Accessible point-of-care technologies that can provide immunoassay and molecular modalities could dramatically enhance diagnostics, particularly for infectious disease control in low-resource settings. Solid-state nanopores are simple and durable sensors with low-energy instrumentation requirements. While nanopore sensors have demonstrated efficacy for nucleic acid targets, selective detection and quantification of target proteins from sample background has not been demonstrated. We present a simple approach for electronic detection and quantification of target proteins that combines novel biomolecular engineering methods, a portable reader device and disposable nanopore test strips. The target of interest can be varied by swapping the binding domain on our engineered detection reagent, which eficiently binds in the bulk-phase to the target and subsequently generates a unique signature when passing through the pore. We show modularity of the detection reagent for two HIV antibodies, TNFα and tetanus toxin as targets. A saliva swab-to-result is demonstrated for clinically relevant HIV antibody levels (0.4–20 mg/liter) in under 60 seconds. While other strip-like assays are qualitative, the presented method is quantitative and sets the stage for simultaneous immunoassay and molecular diagnostic functionality within a single portable platform.

Investigating the mechanisms of action (MOAs) of bioactive compounds and the deconvolution of their cellular targets is an important and challenging undertaking. Drug resistance in model organisms such as S. cerevisiae has long been a means for discovering drug targets and MOAs. Strains are selected for resistance to a drug of interest, and the resistance mutations can often be mapped to the drug’s molecular target using classical genetic techniques. Here we demonstrate the use of next generation sequencing (NGS) to identify mutations that confer resistance to two well-characterized drugs, benomyl and rapamycin. Applying NGS to pools of drug-resistant mutants, we develop a simple system for ranking single nucleotide polymorphisms (SNPs) based on their prevalence in the pool, and for ranking genes based on the number of SNPs that they contain. We clearly identified the known targets of benomyl (TUB2) and rapamycin (FPR1) as the highest-ranking genes under this system. The highest-ranking SNPs corresponded to specific amino acid changes that are known to confer resistance to these drugs. We also found that by screening in a pdr1Δ null background strain that lacks a transcription factor regulating the expression of drug efflux pumps, and by pre-screening mutants in a panel of unrelated anti-fungal agents, we were able to mitigate against the selection of multi-drug resistance (MDR) mutants. We call our approach “Mutagenesis to Uncover Targets by deep Sequencing, or “MUTseq”, and show through this proof-of-concept study its potential utility in characterizing MOAs and targets of novel compounds.a