Back

Prion-like α-synuclein pathology in the brains of infants: Krabbe disease as a novel seed-competent α-synucleinopathy

Hatton, C.; Ghanem, S. S.; Koss, D.; Abdi, I. Y.; Gibbons, E.; Guerreiro, R.; Bras, J.; International DLB Genetics Consortium, ; Walker, L.; Gelpi, E.; Heywood, W.; Outeiro, T. F.; Attems, J.; McFarland, B.; Forsyth, R.; El-Agnaf, O. M.; Erskine, D.

2021-10-14 pathology
10.1101/2021.10.12.463948 bioRxiv
Show abstract

Krabbe disease (KD) is an infantile neurodegenerative disorder resulting from pathogenic variants in the GALC gene which causes accumulation of the toxic sphingolipid psychosine. GALC variants are associated with increased risk of Lewy body diseases (LBD), an umbrella term for age-associated neurodegenerative diseases in which the protein -synuclein aggregates into Lewy bodies. To explore whether -synuclein in KD has pathological similarities to that in LBD, we compared post-mortem KD tissue to that of infant control cases and identified alterations to -synuclein localisation and expression of modifications associated with LBD. To determine whether -synuclein in KD displayed pathogenic properties associated with LBD we evaluated its seeding capacity using the real-time quaking-induced conversion assay. Strikingly, seeded aggregation of -synuclein resulted in the formation of fibrillar aggregates similar to those observed in LBD, confirming the prion-like capacity of KD-derived -synuclein. These observations constitute the first report of prion-like -synuclein in the brain tissue of infants and challenge the putative view that -synuclein pathology is merely an age-associated phenomenon, instead suggesting it can result from alterations to biological processes such as sphingolipid homeostasis. Our findings have important implications for understanding the mechanisms underlying Lewy body formation in LBD.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.