PI3K/Akt signaling and differential gene expression analysis.

<p>(A) Schematic diagram for the regulation of PI3K-Akt signaling pathway. (B) Overview of temporal differential gene expression in rhesus macaques infected with A/Anhui/2/2005 (H5N1) at different time points. A color scale indicating expression levels for the heat map is shown at the top right. Genes exhibited up-regulated expression pattern over time are highlighted in red.</p

Maximum-likelihood phylogeny of the HA gene.

<p>The identical phylogeny with virus names is provided in Figure S1D. Coloured boxes adjacent to branch tips show the group classification of each gene segment of HPAI H5N1. Reassortant subgroups (R1, R2, R3) are indicated with square brackets. Six isolates sampled from southeast China are designated as blue circles. The asterisks denote the phylogenetic position of eleven recombinant viruses (CK/HuB/wj/97, CK/HB/108/02, CK/HB/718/01, DK/ZJ/bj/02, CK/GS/44/04, ML/GX/wt/04, CK/JX/25/04, DK/HN/8/08, DK/EC/108/08, CK/GZ/7/08, CK/SD/A-1/09).</p

Estimation of selection pressure and sequence variability for H5N1 influenza virus.

a<p>FEL, iFEL, SLAC significance levels are indicated by <i>P</i> values and site under positive selection (<i>P</i><0.05) are detected by at least one method.</p>b<p>The dN/dS ratios are estimated using the FEL method available in the Hyphy package.</p>c<p>The variability of each segment is calculated at the amino acid level.</p

Bootscan analysis and GARD estimates of concatenated influenza virus genomes.

<p>CK/JX/25/04, CK/SD/A-1/09, CK/GZ/7/08 and DK/EC/108/08 were used as query sequences in (A), (B), (C) and (D), respectively. Schematic diagram of concatenated influenza virus genomes was showed at the top. Consensus sequences representing viral groups, window size of 1,000 bp and step size of 40 bp, were used for bootscan analysis.</p

Sequence variation along the non-structure 1 protein (NS1) sequence.

<p>(A) Number of polymorphisms (variants occurring in more than 1% sequences examined) at each position. (B) Schematic representation of the NS1 protein of H5N1, together with its known interactors. (C) Variation within RNA binding domain (RBD) and effector domain (ED) of NS1. Position containing 2 polymorphisms are coloured green, 3 polymorphisms are coloured cornflower blue and 4 or above are coloured hot pink and red, respectively. Residue positions have been imposed upon the 3D structure of NS1 from the Protein Data Bank (3F5T). (D) Panel shows the distribution of non-synonymous (dN) and synonymous (dS) substitution (the number of substitutions per site) along the NS sequence.</p

Summary of the mosaic sequence identified in this study.

<p>NS: No significant P-value was recorded for this recombination event.</p

<i>Agaricus bisporus</i>-derived β-glucan enter macrophages and adipocytes by CD36 receptor

β-glucans are a heterogeneous group of natural polysaccharides. They are ubiquitously found in bacterial or fungal cell walls, cereals, seaweed, and mushrooms. The beneficial role of β-glucan in tumor, insulin resistance, dyslipidemia, hypertension, and obesity is being continuously documented. Ample evidence showed that β-glucan could act on several receptors, such as Dectin, complement receptor (CR3), TLR-2, 4, 6 and scavenger. Based on the above, we wanted to explore whether agaricus bisporus-derived β-glucan acted on these receptors on Raw 264.7 macrophages and 3T3-L1 adipocytes.</p

Integrative comparison of the genomic and transcriptomic landscape between prostate cancer patients of predominantly African or European genetic ancestry

Men of predominantly African Ancestry (AA) have higher prostate cancer (CaP) incidence and worse survival than men of predominantly European Ancestry (EA). While socioeconomic factors drive this disparity, genomic factors may also contribute to differences in the incidence and mortality rates. To compare the prevalence of prostate tumor genomic alterations and transcriptomic profiles by patient genetic ancestry, we evaluated genomic profiles from The Cancer Genome Atlas (TCGA) CaP cohort (n = 498). Patient global and local genetic ancestry were estimated by computational algorithms using genotyping data; 414 (83.1%) were EA, 61 (12.2%) were AA, 11 (2.2%) were East Asian Ancestry (EAA), 10 (2.0%) were Native American (NA), and 2 (0.4%) were other ancestry. Genetic ancestry was highly concordant with self-identified race/ethnicity. Subsequent analyses were limited to 61 AA and 414 EA cases. Significant differences were observed by ancestry in the frequency of SPOP mutations (20.3% AA vs. 10.0% EA; p = 5.6×10−03), TMPRSS2-ERG fusions (29.3% AA vs. 39.6% EA; p = 4.4×10−02), and PTEN deletions/losses (11.5% AA vs. 30.2% EA; p = 3.5×10−03). Differentially expressed genes (DEGs) between AAs and EAs showed significant enrichment for prostate eQTL target genes (p = 8.09×10−48). Enrichment of highly expressed DEGs for immune-related pathways was observed in AAs, and for PTEN/PI3K signaling in EAs. Nearly one-third of DEGs (31.3%) were long non-coding RNAs (DE-lncRNAs). The proportion of DE-lncRNAs with higher expression in AAs greatly exceeded that with lower expression in AAs (p = 1.2×10−125). Both ChIP-seq and RNA-seq data suggested a stronger regulatory role for AR signaling pathways in DE-lncRNAs vs. non-DE-lncRNAs. CaP-related oncogenic lncRNAs, such as PVT1, PCAT1 and PCAT10/CTBP1-AS, were found to be more highly expressed in AAs. We report substantial heterogeneity in the prostate tumor genome and transcriptome between EA and AA. These differences may be biological contributors to racial disparities in CaP incidence and outcomes.</div

Characterization of the DE-lncRNAs <i>PVT1</i>, <i>PCAT1</i>, and <i>PCAT10/CTBP1-AS</i> in TCGA sample.

(A) Circle plot shows the percentage of 1,868 DE-lncRNAs that have been characterized based on related publications in PubMed (left). Red and gray indicate characterized and uncharacterized DE-lncRNAs, respectively. Characterized DE-lncRNAs are defined as the lncRNAs having at least one related publication. The characterized DE-lncRNAs were further ranked into three groups, with a darker shade of red indicating a greater number of related publications. Pie charts show the percentages of the characterized DE-lncRNAs with cancer-related (light blue) or CaP-related (dark blue) results among each group (right). (B) Crude and age- and Gleason score-adjusted expression levels of PVT1, PCAT1, and PCAT10 in AA and EA patients. (C) Expression levels of PVT1, PCAT1, and PCAT10 in prostate tumors and tumor-adjacent normal prostate tissues. (D) Percentile ranks of enrichment in AA men for PVT1, PCAT1, and PCAT10 across 33 TCGA cancer types. Color of bars reflects the fold change of expression (AA vs EA) ranks in a specific cancer type relative to others. High relative expression of PCAT1 and PCAT10 among AA men is specific to CaPs, while high relative expression of PVT1 occurs across cancer types. (E) AR binding signals at genomic loci surrounding or containing PCAT1 (left) and PCAT10 (right) from AR ChIP-seq analysis of two CaP cell lines (LNCaP, VCaP) treated with synthetic androgen agonists (R1881, DHT). (F) Expression levels of PVT1 and PCAT1 by copy number status (left). Number of men and frequency of SCNAs at 8q24.21 within categories of Gleason score and genetic ancestry (right).</p

Differences in the prostate tumor transcriptome by genetic ancestry.

(A) Heatmap of expression of 220 genes differentially expressed between genetic ancestry groups with fold-change >2. Red denotes a gene more highly expressed in AA men, and blue more highly expressed in EA men. Genetic ancestry is shown using EIGENSTRAT classification (top) and STRUCTURE-estimated composition (bottom). (B) Circle plots showing (from inner to outer): 1) distribution of types of differentially expressed genes (DEGs); 2) fold-change in gene expression comparing AA to EA men in tumor tissue; 3) fold-change in gene expression comparing AA to EA men in tumor-adjacent normal tissue; 4) fold-change in gene expression comparing tumor to tumor-adjacent normal tissue; 5) fold-change in gene expression comparing AA to EA men for protein-coding genes evaluated in Wallace et al microarray dataset. (C) Enrichment in DEGs for genes regulated by eQTLs in normal prostate tissues (eGenes) and genes identified in prior GWAS or TWAS of CaP risk. The proportion of eGenes was higher among the DEGs than non-DEGs and increased with more stringent cutoffs in the FDR (upper plot). The prevalence odds of eGenes were higher among the DEGs, while the prevalence odds of GWAS and TWAS genes did not differ between DEGs and non-DEGs (lower plot). (D) Gene sets identified through gene set enrichment analysis that were significantly activated (upper plot; n = 18) and repressed (lower plot; n = 13) in tumors of AA men (FDR corrected pet al. Many gene sets pertaining to immune-related signaling were activated in AA tumors, while sets related to PTEN/PI3K were repressed in AA tumors. (E) Network plot of the 18 gene sets significantly activated in AA tumors. Nodes are grouped by gene set function. Node size is proportional to the number of genes in the gene set and node shading reflects the Normalized Enrichment Score (NES). An edge indicates there are shared genes between gene sets and edge shading reflects the number of shared genes. (F) Enrichment plots of the Wallace Prostate Cancer Race (Up), T Cytotoxic Cell Surface Molecules, Wallace Prostate Cancer Race (Down), and PTEN dependent cell cycle and apoptosis gene sets.</p