The Interaction between Plants and Bacteria in the Remediation of Petroleum Hydrocarbons: An Environmental Perspective
Bibliographic record
Abstract
Widespread pollution of terrestrial ecosystems with petroleum hydrocarbons (PHCs) has generated a need for remediation and, given that many PHCs are biodegradable, bio- and phyto-remediation are often viable approaches for active and passive remediation. This review focuses on phytoremediation with particular interest on the interactions between and use of plant – associated bacteria to restore PHC polluted sites. Plant-associated bacteria include endophytic, phyllospheric and rhizospheric bacteria, and cooperation between these bacteria and their host plants allows for greater plant survivability and treatment outcomes in contaminated sites. Bacterially-driven PHC bioremediation is attributed to the presence of diverse suites of metabolic genes for aliphatic and aromatic hydrocarbons, along with a broader suite of physiological properties including biosurfactant production, biofilm formation, chemotaxis to hydrocarbons, and flexibility in cell-surface hydrophobicity. In soils impacted by PHC contamination, microbial bioremediation generally relies on the addition of high-energy electron acceptors (e.g. oxygen) and fertilization to supply limiting nutrients (e.g. nitrogen, phosphorous, potassium) in the face of excess PHC carbon. As an alternative, the addition of plants can greatly improve bioremediation rates and outcomes as plants provide microbial habitats, improve soil porosity (thereby increasing mass transfer of substrates and electron acceptors), and exchange limiting nutrients with their microbial counterparts. In return, plant-associated microorganisms improve plant growth by reducing soil toxicity through contaminant removal, producing plant growth promoting metabolites, liberating sequestered plant nutrients from soil, fixing nitrogen, and more generally establishing the foundations of soil nutrient cycling. In a practical and applied sense, the collective action of plants and their associated microorganisms is advantageous for remediation of PHC contaminated soil in terms of overall cost and success rates for in situ implementation in a diversity of environments. Mechanistically, there remain biological unknowns that present challenges for applying bio- and phyto-remediation technologies without having a deep prior understanding of individual target sites. In this review, evidence from traditional and modern omics technologies is discussed to provide a framework for plant-microbe interactions during PHCs remediation. The potential for integrating multiple molecular and computational techniques to evaluate linkages between microbial communities, plant communities and ecosystem processes is explored with an eye on
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".