Impact of SARS-CoV-2 Mutations on PCR Assay Sequence Alignment

Using machine learning models to predict the impact of template mismatches on polymerase chain reaction assay performance

Molecular assays are critical tools for the diagnosis of infectious diseases. These assays have been extremely valuable during the COVID pandemic, used to guide both patient management and infection control strategies. Sustained transmission and unhindered proliferation of the virus during the pandemic resulted in many variants with unique mutations. Some of these mutations could lead to signature erosion, where tests developed using the genetic sequence of an earlier version of the pathogen may produce false negative results when used to detect novel variants. In this study, we assessed the performance changes of 15 molecular assay designs when challenged with a variety of mutations that fall within the targeted region. Using data generated from this study, we trained and assessed the performance of seven different machine learning models to predict whether a specific set of mutations will result in significant change in the performance for a specific test design. The best performing model demonstrated acceptable performance with sensitivity of 82% and specificity of 87% when assessed using tenfold cross validation. Our findings highlighted the potential of using machine learning models to predict the impact of emerging mutations on the performance of specific molecular test designs.

Loop-mediated isothermal amplification assays for the detection of antimicrobial resistance elements in Vibrio cholera

BACKGROUND

The bacterium Vibrio cholerae causes diarrheal illness and can acquire genetic material leading to multiple drug resistance (MDR). Rapid detection of resistance-conferring mobile genetic elements helps avoid the prescription of ineffective antibiotics for specific strains. Colorimetric loop-mediated isothermal amplification (LAMP) assays provide a rapid and cost-effective means for detection at point-of-care since they do not require specialized equipment, require limited expertise to perform, and can take less than 30 min to perform in resource limited regions. LAMP output is a color change that can be viewed by eye, but it can be difficult to design primer sets, determine target specificity, and interpret subjective color changes.

METHODS

We developed an algorithm for the in silico design and evaluation of LAMP assays within the open-source PCR Signature Erosion Tool (PSET) and a computer vision application for the quantitative analysis of colorimetric outputs. First, Primer3 calculates LAMP primer sequence candidates with settings based on GC-content optimization. Next, PSET aligns the primer sequences of each assay against large sequence databases to calculate sufficient sequence similarity, coverage, and primer arrangement to the intended taxa, ultimately generating a confusion matrix. Finally, we tested assay candidates in the laboratory against synthetic constructs.

RESULTS

As an example, we generated new LAMP assays targeting drug resistance in V. cholerae and evaluated existing ones from the literature based on in silico target specificity and in vitro testing. Improvements in the design and testing of LAMP assays, with heightened target specificity and a simple analysis platform, increase utility for in-field applications. Overall, 9 of the 16 tested LAMP assays had positive signal through visual and computer vision-based detection methods developed here. Here we show LAMP assays tested on synthetic AMR gene targets for aph(6), varG, floR, qnrVC5, and almG, which allow for resistance to aminoglycosides, penicillins, carbapenems, phenicols, fluoroquinolones, and polymyxins respectively.

Multiplexed discrimination of SARS-CoV-2 variants via duplex-specific nuclease combined MALDI-TOF MS

The frequent mutations in SARS-CoV-2 significantly increase the virus's pathogenicity and transmissibility while also diminishing the effectiveness of vaccines. Consequently, assays capable of rapidly and simultaneously identifying multiple SARS-CoV-2 variants are essential for large-scale applications that aim to monitor the evolution of the virus. In this work, we propose a method combining duplex-specific nuclease (DSN)-assisted cyclic amplification with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) detection, enabling the simultaneous identification of multiple SARS-CoV-2 variants at high-throughput. Due to the high specificity of DSN, single-base mutations can be resolved by the method. With ultra-sensitive detection by MALDI-TOF MS, a limit of detection of 100 pM viral RNA fragment was demonstrated. The assay was used for simultaneous identification and typing of SARS-CoV-2 Alpha, Beta, and Delta variants. The whole assay can be accomplished within 3 h, and the amplification is performed under constant temperature, making the technique simple in operation and efficient. It is also feasible to extend the technique to the detection of many other variants of the virus. We expect that the method can add value to the rapid screening of viral variants and can play an important role in pandemic control.