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Research Paper

Nanopore sequencing reveals methylation changes associated with obesity in circulating cell-free DNA from Göttingen Minipigs

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Article: 2199374 | Received 22 Nov 2022, Accepted 08 Mar 2023, Published online: 10 Apr 2023

References

  • Cristiano S, Leal A, Phallen J, et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature. 2019;570:385–15.
  • Turchinovich A, Baranova A, Drapkina O, et al. Cell-free circulating nucleic acids as early biomarkers for NAFLD and NAFLD-associated disorders. Front Physiol. 2018;9:1256.
  • Peng X, Li H-D, Wu F-X, et al. Identifying the tissues-of-origin of circulating cell-free DNAs is a promising way in noninvasive diagnostics. Brief Bioinform. 2020;22(3):bbaa060.
  • Jylhävä J, Lehtimäki T, Jula A, et al. Circulating cell-free DNA is associated with cardiometabolic risk factors: the health 2000 survey. Atherosclerosis. 2014;233:268–271.
  • Grabuschnig S, Bronkhorst AJ, Holdenrieder S, et al. Putative origins of cell-free DNA in humans: a review of active and passive nucleic acid release mechanisms. Int J Mol Sci. 2020;21:8062.
  • Helmig S, Frühbeis C, Krämer-Albers E-M, et al. Release of bulk cell free DNA during physical exercise occurs independent of extracellular vesicles. Eur J Appl Physiol. 2015;115:2271–2280.
  • Ziegler A, Zangemeister-Wittke U, Stahel RA. Circulating DNA: a new diagnostic gold mine? Cancer Treat Rev. 2002;28:255–271.
  • Lehmann-Werman R, Neiman D, Zemmour H, et al. Identification of tissue-specific cell death using methylation patterns of circulating DNA. Proc Natl Acad Sci U S A. 2016;113:E1826–34.
  • Gala-Lopez BL, Neiman D, Kin T, et al. Beta cell death by cell-free DNA and outcome after clinical islet transplantation. Transplantation. 2018;102:978–985.
  • Zemmour H, Planer D, Magenheim J, et al. Non-invasive detection of human cardiomyocyte death using methylation patterns of circulating DNA. Nat Commun. 2018;9:1–9.
  • Sun K, Jiang P, Chan KA, et al. Plasma DNA tissue mapping by genome-wide methylation sequencing for noninvasive prenatal, cancer, and transplantation assessments. Proc Natl Acad Sci USA. 2015;112:E5503–12.
  • Guo S, Diep D, Plongthongkum N, et al. Identification of methylation haplotype blocks aids in deconvolution of heterogeneous tissue samples and tumor tissue-of-origin mapping from plasma DNA. Nat Genet. 2017;49:635–642.
  • Kang S, Li Q, Chen Q, et al. CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA. Genome Biol. 2017;18:53.
  • Li W, Li Q, Kang S, et al. CancerDetector: ultrasensitive and non-invasive cancer detection at the resolution of individual reads using cell-free DNA methylation sequencing data. Nucleic Acids Res. 2018;46:e89.
  • Martignano F, Munagala U, Crucitta S, et al. Nanopore sequencing from liquid biopsy: analysis of copy number variations from cell-free DNA of lung cancer patients. Mol Cancer. 2021;20:1–6.
  • Lyhne MK, Debes KP, Helgogaard T, et al. Electrocardiography and heart rate variability in Göttingen Minipigs: impact of diurnal variation, lead placement, repeatability and streptozotocin-induced diabetes. J Pharmacol Toxicol Methods. 2022;118:107221.
  • Diemar SS, Sejling A-S, Iversen KK, et al. Influence of acute glycaemic level on measures of myocardial infarction in non-diabetic pigs. Scand Cardiovasc J. 2015;49:376–382.
  • Larsen MO, Rolin B. Use of the Göttingen minipig as a model of diabetes, with special focus on type 1 diabetes research. Ilar J. 2004;45:303–313.
  • Schuleri KH, Boyle AJ, Centola M, et al. The adult Göttingen minipig as a model for chronic heart failure after myocardial infarction: focus on cardiovascular imaging and regenerative therapies. Comp Med. 2008;58:568–579.
  • Schumacher-Petersen C, Christoffersen BØ, Kirk RK, et al. Experimental non-alcoholic steatohepatitis in Göttingen Minipigs: consequences of high fat-fructose-cholesterol diet and diabetes. J Transl Med. 2019;17:1–18.
  • Ludvigsen TP, Kirk RK, Christoffersen BØ, et al. Göttingen minipig model of diet-induced atherosclerosis: influence of mild streptozotocin-induced diabetes on lesion severity and markers of inflammation evaluated in obese, obese and diabetic, and lean control animals. J Transl Med. 2015;13:1–12.
  • Bentsen S, Clemmensen A, Loft M, et al. [68ga]ga-NODAGA-E[(crgdyk)]2 angiogenesis PET/MR in a porcine model of chronic myocardial infarction. Diagnostics. 2021;11:1807.
  • Oxford Nanopore Technologies. Ligation sequencing gDNA - PCR barcoding (SQK-LSK110 with EXP-PBC001). 2022.
  • Oxford Nanopore Technologies. Ligation sequencing gDNA - native barcoding (SQK-LSK109 with EXP-NBD104 and EXP-NBD114). 2022.
  • Wick RR, Judd LM, Holt KE. Performance of neural network basecalling tools for Oxford Nanopore sequencing. Genome Biol. 2019;20:1–10.
  • Lanfear R, Schalamun M, Kainer D, et al. MinIONQC: fast and simple quality control for MinION sequencing data. Bioinformatics. 2019;35:523–525.
  • De Coster W, D’hert S, Schultz DT, et al. NanoPack: visualizing and processing long-read sequencing data. Bioinformatics. 2018;34:2666–2669.
  • Zerbino DR, Achuthan P, Akanni W, et al. Ensembl 2018. Nucleic Acids Res. 2018;46:D754–61.
  • Li H, Birol I. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics. 2018;34:3094–3100.
  • Danecek P, Bonfield JK, Liddle J, et al. Twelve years of SAMtools and BCFtools. Gigascience. 2021;10:giab008.
  • García-Alcalde F, Okonechnikov K, Carbonell J, et al. Qualimap: evaluating next-generation sequencing alignment data. Bioinformatics. 2012;28:2678–2679.
  • Simpson JT, Workman RE, Zuzarte P, et al. Detecting DNA cytosine methylation using Nanopore sequencing. Nat Methods. 2017;14:407–410.
  • Liu Y, Rosikiewicz W, Pan Z, et al. DNA methylation-calling tools for Oxford Nanopore sequencing: a survey and human epigenome-wide evaluation. Genome Bio. 2021;22:1–33.
  • Snajder RH, Stegle O, Bonder MJ. PycoMeth: a toolbox for differential methylation testing from Nanopore methylation calls. bioRxiv 2022.
  • Cheetham SW, Kindlova M, Ewing AD Methylartist: tools for visualising modified bases from Nanopore sequence data. bioRxiv 2021.
  • Rappaport N, Twik M, Plaschkes I, et al. MalaCards: an amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res. 2017;45:D877–87.
  • Zglejc-Waszak K, Waszkiewicz E, Franczak A. Periconceptional undernutrition affects the levels of DNA methylation in the peri-implantation pig endometrium and in embryos. Theriogenology. 2019;123:185–193.
  • L-C L, Dahiya R. MethPrimer: designing primers for methylation PCRs. Bioinformatics. 2002;18:1427–1431.
  • Bindea G, Mlecnik B, Hackl H, et al. ClueGO: a cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 2009;25:1091–1093.
  • Su G, Morris JH, Demchak B, et al. Biological network exploration with cytoscape 3. Curr Protoc Bioinformatics 2014;47:8. 13. 1-8. 24.
  • Bindea G, Galon J, Mlecnik B. CluePedia cytoscape plugin: pathway insights using integrated experimental and in silico data. Bioinformatics. 2013;29:661–663.
  • Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.
  • Camon E, Magrane M, Barrell D, et al. The gene ontology annotation (goa) database: sharing knowledge in UniProt with gene ontology. Nucleic Acids Res. 2004;32:D262–6.
  • Villegas R, Williams SM, Gao YT, et al. Genetic variation in the peroxisome proliferator‐activated receptor (PPAR) and peroxisome proliferator‐activated receptor gamma co‐activator 1 (PGC1) gene families and type 2 diabetes. Ann Hum Genet. 2014;78:23–32.
  • Sun L, Yang Z, Jin F, et al. The Gly482Ser variant of the PPARGC1 gene is associated with type 2 diabetes mellitus in northern Chinese, especially men. Diabet Med. 2006;23:1085–1092.
  • Carrer M, Liu N, Grueter CE, et al. Control of mitochondrial metabolism and systemic energy homeostasis by microRnas 378 and 378. Proc Natl Acad Sci U S A. 2012;109:15330–15335.
  • Rushing A, Sommer EC, Zhao S, et al. Salivary epigenetic biomarkers as predictors of emerging childhood obesity. BMC Med Genet. 2020;21:1–9.
  • Akinci A, Kara A, Özgür A, et al. Genomic analysis to screen potential genes and mutations in children with non-syndromic early onset severe obesity: a multicentre study in Turkey. Mol Biol Rep. 2022;49:1883–1893.
  • Nishimoto S, Fukuda D, Higashikuni Y, et al. Obesity-induced DNA released from adipocytes stimulates chronic adipose tissue inflammation and insulin resistance. Sci Adv. 2016;2:e1501332.
  • Zovico PVC, Neto VHG, Venâncio FA, et al. Cell-free DNA as an obesity biomarker. Physiol Res. 2020;69:515.
  • Haghiac M, Vora NL, Basu S, et al. Increased death of adipose cells, a path to release cell‐free DNA into systemic circulation of obese women. Obesity. 2012;20:2213–2219.
  • Gil A, Aguilera CM, Gil-Campos M, et al. Altered signalling and gene expression associated with the immune system and the inflammatory response in obesity. Br J Nutr. 2007;98:S121–6.
  • Ershow AG. Environmental influences on development of type 2 diabetes and obesity: challenges in personalizing prevention and management. J Diabetes Sci Technol. 2009;3:727–734.
  • Lacy P, Stow JL. Cytokine release from innate immune cells: association with diverse membrane trafficking pathways. Blood. 2011;118:9–18.
  • Medzhitov R, Horng T. Transcriptional control of the inflammatory response. Nat Rev Immunol. 2009;9:692–703.
  • Naidoo V, Naidoo M, Ghai M. Cell‐and tissue‐specific epigenetic changes associated with chronic inflammation in insulin resistance and type 2 diabetes mellitus. Scand J Immunol. 2018;88:e12723.
  • Jacobsen MJ, Mentzel CMJ, Olesen AS, et al. Altered methylation profile of lymphocytes is concordant with perturbation of lipids metabolism and inflammatory response in obesity. J Diabetes Res. 2016;2016:8539057.
  • Simar D, Versteyhe S, Donkin I, et al. DNA methylation is altered in B and NK lymphocytes in obese and type 2 diabetic human. Metabolism. 2014;63:1188–1197.
  • Xu X, Su S, Barnes VA, et al. A genome-wide methylation study on obesity: differential variability and differential methylation. Epigenetics. 2013;8:522–533.
  • Chang R, Zhang Y, Sun J, et al. Maternal pre‐pregnancy body mass index and offspring with overweight/obesity at preschool age: the possible role of epigenome‐wide DNA methylation changes in cord blood. Pediatr Obes. 2022;18:e12969.
  • Dong X-Y, Tang S-Q. Insulin-induced gene: a new regulator in lipid metabolism. Peptides. 2010;31:2145–2150.
  • Illumina. TruSeq™ DNA Nano. 2022.
  • New England Biolabs. Guidelines for PCR Optimization with Taq DNA Polymerase. 2022.
  • Oxford Nanopore Technologies. Ligation Sequencing Kit V14. 2022.
  • Schultz MD, He Y, Whitaker JW, et al. Human body epigenome maps reveal noncanonical DNA methylation variation. Nature. 2015;523:212–216.
  • Zemmour H, Planer D, Magenheim J, et al. Non-invasive detection of human cardiomyocyte death using methylation patterns of circulating DNA. Nat Commun. 2018;9:1443.
  • Swindle MM, Smith AC. Swine in the laboratory: surgery, anesthesia, imaging, and experimental techniques. 3rd ed. Boca Raton: Taylor & Francis; 2015.
  • Worm Ørntoft M-B, Jensen SØ, Hansen TB, et al. Comparative analysis of 12 different kits for bisulfite conversion of circulating cell-free DNA. Epigenetics. 2017;12:626–636.
  • Sabina J, Leamon JH. Bias in whole genome amplification: causes and considerations. In: Kroneis T, editor. Whole genome amplification: methods and protocols. New York, NY: Springer; 2015. p. 15–41.
  • Šestáková Š, Šálek C, Remešová H. DNA methylation validation methods: a coherent review with practical comparison. Biol Proced Online. 2019;21:19.
  • Snellenberg S, Strooper LMAD, Hesselink AT, et al. Development of a multiplex methylation-specific PCR as candidate triage test for women with an HPV-positive cervical scrape. BMC Cancer. 2012;12:551.