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Record W185402728

Studying the physiology of declining Diporeia populations in the Laurentian Great Lakes using metabolomics

2011· article· en· W185402728 on OpenAlexaboutno aff
Suman Kumar Maity

Bibliographic record

VenuePurdue e-Pubs (Purdue University System) · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Invertebrate Ecology and Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyEcology
DOInot available

Abstract

fetched live from OpenAlex

The holarctic amphipod Diporeia spp. used to be the most abundant benthic macroinvertebrate in the Laurentian Great Lakes. Diporeia serve as an important link between benthic and pelagic organisms within the Great Lakes by assimilating carbon from the benthic zone making it available to pelagic food webs. Due to their high lipid content, Diporeia are an ideal prey item for a number of fish species including lake whitefish ( Coregonus clupeaformis ) and slimy sculpin ( Cottus cognatus ). Since the 1990's, Diporeia have been extirpated from much of their former habitats. Their decline has coincided with the introduction and establishment of dreissenid mussels in the Great Lakes region. It has been hypothesized that Diporeia population declines are a result of decreased food availability from increasing competition with dreissenids for diatoms. There is additional evidence of remote effects of mussel pseudo-feces excreted by dreissenid colonies inducing toxic responses in Diporeia. In addition, persistent organic pollutants like polychlorinated biphenyls (PCBs), present in Great Lakes sediments, are known to elicit negative effects on Diporeia . However, stable Diporeia populations still persist in oligotrophic Lake Superior and co-exist with dreissenid colonies in Cayuga Lake. Our research has focused on elucidating the cause(s) of Diporeia decline using metabolomics as an exploratory tool. We conducted a series of laboratory experiments exposing Diporeia to multiple environmental factors (i.e., starvation, diatom diet, presence of quagga colonies, and exposure to PCBs). The physiological response elicited by each stressor was measured by evaluating changes in the metabolite expression pattern of Diporeia. Two separate instrumental platforms, two dimensional gas chromatography- and liquid chromatography both coupled with mass spectrometry, were utilized to study polar and non-polar metabolites, respectively. Starvation resulted in decreased phospholipid abundance and enhanced protein metabolism. Lipid biosynthesis and production of essential amino acids were enhanced in diatom fed Diporeia. Our results have also shown that quagga exposure causes an elevated stress response in Diporeia especially for Lake Michigan organisms, suggesting lake-specific adaptive mechanisms for Diporeia in the Great Lakes. PCB exposure resulted in elevated cysteine and phospholipid metabolism and activation of AhR mediated pathways. Using multivariate analysis, these results were then compared to metabolite profiles from Diporeia (n = 148) collected from different lakes (Cayuga, Huron, Michigan, Ontario and Superior), years (2008–2009) seasons (fall and spring), and population histories (“stable” and “declining”). Amino acid (histidine and tryptophan) and lipid (sphingolipid and phospholipid) metabolism was primarily affected in declining populations. Overall, phospholipid metabolism was primarily impacted in both laboratory and field studies. Based on the analyses of field and experimental samples, it also appears that the presence of dreissenids elicits varied physiological response in Diporeia populations across lakes and availability of food also plays an important role controlling such changes in declining Diporeia population. The next step should involve the development of a cumulative health index based on a selected number of metabolites to be used for assessment of overall health status of Diporeia across Great Lakes.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.098
GPT teacher head0.245
Teacher spread0.148 · 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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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