MODBASE, a database of annotated comparative protein structure models and associated resources.

MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/)

Integrated Analyses of microRNAs Demonstrate Their Widespread Influence on Gene Expression in High-Grade Serous Ovarian Carcinoma

The Cancer Genome Atlas (TCGA) Network recently comprehensively catalogued the molecular aberrations in 487 high-grade serous ovarian cancers, with much remaining to be elucidated regarding the microRNAs (miRNAs). Here, using TCGA ovarian data, we surveyed the miRNAs, in the context of their predicted gene targets.Integration of miRNA and gene patterns yielded evidence that proximal pairs of miRNAs are processed from polycistronic primary transcripts, and that intronic miRNAs and their host gene mRNAs derive from common transcripts. Patterns of miRNA expression revealed multiple tumor subtypes and a set of 34 miRNAs predictive of overall patient survival. In a global analysis, miRNA:mRNA pairs anti-correlated in expression across tumors showed a higher frequency of in silico predicted target sites in the mRNA 3'-untranslated region (with less frequency observed for coding sequence and 5'-untranslated regions). The miR-29 family and predicted target genes were among the most strongly anti-correlated miRNA:mRNA pairs; over-expression of miR-29a in vitro repressed several anti-correlated genes (including DNMT3A and DNMT3B) and substantially decreased ovarian cancer cell viability.This study establishes miRNAs as having a widespread impact on gene expression programs in ovarian cancer, further strengthening our understanding of miRNA biology as it applies to human cancer. As with gene transcripts, miRNAs exhibit high diversity reflecting the genomic heterogeneity within a clinically homogeneous disease population. Putative miRNA:mRNA interactions, as identified using integrative analysis, can be validated. TCGA data are a valuable resource for the identification of novel tumor suppressive miRNAs in ovarian as well as other cancers

Time to Recurrence and Survival in Serous Ovarian Tumors Predicted from Integrated Genomic Profiles

Serous ovarian cancer (SeOvCa) is an aggressive disease with differential and often inadequate therapeutic outcome after standard treatment. The Cancer Genome Atlas (TCGA) has provided rich molecular and genetic profiles from hundreds of primary surgical samples. These profiles confirm mutations of TP53 in ∼100% of patients and an extraordinarily complex profile of DNA copy number changes with considerable patient-to-patient diversity. This raises the joint challenge of exploiting all new available datasets and reducing their confounding complexity for the purpose of predicting clinical outcomes and identifying disease relevant pathway alterations. We therefore set out to use multi-data type genomic profiles (mRNA, DNA methylation, DNA copy-number alteration and microRNA) available from TCGA to identify prognostic signatures for the prediction of progression-free survival (PFS) and overall survival (OS). prediction algorithm and applied it to two datasets integrated from the four genomic data types. We (1) selected features through cross-validation; (2) generated a prognostic index for patient risk stratification; and (3) directly predicted continuous clinical outcome measures, that is, the time to recurrence and survival time. We used Kaplan-Meier p-values, hazard ratios (HR), and concordance probability estimates (CPE) to assess prediction performance, comparing separate and integrated datasets. Data integration resulted in the best PFS signature (withheld data: p-value = 0.008; HR = 2.83; CPE = 0.72).We provide a prediction tool that inputs genomic profiles of primary surgical samples and generates patient-specific predictions for the time to recurrence and survival, along with outcome risk predictions. Using integrated genomic profiles resulted in information gain for prediction of outcomes. Pathway analysis provided potential insights into functional changes affecting disease progression. The prognostic signatures, if prospectively validated, may be useful for interpreting therapeutic outcomes for clinical trials that aim to improve the therapy for SeOvCa patients

MODBASE, a database of annotated comparative protein structure models and associated resources.

MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/)

Layilin Anchors Regulatory T Cells in Skin.

Regulatory T cells (Tregs) reside in nonlymphoid tissues where they carry out unique functions. The molecular mechanisms responsible for Treg accumulation and maintenance in these tissues are relatively unknown. Using an unbiased discovery approach, we identified LAYN (layilin), a C-type lectin-like receptor, to be preferentially and highly expressed on a subset of activated Tregs in healthy and diseased human skin. Expression of layilin on Tregs was induced by TCR-mediated activation in the presence of IL-2 or TGF-β. Mice with a conditional deletion of layilin in Tregs had reduced accumulation of these cells in tumors. However, these animals somewhat paradoxically had enhanced immune regulation in the tumor microenvironment, resulting in increased tumor growth. Mechanistically, layilin expression on Tregs had a minimal effect on their activation and suppressive capacity in vitro. However, expression of this molecule resulted in a cumulative anchoring effect on Treg dynamic motility in vivo. Taken together, our results suggest a model whereby layilin facilitates Treg adhesion in skin and, in doing so, limits their suppressive capacity. These findings uncover a unique mechanism whereby reduced Treg motility acts to limit immune regulation in nonlymphoid organs and may help guide strategies to exploit this phenomenon for therapeutic benefit

MiRNA correlates of molecular subtype, with associated gene expression patterns.

<p>Unsupervised clustering of miRNA expression data had identified three subtypes (C1–C3) of high-grade serous ovarian tumors. For miRNAs and genes differing significantly between the groups (t-test <i>P</i><0.01, fold change>1.5, any subtype compared to the other tumors), predicted miRNA:mRNA functional pairs were defined, based on both anti-correlation in expression and predicted miRNA targeting interaction (both miRanda and TargetScan). For each miRNA:mRNA group (e.g. miRNA high/gene low in C1 versus other tumors), expression patterns are represented as heat maps (rows, miRNAs or gene transcripts; columns, profiled samples).</p

Gene transcripts with miRNA 7mer in the 3′-UTR tend to be anti-correlated with expression of the corresponding miRNA.

<p>Analogous to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0034546#pone-0034546-g004" target="_blank">Figure 4A</a>, scatter plot showing mean correlation versus significance of enrichment for predicted target interactions (enrichment expressed as a Fisher's exact z-score), when separately considering the following potential interactions: 7mer seed sequence in 3′-UTR (black dotted line), 7mer seed sequence in 5′-UTR (black dashed line), 7mer seed sequence in coding sequence region (“cds,” gray dotted line), miRanda prediction (black solid line), TargetScan prediction (gray solid line). Plot uses bins of 10000 miRNA:mRNA pairs (total number of pairs represented: 191 miRNAs X 8547 genes). Fisher's exact z-score of +/−2.57 corresponds to significant enrichment (nominal <i>P</i><0.01) for predicted targets within miRNA:mRNA pairs.</p

Correlations between miRNAs and genes in ovarian cancer.

<p>(<b>A</b>) MiRNAs and their predicted gene targets tend to be anti-correlated within ovarian tumors. Scatter plot showing mean correlation and fraction of predicted target interactions (miRanda-mirSVR score>0.1), using bins of 10000 miRNA:mRNA pairs (total number of pairs represented: 191 miRNAs X 8547 genes). Dashed line corresponds to chance expected baseline fraction (13.2%) of predicted target interactions. Correlations were computed using both Pearson's (gray line) and a simple linear regression model to account for ‘noise’ due to DNA copy alterations (black line). (<b>B</b>) Hierarchical clustering matrix (with Pearson's correlation coefficient as distance metric, Ward's Linkage) of correlation coefficients for all miRNA:mRNA pairs having a strong negative correlation (regression coefficient smaller than −7.0, only negative correlations represented). For each gene cluster, enriched gene classes are indicated. *, significant anti-enrichment (<i>P</i><0.001, one-sided Spearman's rank, TargetScan or miRanda) for predicted targets within miRNA:mRNA correlations.</p

In ovarian tumors, expression patterns of miRNAs (miRs) are influenced by both copy number alteration (CNA) and genomic location.

<p>(<b>A</b>) MiRNAs correlated with CNA. Left plot shows hemizygous loss (N< = 1 copies) versus homozygous deletion (N = 0) for miRNA precursors. Correlation (Pearson's) between precursor DNA copy number and mature miRNA expression is indicated by symbol size, and only miRNAs with a minimum correlation (R>0.3) are included here; cytoband regions in bold represent focal CNA events. Similarly, right plot shows gain (N> = 3) versus amplification (N> = 4) for miRNAs having R>0.3 for precursor copy versus mature expression. (<b>B</b>) miRNAs are frequently coexpressed with neighboring miRNAs. Plot shows relationship between the distance separating miRNA loci and their coordinate expression. Each miRNA was paired with each of the others lying in the same orientation on the same chromosome. For each pair, the distance between the two loci (right axis) was ranked, and the correlation coefficient for their expression was plotted according this rank (left axis). (<b>C</b>) miRNAs are frequently coexpressed with host genes. For each of 188 miRNA-host gene pairs (same orientation), the correlation was computed both between miRNA and host gene expression; pair orderings are the same (“X”, no corresponding mRNA or gene copy data; p-values by two-sided t-statistic).</p