The selected target genes and the related signature functions of the inferred onco-miR perturbed target networks.

a<p>The selected targets with high TargetScan scores and literature evidences (reported in at least 5 PubMed abstracts with key words “liver cancer” or “lung cancer”).</p>b<p>These functional terms (and the corresponding FDRs) are the selected top enriched GO terms of the signature annotated by DAVID web tool.</p

The flowchart of miRHiC.

<p>In the first step, the differentially expressed genes were clustered as hierarchical gene co-expression signatures; then, the most significant enrichment of the miRNA target gene set was found across the hierarchical signatures; and finally, a permutation test was used to estimate the empirical p-value of the enrichment.</p

Inferring the Perturbed microRNA Regulatory Networks in Cancer Using Hierarchical Gene Co-Expression Signatures

<div><p>MicroRNAs (miRNAs), a class of endogenous small regulatory RNAs, play important roles in many biological and physiological processes. The perturbations of some miRNAs, which are usually called as onco-microRNAs (onco-miRs), are significantly associated with multiple stages of cancer. Although hundreds of miRNAs have been discovered, the perturbed miRNA regulatory networks and their functions are still poorly understood in cancer. Analyzing the expression patterns of miRNA target genes is a very useful strategy to infer the perturbed miRNA networks. However, due to the complexity of cancer transcriptome, current methods often encounter low sensitivity and report few onco-miR candidates. Here, we developed a new method, named miRHiC (enrichment analysis of miRNA targets in Hierarchical gene Co-expression signatures), to infer the perturbed miRNA regulatory networks by using the hierarchical co-expression signatures in large-scale cancer gene expression datasets. The method can infer onco-miR candidates and their target networks which are only linked to sub-clusters of the differentially expressed genes at fine scales of the co-expression hierarchy. On two real datasets of lung cancer and hepatocellular cancer, miRHiC uncovered several known onco-miRs and their target genes (such as miR-26, miR-29, miR-124, miR-125 and miR-200) and also identified many new candidates (such as miR-149, which is inferred in both types of cancers). Using hierarchical gene co-expression signatures, miRHiC can greatly increase the sensitivity for inferring the perturbed miRNA regulatory networks in cancer. All Perl scripts of miRHiC and the detailed documents are freely available on the web at <a href="http://bioinfo.au.tsinghua.edu.cn/member/jgu/miRHiC/" target="_blank">http://bioinfo.au.tsinghua.edu.cn/member/jgu/miRHiC/</a>.</p></div

The perturbed miR-149 sub-networks shared by LUC and HCC.

<p>The average log-transformed fold changes of the shared target genes are also shown in the below table.</p

The perturbed miRNA regulatory networks in the two types of cancers inferred by miRHiC.

<p>A) is for lung cancer and B) for hepatocellular cancer. The circle nodes represent the gene co-expression signatures (ClusterID:Size). The diamond nodes represent the inferred onco-miRs. The numbers on the edges represent the sizes of the miRNA target genes overlapped with the corresponding gene co-expression signatures.</p

The onco-miRs inferred by miRHiC with q-value <0.1.

a<p>The q-value is due to empirical p-value <0.0001.</p>b<p>(BS) labels mean that the miRNAs are inferred in more than 50% bootstrapping experiments with q-value <0.1.</p

Surface and Structure Characteristics, Self-Assembling, and Solvent Compatibility of Holocellulose Nanofibrils

Rice straw holocellulose was TEMPO-oxidized and mechanically defibrillated to produce holocellulose nanofibrils (HCNFs) at 33.7% yield (based on original rice straw mass), 4.6% higher yield than cellulose nanofibril (CNF) generated by the same process from pure rice straw cellulose. HCNFs were similar in lateral dimensions (2.92 nm wide, 1.36 nm thick) as CNF, but longer, less surface oxidized (69 vs 85%), and negatively charged (0.80 vs 1.23 mmol/g). HCNFs also showed higher affinity to hydrophobic surfaces than CNFs while still attracted to hydrophilic surfaces. By omitting hemicellulose/silica dissolution step, the two-step 2:1 toluene/ethanol extraction and acidified NaClO₂ (1.4%, pH 3–4, 70 °C, 6 h) delignification process for holocellulose was more streamlined than that of pure cellulose, while the resulting amphiphilic HCNFs were more hydrophobic and self-assembled into much finer nanofibers, presenting unique characteristics for new potential applications

The numbers of differentially methylated probes identified by FastDMA and IMA.

<p>The thresholds for the two softwares to identify differentially methylated probes are all set as pvalue <0.01 and FDR <0.05. Each cell shows the number of differentially methylated probes identified in that specified condition. Overlap: number of probes found by both FastDMA and IMA; Genic: genic region; Island: CpG island region; Promoter: promoter region. FastDMA and IMA find more than 98% probes the same.</p

Several examples for the genomic regions identified as DMRs by FastDMA.

<p>All genomic regions shown in this figure are hypermethylated in cancer and lie in the 5′ region of a nearby gene. The green (normal) and red (tumor) vertical bars represent the methylation levels of the probes located in each region. The cyan horizontal bars show the differentially methylated regions identified by FastDMA. A, B are from BRCA dataset; C, D are from LUAD dataset; E, F are from PRAD dataset.</p

Volcano plots of single probe analysis.

<p>Panel A, B, and C shows the result on BRCA, LUAD and PRAD, respectively. Each dot in the plot represents a beadchip probe. The vertical axis indicates the log-transformed pvalue of that probe calculated by ANCOVA and the horizontal axis indicates the mean difference between the methylation levels in tumor and normal samples (tumor - normal).</p