已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Genome-scale metabolic model of the versatile bacterium Paracoccus denitrificans Pd1222

脱氮副球菌 格式化 甲酸脱氢酶 生物量(生态学) 代谢工程 生物化学 生物 化学 基因 农学 催化作用
作者
Sergio Bordel,Diego Martín-González,Tim Börner,Raúl Muñoz,Fernando Santos-Beneit
出处
期刊:MSystems [American Society for Microbiology]
标识
DOI:10.1128/msystems.01077-23
摘要

ABSTRACT A genome scale metabolic model of the bacterium Paracoccus denitrificans has been constructed. The model containing 972 metabolic genes, 1,371 reactions, and 1,388 unique metabolites has been reconstructed. The model was used to carry out quantitative predictions of biomass yields on 10 different carbon sources under aerobic conditions. Yields on C1 compounds suggest that formate is oxidized by a formate dehydrogenase O, which uses ubiquinone as redox co-factor. The model also predicted the threshold methanol/mannitol uptake ratio, above which ribulose biphosphate carboxylase has to be expressed in order to optimize biomass yields. Biomass yields on acetate, formate, and succinate, when NO 3 − is used as electron acceptor, were also predicted correctly. The model reconstruction revealed the capability of P. denitrificans to grow on several non-conventional substrates such as adipic acid, 1,4-butanediol, 1,3-butanediol, and ethylene glycol. The capacity to grow on these substrates was tested experimentally, and the experimental biomass yields on these substrates were accurately predicted by the model. IMPORTANCE Paracoccus denitrificans has been broadly used as a model denitrifying organism. It grows on a large portfolio of carbon sources, under aerobic and anoxic conditions. These characteristics, together with its amenability to genetic manipulations, make P. denitrificans a promising cell factory for industrial biotechnology. This paper presents and validates the first functional genome-scale metabolic model for P. denitrificans , which is a key tool to enable P. denitrificans as a platform for metabolic engineering and industrial biotechnology. Optimization of the biomass yield led to accurate predictions in a broad scope of substrates.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
852应助canghainayun采纳,获得10
1秒前
老李完成签到,获得积分10
3秒前
nhzz2023完成签到 ,获得积分0
6秒前
这就去学习完成签到 ,获得积分10
7秒前
AZN完成签到,获得积分10
13秒前
哒哒哒完成签到 ,获得积分10
13秒前
FashionBoy应助闪晔狼采纳,获得10
18秒前
u深度完成签到 ,获得积分10
19秒前
闾丘惜萱完成签到,获得积分10
19秒前
正直的山雁完成签到,获得积分10
19秒前
浚稚完成签到 ,获得积分10
19秒前
20秒前
斐然诗完成签到 ,获得积分10
24秒前
科研通AI2S应助肚子幽伤采纳,获得10
25秒前
萌宠发布了新的文献求助10
25秒前
28秒前
回复对方完成签到,获得积分10
29秒前
GGbong发布了新的文献求助10
30秒前
苦逼的医学生陳完成签到 ,获得积分10
32秒前
河鲸完成签到 ,获得积分10
34秒前
冷酷愚志完成签到,获得积分10
35秒前
古炮完成签到 ,获得积分10
37秒前
光亮的半山完成签到,获得积分10
40秒前
嘉心糖完成签到,获得积分10
40秒前
肚子幽伤完成签到,获得积分10
41秒前
41秒前
sen123完成签到 ,获得积分10
42秒前
gwh完成签到 ,获得积分10
42秒前
42秒前
44秒前
48秒前
乐生发布了新的文献求助10
48秒前
闪晔狼发布了新的文献求助10
50秒前
racill完成签到 ,获得积分10
50秒前
KongLG完成签到 ,获得积分10
52秒前
黄毛虎完成签到 ,获得积分10
53秒前
lbz6805完成签到 ,获得积分10
54秒前
54秒前
fei完成签到 ,获得积分10
56秒前
Wei完成签到 ,获得积分10
56秒前
高分求助中
Biology and Ecology of Atlantic Cod 1500
LNG地下式貯槽指針(JGA指-107-19)(Recommended practice for LNG inground storage) 1000
Second Language Writing (2nd Edition) by Ken Hyland, 2019 1000
Generalized Linear Mixed Models 第二版 1000
rhetoric, logic and argumentation: a guide to student writers 1000
QMS18Ed2 | process management. 2nd ed 1000
Eric Dunning and the Sociology of Sport 850
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 免疫学 细胞生物学 电极
热门帖子
关注 科研通微信公众号,转发送积分 2921922
求助须知:如何正确求助?哪些是违规求助? 2565140
关于积分的说明 6937016
捐赠科研通 2222049
什么是DOI,文献DOI怎么找? 1181317
版权声明 588801
科研通“疑难数据库(出版商)”最低求助积分说明 577913