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-ß. 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.

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/).

PIK3CA somatic mutations in breast cancer: Mechanistic insights from Langevin dynamics simulations.

Somatic mutations in PIK3CA (phosphatidylinositol-3 kinase, catalytic subunit, alpha isoform) are reported in breast and other human cancers to concentrate at hotspots within its kinase and helical domains. Most of these mutations cause kinase gain of function in vitro and are associated with oncogenicity in vivo. However, little is known about the mechanisms driving tumor development. We have performed computational structural studies on a homology model of wildtype PIK3CA plus recurrent H1047R, H1047L, and P539R mutations, located in the kinase and helical domains, respectively. The time evolution of the structures show that H1047R/L mutants exhibit a larger area of the catalytic cleft between the kinase N- and C-lobes compared with the wildtype that could facilitate the entrance of substrates. This larger area might yield enhanced substrate-to-product turnover associated with oncogenicity. In addition, the H1047R/L mutants display increased kinase activation loop mobility, compared with the wildtype. The P539R mutant forms more hydrogen bonds and salt-bridge interactions than the wildtype, properties that are associated with enhanced thermostability. Mutant-specific differences in the catalytic cleft and activation loop behavior suggest that structure-based mutant-specific inhibitors can be designed for PIK3CA-positive breast cancers.

POLIR: polarizable, flexible, transferable water potential optimized for IR spectroscopy.

A polarizable, flexible and transferable potential for water, POLIR, is presented. In addition to providing a good description of the usual structural and kinetic properties, POLIR correctly describes the vibrational frequencies, absolute infrared intensities, and HOH angle in clusters, liquid water, and ice, offering the possibility of a comprehensive classical theory of vibrational spectroscopy. The high degree of transferability suggests applications to solvation and to water that is confined, interfacial, and under the extreme conditions encountered in the geological and planetary sciences.

Classical molecular electrostatics: recognition of ligands in proteins and the vibrational Stark effect.

It is shown that classical electrostatics quantitatively describes both the binding of the diatomic ligands XO (X = C, N, O) to the heme group in myoglobin and the dependence of their vibrational frequencies upon an external field, the vibrational Stark effect. The key is a proper treatment of induced dipoles. The results suggest that ligand binding occurs via an "electrostatic bond", a generalization of the standard ionic bond to include induction, and, more generally, that classical electrostatics can replace quantum mechanics for a considerable simplification of some complex problems.

Differential antitumor activity of aflibercept and bevacizumab in patient-derived xenograft models of colorectal cancer.

The recombinant fusion protein aflibercept (ziv-aflibercept in the United States) binds VEGF-A, VEGF-B, and placental growth factor (PlGF). The monoclonal antibody bevacizumab binds VEGF-A. Recent studies hypothesized that dual targeting of VEGF/PlGF is more beneficial than targeting either ligand. We compared activity of aflibercept versus bevacizumab in 48 patient-derived xenograft (PDX) colorectal cancer models. Nude mice engrafted subcutaneously with PDX colorectal cancer tumors received biweekly aflibercept, bevacizumab, or vehicle injections. Differential activity between aflibercept and bevacizumab, determined by mouse (m), human (h), VEGF-A, and PlGF levels in untreated tumors, was measured. Aflibercept induced complete tumor stasis in 31 of 48 models and bevacizumab in 2 of 48. Based on statistical analysis, aflibercept was more active than bevacizumab in 39 of 48 models; in 9 of 39 of these models, bevacizumab was considered inactive. In 9 of 48 remaining models, aflibercept and bevacizumab had similar activity. Tumor levels of hVEGF-A (range 776-56,039 pg/mg total protein) were ∼16- to 1,777-fold greater than mVEGF-A (range 8-159 pg/mg total protein). Tumor levels of mPlGF (range 104-1,837 pg/mg total protein) were higher than hPlGF (range 0-543 pg/mg total protein) in 47 of 48 models. Tumor cells were the major source of VEGF; PlGF was primarily produced by tumor stroma. Because tumor levels of hVEGF-A were far greater than mVEGF-A, bevacizumab's inability to bind mVEGF-A is unlikely to explain higher and more consistent aflibercept activity. Neutralizing PlGF and VEGFR-1 activation may be a factor and should be investigated in future studies. In these colorectal cancer PDX models, aflibercept demonstrated greater antitumor activity than bevacizumab.

Integrated analyses of microRNAs demonstrate their widespread influence on gene expression in high-grade serous ovarian carcinoma.

BACKGROUND: 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. METHODS AND RESULTS: 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. CONCLUSIONS: 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.

BACKGROUND: 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). METHODOLOGY/PRINCIPAL FINDINGS: We implemented a multivariate Cox Lasso model and median time-to-event 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). CONCLUSIONS/SIGNIFICANCE: 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.

An integrated genomic analysis of human glioblastoma multiforme.

Glioblastoma multiforme (GBM) is the most common and lethal type of brain cancer. To identify the genetic alterations in GBMs, we sequenced 20,661 protein coding genes, determined the presence of amplifications and deletions using high-density oligonucleotide arrays, and performed gene expression analyses using next-generation sequencing technologies in 22 human tumor samples. This comprehensive analysis led to the discovery of a variety of genes that were not known to be altered in GBMs. Most notably, we found recurrent mutations in the active site of isocitrate dehydrogenase 1 (IDH1) in 12% of GBM patients. Mutations in IDH1 occurred in a large fraction of young patients and in most patients with secondary GBMs and were associated with an increase in overall survival. These studies demonstrate the value of unbiased genomic analyses in the characterization of human brain cancer and identify a potentially useful genetic alteration for the classification and targeted therapy of GBMs.

Core signaling pathways in human pancreatic cancers revealed by global genomic analyses.

There are currently few therapeutic options for patients with pancreatic cancer, and new insights into the pathogenesis of this lethal disease are urgently needed. Toward this end, we performed a comprehensive genetic analysis of 24 pancreatic cancers. We first determined the sequences of 23,219 transcripts, representing 20,661 protein-coding genes, in these samples. Then, we searched for homozygous deletions and amplifications in the tumor DNA by using microarrays containing probes for approximately 10(6) single-nucleotide polymorphisms. We found that pancreatic cancers contain an average of 63 genetic alterations, the majority of which are point mutations. These alterations defined a core set of 12 cellular signaling pathways and processes that were each genetically altered in 67 to 100% of the tumors. Analysis of these tumors' transcriptomes with next-generation sequencing-by-synthesis technologies provided independent evidence for the importance of these pathways and processes. Our data indicate that genetically altered core pathways and regulatory processes only become evident once the coding regions of the genome are analyzed in depth. Dysregulation of these core pathways and processes through mutation can explain the major features of pancreatic tumorigenesis.

Induction model for molecular electrostatics: application to the infrared spectroscopy of CO liquid.

Far-infrared intermolecular and midinfrared vibrational spectra of CO liquid have been calculated by Fourier transforming the quantum-corrected classical dipole correlation. The time dependence of the coordinates is determined from a standard nonpolarizable force field, and the dipole is determined from the coordinates with a "spectroscopic model" proposed herein. The model includes intramolecular induction and atomic charges, polarizabilities, and permanent dipoles. A good agreement with available experimental spectra is achieved. Our results demonstrate that the use of an anharmonic potential is necessary to reproduce the experimentally observed shift upon going from gas to liquid. The behavior of the simulated dipole time correlation functions suggests that CO liquid at 80 K exhibits aspects of both free rotation and solidlike caging. The proposition of some free rotation present in CO liquid supports Ewing's experimental hypothesis.