Event Abstract

“Next-generation” surveillance in aquaculture: Opportunities and challenges emerging from the use of molecular and genomic data

  • 1 Royal Veterinary College, UK, United Kingdom
  • 2 SAFOSO AG, Switzerland
  • 3 National Veterinary Research Institute, Poland
  • 4 Epi-interactive, New Zealand

Molecular surveillance can be defined as “the systematic, continuous or repeated measurement, collection, collation, analysis, interpretation and timely dissemination of molecular-level information about micro-organisms. These data are then used to describe health hazard occurrence and to contribute to the planning, implementation and evaluation of risk mitigation actions” (Muellner et al., 2015). In molecular surveillance, conventional surveillance approaches are utilized, with molecular typing added where considered necessary to improve the resolution of the data. Of all elements of the disease surveillance cycle, changes mostly affect data collection, sampling design and interpretation of laboratory data are mostly if molecular rather than conventional diagnostic approaches are used. A broad range of molecular typing methods exits including very well established methods such as polymerase chain reaction (PCR) but increasingly methods are extending to DNA-sequence based approaches including whole genome sequencing EFSA, 2013, 2014). The latter allow the accumulation of vast amounts of information from the genome of a single pathogen. This also means that we are moving from an environment of binary testing outcomes (positive vs. negative result) to increasingly complex results providing sequence information on individual genes, a subset of genes, the whole genome of a single pathogen or using metagenomics molecular information from the entire mirco-organism population contained in a sample . As such the interpretation of typing outcomes has become increasingly challenging in a research context alone; but even more so when the information provided has to be linked with applied surveillance outcomes. Due to the complexity of the resulting data, interpretation is no longer black and white but consists of continuous measures of relatedness and similarity which require novel analytical methods and new thinking. While the data is highly useful in epidemiology, for example when molecular markers are used as risk factors the analysis is not without challenges. For example, the time sequence between temporal occurrence of genetic changes and the spatio-temporal scale of a surveillance objective need to be aligned to ensure the scale of the investigations match. Also, sequence data as stored in the currently used databases often lack accompanying information regarding the sampling location, clinical signs and other relevant characteristics that are essential for correct epidemiological interpretation. The latter is further complicated by the possible occurrence of clustering (i.e. several strains may have originated from the same sample) which causes severe problems in correct epidemiological interpretation. Also, when genomic data is derived after a culturing step, the culture(s) selected for analysis may not fully represent the heterogeneity within the sample. And depending on the sampling design, the heterogeneity within the targeted host population may also be biased. To increase the quality of publications based on molecular data, standards have been defined regarding the minimum information that should be provided for correct interpretation (e.g. STROME-ID, STROBE-AMR). At present, molecular information is already used in a range of surveillance settings (Muellner et al., 2011). These include control-focused surveillance such as the use of molecular similarity of isolated pathogens to link cases to an outbreak with a common source. Another objective is to use such data for more strategic surveillance, i.e. to provide feedback on the progress of an intervention programme, to document freedom from a hazard (or a gene) or for elicitating transmission dynamics such as in source attribution of food-borne diseases. In order to be able to draw valid conclusions – regardless of the objective – bias needs to be minimised. Bias in the context of molecular or genomic data has many new faces not only relating to sample size or the representativeness of the sample, but also extending to the typability of a micro-organism, the reproducibility of the typing or sequencing technique as well as the discriminatory power of the analysis. Sensitivity can now relate to analytical or diagnostic sensitivity with a potentially severe impact of the culture step if the latter is implemented. Finally, the protocols currently used for PCR or sequencing preparation steps such as DNA extraction are by no means fully validated and may insert background noise that may be larger than true differences between cases intended to be measured. For the reasons stated above, despite the undisputed strengths and potential in molecular surveillance, there are clearly also substantial weaknesses. Molecular typing and genomic data cannot compensate for sound epidemiological design of surveillance. In order to benefit from the opportunities created by these novel tools, the treats need to be identified, and microbiologists, bioinformatics experts and epidemiologists need to collaborate more closely to assure that the results derived with these methods are valid and provide a robust basis for veterinary and public health decision making. Integrative rather than parallel analysis should be promoted. Good practice guidelines should be promoted for adoption and an intensified dialogue between epidemiologists and microbiologists as well as bioinformatics experts is needed.


EFSA, 2013 & 2014: Scientific Opinion on the evaluation of molecular typing methods for major food-borne microbiological: part 1 and 2. EFSA J. 1, 3502, & 12, 3784.
Muellner et al., 2011: Integration of molecular tools into veterinary and special epidemiology. Spatial Spatio-temp. Epid. 2, 159-171.
Muellner et al., 2015: Next-generation surveillance: An epidemiologists’ perspective on the use of molecular information in food safety and animal health decision-making. Zoon. Publ. Hlth. 63, 351-357.

Keywords: surveillance, Bias (Epidemiology), validity, whole genome sequencing, Molecular Sequence Data

Conference: AquaEpi I - 2016, Oslo, Norway, 20 Sep - 22 Sep, 2016.

Presentation Type: Keynote

Topic: Aquatic Animal Epidemiology

Citation: Stärk KD, Pękala A and Muellner P (2016). “Next-generation” surveillance in aquaculture: Opportunities and challenges emerging from the use of molecular and genomic data. Front. Vet. Sci. Conference Abstract: AquaEpi I - 2016. doi: 10.3389/conf.FVETS.2016.02.00062

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Received: 05 Sep 2016; Published Online: 14 Sep 2016.

* Correspondence: Prof. Katharina D Stärk, Royal Veterinary College, UK, London, United Kingdom, kstaerk@rvc.ac.uk