Biology subjects

Miles, J. T.

Publications and source records attributed to Miles, J. T..

6 recordsLinked to original sources

bioRxiv2026

Identification and SAR optimization of FBXO22-mediated TEAD Targeted Glue degraders

TEAD transcription factors are emerging oncology targets due to their function as key effectors of the Hippo signalling pathway, which is frequently dysregulated in cancer. Here, we report the discovery and development of potent, deep, and rapid-acting TEAD Targeted Glue degraders that leverage an aldehyde-mediated degron mechanism. Our compounds demonstrate exceptional on-pathway selectivity profiles and exhibit the expected Hippo signalling modulation. Mechanistically, we demonstrate that amine-based TEAD degrader scaffolds undergo extracellular conversion to the active aldehyde species, which mediate covalent engagement of FBXO22 C326, triggering TEAD proteasomal degradation. The degraders identified were able to be rationally and systematically optimised for both degradation potency and kinetics, achieving enhanced degradation profiles compared to previously reported FBXO22-targeting approaches, establishing design principles for this degrader class. Our findings highlight strategies for the structure activity relationship (SAR) and rational optimization of aldehyde-mediated degrons to generate novel precision molecular glue degraders against TEAD, a high value oncology target.

cancer biology↗

bioRxiv2025

Developmental relationships between the human alpha rhythm and intrinsic neural timescales are dependent on neural hierarchy

Maturation of human brain structure has been well-studied, but developmental changes to brain physiology are not as well understood. One consistent finding is that the peak alpha rhythm frequency (PAF) increases throughout childhood. Another is that resting-state functional connectivity shifts from sensorimotor regions in children to association regions in adolescents, a reorganization along a hierarchy called the sensorimotor-to-association (S-A) axis. In mature brains, the S-A axis has been parcellated physiologically using the duration of persistent neural activity, known as the intrinsic neural timescale (INT), which increases along the hierarchy. Here we studied the development of PAF and INT in a cohort of epilepsy patients 3 - 33 years of age undergoing intracranial electrocorticographic (ECoG) monitoring. Given the well-known developmental trajectory of PAF, and the ability to delineate hierarchy using INT, we hypothesized that changes to PAF and INT would correlate across development, but that their relationship may be influenced by hierarchy. Consequently, we predicted that age-dependent PAF increases would accompany INT decreases, and we tested whether their relationship varied between sensorimotor and association regions. We found that PAF increased significantly with age in both sensorimotor and association regions, while age-dependent INT decreases were only significant in association regions. Supporting this finding, we found a strong negative relationship between PAF and INT that was specific to association regions. Together, our results suggest that developmental divisions across the S-A axis manifest in the relationships between neurophysiological measures, providing further evidence that asynchronous development along the S-A axis depends on maturation of brain function. New & NoteworthyWe report a novel developmental relationship between the human resting-state alpha rhythm frequency and the duration of intrinsic neural timescales. Using resting-state electrocorticography, we found that alpha frequency increased with age at either end of the sensorimotor-to-association cortical hierarchy, while intrinsic neural timescales only decreased with age in association regions. This negative correlation between alpha frequency and intrinsic timescale was only evident in association regions, further linking functional maturation and cortical hierarchy. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=154 SRC="FIGDIR/small/671637v1_ufig1.gif" ALT="Figure 1"> View larger version (54K): org.highwire.dtl.DTLVardef@151bd4dorg.highwire.dtl.DTLVardef@163403forg.highwire.dtl.DTLVardef@10f244eorg.highwire.dtl.DTLVardef@118a154_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2025

Rhythmic modulation of dorsal hippocampus across distinct behavioral timescales during spatial set-shifting

Previous work has shown frequency-specific modulation of dorsal hippocampus (dHPC) neural activity during simple behavioral tasks, suggesting shifts in neural population activity throughout different task phases and animal behaviors. Relatively little is known about task-relevant orchestrated shifts in theta, beta, and gamma rhythms across multiple behavioral timescales during a complex task that requires repeated adaptation of behavioral strategies based on changing reward contingencies. To address this gap in knowledge, we used a spatial set-shifting task to determine whether dHPC plays a specific role in strategy switching. The task requires rats to use two spatial strategies on an elevated plus maze: 1) alternating between East and West reward locations or 2) always going to the same reward location (e.g., only East or only West). Across specific timescales (session-based alignments, comparisons of trial types, within trial epochs), dHPC associated differentially with all three temporal categories. Across a session, we observed a decrease in theta and beta power before, and an increase in theta power after, the target strategy changed. Beta power was increased around the point at which rats learn the current rule. Comparing trial types, on trials before a rat learned the correct strategy, beta power increased. Within a single trial, after an incorrect (but not correct) choice, beta and gamma power increased while the rat returned to start a new trial. If gamma (but not beta) power was high during this return, the rat was more likely to make a correct choice on the next trial. On the other hand, low gamma power during the return was associated with incorrect trials. Rhythmic activity in dHPC, therefore, appears to track task demands, with the strength of each rhythmic frequency differentially associating with specific behaviors across three distinct timescales.

neuroscience↗

bioRxiv2023

Flexible decision-making is related to strategy learning, vicarious trial and error, and medial prefrontal rhythms during spatial set-shifting

A hallmark of behavioral flexibility is the ability to update behavior in response to changes in context. Most studies tend to rely on error counting around reward contingency or rule switches to measure flexibility, but these measures are difficult to adapt in a way that allows shorter timescale flexibility estimates. Further, choice accuracy does not account for other markers of flexibility, such as the hesitations and decision reversals humans and other animals often exhibit as decisions unfold, a behavior often called vicarious trial and error (VTE). To relate observable information about decision-making to latent aspects like learning and behavioral flexibility, we quantified changes in decision-making strategy using a previously developed, recency-weighted Bayesian inference algorithm. By comparing models of strategy use with decision history to generate strategy likelihood estimates on a trial-by-trial basis, the algorithm enabled us to identify learning points, and served as the basis for the development of a behavioral flexibility score. Aligning flexibility scores to learning points showed that flexibility peaked around estimated learning points and near peaks in VTE rate. However, we occasionally observed VTE during periods of low flexibility, where it often led to incorrect choices, suggesting the likely existence of multiple VTE-types. Additionally, we built on the decades of research suggesting a prominent role for the medial prefrontal cortex in enabling behavioral flexibility by recording field potentials from the medial prefrontal cortex during task performance. We observed changes in different field potential frequency bands that varied with respect to the different behavioral measures we used to characterize learning and decision-making. Overall, we demonstrate the use of multiple measures that jointly assess relationships between learning, behavioral flexibility, and decision-making behaviors. Further, we used these complementary measures to demonstrate that a particular decision-making behavior, VTE, was likely to be a marker of deliberation at some times, and uncertainty at others. Finally, we validate these measures by showing that theta, beta, and gamma rhythms in the medial prefrontal cortex vary with respect to both observable and latent aspects of behavior.

neuroscience↗

bioRxiv2023

The medial prefrontal cortex during flexible decisions: Evidence for its role in distinct working memory processes

During decisions that involve working memory, task-related information must be encoded, maintained across delays, and retrieved. Few studies have attempted to causally disambiguate how different brain structures contribute to each of these components of working memory. In the present study, we used transient optogenetic disruptions of rat medial prefrontal cortex (mPFC) during a serial spatial reversal learning (SSRL) task to test its role in these specific working memory processes. By analyzing numerous performance metrics, we found: 1) mPFC disruption impaired performance during only the choice epoch of initial discrimination learning of the SSRL task, 2) mPFC disruption impaired performance in dissociable ways across all task epochs (delay, choice, return) during flexible decision-making, 3) mPFC disruption resulted in a reduction of the typical vicarious-trial-and-error (VTE) rate modulation that was related to changes in task demands. Taken together, these findings suggest that the mPFC plays an outsized role in working memory retrieval, becomes involved in encoding and maintenance when recent memories conflict with task demands, and enables animals to flexibly utilize working memory to update behavior as environments change.

neuroscience↗

bioRxiv2021

A machine learning approach for detecting vicarious trial and error behaviors

Vicarious trial and error behaviors (VTEs) indicate periods of indecision during decision-making, and have been proposed as a behavioral marker of deliberation. In order to understand the neural underpinnings of these putative bridges between behavior and neural dynamics, researchers need the ability to readily distinguish VTEs from non-VTEs. Here we utilize a small set of trajectory-based features and standard machine learning classifiers to identify VTEs from non-VTEs for rats performing a spatial delayed alternation task (SDA) on an elevated plus maze. We also show that previously reported features of the hippocampal field potential oscillation can be used in the same types of classifiers to separate VTEs from non-VTEs with above chance performance. However, we caution that the modest classifier success using hippocampal population dynamics is not sufficient for identifying trials where VTEs occur, and show that combining oscillation-based features with trajectory-based features degrades classifier performance compared to trajectory-based features alone. Overall, we propose a standard set of features useful for trajectory-based VTE classification and support previous suggestions that VTEs are supported by a network including, but likely extending beyond, the hippocampus.

neuroscience↗