Functional characterisation of the amyotrophic lateral sclerosis risk locus GPX3/TNIP1

Restuadi Restuadi; Frederik J Steyn; Edor Kabashi; Shyuan T Ngo; Fei-Fei Cheng; Marta F Nabais; Mike J Thompson; Ting Qi; Yang Wu; Anjali K Henders; Leanne Wallace; Chris R Bye; Bradley J Turner; Laura Ziser; Susan Mathers; Pamela A McCombe; Merrilee Needham; David Schultz; Matthew C Kiernan; Wouter van Rheenen; Leonard H van den Berg; Jan H Veldink; Roel Ophoff; Alexander Gusev; Noah Zaitlen; Allan F McRae; Robert D Henderson; Naomi R Wray; Jean Giacomotto; Fleur C Garton

doi:10.1186/s13073-021-01006-6

Functional characterisation of the amyotrophic lateral sclerosis risk locus GPX3/TNIP1

Genome Med. 2022 Jan 19;14(1):7. doi: 10.1186/s13073-021-01006-6.

Authors

Restuadi Restuadi¹, Frederik J Steyn^{2

3

4}, Edor Kabashi^{5

6}, Shyuan T Ngo^{4

7

8}, Fei-Fei Cheng¹, Marta F Nabais^{1

9}, Mike J Thompson^{10

11}, Ting Qi¹, Yang Wu¹, Anjali K Henders¹, Leanne Wallace¹, Chris R Bye¹², Bradley J Turner¹², Laura Ziser¹, Susan Mathers¹³, Pamela A McCombe^{3

4}, Merrilee Needham^{14

15

16}, David Schultz¹⁷, Matthew C Kiernan¹⁸, Wouter van Rheenen¹⁹, Leonard H van den Berg¹⁹, Jan H Veldink¹⁹, Roel Ophoff^{10

11}, Alexander Gusev^{20

21}, Noah Zaitlen^{10

11

22

23}, Allan F McRae¹, Robert D Henderson^{3

4

7}, Naomi R Wray^{1

7}, Jean Giacomotto^{7

24}, Fleur C Garton²⁵

Affiliations

¹ Institute for Molecular Bioscience, The University of Queensland, QLD, Brisbane, 4072, Australia.
² School of Biomedical Sciences, The University of Queensland, QLD, Brisbane, 4072, Australia.
³ Department of Neurology, Royal Brisbane and Women's Hospital, QLD, Brisbane, 4029, Australia.
⁴ Centre for Clinical Research, The University of Queensland, QLD, Brisbane, 4019, Australia.
⁵ Imagine Institute, Institut National de la Santé et de la Recherche Médicale (INSERM) Unité 1163, Paris Descartes Université, 75015, Paris, France.
⁶ Sorbonne Université, Université Pierre et Marie Curie (UPMC), Université de Paris 06, INSERM Unité 1127, Centre National de la Recherche Scientifique (CNRS) Unité Mixte de Recherche 7225, Institut du Cerveau et de la Moelle Épinière (ICM), 75013, Paris, France.
⁷ Queensland Brain Institute, The University of Queensland, QLD, Brisbane, 4072, Australia.
⁸ Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, QLD, Brisbane, 4072, Australia.
⁹ University of Exeter Medical School, RILD Building, RD&E Hospital Wonford, Barrack Road, Exeter, EX2 5DW, UK.
¹⁰ Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA.
¹¹ Department of Bioinformatics, University of California Los Angeles, Los Angeles, CA, USA.
¹² Florey Institute for Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, 3052, Australia.
¹³ Calvary Health Care Bethlehem, Parkdale, VIC, 3195, Australia.
¹⁴ Fiona Stanley Hospital, Perth, WA, 6150, Australia.
¹⁵ Notre Dame University, Fremantle, WA, 6160, Australia.
¹⁶ Institute for Immunology and Infectious Diseases, Murdoch University, Perth, WA, 6150, Australia.
¹⁷ Department of Neurology, Flinders Medical Centre, Bedford Park, SA, 5042, Australia.
¹⁸ Brain & Mind Centre, University of Sydney, Institute of Clinical Neurosciences, Royal Prince Alfred Hospital, Sydney, NSW, 2006, Australia.
¹⁹ Department of Neurology, University Medical Center Utrecht Brain Center, Utrecht University, Utrecht, The Netherlands.
²⁰ Department of Medical Oncology, Dana-Farber Cancer Institute and Harvard Medical School, Boston, MA, USA.
²¹ Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA.
²² Department of Neurology, University of California Los Angeles, Los Angeles, CA, 90095, USA.
²³ Department of Medicine, University of California San Francisco, San Francisco, CA, 94158, USA.
²⁴ Queensland Centre for Mental Health Research, West Moreton Hospital and Health Service, Wacol, QLD, 4076, Australia.
²⁵ Institute for Molecular Bioscience, The University of Queensland, QLD, Brisbane, 4072, Australia. f.garton@uq.edu.au.

Abstract

Background: Amyotrophic lateral sclerosis (ALS) is a complex, late-onset, neurodegenerative disease with a genetic contribution to disease liability. Genome-wide association studies (GWAS) have identified ten risk loci to date, including the TNIP1/GPX3 locus on chromosome five. Given association analysis data alone cannot determine the most plausible risk gene for this locus, we undertook a comprehensive suite of in silico, in vivo and in vitro studies to address this.

Methods: The Functional Mapping and Annotation (FUMA) pipeline and five tools (conditional and joint analysis (GCTA-COJO), Stratified Linkage Disequilibrium Score Regression (S-LDSC), Polygenic Priority Scoring (PoPS), Summary-based Mendelian Randomisation (SMR-HEIDI) and transcriptome-wide association study (TWAS) analyses) were used to perform bioinformatic integration of GWAS data (N_cases = 20,806, N_controls = 59,804) with 'omics reference datasets including the blood (eQTLgen consortium N = 31,684) and brain (N = 2581). This was followed up by specific expression studies in ALS case-control cohorts (microarray N_total = 942, protein N_total = 300) and gene knockdown (KD) studies of human neuronal iPSC cells and zebrafish-morpholinos (MO).

Results: SMR analyses implicated both TNIP1 and GPX3 (p < 1.15 × 10^-6), but there was no simple SNP/expression relationship. Integrating multiple datasets using PoPS supported GPX3 but not TNIP1. In vivo expression analyses from blood in ALS cases identified that lower GPX3 expression correlated with a more progressed disease (ALS functional rating score, p = 5.5 × 10^-3, adjusted R² = 0.042, B_effect = 27.4 ± 13.3 ng/ml/ALSFRS unit) with microarray and protein data suggesting lower expression with risk allele (recessive model p = 0.06, p = 0.02 respectively). Validation in vivo indicated gpx3 KD caused significant motor deficits in zebrafish-MO (mean difference vs. control ± 95% CI, vs. control, swim distance = 112 ± 28 mm, time = 1.29 ± 0.59 s, speed = 32.0 ± 2.53 mm/s, respectively, p for all < 0.0001), which were rescued with gpx3 expression, with no phenotype identified with tnip1 KD or gpx3 overexpression.

Conclusions: These results support GPX3 as a lead ALS risk gene in this locus, with more data needed to confirm/reject a role for TNIP1. This has implications for understanding disease mechanisms (GPX3 acts in the same pathway as SOD1, a well-established ALS-associated gene) and identifying new therapeutic approaches. Few previous examples of in-depth investigations of risk loci in ALS exist and a similar approach could be applied to investigate future expected GWAS findings.

Keywords: Computational biology; Disease progression; Genes; Genome-wide association study; MND; Motor neurone disease; Neurodegenerative diseases; Quantitative trait loci; Regulator; Zebrafish.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Amyotrophic Lateral Sclerosis* / genetics
Animals
Genetic Predisposition to Disease
Genome-Wide Association Study / methods
Humans
Neurodegenerative Diseases*
Polymorphism, Single Nucleotide
Zebrafish / genetics

Abstract

Publication types

MeSH terms

Grants and funding