scholarly journals Gene Expression Profile Changes in Cotton Root and Hypocotyl Tissues in Response to Infection with Fusarium oxysporum f. sp. vasinfectum

2004 ◽  
Vol 17 (6) ◽  
pp. 654-667 ◽  
Author(s):  
Caitriona Dowd ◽  
Iain W. Wilson ◽  
Helen McFadden

Microarray analysis of large-scale temporal and tissue-specific plant gene expression changes occurring during a susceptible plant-pathogen interaction revealed different gene expression profile changes in cotton root and hypocotyl tissues. In hypocotyl tissues infected with Fusarium oxysporum f. sp. vasinfectum, increased expression of defense-related genes was observed, whereas few changes in the expression levels of defense-related genes were found in infected root tissues. In infected roots, more plant genes were repressed than were induced, especially at the earlier stages of infection. Although many known cotton defense responses were identified, including induction of pathogenesis-related genes and gossypol biosynthesis genes, potential new defense responses also were identified, such as the biosynthesis of lignans. Many of the stress-related gene responses were common to both tissues. The repression of drought-responsive proteins such as aquaporins in both roots and hypocotyls represents a previously unreported response of a host to pathogen attack that may be specific to vascular wilt diseases. Gene expression results implicated the phytohormones ethylene and auxin in the disease process. Biochemical analysis of hormone level changes supported this observation.

PLoS ONE ◽  
2017 ◽  
Vol 12 (3) ◽  
pp. e0170358 ◽  
Author(s):  
Tao Lu ◽  
Ye Zhang ◽  
Yared Kidane ◽  
Alan Feiveson ◽  
Louis Stodieck ◽  
...  

2004 ◽  
Vol 10 (11) ◽  
pp. 3629-3638 ◽  
Author(s):  
Eiji Tamoto ◽  
Mitsuhiro Tada ◽  
Katsuhiko Murakawa ◽  
Minoru Takada ◽  
Gaku Shindo ◽  
...  

2013 ◽  
Vol 189 (4S) ◽  
Author(s):  
Trilla Enrique ◽  
Lorente David ◽  
López-Pacios Miguel Angel ◽  
Cuadros Thaïs ◽  
Vilà Maya ◽  
...  

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 2043-2043
Author(s):  
Hiroyuki Mano ◽  
Yoshihiro Yamashita

Abstract AML is a clonal disorder of immature hematopoietic blasts and has a variable clinical outcome. Current classification of AML is based predominantly on the cytogenetic abnormalities and morphology of the malignant blasts and is not always helpful for optimization of treatment strategy. It is, for instance, very difficult to predict the prognosis of AML patients with a normal karyotype, who constitute ~50% of the AML population. DNA microarray analysis has the potential to provide a novel stratification scheme for AML patients, which is based on gene expression profile, and might help to predict the prognosis of, and optimize the treatment strategy for, each affected individual. However, leukemic blasts derived from bone marrow (BM) of AML-related disorders, are not homogeneous. The blasts may constitute from 20% to almost 100% of mononuclear cells (MNCs) in the marrow. Furthermore, given that many leukemic blasts possess the ability to differentiate to a certain extent, the marrow of AML patients contains not only the immature blasts (leukemic stem clone) but also differentiated blasts. A simple comparison of BM MNCs among heterogeneous AML patients is thus likely to reveal a large number of changes in gene expression that only reflect differences either in the percentage of blasts or in the differentiation ability of the blasts. To minimize such population-shift effects in microarray analyses, we established a large-scale cell depository “Blast Bank” for the storage of CD133 (AC133)-positive hematopoietic stem cell-like fractions from individuals with a wide range of hematopoietic disorders. In the present study, we have used Affymetrix HGU133 A&B microarrays to measure the expression profiles of ~33,000 genes in the Blast Bank specimens of 99 adults with AML-related disorders: 83 individuals with AML and 16 patients in the RAEB stage of MDS. In contrast to the previous microarray analyses of BM MNCs of AML, unsupervised hierarchical clustering of the subjects based on the expression profile did not separate the patients into FAB subtype-matched subgroups. Comparison of gene expression profile between the long-time and short-time survivors has identified a small number of outcome-related genes. Supervised class prediction, based on these genes, with k-nearest neighbor method or Cox proportional hazard model both succeeded to clearly separate individuals into subgroups with statistically distinct prognoses. Our analysis may pave a way toward the expression profile-based novel stratification scheme for AML.


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