The expression of class I patatin gene fusions in transgenic potato varies with both gene and cultivar

1991 ◽  
Vol 16 (1) ◽  
pp. 153-160 ◽  
Author(s):  
K. S. Blundy ◽  
M. A. C. Blundy ◽  
D. Carter ◽  
F. Wilson ◽  
W. D. Park ◽  
...  
1995 ◽  
Vol 11 (6) ◽  
pp. 96-103 ◽  
Author(s):  
I. M. Yefimenko ◽  
T. V. Medvedeva ◽  
P. G. Kovalenko ◽  
K. G. Gazaryan ◽  
A. P. Galkin

2005 ◽  
Vol 2 (1) ◽  
pp. 7-11 ◽  
Author(s):  
Si Huai-Jun ◽  
Liu Jun ◽  
Xie Cong-Hua

AbstractAn antisense class I patatin gene under control of the CaMV 35S promoter was introduced into potato (Solanum tuberosum) cultivar E-potato 3 using the Agrobacterium tumefaciens system. PCR amplification and PCR–Southern blot analysis indicated that the antisense class I patatin gene had been integrated into the potato genome. Northern hybridization analysis showed that the antisense gene transcribed normally in the transgenic potato plants and resulted in a reduction of endogenous class I patatin mRNA. Total soluble protein content and lipid acyl hydrolase activity of microtubers, derived from transformed plants, decreased by a maximum of 36.4% and 31.4%, respectively, compared with control plants. The expression of this antisense gene also resulted in reductions of the plantlets forming tubers, tubers per plantlet and the effective tubers (≥50 mg) of the transformed plants.


1989 ◽  
Vol 8 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Mario Rocha-Sosa ◽  
Uwe Sonnewald ◽  
Wolf Frommer ◽  
Marina Stratmann ◽  
Jeff Schell ◽  
...  
Keyword(s):  
Class I ◽  

2008 ◽  
Vol 88 (4) ◽  
pp. 593-598 ◽  
Author(s):  
Huaijun Si ◽  
Jun Liu ◽  
Jian Huang ◽  
Conghua Xie

Expression of a class I patatin cDNA clone, SK24-1, in Escherichia coli revealed that the cDNA clone possessed lipid acyl hydrolase (LAH) activity. Transformed potato plants were obtained via Agrobacterium-mediated transformation using the chimeric constructs containing the sense and antisense cDNA under the control cauliflower mosaic virus 35S (CaMV 35S) promoter. In some sense transformed plants, both sense patatin RNA and LAH activity were increased and further resulted in a significant increase of percentage of plantlets that formed microtubers and numbers of microtubers per plantlet in vitro. All antisense plants displayed a reduction in LAH activity. Both sense and antisense RNA could be detected in antisense plants, but transcripts of antisense RNA resulted in a reduction of endogenous sense RNA. Moreover, expression of antisense cDNA in some antisense transformed plants led to a significant decrease in the number of microtubers formed. These results suggest that SK24-1 was involved in regulating microtuber formation. Key words: Patatin, potato, Escherichia coli, sense RNA, antisense RNA


2020 ◽  
Vol 8 (Suppl 2) ◽  
pp. A8-A8
Author(s):  
YW Asmann ◽  
Y Ren ◽  
DP Wickland ◽  
V Sarangi ◽  
S Tian ◽  
...  

BackgroundTumors acquire numerous mutations during development and progression. These mutations give rise to neoantigens that can be recognized by T cells and generate antibodies. Tumor mutational burden (TMB) is correlated with, and has often been used as a surrogate of, neoantigen load, although that relationship is different depending on cancer types. Recent studies reported correlations between higher TMB and better overall survival after immune checkpoint blockade therapies in bladder, colorectal, head and neck, and lung cancers but not in breast cancer. On the other hand, the relationship between neoantigen load and survival has been controversial in literature. Higher neoantigen load has been linked to better overall survival in ovarian cancer and melanoma, but worse survival in multiple myeloma. Recently, no clear associations were found between neoantigen load and survival in 33 cancer types although only class-I restricted neoantigens were included.Materials and MethodsWe developed a bioinformatics workflow, REAL-neo, for identification, quality control (QC), and prioritization of both class-I and class-II human leukocyte antigen (HLA) bound neoantigens that arise from tumor somatic single nucleotide mutations (SNM), small insertions and deletions (INDEL), and gene fusions. The correlations between TMB and neoantigen load per sample were calculated using Pearson Correlation Coefficient. TMB and neoantigen load comparisons between various groups were performed using Student’s t-test. The survival analyses were performed using the Cox proportional hazards models while correcting for covariates.ResultsWe applied REAL-neo to 835 primary breast tumors in the Cancer Genome Atlas (TCGA) and performed comprehensive profiling and characterization of the predicted neoantigens. SNMs contributed to only 6.25% of the total neoantigens (# of class-I vs. class-II neoantigens = 1: 3.5); INDELs accounted for 57.17% of the total (class-I : class-II= 1:2), and gene fusions were responsible for 36.58% of the total (class-I : class-II = 1:2.2). TMB were positively correlated with total and each sub-categories of neoantigen load (class I: SNM: r = 0.59, p < 2.2E-16; INDEL: r = 0.28, p < 2.2E-16; gene fusion: r = 0.26, p = 2.01E-11; class II: SNM: r = 0.47, p < 2.2E-16; INDEL: r = 0.16, p = 1.7E-05; gene fusion: r = 0.31, p = 4.37E-13). The vast majority (99.75%) of the predicted neoantigens occurred in ≤1% of the cases and 83.76% were patient-specific found in one patient only. Tumors with somatic and germline functional mutations in BRCA1 or BRCA2 genes had higher TMB (p = 2.76E-06) and overall neoantigen load (p = 0.009). Lower HLA class-I and class-II restricted neoantigen loads from SNM and INDEL were found to predict worse overall survival independent of TMB, breast cancer subtypes, tumor infiltrating lymphocyte (TIL) levels, tumor stage, and age at diagnosis (class-I: HR = 1.81, p = 0.04; class-II: HR = 1.89, p = 0.042).ConclusionsOur study highlighted the importance of accurate and comprehensive neoantigen profiling and QC, and is the first to report the predictive value of neoantigen load for overall survival in breast cancer. This work was support by the State of Florida Cancer Center Grant, the bioinformatics program of Mayo Clinic Center for Individualized Medicine, and the Mayo Clinic inter-SPORE development grant.Disclosure InformationY.W. Asmann: None. Y. Ren: None. D.P. Wickland: None. V. Sarangi: None. S. Tian: None. J.M. Carter: None. A.S. Mansfield: None. M.S. Block: None. M.E. Sherman: None. K.L. Knutson: None. Y. Lin: None.


2017 ◽  
Vol 23 (24) ◽  
pp. 7596-7607 ◽  
Author(s):  
Jennifer L. Kalina ◽  
David S. Neilson ◽  
Yen-Yi Lin ◽  
Phineas T. Hamilton ◽  
Alexandra P. Comber ◽  
...  

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