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VMP1-deficient Chlamydomonas exhibits severely aberrant cell morphology and disrupted cytokinesies
(2014)
Background: The versatile Vacuole Membrane Protein 1 (VMP1) has been previously investigated in six species. It has been shown to be essential in macroautophagy, where it takes part in autophagy initiation. In addition, VMP1 has been implicated in organellar biogenesis; endo-, exo- and phagocytosis, and protein secretion; apoptosis; and cell adhesion. These roles underly its proven involvement in pancreatitis, diabetes and cancer in humans.
Results: In this study we analyzed a VMP1 homologue from the green alga Chlamydomonas reinhardtii. CrVMP1 knockdown lines showed severe phenotypes, mainly affecting cell division as well as the morphology of cells and organelles. We also provide several pieces of evidence for its involvement in macroautophagy.
Lafora disease (LD, OMIM #254780) is a rare, recessively inherited neurodegenerative disease with adolescent onset, resulting in progressive myoclonus epilepsy which is fatal usually within ten years of symptom onset. The disease is caused by loss-of-function mutations in either of the two genes EPM2A (laforin) or EPM2B (malin). It characteristically involves the accumulation of insoluble glycogen-derived particles, named Lafora bodies (LBs), which are considered neurotoxic and causative of the disease. The pathogenesis of LD is therefore centred on the question of how insoluble LBs emerge from soluble glycogen. Recent data clearly show that an abnormal glycogen chain length distribution, but neither hyperphosphorylation nor impairment of general autophagy, strictly correlates with glycogen accumulation and the presence of LBs. This review summarizes results obtained with patients, mouse models, and cell lines and consolidates apparent paradoxes in the LD literature. Based on the growing body of evidence, it proposes that LD is predominantly caused by an impairment in chain-length regulation affecting only a small proportion of the cellular glycogen. A better grasp of LD pathogenesis will further develop our understanding of glycogen metabolism and structure. It will also facilitate the development of clinical interventions that appropriately target the underlying cause of LD.
Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low-cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker-assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.