Bartlett, J. M. | bioRxiv Science

bioRxiv2020

Effects of pro-depressant and immunomodulatory drugs on biases in decision-making in the rat judgement bias task

Studies in human and non-human species suggest that decision-making behaviour can be biased by affective state, also termed an affective bias. To study these behaviours in non-human species, judgement bias tasks have been developed. Animals are trained to associate specific cues (tones) with a positive or negative/less positive outcome. Animals are then presented with intermediate ambiguous cues and affective biases quantified by observing whether animals make more optimistic or more pessimistic choices. Here we use a high versus low reward judgement bias task and test whether pharmacologically distinct compounds, which induce negative biases in learning and memory, have similar effects on decision-making: tetrabenazine (0.0-1.0mg/kg), retinoic acid (0.0-10.0mg/kg) and rimonabant (0.0-10.0mg/kg). We also tested immunomodulatory compounds: interferon- (0-100units/kg), lipopolysaccharide (0.0-10.0g/kg) and corticosterone (0.0-10.0mg/kg). We observed no specific effects in the judgement bias task with any acute treatment except corticosterone which induced a negative bias. We have previously observed a similar lack of effect with acute but not chronic psychosocial stress and so next tested decision-making behaviour following chronic interferon-alpha. Animals developed a negative bias which was sustained even after treatment was ended. These data suggest that decision-making behaviour in the task is sensitive to chronic but not acute effects of most pro-depressant drugs or immunomodulators, but exogenous administration of acute corticosterone induces pessimistic behaviour. This work supports our hypothesis that biases in decision-making develop over a different temporal scale to those seen with learning and memory which may be relevant in the development and perpetuation of mood disorders. Graphical abstract and textDecision-making bias in rats, measured using a judgement bias task, is not altered by acute treatments with pro-depressant or immunomodulatory drugs, but becomes more negative following chronic treatment. The time course of change in decision-making bias reflects the subjective reporting of changes in depression symptoms in humans treated with these drugs. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=163 SRC="FIGDIR/small/358655v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@93dd2corg.highwire.dtl.DTLVardef@19d7e41org.highwire.dtl.DTLVardef@cc1ac1org.highwire.dtl.DTLVardef@1f0775c_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2018

Network-Based Biomarkers Enable Cross-Disease Biomarker Discovery

Biomarkers lie at the heart of precision medicine, biodiversity monitoring, agricultural pathogen detection, amongst others. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers almost always involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. We therefore created SIMMS: an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We applied SIMMS to multiple data-types across four diseases, and in each it reproducibly identified subtypes, made superior predictions to the best bespoke approaches, and identified known and novel signaling nodes. To demonstrate its ability on a new dataset, we measured 33 genes/nodes of the PIK3CA pathway in 1,734 FFPE breast tumours and created a four-subnetwork prediction model. This model significantly out-performed existing clinically-used molecular tests in an independent 1,742-patient validation cohort. SIMMS is generic and can work with any molecular data or biological network, and is freely available at: https://cran.r-project.org/web/packages/SIMMS.

bioinformatics↗