ArticlemedRxiv : the preprint server for health sciences2024
Sensitive Pathogen Detection and Drug Resistance Characterization Using Pathogen-Derived Enzyme Activity Amplified by LAMP or CRISPR-Cas.
Article in medRxiv : the preprint server for health sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed, 3 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
Pathogens encapsulate or encode their own suite of enzymes to facilitate replication in the host. The pathogen-derived enzymes possess specialized activities that are essential for pathogen replication and have naturally been candidates for drug targets. Phenotypic assays detecting the activities of pathogen-derived enzymes and characterizing their inhibition under drugs offer an opportunity for pathogen detection, drug resistance testing for individual patients, and as a research tool for new drug development. Here, we used HIV as an example to develop assays targeting the reverse transcriptase (RT) enzyme encapsulated in HIV for sensitive detection and phenotypic characterization, with the potential for point-of-care (POC) applications. Specifically, we targeted the complementary (cDNA) generation activity of the HIV RT enzyme by adding engineered RNA as substrates for HIV RT enzyme to generate cDNA products, followed by cDNA amplification and detection facilitated by loop-mediated isothermal amplification (LAMP) or CRISPR-Cas systems. To guide the assay design, we first used qPCR to characterize the cDNA generation activity of HIV RT enzyme. In the LAMP-mediated Product-Amplified RT activity assay (LamPART), the cDNA generation and LAMP amplification were combined into one pot with novel assay designs. When coupled with direct immunocapture of HIV RT enzyme for sample preparation and endpoint lateral flow assays for detection, LamPART detected as few as 20 copies of HIV RT enzyme spiked into 25μL plasma (fingerstick volume), equivalent to a single virion. In the Cas-mediated Product-Amplified RT activity assay (CasPART), we tailored the substrate design to achieve a LoD of 2e4 copies (1.67fM) of HIV RT enzyme. Furthermore, with its phenotypic characterization capability, CasPART was used to characterize the inhibition of HIV RT enzyme under antiretroviral drugs and differentiate between wild-type and mutant HIV RT enzyme for potential phenotypic drug resistance testing. Moreover, the CasPART assay can be readily adapted to target the activity of other pathogen-derived enzymes. As a proof-of-concept, we successfully adapted CasPART to detect HIV integrase with a sensitivity of 83nM. We anticipate the developed approach of detecting enzyme activity with product amplification has the potential for a wide range of pathogen detection and phenotypic characterization.
Identifiers
What OpenQuestion holds
Registered trials
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.