Gene expression profiling of the Notch-AhR-IL22 axis at homeostasis and in response to tissue injury

Notch and interleukin-22 (IL-22) signaling are known to regulate tissue homeostasis and respond to injury in humans and mice, and the induction of endogenous aryl hydrocarbon receptor (Ahr) ligands through Notch links the two pathways in a hierarchical fashion. However in adults, the species-, organ- and injury-specific gene expression of the Notch-AhR-IL22 axis components is unknown. We therefore performed gene expression profiling of DLL1, DLL3, DLL4, DLK1, DLK2, JAG1, JAG2, Notch1, Notch2, Notch3, Notch4, ADAM17/TNF-α ADAM metalloprotease converting enzyme (TACE), PSEN1, basigin (BSG)/CD147, RBP-J, HES1, HES5, HEY1, HEYL, AHR, ARNT, ARNT2, CYP1A1, CYP24A1, IL-22, IL22RA1, IL22RA2, IL10RB, and STAT3 under homeostatic conditions in ten mature murine and human organs. Additionally, the expression of these genes was assessed in murine models of acute sterile inflammation and progressive fibrosis. We show that there are organ-specific gene expression profiles of the Notch-AhR-IL22 axis in humans and mice. Although there is an overall interspecies congruency, specific differences between human and murine expression signatures do exist. In murine tissues with AHR/ARNT expression CYP1A1 and IL-22 were correlated with HES5 and HEYL expression, while in human tissues no such correlation was found. Notch and AhR signaling are involved in renal inflammation and fibrosis with specific gene expression changes in each model. Despite the presence of all Notch pathway molecules in the kidney and a model-specific induction of Notch ligands, IL-22 was only up-regulated in acute inflammation, but rapidly down-regulated during regeneration. This implies that for targeting injury responses, e.g. via IL-22, species-specific differences, injury type and time points have to be considered.

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Automated Sorting of Monocytes from Whole Blood for Reproducible Gene Expression Profiling.

Blood ◽

10.1182/blood.v110.11.3840.3840 ◽

2007 ◽

Vol 110 (11) ◽

pp. 3840-3840

Author(s):

Carsten Poggel ◽

Timo Adams ◽

Sabine Martin ◽

Carola Pickel ◽

Nicole Prahl ◽

...

Keyword(s):

Gene Expression ◽

Blood Cell ◽

Gene Expression Profiling ◽

Whole Blood ◽

Expression Profiling ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Cell Types ◽

Specific Gene

Abstract Microarray-based gene expression profiling has been used to develop clinically relevant molecular classifiers for many different diseases. Furthermore, it has been shown for various chronic diseases that specific gene expression patterns are reflected at the level of blood cells. However, blood is a complex tissue comprising numerous cell types. Therefore, the contribution of rare cell types to a whole blood expression profile might not be detected and a substantial proportion of what is usually reported as “up-regulation” or “down-regulation” might actually be the result of a shift in cell populations and not of a true regulatory process. In order to circumvent these problems, several techniques have been established to analyze purified subpopulations rather than whole blood samples. Previously, it has been shown, for example, that reproducible gene expression profiles can be generated by positive selection of blood cell subsets from PBMCs1. As the preparation of PBMCs by, for example, Ficoll is time-consuming, inconvenient, and not amenable to automation, we have set up a combined direct whole blood cell separation and gene expression profiling protocol. By using Whole Blood CD14 MicroBeads in combination with the autoMACS Pro™ Separator, the separation protocol generally allowed enrichment of monocytes from whole blood within 30 min with purities higher than 90%. In combination with the depletion of neutrophils, the major source of contaminating RNA, purities increased to over 95% for all tested blood donors. Monocytes included the CD14bright/CD16− as well as the CD14dim/CD16+ populations. To assess the reproducibility of gene expression profiles and the influence of several experimental parameters, monocytes were sorted from 5 ml whole blood. RNA was extracted and hybridized to microarrays and the Pearson correlation coefficients of pairwise comparisons were calculated. Technical repeats of monocyte analysis from blood donated at different days showed a higher correlation coefficient than whole blood RNA. Blood storage at room temperature resulted in a strong deregulation of many genes, whereas blood stored at 4°C showed minimal changes, which is in agreement with previous studies. Skipping the centrifugation step, which is used to remove unbound MicroBeads did not alter the gene expression profiles. Incubation of sorted cells in PrepProtect™ Stabilization Buffer showed no alteration of gene expression thus enabling the shipping of cells without liquid nitrogen. Monocytes play a crucial role in diseases like atherosclerosis. Our rapid and simple protocol for combined direct cell sorting from whole blood and gene expression profiling of monocytes might help to ease the discovery of new biomarkers and to screen and monitor patients. 1 Lyons et al., BMC Genomics (2007), 8:64.

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Faculty Opinions recommendation of Ketamine and Imipramine Reverse Transcriptional Signatures of Susceptibility and Induce Resilience-Specific Gene Expression Profiles.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726695455.793533096 ◽

2017 ◽

Author(s):

E Ronald de Kloet ◽

Lisa van Weert ◽

Onno Meijer

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Specific Gene Expression

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Effectiveness of Gene Expression Profiling for Response Prediction of Rectal Adenocarcinomas to Preoperative Chemoradiotherapy

Journal of Clinical Oncology ◽

10.1200/jco.2005.00.406 ◽

2005 ◽

Vol 23 (9) ◽

pp. 1826-1838 ◽

Cited By ~ 249

Author(s):

B. Michael Ghadimi ◽

Marian Grade ◽

Michael J. Difilippantonio ◽

Sudhir Varma ◽

Richard Simon ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Response Prediction ◽

Preoperative Chemoradiotherapy ◽

Response To Therapy ◽

Phase Iii ◽

Correct Prediction

Purpose There is a wide spectrum of tumor responsiveness of rectal adenocarcinomas to preoperative chemoradiotherapy ranging from complete response to complete resistance. This study aimed to investigate whether parallel gene expression profiling of the primary tumor can contribute to stratification of patients into groups of responders or nonresponders. Patients and Methods Pretherapeutic biopsies from 30 locally advanced rectal carcinomas were analyzed for gene expression signatures using microarrays. All patients were participants of a phase III clinical trial (CAO/ARO/AIO-94, German Rectal Cancer Trial) and were randomized to receive a preoperative combined-modality therapy including fluorouracil and radiation. Class comparison was used to identify a set of genes that were differentially expressed between responders and nonresponders as measured by T level downsizing and histopathologic tumor regression grading. Results In an initial set of 23 patients, responders and nonresponders showed significantly different expression levels for 54 genes (P < .001). The ability to predict response to therapy using gene expression profiles was rigorously evaluated using leave-one-out cross-validation. Tumor behavior was correctly predicted in 83% of patients (P = .02). Sensitivity (correct prediction of response) was 78%, and specificity (correct prediction of nonresponse) was 86%, with a positive and negative predictive value of 78% and 86%, respectively. Conclusion Our results suggest that pretherapeutic gene expression profiling may assist in response prediction of rectal adenocarcinomas to preoperative chemoradiotherapy. The implementation of gene expression profiles for treatment stratification and clinical management of cancer patients requires validation in large, independent studies, which are now warranted.

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Cell Type-specific Gene Expression Profiling in the Mouse Liver

Journal of Visualized Experiments ◽

10.3791/60242 ◽

2019 ◽

Author(s):

Amber W. Wang ◽

Adam M. Zahm ◽

Kirk J. Wangensteen

Keyword(s):

Gene Expression ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Mouse Liver ◽

Specific Gene ◽

Cell Type ◽

Specific Gene Expression ◽

Cell Type Specific

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Establishment of Cell Lines That Exhibit Correct Ontogenic Stage-Specific Gene Expression Profiles From Tissues of Yeast Artificial Chromosome Transgenic Mice Using Chemically Induced Growth Signals

YAC Protocols ◽

10.1385/1-59745-158-4:163 ◽

2006 ◽

pp. 163-174

Author(s):

C. Anthony Blau ◽

Kenneth R. Peterson

Keyword(s):

Gene Expression ◽

Transgenic Mice ◽

Cell Lines ◽

Yeast Artificial Chromosome ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Artificial Chromosome ◽

Specific Gene ◽

Specific Gene Expression ◽

Chemically Induced

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Region-specific gene expression profiles in the left atria of patients with valvular atrial fibrillation

Heart Rhythm ◽

10.1016/j.hrthm.2012.11.013 ◽

2013 ◽

Vol 10 (3) ◽

pp. 383-391 ◽

Cited By ~ 28

Author(s):

Yung-Hsin Yeh ◽

Chi-Tai Kuo ◽

Yun-Shien Lee ◽

Yuan-Min Lin ◽

Stanley Nattel ◽

...

Keyword(s):

Gene Expression ◽

Atrial Fibrillation ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Specific Gene ◽

Specific Gene Expression

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Gene Expression Profiling Reveals Fundamental Differences between Leukemic Stem Cells (Lin-CD34+CD38−) and Mature Blasts (Lin−CD34+CD38+) of Acute Myeloid Leukaemia (AML) Patients.

Blood ◽

10.1182/blood.v106.11.1377.1377 ◽

2005 ◽

Vol 106 (11) ◽

pp. 1377-1377

Author(s):

Kazem Zibara ◽

Daniel Pearce ◽

David Taussig ◽

Spyros Skoulakis ◽

Simon Tomlinson ◽

...

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Populations ◽

Leukemic Stem Cells ◽

Significant Differential Expression ◽

New Genes

Abstract The identification of LSC has important implications for future research as well as for the development of novel therapies. The phenotypic description of LSC now enables their purification and should facilitate the identification of genes that are preferentially expressed in these cells compared to normal HSC. However, gene-expression profiling is usually conducted on mononuclear cells of AML patients from either peripheral blood and/or bone marrow. These samples contain a mixture of blasts cells, normal hematopoietic cells and limited number of leukemic stem cells. Thus, this results in a composite profile that obscure differences between LSC and blasts cells with low proliferative potential. The aim of this study was to compare the gene expression profile of highly purified LSC versus leukemic blasts in order to identify genes that might have important roles in driving the leukemia. For this purpose, we analyzed the gene expression profiles of highly purified LSCs (Lin−CD34+CD38−) and more mature blast cells (Lin−CD34+CD38+) isolated from 7 adult AML patients. All samples were previously tested for the ability of the Lin−CD34+CD38− cells but not the Lin−CD34+CD38+ fraction to engraft using the non-obese diabetic/severe combined immuno-deficiency (NOD-SCID) repopulation assay. Affymetrix microarrays (U133A chip), containing 22,283 genes, were used for the analysis. Comparison of Lin-CD34+CD38- cell population to the Lin−CD34+CD38+ cell fraction showed 5421 genes to be expressed in both fractions. Comparative analysis of gene-expression profiles showed statistically significant differential expression of 133 genes between the 2 cell populations. Most of the genes were downregulated in the LSC-enriched fraction, compared to the more differentiated fraction. Gene ontology was used to determine the categories of the up-regulated transcripts. These transcripts, which are selectively expressed, include a number of known genes (e.g., receptors, signalling genes, proliferation and cell cycle genes and transcription factors). These genes play important roles in differentiation, self-renewal, migration and adhesion of HSCs. Among the genes showing the highest differences in expression levels were the following: ribonucleotide reductase M2 polypeptide, thymidylate synthetase, ZW10 interactor, cathepsin G, azurocidin 1, topoisomerase II, CDC20, nucleolar and spindle associated protein 1, Rac GTPase activating protein 1, leukocyte immunoglobulin-like receptor, proliferating cell nuclear antigen, myeloperoxidase, cyclin A1 (RRM2, TYMS, ZWINT, CTSG, AZU1, TOP2A, CDC20, NUSAP1, RACGAP1, LILRB2, PCNA, MPO, CCNA1). Some transcripts detected have not been implicated in HSC functions, and others have unknown function so far. This work identifies new genes that might play a role in leukemogenesis and cancer stem cells. It also leads to a better description and understanding of the molecular phenotypes of these 2 cell populations. Hence, in addition to being a more efficient way to further understand the biology of LSC, this should also provide a more efficient way of identifying new therapeutics and diagnostic targets.

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Association of specific gene expression profiles with a pathologic complete response to neoadjuvant therapy in esophageal adenocarcinoma.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.4_suppl.51 ◽

2017 ◽

Vol 35 (4_suppl) ◽

pp. 51-51

Author(s):

Patrick James McLaren ◽

Anthony P Barnes ◽

Willy Z Terrell ◽

Gina M. Vaccaro ◽

Jack Wiedrick ◽

...

Keyword(s):

Gene Expression ◽

Esophageal Cancer ◽

Neoadjuvant Therapy ◽

Esophageal Adenocarcinoma ◽

Expression Profiles ◽

Pathologic Complete Response ◽

Gene Expression Profiles ◽

Complete Response ◽

Specific Gene ◽

Specific Gene Expression

51 Background: Predicting prognosis in esophageal cancer remains an unrealized goal despite studies linking constellations of genes to therapeutic response. In this study, we analyzed specific predictor genes expressed in tumor specimens from our institutional repository. Our aim was to determine if specific gene expression profiles are associated with pathologic complete response (pCR) after neoadjuvant chemo-radiotherapy (CRT). Methods: We investigated eleven genes identified from prior studies (CCL28, SPARC, S100A2, SPRR3, SIRT2, NOV, PERP, PAPSS2, DCK, DKK3, ALDH1) that have significant association with esophageal cancer progression. Patients with esophageal adenocarcinoma treated with neoadjuvant CRT followed by esophagectomy at our institution between January 2011 and July 2015 were included. Quantitative real-time polymerase chain reaction was conducted on pre-treatment biopsy specimens to determine gene expression. Patients were classified into two groups: 1) pCR and, 2) no or poor response (NR) after CRT based on final pathology report. An omnibus test using Mahalanobis distance was applied to evaluate overall genetic expression differences between groups. Log-rank tests compared the differential expression of individual genes. Results: 29 patients (11 pCR and 18 NR) were analyzed. Overall, gene expression profiles were significantly different between pCR and NR patients (p < 0.01). In particular, CCL28 was over-expressed in pCR (Log-HR: 1.53, 95%CI: 0.46-2.59, p = 0.005), and DKK3-was under-expressed in pCR patients (Log-HR: -1.03 95%CI: -1.97, -0.10, p = 0.031). Conclusions: Esophageal adenocarcinoma patients with a pCR after neoadjuvant therapy have genetic profiles that are significantly different from typical NR profiles. In our population, the genes CCL28 and DKK3 are potential predictors of treatment response.

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