Biology subjects

McCoy, B.

Publications and source records attributed to McCoy, B..

5 recordsLinked to original sources

bioRxiv2024

Mapping the canine microbiome: Insights from the Dog Aging Project

Companion dogs (Canis lupus familiaris) offer a unique model for studying the gut microbiome and its relation to aging due to their cohabitation with humans, sharing similar environments, diets, and healthcare practices. Here, we present the Dog Aging Project (DAP) Precision cohort, the largest population-wide study of the canine gut microbiome to date. This cohort encompasses over 900 dogs of diverse breeds, environments, and demographics living across the United States. Coupling fecal shotgun metagenomic sequencing with comprehensive phenotypic and environmental surveys and clinical lab tests, we explore the intricate relationships between microbiome composition, aging, and key factors such as diet, health, and living conditions. Our analyses identify various factors associated with microbiome composition. In addition, we find a gradual shift in microbiome composition with age, which allows us to develop a novel metagenomics-based "clock" to predict biological aging based on microbial signatures. Overall, these findings provide an unprecedented and detailed understanding of the role the gut microbiome plays in our four-legged companions, offering both potential applications in veterinary medicine and an exciting model for aging research.

microbiology↗

bioRxiv2024

Anxious individuals are more sensitive to changes in outcome variability and value differences in dynamic environments

Anxiety is known to alter learning in uncertain environments. Standard experimental paradigms and computational models addressing these differences have mainly assessed the impact of volatility, and anxious individuals have been shown to have a reduced learning rate when moving from a stable to volatile environment. Previous research has not, however, independently assessed the impact of both changes in volatility, i.e., reversals in reward contingency, and changes in outcome variability (noise) in the same individuals. Here we use a simple probabilistic reversal learning paradigm to independently manipulate the level of volatility and noise at the experimental level in a fully orthogonal design. We replicate general increases, irrespective of anxiety levels, in both positive and negative learning rates when moving from low to high volatility, but only in the context of low noise. When low volatility is combined with high noise, more anxious individuals display negative learning rates similar to high volatility with high noise, whereas those lower in anxiety show the usual negative learning rate increase from low to high volatility. Within-individual increases in lose-shift responses from low to high noise conditions scale with levels of anxious traits, but this occurs under low volatility only. We furthermore find that people with higher anxious traits are more accurate overall and utilize a more exploitative decision-making strategy in this dynamic environment. Our findings suggest that changes in both sources of uncertainty, volatility and noise, should be carefully considered when assessing learning, particularly in relation to anxiety and other neuropsychiatric conditions, and implicate anxiety-related differences in dopaminergic and noradrenergic neurotransmitter signalling when learning in highly changeable environments.

animal behavior and cognition↗

bioRxiv2024

A duplexed CRISPRi-based approach to predict synergistic antibiotic targets in Escherichia coli.

Co-repressing drug targets with CRISPR interference (CRISPRi) has the potential to predict both synergistic and additive drug combinations. In a screen experiment with Escherichia coli, pairwise gene co-repression assays correctly predicted the interaction type observed for 36 antibiotic pairs in checkerboard assays. Furthermore, we used this approach to identify promising synergistic interactions with a gene currently not targeted by clinical antibiotics. CRISPRi can systematically test the impact of gene inhibitions and identify novel drug targets.

microbiology↗

bioRxiv2020

Attention and reinforcement learning in Parkinson's disease

Dopamine is known to be involved in several important cognitive processes, most notably in learning from rewards and in the ability to attend to task-relevant aspects of the environment. Both of these features of dopaminergic signalling have been studied separately in research involving Parkinsons disease (PD) patients, who exhibit diminished levels of dopamine. Here, we tie together some of the commonalities in the effects of dopamine on these aspects of cognition by having PD patients (ON and OFF dopaminergic medication) and healthy controls (HCs) perform two tasks that probe these processes. Within-patient behavioural measures of distractibility, from an attentional capture task, and learning performance, from a probabilistic classification reinforcement learning task, were included in one model to assess the role of distractibility during learning. Dopamine medication state and distractibility level were found to have an interactive effect on learning performance; less distractibility in PD ON was associated with higher accuracy during learning, and this was altered in PD OFF. Functional magnetic resonance imaging (fMRI) data acquired during the learning task furthermore allowed us to assess multivariate patterns of positive and negative outcomes in fronto-striatal and visual brain regions involved in both learning processes and the executive control of attention. Here, we demonstrate that while PD ON show a clearer distinction between outcomes than OFF in dorsolateral prefrontal cortex (DLPFC) and putamen, PD OFF show better distinction of activation patterns in visual regions that respond to the stimuli presented during the task. These results demonstrate that dopamine plays a key role in modulating the interaction between attention and learning at the level of both behaviour and activation patterns in the brain.

neuroscience↗

bioRxiv2018

Dopaminergic medication reduces striatal sensitivity to negative outcomes in Parkinson’s disease

Reduced levels of dopamine in Parkinsons disease (PD) contribute to changes in learning, resulting from the loss of midbrain dopamine neurons that transmit a teaching signal to the striatum. Dopamine medication used by PD patients has previously been linked to either behavioral changes during learning itself or adjustments in approach and avoidance behavior after learning. To date, however, very little is known about the specific relationship between dopaminergic medication-driven differences during learning and subsequent changes in approach/avoidance tendencies in individual patients. We assessed 24 PD patients on and off dopaminergic medication and 24 healthy controls (HC) performing a probabilistic reinforcement learning task, while undergoing functional magnetic resonance imaging. During learning, medication in PD reduced an overemphasis on negative outcomes. When patients were on medication, learning rates were lower for negative (but not positive) outcomes and concurrent striatal BOLD responses showed reduced prediction error sensitivity. Medication-induced shifts in negative learning rates were predictive of changes in approach/avoidance choice patterns after learning, and these changes were accompanied by striatal BOLD response alterations. These findings highlight dopamine-driven learning differences in PD and provide new insight into how changes in learning impact the transfer of learned value to approach/avoidance responses in novel contexts.

neuroscience↗