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Animals : an open access journal from MDPI2023; 14(1); 116; doi: 10.3390/ani14010116

A Functional Single-Nucleotide Polymorphism Upstream of the Collagen Type III Gene Is Associated with Catastrophic Fracture Risk in Thoroughbred Horses.

Abstract: Fractures caused by bone overloading are a leading cause of euthanasia in Thoroughbred racehorses. The risk of fatal fracture has been shown to be influenced by both environmental and genetic factors but, to date, no specific genetic mechanisms underpinning fractures have been identified. In this study, we utilised a genome-wide polygenic risk score to establish an in vitro cell system to study bone gene regulation in horses at high and low genetic risk of fracture. Candidate gene expression analysis revealed differential expression of and genes in osteoblasts derived from high- and low-risk horses. Whole-genome sequencing of two fracture cases and two control horses revealed a single-nucleotide polymorphism (SNP) upstream of that was confirmed in a larger cohort to be significantly associated with fractures. Bioinformatics tools predicted that this SNP may impact the binding of the transcription factor SOX11. Gene modulation demonstrated SOX11 is upstream of , and the region binds to nuclear proteins. Furthermore, luciferase assays demonstrated that the region containing the SNP has promoter activity. However, the specific effect of the SNP depends on the broader genetic background of the cells and suggests other factors may also be involved in regulating expression. In conclusion, we have identified a novel SNP that is significantly associated with fracture risk and provide new insights into the regulation of the gene.
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Publication Date: 2023-12-28 PubMed ID: 38200847PubMed Central: PMC10778232DOI: 10.3390/ani14010116Google Scholar: Lookup
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  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

The research article discusses a detected variation in a single DNA building block—referred to as Single Nucleotide Polymorphism (SNP)—in Thoroughbred horses linked to a higher risk of catastrophic fractures. This SNP is positioned upstream of the COL3A1 gene, which is responsible for the production of a type of collagen stitched into bones and cartilage. The presence of SNP is believed to interfere with the regulatory activities of the SOX11 transcription factor, thus affecting the function of COL3A1 and increasing the fracture risk.

Genome-Wide Polygenic Risk Score

  • The researchers employed a genome-wide polygenic risk score for their investigation. This is a method that focuses on associations between genetic variations spread across the entire genome and certain characteristics or diseases—in this case, the likelihood of fracture in Thoroughbred horses.

Candidate Gene Expression Analysis

  • They conducted a candidate gene expression analysis on genes in osteoblasts—cells responsible for bone formation—sourced from high-and-low risk horses. The identified COL3A1 gene showed distinct expression levels in those cells.

Identifying the SNP

  • By examining the DNA data from two fracture cases and two control horses in this study, the identified SNP was pinpointed to be upstream of the COL3A1 gene.
  • Further investigations confirmed that this specific SNP is significantly linked with fractures. This likely impacts the binding of the SOX11 transcription factor that regulates the COL3A1 gene.

Effects of the SNP

  • SOX11’s position has been found to be upstream of the COL3A1 gene. It means that it potentially influences the genetic expression of COL3A1.
  • The region containing this SNP demonstrated promoter activity, which further underlines its influence on the activity of genes downstream.
  • However, the specific implications of this SNP seem to hinge on a broader genetic context. It suggests that other factors too play a role in controlling COL3A1 expression and consequently, the susceptibility to fractures.

Conclusion

  • This investigation has marked the discovery of a new SNP that has substantial association with the risk of fractures in Thoroughbred horses.
  • Further, it has provided new insights into the regulation of the COL3A1 gene which is crucial for understanding the genetic basis of catastrophic bone injuries.

Cite This Article

APA
Palomino Lago E, Baird A, Blott SC, McPhail RE, Ross AC, Durward-Akhurst SA, Guest DJ. (2023). A Functional Single-Nucleotide Polymorphism Upstream of the Collagen Type III Gene Is Associated with Catastrophic Fracture Risk in Thoroughbred Horses. Animals (Basel), 14(1), 116. https://doi.org/10.3390/ani14010116

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 14
Issue: 1
PII: 116

Researcher Affiliations

Palomino Lago, Esther
  • Department of Clinical Sciences and Services, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield AL9 7TA, UK.
Baird, Arabella
  • Animal Health Trust, Lanwades Park, Kentford, Newmarket CB8 7UU, UK.
Blott, Sarah C
  • School of Veterinary Medicine and Science, University of Nottingham, Nottingham LE12 5RD, UK.
McPhail, Rhona E
  • Animal Health Trust, Lanwades Park, Kentford, Newmarket CB8 7UU, UK.
Ross, Amy C
  • Department of Clinical Sciences and Services, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield AL9 7TA, UK.
Durward-Akhurst, Sian A
  • Department of Veterinary Clinical Sciences, University of Minnesota, Saint Paul, MN 55108, USA.
Guest, Deborah J
  • Department of Clinical Sciences and Services, The Royal Veterinary College, Hawkshead Lane, North Mymms, Hatfield AL9 7TA, UK.

Grant Funding

  • vet/prj/792 / Horserace Betting Levy Board
  • N/A / Anne Duchess of Westminster Charitable Trust
  • N/A / Alborada Trust

Conflict of Interest Statement

E. Palomino Lago and D.J. Guest are affiliated with The Royal Veterinary College, which holds patent WO 2015/019097 “Predictive Method for Bone Fracture Risk in Horses” in relation to this work. This patent claims a method of predicting fracture risk in horses using one or more genetic variations from within the associated region on ECA18. None of the other authors have any other competing interests to declare.

References

This article includes 95 references
  1. Parkin T.D., Clegg P.D., French N.P., Proudman C.J., Riggs C.M., Singer E.R., Webbon P.M., Morgan K.L.. Risk of fatal distal limb fractures among Thoroughbreds involved in the five types of racing in the United Kingdom.. Vet. Rec. 2004;154:493–497.
    doi: 10.1136/vr.154.16.493pubmed: 15130054google scholar: lookup
  2. McKee S.L.. An update on racing fatalities in the UK.. Equine Vet. Ed. 1995;7:202–204.
    doi: 10.1111/j.2042-3292.1995.tb01225.xgoogle scholar: lookup
  3. Georgopoulos S.P., Parkin T.D.. Risk factors for equine fractures in Thoroughbred flat racing in North America.. Prev. Vet. Med. 2017;139:99–104.
    doi: 10.1016/j.prevetmed.2016.12.006pubmed: 28017453google scholar: lookup
  4. Parkin T.D., Clegg P.D., French N.P., Proudman C.J., Riggs C.M., Singer E.R., Webbon P.M., Morgan K.L.. Race- and course-level risk factors for fatal distal limb fracture in racing Thoroughbreds.. Equine Vet. J. 2004;36:521–526.
    doi: 10.2746/0425164044877332pubmed: 15460077google scholar: lookup
  5. Verheyen K., Price J., Lanyon L., Wood J.L.N.. Exercise distance and speed affect the risk of fracture in racehorses.. Bone 2006;39:1322–1330.
    doi: 10.1016/j.bone.2006.05.025pubmed: 16926125google scholar: lookup
  6. Anthenill L.A., Stover S.M., Gardner I.A., Hill A.E.. Risk factors for proximal sesamoid bone fractures associated with exercise history and horseshoe characteristics in Thoroughbred racehorses.. AJVR 2007;68:760–771.
    doi: 10.2460/ajvr.68.7.760pubmed: 17605612google scholar: lookup
  7. Kristoffersen M., Parkin T.D., Singer E.R.. Catastrophic biaxial proximal sesamoid bone fractures in UK Thoroughbred races (1994–2004): Horse characteristics and racing history.. Equine Vet. J. 2010;45:420–424.
    doi: 10.1111/j.2042-3306.2010.00079.xpubmed: 20636778google scholar: lookup
  8. Welsh C.E., Lewis T.W., Blott S.C., Mellor D.J., Stirk A.J., Parkin T.D.. Estimates of genetic parameters of distal limb fracture and superficial digital flexor tendon injury in UK Thoroughbred racehorses.. Vet. J. 2014;200:253–256.
    doi: 10.1016/j.tvjl.2014.03.005pubmed: 24679457google scholar: lookup
  9. Blott S.C., Swinburne J.E., Sibbons C., Fox-Clipsham L.Y., Helwegen M., Hillyer L., Parkin T.D., Newton J.R., Vaudin M.. A genome-wide association study demonstrates significant genetic variation for fracture risk in Thoroughbred racehorses.. BMC Genom. 2014;15:147.
    doi: 10.1186/1471-2164-15-147pmc: PMC4008154pubmed: 24559379google scholar: lookup
  10. Blott S.C., Swinburne J.E.. Predictive Method for Bone Fracture Risk in Horses and Humans.. 2015.
  11. Tozaki T., Kusano K., Ishikawa Y., Kushiro A., Nomura M., Kikuchi M., Kakoi H., Hirota K., Miyake T., Hill E.W.. A candidate-SNP retrospective cohort study for fracture risk in Japanese Thoroughbred racehorses.. Anim. Genet. 2020;51:43–50.
    doi: 10.1111/age.12866pubmed: 31612520google scholar: lookup
  12. Bulathsinhala L., Hughes J.M., McKinnon C.J., Kardouni J.R., Guerriere K.I., Popp K.L., Matheny R.W. Jr., Bouxsein M.L.. Risk of Stress Fracture Varies by Race/Ethnic Origin in a Cohort Study of 1.3 Million US Army Soldiers.. J. Bone Miner. Res. 2017;32:1546–1553.
    doi: 10.1002/jbmr.3131pubmed: 28300324google scholar: lookup
  13. Korvala J., Hartikka H., Pihlajamaki H., Solovieva S., Ruohola J.P., Sahi T., Barral S., Ott J., Ala-Kokko L., Mannikko M.. Genetic predisposition for femoral neck stress fractures in military conscripts.. BMC Genet. 2010;11:95.
    doi: 10.1186/1471-2156-11-95pmc: PMC2975640pubmed: 20961463google scholar: lookup
  14. Zhao L., Chang Q., Huang T., Huang C.. Prospective cohort study of the risk factors for stress fractures in Chinese male infantry recruits.. J. Int. Med. Res. 2016;44:787–795.
    doi: 10.1177/0300060516639751pmc: PMC5536631pubmed: 27207942google scholar: lookup
  15. Varley I., Greeves J.P., Sale C., Friedman E., Moran D.S., Yanovich R., Wilson P.J., Gartland A., Hughes D.C., Stellingwerff T.. Functional polymorphisms in the P2X7 receptor gene are associated with stress fracture injury.. Purinergic Signal. 2016;12:103–113.
    doi: 10.1007/s11302-016-9495-6pmc: PMC4749527pubmed: 26825304google scholar: lookup
  16. Warden S.J., Burr D.B., Brukner P.D.. Stress fractures: Pathophysiology, epidemiology, and risk factors.. Curr. Osteoporos. Rep. 2006;4:103–109.
    doi: 10.1007/s11914-996-0029-ypubmed: 16907999google scholar: lookup
  17. Warden S.J., Creaby M.W., Bryant A.L., Crossley K.M.. Stress fracture risk factors in female football players and their clinical implications.. Br. J. Sports Med. 2007;41((Suppl. 1)):i38–i43.
    doi: 10.1136/bjsm.2007.037804pmc: PMC2465247pubmed: 17584950google scholar: lookup
  18. Long P., Samnakay P., Jenner P., Rose S.. A yeast two-hybrid screen reveals that osteopontin associates with MAP1A and MAP1B in addition to other proteins linked to microtubule stability, apoptosis and protein degradation in the human brain.. Eur. J. Neurosci. 2012;36:2733–2742.
    doi: 10.1111/j.1460-9568.2012.08189.xpubmed: 22779921google scholar: lookup
  19. Logan N.J., Camman M., Williams G., Higgins C.A.. Demethylation of ITGAV accelerates osteogenic differentiation in a blast-induced heterotopic ossification in vitro cell culture model.. Bone 2018;117:149–160.
    doi: 10.1016/j.bone.2018.09.008pmc: PMC6218666pubmed: 30219480google scholar: lookup
  20. Wang L., Shi X., Zhao R., Halloran B.P., Clark D.J., Jacobs C.R., Kingery W.S.. Calcitonin-gene-related peptide stimulates stromal cell osteogenic differentiation and inhibits RANKL induced NF-kappaB activation, osteoclastogenesis and bone resorption.. Bone 2010;46:1369–1379.
    doi: 10.1016/j.bone.2009.11.029pmc: PMC2854244pubmed: 19962460google scholar: lookup
  21. Park G., Park S.-Y., Lee E.-H., Lee Y.-J., Kim S.-Y., Kim S.-H., Kim Y.-H., Kim I.-S., Kim J.-E.. High trabecular bone mass induced by reduced function of osteoclasts in GULP1-deficient mice; Proceedings of the European Calcified Tissue Society Conference ECTS; Rome, Italy.. 14–17 May 2016.
  22. Volk S.W., Shah S.R., Cohen A.J., Wang Y., Brisson B.K., Vogel L.K., Hankenson K.D., Adams S.L.. Type III collagen regulates osteoblastogenesis and the quantity of trabecular bone.. Calcif. Tissue Int. 2014;94:621–631.
    doi: 10.1007/s00223-014-9843-xpmc: PMC4335719pubmed: 24626604google scholar: lookup
  23. Hong D., Chen H.X., Yu H.Q., Liang Y., Wang C., Lian Q.Q., Deng H.T., Ge R.S.. Morphological and proteomic analysis of early stage of osteoblast differentiation in osteoblastic progenitor cells.. Exp. Cell Res. 2010;316:2291–2300.
    doi: 10.1016/j.yexcr.2010.05.011pmc: PMC4580249pubmed: 20483354google scholar: lookup
  24. Pereira M., Ko J.-H., Logan J., Protheroe H., Kim K.-B., Tan A.L.M., Croucher P.I., Park K.-S., Rotival M., Petretto E.. A trans-eQTL network regulates osteoclast multinucleation and bone mass.. eLife 2020;9:e55549.
    doi: 10.7554/eLife.55549pmc: PMC7351491pubmed: 32553114google scholar: lookup
  25. Wu L.-F., Zhu D.-C., Wang B.-H., Lu Y.-H., He P., Zhang Y.-H., Gao H.-Q., Zhu X.-W., Xia W., Zhu H.. Relative abundance of mature myostatin rather than total myostatin is negatively associated with bone mineral density in Chinese.. J. Cell. Mol. Med. 2018;22:1329–1336.
    doi: 10.1111/jcmm.13438pmc: PMC5783859pubmed: 29247983google scholar: lookup
  26. Bialek P., Parkington J., Li X., Gavin D., Wallace C., Zhang J., Root A., Yan G., Warner L., Seeherman H.J.. A myostatin and activin decoy receptor enhances bone formation in mice.. Bone 2014;60:162–171.
    doi: 10.1016/j.bone.2013.12.002pubmed: 24333131google scholar: lookup
  27. Burger M.G., Steinitz A., Geurts J., Pippenger B.E., Schaefer D.J., Martin I., Barbero A., Pelttari K.. Ascorbic Acid Attenuates Senescence of Human Osteoarthritic Osteoblasts.. Int. J. Mol. Sci. 2017;18:2517.
    doi: 10.3390/ijms18122517pmc: PMC5751120pubmed: 29186811google scholar: lookup
  28. Kim S., Koga T., Isobe M., Kern B.E., Yokochi T., Chin Y.E., Karsenty G., Taniguchi T., Takayanagi H.. Stat1 functions as a cytoplasmic attenuator of Runx2 in the transcriptional program of osteoblast differentiation.. Genes Dev. 2003;17:1979–1991.
    doi: 10.1101/gad.1119303pmc: PMC196253pubmed: 12923053google scholar: lookup
  29. Tajima K., Takaishi H., Takito J., Tohmonda T., Yoda M., Ota N., Kosaki N., Matsumoto M., Ikegami H., Nakamura T.. Inhibition of STAT1 accelerates bone fracture healing.. J. Orthop. Res. 2010;28:937–941.
    doi: 10.1002/jor.21086pubmed: 20063384google scholar: lookup
  30. Xiao L., Naganawa T., Obugunde E., Gronowicz G., Ornitz D.M., Coffin J.D., Hurley M.M.. Stat1 controls postnatal bone formation by regulating fibroblast growth factor signaling in osteoblasts.. J. Biol. Chem. 2004;279:27743–27752.
    doi: 10.1074/jbc.M314323200pubmed: 15073186google scholar: lookup
  31. Yu Y., Newman H., Shen L., Sharma D., Hu G., Mirando A.J., Zhang H., Knudsen E., Zhang G.-F., Hilton M.J.. Glutamine Metabolism Regulates Proliferation and Lineage Allocation in Skeletal Stem Cells.. Cell Metab. 2019;29:966–978.e964.
    doi: 10.1016/j.cmet.2019.01.016pmc: PMC7062112pubmed: 30773468google scholar: lookup
  32. Groza T., Gomez F.L., Mashhadi H.H., Muñoz-Fuentes V., Gunes O., Wilson R., Cacheiro P., Frost A., Keskivali-Bond P., Vardal B.. The International Mouse Phenotyping Consortium: Comprehensive knockout phenotyping underpinning the study of human disease.. Nucleic Acids Res. 2022;51:D1038–D1045.
    doi: 10.1093/nar/gkac972pmc: PMC9825559pubmed: 36305825google scholar: lookup
  33. Flint J., Mackay T.F.. Genetic architecture of quantitative traits in mice, flies, and humans.. Genome Res. 2009;19:723–733.
    doi: 10.1101/gr.086660.108pmc: PMC3647534pubmed: 19411597google scholar: lookup
  34. Li Z.-k., Zhou Q.. Cellular models for disease exploring and drug screening.. Protein Cell. 2010;1:355–362.
    doi: 10.1007/s13238-010-0027-9pmc: PMC4875092pubmed: 21203947google scholar: lookup
  35. Park I.-H., Arora N., Huo H., Maherali N., Ahfeldt T., Shimamura A., Lensch M.W., Cowan C., Hochedlinger K., Daley G.Q.. Disease-specific induced pluripotent stem (iPS) cells.. Cell. 2008;134:877–886.
    doi: 10.1016/j.cell.2008.07.041pmc: PMC2633781pubmed: 18691744google scholar: lookup
  36. Barral S., Kurian M.A.. Utility of Induced Pluripotent Stem Cells for the Study and Treatment of Genetic Diseases: Focus on Childhood Neurological Disorders.. Front. Mol. Neurosci. 2016;9:78.
    doi: 10.3389/fnmol.2016.00078pmc: PMC5012159pubmed: 27656126google scholar: lookup
  37. Chamberlain S.J.. Disease modelling using human iPSCs.. Hum. Mol. Genet. 2016;25:R173–R181.
    doi: 10.1093/hmg/ddw209pubmed: 27493026google scholar: lookup
  38. Dobrindt K., Zhang H., Das D., Abdollahi S., Prorok T., Ghosh S., Weintraub S., Genovese G., Powell S.K., Lund A.. Publicly Available hiPSC Lines with Extreme Polygenic Risk Scores for Modeling Schizophrenia.. Complex. Psychiatry. 2021;6:68–82.
    doi: 10.1159/000512716pmc: PMC7923934pubmed: 34883504google scholar: lookup
  39. Coleman J.R.I.. Feasibility and application of polygenic score analysis to the morphology of human-induced pluripotent stem cells.. Mol. Genet. Genom. 2022;297:1111–1122.
    doi: 10.1007/s00438-022-01905-2pmc: PMC9250464pubmed: 35633379google scholar: lookup
  40. Choi S.W., Mak T.S.-H., O’Reilly P.F.. Tutorial: A guide to performing polygenic risk score analyses.. Nat. Protoc. 2020;15:2759–2772.
    doi: 10.1038/s41596-020-0353-1pmc: PMC7612115pubmed: 32709988google scholar: lookup
  41. Chung W.. Statistical models and computational tools for predicting complex traits and diseases.. Genom. Inform. 2021;19:e36.
    doi: 10.5808/gi.21053pmc: PMC8752975pubmed: 35012283google scholar: lookup
  42. Yang J., Lee S.H., Goddard M.E., Visscher P.M.. GCTA: A tool for genome-wide complex trait analysis.. Am. J. Hum. Genet. 2011;88:76–82.
    doi: 10.1016/j.ajhg.2010.11.011pmc: PMC3014363pubmed: 21167468google scholar: lookup
  43. Purcell S., Neale B., Todd-Brown K., Thomas L., Ferreira M.A.R., Bender D., Maller J., Sklar P., de Bakker P.I.W., Daly M.J.. PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses.. Am. J. Hum. Genet. 2007;81:559–575.
    doi: 10.1086/519795pmc: PMC1950838pubmed: 17701901google scholar: lookup
  44. Livak K.J., Schmittgen T.D.. Analysis of relative gene expression data using real time quantitative PCR and the 2-deltadeltaCT Method.. Methods 2001;25:402–408.
    doi: 10.1006/meth.2001.1262pubmed: 11846609google scholar: lookup
  45. Xie F., Xiao P., Chen D., Xu L., Zhang B.. miRDeepFinder: A miRNA analysis tool for deep sequencing of plant small RNAs.. Plant Mol. Biol. 2012;80:75–84.
    doi: 10.1007/s11103-012-9885-2pubmed: 22290409google scholar: lookup
  46. Schneider C.A., Rasband W.S., Eliceiri K.W.. NIH Image to ImageJ: 25 years of image analysis.. Nat. Methods. 2012;9:671–675.
    doi: 10.1038/nmeth.2089pmc: PMC5554542pubmed: 22930834google scholar: lookup
  47. McKenna A., Hanna M., Banks E., Sivachenko A., Cibulskis K., Kernytsky A., Garimella K., Altshuler D., Gabriel S., Daly M.. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data.. Genome Res. 2010;20:1297–1303.
    doi: 10.1101/gr.107524.110pmc: PMC2928508pubmed: 20644199google scholar: lookup
  48. Van der Auwera G.A., Carneiro M.O., Hartl C., Poplin R., Del Angel G., Levy-Moonshine A., Jordan T., Shakir K., Roazen D., Thibault J.. From FastQ data to high confidence variant calls: The Genome Analysis Toolkit best practices pipeline.. Curr. Protoc. Bioinform. 2013;43:11.10.11–11.10.33.
    doi: 10.1002/0471250953.bi1110s43pmc: PMC4243306pubmed: 25431634google scholar: lookup
  49. McLaren W., Gil L., Hunt S.E., Riat H.S., Ritchie G.R.S., Thormann A., Flicek P., Cunningham F.. The Ensembl Variant Effect Predictor.. Genome Biol. 2016;17:122.
    doi: 10.1186/s13059-016-0974-4pmc: PMC4893825pubmed: 27268795google scholar: lookup
  50. Gupta S., Stamatoyannopoulos J.A., Bailey T.L., Noble W.S.. Quantifying similarity between motifs.. Genome Biol. 2007;8:R24.
    doi: 10.1186/gb-2007-8-2-r24pmc: PMC1852410pubmed: 17324271google scholar: lookup
  51. Jagannathan V., Gerber V., Rieder S., Tetens J., Thaller G., Drögemüller C., Leeb T.. Comprehensive characterization of horse genome variation by whole-genome sequencing of 88 horses.. Anim. Genet. 2019;50:74–77.
    doi: 10.1111/age.12753pubmed: 30525216google scholar: lookup
  52. Tozaki T., Ohnuma A., Kikuchi M., Ishige T., Kakoi H., Hirota K.-I., Kusano K., Nagata S.-I.. Rare and common variant discovery by whole-genome sequencing of 101 Thoroughbred racehorses.. Sci. Rep. 2021;11:16057.
    doi: 10.1038/s41598-021-95669-1pmc: PMC8346562pubmed: 34362995google scholar: lookup
  53. Durward-Akhurst S.A., Schaefer R.J., Grantham B., Carey W.K., Mickelson J.R., McCue M.E.. Genetic Variation and the Distribution of Variant Types in the Horse.. Front. Genet. 2021;12:758366.
    doi: 10.3389/fgene.2021.758366pmc: PMC8676274pubmed: 34925451google scholar: lookup
  54. Robinson J.T., Thorvaldsdóttir H., Winckler W., Guttman M., Lander E.S., Getz G., Mesirov J.P.. Integrative genomics viewer.. Nat. Biotechnol. 2011;29:24–26.
    doi: 10.1038/nbt.1754pmc: PMC3346182pubmed: 21221095google scholar: lookup
  55. Luo Y., Hitz B.C., Gabdank I., Hilton J.A., Kagda M.S., Lam B., Myers Z., Sud P., Jou J., Lin K.. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal.. Nucleic Acids Res. 2020;48:D882–D889.
    doi: 10.1093/nar/gkz1062pmc: PMC7061942pubmed: 31713622google scholar: lookup
  56. Liew C.-G., Draper J.S., Walsh J., Moore H., Andrews P.W.. Transient and Stable Transgene Expression in Human Embryonic Stem Cells.. Stem Cells. 2007;25:1521–1528.
    doi: 10.1634/stemcells.2006-0634pubmed: 17379764google scholar: lookup
  57. Palomino Lago E., Jelbert E.R., Baird A., Lam P.Y., Guest D.J.. Equine induced pluripotent stem cells are responsive to inflammatory cytokines before and after differentiation into musculoskeletal cell types.. In Vitr. Cell. Dev. Biol. Anim. 2023;59:514–527.
    doi: 10.1007/s11626-023-00800-3pmc: PMC10520172pubmed: 37582999google scholar: lookup
  58. Smith E.J., Beaumont R.E., McClellan A., Sze C., Palomino Lago E., Hazelgrove L., Dudhia J., Smith R.K.W., Guest D.J.. Tumour necrosis factor alpha, interleukin 1 beta and interferon gamma have detrimental effects on equine tenocytes that cannot be rescued by IL-1RA or mesenchymal stromal cell-derived factors.. Cell Tissue Res. 2023;391:523–544.
    doi: 10.1007/s00441-022-03726-6pmc: PMC9974687pubmed: 36543895google scholar: lookup
  59. Cooper G.M., Stone E.A., Asimenos G., Green E.D., Batzoglou S., Sidow A.. Distribution and intensity of constraint in mammalian genomic sequence.. Genome Res. 2005;15:901–913.
    doi: 10.1101/gr.3577405pmc: PMC1172034pubmed: 15965027google scholar: lookup
  60. Haseeb A., Lefebvre V.. The SOXE transcription factors-SOX8, SOX9 and SOX10-share a bi-partite transactivation mechanism.. Nucleic Acids Res. 2019;47:6917–6931.
    doi: 10.1093/nar/gkz523pmc: PMC6649842pubmed: 31194875google scholar: lookup
  61. Haseeb A., Kc R., Angelozzi M., de Charleroy C., Rux D., Tower R.J., Yao L., Pellegrino da Silva R., Pacifici M., Qin L.. SOX9 keeps growth plates and articular cartilage healthy by inhibiting chondrocyte dedifferentiation/osteoblastic redifferentiation.. Proc. Natl. Acad. Sci. USA. 2021;118:e2019152118.
    doi: 10.1073/pnas.2019152118pmc: PMC7923381pubmed: 33597301google scholar: lookup
  62. Dy P., Penzo-Méndez A., Wang H., Pedraza C.E., Macklin W.B., Lefebvre V.. The three SoxC proteins--Sox4, Sox11 and Sox12--exhibit overlapping expression patterns and molecular properties.. Nucleic Acids Res. 2008;36:3101–3117.
    doi: 10.1093/nar/gkn162pmc: PMC2396431pubmed: 18403418google scholar: lookup
  63. Gadi J., Jung S.H., Lee M.J., Jami A., Ruthala K., Kim K.M., Cho N.H., Jung H.S., Kim C.H., Lim S.K.. The transcription factor protein Sox11 enhances early osteoblast differentiation by facilitating proliferation and the survival of mesenchymal and osteoblast progenitors.. J. Biol. Chem. 2013;288:25400–25413.
    doi: 10.1074/jbc.M112.413377pmc: PMC3757203pubmed: 23888050google scholar: lookup
  64. Kassem M., Ankersen L., Eriksen E.F., Clark B.F.C., Rattan S.I.S.. Demonstration of cellular aging and senescence in serially passaged long-term cultures of human trabecular osteoblasts.. Osteoporos. Int. 1997;7:514–524.
    doi: 10.1007/BF02652556pubmed: 9604046google scholar: lookup
  65. Lorenz K., Sicker M., Schmelzer E., Rupf T., Salvetter J., Schulz-Siegmund M., Bader A.. Multilineage differentiation potential of human dermal skin-derived fibroblasts.. Exp. Dermatol. 2008;17:925–932.
    doi: 10.1111/j.1600-0625.2008.00724.xpubmed: 18557932google scholar: lookup
  66. Halcsik E., Forni M.F., Fujita A., Verano-Braga T., Jensen O.N., Sogayar M.C.. New insights in osteogenic differentiation revealed by mass spectrometric assessment of phosphorylated substrates in murine skin mesenchymal cells.. BMC Cell Biol. 2013;14:47.
    doi: 10.1186/1471-2121-14-47pmc: PMC3819743pubmed: 24148232google scholar: lookup
  67. Glynn E.R., Londono A.S., Zinn S.A., Hoagland T.A., Govoni K.E.. Culture conditions for equine bone marrow mesenchymal stem cells and expression of key transcription factors during their differentiation into osteoblasts.. J. Anim. Sci. Biotechnol. 2013;4:40.
    doi: 10.1186/2049-1891-4-40pmc: PMC3874597pubmed: 24169030google scholar: lookup
  68. Nagy K., Sung H.-K., Zhang P., Laflamme S., Vincent P., Agha-Mohammadi S., Woltjen K., Monetti C., Michael I.P., Smith L.C.. Induced pluripotent stem cell lines derived from equine fibroblasts.. Stem Cell Rev. Rep. 2011;7:693–702.
    doi: 10.1007/s12015-011-9239-5pmc: PMC3137777pubmed: 21347602google scholar: lookup
  69. Bavin E.P., Smith O., Baird A., Smith L.C., Guest D.J.. Equine Induced Pluripotent Stem Cells have a Reduced Tendon Differentiation Capacity Compared to Embryonic Stem Cells.. Front. Vet. Sci. 2015;2:55.
    doi: 10.3389/fvets.2015.00055pmc: PMC4672282pubmed: 26664982google scholar: lookup
  70. Sharma R., Livesey M.R., Wyllie D.J., Proudfoot C., Whitelaw B., Hay D.C., Donadeu X.. Generation of functional neurons from feeder-free, keratinocyte-derived equine induced pluripotent stem cells.. Stem Cells Dev. 2014;23:1524–1534.
    doi: 10.1089/scd.2013.0565pubmed: 24548115google scholar: lookup
  71. Sun T.C., Riggs C.M., Cogger N., Wright J., Al-Alawneh J.I.. Noncatastrophic and catastrophic fractures in racing Thoroughbreds at the Hong Kong Jockey Club.. Equine Vet. J. 2019;51:77–82.
    doi: 10.1111/evj.12953pubmed: 29672909google scholar: lookup
  72. Baird A., Lindsay T., Everett A., Iyemere V., Paterson Y.Z., McClellan A., Henson F.M.D., Guest D.J.. Osteoblast differentiation of equine induced pluripotent stem cells.. Biol. Open. 2018;7:bio033514.
    doi: 10.1242/bio.033514pmc: PMC5992527pubmed: 29685993google scholar: lookup
  73. Guest D.J., Ousey J.C., Smith M.R.W.. Defining the expression of marker genes in equine mesenchymal stromal cells.. Stem Cells Cloning Adv. Appl. 2008;1:1–9.
    doi: 10.2147/SCCAA.S3824pmc: PMC3781685pubmed: 24198500google scholar: lookup
  74. Burk J., Ribitsch I., Gittel C., Juelke H., Kasper C., Staszyk C., Brehm W.. Growth and differentiation characteristics of equine mesenchymal stromal cells derived from different sources.. Vet. J. 2013;195:98–106.
    doi: 10.1016/j.tvjl.2012.06.004pubmed: 22841420google scholar: lookup
  75. Radcliffe C.H., Flaminio J.B.F., Fortier L.A.. Temporal analysis of equine bone marrow aspirate during establishment of putative mesenchymal progenitor cell populations.. Stem Cells Dev. 2010;19:269–282.
    doi: 10.1089/scd.2009.0091pmc: PMC3138180pubmed: 19604071google scholar: lookup
  76. Chung C.-H., Golub E.E., Forbes E., Tokuoka T., Shapiro I.M.. Mechanism of action of β-glycerophosphate on bone cell mineralization.. Calcif. Tissue Int. 1992;51:305–311.
    doi: 10.1007/BF00334492pubmed: 1422975google scholar: lookup
  77. Liu W., Toyosawa S., Furuichi T., Kanatani N., Yoshida C., Liu Y., Himeno M., Narai S., Yamaguchi A., Komori T.. Overexpression of Cbfa1 in osteoblasts inhibits osteoblast maturation and causes osteopenia with multiple fractures.. J. Cell Biol. 2001;155:157–166.
    doi: 10.1083/jcb.200105052pmc: PMC2150799pubmed: 11581292google scholar: lookup
  78. Yen J.L., Lin S.P., Chen M.R., Niu D.M.. Clinical features of Ehlers-Danlos syndrome.. J. Formos. Med. Assoc. Taiwan Yi Zhi. 2006;105:475–480.
    doi: 10.1016/S0929-6646(09)60187-Xpubmed: 16801035google scholar: lookup
  79. Kingsley N.B., Hamilton N.A., Lindgren G., Orlando L., Bailey E., Brooks S., McCue M., Kalbfleisch T.S., MacLeod J.N., Petersen J.L.. “Adopt-a-Tissue” Initiative Advances Efforts to Identify Tissue-Specific Histone Marks in the Mare.. Front. Genet. 2021;12:649959.
    doi: 10.3389/fgene.2021.649959pmc: PMC8033197pubmed: 33841506google scholar: lookup
  80. Huber C.D., Kim B.Y., Lohmueller K.E.. Population genetic models of GERP scores suggest pervasive turnover of constrained sites across mammalian evolution.. PLoS Genet. 2020;16:e1008827.
    doi: 10.1371/journal.pgen.1008827pmc: PMC7286533pubmed: 32469868google scholar: lookup
  81. Beisser A.L., McClure S., Wang C., Soring K., Garrison R., Peckham B.. Evaluation of catastrophic musculoskeletal injuries in Thoroughbreds and Quarter Horses at three Midwestern racetracks.. J. Am. Vet. Med. Assoc. 2011;239:1236–1241.
    doi: 10.2460/javma.239.9.1236pubmed: 21999798google scholar: lookup
  82. Kido T., Sikora-Wohlfeld W., Kawashima M., Kikuchi S., Kamatani N., Patwardhan A., Chen R., Sirota M., Kodama K., Hadley D.. Are minor alleles more likely to be risk alleles?. BMC Med. Genom. 2018;11:3.
    doi: 10.1186/s12920-018-0322-5pmc: PMC5775585pubmed: 29351777google scholar: lookup
  83. Pritchard J.K., Cox N.J.. The allelic architecture of human disease genes: Common disease-common variant...or not?. Hum. Mol. Genet. 2002;11:2417–2423.
    doi: 10.1093/hmg/11.20.2417pubmed: 12351577google scholar: lookup
  84. Pritchard J.K.. Are rare variants responsible for susceptibility to complex diseases?. Am. J. Hum. Genet. 2001;69:124–137.
    doi: 10.1086/321272pmc: PMC1226027pubmed: 11404818google scholar: lookup
  85. Gordon A.R., Outram S.V., Keramatipour M., Goddard C.A., Colledge W.H., Metcalfe J.C., Hager-Theodorides A.L., Crompton T., Kemp P.R.. Splenomegaly and modified erythropoiesis in KLF13-/-mice.. J. Biol. Chem. 2008;283:11897–11904.
    doi: 10.1074/jbc.M709569200pubmed: 18285334google scholar: lookup
  86. Yao W., Jiao Y., Zhou Y., Luo X.. KLF13 suppresses the proliferation and growth of colorectal cancer cells through transcriptionally inhibiting HMGCS1-mediated cholesterol biosynthesis.. Cell Biosci. 2020;10:76.
    doi: 10.1186/s13578-020-00440-0pmc: PMC7281930pubmed: 32523679google scholar: lookup
  87. Lavallée G., Andelfinger G., Nadeau M., Lefebvre C., Nemer G., Horb M.E., Nemer M.. The Kruppel-like transcription factor KLF13 is a novel regulator of heart development.. EMBO J. 2006;25:5201–5213.
    doi: 10.1038/sj.emboj.7601379pmc: PMC1630408pubmed: 17053787google scholar: lookup
  88. Leclerc N., Luppen C.A., Ho V.V., Nagpal S., Hacia J.G., Smith E., Frenkel B.. Gene expression profiling of glucocorticoid-inhibited osteoblasts.. J. Mol. Endocrinol. 2004;33:175–193.
    doi: 10.1677/jme.0.0330175pubmed: 15291752google scholar: lookup
  89. Martin K.M., Metcalfe J.C., Kemp P.R.. Expression of Klf9 and Klf13 in mouse development.. Mech. Dev. 2001;103:149–151.
    doi: 10.1016/S0925-4773(01)00343-4pubmed: 11335124google scholar: lookup
  90. Xu L., Huang S., Hou Y., Liu Y., Ni M., Meng F., Wang K., Rui Y., Jiang X., Li G.. Sox11-modified mesenchymal stem cells (MSCs) accelerate bone fracture healing: Sox11 regulates differentiation and migration of MSCs.. FASEB J. 2015;29:1143–1152.
    doi: 10.1096/fj.14-254169pubmed: 25466891google scholar: lookup
  91. Vogel C., Marcotte E.M.. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses.. Nat. Rev. Genet. 2012;13:227–232.
    doi: 10.1038/nrg3185pmc: PMC3654667pubmed: 22411467google scholar: lookup
  92. Qin J.Y., Zhang L., Clift K.L., Hulur I., Xiang A.P., Ren B.-Z., Lahn B.T.. Systematic Comparison of Constitutive Promoters and the Doxycycline-Inducible Promoter.. PLoS ONE. 2010;5:e10611.
    doi: 10.1371/journal.pone.0010611pmc: PMC2868906pubmed: 20485554google scholar: lookup
  93. Wiebe M.S., Nowling T.K., Rizzino A.. Identification of novel domains within Sox-2 and Sox-11 involved in autoinhibition of DNA binding and partnership specificity.. J. Biol. Chem. 2003;278:17901–17911.
    doi: 10.1074/jbc.M212211200pubmed: 12637543google scholar: lookup
  94. Kan A., Ikeda T., Fukai A., Nakagawa T., Nakamura K., Chung U.-I., Kawaguchi H., Tabin C.J.. SOX11 contributes to the regulation of GDF5 in joint maintenance.. BMC Dev. Biol. 2013;13:4.
    doi: 10.1186/1471-213X-13-4pmc: PMC3760452pubmed: 23356643google scholar: lookup
  95. Bragdon B.C., Bahney C.S.. Origin of Reparative Stem Cells in Fracture Healing.. Curr. Osteoporos. Rep. 2018;16:490–503.
    doi: 10.1007/s11914-018-0458-4pmc: PMC6041151pubmed: 29959723google scholar: lookup

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