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Investigating metabolic changes in neurodegeneration


Project Description


This 3-year PhD will study the increasingly recognised association between metabolic disease and neurodegeneration using in vitro cell culture methods. Specific metabolic differences in neurodegeneration-prone neurons will be used to identify potential biomarkers of these changes.

The student will be trained in techniques including cell culture, protein analysis (Western blotting), RNA/qRT-PCR analysis, immunohistochemistry/immunofluorescence and mass spectrometry.

Findings from this study could provide new insights into the association of neurodegenerative diseases with dysregulated metabolism.

Applicants should have or be expecting to obtain at least an Upper Second (2i) class degree in Cell Biology, Neuroscience, Biomedical Science, Biochemistry or a related subject. Experience in laboratory work, particularly in cell culture and analysis, would be an advantage, but training can be given. The student should be enthusiastic, self-motivated, and committed to carrying out their own research.



Funding Notes

No funding is available; only self-funded applications can be considered.

References

Gentile L, Cebria F, Bartscherer K (2011). The planarian flatworm: an in vivo model for stem cell biology and nervous system regeneration. Dis Mod & Mech 4, 12-19.

Zhang Y-F, Ye B-P, Wang D-Y (2008). Molecular actions guiding neural regeneration in planarian. Neurosci Bull 24 (5): 329-337.

Cattin AL, Burden JJ, Van Emmenis L, Mackenzie FE, Hoving JJ, et al (2015) Macrophage-Induced Blood Vessels Guide Schwann-Cell Mediated Regeneration of Peripheral Nerves. Cell 162 (5):1127-39.

Wong F, Fan L, Wells S, Hartley R, Mackenzie FE, Oyebode O, et al (2009) Axonal and neuromuscular synaptic phenotypes in Wld(S), SOD1(G93A) and ostes mutant mice identified by fiber-optic confocal microendoscopy. Mol Cell Neurosci 42 (4): 296-307.

How good is research at Kingston University in Allied Health Professions, Dentistry, Nursing and Pharmacy?

FTE Category A staff submitted: 17.22

Research output data provided by the Research Excellence Framework (REF)

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