Laboratory Analysis

The samples taken in the field are subject to laboratory investigations for:

  1. The Identification and quantification of nucleic acids of SARS-CoV-2, influenza, RSV, enterovirus, enterovirus D68, poliovirus, noroviruses, hepatitis A and E viruses, human adenoviruses and sapoviruses (herein referred to as ‘the target viruses’)
  2.  Sequencing of SARS-CoV-2 variants to identify potential variants of concern (VOCs). Sequencing of enteroviruses.
  3. The elution of intact and infectious viral particles.
  4. The enrichment of potentially harmful viruses for further analysis.
  5. Analysis for chemical and physical markers of wastewater quality (e.g. pH, ammonium, phosphate, electrical conductivity, turbidity, dissolved carbon, metals, BOD)
  6. Quantification and analysis for antimicrobial resistance genes and mobile genetic elements using high throughput qPCR and metagenomics.
  7. Analysis of pharmaceuticals (with Univ Bath).
  8. Recovery, culturing and analysis of Clostridium difficile (with the Anaerobe Reference Lab, PHW).
  9. Analysis of Cryptosporidium (with the Crypto Reference Lab, PHW).

See our publications for details of the methods.

Some of the aims and approaches are summarised below, however, please contact us for more information:

Optimization of viral recovery from environmental samples

We continue to evaluate and optimise standard methods for the recovery of target viruses, fungi, protists and bacteria from wastewater (influent and effluent samples), shellfish, river and lake water, sediments, estuarine and marine waters.

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Although previous studies have focused on the extraction of specific members of our target viruses from a subset of the environmental matrices described here, there are combinations of viral target vs. environmental matrix that have not previously been addressed.

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We have compared the efficiency of traditional extraction processes and compare outcomes with novel approaches. For sensitive quantification, quantitative polymerase chain reaction (qPCR) and reverse transcription qPCR (RT-qPCR) are used alongside RT-LAMP, digital droplet-PCR (dPCR), targeting a representative sequence of the genomes of target viruses. We use HT-qPCR for AMR analysis combined with metagenomics on the Illumina NovaSeq.

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Sample preparation for metagenomics analysis

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Metagenomics is defined as “the direct genetic analysis of genomes contained within an environmental sample”. Viral genomes consist of either double stranded (ds) or single stranded (ss) RNA or DNA molecules and genomic libraries for both can be produced from each environmental sample. To produce a paired end library for sequencing:

  • The complement for each harvested DNA strand is synthesised to produce dsDNA from ssDNA viruses, whilst doubling the numbers of dsDNA molecules.
  • Viral RNA is then converted to DNA by reverse transcription and then made into ds complimentary DNA (cDNA).

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The DNA and an RNA library produced from each environmental sample and barcoded samples sequenced in pools, enables the generation of sequence datasets in numbers and depths of coverage that have only recently become possible.

Exploring viral infectivity in environmental samples

Current viral diagnostic methods rely on genome detection by PCR as most enteric viruses cannot be routinely cultured. A key question is whether the viral genomes detected by PCR originate from viable viruses capable of human infection. Virus inactivation by environmental stressors act via the disruption of the viral genome and/or capsid protein.

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In the absence of culture, the available options are to attempt to determine the integrity of virus genome, and/or capsid, as a marker for infectivity, to supplement PCR. An alternative is to use a surrogate cultivatable virus, as an index for infectivity. Our approach is to establish possible methods for determining virus integrity (both genome and capsid approaches) and to evaluate these using laboratory infectivity models. For example, RNA bacteriophage, a candidate surrogate, can be used in parallel and performance compared with the infectivity models. Similar approaches using Phi 6 can be done for SARS-CoV-2.

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