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Record W7057267394

Investigation of the microbial diversity and characterization of new natural products produced by fungi and actinobacteria of Frobisher Bay

2017· article· en· W7057267394 on OpenAlexaboutno aff

Bibliographic record

VenueIslandScholar (University of Prince Edward Island) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsActinobacteriaBayArcticExtreme environmentBiodiversityMicrobial ecologyMicroorganism
DOInot available

Abstract

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Natural products (NPs) are an important source of pharmaceutical agents and are produced by a wide range of micro and macro organisms. Microorganisms, specifically bacteria within the order Actinomycetales and fungi, are prolific producers of NPs and are responsible for upwards of 70% of all clinically approved antibiotics. Due to the intensive investigation of these microorganisms, specifically from the terrestrial environment for NP discovery, the rate of reisolation of known compounds is high. One way to overcome this issue is to investigate unexplored and underexplored environments as a source of biodiverse microorganisms for natural product discovery.\nCanada’s Arctic remains a vast and largely undiscovered landscape due to its inaccessibility and harsh environment. Being so, the Arctic provides a unique niche, where members of the microbial community have evolved to “fit” this distinctive environment and must be capable of withstanding extreme cold, limited environmental resources and a range of other physical and biological factors. It is hypothesized Arctic microorganisms will have a unique secondary metabolome compared to their tropical counterparts based on environmental selection. Due to a lack of investigation of Canada’s Arctic for natural products, Frobisher Bay was selected as the study location for this investigation. The aims of this thesis were to characterize the bacterial and fungal community of Frobisher Bay and to discover new NPs from these microorganisms.\nIn order to assess the microbial community within Frobisher Bay, 454-pyrosequencing of the 16S rRNA gene and ITS region was used to determine the bacterial and fungal diversity respectively within sediment samples from Frobisher Bay. In order to achieve greater sequencing depth within the prolific NP producing Actinobacteria, Actinobacteria-specific 16S rRNA primers were used in addition to universal 16S rRNA primers. Overall, sites within Frobisher Bay were found to host high levels of taxonomically diverse microorganisms. The presence of large numbers of unknown phylotypes and the immense taxonomic diversity uncovered, make this region an intriguing area to explore from a NPs perspective.\nUsing a variety of isolation techniques, Actinobacteria and fungi were cultured from sediment samples collected from Frobisher Bay. In total, 90 Actinobacteria representing 25 distinct species, and 354 fungal isolates representing 54 species were cultured from this region. Of the fungal isolates, 9 appeared to be putatively novel based on sequencing of barcoding genes and morphological investigations. These isolates were particularly interesting from a NPs perspective, as they offered an untapped resource for NP discovery.\nIn order to prioritize isolates based on the production of new NPs, an LC-HRMS based screening method was used. This resulted in the isolation and characterization of several new NPs including a new hirsutellic acid analog obtained from Simplicillium aogashimaense RKAG 563, a new reduced perylene quinone compound obtained from Cadophora viticola RKAG 170 and two new cameronic acid analogs from Botrytis caroliniana RKAG 208. Investigation of the putatively novel fungal isolates was particularly fruitful for natural product discovery and resulted in the isolation of 17 new natural products. Investigation of Mortierella sp. RKAG 110 resulted in the characterization of mortiamides A-D, new cyclic heptapeptides containing five amino acids in the non-natural D-configuration. Examination of Sesquicillium spp. RKAG 571 and 186 led to the isolation of the new 11 residue peptaibols, tariuqins A-F containing the non-proteogenic amino acids (R)- and (S)-isovaline and aminoisobutyric acid, and to the isolation of the new cyclic decapeptides, auyuittuqamide A-D, containing three N-methylated amino acids. Lastly, exploration of putatively novel Tolypocladium species led to the isolation of several new tetramic acid containing compounds, iqalisetin A and B, and tolypoalbin.\nDue to the permanently cold environment from which they were isolated, the effect of fermentation temperature on NP product production in Actinobacteria from Frobisher Bay was investigated. As most standard lab fermentations occur at a non-ecologically relevant temperature of 30°C, fermentations at colder, more ecologically relevant temperatures (4°C and 15°C) was undertaken. Differences in NP production at each fermentation temperature were assessed using an LC-HRMS based chemical metabolomics method on a subset of cultured actinomycetes. Within the 15°C fermentations, the de novo induction of actinomycin was observed in Streptomyces sp. RKAG 337 and the upregulated production of two new compounds, landomycin AA and AB from Streptomyces sp. RKAG 290 was observed. Due to this upregulation, sufficient material was produced to enable structural characterization of these two compounds. The use of fermentation temperature to induce or increase the production of NPs is a useful tool to access previously inaccessible chemical diversity.\nOverall, Frobisher Bay is a rich resource for microorganisms as assessed by culture independent and dependent methods. The isolation of a large number of new NPs from microorganisms from this region, highlights the Arctic as a promising resource for NP discovery, reinforcing the notion that investigating unexplored environments for NP discovery is a very valuable tool in NPs research.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.196
Teacher spread0.186 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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