Long-term and high-concentration heavy-metal contamination strongly influences the microbiome and functional genes in Yellow River sediments

基因组 拟杆菌 支流 蛋白质细菌 厚壁菌 微生物群 沉积物 生物 基因 遗传学 16S核糖体RNA 地图学 古生物学 地理
作者
Yong Chen,Yiming Jiang,Haiying Huang,Lichao Mou,Jinlong Ru,Jianhua Zhao,Shan Xiao
出处
期刊:Science of The Total Environment [Elsevier]
卷期号:637-638: 1400-1412 被引量:301
标识
DOI:10.1016/j.scitotenv.2018.05.109
摘要

The world is facing a hard battle against soil pollution such as heavy metals. Metagenome sequencing, 16S rRNA sequencing, and quantitative polymerase chain reaction (qPCR) were used to examine microbial adaptation mechanism to contaminated sediments under natural conditions. Results showed that sediment from a tributary of the Yellow River, which was named Dongdagou River (DDG) supported less bacterial biomass and owned lower richness than sediment from Maqu (MQ), an uncontaminated site in the upper reaches of the Yellow River. Additionally, microbiome structures in these two sites were different. Metagenome sequencing and functional gene annotations revealed that sediment from DDG contains a larger number of genes related to DNA recombination, DNA damage repair, and heavy-metal resistance. KEGG pathway analysis indicated that the sediment of DDG contains a greater number of enzymes associated with heavy-metal resistance and reduction. Additionally, the bacterial phyla Proteobacteria, Bacteroidetes, and Firmicutes, which harbored a larger suite of metal-resistance genes, were found to be the core functional phyla in the contaminated sediments. Furthermore, sediment in DDG owned higher viral abundance, indicating virus-mediated heavy-metal resistance gene transfer might be an adaptation mechanism. In conclusion, microbiome of sediment from DDG has evolved into an integrated system resistant to long-term heavy-metal pollution.
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
刚刚
刚刚
刚刚
CC发布了新的文献求助10
刚刚
yuhang完成签到,获得积分20
刚刚
刚刚
我又帅又红又专完成签到,获得积分20
1秒前
闾丘惜萱完成签到,获得积分10
1秒前
lizhiqian2024发布了新的文献求助10
1秒前
人123456发布了新的文献求助10
1秒前
2秒前
2秒前
可爱的函函应助廉洁采纳,获得10
2秒前
岁月荣耀发布了新的文献求助10
3秒前
neyney完成签到,获得积分10
4秒前
4秒前
初空月儿完成签到,获得积分10
4秒前
yuhang发布了新的文献求助10
4秒前
无水乙醚完成签到,获得积分10
4秒前
5秒前
popo6150完成签到,获得积分10
5秒前
橘子胡关注了科研通微信公众号
5秒前
今后应助ZJ0315采纳,获得10
5秒前
闪闪含巧完成签到,获得积分10
6秒前
LCL168发布了新的文献求助10
6秒前
Akim应助毛毛虫采纳,获得10
6秒前
宝宝发布了新的文献求助10
6秒前
甘氨酸完成签到,获得积分0
7秒前
yygz0703发布了新的文献求助10
7秒前
8秒前
乐观小蕊完成签到 ,获得积分10
8秒前
8秒前
李健应助lizhiqian2024采纳,获得10
9秒前
9秒前
深情安青应助嗯哼采纳,获得10
9秒前
11秒前
12秒前
Mon完成签到 ,获得积分10
12秒前
LCL168完成签到,获得积分20
12秒前
小金完成签到,获得积分10
12秒前
高分求助中
Tracking and Data Fusion: A Handbook of Algorithms 1000
Models of Teaching(The 10th Edition,第10版!)《教学模式》(第10版!) 800
La décision juridictionnelle 800
Rechtsphilosophie und Rechtstheorie 800
Academic entitlement: Adapting the equity preference questionnaire for a university setting 500
Arkiv för kemi 400
Machine Learning in Chemistry 400
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 2877336
求助须知:如何正确求助?哪些是违规求助? 2490329
关于积分的说明 6741288
捐赠科研通 2172046
什么是DOI,文献DOI怎么找? 1154161
版权声明 586070
科研通“疑难数据库(出版商)”最低求助积分说明 566681