Additional file 1 of Integrated genomic analysis of biological gene sets with applications in lung cancer prognosis

Supplementary information. Figure A Internal power simulation across various disease-model settings for moderately sized gene sets Figure B Power simulations comparing variance-component-based total effect gene set testing procedures to existing methods under mixture disease-model settings Table C : Davies approximation p-values for gene sets signficantly associated with lung cancer in TCGA subjects after Bonferroni correction Table D Counts of overlapping significant BIOCARTA/ KEGG gene sets associated with one-year lung cancer survival status by iTEGS, iNOTE, and GSAA Table E Counts of overlapping significant lung cancer gene sets associated with pathological stage of tumor at diagnosis by iTEGS, iNOTE, and GSAA; Table E.2: Variance component-based total effect test p-values for lung cancer gene sets significantly associated with pathological stage of tumor after Bonferroni correction. (PDF 2410 kb

Additional file 2 of Integrated genomic analysis of biological gene sets with applications in lung cancer prognosis

iNote installable R package. (TAR 319 kb

Additional file 1: of Discordant anti-mĂźllerian hormone (AMH) and follicle stimulating hormone (FSH) among women undergoing in vitro fertilization (IVF): which one is the better predictor for live birth?

Figure S1. Estimated generalized additive mixed models (GAMMs) on different ages without adjusting for centers. A) AMH, and B) FSH. Figure S2. Joint model of AMH and FSH on predicting live birth rates without adjusting for centers. A) 30 year old, B) 35 year old, C) 37 year old, and D) 40 year old. Figure S3. Estimated generalized additive mixed models (GAMMs) on age and BMI. A) AMH, and B) FSH. Figure S4. Joint effect of AMH and FSH on predicting live birth rates for patients with four combination of age and BMI. Figure S5. Estimated generalized additive mixed models (GAMMs) using only first cycle of each patient. A) AMH, and B) FSH. Figure S6. Joint effect model of AMH and FSH on predicting live birth rate using only first cycle of each patient. A) 30 year old, B) 35 year old, C) 37 year old, and D) 40 year old. (DOCX 999 kb

Epigenome-wide profiling of DNA methylation in paired samples of adipose tissue and blood

<p>Many epigenetic association studies have attempted to identify DNA methylation markers in blood that are able to mirror those in target tissues. Although some have suggested potential utility of surrogate epigenetic markers in blood, few studies have collected data to directly compare DNA methylation across tissues from the same individuals. Here, epigenomic data were collected from adipose tissue and blood in 143 subjects using Illumina HumanMethylation450 BeadChip array. The top axis of epigenome-wide variation differentiates adipose tissue from blood, which is confirmed internally using cross-validation and externally with independent data from the two tissues. We identified 1,285 discordant genes and 1,961 concordant genes between blood and adipose tissue. RNA expression data of the two classes of genes show consistent patterns with those observed in DNA methylation. The discordant genes are enriched in biological functions related to immune response, leukocyte activation or differentiation, and blood coagulation. We distinguish the CpG-specific correlation from the within-subject correlation and emphasize that the magnitude of within-subject correlation does not guarantee the utility of surrogate epigenetic markers. The study reinforces the critical role of DNA methylation in regulating gene expression and cellular phenotypes across tissues, and highlights the caveats of using methylation markers in blood to mirror the corresponding profile in the target tissue.</p

CPEB controls the synthesis of Stat3 and PTEN.

<p>(A and B) 3′ UTR sequences of Stat3 and PTEN from human, mouse and cow. The nucleotides in bold represent putative CPEs. (C and D) Western blots of Stat3 and PTEN following CPEB depletion in HepG2 cells. In panels C-H, tubulin served as a negative or input control. (E and F) Quasi-quantitative RT-PCR for Stat3 and PTEN RNAs following CPEB depletion. (G and H) HepG2 cells depleted of CPEB were pulse labeled with ³⁵S-methionine for 15 min followed by Stat3, PTEN and tubulin (as a control) immunoprecipitation and SDS-PAGE analysis. These same proteins were also analyzed by western blots. (I) Representation of Renilla and firefly luciferase RNAs that were electroporated into HepG2 cells. Renilla luciferase RNA, which contained an irrelevant 3′ UTR, served as a normalization control. Firefly luciferase contained the Stat3 or PTEN 3′ UTRs as noted in panels A and B; in some cases, the CPEs in these 3′ UTRs were mutated. (J and K) The firefly and Renilla RNAs noted above were electroporated into HepG2 cells, some of which were depleted of CPEB. Firefly luciferase was normalized to the Renilla luciferase transfection control; luciferase activity derived from all RNAs was then made relative to the control shRNA. The Stat3 and PTEN data were analyzed with ANOVA; p values were 0.009 and 0.005, respectively. The asterisk refers to statistical significance (p<0.05). Data are represented as mean +/− SEM. The firefly and Renilla luciferase RNAs were also analyzed for relative stability by quasi-quantitative RT-PCR; all the RNAs had similar stabilities. At least 3 animals were used for each experiment.</p

Dramatic and widespread changes in insulin signaling molecules in CPEB knockout mice.

<p>(A) Western blot analysis and quantification of IRS1, IRS2, PTEN, PDK1, phospho-Stat3 (S727), total Stat3, Socs3, and tubulin as a loading control from WT and CPEB knockout liver, fat, and muscle. (B) Quantitative RT-PCR analysis of mRNAs encoding IRS1, IRS2, PTEN, PDK1, Stat3, and Socs3 mRNAs from WT and <i>Cpeb1</i> KO liver. Data are represented as mean +/− SEM. At least 3 animals per group were used for the experiment. Asterisks refer to statistical significance at the p<0.05 (*) or p<0.01 (**) levels (Student's t test).</p

CPEB mediates insulin signaling in the liver.

<p>(A) Western blot and quantification of total and phospho-Akt (serine 473 and threonine 308) from liver of WT and <i>Cpeb1</i> KO mice, some of which were injected with insulin. The pAkt (Ser473) and pAkt (Thr308) data were analyzed with ANOVA with (p<0.05, *; p<0.01, **). Data are represented as mean +/− SEM. In this and all panels, at least 3 animals per group were used for the experiment. (B,C) Phospho-Akt (threonine 308) in CPEB KO fat and muscle, respectively. Analysis as in panel A. (D) Examination of insulin signaling molecules in WT and CPEB KO liver. Analysis as in panel A.</p

Proposed model for CPEB-dependent regulation of the insulin-signaling ribonome regulation.

<p>The dark gray boxes refer to mRNAs that contain conserved CPEs and can potentially be regulated by CPEB. The light gray boxes refer to mRNAs that contain conserved CPEs but because they are not co-immunoprecipitated with CPEB, are probably not regulated by this protein in the current settings. The striped boxes refer to mRNAs that contain conserved CPEs and are regulated by CPEB. Insulin mRNA does not contain a CPE.</p

<i>Cpeb1</i> KO mice are insulin-resistant.

<p>(A) WT and KO mice were fed a normal chow diet and then examined for glucose tolerance test, serum insulin levels, and insulin tolerance. ANOVA values are as indicated in the figure. (B) Measurements for lean mass, fat mass, and total body mass of WT and <i>Cpeb1</i> KO mice fed a high fat diet. (C) Animals fed a high fat diet were subjected to euglycemic clamp analysis that determined glucose infusion rate (GIR), glucose turnover, whole body glycolysis, glycogen synthesis, hepatic glucose production (HGP), and liver insulin action (the ratio of basal to clamped HGP). The HGP data were analyzed with ANOVA with a value of 0.006. The asterisks in this panel as well as panel D refer to statistical significance (p<0.05). (D) Following the euglycemic clamp, liver proteins from WT and KO animals were probed on western blots for total and phospho-Akt (S473 and T308). The pAkt (Ser473) and pAkt (Thr308) data were analyzed with ANOVA with suggestive values of 0.01999 and 0.08335 values respectively. Data are represented as mean +/− SEM. At least 3 animals per group were used for the Western blots and at least 6 animals per group were used to measure the physiological parameters.</p