Omics for the environment

23rd February 2024

Multi-omics data, usually used for biomedical research, can help provide ecologists with a deeper understanding of the health of ecosystems

Historically, environmental monitoring has focused on assessing the presence and toxic effects of known contaminants. These approaches are limited in their ability to detect other chemicals and contaminants, as well as the combined impacts of multiple sublethal stressors.

More importantly, they can fail to capture subtle shifts in ecosystem function, species abundances or animal physiology caused by changing environmental or climatic conditions. Environmental or policy decisions are often based not on the actual health of the ecosystem, but on the aim of keeping specific contaminants or nutrients above or below an arbitrary legislative level.

If the goal is a healthy and sustainable environment, then a lack of detailed indicators of ecosystem health and function hinders meaningful policy development and effective action.

Switching to ‘untargeted’ monitoring of contaminants while also monitoring organism health and other environmental factors would provide more effective monitoring. However, even this does not capture subtle biochemical changes in organisms indced by chronic toxicant exposure or a new environmental change.

In a recent review for Emerging Topics in Life Science, David J Beale and colleagues from several research institutions and agencies in Australia explored how bringing multiple ‘omics’ based assessments together can create a deeper understanding of the health of the environment – particularly in the microbial communities that are so fundamental to the health and remediation of ecosystems.

Ecosystem surveillance

Environmental DNA (eDNA) has emerged as a key tool for ecologists and environmental scientists in recent years, with its use in monitoring largely focused on the presence or absence of indicator organisms.

“These observations do not necessarily inform us of the functional performance of an ecosystem,” write Beale et al. In microbial populations in particular, metabolic functions can be decoupled from taxonomy (through gene loss or horizontal gene transfer) and organisms can exhibit a range of different metabolic capabilities depending on environmental conditions.

Multi-omics can help environmental scientists understand the active genetic pathways in an ecosystem and how they are changing. 

Metabarcoding of functional genes found in eDNA, or whole-metagenome sequencing, have emerged as tools to help survey biological communities and their predicted function, but still these only target the genetic or functional potential of organisms or communities, not the actual functional activity or response to change. Assessment of transcriptomes – that is, the sequences of RNA from transcribed genes – takes researchers “a step closer to realised function”, helping to identify active organisms and genetic pathways, write Beale et al.

Even then, the presence of specific RNA transcripts does not indicate the associated function is actually taking place, since regulation can occur after expression. That is where data from the proteome (proteins that have actually been expressed) and metabolome (evidence of the proteins’ catalytic activity) can provide even stronger indications on the functional health of the ecosystem.

Omics for ecosystems

Multi-omics, integrative omics or ‘panomics’ involves combining data sets from multiple ‘omes’ such as the genome, proteome, transcriptome, epigenome, lipidome, metabolome and microbiome (or the metagenome, metaproteome and so on of mixed populations).

Analysis of these combined data sets, often using machine learning tools, can help research groups find new links, associations and relationships between the various elements to better understand the underlying mechanisms and dynamics of the system.

Such tools have predominantly been developed and used for medical and clinical research, helping identify new drug targets or biomarkers of disease. The use of multi-omics approaches in ecology and environmental science is less well developed but it has the power to dramatically improve how we monitor and manage polluted or threatened environments.

Beale et al point to a number of cases where multi-omics assays have been deployed in an environmental context so far. These range from assays of the functional responses of microbes in soils impacted by drought to the measurement of the biological impacts of offshore oil and gas drilling across multiple trophic levels.

Environmental omics studies have been conducted in soil, river, marine, sediment, root system and permafrost communities, although they are predominantly focused on microbial communities. None have yet combined data from more than three of the major domains of microbiome, genome, proteome, metabolome and transcriptome.

Limitations

The application of omics approaches for ecological monitoring can be and is limited by a range of factors, particularly gaps in reference data. For example, “there is a paucity of proteome sequence databases available for non-model species,” write Beale et al. “The incompleteness of sequence databases and their limited annotations are a bottleneck for environmental proteomics experiments.”

Likewise, many of the ‘features’ of metabolic data sets have not yet been identified, limiting the knowledge and understanding of the environmental process being analysed.

Collecting robust environmental metadata around these unidentified features could enable them to be correlated and characterised, even if we don’t know their specific function, write the authors. (For example, ‘metabolite feature X always occurs within Y environments with high metal loads’.)

Sampling multiple ‘omes’ could help detect changes before significant harm occurs

Analysis of complex environments can also be hindered by the varied substances found within soils or sediments, which can hinder the extraction and recovery of key biomolecules. While commercial kits are available for the co-extraction of DNA and RNA, “the inclusion of proteins and metabolites requires more research and development”, the authors write.

Next steps

Beale et al suggest that, in future, instead of ecologists creating separate libraries for pollutants and their effects, species’ genomes and their metabolites and so on, a library of metabolite profiles for model species exposed to specific pollutants or mixtures of pollutants should be developed. Advanced machine learning-based tools could aid the organisation and analysis of such a database, forming a dedicated tool to facilitate environmental monitoring in complex environments.

Bringing a multi-omics approach from biomedicine to environmental sciences has enormous potential benefits. Not only would it lead to a greater understanding of ecosystems, their makeup and how they are impacted by environmental change, but it could enable scientists to detect physiological change before significant harm occurs. It could also help elucidate how certain organisms help to rebalance ecosystems or biodegrade pollutants.

Integrating further environmental metadata with monitoring assays can help us better understand the complex interactions between biological systems, anthropogenic environmental changes, natural environmental variations, and diurnal and biogeochemical cycles, which remains a significant research challenge.

New libraries and databases will be needed to help ecologists and environmental scientists make sense of omics data 

Beale et al hope their review will be a starting point for further efforts to improve environmental monitoring by integrating comprehensive chemical assessments and molecular biology-based approaches. The paper provides a list of tools and applications for integrating multi-omics data sets for omics-based ecosurveillance, with a breakdown of their features and sources.

The authors write that “bringing multiple levels of omics technology-based assessment together into a systems-wide eco-surveillance approach will bring a greater understanding of the environment, particularly the microbial communities upon which we ultimately rely to remediate perturbed ecosystems.”

• This article is adapted from a recent review of omics data in the RSB co-owned journal Emerging Topics in Life Sciences. Beale, D. J. et al. Omics-based ecosurveillance for the assessment ecosystem function, health, and resilience. Emerg. Top. Life Sci. 6, 185–199 (2022).

Reference

1) Karthikeyan, S. et al. Integrated omics elucidate the mechanisms driving the rapid biodegradation of Deepwater Horizon oil in intertidal sediments undergoing oxic-anoxic cycles. Environ. Sci. Technol. 54, 10088–10099 (2020).

This article is adapted from a recent review of omics data in the RSB co-owned journal Emerging Topics in Life Sciences. Beale, D. J. et al. Omics-based ecosurveillance for the assessment ecosystem function, health, and resilience. Emerg. Top. Life Sci. 6, 185–199 (2022).