DataSheet1_Blood cell traits and risk of glaucoma: A two-sample mendelian randomization study.ZIP

Importance: Glaucoma is the second leading cause of blindness in the world. The causal direction and magnitude of the association between blood cell traits and glaucoma is uncertain because of the susceptibility of observational studies to confounding and reverse causation.Objective: To explore whether there is a causal relationship of blood cell traits including white blood cell (WBC) count (WBCC) and its subtypes [basophil cell count (BASO), monocyte cell count (MONO), lymphocyte cell count (LYMPH), eosinophil cell count (EOS), neutrophil cell count (NEUT)], red blood cell (RBC) count (RBCC), red blood distribution width (RDW), platelet count (PLT), and plateletcrit (PCT) on glaucoma risk.Methods: A two-sample Mendelian randomization (MR) analysis was conducted. Genome-wide significant single nucleotide polymorphisms (SNPs) from published genome-wide association studies (GWAS) on human blood cell traits were utilized as exposure instruments and the dataset for outcome was from the GWAS summary data of glaucoma. In the univariable MR analysis, we examined the association between genetic evidence of blood cell traits and glaucoma. To further investigate the potential causal mechanisms underlying the observed association, we performed multivariable MR analysis with three models, taking into account the mediator effect of inflammation and oxidative stress. According to Bonferroni-corrected for the 10 exposures in 3 methods, the MR study yielded a statistically significant p-value of 0.0017.Results: Genetically BASO, PCT, LYMPH, and PLT were potentially positively associated with glaucoma in the European ancestry [BASO: Odds ratio (OR) = 1.00122, 95% confidence interval (CI), 1.00003–1.00242, p = 0.045; PCT: OR = 1.00078, 95% CI, 1.00012–1.00143, p = 0.019; LYMPH: OR = 1.00076, 95% CI, 1.00002–1.00151, p = 0.045; PLT: OR = 1.00065, 95% CI, 1.00006–1.00123, p = 0.030], There was insufficient evidence to support a causal association of MONO, NEUT, EOS, WBCC, RBCC and RDW (MONO: OR = 1.00050, p = 0.098; NEUT: OR = 1.00028, p = 0.524; EOS: OR = 1.00020, p = 0.562; WBCC: OR = 1.00008, p = 0.830; RBCC: OR = 0.99996, p = 0.920; RDW: OR = 0.99987, p = 0.734) with glaucoma. The multivariable MR with model 1, 2, and 3 demonstrated that BASO, PCT, LYMPH, and PLT were still potentially genetically associated with the risk of glaucoma.Conclusion: Our study reveals a genetic predisposition to higher LYMPH, BASO, PLT, and PCT are associated with a higher risk of glaucoma, whereas WBCC, MONO, EOS, NEUT, RBCC, and RDW are not associated with the occurrence of glaucoma. This finding also supports previous observational studies associating immune components with glaucoma, thus provide guidance on the predication and prevention for glaucoma.</p

Crystallization-Driven Co-Assembly of Micrometric Polymer Hybrid Single Crystals and Nanometric Crystalline Micelles

In the present work, crystallization-driven coassembly of micrometric polymer single crystals and nanometric block copolymer micelles was achieved. The hybrid single crystals are first formed by cocrystallization of polyethylene (PE) homopolymer and polyethylene-<i>b</i>-poly­(<i>tert</i>-butyl acrylate) (PE-<i>b</i>-P<i>t</i>BA) block copolymer (BCP) in DMF or DMF/<i>o</i>-xylene mixed solvent. The morphology of the obtained hybrid single crystals can be regulated via changing the solvent composition, crystallization temperature and mass ratio of BCP/homopolymer. Because of the difference in crystallization rate, the distribution of PE-<i>b</i>-P<i>t</i>BA BCP in the hybrid single crystals may be inhomogeneous, leading to a concave gradient surface structure. The hybrid single crystals have a double-layer structure, in which PE homopolymer chains adopt extended conformation and the PE blocks in PE-<i>b</i>-P<i>t</i>BA are probably once-folded. After the PE homopolymer is consumed, cylindrical micelles of PE-<i>b</i>-P<i>t</i>BA can further epitaxially grow on the lateral surface of the hybrid single crystals and “ciliate paramecium-like” coassemblies are yielded. The single crystal/micelles coassemblies can be prepared either by one-step method, in which PE and PE-<i>b</i>-P<i>t</i>BA are added together in a single step, or by two-step method, in which the hybrid single crystals are prepared in the first step and extra PE-<i>b</i>-P<i>t</i>BA is added in the second step to grow BCP micelles. This work provided a simple route to construct hierarchical assemblies composed of objects with different scales by using crystallization as the key driving force

Hydrogen-Bonding-Mediated Fragmentation and Reversible Self-assembly of Crystalline Micelles of Block Copolymer

Two hydrogen (H)-bond donors, phenol and l-threonine, were added into the aqueous solutions containing crystalline micelles of a poly­(ε-caprolactone)-<i>b</i>-poly­(ethylene oxide) (PCL-<i>b</i>-PEO) block copolymer, respectively. Dynamic light scattering (DLS) characterization showed that the micellar size became smaller after addition of phenol. Transmission electron microscopy (TEM) results revealed that the long crystalline cylindrical micelles formed in the neat aqueous solution were fragmented into short cylinders and even quasi-spherical micelles, as the phenol concentration increased. By contrast, the spherical PCL-<i>b</i>-PEO crystalline micelles could be transformed into short cylinders and then long cylinders after addition of l-threonine. Reversible morphological transformation was realized for the PCL-<i>b</i>-PEO crystalline micelles by adding these two H-bond donors alternately. It is confirmed that both phenol and l-threonine could form H-bonds with PEO. We proposed that, the micellar corona was swollen by phenol, leading to fragmentation of the micellar core, whereas the PEO blocks in the micellar corona was dynamically cross-linked by l-threonine beacuse of its multiple H-bond-donation groups, resulting in a smaller reduced tethering density

The inhibitory potential of PYC was also fully replicated in mouse primary brain microglia.

<p>Cells were treated with PYC or vehicle at the indicated concentrations for 1h. LPS (500ng/mL) was then added and further incubated for 24 h. Ribosomal RNAs were used as the total RNA loading control. mRNA levels was assessed by real-time PCR, and the mRNA level in the control (no stimuli) was arbitrarily designated as 1 for comparison. The data represent the means ± SD of at least 3 independent experiments. *P< 0.05 compared with LPS alone.</p

Power in structured populations at different levels.

<p>Three methods were employed to examine these populations, including GLM, MLM and FarmCPU. The top panel <b>(a</b> to <b>e)</b> and bottom panel <b>(f</b> to <b>j)</b> display the low and high levels of population structure, represented by <i>Arabidopsis</i> and human populations, respectively. The dataset from <i>Arabidopsis</i> population consists of 1,178 individuals genotyped with 250,000 SNPs. The dataset from human population consists of 1,500 individuals genotyped with 500,000 SNPs. The population structures are displayed by the scatter plot on the first two principal components derived from 10% of SNPs sampled randomly from <i>Arabidopsis thaliana</i> <b>(a)</b> and human <b>(f)</b>, respectively. Additive genetic effects were simulated with 10 and 100 QTNs. The QTNs were randomly sampled from all the SNPs in each dataset. Residuals with normal distribution were added to the genetic effect to form phenotypes with heritability of 0.5. Power was examined under different levels of FDR and Type I error. A positive SNP is considered a true positive if a QTN is within a distance of 50,000 base pairs on either side, otherwise is considered a false positive. Power under different levels of FDR is displayed in subfigures <b>b, c, g,</b> and <b>h</b>. Power under different levels of Type I error is displayed in subfigures <b>d, e, i</b>, and <b>j</b>.</p

Conceptual development and procedure of FarmCPU.

<p>The proposed method, FarmCPU, was inspired by the method development demonstrated on the left panel <b>(a)</b>. These methods start with a naïve model (e.g. t-test) that tests marker effect, one at a time, i.e. i^th marker (s_i), on the phenotype (<b>y</b>) with a residual effect (<b>e</b>). Next, GLM controls false positives by fitting population structure (<b>Q</b>) as covariates to adjust the test on genetic markers indicated by the blue arrows. MLM fits both <b>Q</b> and kinship (<b>K</b>) as covariates. However, both <b>Q</b> and <b>K</b> remain constant for testing all the markers. Neither <b>Q</b> nor <b>K</b> receives adjustment from association tests on markers. MLMM add pseudo QTNs as additional covariates (<b>S</b>). These pseudo QTNs are estimated through a stepwise regression procedure. Consequently, these pseudo QTNs receive adjustment from association tests on markers as indicated by the red arrow. However, both <b>Q</b> and <b>K</b> remain constant for testing all the markers. Although similar to MLM, FaST-LMM-Select controls false positives by fitting <b>Q</b> and <b>K</b> as covariates; the <b>K</b> of FaST-LMM-Select is incorporated with association tests on markers as indicated by the red arrow. However, <b>Q</b> remains constant. FarmCPU completely removes the confounding between the testing marker and both <b>K</b> and <b>Q</b> by combining MLMM and FaST-LMM-Select, but allowing a fixed effect model and a random effect model to perform separately. The fixed effect model contains the testing marker and pseudo QTNs to control false positives. The pseudo QTNs are selected from associated markers and evaluated by the random effect model, with <b>K</b> defined by the pseudo QTNs. The fixed effect model and random effect model are used iteratively until a stage of convergence is reached, that is, when no new pseudo QTNs are added. The right panel <b>(b)</b> displays the fixed effect model above the dashed line and the random effect models below the dashed line. The t pseudo QTNs (<b>S</b>_<b>1</b> to <b>S</b>_<b>t</b>) are fitted as covariates to test markers one at a time, e.g., i^th marker (<b>s</b>_<b>i</b>) in the fixed model. As the pseudo QTNs are fitted as covariates for each marker, Not Available (NA) is assigned as the test statistic for all markers that are also pseudo QTNs—as the genetic marker is completely co-linear to the pseudo QTN marker. However, each pseudo QTN has a test statistic corresponding to every marker, creating a matrix (lightly shaded) with elements of P_ij, i = 1 to t and j = 1 to m. The most significant P value of each pseudo QTN (the vector on the right of shaded area) is used as the substitution for the NA of the corresponding marker. The pseudo QTNs are optimized by using the SUPER method in the random model to incorporate both test statistics from the fixed effect model and genetic map information in the genotype data. The random effects are the individuals’ genetic effects (<b>u</b>) with variance and covariance matrix, Var(<b>u</b>), defined by the Singular Value Decomposition (SVD) on the pseudo QTNs by using the FaST-LMM algorithm. The updated set of pseudo QTNs go back into the fixed model. The process continuously repeats until no more pseudo QTNs are added.</p

Reanalysis of 107 traits and power enrichment evaluation on 23 flowering time traits in <i>Arabidopsis thaliana</i>.

<p>Four methods were employed to reanalyze the 107 traits of 199 <i>Arabidopsis thaliana</i> samples genotyped at 250,000 SNPs <b>(a)</b>, including a naïve method (t-test), GLM, MLM, and FarmCPU. The first three PCs were included in the GLM and MLM to control population structure. FarmCPU did not use any PCs. The horizontal axis indicates the 107 traits grouped into four categories: resistance, developmental, ionomics, and flowering time. The vertical axis indicates the number of associated SNPs at three significance levels (0.01, 0.05 and 0.1) after Bonferroni multiple test corrections. The previous results were replicated by using the naïve and MLM methods. The naïve method, without any control on population structure and kinship, generates many associated SNPs. The associations due to genetic linkage to known genes are indistinguishable from the background noise. In contrast, the MLM method controls the inflation of P values well; however, the associations due to genetic linkage to known genes are also weakened and indistinguishable from the background. The GLM method generates results that are between the naïve method and the MLM method. Interestingly, for each flowering time trait, FarmCPU revealed multiple genetic loci. Enrichment analysis was performed to evaluate the four statistical methods <b>(b)</b> on the 23 flowering time traits by using flowering time genes. The random hits are expected to have an enrichment coefficient of 1. For the first hit, the enrichment coefficients are 2.4, 2.4, 3.8, and 8.9 for t-test, GLM, MLM, and FarmCPU, respectively. For the top ten hits, the enrichment coefficients are 1.7, 2.3, 2.8, and 4.0 for t-test, GLM, MLM, and FarmCPU, respectively.</p

LPS increased the levels of PLIN2 mRNA and protein in a dose and time dependent manner in BV2 microglia.

<p>(A and B): LPS stimulated PLIN2 mRNA and protein expression in a dose-dependent manner. Cells were incubated with vehicle or indicated concentrations of LPS for 24 h. (C and D): LPS enhanced PLIN2 mRNA and protein expression in a time-dependent manner. Cells were incubated with vehicle or LPS (500ng/mL) for 6, 12 or 24h. Ribosomal RNAs and GAPDH were used as the total RNA or protein loading control, respectively. The PLIN2 mRNA level in the control (no stimuli) was arbitrarily designated as 1 for comparison. Levels of PLIN2 protein were quantified by the NIH Image processing and analysis program. *P< 0.05 compared with LPS alone. ^#P< 0.05. Experiments were repeated 3 times and representative results are shown.</p

Computing time and memory usage of five software packages.

<p>Three statistical models were performed by the five packages: 1) GLM by PLINK; 2) MLMs by EMMAX, GenABEL, and MLMM; and 3) FarmCPU by FarmCPU. Computing time <b>(a)</b> and memory usage <b>(b)</b> in response to sample size are displayed. The analyses were performed on a laptop (Asus A53S) running a Linux system (Ubuntu 12.10, 64 bit) with a 4.0 Gb of Random-Access Memory (RAM) and an Inter duo Core i3-2310M processor at 2.1 GHz. One core was used for this test. All datasets had 60,000 markers, but response was measured as a function of sample size. The last data point indicates the maximum sample size each software package could process without freezing the computer, except for PLINK and FarmCPU. The limitations for these two software packages were not reached with the maximum sample size examined.</p

PYC suppressed LPS-induced NO production in BV2 microglia.

<p>Cells were incubated with PYC (10, 25, 50 μg/mL) or DMSO (vehicle) for 1 h before addition of LPS (500ng/mL) for 24h (A) or cells were incubated with PYC (50 μg/mL) or vehicle in indicated time before or after LPS stimulation for 24h (B). The culture supernatants were collected and analyzed for nitrite production using Griess reagent and a standard curve was created using NaNO₂ in culture medium. Data are represented as mean ± SD from at least 3 independent experiments. *P< 0.05 compared with LPS alone. ^#P< 0.05.</p