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A day in the life of mitochondria reveals shifting workloads.

Tobias W Weinrich | Jaimie Hoh Kam | Bill T Ferrara | Elinor P Thompson | John Mitrofanis | Glen Jeffery
Scientific reports | 2019

Mitochondria provide energy for cellular function. We examine daily changing patterns of mitochondrial function and metabolism in Drosophila in vivo in terms of their complex (I-IV) activity, ATP production, glycolysis, and whole fly respiration in the morning, afternoon and night. Complex activity and respiration showed significant and unexpected variation, peaking in the afternoon. However, ATP levels by contrast are >40% greater in the morning and lowest at night when glycolysis peaks. Complex activity modulation was at the protein level with no evidence for differential transcription over the day. Timing differences between increased ATP production and peaks of complex activity may result from more efficient ATP production early in the day leaving complex activity with spare capacity. Optical stimulation of mitochondria is only possible in the mornings when there is such spare capacity. These results provide first evidence of shifts in cellular energy capacity at the organism level. Understanding their translation may be significant to the chosen timing of energy demanding interventions to improve function and health.

Pubmed ID: 31554906

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Associated grants

  • Agency: RCUK | Biotechnology and Biological Sciences Research Council (BBSRC), International
    Id: BB/N000250/1

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NormFinder (tool)

RRID:SCR_003387

Software for identifying the optimal normalization gene among a set of candidates. It ranks the set of candidate normalization genes according to their expression stability in a given sample set and given experimental design. It can analyze expression data obtained through any quantitative method e.g. real time RT-PCR and microarray based expression analysis. NormFinder.xla adds the NormFinder functionality directly to Excel. A version for R is also available.

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geNORM (tool)

RRID:SCR_006763

Software to determine most stable reference (housekeeping) genes from set of tested candidate reference genes in given sample panel. From this, gene expression normalization factor can be calculated for each sample based geometric mean of user-defined number of reference genes.

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