scholarly journals Gene expression profiling in treatment-naive schizophrenia patients identifies abnormalities in biological pathways involving AKT1 that are corrected by antipsychotic medication

2013 ◽  
Vol 16 (7) ◽  
pp. 1483-1503 ◽  
Author(s):  
Nishantha Kumarasinghe ◽  
Natalie J. Beveridge ◽  
Erin Gardiner ◽  
Rodney J. Scott ◽  
Surangi Yasawardene ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drug Treatment ◽  
Antipsychotic Drug ◽  
Antipsychotic Medication ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Canonical Pathways ◽  
Treatment Naïve ◽  
Antipsychotic Drug Treatment

Abstract Distinct gene expression profiles can be detected in peripheral blood mononuclear cells (PBMCs) in patients with schizophrenia; however, little is known about the effects of antipsychotic medication. This study compared gene expression profiles in PMBCs from treatment-naive patients with schizophrenia before and after antipsychotic drug treatment. PBMCs were obtained from 10 treatment-naive schizophrenia patients before and 6 wk after initiating antipsychotic drug treatment and compared to PMBCs collected from 11 healthy community volunteers. Genome-wide expression profiling was conducted using Illumina HumanHT-12 expression bead arrays and analysed using significance analysis of microarrays. This analysis identified 624 genes with altered expression (208 up-regulated, 416 down-regulated) prior to antipsychotic treatment (p < 0.05) including schizophrenia-associated genes AKT1, DISC1 and DGCR6. After 6–8 wk treatment of patients with risperidone or risperidone in combination with haloperidol, only 106 genes were altered, suggesting that the treatment corrected the expression of a large proportion of genes back to control levels. However, 67 genes continued to show the same directional change in expression after treatment. Ingenuity® pathway analysis and gene set enrichment analysis implicated dysregulation of biological functions and pathways related to inflammation and immunity in patients with schizophrenia. A number of the top canonical pathways dysregulated in treatment-naive patients signal through AKT1 that was up-regulated. After treatment, AKT1 returned to control levels and less dysregulation of these canonical pathways was observed. This study supports immune dysfunction and pathways involving AKT1 in the aetiopathophysiology of schizophrenia and their response to antipsychotic medication.

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

2005 ◽  
Vol 23 (9) ◽  
pp. 1826-1838 ◽  
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.

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 ◽  
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.

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

2017 ◽  
Vol 37 (6) ◽  
Author(s):  
Marc Weidenbusch ◽  
Severin Rodler ◽  
Shangqing Song ◽  
Simone Romoli ◽  
Julian A. Marschner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Tissue Injury ◽  
Expression Profiles ◽  
Notch Pathway ◽  
Gene Expression Profiles ◽  
Sterile Inflammation ◽  
Specific Gene ◽  
Specific Gene Expression

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.

scholarly journals In silico gene expression profiling in Cannabis sativa

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 69 ◽  
Author(s):  
Luca Massimino
Keyword(s):  
Gene Expression ◽  
Expression Profiling ◽  
Developmental Stages ◽  
Cannabis Sativa ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Functional Genomic ◽  
Functional Genomic Data ◽  
Starting Point ◽  
Scientific Papers

The cannabis plant and its active ingredients (i.e., cannabinoids and terpenoids) have been socially stigmatized for half a century. Luckily, with more than 430,000 published scientific papers and about 600 ongoing and completed clinical trials, nowadays cannabis is employed for the treatment of many different medical conditions. Nevertheless, even if a large amount of high-throughput functional genomic data exists, most researchers feature a strong background in molecular biology but lack advanced bioinformatics skills. In this work, publicly available gene expression datasets have been analyzed giving rise to a total of 40,224 gene expression profiles taken from cannabis plant tissue at different developmental stages. The resource presented here will provide researchers with a starting point for future investigations with Cannabis sativa.

Genome-Wide Profiling of Endothelial Progenitor Cells in Multiple Myeloma: Overlaps with Myeloma Tumor Cells and Common Cancer Gene-Pathways.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 394-394
Author(s):  
Marc J. Braunstein ◽  
Daniel R. Carrasco ◽  
Fabien Campagne ◽  
Piali Mukherjee ◽  
Kumar Sukhdeo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Progenitor Cells ◽  
Tumor Cells ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Biomarkers ◽  
Genome Wide

Abstract Background: In multiple myeloma (MM), bone-marrow-derived endothelial progenitor cells (EPCs) contribute to tumor neoangiogenesis, and their levels covary with tumor mass and prognosis. Recent X-chromosome inactivation studies showed that EPCs are clonally restricted in MM. In addition, high-resolution array comparative genomic hybridization (aCGH) found that the genomes of EPCs and MM cells display similar chromosomal gains and losses in the same patient. In this study, we performed an integrative analysis of EPCs and tumor cells by genome-wide expression profiling, and applied a bioinformatics approach that leverages gene expression data from cancer datasets to mine MM gene pathways common to multiple tumor tissues and likely involved in MM pathogenesis. Methods: Confluent EPCs (&gt;98% vWF/CD133/KDR+ and CD38−) were outgrown from 22 untreated MM patients’ bone marrow aspirates by adherence to laminin. The fractions enriched for tumor cells were &gt;50% CD38+. For gene expression profiling, total RNA from EPCs, MM cells, and control HUVECs were hybridized to cDNA microarrays, and comparisons were made by analysis of variance. Results: Two sets of EPC gene profiles were of particular interest. The first contained genes that differ significantly between EPCs and HUVEC, but not between EPCs and tumor (Profile 1). We hypothesize that this profile is a consequence of the clonal identity previously reported between EPCs and tumor, and that a subset of these genes is largely responsible for MM progression. The second set of important EPC genes are differentially regulated compared both to HUVECs and to tumor cells (Profile 2). These genes may represent the profile of EPCs that are clonally diverse from tumor cells but nevertheless display common gene expression patterns with other cancers. Profile 2 genes may also represent genes that confer a predisposition to clonal transformation of EPCs. When genes in Profile 1 and Profile 2 were overlapped with published lists of cancer biomarkers, significant similarities (P&lt;.05) were apparent. The largest overlaps were observed with the HM200 gene list, a list composed of 200 genes most consistently differentially expressed in human/mouse cancers (Campagne and Skrabanek, BMC Bioinformatics 2006). More than 80% of genes in either EPC profile have not been previously characterized in MM, but have been identified as cancer biomarkers in other cancer studies. These genes will be presented and discussed in the context of MM. Current studies are aimed at integrating Profile 1 and Profile 2 genes in each patient with chromosomal copy number abnormalities (CNAs) found in EPCs, and also with clinical stage and disease severity, in order to elucidate the pathogenic information that the profiles hold. Conclusions: The genomes of EPCs display ranges of overlap with tumor cells in MM, evidenced by gene expression profiles with varying similarity to those found in MM tumor cells. More importantly, MM EPC gene expression profiles, in contrast to normal endothelial cells, contain cancer biomarker genes in tumors not yet associated with MM. Results strongly support the concept that EPCs are an integral part of the neoplastic process in MM.

Reconstructing and analysing cellular states, space and time from gene expression profiles of many cells and single cells

2015 ◽  
Vol 11 (10) ◽  
pp. 2690-2698 ◽  
Author(s):  
Mirko Francesconi ◽  
Ben Lehner
Keyword(s):  
Gene Expression ◽  
Genetic Variation ◽  
Expression Profiling ◽  
Biological Sample ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
High Dimensional ◽  
Dimensional Measurement ◽  
P Gene

Gene expression profiling is a fast, cheap and standardised analysis that provides a high dimensional measurement of the state of a biological sample, including of single cells. Computational methods to reconstruct the composition of samples and spatial and temporal information from expression profiles are described, as well as how they can be used to describe the effects of genetic variation.

scholarly journals Prediction of cytogenetic abnormalities with gene expression profiles

Blood ◽  
2012 ◽  
Vol 119 (21) ◽  
pp. e148-e150 ◽  
Author(s):  
Yiming Zhou ◽  
Qing Zhang ◽  
Owen Stephens ◽  
Christoph J. Heuck ◽  
Erming Tian ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Clinical Parameters ◽  
Proof Of Concept ◽  
Accuracy Rate ◽  
Cytogenetic Abnormalities

Abstract Cytogenetic abnormalities are important clinical parameters in various types of cancer, including multiple myeloma. We developed a model to predict cytogenetic abnormalities in patients with multiple myeloma using gene expression profiling and validated it by different cytogenetic techniques. The model has an accuracy rate up to 0.89. These results provide proof of concept for the hypothesis that gene expression profiling is a superior genomic method for clinical molecular diagnosis and/or prognosis.

Donor T Cell Gene Expression and GVHD.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. sci-53-sci-53
Author(s):  
Claude Perreault
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
T Cell ◽  
Cd8 T Cells ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Qrt Pcr ◽  
One Year ◽  
Donor Gene

GVHD is initiated by donor T cell responses to host alloantigens. However, the occurrence and severity of GVHD are not determined solely by the level of histoincompatibility between donor and recipient. Two MHC-identical subjects will display over 50 minor histocompatibility antigen differences. If histoincompatibility is sufficient for triggering GVHD, the rate of GVHD in MHC-matched recipients of allogeneic hematopoietic cell transplantation (HCT) that receive no immunosuppressive agents should be 100%. Under these conditions, however, GVHD is found in only 50% and 73% of mouse and human recipients, respectively. Histoincompatibility is thus necessary but not sufficient to elicit GVHD. We tested the hypothesis that some donors may be “stronger alloresponders” than others, and consequently more likely to elicit GVHD. To this end, we studied the gene expression profiles of CD4 and CD8 T cells from 50 HCT donors using microarrays and qRT-PCR. We found that gene expression profiling before HCT was able to distinguish those donors whose cells caused GVHD from those whose cells did not. The “dangerous donor” trait (GVHD+ recipient) is under polygenic control and is shaped by the activity of genes that regulate TGF-β signaling and cell proliferation. The donor gene profile defined on day 0 shows strong correlation with that of recipient CD4 and CD8 T cells harvested one year post-AHCT. The latter correlation provides compelling evidence that a significant portion of the differential gene profiles between GVHD+ and GVHD– donors is imprinted at the hematopoietic stem cell level. Moreover, stability of the gene expression profiles over a one-year period suggests that the profiles result from inherited genetic traits as opposed to environmental factors. The gene with the best GVHD-predictive accuracy was SMAD3, a key component of the TGF-β pathway. By testing a cohort of 450 subjects using qRT-PCR, we found that amounts of SMAD3 transcripts varied over a 6-fold range. In mice and humans, SMAD3 is constitutively activated (as evidenced by phosphorylation and accumulation in the nucleus) in many leukocyte subsets. We found in mice that induction of TGF-β signaling in donor T cells is an early event following AHCT and that Smad3-deficient donors trigger more severe GVHD than wild-type littermates. These findings strongly suggest that the donor gene expression profile has a dominant influence on the occurrence of GVHD. In allogeneic HCT, the ability to discriminate strong and weak alloresponders using gene expression profiling could help select low-risk donors and permit tailoring GVHD prophylaxis regimens according to the probability of GVHD occurrence.

Infant Acute Lymphoblastic Leukemias Are Pan-Sensitive to Obatoclax Across molecular/Cytogenetic Subtypes, Especially MLL-ENL, and gene Expression Profiles Determine Obatoclax IC50: A Report on the Children's Oncology Group (COG) P9407 Trial

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 2757-2757 ◽  
Author(s):  
Karen A. Urtishak ◽  
Li-San Wang ◽  
Richard Harvey ◽  
Susan R Atlas ◽  
I-Ming L. Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profiling ◽  
Expression Profiling ◽  
Expression Profiles ◽  
Single Agent ◽  
Gene Expression Profiles ◽  
Poor Survival ◽  
Molecular Cytogenetic ◽  
Ic50 Values ◽  
Mtt Assays

Abstract Abstract 2757 Introduction: The outcome of infants with acute lymphoblastic leukemia (ALL) remains poor because of the association of frequently occurring MLL translocations with drug resistance and vulnerability of the very young to treatment complications. The two most common MLL partner genes in infant ALL, AF4 (AFF1) and ENL (MLLT1), are associated with particularly poor survival. Better therapies are urgent. One candidate is obatoclax (GeminX Biotechnologies, Inc.), which targets interactions of pan-anti-apoptotic BCL-2 family proteins with BH3 proteins and is now in a Phase I trial for relapsed/refractory pediatric cancers (COG ADVL0816). Previously we showed potent single agent in vitro activity of obatoclax against MLL-rearranged infant ALL (Zhang ASH 2008). Here we evaluate correlations of obatoclax activity with MLL translocation status and gene expression profiles in a large number of cases of infant ALL to define molecular determinants of sensitivity. Methods: Bone marrow, peripheral blood or apheresis samples from the time of diagnosis in 54 infants (age 1–365 d, median 168 d; WBC count 15–1230×103/μL, median 445×103/μL) with ALL (n=52) or bilineal acute leukemia (n=2) were examined, 48 of which were from the COG P9407 trial. By molecular/cytogenetic classification, the cases were MLL-AF4+ (n= 28), MLL-ENL+ (n= 11), other MLL rearrangement positive (other MLL+) (n= 8) or MLL germline (MLL-) (n= 7). Single agent IC50 values from MTT assays after 72 h obatoclax exposures were determined in all cases (including 13 previously tested; Zhang ASH 2008) by plotting the surviving fractions. IC50s in the MLL-AF4+ group were compared to those in each of the other 3 molecular/cytogenetic groups by Wilcoxon's test. Gene expression profiling was performed on Affymetrix HG_U133 Plus2.0 arrays in 47 of the 48 COG P9407 cases. Spearman test was used to identify correlation between log2 expression levels for each probeset and IC50 values across subjects. A heatmap of significant probesets (p≤0.001) was generated by transforming expression levels to z-scores and ordering rows and columns by complete linkage hierarchical clustering. Ingenuity pathway analysis was applied to all probesets with p≤0.01 to identify pathways significantly correlated with IC50. Additional MTT assays were initiated to test sensitivity to agents targeting these pathways. Results: Even though most cases in all 4 groups were sensitive to obatoclax as indicated by IC50s within a clinically achievable range, MLL translocation status still had a significant effect on IC50. MLL-AF4+ cases were least sensitive and MLL-ENL+ cases were most sensitive to obatoclax. Respective IC50 ranges across all 54 cases were: MLL-AF4+, 26–918 nM; MLL-ENL+, 13–294 nM; other MLL+ 10–356 nM; MLL−, 31–488. Compared to MLL-AF4+, the IC50s in MLL-ENL+ cases were significantly lower (p=0.047), IC50s in other MLL+ cases were lower but the difference did not achieve significance (p=0.10), and IC50s in MLL- cases were not significantly different (p=0.64). In the 47 COG P9407 cases studied by MTT assay and gene expression profiling, 450 probesets defined a cluster of 16 cases with higher IC50s, which were predominantly MLL-AF4+ (68.7%). Ingenuity analysis identified significant correlations of the following canonical pathways with the IC50 in the same 47 cases: glycolysis/gluconeogenesis, mTOR signaling, regulation of eIF4 and p70S6K signaling, EIF2 signaling, and fructose and mannose metabolism. In preliminary analyses, cell lines with t(4;11) exhibited time and dose-dependant sensitivity to the eIF4e inhibitor ribavirin. Conclusions: In infant ALL, obatoclax has broad-spectrum activity and there is pan-sensitivity across MLL translocation subtypes and MLL− cases. Still specific MLL partner genes have a strong effect on obatoclax IC50 and there is exquisite sensitivity in MLL-ENL+ cases. This result is important because MLL-ENL is associated with particularly poor survival when conventional therapies are used. The association of differentially expressed genes in canonical cell signaling and metabolism pathways with differences in obatoclax sensitivity forms the basis to combine obatoclax with targeted agents directed at restoring these pathways to enhance responsiveness even further. Disclosures: Felix: None: Patent not licensed.

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