c-5p inhibitor. Cell proliferation was determined by CCK8 assay. c Effects of si-circCORO1C and let-7c-5p inhibitor on the proliferation of FD-LSC-1 and TU-177 cells were evaluated by EdU staining. d Colony formation assays were performed to evaluate the proliferative ability of FD-LSC-1 and TU-177 cells transfected with si-circCORO1C or co-transfected with si-circCORO1C and let-7c-5p inhibitor. e Effects of si-circCORO1C and let-7c-5p inhibitor on the migration and invasion of FD-LSC-1 and TU-177 cells were evaluated by Transwell migration and invasion assays. f FD-LSC-1 and TU-177 cells were transfected with si-circCORO1C or co-transfected with si-circCORO1C and let-7c-5p inhibitor. Cells were stained with Annexin V-FITC and PI, and the percentage of apoptotic cells was detected by flow cytometry. Data are presented as the means ± SD of three independent experiments. *P < 0.05; **P < 0.001 PBX3 is a direct target of let-7c-5p and functions as driver gene in LSCC According to ceRNA theory, circCORO1C is positively correlated with the expression of downstream target genes, while the target gene is negatively correlated with let-7c-5p expression. We predicted the possible let-7c-5p target genes by miRanda, PicTar, PITA, and TargetScan, and 257 genes intersected by these four programs were obtained (Fig. 6a; Additional file 1: Table S8). Then we intersected these 257 genes with the mRNAs that were found to be upregulated in LSCC tissues upon RNA sequencing, and 51 intersected genes were obtained (Fig. 6b; Additional file 1: Table S9). Next, we analyzed the expression correlation of circCORO1C and let-7c-5p with the 51 genes using RNA sequencing data of 57 pairs of LSCC samples. Pearson correlation analysis indicated that circCORO1C was positively correlated with PBX3, while let-7c-5p was negatively correlated with PBX3 in LSCC and ANM tissues (Fig. 6c and d). RNA sequencing data showed that PBX3 was upregulated in 73.7% (42/57) of LSCC tissues (Fig. 6ec and d). Moreover, analysis of the transcriptomic data of TCGA database found that PBX3 was upregulated in both HNSCC and LSCC (Fig. 6f). In addition, overexpression of let-7c-5p significantly decreased the expression of PBX3 mRNA and protein (Fig. 6g), while downregulation of let-7c-5p remarkably increased it in FD-LSC-1 and TU-177 cells (Fig. 6h). Fig. 6 PBX3 is a direct target gene of let-7c-5p, which acted as an oncogene in LSCC cells. a Venn analysis of the target genes of let-7c-5p predicted by miRanda, PicTar, PITA, and TargetScan. b Integrated analysis of bioinformatics-predicted target genes and RNA sequencing data of 57 pairs of LSCC tissues was performed to screen for let-7c-5p target genes. c & d Correlation analysis between circCORO1C (c) or let-7c-5p (d) and PBX3 expression using RNA sequencing data of 57 pairs of LSCC tissues and matched ANM tissues. e PBX3 expression in RNA sequencing data of 57 pairs of LSCC tissues and matched ANM tissues. The expression levels of PBX3 in each LSCC tissue were normalized to corresponding matched ANM tissue. f Analysis of PBX3 expression in HNSCC and LSCC tissues using transcriptome sequencing data from TCGA database. g & h FD-LSC-1 and TU-177 cells were transfected with let-7c-5p mimics (g), let-7c-5p inhibitor (h) or NC, and PBX3 expression was detected by qPCR and western blotting. i HEK293T cells were co-transfected with let-7c-5p mimics and wild-type or mutant PBX3 3′ UTR reporter plasmids, and luciferase reporter assays were performed to evaluate the effect of let-7c-5p on luciferase activity. j FD-LSC-1 and TU-177 cells were transfected with let-7c-5p mimics or co-transfected with let-7c-5p mimics and PBX3 overexpression plasmids, and CCK8 assay was performed to detect cell proliferation. k & l FD-LSC-1 (k) and TU-177 (l) cells were transfected with let-7c-5p mimics or co-transfected with let-7c-5p mimics and PBX3 overexpression plasmids. Changes in cell migration and invasion capacity were evaluated by Transwell assays. Data are presented as the means ± SD of three independent experiments. *P < 0.05; **P < 0.001 To demonstrate that let-7c-5p interacts directly with the 3′ UTR of PBX3, we constructed wild-type (WT) PBX3 3′ UTR and let-7c-5p binding-site mutant (Mut) luciferase reporter plasmids. The wild-type and mutant reporter vectors were co-transfected with let-7c-5p mimics in cells. Luciferase reporter assays showed that let-7c-5p mimics significantly decrease the luciferase activity of WT, while the luciferase activity of the Mut group was not significantly changed (Fig. 6i), indicating that let-7c-5p suppresses PBX3 expression by directly binding to the 3′ UTR of PBX3 mRNA. Subsequently, we investigated the functions of PBX3 in LSCC cells. Overexpression of PBX3 promoted the proliferation, migration, and invasion of LSCC cells (Fig. 6j–l). Notably, we observed that overexpression of PBX3 counteracted the inhibitory effects of let-7c-5p on LSCC cell proliferation, migration, and invasion (Fig. 6j–l). Collectively, these findings suggested that PBX3 is a driver gene and a direct target of let-7c-5p in LSCC. circCORO1C facilitates the malignant progression of LSCC cells by targeting PBX3 To investigate whether circCORO1C promoted the malignant progression of LSCC cells by regulating the downstream target gene PBX3, we simultaneously transfected PBX3 overexpression plasmid and si-circCORO1C into FD-LSC-1 and TU-177 cells (Fig. 7a) and detected changes in the cell phenotypes. CCK-8 assay and EdU staining were performed, and the results showed that overexpression of PBX3 could inhibit the decrease in cell proliferation caused by circCORO1C knockdown (Fig. 7b and c). Consistently, overexpression of PBX3 rescued the decreased colony formation ability by circCORO1C knockdown (Fig. 7d). Furthermore, Transwell assay showed that overexpression of PBX3 could reverse the decline in cell migration and invasion ability caused by circCORO1C knockdown (Fig. 7e). We also detected protein changes in EMT marker genes by western blotting. circCORO1C knockdown enhanced the expression of E-cadherin while inhibiting the expression of N-cadherin, Vimentin, and Slug (Fig. 7f), and overexpression of PBX3 could reverse the regulatory effects of circCORO1C on these EMT markers. Compared with the si-circCORO1C group, the expression of E-cadherin was reduced, and the expression of N-cadherin, Vimentin, and Slug were increased in the group cotransfected with si-circCORO1C and PBX3 overexpression plasmid (Fig. 7f). These findings indicated that circCORO1C promoted the proliferation, migration, and invasion phenotype of LSCC cells by specifically upregulating the expression of the target gene PBX3 and affecting the EMT process at the same time. Fig. 7 CircCORO1C contributed to the malignant phenotype of LSCC cells through regulating the expression of PBX3. a FD-LSC-1 and TU-177 cells were transfected with si-circCORO1C or co-transfected with si-circCORO1C and PBX3 overexpression plasmids. PBX3 expression was detected by qPCR. b & c FD-LSC-1 and TU-177 cells were transfected with si-circCORO1C or co-transfected with si-circCORO1C and PBX3 overexpression plasmids. Cell proliferation was determined by CCK8 assay (b) and EdU staining (c). d FD-LSC-1 and TU-177 cells were transfected with si-circCORO1C or co-transfected with si-circCORO1C and PBX3 overexpression plasmids. Cell proliferation ability was evaluated by colony formation assay. e Effects of circCORO1C knockdown and overexpression of PBX3 on the migration and invasion of FD-LSC-1 and TU-177 cells were determined by Transwell assays. f E-cadherin, N-cadherin, Vimentin, and Slug expression in FD-LSC-1 and TU-177 cells with knockdown of circCORO1C and overexpression of PBX3 were detected by western blotting. Data are presented as the means ± SD of three independent experiments. *P < 0.05; **P < 0.001 circCORO1C enhances the growth of xenograft tumors of LSCC cells in vivo To investigate the regulatory effect of circCORO1C on LSCC under in vivo conditions, we constructed a shRNA lentiviral plasmid targeting circCORO1C and screened FD-LSC-1 cells following stable knockdown of circCORO1C (sh-circCORO1C). Next, we constructed xenograft tumor models of nude mice by subcutaneously injecting stably transfected FD-LSC-1 cells. The xenograft tumors formed by circCORO1C-deficient LSCC cells had a significantly smaller volume than those of the control group (sh-NC) (Fig. 8a), and the tumor weight was also significantly lower than the sh-NC group (Fig. 8b). The total RNA of xenograft tumors was extracted, and qPCR was used to detect the expression of circCORO1C, let-7c-5p, and PBX3. The results confirmed decreased circCORO1C and PBX3 expression, while let-7c-5p was increased in tumors with circCORO1C knockdown (Fig. 8c). Furthermore, hematoxylin and eosin (H&E) staining showed that knockdown of circCORO1C remarkably reduced the number of lesions (Fig. 8d). IHC staining demonstrated that the expression of PBX3 and proliferation marker Ki67 was decreased in sh-circCORO1C xenograft tumors (Fig. 8e). In addition, the changes in EMT marker E-cadherin, N-cadherin, Vimentin, and Slug expression were determined by IHC. The results revealed that knockdown of circCORO1C attenuated the mesenchymal phenotype (Fig. 8e). These data confirmed that circCORO1C promoted the malignant progression of LSCC in vivo. Fig. 8 CircCORO1C promoted the tumor growth of LSCC cells in vivo. a Nude mice were subcutaneously injected with negative control (sh-NC) and shRNA-circCORO1C stably transfected FD-LSC-1 cells. After 25 days, tumors were dissected and imaged (left). Starting from day 7 after injection, the tumor volume was measured every 3 days, and the tumor growth curve was plotted (right). b Tumor weight was calculated on the day the mice were killed. Data represents mean ± SD (n = 6 each group). c Expression levels of circCORO1C, let-7c-5p, and PBX3 in xenograft tumors were determined by qPCR. d H&E staining revealed the structure of xenograft tumors derived from sh-NC and sh-circCORO1C LSCC cells. Scale bar, 200 μm. e Changes in PBX3, Ki67, E-cadherin, N-cadherin, and Vimentin expression in xenograft tumors were detected by IHC staining. Scale bar, 20 μm. f Schematic illustration of the regulation of LSCC malignant progression by the circCORO1C–let-7c-5p–PBX3 axis. *P < 0.05; **P < 0.001 Discussion Studies have shown that circRNA has important regulatory effects in a variety of biological processes, especially in the occurrence, development, and metastasis of various malignant tumors [22–24]. circRNA expression profiling revealed a series of differentially expressed circRNAs in LSCC tissues [25, 26], suggesting that circRNA may have important roles in the occurrence and progression of LSCC. In this study, we performed large-scale RNA sequencing of LSCC and matched ANM tissues, and established the circRNA, miRNA, and mRNA expression profiles of LSCC tissues. We identified and verified that circCORO1C was highly expressed in LSCC tissues and cells, and its expression levels were correlated with clinicopathological parameters and LSCC patient survival. Loss-of-function experiments demonstrated that circCORO1C promoted the proliferation, migration, and invasion of LSCC cells and inhibited their apoptosis. Mechanistic studies showed that circCORO1C bound to let-7c-5p and attenuated the inhibition of let-7c-5p on the target gene PBX3, leading to PBX3 accumulation and enhancing the proliferation, migration, and invasion of LSCC cells. The CORO1C-encoded WD repeat protein family member regulates actin-dependent processes through F-actin assembly [27]. Studies have shown that CORO1C promotes the metastases of breast cancer and lung squamous cell carcinoma [28, 29]. Cheng et al. reported that CORO1C is highly expressed in gastric cancer tissues, and in vitro experiments demonstrated that CORO1C promotes the proliferation, migration, and invasion of gastric cancer cells [30]. However, it is unclear whether circRNA is formed by CORO1C, and the roles of CORO1C-formed circRNA in disease or normal physiological processes have not yet been reported. In this study, RNA sequencing data analysis and experiments demonstrated that circCORO1C, which was highly expressed in LSCC tissues, was composed of exons 7 and 8 of CORO1C. Treatment with actinomycin D showed that the half-life of circCORO1C was significantly longer than that of linear CORO1C RNA. RNase R has 3′ to 5′ exoribonuclease activity that digests all linear RNAs except circular RNA structures [31]. When treated with RNase R, there is no significant change in circCORO1C level, proving that it has high stability as previously reported circRNA [32, 33]. Importantly, we found that the high expression of circCORO1C was positively correlated with advanced T stage, cervical lymph node metastasis, and clinical stage of LSCC, as well as poor prognosis in patients with LSCC, suggesting that circCORO1C may exert an important regulatory effect on the occurrence and development of LSCC. RNA sequencing and bioinformatics analysis indicated that circRNA has an important regulatory effect in the occurrence and development of head and neck tumors [34]. Experimental studies further demonstrated that circHIPK3 promotes cell proliferation and invasion in nasopharyngeal carcinoma [35], while Hsa_circ_0005379 inhibits the cell migration, invasion, proliferation, and in vivo tumorigenesis of oral squamous cell carcinoma [36]. CircRNAs CDR1as and hsa_circ_0023028 promote the proliferation, migration, and invasion of LSCC cells [37, 38]. Moreover, our previous studies found that the circRNA hg19_circ_0005033, which is highly expressed in LSCC stem cells, promotes proliferation, migration, invasion, and chemotherapy resistance [19]. There are very few LSCC cell lines available, among which FD-LSC-1 and TU-177 are well-characterized [18, 39]. Our data showed that expression of circCORO1C in FD-LSC-1 and TU-177 cells was higher than that in normal control cell lines. Therefore, we used these two cell lines to investigate the role of circCORO1C in LSCC cells. Consistent results showed that knockdown of circCORO1C inhibited cell proliferation, migration, invasion, and promoted apoptosis of LSCC, indicating that circCORO1C acts as an important oncogene to promote the malignant progression of LSCC. Transcripts with the same miRNA binding site, such as circRNA, mRNA, and lncRNA, regulate the expression of each other by competitively binding miRNAs. These molecules form a complex and precise post-transcriptional regulatory network, namely the ceRNA network [40]. As an important member of the ceRNA network, circRNA is involved in the formation of the circRNA–miRNA–mRNA axis, which has regulatory functions in a variety of diseases and is the most reported mechanism of action of circRNA [41–43]. In this study, we found that circCORO1C was localized to the cytoplasm, suggesting that it functions as a ceRNA [44]. let-7c-5p has been demonstrated to have anti-tumor effects in malignant tumors including non-small cell lung cancer and liver cancer [45, 46]. The combined bioinformatics prediction and transcriptomic analysis showed that let-7c-5p may bind to circCORO1C. We further demonstrated that let-7c-5p expression levels in LSCC were significantly lower than that in adjacent normal tissues, and overexpression of let-7c-5p inhibited cell proliferation, migration, and invasion in LSCC. The luciferase reporter assay and AGO2 RIP experiments demonstrated that let-7c-5p bound to circCORO1C, while rescue experiments revealed that inhibition of let-7c-5p reversed the inhibitory effect of knockdown of circCORO1C on LSCC malignant phenotypes. These findings indicated that circCORO1C sponged let-7c-5p to exert tumor-promoting functions in LSCC cells. PBX3 is highly expressed in a variety of cancer tissues, such as prostate and cervical cancer [14, 16]. Han et al. demonstrated that PBX3 expression is a critical determinant for maintaining the characteristics of tumor-initiating cells in hepatocellular carcinoma [17]. In this study, we found that PBX3 expression was upregulated in LSCC tissues, and functional studies indicated that PBX3 promoted cell proliferation, migration, and invasion in LSCC. Our data revealed that PBX3 was a direct target of let-7c-5p, and circCORO1C competitively bound to let-7c-5p and relieved the inhibitory effect of let-7c-5p on PBX3 expression, thereby upregulating PBX3 expression. We further confirmed that circCORO1C promoted the malignant progression of LSCC cells by upregulating PBX3. EMT is the basis of tumor cell migration and invasion [47, 48], and PBX3 is an essential regulator of the EMT signaling network [13]. We observed that changes in the expression levels of circCORO1C or PBX3 affected the expression of EMT markers, indicating that the circCORO1C–let-7c-5p–PBX3 axis promoted the migration and invasion of LSCC cells by regulating EMT. Finally, we demonstrated that knockdown of circCORO1C inhibited the growth of LSCC cell xenograft tumors through preclinical models and verified the regulatory relationship of the circCORO1C–let-7c-5p–PBX3 axis in vivo. In future, exploring the upstream regulator of circCORO1C and developing non-invasive circCORO1C detection methods in LSCC and other HNSCC types will be of great significance in promoting clinical translation. Conclusions In summary, our data revealed that circCORO1C competitively binds let-7c-5p to eliminate its inhibitory effect on PBX3, thereby promoting LSCC cell proliferation, migration, and invasion (Fig. 8f). High expression of circCORO1C is an important marker of poor prognosis for LSCC. These findings provide new insights into the occurrence and progression of LSCC and indicate the potential of circCORO1C as a biomarker and therapeutic target for LSCC. Supplementary information Additional file 1: Table S1. Clinical features of 57 LSCC samples for RNA sequencing. Table S2. Clinical features of 107 LSCC samples for qPCR validation. Table S3. Differentially expressed circRNAs in LSCC tissues. Table S4. Differentially expressed miRNAs in LSCC tissues. Table S5. Differentially expressed mRNAs in LSCC tissues. Table S6. Primer sequences for RT-PCR and qPCR analysis. Table S7. Prediction of circCORO1C and miRNA interaction by seedVicious. Table S8. let-7c-5p target gene prediction by ENCORI. Table S9. Intersection of predicted let-7c-5p targets and upregulated mRNAs in LSCC tissues Additional file 2: Figure S1. RNA sequencing and high-content screening reveals that circCORO1C affects the proliferation of LSCC cells. a Flowchart showing the steps for identifying functional circRNAs in LSCC. b Validation of circRNA expression in LSCC tissues by RT-PCR and Sanger sequencing. c High-content screening of circRNAs that affect the proliferation of LSCC cells. GFP-labeled FD-LSC-1 cells were transfected with siRNAs targeting the indicated circRNA. After 24 h transfection, cells were seeded into 96-well plates, and the cell number was counted at the indicated time points. Representative images (left) and fold change in cell count (right) are shown. Data are presented as the means ± SD of three independent experiments. *P < 0.05. Figure S2. FD-LSC-1 cells were transfected with let-7c-5p mimics or NC mimics for 48 h, then RIP assay was performed using AGO2 antibody and circCORO1C levels were measured by qPCR. **P < 0.001. Abbreviations LSCC Laryngeal squamous cell carcinoma circRNA Circular RNA ceRNA Competing endogenous RNA EMT Epithelial–mesenchymal transition ANM Adjacent normal mucosa qPCR Quantitative real-time PCR FISH Fluorescence in situ hybridization EdU 5-Ethynyl-2′-deoxyuridine RIP RNA immunoprecipitation 3′ UTR 3′-untranslated region HNSCC Head and neck squamous cell carcinoma Supplementary information Supplementary information accompanies this paper at 10.1186/s12943-020-01215-4. We thank Prof. Tao Bai from the Department of Pathology, The First Hospital of Shanxi Medical University for pathological analysis of LSCC sections. Authors’ contributions WG, WX, HZL, and CMA conceived the study and participated in the study design. YYW, YLZ, XWZ, FSD, YL, MN and HNG performed cell culture, colony formation, western blots, RIP, and flow cytometry assays. YYW, XWZ, LD, WQL, XTX, YFB and RH performed bioinformatics analysis, IHC, and pathological diagnosis. JBQ, YXQ, HLL, YZ, TY, and LL performed high-content screening, EdU staining and luciferase reporter assays. YYW, YLZ, XWZ, FSD, YL, LSZ and HNG contributed FISH and qPCR experiments. YYW, WQL and YLZ performed xenograft experiments. YL, YFB and SXW contributed to the clinical samples collection, follow-up and clinical data analysis. YYW, YLZ and XWZ performed primer design and plasmid construction. YYW, YJG, WG, WX, HZL, and CMA analyzed the data, organized figures and wrote the manuscript. All authors read and approved the final manuscript. Funding This work was supported by the National Natural Science Foundation of China (grants: 81872210, 81802793, and 81802948), Postdoctoral Research Foundation of China (grants: 2016 M591412 and 2017 M610174), The Excellent talent science and technology innovation project of Shanxi Province (grants: 201605D211029, 201705D211018, and 201805D211007), Youth Science and Technology Research Fund of Shanxi Province (grants: 201901D211486, 201901D211490), Shanxi Province Scientific and Technological Achievements Transformation Guidance Foundation (grants: 201604D131002, 201604D132040, and 201804D131043), Youth Foundation of The First Hospital Affiliated with Shanxi Medical University (grant: YQ1503), Youth Top Talent Program Fund of Shanxi Province, Fund of Shanxi “1331” Project. Availability of data and materials RNA sequencing raw data and normalized results were deposited at GEO database (GSE127165, GSE133632). All data that support the findings of this study are available from the corresponding authors upon reasonable request. Ethics approval and consent to participate The clinical samples were obtained with the consent of patients and approved by The Medical Ethics Committee of The First Hospital of Shanxi Medical University. Informed consent per institutional guidelines was obtained from all patients who agreed to participate in this study. Animal experiments were conducted according to the Health Guide for the Care and Use of Laboratory Animals approved by the Animal Experimental Research Ethics Committee of Shanxi Medical University. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Yongyan Wu, Yuliang Zhang, Xiwang Zheng, Fengsheng Dai and Yan Lu contributed equally to this work. 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PMC008xxxxxx/PMC8206691.txt

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PMC008xxxxxx/PMC8206706.txt

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