转录调控因子BglR对东方肉座菌纤维素酶表达的影响
Journal of Xiamen University(Natural Science)（2018）
Abstract
东方肉座菌(Trichoderma orientalis)EU7-22菌株是一株高效分泌纤维素酶的丝状真菌,对生物质转化具有重要价值.采用交错式热不对称PCR(TAIL-PCR)方法克隆得到纤维素酶转录调控因子bglr基因及其上下游序列,并经同源交换获得bglr基因敲除的菌株EU7-22Δbglr.其生产的滤纸酶、外切葡聚糖酶、木聚糖酶活力和分泌蛋白最高值较对照菌株分别增加了39％,22％,16％和20％,而β-葡萄糖苷酶活力下降47％,β-葡萄糖苷酶基因bgl1的表达量显著降低.进一步分析发现BglR的缺失抑制了菌株EU7-22在以特定多糖/寡糖为碳源的培养基上的生长,且引起发酵液pH值的改变.上述结果证明BglR对β-葡萄糖苷酶起正调控作用,可为构建高效降解纤维素的工程菌株提供参考.
More求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined