Rapid characterization of feline leukemia virus infective stages by a novel nested recombinase polymerase amplification (RPA) and reverse transcriptase-RPA

Feline leukemia virus (FeLV) is a major viral disease in cats, causing leukemia and lymphoma. The molecular detection of FeLV RNA and the DNA provirus are important for staging of the disease. However, the rapid immunochromatographic assay commonly used for antigen detection can only detect viremia at the progressive stage. In this study, nested recombinase polymerase amplification (nRPA) was developed for exogenous FeLV DNA provirus detection, and reverse transcriptase polymerase amplification (RT-RPA) was developed for the detection of FeLV RNA. The approaches were validated using 108 cats with clinicopathologic abnormalities due to FeLV infection, and from 14 healthy cats in a vaccination plan. The nRPA and RT-RPA assays could rapidly amplify the FeLV template, and produced high sensitivity and specificity. The FeLV detection rate in regression cats by nRPA was increased up to 45.8% compared to the rapid immunochromatographic assay. Hence, FeLV diagnosis using nRPA and RT-RPA are rapid and easily established in low resource settings, benefiting FeLV prognosis, prevention, and control of both horizontal and vertical transmission.

Rapid Characterization of Complex Killer Cell Immunoglobulin-Like Receptor (KIR) Regions Using Cas9 Enrichment and Nanopore Sequencing

Long-read sequencing approaches have considerably improved the quality and contiguity of genome assemblies. Such platforms bear the potential to resolve even extremely complex regions, such as multigenic immune families and repetitive stretches of DNA. Deep sequencing coverage, however, is required to overcome low nucleotide accuracy, especially in regions with high homopolymer density, copy number variation, and sequence similarity, such as the MHC and KIR gene clusters of the immune system. Therefore, we have adapted a targeted enrichment protocol in combination with long-read sequencing to efficiently annotate complex KIR gene regions. Using Cas9 endonuclease activity, segments of the KIR gene cluster were enriched and sequenced on an Oxford Nanopore Technologies platform. This provided sufficient coverage to accurately resolve and phase highly complex KIR haplotypes. Our strategy eliminates PCR-induced amplification errors, facilitates rapid characterization of large and complex multigenic regions, including its epigenetic footprint, and is applicable in multiple species, even in the absence of a reference genome.

Novel Growth Age Characterization for Fresh Herbal Ginseng Based on Quantitative Counting of the Calcium Oxalate Crystals through FEG-ESEM

To reveal the accumulation of the calcium oxalate crystals (COH Crystals) during the growth and development of the taproot of Panax ginseng, and develop a novel and rapid characterization method to evaluate the growth age of commercial ginseng, multiple methods in micro characterization techniques of SAXS, Micro-CT, FEG-ESEM and Micro-Raman were used to identify the COH Crystals and establish a quantitative counting method for growth age identification. In this study, a cross-analysis with multiple methods proved for the first time with a Raman and Energy spectrum that the high-density particles widely distributed in the parenchyma cells of the xylem and cortex are COH Crystals; we also first realized quantitative counting of the COH Crystals on the cross-section of fresh ginseng samples. Moreover, catering to the testing requirements of the modern trading of fresh ginseng products, we also specifically established an interesting and useful mathematical equation (Y = 2.3797X − 1.2404) for growth age identification. The technology and strategy in this study effectively compensated for the shortcomings of chemical testing and other methods in technical limitations; hence, the application of more ginseng varieties to perform the technical optimization is expected.

Machine Learning Prediction of Resistance to Subinhibitory Antimicrobial Concentrations from Escherichia coli Genomes

Predicting bacterial growth from genome sequences is important for a rapid characterization of strains in clinical diagnostics and to disclose candidate novel targets for anti-infective drugs. Previous studies have dissected the relationship between bacterial growth and genotype in mutant libraries for laboratory strains, yet no study so far has examined the predictive power of genome sequence in natural strains.