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Coding Polymorphisms in Zinc Transporter Gene SLC39A12 (ZIP12) Associated with Brain MRI Pattern Differences Have Reduced Zinc Transport Activity

ABSTRACT: Abstract Objectives Zinc is important for brain function. The SLC39A12 gene encodes ZIP12, which is a zinc transport protein highly expressed in the brain. Our goal is to determine whether human slc39a12 coding polymorphisms associated with differences in susceptibility-weighted brain MRI (swMRI) patterns affect zinc transport ability. Methods We used summary statistics for a genome-wide association study (GWAS) on swMRI patterns for caudate, putamen, and pallidum of 7778 human subjects aged 40–69 years from the UK BioBank. Human SLC39A12 coding polymorphisms rs10764176 and rs72778328 were associated with a reduction in relaxation spin time during swMRI in the putamen or pallidum but not caudate and met a significance cutoff of P < 5.0 × 10–8. Haploblock determination and linkage disequilibrium (LD) between the variants was determined by SNPclip, LDmatrix, and LDpair software and the European 1000 genomes LD reference panel. The effects of the ZIP12 polymorphisms on cell zinc status and zinc uptake were measured in Chinese hamster ovary (CHO) cells transfected with the reference ZIP12 version or coding variants. Cell zinc status was assessed after 18 hours by metal-response element reporter activation (n = 8). Cell zinc uptake activity was measured by stable isotope Zinc-70 uptake (n = 6) relative to endogenous Zinc-68 content using inductively coupled plasma mass spectrometry (ICP-MS). Statistical comparisons were performed using ANOVA. Significance was set at P < 0.05. Results Variants rs10764176 and rs72778328 are present on independent haploblocks and are in linkage equilibrium with each other. Variants rs10764176 and rs72778328 have a reduction in MRE reporter activation (41.7% [P < 0.05] and 55.6% [P < 0.01], respectively) and in exogenous Zinc-70 uptake after 20 minutes (37.6% [P < 0.001] and 54.2% [P < 0.001], respectively) compared to the reference ZIP12. Conclusions Variants rs10764176 and rs72778328 are on distinct haploblocks. These ZIP12 coding variants that are associated with altered brain swMRI patterns also have reduced zinc uptake activity. Funding Sources This work was funded by grants from the Oklahoma Center for the Advancement of Science and Technology and the Oklahoma Agricultural Experiment Station.

SUBMITTER: Chambers E

PROVIDER: S-EPMC7259093 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Project description:Abstract Objectives The SLC39A8 gene encodes a divalent metal transporter, ZIP8. ZIP8 polymorphisms are associated with pleiotropic effects including altered risks for schizophrenia. Our objective is to determine the different brain MRI phenotypes associated at or near the SLC39A8 (ZIP8) genetic locus using a phenome-wide association (PheWAS) approach followed by joint and conditional association analysis. Methods Using the summary statistics database containing brain MRI genome-wide association study (GWAS) data, we systematically selected all brain MRI phenotypes which were associated with single-nucleotide polymorphisms (SNPs) within 1 million bp of the SLC39A8 genetic locus, as defined as reaching a P-value significance cutoff of P < 5.0 × 10–8. For all brain MRI phenotypes reaching significance, we used GCTA-COJO to determine the number of independent association signals using settings of P < 1.0 × 10–5 and a window of 10 million base pairs. Using SNPclip and the European 1000 Genomes linkage panel with a linkage disequilibrium cutoff of r2 > 0.8, we identified SNP candidates at each index SNP. Linkage equilibrium for brain phenotypes with multiple independent signals was confirmed by LDpair. Results We identified 25 brain MRI phenotypes that vary due to MRI type and brain region that all contain a SNP associated with the SLC39A8 locus. All of these datasets have at least 1 index SNP directly labeling or in high linkage disequilibrium with rs13107325, which encodes a missense mutation in the SLC39A8 (ZIP8). Among the 25 datasets, an additional 4 association signals were identified by GCTA-COJO and confirmed to be in linkage equilibrium with rs13107325 using LDpair. For these additional association signals, probable causative SNPs were identified from the index SNP using SNPclip. Conclusions From the 25 brain MRI phenotypes, we identified new probable causative SNPs in addition to a previously reported missense SNP (rs13107325) associated with schizophrenia. This study provides leads into how SNPs in genes involved in trace metal transport influence brain structures and affect risks for schizophrenia. Funding Sources This work was funded by grants from the Oklahoma Center for the Advancement of Science and Technology and the Oklahoma Agricultural Experiment Station.

Project description:We explored the performance of structure-based computational analysis in four neurodegenerative conditions [Ataxia (AT, n = 16), Huntington's Disease (HD, n = 52), Alzheimer's Disease (AD, n = 66), and Primary Progressive Aphasia (PPA, n = 50)], all characterized by brain atrophy. The independent variables were the volumes of 283 anatomical areas, derived from automated segmentation of T1-high resolution brain MRIs. The segmentation based volumetric quantification reduces image dimensionality from the voxel level [on the order of [Formula: see text](106)] to anatomical structures [[Formula: see text](102)] for subsequent statistical analysis. We evaluated the effectiveness of this approach on extracting anatomical features, already described by human experience and a priori biological knowledge, in specific scenarios: (1) when pathologies were relatively homogeneous, with evident image alterations (e.g., AT); (2) when the time course was highly correlated with the anatomical changes (e.g., HD), an analogy for prediction; (3) when the pathology embraced heterogeneous phenotypes (e.g., AD) so the classification was less efficient but, in compensation, anatomical and clinical information were less redundant; and (4) when the entity was composed of multiple subgroups that had some degree of anatomical representation (e.g., PPA), showing the potential of this method for the clustering of more homogeneous phenotypes that can be of clinical importance. Using the structure-based quantification and simple linear classifiers (partial least square), we achieve 87.5 and 73% of accuracy on differentiating AT and pre-symptomatic HD patents from controls, respectively. More importantly, the anatomical features automatically revealed by the classifiers agreed with the patterns previously described on these pathologies. The accuracy was lower (68%) on differentiating AD from controls, as AD does not display a clear anatomical phenotype. On the other hand, the method identified PPA clinical phenotypes and their respective anatomical signatures. Although most of the data are presented here as proof of concept in simulated clinical scenarios, structure-based analysis was potentially effective in characterizing phenotypes, retrieving relevant anatomical features, predicting prognosis, and aiding diagnosis, with the advantage of being easily translatable to clinics and understandable biologically.

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Coding Polymorphisms in Zinc Transporter Gene SLC39A12 (ZIP12) Associated with Brain MRI Pattern Differences Have Reduced Zinc Transport Activity

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