Unknown,Transcriptomics,Genomics,Proteomics

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Immune response analysis

ABSTRACT: Microarray profiling of leukocytes from drug treated tumor bearing mice. After dosing with the drug, blood was collected at 9th day and RBCs were lysed. Leukocytes were sent for array hybridization and scanning. 2 Control samples (without drug tumor bearing), 3 Responders (drug treated reduced tumor), 3 Non- Responders (drug treated, tumor size not reduced) Array analysis: Software used- Genespring GX14.5 Normalization method: Percentile shift Data processing and analysis. Agilent one-color microarray data were processed to obtain background-corrected gProcessedSignal values and analyzed in GeneSpring GX using 75th percentile shift normalization. Signal intensities were log2-transformed and normalized across arrays, after which fold-change values were calculated relative to a control reference derived from both control samples. Individual fold values for each sample represent normalized log2 deviations from the control baseline; therefore, control samples were centered around zero and showed symmetric positive and negative fold values by design. For comparison, one representative control sample was used and displayed, while both controls were included in normalization and fold-change calculations. For B cell proloferation genes - B cell activation Gene Ontology Term (GO:0042113) and MGI data base were used Exclusion criteria: Probes flagged as “Compromised” were excluded from downstream analysis. In case of multiple annotations: representative reads were taken In case of double annotations: higher value was considered

ORGANISM(S): Mus musculus

SUBMITTER: HEENA AGGARWAL

PROVIDER: E-MTAB-16828 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Background: Results obtained from our previous study using a label-free quantitation (LF) proteomic approach in which dynamic range compression was used to profile lower abundance proteins, demonstrated few proteins that were differentially abundant within the synovial fluid (SF) when comparing Autologous Chondrocyte Implantation (ACI) responders and non-responders at baseline (1). This study builds upon our previous findings by assessing higher abundance proteins within these SFs; providing a more global proteome analysis from which we can understand more fully the biology underlying ACI success or failure. Methods: Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomics was used to assess SFs from ACI responders (mean Lysholm improvement of 33; n=14) and non-responders (mean Lysholm decrease of 14; n=13) at the two stages of surgery (cartilage harvest and chondrocyte implantation). Differentially abundant proteins were investigated using pathway analyses and the iTRAQ proteomic dataset was combined with our published proteomic dataset of dynamically compressed SFs, from which an interactome network model of systemic protein interactions was generated. Results: iTRAQ proteomics has confirmed our previous finding that there is a marked proteome shift in response to cartilage harvest (70 and 54 proteins demonstrating ≥2.0 fold change between Stages I and II in responders and non-responders, respectively) and has highlighted 28 proteins that were differentially abundant between responders and non-responders to ACI, that were not found in the LF study; 16 of which were altered at baseline. Two protein abundance changes (Complement C1S subcomponent and Matrix metalloproteinase 3 (MMP3), have been biochemically validated. Combination of the iTRAQ and LF proteomic datasets has generated in-depth SF proteome information that has been used to generate interactome networks representing ACI success or failure, from which functional pathways that are dysregulated in ACI non-responders have been identified. Conclusions: Several candidate biomarkers for baseline prediction of ACI outcome have been identified. A holistic overview of the SF proteome in responders and non-responders to ACI has been profiled providing a better understanding of the biological pathways underlying clinical outcome, particularly the differential response to cartilage harvest in non-responders.

Project description:Ewing Sarcoma is caused by a pathognomonic genomic translocation that places an N-terminal EWSR1 gene in approximation with one of several ETS genes (typically FLI1). This aberration, in turn, alters the transcriptional regulation of more than five hundred genes and perturbs a number of critical pathways that promote oncogenesis, cell growth, invasion, and metastasis. Among them, translocation-mediated up-regulation of the insulin-like growth factor receptor 1 (IGF-1R) and mammalian target of rapamycin (mTOR) are of particular importance since they work in concert to facilitate IGF-1R expression and ligand-induced activation, respectively, of proven importance in ES transformation. When used as a single agent in Ewing sarcoma therapy, IGF-1R or mTOR inhibition leads to rapid counter-regulatory effects that blunt the intended therapeutic purpose. Therefore, identify new mechanisms of resistance that are used by Ewing sarcoma to evade cell death to single-agent IGF-1R inhibition might suggest a number of therapeutic combinations that could improve its clinical activity. TC32 and TC71 ES clones with acquired resistance to OSI-906 or NVP-ADW-742 were generated by maintaining the corresponding parental cell lines with increasing concentrations of the agents (up to 2.3 Î¼M for OSI-906, 1.5 Î¼M for NVP-ADW-742) for 7 months. All parental and acquired drug resistant cell lines were tested twice per year for mycoplasma contamination using the MycoAlert Detection Kit (Lonza Group Ltd.) according to the manufacturerâs protocol and validated using short-tandem repeat fingerprinting with an AmpFLSTR Identifier kit as previously described. Herein, we determine subtle differences in acquired mechanism of resistance by two promising small molecule inhibitors of IGF-1R/IR-Î±. OSI-906, which inhibits IGF-1R and IR, and NVP-ADW-742, which inhibits only IGF-1R, were evaluated using in vitro assays to decipher the mechanism(s) by which IGF-1R inhibition induces drug resistance in Ewing sarcoma cells. The preparation of extracted proteins from sensitive and acquired resistant Ewing sarcoma cells to OSI-906 and NVP-ADW-742 for reverse-phase protein lysate array (RPPA) analysis were prepared using the same array. Lysates were processed, spotted onto nitrocellulose-coated FAST slides, probed with 115 validated primary antibodies, and detected using a DakoCytomation-catalyzed system with secondary antibodies. MicroVigene software program (VigeneTech) was used for automated spot identification, background correction, and individual spot-intensity determination. Expression data was normalized for possible unequal protein loading, taking into account the signal intensity for each sample for all antibodies tested. Log2 values were media-centered by protein to account for variability in signal intensity by time and were calculated using the formula log2 signal â log2 median. Principal component analysis was used to check for a batch effect and feature-by-feature two-sample t-tests were used to assess differences between sensitive and resistant cell lines to drug treatments. We also used feature-by-feature one-way analysis of variance (ANOVA) followed by the Tukey test to perform pair comparisons for all groups. Beta-uniform mixture models were used to fit the resulting p value distributions to adjust for multiple comparisons. The cutoff p values and number of significant proteins were computed for several different false discovery rates (FDRs). Biostatistical analyses comparing two groups were performed using an unpaired t-test with Gaussian distribution followed by the Welch correction. To distinguish between treatment groups, we used one-way ANOVA with the Geisser-Greenhouse correction. Differences with p values <0.05 were considered significant. Within clustered image maps (CIM), unsupervised double hierarchical clustering used the Pearson correlation distance and Wardâs linkage method as the clustering algorithm to link entities (proteins) and samples.

Project description:Myasthenia gravis (MG) is an autoimmune disease affecting the neuromuscular junction, whose clinical hallmark is muscle weakness and early fatigability. Azathioprine (AZA) is commonly used in Myasthenia Gravis therapy. AZA is a purine antagonist, which inhibits the cell cycle in the resting and DNA synthesis phases. It is usually used as an immunosuppressant to block T- and B-cell proliferation. AZA, bioconverted to 6-mercaptopurine by glutathione S-transferase (GST), can be metabolized either through the hypoxanthine phosphoribosyl transferase pathway to active 6-thioguanine nucleotides (6-TGN) or through the thiopurine S-methyltransferase (TPMT) pathway to inactive methyl-thiopurine metabolites. The incorporation of active 6-TGNs into DNA, causing breaks in DNA strands resulting in interference with RNA production and thereby protein synthesis, is responsible for the drug effect. The response to AZA is determined by which metabolic pathway is being favored. While balanced use of both HPRT and TPMT pathways results in responsiveness to AZA, hyperactivity of TPMT skews the balance towards the TPMT pathway and results in unresponsiveness to AZA with no pharmacological effect. In contrast, low TPMT activity skews the balance towards the HPRT pathway, resulting in increasing side-effects due to the accumulation of 6-TGNs. Current treatments for MG therapy are often inadequate because less than 40% of patients achieve complete remission with available drugs. This reflects the lack of drugs acting on target sites for which there is strong evidence of pathogenicity and the inability to identify responder patients. No criteria for responsiveness are available, exposing patients to unpredictable failures and unpredictable side effects. These individuals are at particular risk for adverse drug reaction (ADRs) or therapeutic failure. Genetic profiling with the Affymetrix drug metabolizing enzymes and transports genotyping array offers the ability to determine 1,931 variants and 225 genes involved in drug metabolism and disposition. Accordingly, the study of well-known drug-metabolizing genes can be involved in the specific metabolic pathway of a drug, which is more likely to define genotype-phenotype association and thereby genotype profiles relevant to drug response that can be applied as predictive biomarkers for pharmacological treatment.

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Immune response analysis

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