Search results for: deciphering

Authors: Rajeswari Raguraman, Sowmya Parameswaran, Krishnakumar Subramanian, Jagat Kanwar, Rupinder Kanwar

Abstract:

Background: Tumor microenvironment has been implicated in several cancers to regulate cell growth, invasion and metastasis culminating in outcome of therapy. Tumor stroma consists of multiple cell types that are in constant cross-talk with the tumor cells to favour a pro-tumorigenic environment. Not much is known about the existence of tumor microenvironment in the pediatric intraocular malignancy, Retinoblastoma (RB). In the present study, we aim to understand the multiple stromal cellular subtypes and tumor stromal interactions expressed in RB tumors. Materials and Methods: Immunohistochemistry for stromal cell markers CD31, CD68, alpha-smooth muscle (α-SMA), vimentin and glial fibrillary acidic protein (GFAP) was performed on formalin fixed paraffin embedded tissues sections of RB (n=12). The differential expression of stromal target molecules; fibroblast activation protein (FAP), tenascin-C (TNC), osteopontin (SPP1), bone marrow stromal antigen 2 (BST2), stromal derived factor 2 and 4 (SDF2 and SDF4) in primary RB tumors (n=20) and normal retina (n=5) was studied by quantitative reverse transcriptase polymerase chain reaction (qRT-PCR) and Western blotting. The differential expression was correlated with the histopathological features of RB. The interaction between RB cell lines (Weri-Rb-1, NCC-RbC-51) and Bone marrow stromal cells (BMSC) was also studied using direct co-culture and indirect co-culture methods. The functional effect of the co-culture methods on the RB cells was evaluated by invasion and proliferation assays. Global gene expression was studied by using Affymetrix 3’ IVT microarray. Pathway prediction was performed using KEGG and the key molecules were validated using qRT-PCR. Results: The immunohistochemistry revealed the presence of several stromal cell types such as endothelial cells (CD31+;Vim+/-); macrophages (CD68+;Vim+/-); Fibroblasts (Vim+; CD31-;CD68- );myofibroblasts (α-SMA+/ Vim+) and invading retinal astrocytes/ differentiated retinal glia (GFAP+; Vim+). A characteristic distribution of these stromal cell types was observed in the tumor microenvironment, with endothelial cells predominantly seen in blood vessels and macrophages near actively proliferating tumor or necrotic areas. Retinal astrocytes and glia were predominant near the optic nerve regions in invasive tumors with sparse distribution in tumor foci. Fibroblasts were widely distributed with rare evidence of myofibroblasts in the tumor. Both gene and protein expression revealed statistically significant (P<0.05) up-regulation of FAP, TNC and BST2 in primary RB tumors compared to the normal retina. Co-culture of BMSC with RB cells promoted invasion and proliferation of RB cells in direct and indirect contact methods respectively. Direct co-culture of RB cell lines with BMSC resulted in gene expression changes in ECM-receptor interaction, focal adhesion, IL-8 and TGF-β signaling pathways associated with cancer. In contrast, various metabolic pathways such a glucose, fructose and amino acid metabolism were significantly altered under the indirect co-culture condition. Conclusion: The study suggests that the close interaction between RB cells and the stroma might be involved in RB tumor invasion and progression which is likely to be mediated by ECM-receptor interactions and secretory factors. Targeting the tumor stroma would be an attractive option for redesigning treatment strategies for RB.

Keywords: gene expression profiles, retinoblastoma, stromal cells, tumor microenvironment

Procedia PDF Downloads 384

Authors: Danyi Wang, Andrew Schriefer, Dennis O'Rourke, Brajendra Kumar, Yang Liu, Fei Zhong, Juergen Scheuenpflug, Zheng Feng

Abstract:

Purpose: It has been recognized that the microbiome plays critical roles in disease pathogenesis, including cancer, autoimmune disease, and multiple sclerosis. To develop a clinical-grade assay for exploring microbiome-derived clinical biomarkers across disease areas, a two-phase approach is implemented. 1) Identification of the optimal sample preparation reagents using pre-mixed bacteria and healthy donor stool samples coupled with proprietary Sigma-Aldrich® bioinformatics solution. 2) Exploratory analysis of patient samples for enabling precision medicine. Study Procedure: In phase 1 study, we first compared the 16S sequencing results of two ATCC® microbiome standards (MSA 2002 and MSA 2003) across five different extraction kits (Kit A, B, C, D & E). Both microbiome standards samples were extracted in triplicate across all extraction kits. Following isolation, DNA quantity was determined by Qubit assay. DNA quality was assessed to determine purity and to confirm extracted DNA is of high molecular weight. Bacterial 16S ribosomal ribonucleic acid (rRNA) amplicons were generated via amplification of the V3/V4 hypervariable region of the 16S rRNA. Sequencing was performed using a 2x300 bp paired-end configuration on the Illumina MiSeq. Fastq files were analyzed using the Sigma-Aldrich® Microbiome Platform. The Microbiome Platform is a cloud-based service that offers best-in-class 16S-seq and WGS analysis pipelines and databases. The Platform and its methods have been extensively benchmarked using microbiome standards generated internally by MilliporeSigma and other external providers. Data Summary: The DNA yield using the extraction kit D and E is below the limit of detection (100 pg/µl) of Qubit assay as both extraction kits are intended for samples with low bacterial counts. The pre-mixed bacterial pellets at high concentrations with an input of 2 x106 cells for MSA-2002 and 1 x106 cells from MSA-2003 were not compatible with the kits. Among the remaining 3 extraction kits, kit A produced the greatest yield whereas kit B provided the least yield (Kit-A/MSA-2002: 174.25 ± 34.98; Kit-A/MSA-2003: 179.89 ± 30.18; Kit-B/MSA-2002: 27.86 ± 9.35; Kit-B/MSA-2003: 23.14 ± 6.39; Kit-C/MSA-2002: 55.19 ± 10.18; Kit-C/MSA-2003: 35.80 ± 11.41 (Mean ± SD)). Also, kit A produced the greatest yield, whereas kit B provided the least yield. The PCoA 3D visualization of the Weighted Unifrac beta diversity shows that kits A and C cluster closely together while kit B appears as an outlier. The kit A sequencing samples cluster more closely together than both the other kits. The taxonomic profiles of kit B have lower recall when compared to the known mixture profiles indicating that kit B was inefficient at detecting some of the bacteria. Conclusion: Our data demonstrated that the DNA extraction method impacts DNA concentration, purity, and microbial communities detected by next-generation sequencing analysis. Further microbiome analysis performance comparison of using healthy stool samples is underway; also, colorectal cancer patients' samples will be acquired for further explore the clinical utilities. Collectively, our comprehensive qualification approach, including the evaluation of optimal DNA extraction conditions, the inclusion of positive controls, and the implementation of a robust qualified bioinformatics pipeline, assures accurate characterization of the microbiota in a complex matrix for deciphering the deep biology and enabling precision medicine.

Keywords: 16S rRNA sequencing, analytical validation, bioinformatics pipeline, metagenomics

Procedia PDF Downloads 170