Detecting eDNA – The Importance of Assay Specificity and Sensitivity Part I: An Introduction to the MIQE Guidelines and DNA Sequence Database Generation & Curation for qPCR Assay Design

MIQE – a Brief Introduction

In 2009, Bustin and colleagues[1] codified a set of minimal requirements for the publication of quantitative real-time polymerase chain reaction (qPCR) data, ostensibly for use in the fields of clinical medicine and molecular biology, as a means of ensuring rigorous datasets that enable investigators to quantify subtle changes in the expression of particular genes or in the estimation of viral load, for instance. In gene expression studies, fractional changes in the production of intracellular messenger molecules, called mRNAs, which convey genetic information encoded in genes to their proteinaceous or final form, need to be perceived so as to test crucial hypotheses of physiological, medical and even evolutionary and ecological import. Bustin et al. address some of the minimal mandatory standards that need to be reported to ensure continuing robust detection of nucleic acids at low concentrations. These standards were termed the MIQE guidelines: Minimal Information for the publication of Quantitative PCR Experiments.

We will discuss these guidelines and how they compare with eDNA standards in future posts, but for now we will divulge the chief difference between the studies that invoke MIQE and those that involve environmental DNA quantification using qPCR: that concentrations of eDNA, depending upon the circumstances in which it is collected, is likely to be several orders of magnitude lower than many observed changes in the level of mRNA expression or absolute levels of viral genomes present within tissues. Consequently, we need to modify MIQE standards to ensure that eDNA surveys are protected from accusations of too lax standards that may result in the significant incurrence of Types I and II error (false positive and negatives, respectively). Like ancient DNA (aDNA) before, eDNA detection has to elevate these standards to a higher level given the much more ephemeral nature of the target molecules in contemporaneous natural systems.

The Importance of Specificity and Sensitivity

Shared with aDNA and biomedical applications of qPCR assays, is a vital dependency on two touchstones of molecular-based detections: specificity (or: what is the likelihood of incorrectly detecting a non-target, which may lead to Type I error if not wholly specific to the target(s)?) and sensitivity (or: what is the likelihood of detecting extremely low concentrations of the target species, which, if unquantified, may lead to Type II error?)[2]. The very first step to minimize these potential sources of error is to design extremely robust assays in silico, which depends, crucially, on having substantial genomic data from target and sympatric (co-distributed) non-target organisms with which to design highly discriminative assays. To do so, genomic information is paramount; one must collect and curate a significant body of genetic sequence information.

Information is Key – An Evolutionary and Population Genetics Rationale for Data Generation and Curation

How much genomic information is enough? And how can we account for high levels of within-species genetic variation, and low levels of genomic sequence divergence between closely-related, sister and/or cryptic species? All excellent questions, and each needs to be answered satisfactorily, or one cannot with good conscience publish or make available a generic assay for the target species in question.

Coda

In this post, we have broadly discussed gathering and curating data for designing a species-specific assay to be generally deployed across a species’ range, or at least in the populations from which sequence data were generated. Here at PBI, we are also developing novel ways to tease apart even extremely closely-related species with low levels of nucleotide variation. In a similar vein, we also hope to design population-specific assays that may be able to target individual evolutionary significant units (ESUs) and management or conservation units (M/CUs)[9].

In the next blog, we shall develop further the required minimal standards of a reliable, robust and reputable eDNA qPCR assay, and how these requirements extend and embellish those of the MIQE guidelines. We shall treat in some detail the concepts LOD (limit of detection) and LOQ (limit of quantification) and ask: what are the crucial assay requisite parameters (CARP) for designing, optimizing and validating eDNA assays?

[1] Bustin et al. (2009). The MIQE guidelines: minimum information for publication of qPCR experiments. Clinical Chemistry 55doi.org/41373/clinch.2008.112797.

[2] Lahoz-Monfort et al. (2015). Statistical approaches to account for false positive errors in environmental DNA samples. Molecular Ecology Resources16, doi: 10.1111/1755-0998.12486.

[3] Hale et al. (2012). Sampling for Microsatellite-Based Population Genetic Studies: 25 to 30 individuals per population Is enough to accurately estimate allele frequencies.  PloS One, doi: 10.1371/journal.pone/0045170

[4] Luo et al. (2018). Biparental inheritance of Mitochondrial DNA in humans. Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.1810946115

[5] Benasson et al. (2001). Mitochondrial pseudogenes: evolution’s misplaced witnesses. Trends in Ecology and Evolution16, 314-321.

[6] Smith & Smith (1996). Synonymous nucleotide divergence: what is “saturation”? Genetics142, 1033-1036.

[7] Felsenstein J (2003). Inferring Phylogenies, Sunderland (Sinauer).

[8] Crête-Lafrenière et al. (2012). Framing the Salmonidae family phylogenetic portrait: a more complete pictire from increased taxon sampling. PloS One, doi: 10.1371/journalpone.0046662.

[9] Palsbøll et al. (2006). Identification of management units using population genetic data. Trends in Ecology and Evolution22, 11-16.

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