Additional file 3: of Genetic differences among ethnic groups

The minor allele frequencies of the 299 optimal SNPs in each ethnic group. Each page is a SNP. (PDF 238 kb

Additional file 2: of Genetic differences among ethnic groups

The 299 optimal SNPs that can classify the nine ethnic groups with accuracy greater than 90 %. The order is the mRMR rank, and the SNP name includes the dbSNP ID and the minor allele. (XLSX 37 kb

Predicting A-to-I RNA Editing by Feature Selection and Random Forest

<div><p>RNA editing is a post-transcriptional RNA process that provides RNA and protein complexity for regulating gene expression in eukaryotes. It is challenging to predict RNA editing by computational methods. In this study, we developed a novel method to predict RNA editing based on a random forest method. A careful feature selection procedure was performed based on the Maximum Relevance Minimum Redundancy (mRMR) and Incremental Feature Selection (IFS) algorithms. Eighteen optimal features were selected from the 77 features in our dataset and used to construct a final predictor. The accuracy and <i>MCC</i> (Matthews correlation coefficient) values for the training dataset were 0.866 and 0.742, respectively; for the testing dataset, the accuracy and <i>MCC</i> were 0.876 and 0.576, respectively. The performance was higher using 18 features than all 77, suggesting that a small feature set was sufficient to achieve accurate prediction. Analysis of the 18 features was performed and may shed light on the mechanism and dominant factors of RNA editing, providing a basis for future experimental validation.</p></div

The prediction performance of the final model using 18 features, by 10-fold cross validation.

<p>The prediction performance of the final model using 18 features, by 10-fold cross validation.</p

The basic demographics of habitually unshod and shod runners.

<p>Note: Mean±Standard Deviation; BMI-body mass index.</p><p>The basic demographics of habitually unshod and shod runners.</p

The 18 optimal features selected in this study and their descriptions.

<p>The 18 optimal features selected in this study and their descriptions.</p

Phosphorylation of R1628P mutant by Cdk5 increases the LRRK2 activity and causes cell death.

<p><b>a.</b> Plasmids of HA-tagged wild-type (WT) or R1628P mutant LRRK2 were cotransfected with Cdk5/p35 in HEK293 cells, as indicated. After 24 h, LRRK2 was immune precipitated in the lysates by anti-HA antibody, and <i>in vitro</i> LRRK2 kinase assay was performed to measure the LRRK2 kinas activity using purified MBP protein as substrate. HA-LRRK2, Cdk5, and p35 levels were determined by Western blotting as a loading control. <b>b.</b> The graph is the quantification of LRRK2 kinase activities in Fig 3A. The numbers are relative values, with WT w/o Cdk5/p35 set to 1. <b>c.</b> Plasmids of HA-tagged wild-type (WT), R1628P, and S1627A:R1628P mutant LRRK2 were cotransfected with Cdk5 or dominant-negative form of Cdk5 (dnCdk5) and p35 in HEK293 cells, as indicated. After 24 h, LRRK2 was immune precipitated in the lysates by anti-HA antibody, and <i>in vitro</i> LRRK2 kinase activity was measured for 30min using LRRKtide as substrate. The results represent as the mean ± SD, **P<0.01 (compared with WT:control group) ##P<0.01 (compared with WT:Cdk5/p35 group) (ANOVA). <b>d.</b> Phosphorylation mimic of S1627 (S1627D) increased the LRRK2 kinase activity. HEK293 cells were transfected with HA-tagged LRRK2 plasmids, including wild-type (WT), R1628P, S1627D and S1627D:R1628P double mutant. LRRK2 kinase activities were measured as above. <b>e.</b> The graph is the quantification of LRRK2 kinase activities in Fig 3D. The numbers are relative values, with WT set to 1. **P<0.01 (ANOVA) <b>f.</b> HEK293 cells were transfected with HA-tagged LRRK2 plasmids, including wild-type (WT), R1628P, S1627D and S1627D:R1628P. LRRK2 kinase activities were measured as above for indicated period of time using LRRKtide as substrate. **P<0.01 (compared with WT group, ANOVA)</p

Parkinson-Related LRRK2 Mutation R1628P Enables Cdk5 Phosphorylation of LRRK2 and Upregulates Its Kinase Activity

<div><p>Background</p><p>Recent studies have linked certain single nucleotide polymorphisms in the <i>leucine-rich repeat kinase 2 (LRRK2)</i> gene with Parkinson’s disease (PD). Among the mutations, <i>LRRK2</i> c.4883G>C (R1628P) variant was identified to have a significant association with the risk of PD in ethnic Han-Chinese populations. But the molecular pathological mechanisms of R1628P mutation in PD is still unknown.</p><p>Principle Findings</p><p>Unlike other LRRK2 mutants in the Roc-COR-Kinase domain, the R1628P mutation didn’t alter the LRRK2 kinase activity and promote neuronal death directly. LRRK2 R1628P mutation increased the binding affinity of LRRK2 with Cyclin-dependent kinase 5 (Cdk5). Interestingly, R1628P mutation turned its adjacent amino acid residue S1627 on LRRK2 protein to a novel phosphorylation site of Cdk5, which could be defined as a typical type II (+) phosphorylation-related single nucleotide polymorphism. Importantly, we showed that the phosphorylation of S1627 by Cdk5 could activate the LRRK2 kinase, and neurons ectopically expressing R1628P displayed a higher sensitivity to 1-methyl-4-phenylpyridinium, a bioactive metabolite of environmental toxin MPTP, in a Cdk5-dependent manner.</p><p>Conclusion</p><p>Our data indicate that Parkinson-related LRRK2 mutation R1628P leads to Cdk5 phosphorylation of LRRK2 at S1627, which would upregulate the kinase activity of LRRK2 and consequently cause neuronal death.</p></div

R1628P mutation do not alter the LRRK2 activity and cause neuronal toxicity directly.

<p><b>a.</b> Kinase activity of LRRK2 mutants tested by kinase assay using MBP as substrate. HEK293 cells were transfected with HA-tagged WT LRRK2 or mutants in the Roc-COR-Kinase domain, including R1441C, R1628P, Y1669C, I2012T, G2019S, I2020T, and kinase-inactive mutant D1994N as indicated. After 24 h, LRRK2 was immunoprecipitated in the lysates by anti-HA antibody, and <i>in vitro</i> LRRK2 kinase assay was performed to measure the LRRK2 kinas activity using purified MBP protein as substrate. The bottom panel shows equal expression of HA-LRRK2 and MBP used in the lysates. <b>b.</b> The graph is the quantification of LRRK2 kinase activities in Fig 1A. The numbers are relative values, with WT set to 1. <b>c.</b> Kinase activity of LRRK2 mutants tested by kinase assay using LRRKtide as substrate. HA-tagged WT LRRK2 or mutants were immunoprecipitated as above, then <i>in vitro</i> LRRK2 kinase assay was performed using LRRKtide as substrate. <b>d.</b> Overexpression of R1628P mutation do not cause neuronal cell death. Primary-cultured cortical neurons were transfected with GFP-tagged WT LRRK2 or mutants in the Roc-COR-Kinase domain, including R1441C, R1628P, Y1669C, I2012T, G2019S, I2020T, and kinase-inactive mutant D1994N. After 48 h transfection, the dead cells were labeled with EthD-1 in red, and 200 GFP-positive neurons were counted to calculate the percentage of cell death. All the above results represent at least three independent experiments as the mean ± SD, *P<0.05, **P<0.01 (ANOVA).</p

The Schematic diagram shows that the R1628P genetic mutation of LRRK2 provide a potential “two-hit” target of environment toxic MPP+-induced Cdk5 activation, and Cdk5 could phosphorylate the adjacent amino residue S1627 of R1628P mutation, thus activate LRRK2 kinase activity and cause neuronal death.

<p>The Schematic diagram shows that the R1628P genetic mutation of LRRK2 provide a potential “two-hit” target of environment toxic MPP+-induced Cdk5 activation, and Cdk5 could phosphorylate the adjacent amino residue S1627 of R1628P mutation, thus activate LRRK2 kinase activity and cause neuronal death.</p