scholarly journals 1N1630 Functional analysis of the MAP4 Pro-rich region mutant polypeptides

2002 ◽  
Vol 42 (supplement2) ◽  
pp. S85
Author(s):  
M. Aosaki ◽  
K. Matsushima ◽  
K. Tokuraku ◽  
S. Kotani
Genetics ◽  
2009 ◽  
Vol 183 (3) ◽  
pp. 853-860 ◽  
Author(s):  
Leila Feiz ◽  
Brian S. Beecher ◽  
John M. Martin ◽  
Michael J. Giroux

In planta analysis of protein function in a crop plant could lead to improvements in understanding protein structure/function relationships as well as selective agronomic or end product quality improvements. The requirements for successful in planta analysis are a high mutation rate, an efficient screening method, and a trait with high heritability. Two ideal targets for functional analysis are the Puroindoline a and Puroindoline b (Pina and Pinb, respectively) genes, which together compose the wheat (Triticum aestivum L.) Ha locus that controls grain texture and many wheat end-use properties. Puroindolines (PINs) together impart soft texture, and mutations in either PIN result in hard seed texture. Studies of the PINs' mode of action are limited by low allelic variation. To create new Pin alleles and identify critical function-determining regions, Pin point mutations were created in planta via EMS treatment of a soft wheat. Grain hardness of 46 unique PIN missense alleles was then measured using segregating F2:F3 populations. The impact of individual missense alleles upon PIN function, as measured by grain hardness, ranged from neutral (74%) to intermediate to function abolishing. The percentage of function-abolishing mutations among mutations occurring in both PINA and PINB was higher for PINB, indicating that PINB is more critical to overall Ha function. This is contrary to expectations in that PINB is not as well conserved as PINA. All function-abolishing mutations resulted from structure-disrupting mutations or from missense mutations occurring near the Tryptophan-rich region. This study demonstrates the feasibility of in planta functional analysis of wheat proteins and that the Tryptophan-rich region is the most important region of both PINA and PINB.


Author(s):  
L. A. Giannuzzi ◽  
A. S. Ramani ◽  
P. R. Howell ◽  
H. W. Pickering ◽  
W. R. Bitler

The δ phase is a Zn-rich intermetallic, having a composition range of ∼ 86.5 - 92.0 atomic percent Zn, and is stable up to 665°C. The stoichiometry of the δ phase has been reported as FeZn7 and FeZn10 The deviation in stoichiometry can be attributed to variations in alloy composition used by each investigator. The structure of the δ phase, as determined by powder x-ray diffraction, is hexagonal (P63mc or P63/mmc) with cell dimensions a = 1.28 nm, c = 5.76 nm, and 555±8 atoms per unit cell. Later work suggested that the layer produced by hot-dip galvanizing should be considered as two distinct phases which are characterized by their morphological differences, namely: the iron-rich region with a compact appearance (δk) and the zinc-rich region with a columnar or palisade microstructure (δp). The sub-division of the δ phase was also based on differences in diffusion behavior, and a concentration discontinuity across the δp/δk boundary. However, work utilizing Weisenberg photographs on δ single crystals reported that the variation in lattice parameters with composition was small and hence, structurally, the δk phase and the δp phase were the same and should be thought of as a single phase, δ. Bastin et al. determined the average cell dimensions to be a = 1.28 nm and c = 5.71 nm, and suggested that perhaps some kind of ordering process, which would not be observed by x-ray diffraction, may be responsible for the morphological differences within the δ phase.


Author(s):  
S. E. Keckler ◽  
D. M. Dabbs ◽  
N. Yao ◽  
I. A. Aksay

Cellular organic structures such as wood can be used as scaffolds for the synthesis of complex structures of organic/ceramic nanocomposites. The wood cell is a fiber-reinforced resin composite of cellulose fibers in a lignin matrix. A single cell wall, containing several layers of different fiber orientations and lignin content, is separated from its neighboring wall by the middle lamella, a lignin-rich region. In order to achieve total mineralization, deposition on and in the cell wall must be achieved. Geological fossilization of wood occurs as permineralization (filling the void spaces with mineral) and petrifaction (mineralizing the cell wall as the organic component decays) through infiltration of wood with inorganics after growth. Conversely, living plants can incorporate inorganics into their cells and in some cases into the cell walls during growth. In a recent study, we mimicked geological fossilization by infiltrating inorganic precursors into wood cells in order to enhance the properties of wood. In the current work, we use electron microscopy to examine the structure of silica formed in the cell walls after infiltration of tetraethoxysilane (TEOS).


2003 ◽  
Vol 19 (3) ◽  
pp. 164-174 ◽  
Author(s):  
Stephen N. Haynes ◽  
Andrew E. Williams

Summary: We review the rationale for behavioral clinical case formulations and emphasize the role of the functional analysis in the design of individualized treatments. Standardized treatments may not be optimally effective for clients who have multiple behavior problems. These problems can affect each other in complex ways and each behavior problem can be influenced by multiple, interacting causal variables. The mechanisms of action of standardized treatments may not always address the most important causal variables for a client's behavior problems. The functional analysis integrates judgments about the client's behavior problems, important causal variables, and functional relations among variables. The functional analysis aids treatment decisions by helping the clinician estimate the relative magnitude of effect of each causal variable on the client's behavior problems, so that the most effective treatments can be selected. The parameters of, and issues associated with, a functional analysis and Functional Analytic Clinical Case Models (FACCM) are illustrated with a clinical case. The task of selecting the best treatment for a client is complicated because treatments differ in their level of specificity and have unequally weighted mechanisms of action. Further, a treatment's mechanism of action is often unknown.


1958 ◽  
Vol 3 (6) ◽  
pp. 158-160
Author(s):  
LAWRENCE SCHLESINGER

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