Evidence map›Paper›PMID 38459433›Full record

ArticleBMC genomics2024

High genetic diversity of the himalayan marmot relative to plague outbreaks in the Qinghai-Tibet Plateau, China.

Ying Ma, Pengbo Liu, Ziyan Li, Yujuan Yue, Yanmei Zhao, Jian He, Jiaxin Zhao, Xiuping Song, Jun Wang, Qiyong Liu and 1 more

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.1field-weighted citation impact, top 22% of its field
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

3 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
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

11 authors at 3 institutions in 1 country.

Ying Ma *Qinghai Institute for Endemic Disease Prevention and Control, Xining, 811602, China.
Pengbo Liu *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, 102206, China.
Ziyan LiCollege of Life Sciences, WuHan University, Wuhan, 430072, China.
Yujuan YueNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, 102206, China.
Yanmei ZhaoQinghai Institute for Endemic Disease Prevention and Control, Xining, 811602, China.
Jian HeQinghai Institute for Endemic Disease Prevention and Control, Xining, 811602, China.
Jiaxin ZhaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, 102206, China.
Xiuping SongNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, 102206, China.
Jun WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, 102206, China.
Qiyong LiuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, 102206, China.
Liang LuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Chinese Center for Disease Control and Prevention, National Institute for Communicable Disease Control and Prevention, Beijing, 102206, China. luliang@icdc.cn.
National Institute for Communicable Disease Control and Prevention · CNQinghai Institute for Endemic Diease Prevention and Control · CNWuhan University · CN

Funding

the National Natural Science Foundation of China 32090023the Qinghai Provincial Department of Science and Technology 2022-ZJ-750
6 · The paper itself

Abstract

Plague, as an ancient zoonotic disease caused by Yersinia pestis, has brought great disasters. The natural plague focus of Marmota himalayana in the Qinghai-Tibet Plateau is the largest, which has been constantly active and the leading source of human plague in China for decades. Understanding the population genetics of M. himalayana and relating that information to the biogeographic distribution of Yersinia pestis and plague outbreaks are greatly beneficial for the knowledge of plague spillover and arecrucial for pandemic prevention. In the present research, we assessed the population genetics of M. himalayana. We carried out a comparative study of plague outbreaks and the population genetics of M. himalayana on the Qinghai-Tibet Plateau. We found that M. himalayana populations are divided into two main clusters located in the south and north of the Qinghai-Tibet Plateau. Fourteen DFR genomovars of Y. pestis were found and exhibited a significant region-specific distribution. Additionally, the increased genetic diversity of plague hosts is positively associated with human plague outbreaks. This insight gained can improve our understanding of biodiversity for pathogen spillover and provide municipally directed targets for One Health surveillance development, which will be an informative next step toward increased monitoring of M. himalayana dynamics.

Indexed as

MarmotaYersinia pestisAnimalsChinaDisease OutbreaksGenetic VariationHumansTibetMarmota himalayanaPlaguePopulation geneticsYersinia pestisZoonotic spillover

Identifiers

PMID38459433
PMCPMC10921737
OpenAlexW4392597502

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.