Dinu, Valentin

Obesity is a persistent public health issue, often resulting in metabolic complications such as insulin resistance (IR). The secretion of pro-inflammatory cytokines from adipose tissue (AT) is increased during obesity, contributing to the impairment of systemic insulin sensitivity. While interventions in animal models have shown that reducing inflammation restores insulin sensitivity, human studies reducing systemic inflammation have produced inconsistent results. We recently demonstrated that three months of high-dose (4 g/daily) ω-3PUFA (fish oil, FO) supplementation improved insulin sensitivity, and decreased systemic and AT inflammation in individuals with obesity (BMI ≥ 30 kg/m2). Given recent studies highlighting the involvement of microRNA (miRNA) in inflammatory cytokine production, we investigated the effect of ω-3PUFA supplementation on AT miRNA expression in this cohort. AT biopsies were collected before and after ω-3PUFA supplementation. miRNA was processed on the Affymetrix miRNA 4.0 GeneChip and analyzed using existing inflammatory gene sets sourced from MSigDB. Unbiased, differentially expressed miRNA analysis identified miR-4498 and miR-5689 as significantly increased after three months of ω-3PUFA supplementation. Real-time PCR confirmed bioinformatic analysis findings. Our study reports the modulation of miRNA in AT, reductions in systemic and AT markers of inflammation, and the improvement of IR post ω-3PUFA supplementation. Further research is needed to elucidate the link between miR-4498, miR-5689, and whole-body insulin sensitivity.

Objectives: Gain insights into the pathophysiology of idiopathic subglottic stenosis (iSGS) by investigating differences in transcriptome of subglottic mucosal tissue between patients with iSGS and controls, and between tracheal and subglottic tissue within patients. Methods: RNA sequencing was conducted on biopsied mucosal samples collected from subglottic and tracheal (in-patient control) regions in iSGS patients, and from subglottis in controls. The gene expression differences were validated on a protein level by (1) staining the tissue samples obtained from a second cohort of patients and controls; and (2) in vitro functional assays using primary subglottic epithelial cells from both iSGS patients and healthy donors. Results: We found 7 upregulated genes in the subglottic region of iSGS patients relative to both the tracheal mucosa and subglottic region of controls. A gene ontology enrichment analysis found that the epithelial cell differentiation and cornification pathways are significant, involving specifically 3 of the genes: involucrin ( IVL), small proline rich protein 1B ( SPRR1B), and keratin 16 ( KRT16). Involvement of these pathways suggests squamous metaplasia of the epithelium. Histological analyses of epithelium in subglottic mucosal biopsies revealed squamous metaplasia in 41% of the samples from iSGS patients and in 25% from controls. Immunohistochemical evaluation of the samples presented with squamous epithelium revealed increased expression of the protein encoded by SPRR1B, hyperproliferative basal cells, shedding of apical layers, and accompanying lesions in iSGS compared to CTRL. Cultured primary subglottic epithelial cells from iSGS patients had higher proliferation rates compared to healthy donors and squamous metaplastic differentiation formed thinner epithelia with increased expression proteins encoded by INV, SPRR1B, and KRT16, suggesting intrinsic dysfunction of basal cells in iSGS. Conclusions: Abnormal squamous differentiation of epithelial cells may contribute to the pathogenesis of iSGS. Patients having metaplastic epithelial phenotype may be sensitive to drugs that reverse it to a normal phenotype.

AbstractUnderstanding intratumor heterogeneity is critical for studying tumorigenesis and designing personalized treatments. To decompose the mixed cell population in a tumor, subclones are inferred computationally based on variant allele frequency (VAF) from bulk sequencing data. In this study, we showed that sequencing depth, mean VAF, and variance of VAF of a subclone are confounded. Without considering this effect, current methods require deep-sequencing data (>300× depth) to reliably infer subclones. Here, we present a novel algorithm that incorporates depth-variance and mean-variance dependencies in a clustering error model and successfully identifies subclones in tumors sequenced at depths of as low as 30×. We implemented the algorithm as a model-based adaptive grouping of subclones (MAGOS) method. Analyses of computer simulated data and empirical sequencing data showed that MAGOS outperformed existing methods on minimum sequencing depth, decomposition accuracy, and computation efficiency. The most prominent improvements were observed in analyzing tumors sequenced at depths between 30× and 200×, whereas the performance was comparable between MAGOS and existing methods on deeply sequenced tumors. MAGOS supports analysis of single-nucleotide variants and copy number variants from a single sample or multiple samples of a tumor. We applied MAGOS to whole-exome data of late-stage liver cancers and discovered that high subclone count in a tumor was a significant risk factor of poor prognosis. Lastly, our analysis suggested that sequencing multiple samples of the same tumor at standard depth is more cost-effective and robust for subclone characterization than deep sequencing a single sample. MAGOS is available at github (https://github.com/liliulab/magos).

Background. Our publication of the new pathways of topological rank analysis (PoTRA) algorithm demonstrated a novel approach for using the Google Search PageRank algorithm to analyze gene expression networks to identify biological pathways significantly disrupted in hepatocellular carcinoma. In order to apply the PoTRA algorithm to analyze other cancer gene expression data sets, of various sizes and normal:tumor ratio composition, two important questions must be answered: 1. What is the optimal normal:tumor sample ratio?; and 2. What is the minimum number of samples that should be used for PoTRA analysis? To address these questions, the average standard deviation (SD) in PoTRA-ranked mRNA mediated dysregulated pathways was studied using randomly sampled data sets with various normal:tumor ratios and sizes drawn from the TCGA Breast Invasive Carcinoma (TCGA-BRCA) project. Methods. To identify the optimal normal:tumor sample ratios, the SD analysis used random combinations of 1:N unbalanced normal:tumor data sets: (1:1, 1:2, 1:3, 1:5, 1:7, 1:9). To identify the minimum sample size, random resampling of normal and tumor samples of various sizes are used: (3 vs 3), (5 vs 5), (10 vs 10), (25 vs 25), (50 vs 50), (75 vs 75), (100 vs 100), and (113 vs 113). Results. This analysis suggests that the 1:1 ratio achieves the lowest average rank variation and that the minimum sample size of 50 normal and 50 tumor samples reaches a steady state in the average rank variation. Conclusion. In conclusion, future applications of the PoTRA algorithm to analyze gene expression data sets such as TCGA should use balanced data sets as well as a minimum sample size of 50 for both normal and tumor to ensure the most robust performance.

Abstract Motivation: The Biological Reference Repository (BioR) is a toolkit for annotating variants. BioR stores public and user-specific annotation sources in indexed JSON-encoded flat files (catalogs). The BioR toolkit provides the functionality to combine and retrieve annotation from these catalogs via the command-line interface. Several catalogs from commonly used annotation sources and instructions for creating user-specific catalogs are provided. Commands from the toolkit can be combined with other UNIX commands for advanced annotation processing. We also provide instructions for the development of custom annotation pipelines. Availability and implementation: The package is implemented in Java and makes use of external tools written in Java and Perl. The toolkit can be executed on Mac OS X 10.5 and above or any Linux distribution. The BioR application, quickstart, and user guide documents and many biological examples are available at http://bioinformaticstools.mayo.edu . Contact: Kocher.JeanPierre@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Motivation: Modern techniques have produced many sequence annotation databases and protein structure portals, but these Web resources are rarely integrated in ways that permit straightforward exploration of protein functional residues and their co-localization. Results: We have created the AMASS database, which maps 1D sequence annotation databases to 3D protein structures with an intuitive visualization interface. Our platform also provides an analysis service that screens mass spectrometry sequence data for post-translational modifications that reside in functionally relevant locations within protein structures. The system is built on the premise that functional residues such as active sites, cancer mutations and post-translational modifications within proteins may co-localize and share common functions. Availability and implementation: AMASS database is implemented with Biopython and Apache as a freely available Web server at amass-db.org. Contact: clinton.mielke@gmail.com