Academic literature on the topic 'Leukaemia; microarray; gene expression'
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Journal articles on the topic "Leukaemia; microarray; gene expression"
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Noman, Helal Mohammed Mohammed Ahmed, Yahya Saleh Al-Matary, Subbaiah Chary Nimmagadda, Pradeep Kumar Patnana, Longlong Liu, Lanying Wei, Daria Frank, Georg Lenz, and Cyrus Khandanpour. "Leukaemia Cells Induced Metabolic Alterations in AML Associated Mesenchymal Stem Cells Via Notch Signalling." Blood 138, Supplement 1 (November 5, 2021): 4347. http://dx.doi.org/10.1182/blood-2021-144468.
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Abstract Introduction: Acute myeloid leukaemia (AML) is a haematological malignancy with a high relapse rate and poor prognosis. Leukaemia cell proliferation is dependent on its interaction with the bone marrow (BM) microenvironment. AML associated mesenchymal stem cells (AML-MSCs) supported the proliferation of leukaemia cells and contributed to disease progression. Stromal microenvironment promoted a metabolic switch but precise underlying molecular mechanisms are poorly understood. Previous studies have demonstrated transfer of functional mitochondria from AML-MSCs to AML blasts facilitating energy requirements. To further improve our understanding of the crosstalk between leukaemia and AML-MSCs, we sought to determine contribution of AML-MSCs and signalling cascades regulating metabolic processes. Methods: Sorted MSCs from non-leukaemic and MLL-AF9 leukaemic mice were isolated, and gene expression profiling was performed using RNA microarray. Additionally sorted MSCs from long-term cultures were cultured alone or with MLL-AF9 leukaemia cells and analysed by RNA-sequencing. Gene set enrichment analysis (GSEA) was used to identify the hallmark gene sets overrepresented in AML-MSCs. We further cocultured murine wild type BM-MSCs alone or together with murine AML cells (C1498 and MLL-AF9) or the control lineage negative cells (Lin -). Metabolic alterations, oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were analysed by Agilent Seahorse XFe96 analyser. Additionally, glucose consumption, lactate secretion and mitochondrial DNA copy number were measured. Results: Microarray analysis in sorted MSCs from leukaemic and non-leukaemic mice have identified hallmark oxidative phosphorylation (p<0.01, NES=-1.6) and glycolysis (p<0.01, NES=-1.3) gene sets to be negatively enriched in AML-MSCs. Interestingly, both the gene sets were also negatively enriched in sorted AML-MSCs when cocultured with leukaemia but not control cells. To validate these findings, we analysed OCR and EACR in WT-MSCs in an identical setting. The oxidative phosphorylation was significantly decreased in MSCs cocultured with C1498 (p<0.0001) and MLL-AF9 (p<0.005) but not with Lin - cells. Interestingly, glycolysis rate, glucose consumption, lactate secretion were significantly decreased in MSCs cocultured with leukaemia cells. Mitochondrial DNA copy number were significantly decreased in MSCs cocultured with C1498 (p<0.001) or MLL-AF9 (p<0.005) but not with control cells. Recent evidence from the lab has demonstrated an essential role for Notch signalling in the leukaemia and AML-MSCs interaction. To functionally determine the crosstalk of leukaemia-MSC interaction and subsequent Notch signalling, we ectopically expressed the Notch intracellular domain (Notch-ICN1) to mimic Notch activation in a murine stromal cell line, MS-5. Confirming Notch activation, Hes1 mRNA expression (encoding a transcriptional target of Notch signalling) was significantly increased in these cells. Underscoring a role for Notch signalling and activation, Notch-ICN1 overexpression in MS-5 cells demonstrated less oxidative phosphorylation and glycolysis rates as compared to MS-5 cells transduced with empty vector. Conclusion: In line with our microarray and GSEA analysis, our findings confirmed that leukaemia cells indeed induced metabolic alterations decreasing oxidative phosphorylation and glycolysis, and thereby potentially altering AML-MSCs function. At the molecular level, Notch signalling (via upregulated Notch1 and 2 expressions and Notch-ICN) in AML-MSCs contributed to metabolic alterations. Therefore, therapeutically interfering this pathway could target the bidirectional interaction between leukaemia and AML-MSCs improving therapeutic efficacy of AML. Disclosures Khandanpour: GSK: Honoraria; Takeda: Honoraria; Janssen: Honoraria; AstraZeneca: Honoraria, Research Funding; Pfizer: Honoraria; Sanofi: Honoraria, Research Funding; BMS/Celgene: Honoraria.
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Mills, Ken I., Torsten Haferlach, Jesus M. Hernandez, Wolf-Karsten Hofmann, Alexander Kohlmann, Mickey Williams, and Lothar Wieczorek. "An International Multi-Center Microarray Study for the Molecular Classification of Leukemia Identifies Novel Sub-Groupings in MDS Overlapping with AML." Blood 108, no. 11 (November 16, 2006): 852. http://dx.doi.org/10.1182/blood.v108.11.852.852.
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Abstract Microarrays can identify robust gene expression signatures associated with distinct sub-classes of paediatric and adult leukemias. Recently, the MILE (Microarray Innovations in LEukemia) study has analysed 1901 expression profiles from retrospective samples in 11 centres (ELN: 7, USA: 3, Singapore: 1). MILE has compared the microarray classification accuracy of 16 acute and chronic leukaemia subclasses, MDS, and non-leukaemia as control group, to routine diagnostic workup. The achieved cross-validation accuracy was very high for the leukaemia subclasses: ~96%. Included in the study were 175 samples diagnosed as MDS, however, only 49.1% of these samples were correctly called as MDS from their underlying gene expression profiles. The remainder were approximately equally split between a call of “non-leukaemia” (24%) and “AML” (24.6%). A further sub-division of MDS samples called “AML”: 81% called as “AML with normal or other cytogenetics”; and the remainder as “AML with complex cytogenetics”. MDS is a heterogeneous group of disorders with a wide range in blast cell count, cytogenetics and number of cytopenias. Our analysis showed that neither study centre nor age were a factor in differentiating between “MDS”, “AML” or “non-leukaemia”. However, WHO classification was highly correlated with the microarray classification result; specifically RAEB(I or II) was associated with “AML” call (p < 0.0001). RA/RARS was highly correlated with “MDS” or “non-leukaemia” calls. Furthermore, IPSS was significantly correlated with call (p>0.0001): 65% of patients with an IPSS score of Int-2 or above were classified as “AML”. Examination of the individual components of the IPSS showed that two patients classified as “AML” had a blast count of >20%, under the WHO definition these would be defined as AML and were excluded. Individually, the blast, karyotype and cytopenia contributions were highly significant (p<0.0001, <0.013 and <0.0001 respectively) when comparing “AML”, “MDS” and “non-leukemia” calls. All the “non-leukemia” patients had <10% blasts, with 85% (34/40) having a cytogenetic score of 0 (Normal or Good (1 of: -Y, del(5q), del(20q))) and 82.5% having only 0 or 1 cytopenias. In contrast, 45% of the “AML” samples had between 11 & 20% blasts, 32% with intermediate (0.5) or poor/complex (1) cytogenetic score and 79% had 2 or 3 cytopenias. Furthermore, survival data was available for 122 of the diagnosed MDS patients and showed that MDS patients called “AML” had a trend towards shorter survival (2P=0.2) than those called “MDS” or “non-leukaemia”. These analyses, in combination with gene expression signatures, may contribute to a redefinition of MDS classifications.
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Guo, Dachuan, Alex Fong, Andy Lail, Maree O’Sullivan, Glenn Stone, Harri Kiiveri, Michael Henry, Dietrich Stephan, Luce Dalla-Pozza, and Daniel R. Catchpoole. "Simplifying Complex Microarray Data To Derive Gene Expression Profiles Which Identify Childhood Acute Lymphoblastic Leukaemia Patients at Risk of Relapse." Blood 106, no. 11 (November 16, 2005): 4506. http://dx.doi.org/10.1182/blood.v106.11.4506.4506.
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Abstract The optimal treatment of patients with childhood acute lymphoblastic leukaemia (ALL) depends on establishing accurate diagnosis. Our investigations seek to strategically develop the application of microarray gene expression profiling to identify ALL patients with clinically homogenous presentations but which may respond differently to established treatment regimens. We have determined the gene expression profiles of ALL bone marrow (BM) samples taken from patients at diagnosis. Data analysis has focussed on the use of a novel and innovative statistical technology, Gene-RaVE. This series of patent protected algorithms builds a multinomial regression model using Bayesian variable selection. Gene-RaVE leads to the generation of a parsimonious and simple diagnostic gene expression signature, but which provides increased predictive ability over current analysis approaches. We describe our analysis of both Affymetrix (HU133A) and cDNA (10.5K) microarray gene expression profiles generated from diagnostic BM from >100 ALL patients covering the broad ALL subtypes including T and B lineage as well as T cell lymphoma leukaemia. Comparison of gene expression data failed to identify clearly distinguishing profiles between patients identified as ‘standard risk’ from ‘medium risk’ according to BFM95 clinical criteria. Gene expression profiles from a cohort of ALL patients, identified as ‘standard risk’ at diagnosis, were compared on the basis of their overall clinical outcome: relapse within 2 yrs vs non-relapse. Using a range of analyses including t-test, Gene-RaVE, discriminant analysis approaches and principle component analysis, we have discovered that small subsets of genes (<10), all of which included Nedd4BP3 and Ribosomal Protein L38 (RPL38), can be used to distinguish the two outcome groups. Subsequent validation using real time PCR supports the increase in Nedd4BP3 expression in standard risk patients which do not respond well to established treatment regimens. The Gene-RaVE algorithm also provides a generic framework for survival analysis. This approach indicates that the expression of these Nedd4BP3, RPL38 and inositol 1, 4, 5-triphosphate receptor, type 2 can be used to build a survival ‘index’ which correlates with the time to a relapse event in standard risk childhood ALL patients. Our results are suggestive of a way forward in the development of an informative, yet efficient diagnostic tool for this childhood malignancy using microarray gene expression analysis technology.
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Zhao, Pan, Yuhuan Zheng, and Ting Niu. "SLC2A5 Overexpression in Childhood Philadelphia Chromosome Positive Acute Lymphoblastic Leukaemia." Blood 132, Supplement 1 (November 29, 2018): 5286. http://dx.doi.org/10.1182/blood-2018-99-118875.
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Abstract To study glycolysis/glycogenesis-related genes expression in childhood B cell acute lymphoblastic leukaemia (B-ALL), we performed a microarray-based analysis using published gene expression profiles. We found that gene SLC2A5, which encoded fructose transporter GLUT5 that facilitated cell fructose uptake, was up-regulated in Philadelphia chromosome positive ALL (Ph+ALL). Microarray-based analyses also suggested that SLC2A5 expression was significantly down-regulated in childhood B-ALL with t(1;19) or 11q23 mutation. High SLC2A5 expression was found in the patients who had recurrence within 3 years, early relapse, shortened complete remission duration, and positive minimal residue disease (MRD) status after treatment. The overexpression of SLC2A5 at both mRNA level and protein level in Ph+ALL was confirmed in a validation cohort of childhood B-ALL. We also validated the correlation of SLC2A5 expression and MRD status. In a mechanistic study using a human Ph+ALL cell line, we found that BCR-ABL kinase might regulate GLUT5 expression via c-myc. The tyrosine kinase inhibitors imatinib and dasatinib repressed GLUT5 expression and the cell uptake of fructose. Fructose protected the tumour cells from nutrition deficiency and drug-induced cell death. Overall, our findings showed that SLC2A5 was up-regulated in childhood Ph+ALL. The expression of SLC2A5 correlated with childhood B-ALL clinical factors, such as MRD status. Since TKI was able to inhibit GLUT5 expression, repression of fructose utility after TKI treatment contributes to TKI-induced Ph+ALL cytotoxicity. Targeting GLUT5 might be promising in B-ALL treatment, especially for Ph+ALL patients with high expression of SLC2A5. Disclosures No relevant conflicts of interest to declare.
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Chen, Xiao Zhou. "LTSA Algorithm for Dimension Reduction of Microarray Data." Advanced Materials Research 645 (January 2013): 192–95. http://dx.doi.org/10.4028/www.scientific.net/amr.645.192.
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Dimension reduction is an important issue to understand microarray data. In this study, we proposed a efficient approach for dimensionality reduction of microarray data. Our method allows to apply the manifold learning algorithm to analyses dimensionality reduction of microarray data. The intra-/inter-category distances were used as the criteria to quantitatively evaluate the effects of data dimensionality reduction. Colon cancer and leukaemia gene expression datasets are selected for our investigation. When the neighborhood parameter was effectivly set, all the intrinsic dimension numbers of data sets were low. Therefore, manifold learning is used to study microarray data in the low-dimensional projection space. Our results indicate that Manifold learning method possesses better effects than the linear methods in analysis of microarray data, which is suitable for clinical diagnosis and other medical applications.
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Ichim, Christine V., Mahadeo A. Sukhai, J. Brandwein, Mark D. Minden, Aaron D. Schimmer, Andre C. Schuh, Suzanne Kamel-Reid, Norman N. Iscove, and Richard A. Wells. "EAR-2: Identification of a Gene Involved in Maintenance of Clonogenicity in Haematopoiesis." Blood 104, no. 11 (November 16, 2004): 3226. http://dx.doi.org/10.1182/blood.v104.11.3226.3226.
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Abstract Primary acute myelogenous leukaemia (AML) samples are heterogeneous in clonogenicity, both among patients and within the leukaemic cell population of a single patient. To explain this heterogeneity the leukaemia stem cell model postulates that leukaemic hematopoiesis is organized in a hierarchy, sustained by leukaemia stem cells that may either self-renew or differentiate aberrantly to give rise to blasts that can no longer proliferate. This process is akin to the irreversible growth arrest entered by terminally differentiating normal blood cells. We wished to identify genes associated with clonogenicity in AML. To obtain pure populations of cells of defined growth abilities, we analyzed low passage cultures of the cell line OCI-AML4. This cell line resembles primary AML cells in several important respects; it is growth factor-dependent, contains a low proportion of clonogenic cells, and has a relatively simple karyotype. Clones consisting of four cells were micromanipulated so that a single cell was sampled for global RT-PCR while its three clonal siblings served as reporters of clonogenicity. By microarray analysis we found the orphan nuclear receptor EAR-2 to be expressed four-fold lower in leukemia single cells that spontaneously lose proliferative ability, compared to single cells with greater proliferative capacity. EAR-2 is a member of the COUP transcription factor family, which play roles in various developmental processes through interactions with nuclear receptors and other transcription factors. We assessed expression of EAR-2 in monoblastic leukaemia U937 cells induced to differentiate with a variety of induction agents. Treatment with dimethylsulfoxide, phorbol ester, vitamin D3, and all trans retinoic acid (ATRA) all induced significant decreases in EAR-2 expression. This phenomenon was also seen in a mouse model of acute promyelocytic leukaemia (APL). When primary bone marrow cultures of hCG-NuMA-RAR transgenic mice were induced to differentiate with ATRA, an average decrement in EAR-2 expression of 5.58 fold was observed (p<0.005). Since aberrant differentiation is an invariant feature of AML, we hypothesized that the overall expression of EAR-2 would be greater in AML patients relative to healthy controls. Analysis by quantitative RT-PCR of 15 AML, 10 CMML, 12 MDS and 16 normal bone marrow samples showed that EAR-2 is overexpressed in all three disease categories (p<0.0009 AML, 0.03 CMML, 0.0003 MDS). To characterize the effect of forced expression of EAR-2 on clonogenicity we transduced U937 cells with a retrovirus encoding either EAR-2 (U937-EAR2) or EGFP (U937-GFP). Analysis of FACS-purified U937-EAR2 and U937-GFP cultures showed that forced expression of EAR-2 reduces the doubling time of these populations (U937-EAR2 = 24h; U937-GFP = 34h; p<< 0.001), while no significant difference was observed in cell cycle profile. The decrease in doubling time of U937-EAR2 cells may reflect a decrease in the rate of cell loss in the population, consistent with the hypothesis that EAR-2 functions as a repressor of terminal differentiation. We have observed that expression of the orphan nuclear receptor EAR-2 is positively associated with maintenance of proliferative capacity and negatively associated with differentiation. These observations establish the importance of EAR-2 in the regulation of clonogenicity and terminal differention.
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Greiner, Jochen, Elliott Brown, Lars Bullinger, Robert K. Hills, Vanessa Morris, Hartmut Döhner, Ken I. Mills, and Barbara-ann Guinn. "Survivin’ Acute Myeloid Leukaemia—A Personalised Target for inv(16) Patients." International Journal of Molecular Sciences 22, no. 19 (September 28, 2021): 10482. http://dx.doi.org/10.3390/ijms221910482.
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Despite recent advances in therapies including immunotherapy, patients with acute myeloid leukaemia (AML) still experience relatively poor survival rates. The Inhibition of Apoptosis (IAP) family member, survivin, also known by its gene and protein name, Baculoviral IAP Repeat Containing 5 (BIRC5), remains one of the most frequently expressed antigens across AML subtypes. To better understand its potential to act as a target for immunotherapy and a biomarker for AML survival, we examined the protein and pathways that BIRC5 interacts with using the Kyoto Encyclopedia of Genes and Genomes (KEGG), search tool for recurring instances of neighbouring genes (STRING), WEB-based Gene Set Analysis Toolkit, Bloodspot and performed a comprehensive literature review. We then analysed data from gene expression studies. These included 312 AML samples in the Microarray Innovations In Leukemia (MILE) dataset. We found a trend between above median levels of BIRC5 being associated with improved overall survival (OS) but this did not reach statistical significance (p = 0.077, Log-Rank). There was some evidence of a beneficial effect in adjusted analyses where above median levels of BIRC5 were shown to be associated with improved OS (p = 0.001) including in Core Binding Factor (CBF) patients (p = 0.03). Above median levels of BIRC5 transcript were associated with improved relapse free survival (p < 0.0001). Utilisation of a second large cDNA microarray dataset including 306 AML cases, again showed no correlation between BIRC5 levels and OS, but high expression levels of BIRC5 correlated with worse survival in inv(16) patients (p = 0.077) which was highly significant when datasets A and B were combined (p = 0.001). In addition, decreased BIRC5 expression was associated with better clinical outcome (p = 0.004) in AML patients exhibiting CBF mainly due to patients with inv(16) (p = 0.007). This study has shown that BIRC5 expression plays a role in the survival of AML patients, this association is not apparent when we examine CBF patients as a cohort, but when those with inv(16) independently indicating that those patients with inv(16) would provide interesting candidates for immunotherapies that target BIRC5.
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Osuji, Nnenna, Ilaria Del Giudice, Tim Dexter, Estella Matutes, Vasantha Brito-Babapulle, David Gonzalez, Brian A. Walker, and Daniel Catovsky. "Gene Expression Reveals Two Distinct Biological Groups within T-Cell Prolymphocytic Leukaemia." Blood 106, no. 11 (November 16, 2005): 4366. http://dx.doi.org/10.1182/blood.v106.11.4366.4366.
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Abstract T-cell prolymphocytic leukemia (T-PLL) is rare and presents with widespread disease. Indolent presentations are seen but eventually progress. The disease shows marked chemoresistance and is best treated with the monoclonal anti-CD52 antibody (CAMPATH). Prolymphocytes show a post-thymic phenotype and are CD4+CD8− (65%), CD4−CD8+ (10%) or CD4+CD8+ (25%). This double positive phenotype, raises questions about the putative ontology of T-PLL. Morphological heterogeneity, with typical (75%), small cell (20%) and cerebriform/sezary-like variants (5%) is described. Inversions or reciprocal translocations of chromosome 14 involving breakpoints at q11 (TCR a/d) and q32.1 (TCL1 and TCL1b) are seen (~ 80%). Other common abnormalities involve chromosome 8, translocation (X;14)(q28;q11) and, ATM (11q23). We investigated the clinico-pathological heterogeneity in T-PLL, at the level of the transcriptome and evaluated the ability of gene expression profiling to sub-classify T-PLL. Total RNA was extracted from blood prolymphocytes (>92% purity) of 22 patients. cDNA synthesis followed by biotin-labelled cRNA synthesis was carried out as per Affymetrix protocols. Fragmented cRNA was hybridized to the Human U133 PLUS2 GeneChip array (54K probes). Microarray services were provided by MRC geneservice (UK HGMP Resource Centre). Hierarchical clustering of samples was performed using a filtered gene set (12,456) and >4 different algorithims. Prediction analysis for micoarray (PAM) and significance analysis of microarray (SAM) were used to evaluate class performance, and partition genes using pre-defined labels of immunophenotype, karyotype, response and morphology. Validation was performed by RT-PCR in a subset of genes.Unsupervised analysis robustly and reproducibly partitioned samples into 2 groups; A (n=8) and B (n=14). SAM analysis identified 4487 differentially expressed transcripts (false discovery rates <1%), >40% of which showed >2-fold difference in expression between the groups. There was no statistical difference in age, immunophenotype or karyotype betweeen groups, however, differential response to CAMPATH was seen. PAM analysis refined a sub-group of ~123 genes which most efficiently differentiated these groups. Group A showed significantly higher rates of non-response and progressive disease as compared to group B (n=14, p=0.036). Key differences related to apoptosis and cell-cycle associated gene expression. Down regulation of caspases (CASP1, CASP2,CASP4, CARD8 and CASP8AP2), cyclins (CCNC, CCND2, CCND3, CCNG1, CCNI, CCNT2), bcl-2, HDAC1, HIPK2, IL6R and ATM were frequent in group A with upregulation of genes implicated in NF-kB (TRAF4, SQSTM1) and TNF pathways (LMNA, ARTS-1), as well as transcription factors such as ATF-3. CD52 expression was ~2-fold higher in group B and may explain in part, differential responses to CAMPATH. RT-PCR validated gene expression data for LMNA and ATF-3. Despite the small numbers, algorithim-independent segregation into 2 consistent groups, in conjunction with the magnitude of gene differences, presence of many mutually exclusive divisions, and low prediciton errors, imply that the 2 identified profiles arise from fundamental differences at a regulatory level and thus likely represent a generalisable classification for T-PLL. Differential responses to CAMPATH may be a sub-feature of this grouping.
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Saha, Sujay, Anupam Ghosh, Dibyendu Bikash Seal, and Kashi Nath Dey. "An Improved Fuzzy Based Missing Value Estimation in DNA Microarray Validated by Gene Ranking." Advances in Fuzzy Systems 2016 (2016): 1–19. http://dx.doi.org/10.1155/2016/6134736.
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Most of the gene expression data analysis algorithms require the entire gene expression matrix without any missing values. Hence, it is necessary to devise methods which would impute missing data values accurately. There exist a number of imputation algorithms to estimate those missing values. This work starts with a microarray dataset containing multiple missing values. We first apply the modified version of the fuzzy theory based existing method LRFDVImpute to impute multiple missing values of time series gene expression data and then validate the result of imputation by genetic algorithm (GA) based gene ranking methodology along with some regular statistical validation techniques, like RMSE method. Gene ranking, as far as our knowledge, has not been used yet to validate the result of missing value estimation. Firstly, the proposed method has been tested on the very popular Spellman dataset and results show that error margins have been drastically reduced compared to some previous works, which indirectly validates the statistical significance of the proposed method. Then it has been applied on four other 2-class benchmark datasets, like Colorectal Cancer tumours dataset (GDS4382), Breast Cancer dataset (GSE349-350), Prostate Cancer dataset, and DLBCL-FL (Leukaemia) for both missing value estimation and ranking the genes, and the results show that the proposed method can reach 100% classification accuracy with very few dominant genes, which indirectly validates the biological significance of the proposed method.
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Ede, Ben Christopher, Paraskevi Diamanti, Charlotte V. Cox, and Allison Blair. "A Novel Combination Therapy for Paediatric T Cell Acute Lymphoblastic Leukaemia." Blood 126, no. 23 (December 3, 2015): 3767. http://dx.doi.org/10.1182/blood.v126.23.3767.3767.
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Abstract T cell acute lymphoblastic leukaemia (T-ALL) is a rare form of leukaemia that accounts for approximately 15% of paediatric ALL cases. Unfortunately, approximately 20% of patients do not achieve long term remission as a result of failure of therapy to eradicate the disease. T-ALL is a highly heterogeneous disease that displays a spectrum of immunophenotypes, chromosomal aberrations and gene expression profiles. This heterogeneity has prompted research into more targeted therapies, with the aim of overcoming drug resistance often found with standard chemotherapeutic regimens. Here, we build upon use of the drug Parthenolide (PTL), which has shown promise in treatment of T-ALL and other leukaemias such as BCP-ALL and AML, in combination with ABT-263, a BCL-2 family antagonising agent. Bone marrow samples from 10 T-ALL cases, taken at diagnosis, were treated with PTL in vitro for 24 hours then viability was assessed using the annexin V / PI flow cytometric assay. Variable cytotoxic effects were observed in samples treated with PTL (1-10µM), with half maximal inhibitory concentrations ranging from 2.6-10 µM. At the highest dose tested, the proportion of surviving cells ranged from 5.79-56% (median 35.33%). BM from 5 of these samples was used for whole genome microarray (WGA) analysis. We compared gene expression in bulk ALL and in specific subpopulations, known to have leukaemia initiating capacity in vivo; CD34+/CD7+, CD34+/CD7-, CD34-/CD7+ and CD34-/CD7- cells. WGA data demonstrated that CD34+/CD7- was the only subpopulation to express significantly lower levels (5.38 fold) of the pro-apoptotic gene Bcl-2L11 (BIM) compared to the unsorted bulk T-ALL cells, p=0.006. Interestingly, we have previously shown that CD34+/CD7- cells from a few patients were resistant to PTL treatment in vivo compared to unsorted cells. To validate these results, mRNA and relative protein quantification was performed by qPCR and western blotting in bulk material from 8 of the 10 samples, 3 of which had been analysed by microarray for BIM expression. We found that the gene and protein expression levels of BIM were negatively correlated with PTL resistance in vitro, p≤0.0001 and p=0.049 respectively. This suggests that reduced BIM expression is related to PTL resistance. We next evaluated the effects of combining PTL and ABT-263 on T-ALL cells in vitro. ABT-263 is a BH3 protein mimetic, like BIM it promotes apoptosis by blocking the inhibitory effects that BCL-2 anti-apoptotic proteins have on pro-apoptotic proteins. The effects of combining the drugs were assessed in 7 of the original 10 samples. Unsorted ALL cells were incubated with PTL and ABT-263 for 24 hours, before viability was analysed by flow cytometry and drug synergy was calculated via the Chou Talalay method. This drug combination showed enhanced cytotoxicity to T-ALL cells compared to PTL (p=0.0282) or ABT-263 (p=0.0358) alone. Moreover, the highest combined dose tested (2.5µM PTL with 0.25µM ABT-263) killed 86.1±9% cells cf 71.8±18% with ABT-263 alone and only 21.7±11% with PTL alone. The combination also showed synergism with a combination index value below 1 in all doses tested. Previous findings in our laboratory have shown that in vivo PTL treatment eliminated childhood leukaemia in NOD/LtSz-scid IL-2Rγc null (NSG) mice, in most cases tested. It may be possible to further enhance this toxicity using ABT-263 alone or in combination with PTL. NSG mice were inoculated with unsorted T-ALL cells and leukaemia was allowed to establish until levels in peripheral blood (PB) exceeded 0.1%. NSG mice were subsequently treated orally for 21 days with 100mg/kg of ABT-263 or placebo and leukaemia burden was monitored weekly in PB aspirates. Twenty-eight days following commencement of treatment, leukaemia burden in the placebo treated group was 80.73±2.94% and the animals were electively culled. In contrast, disease burden was significantly lower in the treated animals at this stage (35.2±2.1%, p=0.004). ABT-263 treatment has significantly improved survival of all xenografts to date, (P<0.014). In summary, we have shown that PTL resistance is related to the expression of BIM. By combining PTL with ABT-263, which mimics the pro-apoptotic action of BIM, the drugs work synergistically to enhance T-ALL cytotoxicity in vitro. Ongoing in vivo studies will assess the full potential of this combination therapy for paediatric T-ALL. Disclosures No relevant conflicts of interest to declare.
Dissertations / Theses on the topic "Leukaemia; microarray; gene expression"
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com, Darcelle@gmail, and Darcelle Natalie Dixon. "Identification of Downstream Target Genes of the T-cell Oncoprotein HOX11 by Global Gene Expression Profiling." Murdoch University, 2004. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20040929.143814.
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HOX11 is a homeodomain transcription factor that has been implicated in leukaemic transformation associated with T-cell acute lymphoblastic leukaemia (T-ALL). Its role in leukaemogenesis remains enigmatic, nevertheless, in vitro and in vivo studies have provided additional evidence supporting the role of HOX11 as an oncogene. The mechanism by which HOX11 transforms cells is yet to be elucidated, however, HOX11 has been postulated to function by binding regulatory elements within the promoter regions of specific target genes in order to control gene transcription. The identification of transcriptional targets is thus thought to be critical to our understanding of the pathways controlled by this master gene regulator. To date, only three candidate HOX11 target genes have been reported and given that HOX11 overexpression can have a profound impact on cell behaviour, it is likely that many more exist. In this study, we sought to further understand the role of HOX11 in tumorigenesis by: 1) The identification of novel putative HOX11 target genes by profiling gene expression in response to HOX11 in a number of cell lines using a combination of RDA, cDNA microarray and GeneChip approaches and 2) confirming target gene status by assessing whether the proximal promoters of the leading candidates identified are transcriptionally regulated by HOX11.
To identify genes whose expression was altered by HOX11, three techniques were employed, namely representational difference analysis, cDNA microarray and Affymetrix GeneChip array. Because of the relative novelty of these technologies, all three methods were employed in a complementary manner. While representational difference analysis did not require dedicated equipment and enabled the identification of novel genes, the technique was labour-intensive and also exhibited a number of problems including high levels of background. Emphasis was therefore placed on the more systematic microarray approaches that enabled a global investigation of expression patterns and thus the identification of a range of candidate target genes. Initially, this involved cDNA microarray experiments, however, during the course of this work Affymetrix GeneChip technology became available. The latter was identified as the most appropriate technology for the identification of candidate target genes because of its relative ease of use, as well as its employment of multiple independent probe pairs which greatly improved background noise, increased the range and accuracy of detection, minimized the effects of cross hybridization and drastically reduced the rate of false positives and miscalls.
Using these combined approaches, several genes of interest were identified which were differentially regulated in the presence of HOX11 and thus may represent oncogenically or physiologically relevant target genes. These included OSTEOPONTIN, PAG, GUANOSINE DIPHOSPHATE DISSOCIATION INHIBITOR 3, SUR8, GAS3, C-KIT, VEGFC, NOR1 and SMARCD3. In order to confirm their role as target genes, four candidates (C-KIT, VEGFC, NOR1 and SMARCD3) were characterized in terms of the ability of their proximal promoters to be transcriptionally regulated by HOX11 using luciferase reporter assays. Significant repression of the proximal promoters of C-KIT and VEGFC by HOX11 was observed, which provided further evidence for their status as target genes. This repression was, however, in stark contrast to the transcriptional activation seen when the C-KIT and VEGFC proximal promoters were co-transfected with a HOX11 mutant lacking the third helix of the DNA-binding homeodomain. This unexpected finding suggested that the transcriptional activity of HOX11 is complex and highly context-dependent, and in particular, highlighted the importance of an intact homeodomain for HOX11 function.
C-KIT and VEGFC are both involved in tyrosine kinase signal transduction pathways, as a receptor tyrosine kinase and tyrosine kinase ligand, respectively. C-KIT plays an important role in the survival and self-renewal of haematopoietic cells. It is a previously identified and relatively well characterized oncogene known to be regulated by other transcription factors (SCL/TAL1 and LMO) implicated in the pathogenesis of T-ALL. VEGFC is a member of the vascular endothelial growth factor family that functions in angiogenesis and lymphangiogenesis. A paracrine loop involving VEGFC and its receptor VEGFR-3 has previously been implicated in leukaemic cell survival. While further work is required in order to confirm the status of VEGFC and C-KIT as oncogenically-relevant HOX11 target genes and to characterize their exact mode of regulation, these findings implicate receptor tyrosine kinases in HOX11-mediated tumorigenesis and underscore their potential importance as therapeutic targets in haematological malignancies.
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Loi, To Ha Clinical School St Vincent's Hospital Faculty of Medicine UNSW. "Gene expression profiling in Philadelphia positive acute lymphoblastic leukaemia treated with Imatinib -- a novel role of PKC epsilon signalling." Publisher:University of New South Wales. Clinical School - St Vincent's Hospital, 2008. http://handle.unsw.edu.au/1959.4/43343.
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Philadelphia positive (Ph+) Acute Lymphoblastic Leukaemia (ALL) is characterised by the presence of the BCR-ABL fusion gene, which encodes a protein tyrosine kinase with aberrant activity. Imatinib, a chemical Bcr-Abl inhibitor, is rarely effective in Ph+ ALL patients as a single agent. In this study, insight into molecular and signalling changes occurring in Ph+ ALL during Imatinib therapy were investigated using cDNA microarrays. An optimal microarray assay was established to examine the gene expression changes in leukaemic cells from Ph+ ALL patients treated with Imatinib. Over 500 genes with ≥1.5-fold up- or down-regulation were identified. Based on gene ontology and novelty to Bcr-Abl signalling, six genes were selected and expression changes in five of these genes (PKCε, PINK1, SPRY2, ATF4 and PECAM1) confirmed by real time RT-PCR in Imatinib treated primary Ph+ ALL cells or the SUP-B15 cell line. The functional role of Protein Kinase C epsilon (PKCε) in response to Imatinib was further investigated using the Ph+ lymphoid and myeloid cell lines, SUP-B15 and K562. Detection of Imatinib-induced apoptosis by annexin V and PI staining demonstrated that SUP-B15 cells were less sensitive to Imatinib compared to K562 cells. PKCε mRNA was 50-fold higher in Ph+ ALL cells than Ph+ myeloid cells. In SUP-B15 cells, Imatinib upregulated PKCε mRNA but the protein was reduced by proteolytic cleavage. Inhibition of caspases showed that this cleaved product was not required for Imatinib induced-apoptosis. The treatment of SUP-B15 and primary Ph+ ALL cells with TAT-εV1-2 peptide, a specific inhibitor of PKCε, increased Imatinib-induced apoptosis. While the forced overexpression of PKCε in K562 cells reduced Imatinib-induced apoptosis. This increased expression of PKCε was associated with the increase of survival and anti-apoptotic proteins, Akt and Bcl-2. In summary, Gene expression profiling of Ph+ ALL cells during Imatinib therapy identified PKCε as an Imatinib responsive gene. A novel role of PKCε in Ph+ ALL response to imatinib is proposed. Experimental data presented in this thesis indicate that PKCε mediates pro-survival/anti-apoptosis signals in Ph+ ALL thereby reducing Imatinib-induced death. Thus, targeting PKCε during Imatinib therapy may be beneficial for the future treatment of Ph+ ALL.
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Quinn, M. F. "Homeobox gene expression in acute leukaemia." Thesis, Queen's University Belfast, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.398094.
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Szeto, Lap Keung. "Clustering analysis of microarray gene expression data /." access full-text access abstract and table of contents, 2005. http://libweb.cityu.edu.hk/cgi-bin/ezdb/thesis.pl?mphil-it-b19885817a.pdf.
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Thesis (M.Phil.)--City University of Hong Kong, 2005."Submitted to Department of Computer Engineering and Information Technology in partial fulfillment of the requirements for the degree of Master of Philosophy" Includes bibliographical references (leaves 70-79)
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Botella, Pérez Cristina. "Multivariate classification of gene expression microarray data." Doctoral thesis, Universitat Rovira i Virgili, 2010. http://hdl.handle.net/10803/9046.
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L'expressiódels gens obtinguts de l'anàliside microarrays s'utilitza en molts casos, per classificar les cèllules. En aquestatesi, unaversióprobabilística del mètodeDiscriminant Partial Least Squares (p-DPLS)s'utilitza per classificar les mostres de les expressions delsseus gens. p-DPLS esbasa en la regla de Bayes de la probabilitat a posteriori. Aquestsclassificadorssónforaçats a classficarsempre.Per superaraquestalimitaciós'haimplementatl'opció de rebuig.Aquestaopciópermetrebutjarlesmostresamb alt riscd'errors de classificació (és a dir, mostresambigüesi outliers).Aquestaopció de rebuigcombinacriterisbasats en els residuals x, el leverage ielsvalorspredits. A més,esdesenvolupa un mètode de selecció de variables per triarels gens mésrellevants, jaque la majoriadels gens analitzatsamb un microarraysónirrellevants per al propòsit particular de classificacióI podenconfondre el classificador. Finalment, el DPLSs'estenen a la classificació multi-classemitjançant la combinació de PLS ambl'anàlisidiscriminant lineal.
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Eijssen, Lars Maria Theo. "Analysis of microarray gene expression data sets." [Maastricht : Maastricht : Universiteit Maastricht] ; University Library, Universiteit Maastricht [host], 2006. http://arno.unimaas.nl/show.cgi?fid=6830.
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Molloy, Timothy John St George Clinical School UNSW. "Gene expression in healing tendon." Awarded by:University of New South Wales. St George Clinical School, 2006. http://handle.unsw.edu.au/1959.4/23939.
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Tendon injury is painful and often debilitating, and is a one of the most prevalent soft tissue injuries encountered in the clinic. While common, the underlying molecular and genetic processes of tendon damage and repair remain poorly understood. The work described herein used genome-wide expression analyses to investigate tendon injury and healing from three perspectives. The first identified novel gene expression in tendon fibroblasts following their stimulation with nitric oxide (NO). Of particular relevance to tendon healing was the observation that stimulated fibroblasts express a number of extracellular matrix (ECM) genes in response to NO in a dose-dependent manner, and that NO significantly affects cellular adhesion, a critical process during tendon repair. These findings will be of use when optimising dosages of NO delivery in future work investigating NO as potential treatment for tendon injuries. The second study examined gene expression in an acute tendon injury model in the rat at 1, 7, and 21 days post injury, roughly representing the inflammation, proliferation, and remodelling phase of wound repair. Several novel genes and pathways were found to be differentially expressed at each stage of healing. Of particular interest were the presence of a significant number of genes related to glutamate signaling, a method of cellular communication that has not previously been shown to exist in tendon. Also upregulated were a number of genes which have previously only been associated with embryonic development. Finally, gene expression in a supraspinatus tendinopathy model in the rat was investigated. Several genetic pathways were identified in tendinopathic tendons which have not previously been associated with the disease, and, analogous to the acute injury model study, glutamate signaling gene overexpression was also prevalent. Further in vitro studies showed that the expression of these genes in tendon fibroblasts were stimulated by glutamate treatment, which in turn upregulated pro-apoptotic pathways causing cell death. This may prove to be an important causative factor in the tendon degeneration seen in tendinopathy, in which apoptosis has been identified as playing a significant role. The results of these studies contribute to a better understanding of the aetiology of several extremely common pathologies of this soft tissue, and may help to develop more targeted therapies for increasing the efficacy of tendon healing in future.
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Laurell, Cecilia. "Microarray Based Gene Expression Analysis in Cancer Research." Doctoral thesis, Stockholm : School of Biotechnology, Royal Institute of Technology, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4244.
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Mohammed, Suhaib. "Consensus network inference of microarray gene expression data." Thesis, University of Exeter, 2016. http://hdl.handle.net/10871/24185.
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Genetic and protein interactions are essential to regulate cellular machinery. Their identification has become an important aim of systems biology research. In recent years, a variety of computational network inference algorithms have been employed to reconstruct gene regulatory networks from post-genomic data. However, precisely predicting these regulatory networks remains a challenge. We began our study by assessing the ability of various network inference algorithms to accurately predict gene regulatory interactions using benchmark simulated datasets. It was observed from our analysis that different algorithms have strengths and weaknesses when identifying regulatory networks, with a gene-pair interaction (edge) predicted by one algorithm not always necessarily consistent with the other. An edge not predicted by most inference algorithms may be an important one, and should not be missed. The naïve consensus (intersection) method is perhaps the most conservative approach and can be used to address this concern by extracting the edges consistently predicted across all inference algorithms; however, it lacks credibility as it does not provide a quantifiable measure for edge weights. Existing quantitative consensus approaches, such as the inverse-variance weighted method (IVWM) and the Borda count election method (BCEM), have been previously implemented to derive consensus networks from diverse datasets. However, the former method was biased towards finding local solutions in the whole network, and the latter considered species diversity to build the consensus network. In this thesis we proposed a novel consensus approach, in which we used Fishers Combined Probability Test (FCPT) to combine the statistical significance values assigned to each network edge by a number of different networking algorithms to produce a consensus network. We tested our method by applying it to a variety of in silico benchmark expression datasets of different dimensions and evaluated its performance against individual inference methods, Bayesian models and also existing qualitative and quantitative consensus techniques. We also applied our approach to real experimental data from the yeast (S. cerevisiae) network as this network has been comprehensively elucidated previously. Our results demonstrated that the FCPT-based consensus method outperforms single algorithms in terms of robustness and accuracy. In developing the consensus approach, we also proposed a scoring technique that quantifies biologically meaningful hierarchical modular networks.
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Morimoto, Shoko. "Global Gene Expression in Haloferax volcanii." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1306873403.
Full textBooks on the topic "Leukaemia; microarray; gene expression"
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McLachlan, Geoffrey J., Kim-Anh Do, and Christophe Ambroise. Analyzing Microarray Gene Expression Data. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/047172842x.
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McLachlan, Geoffrey J., Kim-Anh Do, and Christophe Ambroise. Analyzing Microarray Gene Expression Data. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/047172842x.
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Lee, Mei-Ling Ting. Analysis of microarray gene expression data. Boston: Kluwer Academic, 2004.
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Simone, Mocellin, ed. Microarray technology and cancer gene profiling. New York, N.Y: Kluwer Academic/Plenum Publishers, 2007.
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John, Quackenbush, and Brazma Alvis, eds. Microarray gene expressions data analysis: A beginner's guide. Oxford: Blackwell, 2003.
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Microarray analysis. Hoboken, NJ: Wiley-Liss, 2003.
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Microarray methods for drug discovery. New York: Springer, 2010.
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1966-, Scherer Andreas, ed. Batch effects and noise in microarray experiments, sources, and solutions. Chichester, West Sussex: J. Wiley, 2009.
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Howell, Brandon George. Gene expression profiling of UV-induced skin cancer using cDNA microarray technology. Ottawa: National Library of Canada, 2001.
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Seale, David Andrew. A statistical model of microarray images and an estimator of gene expression ratio. Ottawa: National Library of Canada, 2002.
Find full textBook chapters on the topic "Leukaemia; microarray; gene expression"
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Liew, Alan Wee-Chung, and Xiangchao Gan. "Microarray Gene Expression Data Analysis." In Algorithms in Computational Molecular Biology, 623–50. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470892107.ch28.
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Xu, Ying. "Microarray Gene Expression Data Analysis." In Microbial Functional Genomics, 177–206. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2005. http://dx.doi.org/10.1002/0471647527.ch7.
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Modlich, Olga, and Marc Munnes. "Statistical Framework for Gene Expression Data Analysis." In Microarray Data Analysis, 111–30. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-390-5_6.
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Binder, Hans, Stephan Preibisch, and Hilmar Berger. "Calibration of Microarray Gene-Expression Data." In Methods in Molecular Biology, 375–407. Totowa, NJ: Humana Press, 2009. http://dx.doi.org/10.1007/978-1-59745-545-9_20.
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Nettleton, Dan. "Design of Gene Expression Microarray Experiments." In Design and Analysis of Experiments, 73–108. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2012. http://dx.doi.org/10.1002/9781118147634.ch2.
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Calza, Stefano, and Yudi Pawitan. "Normalization of Gene-Expression Microarray Data." In Methods in Molecular Biology, 37–52. Totowa, NJ: Humana Press, 2010. http://dx.doi.org/10.1007/978-1-60761-842-3_3.
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Petre, I., and C. Buiu. "Microarray Gene Expression Analysis using R." In International Conference on Advancements of Medicine and Health Care through Technology; 12th - 15th October 2016, Cluj-Napoca, Romania, 358–61. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52875-5_74.
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Huber, W., A. von Heydebreck, and M. Vingron. "Analysis of Microarray Gene Expression Data." In Handbook of Statistical Genetics, 201–30. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470061619.ch6.
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Hayes, David Neil, and Matthew Meyerson. "Microarray Approaches to Gene Expression Analysis." In Molecular Diagnostics, 121–48. Totowa, NJ: Humana Press, 2006. http://dx.doi.org/10.1385/1-59259-928-1:121.
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Cheung, Leo Wang-Kit. "Classification Approaches for Microarray Gene Expression Data Analysis." In Next Generation Microarray Bioinformatics, 73–85. Totowa, NJ: Humana Press, 2011. http://dx.doi.org/10.1007/978-1-61779-400-1_5.
Full textConference papers on the topic "Leukaemia; microarray; gene expression"
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Kuruoglu, Ercan E., Diego Salas, and Diego Pablo Ruiz. "Microarray Gene Expression and Stable Laws." In 2007 IEEE 15th Signal Processing and Communications Applications. IEEE, 2007. http://dx.doi.org/10.1109/siu.2007.4298832.
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Yang, Andy C., Hui-Huang Hsu, and Ming-Da Lu. "Applying gene ontology to microarray gene expression data analysis." In 2010 International Conference on System Science and Engineering (ICSSE). IEEE, 2010. http://dx.doi.org/10.1109/icsse.2010.5551740.
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McMunn-Coffran, Cameron, Christina Schweikert, and D. Frank Hsu. "Microarray Gene Expression Analysis Using Combinatorial Fusion." In 2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering (BIBE). IEEE, 2009. http://dx.doi.org/10.1109/bibe.2009.70.
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Qi, Jianlong, and Jian Tang. "Gene Ontology Driven Feature Selection from Microarray Gene Expression Data." In 2006 IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology. IEEE, 2006. http://dx.doi.org/10.1109/cibcb.2006.330968.
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Peeling, Emma, Allan Tucker, Arno P. J. M. Siebes, Michael R. Berthold, Robert C. Glen, and Ad J. Feelders. "Consensus gene regulatory networks: combining multiple microarray gene expression datasets." In COMPLIFE 2007: The Third International Symposium on Computational Life Science. AIP, 2007. http://dx.doi.org/10.1063/1.2793402.
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Ji, Liping, Kenneth Mock, and Kian-lee Tan. "Quick Hierarchical Biclustering on Microarray Gene Expression Data." In Sixth IEEE Symposium on BioInformatics and BioEngineering (BIBE'06). IEEE, 2006. http://dx.doi.org/10.1109/bibe.2006.253323.
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Fleury, Hero, Yoshida, Carter, Barlow, and Swaroop. "Clustering gene expression signals from retinal microarray data." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1004801.
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Fleury, G., A. Hero, S. Yoshida, T. Carter, C. Barlow, and A. Swaroop. "Clustering gene expression signals from retinal microarray data." In Proceedings of ICASSP '02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.5745540.
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German, Donald, Bahman Afsari, Aik Choon Tan, and Daniel Q. Naiman. "Microarray Classification from Several Two-Gene Expression Comparisons." In 2008 Seventh International Conference on Machine Learning and Applications. IEEE, 2008. http://dx.doi.org/10.1109/icmla.2008.152.
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Anaissi, Ali, Paul J. Kennedy, and Madhu Goyal. "Feature Selection of Imbalanced Gene Expression Microarray Data." In Distributed Computing. IEEE, 2011. http://dx.doi.org/10.1109/snpd.2011.12.
Full textReports on the topic "Leukaemia; microarray; gene expression"
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Huang, Shixia, and Harold Varmus. The Use of cDNA Microarray to Study Gene Expression in Wnt-1 Induced Mammary Tumors. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada411264.
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Tooker, B. C., and Timothy S. Stahly. Microarray Analysis of Gene Expression Essential to Energetic Efficiency in a Porcine Model of Obesity. Ames (Iowa): Iowa State University, January 2005. http://dx.doi.org/10.31274/ans_air-180814-1077.
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Szallasi, Zoltan. CDNA Microarray Based Comparative Gene Expression Analysis of Primary Breast Tumors Versus In Vitro Transformed Neoplastic Breast Epithelium. Fort Belvoir, VA: Defense Technical Information Center, December 2001. http://dx.doi.org/10.21236/ada401181.
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Rodriguez, Russell J., and Stanley Freeman. Gene Expression Patterns in Plants Colonized with Pathogenic and Non-pathogenic Gene Disruption Mutants of Colletotrichum. United States Department of Agriculture, February 2009. http://dx.doi.org/10.32747/2009.7592112.bard.
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Fungal plant pathogens are responsible for extensive annual crop and revenue losses throughout the world. To better understand why fungi cause diseases, we performed gene-disruption mutagenesis on several pathogenic Colletotrichum species and demonstrated that pathogenic isolates can be converted to symbionts expressing non-pathogenic lifestyles. One group of nonpathogenic mutants confer disease protection against pathogenic species of Col!etotrichum, Fusarium and Phytophthora; drought tolerance; and growth enhancement to host plants. These mutants have been defined as mutualists and disease resistance correlates to a decrease in the time required for hosts to activate defense systems when exposed to virulent fungi. A second group of non-pathogenic mutants did not confer disease resistance and were classified as commensals. In addition, we have demonstrated that wildtype pathogenic Colletotrichum species can express non-pathogenic lifestyles, including mutualism, on plants they colonize asymptomatically. We have been using wildtype and isogenic gene disruption mutants to characterize gene expression patterns in plants colonized with a pathogen, mutualist or commensal. The US group is contrasting genes expressed during colonization by mutuahstic and commensal mutants of C. magna and a pathogenic wildtype C. coccodes on tomato. The Israeli group is characterizing genes expressed during asymptomatic colonization of tomato by wildtype C. acutatum and a non-pathogenic mutant.To accomplish this we have been utilizing suppressive subtraction hybridization, microarray and sequencing strategies. The expected contribution of this research to agriculture in the US and Israel is: 1) understanding how pathogens colonize certain hosts asymptomatic ally will shed light on the ecology of plant pathogens which has been described as a fundamental deficiency in plant pathology; 2) identifying genes involved in symbiotically conferred disease resistance will help explain why and how pathogens cause disease, and may identify new candidate targets for developing genetically modified disease resistant crop plants.
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Splitter, Gary, and Menachem Banai. Microarray Analysis of Brucella melitensis Pathogenesis. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7709884.bard.
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Original Objectives 1. To determine the Brucella genes that lead to chronic macrophage infection. 2. To identify Brucella genes that contribute to infection. 3. To confirm the importance of Brucella genes in macrophages and placental cells by mutational analysis. Background Brucella spp. is a Gram-negative facultative intracellular bacterium that infects ruminants causing abortion or birth of severely debilitated animals. Brucellosis continues in Israel, caused by B. melitensis despite an intensive eradication campaign. Problems with the Rev1 vaccine emphasize the need for a greater understanding of Brucella pathogenesis that could improve vaccine designs. Virulent Brucella has developed a successful strategy for survival in its host and transmission to other hosts. To invade the host, virulent Brucella establishes an intracellular niche within macrophages avoiding macrophage killing, ensuring its long-term survival. Then, to exit the host, Brucella uses placenta where it replicates to high numbers resulting in abortion. Also, Brucella traffics to the mammary gland where it is secreted in milk. Missing from our understanding of brucellosis is the surprisingly lillie basic information detailing the mechanisms that permit bacterial persistence in infected macrophages (chronic infection) and dissemination to other animals from infected placental cells and milk (acute infection). Microarray analysis is a powerful approach to determine global gene expression in bacteria. The close genomic similarities of Brucella species and our recent comparative genomic studies of Brucella species using our B. melitensis microarray, suqqests that the data obtained from studying B. melitensis 16M would enable understanding the pathogenicity of other Brucella organisms, particularly the diverse B. melitensis variants that confound Brucella eradication in Israel. Conclusions Results from our BARD studies have identified previously unknown mechanisms of Brucella melitensis pathogenesis- i.e., response to blue light, quorum sensing, second messenger signaling by cyclic di-GMP, the importance of genomic island 2 for lipopolysaccharide in the outer bacterial membrane, and the role of a TIR domain containing protein that mimics a host intracellular signaling molecule. Each one of these pathogenic mechanisms offers major steps in our understanding of Brucella pathogenesis. Strikingly, our molecular results have correlated well to the pathognomonic profile of the disease. We have shown that infected cattle do not elicit antibodies to the organisms at the onset of infection, in correlation to the stealth pathogenesis shown by a molecular approach. Moreover, our field studies have shown that Brucella exploit this time frame to transmit in nature by synchronizing their life cycle to the gestation cycle of their host succumbing to abortion in the last trimester of pregnancy that spreads massive numbers of organisms in the environment. Knowing the bacterial mechanisms that contribute to the virulence of Brucella in its host has initiated the agricultural opportunities for developing new vaccines and diagnostic assays as well as improving control and eradication campaigns based on herd management and linking diagnosis to the pregnancy status of the animals. Scientific and Agricultural Implications Our BARD funded studies have revealed important Brucella virulence mechanisms of pathogenesis. Our publication in Science has identified a highly novel concept where Brucella utilizes blue light to increase its virulence similar to some plant bacterial pathogens. Further, our studies have revealed bacterial second messengers that regulate virulence, quorum sensing mechanisms permitting bacteria to evaluate their environment, and a genomic island that controls synthesis of its lipopolysaccharide surface. Discussions are ongoing with a vaccine company for application of this genomic island knowledge in a Brucella vaccine by the U.S. lab. Also, our new technology of bioengineering bioluminescent Brucella has resulted in a spin-off application for diagnosis of Brucella infected animals by the Israeli lab by prioritizing bacterial diagnosis over serological diagnosis.
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Katzir, Nurit, James Giovannoni, Marla Binzel, Efraim Lewinsohn, Joseph Burger, and Arthur Schaffer. Genomic Approach to the Improvement of Fruit Quality in Melon (Cucumis melo) and Related Cucurbit Crops II: Functional Genomics. United States Department of Agriculture, January 2010. http://dx.doi.org/10.32747/2010.7592123.bard.
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Background: Genomics tools for enhancement of melon research, with an emphasis on fruit, were developed through a previous BARD project of the PIs (IS -333-02). These included the first public melon EST collection, a database to relay this information to the research community and a publicly available microarray. The current project (IS-3877- 06) aimed to apply these tools for identification of important genes for improvement of melon (Cucumis melo) fruit quality. Specifically, the research plans included expression analysis using the microarray and functional analyses of selected genes. The original project objectives, as they appeared in the approved project, were: Objective 1: Utilization of a public melon microarray developed under the existing project to characterize melon transcriptome activity during the ripening of normal melon fruit (cv. Galia) in order to provide a basis for both a general view of melon transcriptome activity during ripening and for comparison with existing transcriptome data of developing tomato and pepper fruit. Objective 2: Utilization of the same public melon microarray to characterize melon transcriptome activity in lines available in the collection of the Israeli group, focusing on sugar, organic acids and aroma metabolism, so as to identify potentially useful candidates for functional analysis and possible manipulation, through comparison with the general fruit development profile resulting from (1) above. Objective 3: Expansion of our existing melon EST database to include publicly available gene expression data and query tools, as the US group has done with tomato. Objective 4: Selection of 6-8 candidate genes for functional analysis and development of DNA constructs for repression or over-expression. Objective 5: Creation of transgenic melon lines, or transgenic heterologous systems (e.g. E. coli or tomato), to assess putative functions and potential as tools for molecular enhancement of melon fruit quality, using the candidate gene constructs from (4).
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Katzir, Nurit, James Giovannoni, and Joseph Burger. Genomic approach to the improvement of fruit quality in melon (Cucumis melo) and related cucurbit crops. United States Department of Agriculture, June 2006. http://dx.doi.org/10.32747/2006.7587224.bard.
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Fruit quality is determined by numerous genetic traits that affect taste, aroma, texture, pigmentation, nutritional value and duration of shelf-life. The molecular basis of many of these important traits is poorly understood and it’s understanding offers an excellent opportunity for adding value to agricultural products. Improvement of melon fruit quality was the primary goal of the project. The original objectives of the project were: The isolation of a minimum of 1000 fruit specific ESTs. The development of a microarray of melon fruit ESTs. The analysis of gene expression in melon using melon and tomato fruit enriched microarrays. A comprehensive study of fruit gene expression of the major cucurbit crops. In our current project we have focused on the development of genomics tools for the enhancement of melon research with an emphasis on fruit, specifically the first public melon EST collection. We have also developed a database to relay this information to the research community and developed a publicly available microarray. The release of this information was one of the catalysts for the establishment of the International Cucurbit Genomic Initiative (ICuGI, Barcelona, Spain, July 2005) aimed at collecting and generating up to 100,000 melon EST sequences in 2006, leveraging a significant expansion of melon genomic resources. A total of 1000 ESTs were promised under the original proposal (Objective 1). Non-subtracted mature fruit and young fruit flesh of a climacteric variety in addition to a non-climacteric variety resulted in the majority of additional EST sequences for a total of 4800 attempted reads. 3731 high quality sequences from independent ESTs were assembled, representing 2,467 melon unigenes (1,873 singletons, 594 contigs). In comparison, as of June 2004, a total of 170 melon mRNA sequences had been deposited in GENBANK. The current project has thus resulted in nearly five- fold the number of ESTs promised and ca. 15-fold increase in the depth of publicly available melon gene sequences. All of these sequences have been deposited in GENBANK and are also available and searchable via multiple approaches in the public database (http://melon.bti.cornell.edu). Our database was selected as the central location for presentation of public melon EST data of the International Cucurbit Genomic Initiative. With the available unigenes we recently constructed a microarray, which was successfully applied in hybridizations (planned public release by August 2006). Current gene expression analyses focus on fruit development and on comparative studies between climacteric and non-climacteric melons. Earlier, expression profiling was conducted using macroarrays developed at the preliminary stage of the project. This analysis replaced the study of tomato microarray following the recommendations of the reviewers and the panel of the original project. Comparative study between melon and other cucurbit crops have begun, mainly with watermelon, in collaboration with Dr. Amnon Levi (USDA-ARS). In conclusion, all four objectives have been addressed and achieved. In the continuation project that have been approved we plan to apply the genomic tools developed here to achieve detailed functional analyses of genes associated with major metabolic pathway.
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Heifetz, Yael, and Michael Bender. Success and failure in insect fertilization and reproduction - the role of the female accessory glands. United States Department of Agriculture, December 2006. http://dx.doi.org/10.32747/2006.7695586.bard.
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The research problem. Understanding of insect reproduction has been critical to the design of insect pest control strategies including disruptions of mate-finding, courtship and sperm transfer by male insects. It is well known that males transfer proteins to females during mating that profoundly affect female reproductive physiology, but little is known about the molecular basis of female mating response and no attempts have yet been made to interfere with female post-mating responses that directly bear on the efficacy of fertilization. The female reproductive tract provides a crucial environment for the events of fertilization yet thus far those events and the role of the female tract in influencing them are poorly understood. For this project, we have chosen to focus on the lower reproductive tract because it is the site of two processes critical to reproduction: sperm management (storage, maintenance, and release from storage) and fertilization. E,fforts during this project period centered on the elucidation of mating responses in the female lower reproductive tract The central goals of this project were: 1. To identify mating-responsive genes in the female lower reproductive tract using DNA microarray technology. 2. In parallel, to identify mating-responsive genes in these tissues using proteomic assays (2D gels and LC-MS/MS techniques). 3. To integrate proteomic and genomic analyses of reproductive tract gene expression to identify significant genes for functional analysis. Our main achievements were: 1. Identification of mating-responsive genes in the female lower reproductive tract. We identified 539 mating-responsive genes using genomic and proteomic approaches. This analysis revealed a shift from gene silencing to gene activation soon after mating and a peak in differential gene expression at 6 hours post-mating. In addition, comparison of the two datasets revealed an expression pattern consistent with the model that important reproductive proteins are pre-programmed for synthesis prior to mating. This work was published in Mack et al. (2006). Validation experiments using real-time PCR techniques suggest that microarray assays provide a conservativestimate of the true transcriptional activity in reproductive tissues. 2.lntegration of proteomics and genomics data sets. We compared the expression profiles from DNA microarray data with the proteins identified in our proteomic experiments. Although comparing the two data sets poses analyical challenges, it provides a more complete view of gene expression as well as insights into how specific genes may be regulated. This work was published in Mack et al. (2006). 3. Development of primary reproductive tract cell cultures. We developed primary cell cultures of dispersed reproductive tract cell types and determined conditions for organ culture of the entire reproductive tract. This work will allow us to rapidly screen mating-responsive genes for a variety of reproductive-tract specifi c functions. Scientific and agricultural significance. Together, these studies have defined the genetic response to mating in a part of the female reproductive tract that is critical for successful fertllization and have identified alarge set of mating-responsive genes. This work is the first to combine both genomic and proteomic approaches in determining female mating response in these tissues and has provided important insights into insect reproductive behavior.
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Meir, Shimon, Michael Reid, Cai-Zhong Jiang, Amnon Lers, and Sonia Philosoph-Hadas. Molecular Studies of Postharvest Leaf and Flower Abscission. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7696523.bard.
Full textAbstract:
Original objectives: Understanding the regulation of abscission competence by exploring the nature and function of auxin-related gene expression changes in the leaf and pedicelAZs of tomato (as a model system), was the main goal of the previously submitted proposal. We proposed to achieve this goal by using microarray GeneChip analysis, to identify potential target genes for functional analysis by virus-induced gene silencing (VIGS). To increase the potential of accomplishing the objectives of the previously submitted proposal, we were asked by BARD to show feasibility for the use of these two modern techniques in our abscission system. Thus, the following new objectives were outlined for the one-year feasibility study: 1.to demonstrate the feasibility of the VIGS system in tomato to perform functional analysis of known abscission-related genes; 2. to demonstrate that by using microarray analysis we can identify target genes for further VIGS functional analysis. Background to the topic: It is a generally accepted model that auxin flux through the abscission zone (AZ) prevents organ abscission by rendering the AZ insensitive to ethylene. However, the molecular mechanisms responsible for acquisition of abscission competence and the way in which the auxin gradient modulates it are still unknown. Understanding this basic stage of the abscission process may provide us with future tools to control abscission for agricultural applications. Based on our previous study, performed to investigate the molecular changes occurring in leaf and stem AZs of MirabillisJalapaL., we have expanded our research to tomato, using genomic approaches that include modern techniques for gene discovery and functional gene characterization. In our one-year feasibility study, the US team has established a useful system for VIGS in tomato, using vectors based on the tobacco rattle virus (TRV), a Lcreporter gene for silencing (involved in regulation of anthocyanin biosynthesis), and the gene of interest. In parallel, the Israeli team has used the newly released Affymetrix Tomato GeneChip to measure gene expression in AZ and non-AZ tissues at various time points after flower removal, when increased sensitivity to ethylene is acquired prior to abscission (at 0-8 h), and during pedicelabscission (at 14 h). In addition, gene expression was measured in the pedicel AZ pretreated with the ethylene action inhibitor, 1-methylcyclopropene (1-MCP) before flower removal, to block any direct effects of ethylene. Major conclusions, solutions and achievements: 1) The feasibility study unequivocally established that VIGS is an ideal tool for testing the function of genes with putative roles in abscission; 2) The newly released Affymetrix Tomato GeneChip was found to be an excellent tool to identify AZ genes possibly involved in regulation and execution of abscission. The VIGS-based study allowed us to show that TAPG, a polygalacturonase specifically associated with the tomato AZ, is a key enzyme in the abscission process. Using the newly released Affymetrix Tomato GeneChip we have identified potential abscission regulatory genes as well as new AZ-specific genes, the expression of which was modified after flower removal. These include: members of the Aux/IAAgene family, ethylene signal transduction-related genes, early and late expressed transcription factors, genes which encode post-translational regulators whose expression was modified specifically in the AZ, and many additional novel AZ-specific genes which were previously not associated with abscission. This microarray analysis allowed us to select an initial set of target genes for further functional analysis by VIGS. Implications: Our success in achieving the two objectives of this feasibility study provides us with a solid basis for further research outlined in the original proposal. This will significantly increase the probability of success of a full 3-year project. Additionally, our feasibility study yielded highly innovative results, as they represent the first direct demonstration of the functional involvement of a TAPG in abscission, and the first microarray analysis of the abscission process. Using these approaches we could identify a large number of genes involved in abscission regulation, initiation and execution, and in auxin-ethylene cross-talk, which are of great importance, and could enable their potential functional analysis by VIGS.
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Lichter, Amnon, Gopi K. Podila, and Maria R. Davis. Identification of Genetic Determinants that Facilitate Development of B. cinerea at Low Temperature and its Postharvest Pathogenicity. United States Department of Agriculture, March 2011. http://dx.doi.org/10.32747/2011.7592641.bard.
Full textAbstract:
Botrytis cinerea is the postharvest pathogen of many agricultural produce with table grapes, strawberries and tomatoes as major targets. The high efficiency with which B. cinerea causes disease on these produce during storage is attributed in part due to its exceptional ability to develop at very low temperature. Our major goal was to understand the genetic determinants which enable it to develop at low temperature. The specific research objectives were: 1. Identify expression pattern of genes in a coldenriched cDNA library. 2. Identify B. cinerea orthologs of cold-induced genes 3. Profile protein expression and secretion at low temperature on strawberry and grape supplemented media. 4. Test novel methods for the functional analysis of coldresponsive genes. Objective 1 was modified during the research because a microarray platform became available and it allowed us to probe the whole set of candidate genes according to the sequence of 2 strains of the fungus, BO5.10 and T4. The results of this experiment allowed us to validate some of our earlier observations which referred to genes which were the product of a SSH suppression-subtraction library. Before the microarray became available during 2008 we also analyzed the expression of 15 orthologs of cold-induced genes and some of these results were also validated by the microarray experiment. One of our goals was also to perform functional analysis of cold-induced genes. This goal was hampered for 3 years because current methodology for transformation with ‘protoplasts’ failed to deliver knockouts of bacteriordopsin-like (bR) gene which was our primary target for functional analysis. Consequently, we developed 2 alternative transformation platforms, one which involves an air-gun based technique and another which involves DNA injection into sclerotia. Both techniques show great promise and have been validated using different constructs. This contribution is likely to serve the scientific community in the near future. Using these technologies we generated gene knockout constructs of 2 genes and have tested there effect on survival of the fungus at low temperature. With reference to the bR genes our results show that it has a significant effect on mycelial growth of the B. cinerea and the mutants have retarded development at extreme conditions of ionic stress, osmotic stress and low temperature. Another gene of unknown function, HP1 is still under analysis. An ortholog of the yeast cold-induced gene, CCH1 which encodes a calcium tunnel and was shown to be cold-induced in B. cinerea was recently cloned and used to complement yeast mutants and rescue them from cold-sensitivity. One of the significant findings of the microarray study involves a T2 ribonuclease which was validated to be cold-induced by qPCR analysis. This and other genes will serve for future studies. In the frame of the study we also screened a population of 631 natural B. cinerea isolates for development at low temperature and have identified several strains with much higher and lower capacity to develop at low temperature. These strains are likely to be used in the future as candidates for further functional analysis. The major conclusions from the above research point to specific targets of cold-induced genes which are likely to play a role in cold tolerance. One of the most significant observations from the microarray study is that low temperature does not induce ‘general stress response in B. cinerea, which is in agreement to its exceptional capacity to develop at low temperature. Due to the tragic murder of the Co-PI Maria R. Davis and GopiPodila on Feb. 2010 it is impossible to deliver their contribution to the research. The information of the PI is that they failed to deliver objective 4 and none of the information which relates to objective 3 has been delivered to the PI before the murder or in a visit to U. Alabama during June, 2010. Therefore, this report is based solely on the IS data.