Repurposing anti-inflammasome NRTIs for improving insulin sensitivity and reducing type 2 diabetes development

doi:10.1038/s41467-020-18528-z

Meta-Analysis

. 2020 Sep 23;11(1):4737.

doi: 10.1038/s41467-020-18528-z.

Repurposing anti-inflammasome NRTIs for improving insulin sensitivity and reducing type 2 diabetes development

Jayakrishna Ambati^{1

2

3

4}, Joseph Magagnoli^{5

6}, Hannah Leung^{7

8}, Shao-Bin Wang^{7

8}, Chris A Andrews^{9

10}, Dongxu Fu^{7

8}, Akshat Pandey^{7

8}, Srabani Sahu¹¹, Siddharth Narendran^{7

8}, Shuichiro Hirahara^{7

8}, Shinichi Fukuda^{7

8

12}, Jian Sun^{7

8}, Lekha Pandya^{7

8}, Meenakshi Ambati^{7

8}, Felipe Pereira^{7

8}, Akhil Varshney^{7

8}, Tammy Cummings^{5

6}, James W Hardin¹³, Babatunde Edun^{5

14}, Charles L Bennett^{5

6

15}, Kameshwari Ambati^{7

8}, Benjamin J Fowler¹⁶, Nagaraj Kerur^{7

8

17

18}, Christian Röver¹⁹, Norbert Leitinger¹¹, Brian C Werner²⁰, Joshua D Stein^{6

9

21}, S Scott Sutton^{5

6}, Bradley D Gelfand^{7

8

22}

Affiliations

¹ Center for Advanced Vision Science, University of Virginia School of Medicine, Charlottesville, VA, USA. ja9qr@virginia.edu.
² Department of Ophthalmology, University of Virginia School of Medicine, Charlottesville, VA, USA. ja9qr@virginia.edu.
³ Department of Pathology, University of Virginia School of Medicine, Charlottesville, VA, USA. ja9qr@virginia.edu.
⁴ Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, VA, USA. ja9qr@virginia.edu.
⁵ Dorn Research Institute, Columbia VA Health Care System, Columbia, SC, USA.
⁶ Department of Clinical Pharmacy & Outcomes Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA.
⁷ Center for Advanced Vision Science, University of Virginia School of Medicine, Charlottesville, VA, USA.
⁸ Department of Ophthalmology, University of Virginia School of Medicine, Charlottesville, VA, USA.
⁹ Department of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, MI, USA.
¹⁰ Center for Eye Policy and Innovation, University of Michigan, Ann Arbor, MI, USA.
¹¹ Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA, USA.
¹² Department of Ophthalmology, University of Tsukuba, Ibaraki, Japan.
¹³ Department of Epidemiology & Biostatistics, University of South Carolina, Columbia, SC, USA.
¹⁴ Department of Medicine, Baystate Medical Center, Springfield, MA, USA.
¹⁵ Center for Medication Safety and Efficacy, College of Pharmacy, University of South Carolina, Columbia, SC, USA.
¹⁶ Department of Ophthalmology and Visual Sciences, University of Kentucky, Lexington, KY, USA.
¹⁷ Department of Pathology, University of Virginia School of Medicine, Charlottesville, VA, USA.
¹⁸ Department of Neuroscience, University of Virginia School of Medicine, Charlottesville, VA, USA.
¹⁹ Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
²⁰ Department of Orthopaedics, University of Virginia School of Medicine, Charlottesville, VA, USA.
²¹ Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, MI, USA.
²² Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, VA, USA.

PMID: 32968070
PMCID: PMC7511405
DOI: 10.1038/s41467-020-18528-z

Meta-Analysis

Repurposing anti-inflammasome NRTIs for improving insulin sensitivity and reducing type 2 diabetes development

Jayakrishna Ambati et al. Nat Commun. 2020.

. 2020 Sep 23;11(1):4737.

doi: 10.1038/s41467-020-18528-z.

Authors

Affiliations

¹ Center for Advanced Vision Science, University of Virginia School of Medicine, Charlottesville, VA, USA. ja9qr@virginia.edu.
² Department of Ophthalmology, University of Virginia School of Medicine, Charlottesville, VA, USA. ja9qr@virginia.edu.
³ Department of Pathology, University of Virginia School of Medicine, Charlottesville, VA, USA. ja9qr@virginia.edu.
⁴ Department of Microbiology, Immunology, and Cancer Biology, University of Virginia School of Medicine, Charlottesville, VA, USA. ja9qr@virginia.edu.
⁵ Dorn Research Institute, Columbia VA Health Care System, Columbia, SC, USA.
⁶ Department of Clinical Pharmacy & Outcomes Sciences, College of Pharmacy, University of South Carolina, Columbia, SC, USA.
⁷ Center for Advanced Vision Science, University of Virginia School of Medicine, Charlottesville, VA, USA.
⁸ Department of Ophthalmology, University of Virginia School of Medicine, Charlottesville, VA, USA.
⁹ Department of Ophthalmology and Visual Sciences, University of Michigan Medical School, Ann Arbor, MI, USA.
¹⁰ Center for Eye Policy and Innovation, University of Michigan, Ann Arbor, MI, USA.
¹¹ Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA, USA.
¹² Department of Ophthalmology, University of Tsukuba, Ibaraki, Japan.
¹³ Department of Epidemiology & Biostatistics, University of South Carolina, Columbia, SC, USA.
¹⁴ Department of Medicine, Baystate Medical Center, Springfield, MA, USA.
¹⁵ Center for Medication Safety and Efficacy, College of Pharmacy, University of South Carolina, Columbia, SC, USA.
¹⁶ Department of Ophthalmology and Visual Sciences, University of Kentucky, Lexington, KY, USA.
¹⁷ Department of Pathology, University of Virginia School of Medicine, Charlottesville, VA, USA.
¹⁸ Department of Neuroscience, University of Virginia School of Medicine, Charlottesville, VA, USA.
¹⁹ Department of Medical Statistics, University Medical Center Göttingen, Göttingen, Germany.
²⁰ Department of Orthopaedics, University of Virginia School of Medicine, Charlottesville, VA, USA.
²¹ Department of Health Management and Policy, University of Michigan School of Public Health, Ann Arbor, MI, USA.
²² Department of Biomedical Engineering, University of Virginia School of Medicine, Charlottesville, VA, USA.

PMID: 32968070
PMCID: PMC7511405
DOI: 10.1038/s41467-020-18528-z

Abstract

Innate immune signaling through the NLRP3 inflammasome is activated by multiple diabetes-related stressors, but whether targeting the inflammasome is beneficial for diabetes is still unclear. Nucleoside reverse-transcriptase inhibitors (NRTI), drugs approved to treat HIV-1 and hepatitis B infections, also block inflammasome activation. Here, we show, by analyzing five health insurance databases, that the adjusted risk of incident diabetes is 33% lower in patients with NRTI exposure among 128,861 patients with HIV-1 or hepatitis B (adjusted hazard ratio for NRTI exposure, 0.673; 95% confidence interval, 0.638 to 0.710; P < 0.0001; 95% prediction interval, 0.618 to 0.734). Meanwhile, an NRTI, lamivudine, improves insulin sensitivity and reduces inflammasome activation in diabetic and insulin resistance-induced human cells, as well as in mice fed with high-fat chow; mechanistically, inflammasome-activating short interspersed nuclear element (SINE) transcripts are elevated, whereas SINE-catabolizing DICER1 is reduced, in diabetic cells and mice. These data suggest the possibility of repurposing an approved class of drugs for prevention of diabetes.

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Conflict of interest statement

J.A. is a co-founder of Inflammasome Therapeutics, iVeena Holdings, and iVeena Delivery Systems, and has received consultancy fees from Allergan, Biogen, Boehringer-Ingelheim, Immunovant, Janssen, Olix Pharmaceuticals, Retinal Solutions, and Saksin LifeSciences unrelated to this work. J.A., B.D.G., N.K., S.W., S.F., K.A., S.N., M.A., F.P. and B.J.F. are named as inventors on patent applications filed by or patents issued to the University of Virginia or the University of Kentucky relating to DICER1, Alu, and inflammasome. J.W.H. has received consulting fees from Celgene Corporation unrelated to this work. S.S.S. has received research grants from Boehringer Ingelheim, Gilead Sciences, Portola Pharmaceuticals, and United Therapeutics unrelated to this work. The other authors declare no competing interests.

Figures

**Fig. 1. Forest plot of incident diabetes.**
Hazard ratios based on a Cox proportional-hazards model and adjusted for the confounding variables listed in Supplementary Tables 5, 7, 9, 11, and 13 were estimated separately for each database. The dashed vertical line denotes a hazard ratio of 1.0, which represents no difference in risk between nucleoside reverse-transcriptase inhibitor (NRTI) exposure and non-exposure. The black horizontal bars represent 95% confidence intervals (CI) for unmatched models. The blue horizontal bars represent 95% CI for propensity score-matched models. P values derived from z tests for individual databases are reported. Inverse-variance-weighted random-effects and fixed-effect meta-analyses were performed to obtain a pooled estimate of the adjusted hazard ratio of incident diabetes for NRTI exposure (ever versus never). The prediction interval is reported. The estimate of heterogeneity (τ²) and the results of the statistical test of heterogeneity using the chi-square (χ²) test statistic and its degrees of freedom (df) are shown below the plot. The Higgins I² statistic and its 95% CI are presented. The results of the statistical tests of overall effect, the z test statistics, and corresponding P values are presented. All tests were two-tailed.

**Fig. 2. Bayesian meta-analysis of incident diabetes.**
Hazard ratios based on a Cox proportional-hazards model and adjusted for the confounding variables listed in Supplementary Tables 5, 7, 9, 11, and 13 were estimated separately for each database and are shown in black along with their 95% confidence intervals. A Bayesian meta-analysis was performed using a random-effects model and a weakly informative hierarchical half-Cauchy prior distribution^, for between-study variance with the assumption that it was unlikely for the between-study hazard ratios to vary by more than 3-fold (scale = 0.280). A sensitivity analysis to the choice of the prior by assuming that it was unlikely for the between-study hazard ratios to vary by more than 10-fold was also performed (scale = 0.587). The Bayesian shrinkage estimates and the summary estimates of the adjusted hazard ratio of incident diabetes for NRTI exposure (ever versus never), along with the 95% credible intervals, are shown in red (scale = 0.280) and blue (scale = 0.587). The dashed vertical line denotes a hazard ratio of 1.0, which represents no difference in risk between nucleoside reverse-transcriptase inhibitor (NRTI) exposure and non-exposure. The estimates of heterogeneity (τ²) and the posterior probabilities of a non-beneficial effect for each model are shown below the plot.

**Fig. 3. Expression of DICER1 and *Alu* in human diabetic adipocytes and skeletal myocytes.**
The top panels show the results of western blotting of extracts of proteins from human adipocytes a and human myocytes b isolated from nondiabetic and diabetic persons. Immunoreactive bands corresponding to DICER1 and beta-actin (β-actin) are shown. The bottom panels show bar graphs of the densitometric analyses of the DICER1 western blots in the top panels that have been normalized to β-actin abundance and to the nondiabetic group data. *P = 0.04 (diabetic versus nondiabetic adipocytes), *P = 0.046 (diabetic versus nondiabetic myocytes), two-tailed unpaired Student t test. The top panels show the results of northern blotting of total RNA extracts from human adipocytes c and human myocytes d isolated from nondiabetic and diabetic persons. Hybridization bands corresponding to *Alu* RNA and 5.8S ribosomal RNA (5.8S rRNA) are shown. The bottom panels show bar graphs of the densitometric analyses of the *Alu* northern blots in the top panels that have been normalized to 5.8S rRNA abundance and to the nondiabetic group data. *P = 0.04 (diabetic versus nondiabetic adipocytes), *P = 0.03 (diabetic versus nondiabetic myocytes), two-tailed unpaired Student t test. Data are reported as mean ± s.e.m. n = 5 samples per group a–d. Source data are provided as a Source Data file.

**Fig. 4. Insulin-induced glucose uptake in human adipocytes and skeletal myocytes.**
The results of glucose uptake assays in human adipocytes a and in human skeletal myocytes b are shown. Cells from nondiabetic (Non-DM) persons were treated with either tumor necrosis factor (TNF; 2.5 nM) or with high glucose (25 mM) and high insulin (100 nM) (HG) to induce insulin resistance. Cells from diabetic (DM) and nondiabetic persons were treated with lamivudine (Lam; 100 μM) or phosphate-buffered saline (PBS; vehicle). Glucose uptake measurements were performed by exposing cells to insulin (20 nM) using a fluorescent derivative of glucose, and quantified as relative fluorescence units (RFU) and normalized to baseline (prior to insulin treatment) levels of fluorescence. Data are reported as mean ± s.e.m.* In Non-DM adipocytes, P = 0.02 (HG versus PBS), P = 0.01 (HG + 3TC versus HG), P = 0.01 (TNF versus PBS), P = 0.02 (TNF + 3TC versus TNF), two-tailed unpaired Student t test. In DM adipocytes, P = 0.02 (3TC versus PBS), two-tailed paired Student t test. n = 5 samples per group (a). In Non-DM myocytes, P < 0.001 (HG versus PBS), P = 0.001 (HG + 3TC versus HG), P < 0.001 (TNF versus PBS), P = 0.01 (TNF + 3TC versus TNF), two-tailed unpaired Student t test. In DM myocytes, P = 0.03 (3TC versus PBS), two-tailed paired Student t test. n = 6 (nondiabetic) or 5 (diabetic) samples per group (b). Source data are provided as a Source Data file.

**Fig. 5. Expression of DICER1, B2, and insulin sensitivity in high-fat diet-fed mice.**
a The top three panels show the results of western blotting of extracts of proteins from subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and skeletal muscle tissue isolated from mice fed a normal diet (ND) or a high-fat diet (HFD). Immunoreactive bands corresponding to DICER1 and beta-actin (β-actin) are shown. The bottom three panels show bar graphs of the densitometric analyses of the DICER1 western blots in the top panels that have been normalized to β-actin abundance and to the ND group data. n = 10 (ND) or 9 (HFD) samples per group. *P = 0.01 (ND versus HFD in SAT), P < 0.001 (ND versus HFD in VAT), P = 0.01 (ND versus HFD in Skeletal muscle), two-tailed unpaired Student t test. b The top two panels show the results of northern blotting of total RNA extracts from visceral adipose tissue (VAT) and skeletal muscle tissue isolated from mice fed a normal diet (ND) or a high-fat diet (HFD). Hybridization bands corresponding to B2 RNA and 5.8S ribosomal RNA (5.8S rRNA) are shown. The bottom two panels show bar graphs of the densitometric analyses of the B2 northern blots in the top panels that have been normalized to 5.8S rRNA abundance and to the ND group data. n = 9 (ND) or 10 (HFD) samples per group. *P < 0.001 (ND versus HFD in VAT) and P = 0.001 (ND versus HFD in Skeletal muscle), two-tailed unpaired Student t test. Glucose tolerance test (GTT; c) and insulin tolerance test (ITT; d) measurements, and area under the curve (AUC) quantification in mice fed a normal diet, a high-fat diet and treated with phosphate-buffered saline vehicle (HFD + Vehicle), or a high-fat diet and treated with once-daily intraperitoneal administration of lamivudine (70 mg/kg of body weight) (HFD + Lam). Data are reported as mean ± s.e.m. n = 10 (ND), 8 (HFD + Vehicle), or 10 (HFD + Lam) samples per group c. n = 10 (ND), 8 (HFD + Vehicle), or 9 (HFD + Lam) samples per group d. *In GTT, P < 0.001 (HFD + Vehicle versus Normal Diet) and P = 0.002 (HFD + 3TC versus HFD + Vehicle), two-tailed unpaired Student t test. *In ITT, P < 0.001 (HFD + Vehicle versus Normal Diet) and P = 0.003 (HFD + 3TC versus HFD + Vehicle), two-tailed unpaired Student t test. Source data are provided as a Source Data file.

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References

1. International Diabetes Federation. IDF Diabetes Atlas. 9th edn, (International Diabetes Federation, 2019).
1. Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet. 2014;383:1068–1083. - PMC - PubMed
1. Pickup JC, Crook MA. Is type II diabetes mellitus a disease of the innate immune system? Diabetologia. 1998;41:1241–1248. - PubMed
1. Donath MY, Shoelson SE. Type 2 diabetes as an inflammatory disease. Nat. Rev. Immunol. 2011;11:98–107. - PubMed
1. Masters SL, Latz E, O’Neill LA. The inflammasome in atherosclerosis and type 2 diabetes. Sci. Transl. Med. 2011;3:81ps17. - PubMed

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[1] International Diabetes Federation. IDF Diabetes Atlas. 9th edn, (International Diabetes Federation, 2019).

[2] International Diabetes Federation. IDF Diabetes Atlas. 9th edn, (International Diabetes Federation, 2019).

[3] Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet. 2014;383:1068–1083. - PMC - PubMed

[4] Kahn SE, Cooper ME, Del Prato S. Pathophysiology and treatment of type 2 diabetes: perspectives on the past, present, and future. Lancet. 2014;383:1068–1083. - PMC - PubMed

[5] Pickup JC, Crook MA. Is type II diabetes mellitus a disease of the innate immune system? Diabetologia. 1998;41:1241–1248. - PubMed

[6] Pickup JC, Crook MA. Is type II diabetes mellitus a disease of the innate immune system? Diabetologia. 1998;41:1241–1248. - PubMed

[7] Donath MY, Shoelson SE. Type 2 diabetes as an inflammatory disease. Nat. Rev. Immunol. 2011;11:98–107. - PubMed

[8] Donath MY, Shoelson SE. Type 2 diabetes as an inflammatory disease. Nat. Rev. Immunol. 2011;11:98–107. - PubMed

[9] Masters SL, Latz E, O’Neill LA. The inflammasome in atherosclerosis and type 2 diabetes. Sci. Transl. Med. 2011;3:81ps17. - PubMed

[10] Masters SL, Latz E, O’Neill LA. The inflammasome in atherosclerosis and type 2 diabetes. Sci. Transl. Med. 2011;3:81ps17. - PubMed

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Repurposing anti-inflammasome NRTIs for improving insulin sensitivity and reducing type 2 diabetes development

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Repurposing anti-inflammasome NRTIs for improving insulin sensitivity and reducing type 2 diabetes development

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