Molecular characterization of the hydrocarbon biodegradation process on Canadian Arctic beaches
Notice bibliographique
Résumé
The warming effect of climate change is causing a decrease in sea ice across the Arctic Ocean, especially in the Canadian high Arctic. This is expected to cause an increase in shipping traffic through the Northwest Passage. This comes with the risk of environmental damage to the region with one of the largest concerns being a hydrocarbon spill from a passing ship. Due to the remoteness of the Canadian high Arctic, the response in case of such a spill will be slow and the spilled hydrocarbons might reach the shoreline. Simpler remediation strategies could be preferrable due to the limited resources available for a cleanup in this area. One of said strategies is bioremediation using the native microbial communities that inhabit these beaches. Limited research has been carried out to understand the hydrocarbon biodegradative capabilities of Arctic microbes on shorelines. This limits our understanding of the microbial community and the metabolic processes these microbes are carrying out in response to the presence of hydrocarbons in their environment. This is crucial information that needs to be obtained to understand whether bioremediation will be an effective cleanup strategy. This thesis aims to narrow this knowledge gap by utilizing state-of-the-art molecular techniques to describe the potential and actualized in situ hydrocarbon biodegradation capabilities of Arctic shoreline microorganisms.Initially, I first performed a metagenomic survey of the baseline microbial communities inhabiting 9 Arctic beaches from 4 regions of the Canadian Arctic archipelago to understand the genomic potential available in those beaches before any spill has occurred. The presence of known hydrocarbon-degrading taxa as well as genes involved in hydrocarbon degradation pathways, especially those associated with the degradation of short, medium, and long-chain alkanes was identified. I then compared metagenome-assembled genomes from the survey with genomes of isolates obtained from the same beaches that can grow using hydrocarbons as a source of carbon.To further corroborate the results of the survey, in situ mesocosm experiments consisting of hydrophobic netting covered with three fuels commonly used by the shipping industry: Marine diesel, Bunker C, and Ultra Low Sulfur Fuel Oil (ULSFO) were performed. These nettings were deployed in Assistance Bay (Cornwallis Island, Nunavut) for a month. I then performed 16S rRNA gene amplicon, metagenomic, and metatranscriptomic sequencing on the netting to characterize how the native microbial community of this beach responds when fuels are added to the beach. A shift in the community composition towards taxa commonly associated with hydrocarbon degradation as well as the presence of multiple genes these taxa are using to metabolize aliphatic and aromatic hydrocarbons was observed. Analysis of the remaining fuel showed that 14 – 78% of the compounds were biodegraded, with Marine diesel having the highest degradation and Bunker C having the lowest.Finally, a second round of in situ mesocosm experiments was performed to determine whether allowing biodegradation to occur for a whole year along with the addition of fertilizers would further stimulate the Assistance Bay beach microbiota and lead to higher Marine diesel and ULSFO biodegradation. After a year, I observed more defined differences in the microbial communities of the fuel treatments compared to the controls, but no noticeable differences between the communities of the two fuels, suggesting that similar microbes can metabolize both kinds of fuel. There was no effect of the addition of fertilizers for the microbial composition or the biodegradation performance. The increased duration of the experiment also did not result in more of the fuel being biodegraded, with similar percentages observed after a year (33 – 72%) which could have been caused by the decrease in metabolic activity of most microbes under the sub-zero temperatures experienced in the prolonged Arctic winter
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».