Exploring the Survival Strategies of Aerobic Methanotrophs in Oxygen-Limited Conditions; an Interdisciplinary Approach
Bibliographic record
Abstract
Anthropogenic pollution is a critical threat to life on Earth. Methane is a powerful greenhouse gas, emitted directly to the atmosphere as a polluting byproduct of many industries. It is also emitted from natural sources, and it is produced by microbes whose growth is promoted by other anthropogenic pollutants, such as nitrogen fertilizers and the growing accumulation of human and domestic animal waste. Luckily, nature contains a group of opportunistic, counterbalancing microorganisms capable of consuming methane and converting it into biomass or value-added products. These so-called methanotrophs are the organisms which are featured in this dissertation, and in particular, a sub-group of aerobic methanotrophs that survive in environments where oxygen is transiently available. One model organism representing this group is a methanotroph in the class Gammaproteobacteria, Methylomonas denitrificans FJG1. This bacterium is an obligate aerobe, requiring oxygen to survive, yet it has recently been discovered to simultaneously consume methane and nitrate in a combined metabolic pathway which allows the bacterium to survive for extended durations without access to oxygen. This unique ability also corresponds with a profound phenotypic change from a cream colour to bright pink when oxygen is depleted. The first aim of this thesis was to investigate the molecular responses of M. denitrificans FJG1 to oxygen limitation and its regulation of the methane and denitrification pathways. Searching a combined set of transcriptome and proteome data measuring the gene expression and protein production of M. denitrificans FJG1 collected before, during, and after oxygen depletion, a strong candidate protein for oxygen binding and delivery was identified. This gene was found to be highly upregulated in response to decreasing oxygen availability, and the corresponding protein was produced in very high abundance. The gene, annotated as bacteriohemerythrin, is a homologue, or copy, of a gene for a pink-coloured iioxygen-transport protein found in the blood of annelid worms that lack the usual hemoglobin employed by most animals. In Chapter 2, comparative genomics between M. denitrificans FJG1, other methanotrophic microorganisms, and other non-methanotrophic bacteria revealed ten homologues in the genome of M. denitrificans FJG1 alone. One, designated bhr-00 was specific to methanotrophs in the Methylococcales order. The predicted structure of this Bhr protein is similar to a previously characterized Bhr-Bath protein shown to bind and deliver O2 to support methane oxidation in the methanotroph, Methylococcus capsulatus Bath. In Chapter 3, this discovery of the methanotroph-specific Bhr-00 protein was employed as a biomarker, in correlation with the most often used pmoA biomarker, to identify aerobic methanotroph activity in the anoxic regions of a Canadian Boreal lake (Lake 227) using metatranscriptome data. Aerobic methanotrophs in the order Methylococcales have been identified in bacterial communities of many anoxic environments, yet the metabolism that supports their presence and abundance has not yet been solved, particularly in how they access requisite O2 to support methane oxidation. Both bhr-00 and pmoA transcripts were found in methanotroph metagenome assembled genomes (MAGs) reconstructed from Lake 227 and the metatranscriptome indicated they are upregulated in a manner similar to M. denitrificans FJG1 under oxygen limitation. In addition, genes for gas vesicles and extracellular electron transport were upregulated in some of the methanotroph MAGs indicating they have evolved distinctive methods for utilizing bhr in conjunction with motility and alternative terminal electron acceptors in Lake 227. In Chapter 4, a gene regulatory network (GRN) was developed based on differential gene expression of M. denitrificans FJG1 grown under oxygen limitation and two different nitrogen sources across a six point time course, through the development of a methodology incorporating unsupervised machine learning (ML) algorithms. This methodology was able to capture and interpret complex regulatory patterns contained in the gene expression values iiiof 12 individual sampling points by combining two different ML algorithms (ARACNE and GENIE3) and retaining only those network connections agreed upon by both algorithms. A comprehensive workflow was developed to strategically maximize confidence in these models to interpret the links between genes and their regulators when comparing across growth conditions. Chapter 5 presents an overall conclusion and future research directions for further understanding of methanotrophs in anoxic ecosystems and their genomic regulation as they transition from one physicochemical context to another. Taken together, the work presented in this dissertation identified a key methanotroph-specific protein, Bhr-00, that promotes survival of Methylococcales bacteria in ecosystems with limited oxygen availability. This information connected laboratory observations from a model methanotroph strain with the activity of related methanotrophs in a natural lake ecosystem and showed potential mechanisms that allow methanotrophs in the Methylococcales order to thrive in anoxic zones. Last, a new method for interpreting gene regulatory networks, called ProGRN, was developed with the potential for increasing scientific return on transcriptome data. The resulting GRN revealed regulatory connections between methane oxidizing genes and bhr-00 by way of transcription factors sigma 70 and sigma 24, shedding new light on the regulatory system of methanotrophs when faced with oxygen depletion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".