Variation of natural selection in the Amoebozoa reveals heterogeneity across the phylogeny and adaptive evolution in diverse lineages

生物 进化生物学 自然选择 系统发育学 系统发育树 否定选择 选择(遗传算法) 基因组 遗传学 基因 人工智能 计算机科学
作者
Fang Wang,Yonas I. Tekle
出处
期刊:Frontiers in Ecology and Evolution [Frontiers Media SA]
卷期号:10
标识
DOI:10.3389/fevo.2022.851816
摘要

The evolution and diversity of the supergroup Amoebozoa is complex and poorly understood. The supergroup encompasses predominantly amoeboid lineages characterized by extreme diversity in phenotype, behavior and genetics. The study of natural selection, a driving force of diversification, within and among species of Amoebozoa will play a crucial role in understanding the evolution of the supergroup. In this study, we searched for traces of natural selection based on a set of highly conserved protein-coding genes in a phylogenetic framework from a broad sampling of amoebozoans. Using these genes, we estimated substitution rates and inferred patterns of selective pressure in lineages and sites with various models. We also examined the effect of selective pressure on codon usage bias and potential correlations with observed biological traits and habitat. Results showed large heterogeneity of selection across lineages of Amoebozoa, indicating potential species-specific optimization of adaptation to their diverse ecological environment. Overall, lineages in Tubulinea had undergone stronger purifying selection with higher average substitution rates compared to Discosea and Evosea. Evidence of adaptive evolution was observed in some representative lineages and in a gene (Rpl7a) within Evosea, suggesting potential innovation and beneficial mutations in these lineages. Our results revealed that members of the fast-evolving lineages, Entamoeba and Cutosea, all underwent strong purifying selection but had distinct patterns of codon usage bias. For the first time, this study revealed an overall pattern of natural selection across the phylogeny of Amoebozoa and provided significant implications on their distinctive evolutionary processes.
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
Etiquette完成签到,获得积分10
1秒前
1秒前
sjxbjrndkd完成签到 ,获得积分10
2秒前
顾矜应助lucygaga采纳,获得10
3秒前
小马甲应助苏州小北采纳,获得10
3秒前
huang发布了新的文献求助10
3秒前
米儿发布了新的文献求助10
4秒前
6秒前
NEAC完成签到 ,获得积分10
7秒前
Felix完成签到,获得积分10
7秒前
7秒前
张占完成签到,获得积分10
10秒前
huang完成签到,获得积分10
11秒前
小白先生完成签到,获得积分10
11秒前
lcx发布了新的文献求助10
13秒前
15秒前
16秒前
Agan完成签到,获得积分10
17秒前
yk发布了新的文献求助10
17秒前
177完成签到,获得积分10
18秒前
敏感的香氛完成签到 ,获得积分10
18秒前
阿桃狸子完成签到,获得积分10
20秒前
lucygaga发布了新的文献求助10
20秒前
yayabing完成签到,获得积分10
21秒前
Lucas应助lcx采纳,获得10
22秒前
22秒前
HX应助科研通管家采纳,获得10
22秒前
英俊的铭应助科研通管家采纳,获得10
23秒前
传奇3应助科研通管家采纳,获得10
23秒前
上官若男应助科研通管家采纳,获得10
23秒前
田様应助科研通管家采纳,获得10
23秒前
HX应助科研通管家采纳,获得10
23秒前
shinysparrow应助科研通管家采纳,获得10
23秒前
无花果应助科研通管家采纳,获得10
23秒前
23秒前
无花果应助科研通管家采纳,获得10
23秒前
SciGPT应助科研通管家采纳,获得10
23秒前
爆米花应助科研通管家采纳,获得10
23秒前
完美世界应助科研通管家采纳,获得20
23秒前
23秒前
高分求助中
请在求助之前详细阅读求助说明!!!! 20000
The Three Stars Each: The Astrolabes and Related Texts 900
Yuwu Song, Biographical Dictionary of the People's Republic of China 700
Multifunctional Agriculture, A New Paradigm for European Agriculture and Rural Development 600
Bernd Ziesemer - Maos deutscher Topagent: Wie China die Bundesrepublik eroberte 500
A radiographic standard of reference for the growing knee 400
Glossary of Geology 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 有机化学 工程类 生物化学 纳米技术 物理 内科学 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 电极 光电子学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 2474361
求助须知:如何正确求助?哪些是违规求助? 2139407
关于积分的说明 5452184
捐赠科研通 1863189
什么是DOI,文献DOI怎么找? 926351
版权声明 562833
科研通“疑难数据库(出版商)”最低求助积分说明 495538