Data characterizing the ZMIZ1 molecular phenotype of multiple sclerosis

N Fewings¹, P N Gatt¹, F C McKay¹, G P Parnell², S D Schibeci¹, J Edwards¹, M A Basuki¹, A Goldinger³, M J Fabis-Pedrini⁴, A G Kermode⁵, C P Manrique⁶, J L McCauley⁶, D Nickles⁷, S E Baranzini⁷, T Burke⁸, S Vucic², G J Stewart², D R Booth²

Affiliations

¹ Centre for Immunology and Allergy Research, Westmead Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia.
² Centre for Immunology and Allergy Research, Westmead Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia; Western Clinical School, University of Sydney, Westmead Hospital, Sydney, New South Wales, Australia.
³ University of Queensland Diamantina Institute, Translational Research Institute, Australia; The Queensland Brain Institute, University of Queensland, Australia.
⁴ Western Australian Neuroscience Research Institute, University of Western Australia, Nedlands, Western Australia, Australia; Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia.
⁵ Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia.
⁶ John P. Hussman Institute for Human Genomics and the Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA.
⁷ Department of Neurology, University of California San Francisco, USA.
⁸ Western Clinical School, University of Sydney, Westmead Hospital, Sydney, New South Wales, Australia.

PMID: 28275670
PMCID: PMC5329066
DOI: 10.1016/j.dib.2017.02.040

Data characterizing the ZMIZ1 molecular phenotype of multiple sclerosis

N Fewings et al. Data Brief. 2017.

. 2017 Feb 21:11:364-370.

doi: 10.1016/j.dib.2017.02.040. eCollection 2017 Apr.

Authors

Affiliations

¹ Centre for Immunology and Allergy Research, Westmead Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia.
² Centre for Immunology and Allergy Research, Westmead Institute of Medical Research, University of Sydney, Sydney, New South Wales, Australia; Western Clinical School, University of Sydney, Westmead Hospital, Sydney, New South Wales, Australia.
³ University of Queensland Diamantina Institute, Translational Research Institute, Australia; The Queensland Brain Institute, University of Queensland, Australia.
⁴ Western Australian Neuroscience Research Institute, University of Western Australia, Nedlands, Western Australia, Australia; Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia.
⁵ Institute for Immunology and Infectious Diseases, Murdoch University, Murdoch, Western Australia, Australia.
⁶ John P. Hussman Institute for Human Genomics and the Dr. John T. Macdonald Foundation Department of Human Genetics, University of Miami, Miller School of Medicine, Miami, FL 33136, USA.
⁷ Department of Neurology, University of California San Francisco, USA.
⁸ Western Clinical School, University of Sydney, Westmead Hospital, Sydney, New South Wales, Australia.

PMID: 28275670
PMCID: PMC5329066
DOI: 10.1016/j.dib.2017.02.040

Abstract

The data presented in this article are related to the research article entitled "The autoimmune risk gene ZMIZ1 is a vitamin D responsived marker of a molecular phenotype of multiple sclerosis" Fewings et al. (2017) [1]. Here we identify the set of genes correlated with ZMIZ1 in multiple cohorts, provide phenotypic details on those cohorts, and identify the genes negatively correlated with ZMIZ1 and the cells predominantly expressing those genes. We identify the metabolic pathways in which the molecular phenotype genes are over-represented. Finally, we present the flow cytometry gating strategy we have used to identify the immune cells from blood which are producing ZMIZ1 and RPS6.

Keywords: Gene expression; Molecular phenotype; Multiple sclerosis; ZMIZ1.

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Figures

Fig. 1. — **Fig. 1**
Relative expression in immune cell subsets of ZMIZ1 and the 50 genes whose expression is most highly negatively correlated with ZMIZ1 expression in PAXgene whole blood in multiple sclerosis and healthy controls. (These were determined in the RNASeq cohort: n=32 MS, n=40 healthy controls, . These genes are mostly expressed in lymphocytes. Expression was by RNASeq and colour on heatmap indicates relative expression level: orange is high, blue is low. Cell subsets were ex vivo or in-vitro generated as previously described . Pearson׳s correlation (R) of expression with ZFP36L2 and RPS6 is also shown for each module gene, red is positive correlation and green is negative correlation. ZFP36L2 correlations all less than r=-0.27 (p=0.02), RPS6 correlations all greater than r=0.54 (p=8.9E-07). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2. — **Fig. 2**
Gene pathways, diseases, process networks, processes, and molecular functions enriched for the ZMIZ1 Molecular Module genes (the 200 genes with expression most highly positively correlated with ZMIZ1 gene expression in whole blood). The 20 most over-represented items in the module are shown for A. Pathway maps; B. Pathway map folders (diseases); C. Process networks; D. Processes; E. Molecular functions. Enrichment analysis using Gene Ontology (GO) software (http://geneontology.org/); with items listed in order of significance; p value shows the probability that the module is not over-represented; FDR: false discovery rate; ratio is the number of genes from the ZMIZ1 module in the pathway, compared to the total number of genes in the pathway.

Fig. 3. — **Fig. 3**
Flow cytometric gating of PBMC subsets for determination of ZMIZ1 and RPS6 protein expression. (A) ZMIZ1 panel: progressive gating of subsets of interest, and ZMIZ1 median fluorescence intensity of DC and monocyte subsets relative to isotype control; (B) RPS6 panel: gating of live PBMCs and their median fluorescence intensity of RPS6 relative to isotype control.

See this image and copyright information in PMC

References

1. N.L. Fewings, P.N. Gatt, F.C. McKay, G.P. Parnell, S.D. Schibeci, J. Edwards, et al., The autoimmune risk gene ZMIZ1 is a vitamin D responsive marker of a molecular phenotype of multiple sclerosis, J. Autoimmun. (2017), http://dx.doi.org/10.1016/j.jaut.2016.12.006. - DOI - PubMed
1. Gandhi K.S., McKay F.C., Cox M., Riveros C., Armstrong N., Heard R.N. The multiple sclerosis whole blood mRNA transcriptome and genetic associations indicate dysregulation of specific T cell pathways in pathogenesis. Hum. Mol. Genet. 2010;19:2134–2143. - PubMed
1. Nickles D., Chen H.P., Li M.M., Khankhanian P., Madireddy L., Caillier S.J. Blood RNA profiling in a large cohort of multiple sclerosis patients and healthy controls. Hum. Mol. Genet. 2013;22:4194–4205. - PMC - PubMed
1. Parnell G.P., Gatt P.N., McKay F.C., Schibeci S., Krupa M., Powell J.E. Ribosomal protein S6 mRNA is a biomarker upregulated in multiple sclerosis, downregulated by interferon treatment, and affected by season. Mult. Scler. 2014;20:675–685. - PubMed
1. Parnell G.P., Gatt P.N., Krupa M., Nickles D., McKay F.C., Schibeci S.D. The autoimmune disease-associated transcription factors EOMES and TBX21 are dysregulated in multiple sclerosis and define a molecular subtype of disease. Clin. Immunol. 2014;151:16–24. - PubMed

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Data characterizing the ZMIZ1 molecular phenotype of multiple sclerosis

Affiliations

Data characterizing the ZMIZ1 molecular phenotype of multiple sclerosis

Authors

Affiliations

Abstract

Figures

References

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources

Miscellaneous