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The kick-in system: a novel rapid knock-in strategy.

ABSTRACT: Knock-in mouse models have contributed tremendously to our understanding of human disorders. However, generation of knock-in animals requires a significant investment of time and effort. We addressed this problem by developing a novel knock-in system that circumvents several traditional challenges by establishing stem cells with acceptor elements enveloping a particular genomic target. Once established, these acceptor embryonic stem (ES) cells are efficient at directionally incorporating mutated target DNA using modified Cre/lox technology. This is advantageous, because knock-ins are not restricted to one a priori selected variation. Rather, it is possible to generate several mutant animal lines harboring desired alterations in the targeted area. Acceptor ES cell generation is the rate-limiting step, lasting approximately 2 months. Subsequent manipulations toward animal production require an additional 8 weeks, but this delimits the full period from conception of the genetic alteration to its animal incorporation. We call this system a "kick-in" to emphasize its unique characteristics of speed and convenience. To demonstrate the functionality of the kick-in methodology, we generated two mouse lines with separate mutant versions of the voltage-dependent potassium channel Kv7.2 (Kcnq2): p.Tyr284Cys (Y284C) and p.Ala306Thr (A306T); both variations have been associated with benign familial neonatal epilepsy. Adult mice homozygous for Y284C, heretofore unexamined in animals, presented with spontaneous seizures, whereas A306T homozygotes died early. Heterozygous mice of both lines showed increased sensitivity to pentylenetetrazole, possibly due to a reduction in M-current in CA1 hippocampal pyramidal neurons. Our observations for the A306T animals match those obtained with traditional knock-in technology, demonstrating that the kick-in system can readily generate mice bearing various mutations, making it a suitable feeder technology toward streamlined phenotyping.

SUBMITTER: Tomonoh Y

PROVIDER: S-EPMC3929540 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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The kick-in system: a novel rapid knock-in strategy.

Tomonoh Yuko Y Deshimaru Masanobu M Araki Kimi K Miyazaki Yasuhiro Y Arasaki Tomoko T Tanaka Yasuyoshi Y Kitamura Haruna H Mori Fumiaki F Wakabayashi Koichi K Yamashita Sayaka S Saito Ryo R Itoh Masayuki M Uchida Taku T Yamada Junko J Migita Keisuke K Ueno Shinya S Kitaura Hiroki H Kakita Akiyoshi A Lossin Christoph C Takano Yukio Y Hirose Shinichi S

PloS one 20140219 2

Knock-in mouse models have contributed tremendously to our understanding of human disorders. However, generation of knock-in animals requires a significant investment of time and effort. We addressed this problem by developing a novel knock-in system that circumvents several traditional challenges by establishing stem cells with acceptor elements enveloping a particular genomic target. Once established, these acceptor embryonic stem (ES) cells are efficient at directionally incorporating mutated ...[more]

PMID: 24586341

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Project description:Group analysis of neuroimaging data is a vital tool for identifying anatomical and functional variations related to diseases as well as normal biological processes. The analyses are often performed on a large number of highly correlated measurements using a relatively smaller number of samples. Despite the correlation structure, the most widely used approach is to analyze the data using univariate methods followed by post-hoc corrections that try to account for the data's multivariate nature. Although widely used, this approach may fail to recover from the adverse effects of the initial analysis when local effects are not strong. Multivariate pattern analysis (MVPA) is a powerful alternative to the univariate approach for identifying relevant variations. Jointly analyzing all the measures, MVPA techniques can detect global effects even when individual local effects are too weak to detect with univariate analysis. Current approaches are successful in identifying variations that yield highly predictive and compact models. However, they suffer from lessened sensitivity and instabilities in identification of relevant variations. Furthermore, current methods' user-defined parameters are often unintuitive and difficult to determine. In this article, we propose a novel MVPA method for group analysis of high-dimensional data that overcomes the drawbacks of the current techniques. Our approach explicitly aims to identify all relevant variations using a "knock-out" strategy and the Random Forest algorithm. In evaluations with synthetic datasets the proposed method achieved substantially higher sensitivity and accuracy than the state-of-the-art MVPA methods, and outperformed the univariate approach when the effect size is low. In experiments with real datasets the proposed method identified regions beyond the univariate approach, while other MVPA methods failed to replicate the univariate results. More importantly, in a reproducibility study with the well-known ADNI dataset the proposed method yielded higher stability and power than the univariate approach.

Project description:BackgroundHigh amounts of insoluble substrates exist in the traditional solid-state fermentation (SSF) system. The presence of these substrates complicates the determination of microbial biomass. Thus, enzyme activity is used as the sole index for the optimization of the traditional SSF system, and the relationship between microbial growth and enzyme synthesis is always ignored. This study was conducted to address this deficiency. All soluble nutrients from tea stalk were extracted using water. The aqueous extract was then mixed with polyurethane sponge to establish a modified SSF system, which was then used to conduct tannase production. With this system, biomass, enzyme activity, and enzyme productivity could be measured rationally and accurately. Thus, the association between biomass and enzyme activity could be easily identified, and the shortcomings of traditional SSF could be addressed.ResultsDifferent carbon and nitrogen sources exerted different effects on microbial growth and enzyme production. Single-factor experiments showed that glucose and yeast extract greatly improved microbial biomass accumulation and that tannin and (NH4)2SO4 efficiently promoted enzyme productivity. Then, these four factors were optimized through response surface methodology. Tannase activity reached 19.22 U/gds when the added amounts of tannin, glucose, (NH4)2SO4, and yeast extract were 7.49, 8.11, 9.26, and 2.25%, respectively. Tannase activity under the optimized process conditions was 6.36 times higher than that under the initial process conditions. The optimized parameters were directly applied to the traditional tea stalk SSF system. Tannase activity reached 245 U/gds, which is 2.9 times higher than our previously reported value.ConclusionsIn this study, a modified SSF system was established to address the shortcomings of the traditional SSF system. Analysis revealed that enzymatic activity and microbial biomass are closely related, and different carbon and nitrogen sources have different effects on microbial growth and enzyme production. The maximal tannase activity was obtained under the optimal combination of nutrient sources that enhances cell growth and tannase accumulation. Moreover, tannase production through the traditional tea stalk SSF was markedly improved when the optimized parameters were applied. This work provides an innovative approach to bioproduction research through SSF.

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The kick-in system: a novel rapid knock-in strategy.

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The kick-in system: a novel rapid knock-in strategy.

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