ABSTRACT
Salmonella species are among the major pathogens that cause foodborne illness outbreaks. In this study, we aimed to develop a loop-mediated isothermal amplification (LAMP) assay for the rapid and sensitive detection of Salmonella species. We designed LAMP primers targeting the hilA gene as a universal marker of Salmonella species. A total of seven Salmonella species strains and 11 non-Salmonella pathogen strains from eight different genera were used in this study. All Salmonella strains showed positive amplification signals with the Salmonella LAMP assay; however, there was no non-specific amplification signal for the non-Salmonella strains. The detection limit was 100 femtograms (20 copies per reaction), which was ~1,000 times more sensitive than the detection limits of the conventional polymerase chain reaction (PCR) assay (100 pg). The reaction time for a positive amplification signal was less than 20 minutes, which was less than one-third the time taken while using conventional PCR. In conclusion, our Salmonella LAMP assay accurately detected Salmonella species with a higher degree of sensitivity and greater rapidity than the conventional PCR assay, and it may be suitable for point-of-care testing in the field.
ABSTRACT
Highly pathogenic avian influenza (HPAI) viruses have caused severe respiratory disease and death in poultry and human beings. Although most of the avian influenza viruses (AIVs) are of low pathogenicity and cause mild infections in birds, some subtypes including hemagglutinin H5 and H7 subtype cause HPAI. Therefore, sensitive and accurate subtyping of AIV is important to prepare and prevent for the spread of HPAI. Next-generation sequencing (NGS) can analyze the full-length sequence information of entire AIV genome at once, so this technology is becoming a more common in detecting AIVs and predicting subtypes. However, an analysis pipeline of NGS-based AIV sequencing data, including AIV subtyping, has not yet been established. Here, in order to support the pre-processing of NGS data and its interpretation, we developed a user-friendly tool, named prediction of avian influenza virus subtype (PAIVS). PAIVS has multiple functions that support the pre-processing of NGS data, reference-guided AIV subtyping, de novo assembly, variant calling and identifying the closest full-length sequences by BLAST, and provide the graphical summary to the end users.
ABSTRACT
Highly pathogenic avian influenza (HPAI) viruses have caused severe respiratory disease and death in poultry and human beings. Although most of the avian influenza viruses (AIVs) are of low pathogenicity and cause mild infections in birds, some subtypes including hemagglutinin H5 and H7 subtype cause HPAI. Therefore, sensitive and accurate subtyping of AIV is important to prepare and prevent for the spread of HPAI. Next-generation sequencing (NGS) can analyze the full-length sequence information of entire AIV genome at once, so this technology is becoming a more common in detecting AIVs and predicting subtypes. However, an analysis pipeline of NGS-based AIV sequencing data, including AIV subtyping, has not yet been established. Here, in order to support the pre-processing of NGS data and its interpretation, we developed a user-friendly tool, named prediction of avian influenza virus subtype (PAIVS). PAIVS has multiple functions that support the pre-processing of NGS data, reference-guided AIV subtyping, de novo assembly, variant calling and identifying the closest full-length sequences by BLAST, and provide the graphical summary to the end users.