Evidence map›Paper›PMID 35413624›Full record

ArticleBiosensors & bioelectronics2022

Protein sensors combining both on-and-off model for antibody homogeneous assay.

Jie Li, Jin-Lan Wang, Wen-Lu Zhang, Zeng Tu, Xue-Fei Cai, Yu-Wei Wang, Chun-Yang Gan, Hai-Jun Deng, Jing Cui, Zhao-Che Shu and 6 more

Abstract read
In one paragraph

Article in Biosensors & bioelectronics, 2022. 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
–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

3 citing papers in PubMed.

  1. Review
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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

16 authors.

Jie LiKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China.
Jin-Lan WangKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China.
Wen-Lu ZhangKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China.
Zeng TuDepartment of Immunology and Microbiology, Chongqing Medical University, Chongqing, China.
Xue-Fei CaiKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China.
Yu-Wei WangDepartment of Laboratory Medicine, Chongqing Hospital of Traditional Chinese Medicine, China; Laboratory for Diagnosis and Treatment of Infectious Diseases Integrated Traditional Chinese and Western Medicine, Chongqing Hospital of Traditional Chinese Medicine, China.
Chun-Yang GanKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China.
Hai-Jun DengKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China.
Jing CuiKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China.
Zhao-Che ShuThe First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Quan-Xin LongKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China.
Juan ChenKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China.
Ni TangKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China.
Xue HuThe First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China. Electronic address: huxue@hospital.cqmu.edu.cn.
Ai-Long HuangKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China. Electronic address: ahuang@cqmu.edu.cn.
Jie-Li HuKey Laboratory of Molecular Biology on Infectious Diseases, Ministry of Education, Chongqing Medical University, Chongqing, China. Electronic address: 102564@cqmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein sensors based on allosteric enzymes responding to target binding with rapid changes in enzymatic activity are potential tools for homogeneous assays. However, a high signal-to-noise ratio (S/N) is difficult to achieve in their construction. A high S/N is critical to discriminate signals from the background, a phenomenon that might largely vary among serum samples from different individuals. Herein, based on the modularized luciferase NanoLuc, we designed a novel biosensor called NanoSwitch. This sensor allows direct detection of antibodies in 1 μl serum in 45 min without washing steps. In the detection of Flag and HA antibodies, NanoSwitches respond to antibodies with S/N ratios of 33-fold and 42-fold, respectively. Further, we constructed a NanoSwitch for detecting SARS-CoV-2-specific antibodies, which showed over 200-fold S/N in serum samples. High S/N was achieved by a new working model, combining the turn-off of the sensor with human serum albumin and turn-on with a specific antibody. Also, we constructed NanoSwitches for detecting antibodies against the core protein of hepatitis C virus (HCV) and gp41 of the human immunodeficiency virus (HIV). Interestingly, these sensors demonstrated a high S/N and good performance in the assays of clinical samples; this was partly attributed to the combination of off-and-on models. In summary, we provide a novel type of protein sensor and a working model that potentially guides new sensor design with better performance.

Indexed as

Biosensing TechniquesCOVID-19Antibodies, ViralHumansLuciferasesSARS-CoV-2Antibodies, ViralLuciferasesnanolucAntibody testBiosensorHomogeneous assayNanoLucProtein switch

Identifiers

PMID35413624
PMCPMC8968183

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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.