scholarly journals High-throughput mRNA sequencing of stromal cells from endometriomas and endometrium

Reproduction ◽  
2017 ◽  
Vol 154 (1) ◽  
pp. 93-100 ◽  
Author(s):  
Kadri Rekker ◽  
Merli Saare ◽  
Elo Eriste ◽  
Tõnis Tasa ◽  
Viktorija Kukuškina ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
Complement System ◽  
Stromal Cells ◽  
Cell Types ◽  
Coagulation Cascade ◽  
Expression Levels ◽  
Mrna Sequencing ◽  
Standard Design ◽  
Gene Expression Levels

The aetiology of endometriosis is still unclear and to find mechanisms behind the disease development, it is important to study each cell type from endometrium and ectopic lesions independently. The objective of this study was to uncover complete mRNA profiles in uncultured stromal cells from paired samples of endometriomas and eutopic endometrium. High-throughput mRNA sequencing revealed over 1300 dysregulated genes in stromal cells from ectopic lesions, including several novel genes in the context of endometriosis. Functional annotation analysis of differentially expressed genes highlighted pathways related to cell adhesion, extracellular matrix–receptor interaction and complement and coagulation cascade. Most importantly, we found a simultaneous upregulation of complement system components and inhibitors, indicating major imbalances in complement regulation in ectopic stromal cells. We also performed in vitro experiments to evaluate the effect of endometriosis patients’ peritoneal fluid (PF) on complement system gene expression levels, but no significant impact of PF on C3, CD55 and CFH levels was observed. In conclusion, the use of isolated stromal cells enables to determine gene expression levels without the background interference of other cell types. In the future, a new standard design studying all cell types from endometriotic lesions separately should be applied to reveal novel mechanisms behind endometriosis pathogenesis.

scholarly journals Optimized gene expression from bacterial chromosome by high-throughput integration and screening

2021 ◽  
Vol 7 (7) ◽  
pp. eabe1767
Author(s):  
Tatyana E. Saleski ◽  
Meng Ting Chung ◽  
David N. Carruthers ◽  
Azzaya Khasbaatar ◽  
Katsuo Kurabayashi ◽  
...  
Keyword(s):  
Gene Expression ◽  
High Throughput ◽  
High Throughput Screening ◽  
Efficient Production ◽  
Expression Levels ◽  
Pathway Gene ◽  
Pathway Expression ◽  
Wide Range ◽  
Cell To Cell Variability ◽  
Gene Expression Levels

Chromosomal integration of recombinant genes is desirable compared with expression from plasmids due to increased stability, reduced cell-to-cell variability, and elimination of the need for antibiotics for plasmid maintenance. Here, we present a new approach for tuning pathway gene expression levels via random integration and high-throughput screening. We demonstrate multiplexed gene integration and expression-level optimization for isobutanol production in Escherichia coli. The integrated strains could, with far lower expression levels than plasmid-based expression, produce high titers (10.0 ± 0.9 g/liter isobutanol in 48 hours) and yields (69% of the theoretical maximum). Close examination of pathway expression in the top-performing, as well as other isolates, reveals the complexity of cellular metabolism and regulation, underscoring the need for precise optimization while integrating pathway genes into the chromosome. We expect this method for pathway integration and optimization can be readily extended to a wide range of pathways and chassis to create robust and efficient production strains.

Immunofocus-PD-L1 high throughput quantitative next generation sequencing assay.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. e13521-e13521
Author(s):  
Gareth Haydn Williams ◽  
Robert Paul Thatcher ◽  
Tiffany Eira Haddow ◽  
Keeda-Marie Hardisty ◽  
Marco Loddo
Keyword(s):  
Gene Expression ◽  
Next Generation Sequencing ◽  
High Throughput ◽  
Immune Cell ◽  
Next Generation ◽  
Cut Points ◽  
Expression Levels ◽  
Sequencing Platform ◽  
Generation Sequencing ◽  
Gene Expression Levels

e13521 Background: Immunohistochemical (IHC) assays are presently used as the gold standard predictive tests for immunotherapy but are compromised due to a number of potential variables. Comparative studies have demonstrated differing levels of PD-L1 staining between assays which appears independent of the antibody binding epitope. Secondly, inter-reader reliability even between expert pathologists is problematic particularly for assessment of PD-L1 positive immune cell populations. Methods: To improve predictive testing for anti PD-L1/PD1 immunotherapies we have developed and validated a Next Generation Sequencing Platform, Immunofocus, able to perform high-throughput quantitative PD-L1 gene expression levels in routine diagnostic PWET biopsies. We applied Immunofocus to a cohort of 130 NSCLCs and compared PD-L1 gene expression levels with PD-L1 IHC scores generated using the VENTANA PD-L1 (SP142) Assay. The PD-L1 IHC assessment was carried out double blinded by an independent laboratory. PD-L1 IHC scores were calculated using an algorithm combining tumour proportion score (TPS) with a PD-L1 positive immune cell (IC) score and immune cell area. Results: An exceptionally high degree of correlation was observed between the NGS PD-L1 levels with the combined PD-L1 IHC scores (P < 0.001). Therapeutic cut points for NGS PD-L1 levels were identified corresponding to PD-L1 IHC defined clinical cut points. Notably, ~20% of patients with negative PD-L1 IHC scores showed high NGS PD-L1 expression levels. We hypothesize that these cases represent false negatives and identify a cohort of patients who have shown significant response rates to anti-PD-L1/PD-directed immunotherapies. Conclusions: The Immunofocus NGS PD-L1 assay has potential to greatly improve patient selection for immunotherapy by removing the IHC assay variables and inter-reader variability which compromise current PD-L1 IHC tests while also providing standardized high throughput in the clinical setting. Immunofocus is able to integrate gene expression with somatic mutation analysis allowing capture of networks regulating the immune-checkpoint including for example adaptive and innate resistance pathways, JAK1/2 pathways, differential MHC expression, TEFF gene signature, neoantigen surrogates such as DDR defects and TMB. The integration of NGS PD-L1 expression with other putative biomarkers of response is presently ongoing to further improve prediction of response.

scholarly journals Cellular sources of interleukin-6 and associations with clinical phenotypes and outcomes in pulmonary arterial hypertension

2020 ◽  
Vol 55 (4) ◽  
pp. 1901761 ◽  
Author(s):  
Catherine E. Simpson ◽  
Jenny Y. Chen ◽  
Rachel L. Damico ◽  
Paul M. Hassoun ◽  
Lisa J. Martin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
Severe Disease ◽  
Connective Tissue Diseases ◽  
Cell Types ◽  
Expression Levels ◽  
Clinical Phenotypes ◽  
Pulmonary Arterial ◽  
Gene Expression Levels

The pro-inflammatory cytokine interleukin (IL)-6 has been associated with outcomes in small pulmonary arterial hypertension (PAH) cohorts composed largely of patients with severe idiopathic PAH (IPAH). It is unclear whether IL-6 is a marker of critical illness or a mechanistic biomarker of pulmonary vascular remodelling. We hypothesised that IL-6 is produced by pulmonary vascular cells and sought to explore IL-6 associations with phenotypes and outcomes across diverse subtypes in a large PAH cohort.IL-6 protein and gene expression levels were measured in cultured pulmonary artery smooth muscle cells (PASMCs) and endothelial cells (PAECs) from PAH patients and healthy controls. Serum IL-6 was measured in 2017 well-characterised PAH subjects representing each PAH subgroup. Relationships between IL-6 levels, clinical variables, and mortality were analysed using regression models.Significantly higher IL-6 protein and gene expression levels were produced by PASMCs than by PAECs in PAH (p<0.001), while there was no difference in IL-6 between cell types in controls. Serum IL-6 was highest in PAH related to portal hypertension and connective tissue diseases (CTD-PAH). In multivariable modelling, serum IL-6 was associated with survival in the overall cohort (hazard ratio 1.22, 95% CI 1.08–1.38; p<0.01) and in IPAH, but not in CTD-PAH. IL-6 remained associated with survival in low-risk subgroups of subjects with mild disease.IL-6 is released from PASMCs, and circulating IL-6 is associated with specific clinical phenotypes and outcomes in various PAH subgroups, including subjects with less severe disease. IL-6 is a mechanistic biomarker, and thus a potential therapeutic target, in certain PAH subgroups.

scholarly journals Comparability of reference-based and reference-free transcriptome analysis approaches at the gene expression level

2021 ◽  
Vol 22 (S11) ◽  
Author(s):  
Sung-Gwon Lee ◽  
Dokyun Na ◽  
Chungoo Park
Keyword(s):  
Gene Expression ◽  
De Novo ◽  
Expression Profiles ◽  
Gene Families ◽  
Cell Types ◽  
Model Organisms ◽  
Expression Levels ◽  
Large Gene ◽  
Gene Expression Quantification ◽  
Gene Expression Levels

Abstract Background Lately, high-throughput RNA sequencing has been extensively used to elucidate the transcriptome landscape and dynamics of cell types of different species. In particular, for most non-model organisms lacking complete reference genomes with high-quality annotation of genetic information, reference-free (RF) de novo transcriptome analyses, rather than reference-based (RB) approaches, are widely used, and RF analyses have substantially contributed toward understanding the mechanisms regulating key biological processes and functions. To date, numerous bioinformatics studies have been conducted for assessing the workflow, production rate, and completeness of transcriptome assemblies within and between RF and RB datasets. However, the degree of consistency and variability of results obtained by analyzing gene expression levels through these two different approaches have not been adequately documented. Results In the present study, we evaluated the differences in expression profiles obtained with RF and RB approaches and revealed that the former tends to be satisfactorily replaced by the latter with respect to transcriptome repertoires, as well as from a gene expression quantification perspective. In addition, we urge cautious interpretation of these findings. Several genes that are lowly expressed, have long coding sequences, or belong to large gene families must be validated carefully, whenever gene expression levels are calculated using the RF method. Conclusions Our empirical results indicate important contributions toward addressing transcriptome-related biological questions in non-model organisms.

scholarly journals A Sparse and Low-Rank Regression Model for Identifying the Relationships Between DNA Methylation and Gene Expression Levels in Gastric Cancer and the Prediction of Prognosis

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 854
Author(s):  
Yishu Wang ◽  
Lingyun Xu ◽  
Dongmei Ai
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Dna Methylation ◽  
Regression Model ◽  
The Cancer Genome Atlas ◽  
Low Rank ◽  
Expression Levels ◽  
Rank Regression ◽  
Prediction Of Prognosis ◽  
Gene Expression Levels

DNA methylation is an important regulator of gene expression that can influence tumor heterogeneity and shows weak and varying expression levels among different genes. Gastric cancer (GC) is a highly heterogeneous cancer of the digestive system with a high mortality rate worldwide. The heterogeneous subtypes of GC lead to different prognoses. In this study, we explored the relationships between DNA methylation and gene expression levels by introducing a sparse low-rank regression model based on a GC dataset with 375 tumor samples and 32 normal samples from The Cancer Genome Atlas database. Differences in the DNA methylation levels and sites were found to be associated with differences in the expressed genes related to GC development. Overall, 29 methylation-driven genes were found to be related to the GC subtypes, and in the prognostic model, we explored five prognoses related to the methylation sites. Finally, based on a low-rank matrix, seven subgroups were identified with different methylation statuses. These specific classifications based on DNA methylation levels may help to account for heterogeneity and aid in personalized treatments.

Sign in / Sign up

Export Citation Format

Share Document