Association Between a Directly Translated Cognitive Measure of Negative Bias and Self-reported Psychiatric Symptoms.

BACKGROUND: Negative interpretation biases are thought to be core symptoms of mood and anxiety disorders. However, prior work using cognitive tasks to measure such biases is largely restricted to case-control group studies, which cannot be used for inference about individuals without considerable additional validation. Moreover, very few measures are fully translational (i.e., can be used across animals and humans in treatment-development pipelines). This investigation aimed to produce the first measure of negative cognitive biases that is both translational and sensitive to individual differences, and then to determine which specific self-reported psychiatric symptoms are related to bias. METHODS: A total of 1060 (n = 990 complete) participants performed a cognitive task of negative bias along with psychiatric symptom questionnaires. We tested the hypothesis that individual levels of mood and anxiety disorder symptomatology would covary positively with negative bias on the cognitive task using a combination of computational modeling of behavior, confirmatory factor analysis, exploratory factor analysis, and structural equation modeling. RESULTS: Participants with higher depression symptoms (ß = -0.16, p = .017) who were older (ß = -0.11, p = .001) and had lower IQ (ß = 0.14, p < .001) showed greater negative bias. Confirmatory factor analysis and structural equation modeling suggested that no other psychiatric symptom (or transdiagnostic latent factor) covaried with task performance over and above the effect of depression, while exploratory factor analysis suggested combining depression/anxiety symptoms in a single latent factor. Generating groups using symptom cutoffs or latent mixture modeling recapitulated our prior case-control findings. CONCLUSIONS: This measure, which uniquely spans both the clinical group-to-individual and preclinical animal-to-human generalizability gaps, can be used to measure individual differences in depression vulnerability for translational treatment-development pipelines.

FUS ALS-causative mutations impair FUS autoregulation and splicing factor networks through intron retention.

Mutations in the RNA-binding protein FUS cause amyotrophic lateral sclerosis (ALS), a devastating neurodegenerative disease. FUS plays a role in numerous aspects of RNA metabolism, including mRNA splicing. However, the impact of ALS-causative mutations on splicing has not been fully characterized, as most disease models have been based on overexpressing mutant FUS, which will alter RNA processing due to FUS autoregulation. We and others have recently created knockin models that overcome the overexpression problem, and have generated high depth RNA-sequencing on FUS mutants in parallel to FUS knockout, allowing us to compare mutation-induced changes to genuine loss of function. We find that FUS-ALS mutations induce a widespread loss of function on expression and splicing. Specifically, we find that mutant FUS directly alters intron retention levels in RNA-binding proteins. Moreover, we identify an intron retention event in FUS itself that is associated with its autoregulation. Altered FUS levels have been linked to disease, and we show here that this novel autoregulation mechanism is altered by FUS mutations. Crucially, we also observe this phenomenon in other genetic forms of ALS, including those caused by TDP-43, VCP and SOD1 mutations, supporting the concept that multiple ALS genes interact in a regulatory network.