Evaluation of Viral Recovery Methodologies from Solid Waste Landfill Leachate.
Natália Maria LanzariniRafaela Marinho MataEnrico Mendes SaggioroJosino Costa MoreiraCamille Ferreira MannarinoMarise Pereira MiagostovichPublished in: Food and environmental virology (2020)
Leachate from solid waste landfill is a dark liquid of variable composition and possible source of contamination of groundwater and surface waters. This study aims to assess skimmed milk flocculation and ultracentrifugation as viral concentration methods associated to different nucleic acid extraction protocols in order to establish a methodology for virus recovery from sanitary landfill leachate. Spiking experiments using human adenovirus (HAdV) and bacteriophage PP7 revealed the association of QIAamp Fast DNA Stool mini kit® nucleic acid extraction and ultracentrifugation as an effective method for recovering HAdV (346.18%) and PP7 (523.97%) when compared to organic flocculation method (162.64% for HAdV and 0.61% for PP7) that presented PCR inhibition in all undiluted samples. Ultracentrifugation applied in three landfill samples confirm efficiency of the methodology detecting HAdV in all samples with a mean of 3.44E + 06 ± 1.56E + 06 genomic copies/mL. Nucleotide sequencing characterized HAdV as belonging to group B and F. JC polyomavirus (JCPyV) was also investigated in those samples; however, detection was not observed. Methodologies for detection of viruses in leachate can be useful to generate data for future health risk analysis of workers who have contact with solid urban waste, as well as populations exposed to different environmental matrices contaminated by these effluents.
Keyphrases
- municipal solid waste
- nucleic acid
- health risk
- heavy metals
- sewage sludge
- drinking water
- anaerobic digestion
- sars cov
- human health
- real time pcr
- risk assessment
- endothelial cells
- single cell
- loop mediated isothermal amplification
- health risk assessment
- wastewater treatment
- gene expression
- copy number
- big data
- circulating tumor
- mass spectrometry
- genetic diversity
- deep learning