Evidence map›Paper›PMID 38982550›Full record

ArticleInfectious diseases of poverty2024

Genetic diversity and prevalence of emerging Rickettsiales in Yunnan Province: a large-scale study.

Chun-Hong Du, Rong Xiang, Shuang-Shuang Bie, Xing Yang, Ji-Hu Yang, Ming-Guo Yao, Yun Zhang, Zhi-Hai He, Zong-Ti Shao, Chun-Feng Luo and 10 more

Abstract read
In one paragraph

Article in Infectious diseases of poverty, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 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

20 authors.

Chun-Hong Du *Yunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Rong Xiang *State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, 100071, PR China.
Shuang-Shuang Bie *Yunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Xing Yang *Department of Medical Microbiology and Immunology, School of Basic Medicine, Dali University, Dali, 671000, PR China.
Ji-Hu YangState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, 100071, PR China.
Ming-Guo YaoYunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Yun ZhangYunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Zhi-Hai HeYunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Zong-Ti ShaoYunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Chun-Feng LuoState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, 100071, PR China.
En-Nian PuYunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Yu-Qiong LiYunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Fan WangYunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Zhi LuoYunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Chao-Bo DuYunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Jie ZhaoYunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Miao LiYunnan Key Laboratory for Zoonosis Control and Prevention, Yunnan Institute for Endemic Diseases Control and Prevention, Dali, 671000, PR China.
Wu-Chun CaoState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, 100071, PR China. caowuchun@126.com.
Yi SunState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, 100071, PR China. sunyi7310@sina.com.
Jia-Fu JiangState Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, 100071, PR China. jiangjf2008@139.com.ORCID http://orcid.org/0000-0003-2624-7450

Funding

Key Technologies Research and Development Program 2019YFC1200501Key Technologies Research and Development Program 2022YFC2305001National Natural Science Foundation of China U200221
6 · The paper itself

Abstract

backgroundRickettsia and related diseases have been identified as significant global public health threats. This study involved comprehensive field and systematic investigations of various rickettsial organisms in Yunnan Province.

methodsBetween May 18, 2011 and November 23, 2020, field investigations were conducted across 42 counties in Yunnan Province, China, encompassing small mammals, livestock, and ticks. Preliminary screenings for Rickettsiales involved amplifying the 16S rRNA genes, along with additional genus- or species-specific genes, which were subsequently confirmed through sequencing results. Sequence comparisons were carried out using the Basic Local Alignment Search Tool (BLAST). Phylogenetic relationships were analyzed using the default parameters in the Molecular Evolutionary Genetics Analysis (MEGA) program. The chi-squared test was used to assess the diversities and component ratios of rickettsial agents across various parameters.

resultsA total of 7964 samples were collected from small mammals, livestock, and ticks through Yunnan Province and submitted for screening for rickettsial organisms. Sixteen rickettsial species from the genera Rickettsia, Anaplasma, Ehrlichia, Neoehrlichia, and Wolbachia were detected, with an overall prevalence of 14.72%. Among these, 11 species were identified as pathogens or potential pathogens to humans and livestock. Specifically, 10 rickettsial organisms were widely found in 42.11% (24 out of 57) of small mammal species. High prevalence was observed in Dremomys samples at 5.60%, in samples from regions with latitudes above 4000 m or alpine meadows, and in those obtained from Yuanmou County. Anaplasma phagocytophilum and Candidatus Neoehrlichia mikurensis were broadly infecting multiple genera of animal hosts. In contrast, the small mammal genera Neodon, Dremomys, Ochotona, Anourosorex, and Mus were carrying individually specific rickettsial agents, indicating host tropism. There were 13 rickettsial species detected in 57.14% (8 out of 14) of tick species, with the highest prevalence (37.07%) observed in the genus Rhipicephalus. Eight rickettsial species were identified in 2375 livestock samples. Notably, six new Rickettsiales variants/strains were discovered, and Candidatus Rickettsia longicornii was unambiguously identified.

conclusionsThis large-scale survey provided further insight into the high genetic diversity and overall prevalence of emerging Rickettsiales within endemic hotspots in Yunnan Province. The potential threats posed by these emerging tick-borne Rickettsiales to public health warrant attention, underscoring the need for effective strategies to guide the prevention and control of emerging zoonotic diseases in China.

Indexed as

Genetic VariationPhylogenyRickettsialesTicksAnimalsChinaHumansLivestockMammalsPrevalenceRickettsiaRickettsia InfectionsRNA, Ribosomal, 16SRNA, Ribosomal, 16SCandidatus Rickettsia longicorniiChinaRickettsialesSmall mammalTick

Identifiers

PMID38982550
PMCPMC11234784

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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.