Biology subjects

Garcia, J.

Publications and source records attributed to Garcia, J..

4 recordsLinked to original sources

bioRxiv2018

Side chain to main chain hydrogen bonds stabilize polyglutamine helices in transcription factors

Polyglutamine (polyQ) tracts are regions of low sequence complexity of variable length found in more than one hundred human proteins. These tracts are frequent in activation domains of transcription factors and their length often correlates with transcriptional activity. In addition, in nine proteins, tract elongation beyond specific thresholds causes polyQ disorders. To study the structural basis of the association between tract length, transcriptional activity and disease, here we addressed how the conformation of the polyQ tract of the androgen receptor (AR), a transcription factor associated with the polyQ disease spinobulbar muscular atrophy (SBMA), depends on its length. We found that the tract folds into a helical structure stabilized by unconventional hydrogen bonds between glutamine side chains and main chain carbonyl groups. These bonds are bifurcate with the conventional main chain to main chain hydrogen bonds stabilizing -helices. In addition, since tract elongation provides additional interactions, the helicity of the polyQ tract directly correlates with its length. These findings suggest a plausible rationale for the association between polyQ tract length and AR transcriptional activity and have implications for establishing the mechanistic basis of SBMA.

biophysics↗

bioRxiv2018

A computational model of task allocation in social insects: ecology and interactions alone can drive specialisation

Social insect colonies are capable of allocating their workforce in a decentralised fashion; addressing a variety of tasks and responding effectively to changes in the environment. This process is fundamental to their ecological success, but the mechanisms behind it remain poorly understood. While most models focus on internal and individual factors, empirical evidence highlights the importance of ecology and social interactions. To address this gap we propose a game theoretical model of task allocation. Individuals are characterised by a trait that determines how they split their energy between two prototypical tasks: foraging and regulation. To be viable, a colony needs to learn to adequately allocate its workforce between these two tasks. We study two different processes: individuals can learn relying exclusively on their own experience, or by using the experiences of others via social learning. We find that social organisation can be determined by the ecology alone, irrespective of interaction details. Weakly specialised colonies in which all individuals tend to both tasks emerge when foraging is cheap; harsher environments, on the other hand, lead to strongly specialised colonies in which each individual fully engages in a single task. We compare the outcomes of self-organised task allocation with optimal group performance. Counter to intuition, strongly specialised colonies perform suboptimally, whereas the group performance of weakly specialised colonies is closer to optimal. Social interactions lead to important differences when the colony deals with dynamic environments. Colonies whose individuals rely on their own experience are more exible when dealing with change. Our computational model is aligned with mathematical predictions in tractable limits. This different kind of model is useful in framing relevant and important empirical questions, where ecology and interactions are key elements of hypotheses and predictions.

ecology↗

bioRxiv2017

Homeostatic feedback, not early activity, modulates development of two-state patterned activity in the C. elegans egg-laying circuit

Neuron activity accompanies synapse formation and maintenance, but how early circuit activity contributes to behavior development is not well understood. Here, we use the Caenorhabditis elegans egg-laying motor circuit as a model to understand how coordinated cell and circuit activity develops and drives a robust two-state behavior in adults. Using calcium imaging in behaving animals, we find the serotonergic Hermaphrodite Specific Neurons (HSNs) and vulval muscles show rhythmic calcium transients in L4 larvae before eggs are produced. HSN activity in L4 is tonic and lacks the alternating burst-firing/quiescent pattern seen in egg-laying adults. Vulval muscle activity in L4 is initially uncoordinated but becomes synchronous as the anterior and posterior muscle arms meet at HSN synaptic release sites. However, coordinated muscle activity does not require presynaptic HSN input. Using reversible silencing experiments, we show that neuronal and vulval muscle activity in L4 is not required for the onset of adult behavior. Instead, the accumulation of eggs in the adult uterus renders the muscles sensitive to HSN input. Sterilization or acute electrical silencing of the vulval muscles inhibits presynaptic HSN activity, and reversal of muscle silencing triggers a homeostatic increase in HSN activity and egg release that maintains ~12-15 eggs in the uterus. Feedback of egg accumulation depends upon the vulval muscle postsynaptic terminus, suggesting a retrograde signal sustains HSN synaptic activity and egg release. Our results show that egg-laying behavior in C. elegans is driven by a homeostat that scales serotonin motor neuron activity in response to postsynaptic muscle feedback.\n\nSignificance StatementThe functional importance of early, spontaneous neuron activity in synapse and circuit development is not well understood. Here we show that in the nematode C. elegans, the serotonergic Hermaphrodite Specific Neurons (HSNs) and postsynaptic vulval muscles show activity during circuit development, well before the onset of adult behavior. Surprisingly, early activity is not required for circuit development or the onset of adult behavior, and the circuit remains unable to drive egg laying until fertilized embryos are deposited into the uterus. Egg accumulation potentiates vulval muscle excitability, but ultimately acts to promote burst firing in the presynaptic HSNs during which eggs are laid. Our results suggest that mechanosensory feedback acts at three distinct steps to initiate, sustain, and terminate C. elegans egg-laying circuit activity and behavior.

neuroscience↗

bioRxiv2017

Local Connectome Phenotypes Predict Social, Health, and Cognitive Factors

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample (N=841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions.\n\nAuthor SummaryThe local connectome is the pattern of fiber systems (i.e., number of fibers, orientation, and size) within a voxel and reflects the proximal characteristics of white matter fascicles distributed throughout the brain. Here we show how variability in the local connectome is correlated in a principled way across individuals. This inter-subject correlation is reliable enough that unique phenotype maps can be learned to predict between-subject variability in a range of social, health, and cognitive attributes. This work shows, for the first time, how shared variability across individuals is reflected in the local connectome.

neuroscience↗