Biology subjects

Lindsay, A. J.

Publications and source records attributed to Lindsay, A. J..

3 recordsLinked to original sources

bioRxiv2024

HUB-DT: A tool for unsupervised behavioural discovery and analysis

There has been an expansion in the diversity of tools used to measure various aspects of brain function in behaving animals. While these tools have great potential to transform our understanding of brain function, they are of little value if the behavior of interest is poorly defined or quantified. Traditional methods of behavioural labelling focus on easily quantified gross measure, such as velocity, gate crossing, nosepokes, etc. While these measures are specific and reproducible, they are crude descriptions of behaviour at best. Manually defined behaviours, while providing increased granularity and descriptive power over specific gross measures, suffer from being inexact and somewhat arbitrary. Consistent labelling between human observers is often difficult, and even if manually defined behaviours are subsequently labelled in an automated fashion (via a supervised learning algorithm) these behaviours need to be defined ahead of time, possibly biasing the range of behaviours of interest for a given task. Here we present HUB-DT, a behavioural discovery pipeline built on the frameworks of several existing tools and methods in the space of behavioural categorisation, the specifics of which will be highlighted in this report, and designed to address the requirements of behavioral discovery.

neuroscience↗

bioRxiv2023

Reconfiguration of Behavioral Signals in the Anterior Cingulate Cortex based on Emotional State

Behaviours and their execution depend on the context and emotional state in which they are performed. The contextual modulation of behavior likely relies on regions such as the anterior cingulate cortex (ACC) that multiplex information about emotional/autonomic states and behaviours. The objective of the present study was to understand how the representations of behaviors by ACC neurons become modified when performed in different emotional states. A pipeline of machine learning techniques was developed to categorize and classify complex, spontaneous behaviors from video. This pipeline, termed HUB-DT, discovered a range of statistically separable behaviors during a task in which motivationally significant outcomes were delivered in blocks of trials that created 3 unique emotional contexts. HUB-DT was capable of detecting behaviors specific to each emotional context and was able to identify and segregate the portions of a neural signal related to a behaviour and to emotional context. Overall, [~]10x as many neurons responded to behaviors in a contextually dependent versus a fixed manner, highlighting the extreme impact of emotional state on representations of behaviors that were precisely defined based on detailed analyses of limb kinematics. This type of modulation may be a key mechanism that allows the ACC to modify behavioral output based on emotional states and contextual demands.

neuroscience↗

bioRxiv2023

Condensate formation of the human RNA-binding protein SMAUG1 is controlled by its intrinsically disordered regions and interactions with 14-3-3 proteins

SMAUG1 is a human RNA-binding protein that is known to be dysregulated in a wide range of diseases. It is evolutionarily conserved and has been shown to form condensates containing translationally repressed RNAs. This indicates that condensation is central to proper SMAUG1 function; however, the factors governing condensation are largely unknown. In this work, we show that SMAUG1 drives the formation of liquid-like condensates in cells through its non-conventional C-terminal prion-like disordered region. We use biochemical assays to show that this liquid-liquid phase separation is independent of RNA binding and does not depend on other large, disordered regions that potentially harbor several binding sites for partner proteins. Using a combination of computational predictions, structural modeling, in vitro and in cell measurements, we also show that SMAUG1-driven condensation is negatively regulated by direct interactions with members of the 14-3-3 protein family. These interactions are mediated by four distinct phospho-regulated short linear motifs embedded in the disordered regions of SMAUG1, working synergistically. Interactions between SMAUG1 and 14-3-3 proteins drive the dissolution of condensates, alter the dynamics of the condensed state, and are likely to be intertwined with currently unknown regulatory mechanisms. Our results provide information on how SMAUG1 phase separation is regulated and the first known instance of 14-3-3 proteins being able to completely dissolve condensates by directly interacting with a phase separation driver, which might be a general mechanism in cells to regulate biological condensation. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/527857v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@868746org.highwire.dtl.DTLVardef@1b3318org.highwire.dtl.DTLVardef@1b78868org.highwire.dtl.DTLVardef@5c7e5e_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LISMAUG1 is a human RNA-binding protein capable of condensation with unknown regulation C_LIO_LIA prion-like domain of SMAUG1 drives condensation via liquid-liquid phase separation C_LIO_LISMAUG1 interacts with 14-3-3 proteins via four phospho-regulated short linear motifs C_LIO_LI14-3-3 interactions change the dynamics of SMAUG1 condensates, promoting their dissolution C_LIO_LIThis is the first described regulatory mechanism for SMAUG1-driven condensation C_LI

molecular biology↗