Search bioRxiv⌕ Search

Biology subjects

Armstrong, R. A.

Publications and source records attributed to Armstrong, R. A..

2 recordsLinked to original sources

QT-AMP: Quanti-Tray-based amplicon sequencing for simultaneous quantification and identification of enterococci for microbial source tracking

Enterococcus is ubiquitous in human feces and has been adopted as a useful indicator of human fecal pollution in water. Although regular enterococci monitoring only examines their numbers, identification of human-specific Enterococcus species or genotypes could help in the discrimination of human fecal contamination from other environmental sources. We documented a new approach to characterize enterococci using an amplicon sequencing platform from Quanti Trays after following the counting of most probable numbers (MPN) of enterococci. We named this method as QT-AMP (Quanti-Tray-based amplicon sequencing). We tested surface water samples collected from three rivers in southwest Florida. We detected 11 Enterococcus species from 45 samples in 1.1 million sequence reads. The method detected three rare species and eight cosmopolitan species (Enterococcus faecalis, E.faecium, E. casseliflavus, E. hirae, E. mundtii, E. gallinarum, E. avium, and E. durans) which have been commonly documented in various enterococci isolation studies. It is likely that the approximate detection level of QT-AMP is four orders of magnitude higher than regular 16S rRNA gene amplicon sequencing. QT-AMP revealed that a majority of Enterolert positive signals are actually the mixture of both enterococci and other facultative aerobes and anaerobes. QT-AMP may have the potential to monitor not only enterococci but also other pathogenic bacteria commonly found in natural environments. This QT-AMP could be a powerful tool to streamline the quantification and identification of enterococci and allows us to do more accurate and efficient microbial source tracking in various water management projects and human health risk assessment. HighlightsO_LIA selected primer set (27f-519r) can differentiate over 50 Enterococcus species and is suitable for Illumina amplicon sequencing. C_LIO_LIThe median of the relative contribution of enterococci reads among total sequencing reads was 82.7% and ranged between 0% and 100%. C_LIO_LIEnterolert signals are most likely the mixture of enterococci and other facultative aerobes and anaerobes. C_LIO_LIWe identified eight cosmopolitan enterococci species and three rare species. C_LIO_LIThe median of the relative contribution of non-enterococci reads among total sequencing reads was 17.3%, respectively, and ranged between 0% and 100%. C_LI

microbiology↗

High-Resolution Satellite Imagery to Assess Sargassum Inundation Impacts to Coastal Areas

A change detection analysis utilizing Very High-resolution (VHR) satellite imagery was performed to evaluate the changes in benthic composition and coastal vegetation in La Parguera, southwestern Puerto Rico, attributable to the increased influx of pelagic Sargassum spp and its accumulations in cays, bays, inlets and near-shore environments. Satellite imagery was co-registered, corrected for atmospheric effects, and masked for water and land. A Normalized Difference Vegetation Index (NDVI) and an unsupervised classification scheme were applied to the imagery to evaluate the changes in coastal vegetation and benthic composition. These products were used to calculate the differences from 2010 baseline imagery, to potential hurricane impacts (2018 image), and potential Sargassum impacts (2020 image). Results show a negative trend in Normalized Difference Vegetation Index (NDVI) from 2010 to 2020 for the total pixel area of 24%, or 546,446 m2. These changes were also observed in true color images from 2010 to 2020. Changes in the NDVI negative values from 2018 to 2020 were higher, especially for the Isla Cueva site (97%) and were consistent with the field observations and drone surveys conducted since 2018 in the area. The major changes from 2018 and 2020 occurred mainly in unconsolidated sediments (e.g. sand, mud) and submerged aquatic vegetation (e.g. seagrass, algae), which can have similar spectra limiting the differentiation from multi-spectral imagery. Areas prone to Sargassum accumulation were identified using a combination of 2018 and 2020 true color VHR imagery and drone observations. This approach provides a quantifiable method to evaluate Sargassum impacts to the coastal vegetation and benthic composition using change detection of VHR images, and to separate these effects from other extreme events.

ecology↗