A new molecular diagnostic tool can reveal not only the extent of petrochemical contamination in soils, but also how long it has been present. Developed by researchers at Queen’s University Belfast and partners in Nigeria, the approach uses ratios of hydrocarbon-degrading functional genes as biological markers for pollution severity and age – providing a complementary perspective to chemical analyses alone.
In field studies across the chronically polluted Niger Delta, the team used gas chromatography-mass spectrometry alongside gene analysis to link hydrocarbon levels with specific microbial markers. Fresh spills contained high kerosene concentrations and aerobic degradation genes, while older sites showed no kerosene but retained anaerobic signatures. These patterns, confirmed through in-silico analysis, highlight how microbial communities record both the severity and age of contamination – offering a rapid, biologically informed tool for environmental monitoring, remediation, and accountability.
We reached out to Aliyu Ibrahim Dabai, corresponding author of the study, to learn more about the technology and its potential applications.
Could you describe, in a nutshell, how your diagnostic tool works?
Our tool operates by quantifying the relative abundance of functional microbial genes linked to aerobic and anaerobic hydrocarbon degradation in genomic material (eDNA) obtained from as low as 1 g of contaminated soils. Using quantitative PCR (qPCR), we measure specific catabolic genes, namely Gram-negative (PAH-RHD α-GN) and Gram-positive (PAH-RHD α-GP) PAH dioxygenase (aerobic) and bamA (anaerobic), normalized against universal microbial housekeeping genes like 16S rRNA as a control. The resulting gene ratios (Gram-negative aerobic:anaerobic or Gram-positive aerobic:anaerobic) serve as a proxy for the apparent level and age of pollution. This ratio-based strategy provides a rapid, cost-effective, and scalable way to assess the oil spill (level and age) and bioremediation potential of a contaminated site.
What initially motivated your team to take a closer look at microbial gene signatures as a way to assess crude oil contamination in soil?
When we received soil samples from Niger Delta, Nigeria, we used an analytical approach to determine if the soils were originally polluted using hydrocarbon reference standards. Unfortunately, one set of soils showed peaks for the reference hydrocarbon, while the other showed no evidence of pollution. This was a bit disappointing; we didn’t want to provide misleading information – especially considering the huge pollution history of the Niger Delta. We were therefore motivated to look at alternative methods, particularly as traditional contamination assessment methods focus on chemical profiling tend to show the pollutants that are present, but without biological insights such as microbial response or methods of self-remediation.
As microbiologists, we recognized that the soil microbiome is both a sensor and a response mechanism. Crude oil selectively enriches specific catabolic gene pools, essentially reshaping microbial functionality. Our motivation was to translate these biological signals into actionable data for site assessment, moving beyond chemical quantification and towards functional ecological insight.
Was there a key breakthrough during development?
The turning point came when we validated that the PAH dioxygenase:bamA/16S gene ratio could reliably distinguish between pristine and hydrocarbon-impacted soils, new and old pollution, and confirmed the analytical approach (GC-MS and GCxGC-FID) supports the result – even when total microbial biomass varied. This meant that the tool wasn’t simply measuring contamination but capturing a functional ecological response. This realization linking gene ratios to apparent age, pollutant level and functional ecosystem resilience was transformative. It reframed our tool as not just diagnostic, but predictive of bioremediation capacity.
What was the biggest analytical challenge you faced during development – and how did you overcome it?
Our biggest hurdle was achieving reproducibility across diverse soil types with varying physical and chemical characteristics. Soil matrix complexity can inhibit DNA extraction or distort qPCR efficiency. We addressed this by rigorously optimizing DNA isolation protocols, introducing robust standards, and selecting robust primer sets with high specificity and efficiency across taxonomic groups. These refinements allowed us to achieve consistent quantification across contrasting soil matrices.
How might this approach be adapted for other contaminants, soil types, or field conditions – and what are the main barriers to wider or field-deployable use?
The framework is inherently adaptable – for example, we are currently applying the approach to monitor P mobilisation in soils by quantifying PhoD (encodes alkaline phosphatase) and PqqC (pyrroloquinoline quinone (PQQ) biosynthesis, relates inorganic P solubilisation). For other contaminants such as heavy metals, pesticides, or emerging organics, we can target different functional gene markers (e.g., linA and linE for lindane degradation). The key is identifying relevant gene targets and validating their functional significance in situ. The main barriers include the need for portable, field-deployable qPCR platforms and region-specific gene database limitations. However, these obstacles are increasingly manageable with advances in handheld qPCR and metagenomic annotation .
What are the next steps for your team?
Our next step is to expand the platform for marine and estuarine systems, integrating gene-ratio models into oil spill response frameworks such as hydrodynamic simulators. We are also developing multiplex qPCR panels to simultaneously assess degradation and recovery functions. As well as this, we’re looking to develop a reverse transcription loop-mediated isothermal amplification (RT-LAMP) for deployment in the field.
More broadly, gene-ratio tools can serve as a missing biological layer in risk assessment complementing chemical and toxicological assays. It can also offer early-warning capabilities and predictive insights, positioning them as powerful tools for precision remediation and adaptive environmental management.
Aliyu Ibrahim Dabai is a researcher in Molecular Microbiology and Metagenomics at Agri-Food and Biosciences Institute Belfast, Northern Ireland and visiting lecturer at Usmanu Danfodiyo University, Sokoto, Nigeria
Newsletters
Receive the latest analytical science news, personalities, education, and career development – weekly to your inbox.
