Advancing biodiversity monitoring with environmental DNA and RNA

Rapid monitoring of organism presence and health is crucial for biodiversity conservation in the Anthropocene. Analysis of environmental DNA (eDNA) naturally shed by organisms into their surroundings has become a powerful tool for biodiversity monitoring, enabling species detection without visual observation. I am interested in taking these molecular methods beyond species presence/absence by exploring the potential of environmental RNA (eRNA). By analyzing transcribed genes, which reflect the physiological status of organisms, I aim to use changes in gene expression detected via eRNA analysis as indicators of organism health.

Relevant publications:

Hechler, R.M. 2025. Quantifying species interactions in the Anthropocene. Nature Reviews Biodiversity, 1, 89. [link]

Hechler, R.M., & Cristescu, M.E. (2024). Revealing population demographics with environmental RNA. Molecular Ecology Resources, 24(4), e13951. [link]

Hechler, R.M., Yates, M.C., Chain, F.J.J., & Cristescu, M.E. (2023). Environmental transcriptomics under heat stress: Can environmental RNA reveal changes in gene expression of aquatic organisms? Molecular Ecology, 00, 1–15. [link]

Kagzi, K., Hechler, R.M., Fussmann, G.F., & Cristescu, M.E. (2022). Environmental RNA degrades more rapidly than environmental DNA across a broad range of pH conditions. Molecular Ecology Resources, 22(7), 2640-2650. [link]

1. Can eRNA detect the stress response of aquatic organisms?

Hechler et al. (2023, Molecular Ecology)

Hechler2023_MolecularEcology.pdf

Summary: eRNA was able to recover a subset of genes which reflected the general stress response of Daphnia exposed to an experimental heat wave. Using eRNA, we identified 32 Daphnia genes to be differentially expressed following heat stress. Of these, 17 were also differentially expressed and exhibited similar levels of relative expression in organismal RNA. This was the first evidence that extra-organismal RNA extracted from the environment is representative of the organismal transcriptional signal.
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1.1 Methods

We exposed four replicate Daphnia populations to 20°C and 28°C for seven days
We performed RNA-seq of eRNA and oRNA

1.2 Results

Fig 1. PCA of the top 300 Daphnia genes, as ranked by variance. PCA revealed separation by RNA type and temperature across the PC1 and PC2, respectively (except eRNA_20T1 clusters more closely with 28°C samples than with other 20°C samples).

Fig. 2. Heatmap showing the relative expression (Z-score calculated for each gene) of all commonly significantly differentially expressed Daphnia genes (false discovery rate-adjusted p-value < 0.05) between 20°C and 28°C, in both eRNA and oRNA. Hierarchical clustering analysis revealed four groups: 20°C and 28°C samples, and up and downregulated genes (except sample eRNA_20T1 clusters with 28°C samples). Colours indicate levels of relative expression, with blue and red indicating low and high, respectively.

2. eRNA degrades more rapidly than eDNA across a broad range of pH conditions

Kagzi, Hechler et al. (2022, Molecular Ecology Resources)

Kagzi2022_MolecularEcologyResources.pdf

Although the use and development of molecular biomonitoring tools based on environmental nucleic acids (eDNA and eRNA; collectively known as eNAs) have gained broad interest for the quantification of biodiversity in natural ecosystems, studies investigating the impact of site-specific physicochemical parameters on eNA-based detection methods (particularly eRNA) remain scarce. Here, we used a controlled laboratory microcosm experiment to comparatively assess the environmental degradation of eDNA and eRNA across an acid–base gradient.

eRNA persisted long enough to be detected (up to 57 hours), but degraded much more rapidly than eDNA thereby reducing false-positve species detections.

2.1 Methods

initial stock containers to generate eNA rich water
intermediate large reservoirs to mix eNA rich water
experimental and positive control tanks at various pH levels
sampled between 0 hour and 30 days post Daphnia removal from experimental tanks
quantified eDNA and eRNA concentrations via ddPCR

2.2 Results

Fig 3. COI copy number · μl of (a) eDNA and (b) eRNA detected using droplet digital PCR at each pH level (4, 7 and 10) through time. The “initial” value corresponds to the initial sample reading taken prior to acid/base addition. Note different y-axis scales.

Fig 4. Exponential decay curves depicting mean COI copies/μl detected over time for (a) eDNA at pH 4, (b) eDNA at pH 7, (c) eDNA at pH 10, (d) eRNA at pH 4, (e) eRNA at pH 7, and (f) eRNA at pH 10. Decay curves were fitted to data using the equation N(t) = N0 e–λt, and R2 values indicate the goodness of fit of the model to the data. Note different y-axis scales.

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