Evidence map›Paper›PMID 38378556›Full record

ArticleBMC biology2024

Population genomics of Agrotis segetum provide insights into the local adaptive evolution of agricultural pests.

Ping Wang, Minghui Jin, Chao Wu, Yan Peng, Yanjin He, Hanyue Wang, Yutao Xiao

Abstract read
In one paragraph

Article in BMC biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Ping Wang *Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Minghui Jin *Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Chao Wu *Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Yan PengShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Yanjin HeShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Hanyue WangShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China.
Yutao XiaoShenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Key Laboratory of Gene Editing Technologies (Hainan), Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China. xiaoyutao@caas.cn.ORCID 0000-0003-3203-9375

Funding

Sci-Tech Innovation 2030 Agenda 2022ZD04021
6 · The paper itself

Abstract

backgroundThe adaptive mechanisms of agricultural pests are the key to understanding the evolution of the pests and to developing new control strategies. However, there are few studies on the genetic basis of adaptations of agricultural pests. The turnip moth, Agrotis segetum (Lepidoptera: Noctuidae) is an important underground pest that affects a wide range of host plants and has a strong capacity to adapt to new environments. It is thus a good model for studying the adaptive evolution of pest species.

resultsWe assembled a high-quality reference genome of A. segetum using PacBio reads. Then, we constructed a variation map of A. segetum by resequencing 98 individuals collected from six natural populations in China. The analysis of the population structure showed that all individuals were divided into four well-differentiated populations, corresponding to their geographical distribution. Selective sweep analysis and environmental association studies showed that candidate genes associated with local adaptation were functionally correlated with detoxification metabolism and glucose metabolism.

conclusionsOur study of A. segetum has provided insights into the genetic mechanisms of local adaptation and evolution; it has also produced genetic resources for developing new pest management strategies.

Indexed as

MetagenomicsMothsAnimalsChinaAgrotis segetumEvolutionLocal adaptationPopulation genomics

Identifiers

PMID38378556
PMCPMC10877822

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.