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Publications in Forests by NOMIS researchers

NOMIS Researcher(s)

Published in

September 16, 2023

The expanded hexanucleotide GGGGCC repeat mutation in the C9orf72 gene is the main genetic cause of amyotrophic lateral sclerosis and frontotemporal dementia. Under one disease mechanism, sense and antisense transcripts of the repeat are predicted to bind various RNA-binding proteins, compromise their function and cause cytotoxicity. Here we identify phenylalanine-tRNA synthetase (FARS) subunit alpha (FARSA) as the main interactor of the CCCCGG antisense repeat RNA in cytosol. The aminoacylation of tRNAPhe by FARS is inhibited by antisense RNA, leading to decreased levels of charged tRNAPhe. Remarkably, this is associated with global reduction of phenylalanine incorporation in the proteome and decrease in expression of phenylalanine-rich proteins in cellular models and patient tissues. In conclusion, this study reveals functional inhibition of FARSA in the presence of antisense RNA repeats. Compromised aminoacylation of tRNA could lead to impairments in protein synthesis and further contribute to C9orf72 mutation-associated pathology. © 2023, Springer Nature Limited.

Research field(s)
Health Sciences

NOMIS Researcher(s)

May 16, 2023

Aim: Globally distributed plant trait data are increasingly used to understand relationships between biodiversity and ecosystem processes. However, global trait databases are sparse because they are compiled from many, mostly small databases. This sparsity in both trait space completeness and geographical distribution limits the potential for both multivariate and global analyses. Thus, ‘gap-filling’ approaches are often used to impute missing trait data. Recent methods, like Bayesian hierarchical probabilistic matrix factorization (BHPMF), can impute large and sparse data sets using side information. We investigate whether BHPMF imputation leads to biases in trait space and identify aspects influencing bias to provide guidance for its usage. Innovation: We use a fully observed trait data set from which entries are randomly removed, along with extensive but sparse additional data. We use BHPMF for imputation and evaluate bias by: (1) accuracy (residuals, RMSE, trait means), (2) correlations (bi- and multivariate) and (3) taxonomic and functional clustering (valuewise, uni- and multivariate). BHPMF preserves general patterns of trait distributions but induces taxonomic clustering. Data set–external trait data had little effect on induced taxonomic clustering and stabilized trait–trait correlations. Main Conclusions: Our study extends the criteria for the evaluation of gap-filling beyond RMSE, providing insight into statistical data structure and allowing better informed use of imputed trait data, with improved practice for imputation. We expect our findings to be valuable beyond applications in plant ecology, for any study using hierarchical side information for imputation. © 2023 The Authors. Global Ecology and Biogeography published by John Wiley & Sons Ltd.

Research field(s)
Natural Sciences, Biology, Ecology