Evidence map›Paper›PMID 37884668›Full record

ArticleNature chemistry2024

Heteromultivalency enables enhanced detection of nucleic acid mutations.

Brendan R Deal, Rong Ma, Steven Narum, Hiroaki Ogasawara, Yuxin Duan, James T Kindt, Khalid Salaita

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Article in Nature chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 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

7 authors.

Brendan R DealDepartment of Chemistry, Emory University, Atlanta, GA, USA.
Rong MaDepartment of Chemistry, Emory University, Atlanta, GA, USA.
Steven NarumDepartment of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA.ORCID 0000-0003-3532-2985
Hiroaki OgasawaraDepartment of Chemistry, Emory University, Atlanta, GA, USA.ORCID 0000-0001-8462-562X
Yuxin DuanDepartment of Chemistry, Emory University, Atlanta, GA, USA.
James T KindtDepartment of Chemistry, Emory University, Atlanta, GA, USA.
Khalid SalaitaDepartment of Chemistry, Emory University, Atlanta, GA, USA. k.salaita@emory.edu.ORCID 0000-0003-4138-3477

Funding

National Science Foundation (NSF) 2004126
6 · The paper itself

Abstract

Detecting genetic mutations such as single nucleotide polymorphisms (SNPs) is necessary to prescribe effective cancer therapies, perform genetic analyses and distinguish similar viral strains. Traditionally, SNP sensing uses short oligonucleotide probes that differentially bind the SNP and wild-type targets. However, DNA hybridization-based techniques require precise tuning of the probe's binding affinity to manage the inherent trade-off between specificity and sensitivity. As conventional hybridization offers limited control over binding affinity, here we generate heteromultivalent DNA-functionalized particles and demonstrate optimized hybridization specificity for targets containing one or two mutations. By investigating the role of oligo lengths, spacer lengths and binding orientation, we reveal that heteromultivalent hybridization enables fine-tuned specificity for a single SNP and dramatic enhancements in specificity for two non-proximal SNPs empowered by highly cooperative binding. Capitalizing on these abilities, we demonstrate straightforward discrimination between heterozygous cis and trans mutations and between different strains of the SARS-CoV-2 virus. Our findings indicate that heteromultivalent hybridization offers substantial improvements over conventional monovalent hybridization-based methods.

Indexed as

Nucleic AcidsDNAMutationNucleic Acid HybridizationOligonucleotide ProbesDNANucleic AcidsOligonucleotide Probes

Identifiers

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Registered trials

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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.