Genomic and functional evaluation of <i>TNFSF14</i> in multiple sclerosis susceptibility

Miriam Zuccalà; Nadia Barizzone; Elena Boggio; Luca Gigliotti; Melissa Sorosina; Chiara Basagni; Roberta Bordoni; Ferdinando Clarelli; Santosh Anand; Eleonora Mangano; Domizia Vecchio; Elena Corsetti; Serena Martire; Simona Perga; Daniela Ferrante; Alberto Gajofatto; Andrei Ivashynka; Claudio Solaro; Roberto Cantello; Vittorio Martinelli; Giancarlo Comi; Massimo Filippi; Federica Esposito; Maurizio Leone; Gianluca De Bellis; Umberto Dianzani; Filippo Martinelli-Boneschi; Sandra D'Alfonso

doi:10.1016/j.jgg.2021.03.017

Volume 48 Issue 6

Jun. 2021

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Article Navigation > Journal of Genetics and Genomics > 2021 > 48(6): 497-507

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Genomic and functional evaluation of TNFSF14 in multiple sclerosis susceptibility

doi: 10.1016/j.jgg.2021.03.017

a Department of Health Sciences, Center on Autoimmune and Allergic Diseases (CAAD), UPO, University of Eastern Piedmont, A. Avogadro, Novara 28100, Italy;
b Consorzio Interuniversitario di Biotecnologie (CIB), Trieste 34149, Italy;
c Laboratory of Human Genetics of Neurological Disorders, Institute of Experimental Neurology, Division of Neurosciences, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy;
d National Research Council of Italy, Institute for Biomedical Technologies, Segrate, Milan 20090, Italy;
e Department of Genetic Medicine and Development (GEDEV), Faculty of Medicine, University of Geneva Medical School, Geneva 1211, Switzerland;
f Department of Informatics, Systems and Communications (DISCo), University of Milano-Bicocca, Milan 20126, Italy;
g MS Centre, SCDU Neurology, AOU Maggiore della Carità, Novara 28100, Italy;
h Department of Translational Medicine, Interdisciplinary Research Center of Autoimmune Diseases (IRCAD), University of Eastern Piedmont, Novara, Avogadro University, Novara 28100, Italy;
i Neuroscience Institute Cavalieri Ottolenghi, Orbassano, Turin 10043, Italy;
j Neurobiology Unit, Neurology - CReSM (Regional Referring Center of Multiple Sclerosis), AOU San Luigi Gonzaga, Orbassano, Turin 10043, Italy;
k Department of Neuroscience ‘Rita Levi Montalcini’, University of Turin, Turin 10126, Italy;
l Department of Neuroscience, Biomedicine and Movement Sciences, University of Verona, Verona 37134, Italy;
m UO Neurologia Fondazione IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Foggia 71013, Italy;
n Centro Recupero e Rieducazione Funzionale “Mons L Novarese”, Moncrivello (VC) 13040, Italy;
o Department of Neurology, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy;
p Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan 20132, Italy;
q Vita-Salute San Raffaele University, Milan 20138, Italy;
r Neurophysiology Unit, IRCCS San Raffaele Scientific Institute, Milan 20138, Italy;
s Dino Ferrari Centre, Neuroscience Section, Department of Pathophysiology and Transplantation (DEPT), University of Milan, Milan 20122, Italy;
t Foundation IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Neurology Unit and MS Center, Milan 20122, Italy

Funds:

We thank the International Multiple Sclerosis Genetic Consortium. This work was supported by the Italian Foundation of Multiple Sclerosis (FISM, 2011/R/14 2015/R/10, 2019/R-Multi/033) by the Italian Ministry of Health (RF-2016-02361294) and the AGING Project for Department of Excellence at the Department of Translational Medicine (DIMET), Universita del Piemonte Orientale, Novara, Italy. M.Z. supported by Consorzio Interuniversitario di Biotecnologie (CIB). N.B. and A.I. were partially supported by MultipleMS project (Horizon 2020 European Grant 733161), Stockholm.

Received Date: 2020-07-27
Accepted Date: 2021-03-05
Rev Recd Date: 2021-02-24
Publish Date: 2021-06-20

Abstract

Abstract

Among multiple sclerosis (MS) susceptibility genes, the strongest non-human leukocyte antigen (HLA) signal in the Italian population maps to the TNFSF14 gene encoding LIGHT, a glycoprotein involved in dendritic cell (DC) maturation. Through fine-mapping in a large Italian dataset (4,198 patients with MS and 3,903 controls), we show that the TNFSF14 intronic SNP rs1077667 is the primarily MS-associated variant in the region. Expression quantitative trait locus (eQTL) analysis indicates that the MS risk allele is significantly associated with reduced TNFSF14 messenger RNA levels in blood cells, which is consistent with the allelic imbalance in RNA-Seq reads (P<0.0001). The MS risk allele is associated with reduced levels of TNFSF14 gene expression (P<0.01) in blood cells from 84 Italian patients with MS and 80 healthy controls (HCs). Interestingly, patients with MS are lower expressors of TNFSF14 compared to HC (P<0.007). Individuals homozygous for the MS risk allele display an increased percentage of LIGHT-positive peripheral blood myeloid DCs (CD11c⁺, P = 0.035) in 37 HCs, as well as in in vitro monocyte-derived DCs from 22 HCs (P = 0.04). Our findings suggest that the intronic variant rs1077667 alters the expression of TNFSF14 in immune cells, which may play a role in MS pathogenesis.
- Multiple sclerosis,
- TNFSF14,
- LIGHT,
- Fine-mapping analysis,
- SNV

These authors contributed equally to the work as co-last authors.

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References(56)

References

ANZgene, A.a.N.Z.M.S.G.C., The Australia and New Zealand Multiple Sclerosis Genetics Consortium (ANZgene), 2009. Genome wide association study identifies new multiple sclerosis susceptibility loci on chromosomes 12 and 20. Nat. Genet. 41, 824-828.

Abecasis, G.R., Auton, A., Brooks, L.D., DePristo, M.A., Durbin, R.M., Handsaker, R.E., Kang, H.M., Marth, G.T., McVean, G.A., Consortium, G.P., 2012. An integrated map of genetic variation from 1092 human genomes. Nature 491, 56-65.

Anand, S., Mangano, E., Barizzone, N., Bordoni, R., Sorosina, M., Clarelli, F., Corrado, L., Martinelli Boneschi, F., D'Alfonso, S., De Bellis, G., 2016. Next generation sequencing of pooled samples: guideline for variants. Filter. Sci. Rep. 6, 33735.

Andrews, S., 2015. FastQC: A Quality Control Tool for High Throughput Sequence Data. https://www.bioinformatics.babraham.ac.uk/projects/fastqc/.

Banchereau, J., Palucka, A.K., 2005. Dendritic cells as therapeutic vaccines against cancer. Nat. Rev. Immunol. 5, 296-306.

Bansal, V., 2010. A statistical method for the detection of variants from nextgeneration resequencing of DNA pools. Bioinformatics 26, 318-324.

Beecham, A.H., Patsopoulos, N.A., Xifara, D.K., Davis, M.F., Kemppinen, A., Cotsapas, C., Shah, T.S., Spencer, C., Booth, D., Goris, A., et al., 2013. Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat. Genet. 45, 1353-1360.

Borenstein, M., Hedges, L., Higgins, J., Rothstein, H., 2013. Comprehensive MetaAnalysis Version 3. Biostat., Englewood, NJ.

Cartharius, K., Frech, K., Grote, K., Klocke, B., Haltmeier, M., Klingenhoff, A., Frisch, M., Bayerlein, M., Werner, T., 2005. MatInspector and beyond: promoter analysis based on transcription factor binding sites. Bioinformatics 21, 2933-2942.

Cortes, A., Brown, M.A., 2011. Promise and pitfalls of the Immunochip. Arthritis Res. Ther. 13, 101.

Delaneau, O., Marchini, J., Zagury, J.F., 2011. A linear complexity phasing method for thousands of genomes. Nat. Methods 9, 179-181.

Durelli, L., Conti, L., Clerico, M., Boselli, D., Contessa, G., Ripellino, P., Ferrero, B., Eid, P., Novelli, F., 2009. T-helper 17 cells expand in multiple sclerosis and are inhibited by interferon-beta. Ann. Neurol. 65, 499-509.

Ernst, J., Kellis, M., 2010. Discovery and characterization of chromatin states for systematic annotation of the human genome. Nat. Biotechnol. 28, 817-825.

Frischer, J.M., Bramow, S., Dal-Bianco, A., Lucchinetti, C.F., Rauschka, H., Schmidbauer, M., Laursen, H., Sorensen, P.S., Lassmann, H., 2009. The relation between inflammation and neurodegeneration in multiple sclerosis brains. Brain 132, 1175-1189.

Fuchsberger, C., Abecasis, G.R., Hinds, D.A., 2015. Minimac2: faster genotype imputation. Bioinformatics 31, 782-784.

Granger, S.W., Butrovich, K.D., Houshmand, P., Edwards, W.R., Ware, C.F., 2001. Genomic characterization of LIGHT reveals linkage to an immune response locus on chromosome 19p13.3 and distinct isoforms generated by alternate splicing or proteolysis. J. Immunol. 167, 5122-5128.

Granger, S.W., Rickert, S., 2003. LIGHT-HVEM signaling and the regulation of T cellmediated immunity. Cytokine Growth Factor Rev. 14, 289-296.

Gregory, A.P., Dendrou, C.A., Attfield, K.E., Haghikia, A., Xifara, D.K., Butter, F., Poschmann, G., Kaur, G., Lambert, L., Leach, O.A., et al., 2012. TNF receptor 1 genetic risk mirrors outcome of anti-TNF therapy in multiple sclerosis. Nature 488, 508-511.

Holm, K., Melum, E., Franke, A., Karlsen, T.H., 2010. SNPexp - a web tool for calculating and visualizing correlation between HapMap genotypes and gene expression levels. BMC Bioinf. 11, 600.

Holmes, T.D., Wilson, E.B., Black, E.V., Benest, A.V., Vaz, C., Tan, B., Tanavde, V.M., Cook, G.P., 2014. Licensed human natural killer cells aid dendritic cell maturation via TNFSF14/LIGHT. Proc. Natl. Acad. Sci. U. S. A. 111, 5688-5696.

Howie, B., Fuchsberger, C., Stephens, M., Marchini, J., Abecasis, G.R., 2012. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955-959.

International Multiple Sclerosis Genetics Consortium, 2018. Low-frequency and rare-coding variation contributes to multiple sclerosis risk. Cell 175, 1679-1687.

International Multiple Sclerosis Genetics Consortium, 2019. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility. Science 365, eaav7188.

Jernås, M., Malmeström, C., Axelsson, M., Nookaew, I., Wadenvik, H., Lycke, J., Olsson, B., 2013. MicroRNA regulate immune pathways in T-cells in multiple sclerosis (MS). BMC Immunol. 14, 32.

Kimura, A., Naka, T., Nohara, K., Fujii-Kuriyama, Y., Kishimoto, T., 2008. Aryl hydrocarbon receptor regulates Stat1 activation and participates in the development of Th17 cells. Proc. Natl. Acad. Sci. U. S. A. 105, 9721-9726.

Lappalainen, T., Sammeth, M., Friedländer, M.R., t Hoen, P.A., Monlong, J., Rivas, M.A., Gonzàlez-Porta, M., Kurbatova, N., Griebel, T., Ferreira, P.G., et al., 2013. Transcriptome and genome sequencing uncovers functional variation in humans. Nature 501, 506-511.

Li, H., Durbin, R., 2009. Fast and accurate short read alignment with BurrowsWheeler transform. Bioinformatics 25, 1754-1760.

Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Marth, G., Abecasis, G., Durbin, R., Subgroup, G.P.D.P., 2009a. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078-2079.

Li, Y., Willer, C., Sanna, S., Abecasis, G., 2009b. Genotype imputation. Annu. Rev. Genom. Hum. Genet. 10, 387-406.

Li, Y., Willer, C.J., Ding, J., Scheet, P., Abecasis, G.R., 2010. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816-834.

Livak, K.J., Schmittgen, T.D., 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25, 402-408.

Malmeström, C., Gillett, A., Jernås, M., Khademi, M., Axelsson, M., Kockum, I., Mattsson, N., Zetterberg, H., Blennow, K., Alfredsson, L., et al., 2013. Serum levels of LIGHT in MS. Mult. Scler. 19, 871-876.

Matys, V., Kel-Margoulis, O.V., Fricke, E., Liebich, I., Land, S., Barre-Dirrie, A., Reuter, I., Chekmenev, D., Krull, M., Hornischer, K., et al., 2006. TRANSFAC and its module TRANSCompel: transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 34 (Database issue), D108-D110.

Maña, P., Liñares, D., Silva, D.G., Fordham, S., Scheu, S., Pfeffer, K., Staykova, M., Bertram, E.M., 2013. LIGHT (TNFSF14/CD258) is a decisive factor for recovery from experimental autoimmune encephalomyelitis. J. Immunol. 191, 154-163.

MedCalc Statistical Software version 15.5, 2015. MedCalc Software bvba, O., Belgium. https://www.medcalc.org.

Melé, M., Ferreira, P.G., Reverter, F., DeLuca, D.S., Monlong, J., Sammeth, M., Young, T.R., Goldmann, J.M., Pervouchine, D.D., Sullivan, T.J., et al., 2015. The human transcriptome across tissues and individuals. Science 348, 660-665.

Morel, Y., Truneh, A., Sweet, R.W., Olive, D., Costello, R.T., 2001. The TNF superfamily members LIGHT and CD154 (CD40 ligand) costimulate induction of dendritic cell maturation and elicit specific CTL activity. J. Immunol. 167, 2479-2486.

Moutsianas, L., Jostins, L., Beecham, A.H., Dilthey, A.T., Xifara, D.K., Ban, M., Shah, T.S., Patsopoulos, N.A., Alfredsson, L., Anderson, C.A., et al., 2015. Class Ⅱ HLA interactions modulate genetic risk for multiple sclerosis. Nat. Genet. 47, 1107-1113.

Navone, N.D., Perga, S., Martire, S., Berchialla, P., Malucchi, S., Bertolotto, A., 2014.

Monocytes and CD4+ T cells contribution to the under-expression of NR4A2 and TNFAIP3 genes in patients with multiple sclerosis. J. Neuroimmunol. 272, 99-102.

Parkes, M., Cortes, A., van Heel, D.A., Brown, M.A., 2013. Genetic insights into common pathways and complex relationships among immune-mediated diseases. Nat. Rev. Genet. 14, 661-673.

Pashenkov, M., Huang, Y.M., Kostulas, V., Haglund, M., Söderström, M., Link, H., 2001. Two subsets of dendritic cells are present in human cerebrospinal fluid. Brain 124, 480-492.

Patsopoulos, N.A., Bayer Pharma MS Genetics Working Group; Steering Committees of Studies Evaluating IFNb-1b and a CCR1-Antagonist, ANZgene Consortium, GeneMSA, International Multiple Sclerosis Genetics Consortium, Esposito, F., Reischl, J., Lehr, S., Bauer, D., Heubach, J., et al., 2011. Genomewide meta-analysis identifies novel multiple sclerosis susceptibility loci. Ann. Neurol. 70, 897-912.

Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J., et al., 2007. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559-575.

Ramasamy, A., Trabzuni, D., Guelfi, S., Varghese, V., Smith, C., Walker, R., De, T., Coin, L., de Silva, R., Cookson, M.R., et al., 2014. Genetic variability in the regulation of gene expression in ten regions of the human brain. Nat. Neurosci. 17, 1418-1428.

Romme Christensen, J., Börnsen, L., Ratzer, R., Piehl, F., Khademi, M., Olsson, T., Sørensen, P.S., Sellebjerg, F., 2013. Systemic inflammation in progressive multiple sclerosis involves follicular T-helper, Th17- and activated B-cells and correlates with progression. PLoS One 8, e57820.

Sawcer, S., Hellenthal, G., Pirinen, M., Spencer, C.C., Patsopoulos, N.A., Moutsianas, L., Dilthey, A., Su, Z., Freeman, C., Hunt, S.E., et al., 2011. Genetic risk and a primary role for cell-mediated immune mechanisms in multiple sclerosis. Nature 476, 214-219.

Serafini, B., Rosicarelli, B., Magliozzi, R., Stigliano, E., Capello, E., Mancardi, G.L., Aloisi, F., 2006. Dendritic cells in multiple sclerosis lesions: maturation stage, myelin uptake, and interaction with proliferating T cells. J. Neuropathol. Exp. Neurol. 65, 124-141.

Sherry, S.T., Ward, M.H., Kholodov, M., Baker, J., Phan, L., Smigielski, E.M., Sirotkin, K., 2001. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308-311.

Shui, J.W., Steinberg, M.W., Kronenberg, M., 2011. Regulation of inflammation, autoimmunity, and infection immunity by HVEM-BTLA signaling. J. Leukoc. Biol. 89, 517-523.

Singh, N.P., Hegde, V.L., Hofseth, L.J., Nagarkatti, M., Nagarkatti, P., 2007. Resveratrol (trans-3,5,4'-trihydroxystilbene) ameliorates experimental allergic encephalomyelitis, primarily via induction of apoptosis in T cells involving activation of aryl hydrocarbon receptor and estrogen receptor. Mol. Pharmacol. 72, 1508-1521.

Steri, M., Orrù, V., Idda, M.L., Pitzalis, M., Pala, M., Zara, I., Sidore, C., Faà, V., Floris, M., Deiana, M., et al., 2017. Overexpression of the cytokine BAFF and autoimmunity risk. N. Engl. J. Med. 376, 1615-1626.

Tamada, K., Shimozaki, K., Chapoval, A.I., Zhai, Y., Su, J., Chen, S.F., Hsieh, S.L., Nagata, S., Ni, J., Chen, L., 2000. LIGHT, a TNF-like molecule, costimulates T cell proliferation and is required for dendritic cell-mediated allogeneic T cell response. J. Immunol. 164, 4105-4110.

Vogel, C.F., Khan, E.M., Leung, P.S., Gershwin, M.E., Chang, W.L., Wu, D., Haarmann-Stemmann, T., Hoffmann, A., Denison, M.S., 2014. Cross-talk between aryl hydrocarbon receptor and the inflammatory response: a role for nuclear factorkB. J. Biol. Chem. 289, 1866-1875.

Wang, K., Li, M., Hakonarson, H., 2010. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164.

Westra, H.J., Peters, M.J., Esko, T., Yaghootkar, H., Schurmann, C., Kettunen, J., Christiansen, M.W., Fairfax, B.P., Schramm, K., Powell, J.E., et al., 2013. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat. Genet. 45, 1238-1243.

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