Biology subjects

Jollans, L.

Publications and source records attributed to Jollans, L..

3 recordsLinked to original sources

bioRxiv2020

Stress-primed secretory autophagy drives extracellular BDNF maturation

The stress response is an essential mechanism that strives to maintain homeostasis, and its disruption is implicated in several psychiatric disorders. As a cellular response to stressors, autophagy is activated to regulate homeostasis through protein degradation and recycling. Secretory autophagy is a recently described pathway where autophagosomes fuse with the plasma membrane rather than lysosomes. In this study, we demonstrate that glucocorticoid-mediated stress enhances secretory autophagy, via the stress-responsive co-chaperone FK506-binding protein 51. We identified the matrix metalloproteinase 9 (MMP9) as one of the stress-induced secreted proteins. Using cellular assays and in vivo microdialysis, we further found that stress-enhanced MMP9 secretion increases the cleavage of pro-brain derived neurotrophic factor (proBDNF) to its mature form. BDNF is essential for adult synaptic plasticity and its pathway is associated with major depression and posttraumatic stress disorder. These findings unravel a novel mechanistic link between stress, stress adaptation and the development of psychiatric disorders, with possible therapeutic implications.

molecular biology↗

bioRxiv2020

Predicting future drinking among young adults:using ensemble machine-learning to combine MRI with psychometrics and behaviour

BackgroundWhile most research into predictors of problematic alcohol use has focused on adolescence, young adults are also at elevated risk, and differ from adolescents and adults in terms of exposure to alcohol and neurodevelopment. Here we examined predictors of alcohol use among young adults at a 1-year follow-up using a broad predictive modelling approach. MethodsData in four modalities were included from 128 men aged between 18 and 25 years; functional MRI regions-of-interest from 1) a beer-incentive delay task, and 2) a social alcohol cue-exposure task, 3) grey matter data, and 4) non-neuroimaging data (i.e. psychometric and behavioural). These modalities were combined into an ensemble model to predict follow-up Alcohol Use Disorder Identification (AUDIT) scores, and were tested separately for their contribution. To reveal specificity for the prediction of future AUDIT scores, the same analyses were carried out for current AUDIT score. ResultsThe ensemble resulted in a more accurate estimation of follow-up AUDIT score than any single modality. Only removal of the social alcohol cue-exposure task and of the non-neuroimaging data significantly worsened predictions. Reporting to need a drink in the morning to start the day was the strongest unique predictor of future drinking along with anterior cingulate cortex and cerebellar activity. ConclusionsAlcohol-related task fMRI activity is a valuable predictor for future drinking among young adults alongside non-neuroimaging variables. Multi-modal prediction models best predict future drinking among young adults and may play an important part in the move towards individualized treatment and prevention efforts.

neuroscience↗

bioRxiv2019

Brain-predicted age difference score is related to specific cognitive functions: A multi-site replication analysis

Brain-predicted age difference scores are calculated by subtracting chronological age from brain age, which is estimated using neuroimaging data. Positive scores reflect accelerated ageing and are associated with increased mortality risk and poorer physical function. To date, however, the relationship between brain-predicted age difference scores and specific cognitive functions has not been systematically examined using appropriate statistical methods. First, applying machine learning to 1,359 T1-weighted MRI scans, we predicted the relationship between chronological age and voxel-wise grey matter data. This model was then applied to MRI data from three independent datasets, significantly predicting chronological age in each dataset: Dokuz Eylul University (n=175), the Cognitive Reserve/Reference Ability Neural Network study (n=380), and The Irish Longitudinal Study on Ageing (n=487). Each independent dataset had rich neuropsychological data. Brain-predicted age difference scores were significantly negatively correlated with performance on measures of general cognitive status (two datasets); processing speed, visual attention, and cognitive flexibility (three datasets); visual attention and cognitive flexibility (two datasets); and semantic verbal fluency (two datasets). As such, there is firm evidence of correlations between increased brain-predicted age differences and reduced cognitive function in some domains that are implicated in cognitive ageing.

neuroscience↗