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Record W3204465178 · doi:10.1093/plcell/koab238

Swapping acids: the ins and outs of plant mitochondrial metabolism

2021· letter· en· W3204465178 on OpenAlexaff
Brendan M. O’Leary

Bibliographic record

VenueThe Plant Cell · 2021
Typeletter
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsBiologyPlant metabolismMetabolismMitochondrionBiochemistryGeneRNA

Abstract

fetched live from OpenAlex

Studying membrane-embedded metabolite transporters is experimentally troublesome. Several elements of the membrane environment in which metabolite transporters operate in vivo, such as physical orientation, lipid composition, metabolite concentrations, and electrochemical gradients, are difficult to recapitulate in vitro. Consequently, in many instances, our understanding of which specific transporters move compounds between cellular compartments and contribute to the functioning of the metabolic network in plants remains poorly understood. For example, plant mitochondria are actively engaged in the import and export of mono, di-, and tri- carboxylic acids (e.g. TCA cycle intermediates) as part of their respiratory and biosynthetic functions. Despite our detailed understanding of the metabolic pathways both inside and outside the mitochondria, how metabolite transporters function to connect these pathways remains unclear (Lee and Millar, 2016). In a recent Plant Cell paper, Chun Pong Lee and colleagues (Lee et al., 2021) applied advanced transport and metabolic flux measurements to understand the contribution of a particular transporter, dicarboxylate transporter 2 (DIC2), to mitochondrial metabolism. The first part of the transport equation seemed straightforward, as isolated mitochondria lacking DIC2 displayed lower malate import rates: DIC2 was likely one of several mitochondrial malate importers. However, metabolite transporters typically operate as exchangers and the nature of the exported metabolite is equally important. When treated with external malate, isolated dic2-1 mitochondria displayed pronounced shifts in several internal and external metabolite levels. This complex rearrangement of mitochondrial fluxes and transport activities revealed major metabolic impacts of DIC2 loss, but obscured the identity of the DIC2 export substrate. Follow-up experiments utilized recombinantly expressed DIC2 incorporated into lipid vesicles (proteoliposomes) to assay its enzymatic activity in vitro. High rates of DIC2-dependent malate uptake into lipid vesicles occurred when vesicles were pre-loaded with citrate for exchange. Mitochondria isolated from dic2-1 and fed with malate also hyper-accumulated citrate inside compared to wild type. Together the results strongly indicate that DIC2 is a malate–citrate antiporter. Malate import and citrate export are major, light-dependent features of mitochondrial carbon flux in photosynthetic cells (Sweetlove et al., 2010). The researchers therefore sought to elucidate a functional role for the DIC2 malate–citrate antiporter in leaves. To start, dic2-1 plants displayed reduced growth compared to wild type under all light conditions. Large differences in the metabolomic profile of dic2-1 leaves, including changes in NAD:NADH redox balance, occurred specifically at the transition from light to darkness. This indicated a defect in switching between daytime metabolism and dark metabolism flux modes in dic2-1 mitochondria. Further observations in dic2-1 leaves, including greater rates of respiration and sucrose depletion at night, indicated that inefficient nocturnal mitochondrial metabolism was leading to an early onset of carbon starvation. These symptoms of metabolic inefficiency were corroborated by observations from extended darkness treatments where dic2-1 leaves displayed more rapid deterioration, which coincided with abnormal malate, citrate, and 2-oxoglutarate accumulation. The metabolic phenotype caused by loss of DIC2 was further detailed by tracing the metabolism of 13C-glucose, fed to leaf tissue in the dark. Consistent with its role as a mitochondrial citrate exporter, less labeled carbon flowed into citrate and was instead diverted into 2-oxoglutarate and aspartate (Figure). Overall, the data indicate that DIC2 functions to support nighttime citrate export from mitochondria for vacuolar storage, and that the metabolic rearrangements occurring in the absence of DIC2 are less energetically efficient. The work complements other recent studies in demonstrating how detailed in situ characterization of mitochondrial metabolite transporters leads to a better understanding of how plant metabolism is interconnected (Le et al., 2021). The metabolic phenotype of DIC2 disruption. The flux of labeled carbon from exogenously fed 13C-glucose was measured in WT (blue), dic2 (orange), and dic2 complemented (light blue) leaf discs. Important differences were observed in citrate, 2-oxoglutarate, and aspartate labeling. Adapted from Lee et al. (2021), Figure 7. The metabolic phenotype of DIC2 disruption. The flux of labeled carbon from exogenously fed 13C-glucose was measured in WT (blue), dic2 (orange), and dic2 complemented (light blue) leaf discs. Important differences were observed in citrate, 2-oxoglutarate, and aspartate labeling. Adapted from Lee et al. (2021), Figure 7.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.021
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.004
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0210.019
Insufficient payload (model declined to judge)0.0050.003

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 head0.038
GPT teacher head0.210
Teacher spread0.171 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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Citations1
Published2021
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