The set of multi-analyte prognostic modules identified by the MAPIT algorithm.

<p>Ten eModules (<i>A–J</i>) and three mModules (<i>K–M</i>) are shown. Node colour represents gene expression change of LTS patients compared to STS patients. Red, down-regulation; Green, up-regulation. Shade is proportional to the −log (p-value) of the change. Node border colour represents DNA methylation change of LTS patients compared to STS patients. Red, hypomethylation; Green, hypermethylation. Shade is proportional to the −log (p-value) of the change. Diamond nodes, genes reported to bear somatic mutations in GBM patients. Rectangular nodes, genes identified as GBM prognostic markers in either <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Colman1" target="_blank">[4]</a> or <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Noushmehr1" target="_blank">[6]</a>. Hexagonal nodes, genes both reported to bear somatic mutations and identified as prognostic markers in either <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Colman1" target="_blank">[4]</a> or <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Noushmehr1" target="_blank">[6]</a>. Purple star: genes located in CNV regions identified in GBM patients <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-The1" target="_blank">[37]</a>. Edge, protein-protein interaction. Edge width is proportional to the combined significance of expression changes of the two involved nodes (see Methods for details).</p

Multi-Analyte Network Markers for Tumor Prognosis

<div><p>Deregulation of gene expression, a hallmark of cancer, is caused by both genetic and epigenetic mechanisms. The rapid accumulation of epigenome maps of various cancers suggests a new avenue of research, namely integrating epigenomic data with other types of omic data for cancer diagnosis, prognosis, and biomarker discovery. We introduce the MAPIT algorithm (<u>M</u>ulti <u>A</u>nalyte <u>P</u>athway <u>I</u>nference <u>T</u>ool), to enable principled integration of epigenomic, transcriptomic, and protein interactome data. As a proof-of-principle, we apply MAPIT to glioblastoma multiforme (GBM), the most common and aggressive form of brain tumor. Few predictive markers were reported for the prognosis of GBM patients. By integrating mRNA transcriptome, promoter DNA methylome and protein-protein physical interactome, we find ten expression- and three methylation-based network markers, involving 118 genes. When tested on additional GBM patient samples, the prognostic accuracy of the multi-analyte network markers (73.5%) is 9.7% and 8.6% higher than previous prognostic signatures built on gene expression or DNA methylation alone. Our results highlight the critical role of two novel pathways in the prognosis of GBM patients, small GTPase-mediated protein trafficking and ubiquitination-dependent protein degradation. A better understanding of these two pathways could lead to personalized therapies for subgroups of GBM patients. Our study demonstrates that integrating epigenomic, transcriptomic, and interactomic data can improve the accuracy network-based prognosis markers and lead to novel mechanistic understanding of cancer.</p> </div

Performance comparison of gene-expression-based classifiers for GBM patient prognosis.

<p><b>A</b>) Prognostic accuracy of various marker sets. Classification accuracy is defined as the ratio of the number of correctly classified patients to the total number of patients tested. Expression data of 42 GBM patients was used to derive the eModule set. Top-gene set is top 156 (size-matched to the number of genes in the eModule set) most significantly differentially expressed genes between LTS and STS patients. 38-gene set, a set of 38 discriminative genes reported in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Colman1" target="_blank">[4]</a>. Two hundred thirty seven additional GBM patients from TCGA were used for testing classification accuracy. Error bar is the standard deviation based on 100 leave-one-out cross validations. <b>B</b>) Performance of eModule set and 38-gene set using three external microarray data <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Freije1" target="_blank">[28]</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052973#pone.0052973-Phillips1" target="_blank">[30]</a> from which the 38-gene signature was derived. Numbers in parenthesis indicate number of LTS and STS in each data set, respectively. P-values are based on t-tests comparing the average classification accuracy of the eModule-based classifier and those of other classifiers.</p

Ultrathin oxide controlled photocurrent generation through a metal–insulator– semiconductor heterojunction

Recent advances in nanoscale lasers, amplifiers, and nonlinear optical converters have demonstrated the unprecedented potential of metal–insulator–semiconductor (MIS) structures as a versatile platform to realize integrated photonics at the nanoscale. While the electric field enhancement and confinement have been discussed intensively in MIS based plasmonic structures, little is known about the carrier redistribution across the heterojunction and photocurrent transport through the oxide. Herein, we investigate the photo-generated charge transport through a single CdSe microbelt-Al2O3-Ag heterojunction with oxide thickness varying from 3 nm to 5 nm. Combining photocurrent measurements with finite element simulations on electron (hole) redistribution across the heterojunction, we are able to explain the loss compensation observed in hybrid plasmonic waveguides at substantially reduced pump intensity based on MIS geometry compared to its photonic counterpart. We also demonstrate that the MIS configuration offers a low-dark-current photodetection scheme, which can be further exploited for photodetection applications.</p

Revealing Pathway Dynamics in Heart Diseases by Analyzing Multiple Differential Networks

<div><p>Development of heart diseases is driven by dynamic changes in both the activity and connectivity of gene pathways. Understanding these dynamic events is critical for understanding pathogenic mechanisms and development of effective treatment. Currently, there is a lack of computational methods that enable analysis of multiple gene networks, each of which exhibits differential activity compared to the network of the baseline/healthy condition. We describe the <i>i</i>MDM algorithm to identify both unique and shared gene modules across multiple differential co-expression networks, termed M-DMs (<u>m</u>ultiple <u>d</u>ifferential <u>m</u>odules). We applied <i>i</i>MDM to a time-course RNA-Seq dataset generated using a murine heart failure model generated on two genotypes. We showed that <i>i</i>MDM achieves higher accuracy in inferring gene modules compared to using single or multiple co-expression networks. We found that condition-specific M-DMs exhibit differential activities, mediate different biological processes, and are enriched for genes with known cardiovascular phenotypes. By analyzing M-DMs that are present in multiple conditions, we revealed dynamic changes in pathway activity and connectivity across heart failure conditions. We further showed that module dynamics were correlated with the dynamics of disease phenotypes during the development of heart failure. Thus, pathway dynamics is a powerful measure for understanding pathogenesis. <i>i</i>MDM provides a principled way to dissect the dynamics of gene pathways and its relationship to the dynamics of disease phenotype. With the exponential growth of omics data, our method can aid in generating systems-level insights into disease progression.</p></div

Identifying the DNA-binding motif of MarR.

<p>(<b>A</b>) DNaseI footprinting assays were carried out on the coding and non-coding strands. Protection of the promoter DNA by MarR against DNaseI digestion was tested by increasing the amount of MarR (0–0.6 µM). The ladders are shown and the corresponding nucleotide sequence is listed (lanes 2–4). The protected regions on the coding and non-coding strands are indicated. (<b>B</b>) Sequence and structural characteristics of the promoter DNA region protected by MarR. The regions protected by MarR are underlined. The 21-bp sequences containing the inverted repeats (IR) separated by 1 bp are indicated by a pair of arrows. The translation start codon of MarR is indicated in bold. (<b>C</b>) EMSA assays for the DNA-binding activity of MarR on DNA substrates with wildtype IR sequence and IR-deleted mutant sequences. DNA substrates were co-incubated with 0.2–0.6 µM of the MarR protein. Cold DNA substrate containing IR motif (p4), but not unrelated substrate (p3) which does not contain IR motif, could competitively inhibit the binding of MarR to the labeled DNA substrate (p4*).</p

Construction of the MarR knockout strain of <i>M. smegmatis</i> and southern blot assays.

<p>(<b>A</b>) Schematic representation of the recombination strategy for removing <i>marR</i> from the genome of <i>M. smegmatis</i>. (<b>B</b>) A map of the recombinant vector pMindMs6508KO containing upstream and downstream sequences of <i>marR</i>, and the gene that confers resistance against hygromycin. (<b>C</b>) Schematic representation of the DNA fragments of the wildtype strain and the <i>marR</i> knockout strain treated with the restriction enzyme <i>SalI</i>. The probe is indicated with a black bar. (<b>D</b>) Southern blot assays. A 387 bp probe corresponding to the upstream sequences of <i>marR</i> in <i>M. smegmatis</i> was obtained by PCR and was labeled with digoxigenin dUTP (Boehringer Mannheim Inc., Germany).</p

Assays for the effects of MarR on RIF resistance in <i>M. smegmatis</i>.

<p>Growth curves of the wild-ype, <i>marR</i>-overexpressed, deletion mutant and complementation strains were determined as described in Experimental Procedures. These mycobacterial strains were grown in 7H9 broth in the absence (<b>A</b>) and presence of 4 µg/ml RIF (<b>B</b>). Representative growth curves are shown.</p

Sequence alignment and domain analysis of Ms6508–Ms6510.

<p>(<b>A</b>)The conserved amino acids residues of MarR are highlighted. ST1710, a MarR family regulator in <i>Sulfolobus tokodaii</i>; MTH313, a MarR family regulator in <i>Methanobacterium thermoautotrophicum</i>; b1530, a MarR family regulator in <i>E. coli.</i> (B) Domain assays for MarR and Ms6510. Ms6509 encodes a multidrug ABC transporter ATP-binding family protein and Ms6510 encodes a multidrug ABC transporter family protein.</p

Performance comparison of the <i>i</i>MDM algorithm.

<p><i>i</i>MDM DCN, method using multiple differential co-expression networks; <i>i</i>MDM Co-expression, method using multiple co-expression networks but no differential gene expression information. A, Specificity of the algorithms. Gene modules found by each method were evaluated using a set of gold-standard pathway annotations. Specificity was defined as the fraction of predicted modules that significantly overlaps with reference pathways. B, Sensitivity of the algorithms. Sensitivity was defined as the fraction of reference pathways that significantly overlaps with predicted modules. Pathway overlap P-values were computed using the hypergeometric distribution. P-values for the difference in specificity and sensitivity were computed using Fisher’s exact test. C, Percentage of predicted modules that significantly overlapped with genes whose deletions lead to cardiovascular phenotypes. P-values for the difference in the percentage of overlapped modules was computed using Fisher’s exact test. All p-values were corrected for multiple testing using the method of Benjamin-Hochberg. *, <i>p-value</i> < 0.05.</p