Evidence map›Paper›PMID 35568912›Full record

ArticleJournal of nanobiotechnology2022

Highly potent multivalent VHH antibodies against Chikungunya isolated from an alpaca naïve phage display library.

Qianlin Li, Fuqiang Zhang, Yi Lu, Huan Hu, Jin Wang, Cheng Guo, Qiang Deng, Conghui Liao, Qin Wu, Tingsong Hu and 2 more

Open access · goldAbstract read
In one paragraph

Article in Journal of nanobiotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
5.8field-weighted citation impact, top 3% 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

16 citing papers in PubMed, 36 citations in OpenAlex.

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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

12 authors at 5 institutions in 2 countries.

Qianlin Li *One Health Center of Excellence for Research and Training, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Fuqiang Zhang *Center for Disease Control and Prevention of Southern Theater Command, Guangzhou, 510060, People's Republic of China.
Yi Lu *Health Effects Institute, Boston, 02169, USA.
Huan Hu *One Health Center of Excellence for Research and Training, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Jin WangOne Health Center of Excellence for Research and Training, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Cheng GuoCenter for Infection and Immunity, Mailman School of Public Health, Columbia University, New York, NY, USA.
Qiang DengOne Health Center of Excellence for Research and Training, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Conghui LiaoOne Health Center of Excellence for Research and Training, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Qin WuOne Health Center of Excellence for Research and Training, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China.
Tingsong HuCenter for Disease Control and Prevention of Southern Theater Command, Guangzhou, 510060, People's Republic of China. htsong2001@sina.com.
Zeliang ChenOne Health Center of Excellence for Research and Training, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China. chenzliang5@mail.sysu.edu.cn.
Jiahai LuOne Health Center of Excellence for Research and Training, School of Public Health, Sun Yat-Sen University, Guangzhou, 510080, China. lujiahai@mail.sysu.edu.cn.
National Medical Products Administration · CNSun Yat-sen University · CNGuangzhou Center for Disease Control and Prevention · CNColumbia University · USHealth Effects Institute · US

Funding

the Key-Area Research and Development Program of Guangdong Province 2018B020241002the Key Research and Development Program for the 14th Five-Year Plan from the Ministry of Science and Technology, China 20SWAQX02the National Key Research and Development Program of China 2018YFE0208000
6 · The paper itself

Abstract

backgroundChikungunya virus (CHIKV) is a re-emerged mosquito-borne alphavirus that can cause musculoskeletal diseases, imposing a substantial threat to public health globally. High-affinity antibodies are need for diagnosis and treatment of CHIKV infections. As a potential diagnostic and therapeutic agent, the multivalent VHH antibodies is a promising tookit in nanomedicine. Here, we developed potent multivalent VHH antibodies from an alpaca naïve phage display library targeting the E2 glycoprotein of the CHIKV virus.

resultsIn the present study, we generated 20 VHH antibodies using a naïve phage display library for binders to the CHIKV E2 glycoprotein. Of these, multivalent VHH antibodies Nb-2E8 and Nb-3C5 had specific high-affinity binding to E2 protein within the nanomolar range. The equilibrium dissociation constant (KD) was between 2.59-20.7 nM, which was 100-fold stronger than the monovalent antibodies' affinity. Moreover, epitope mapping showed that Nb-2E8 and Nb-3C5 recognized different linear epitopes located on the E2 glycoprotein domain C and A, respectively. A facile protocol of sandwich ELISA was established using BiNb-2E8 as a capture antibody and HRP-conjugated BiNb-3C5 as a detection antibody. A good linear correlation was achieved between the OD

conclusionsCompared to monovalent antibodies, multivalent VHH antibodies Nb-2E8 and Nb-3C5 showed high affinity and are potential candidates for diagnostic applications to better detect CHIKV virions in sera.

Indexed as

BacteriophagesCamelids, New WorldChikungunya FeverChikungunya virusSingle-Domain AntibodiesAnimalsAntibodies, ViralGlycoproteinsAntibodies, ViralGlycoproteinsSingle-Domain AntibodiesChikungunyaE2 glycoproteinEpitopeNaïve phage display libraryVHH antibody

Identifiers

PMID35568912
PMCPMC9107221
OpenAlexW4280492569

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.