Biology subjects

O'Leary, T.

Publications and source records attributed to O'Leary, T..

24 records · Page 2Linked to original sources

bioRxiv2020

Mapping glycan-mediated galectin-3 interactions by live cell proximity labeling

Galectin-3 is a glycan-binding protein (GBP) that binds {beta}-galactoside glycan structures to orchestrate a variety of important biological events, including the activation of hepatic stellate cells to cause hepatic fibrosis. While the requisite glycan epitopes needed to bind galectin-3 have long been elucidated, the cellular glycoproteins that bear these glycan signatures remain unknown. Given the importance of the three-dimensional arrangement of glycans in dictating GBP interactions, strategies that allow the identification of GBP receptors in live cells, where the native glycan presentation and glycoprotein expression are preserved, possess significant advantages over static and artificial systems. Here, we describe the integration of a proximity labeling method and quantitative mass spectrometry to map the glycan and glycoprotein interactors for galectin-3 in live hepatic stellate cells. Understanding the identity of the glycoproteins and defining the structures of the glycans required for galectin-3 mediated hepatic stellate cell activation will empower efforts to design and develop selective therapeutics to mitigate hepatic fibrosis. SignificanceBecause of the weak interactions between individual glycan-binding proteins (GBP), such as galectin-3, and glycans, strategies that allow the direct interrogation of these interactions in living cells remain limited. Thus, the glycan and glycoprotein ligands that are physiologically relevant for galectin-3 binding are insufficiently described. Here, we used a proximity labeling approach that catalytically tags interactors for galectin-3 and identified its pertinent glycan and glycoprotein counter-receptors in live hepatic stellate cells. This study demonstrates that proximity labeling is a powerful tool for mapping GBP complexes in living cells, and when coupled with chemical inhibitors, it can discriminate between protein-protein and protein-glycan interactions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=57 SRC="FIGDIR/small/058586v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@163b731org.highwire.dtl.DTLVardef@19021ddorg.highwire.dtl.DTLVardef@19f53beorg.highwire.dtl.DTLVardef@133009a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2019

Stable task information from an unstable neural population

Over days and weeks, neural activity representing an animals position and movement in sensorimotor cortex has been found to continually reconfigure or drift during repeated trials of learned tasks, with no obvious change in behavior. This challenges classical theories which assume stable engrams underlie stable behavior. However, it is not known whether this drift occurs systematically, allowing downstream circuits to extract consistent information. We show that drift is systematically constrained far above chance, facilitating a linear weighted readout of behavioural variables. However, a significant component of drift continually degrades a fixed readout, implying that drift is not confined to a null coding space. We calculate the amount of plasticity required to compensate drift independently of any learning rule, and find that this is within physiologically achievable bounds. We demonstrate that a simple, biologically plausible local learning rule can achieve these bounds, accurately decoding behavior over many days.

neuroscience↗

bioRxiv2019

Homeostatic plasticity rules that compensate for cell size are susceptible to channel deletion

Neurons can increase in size dramatically during growth. In many species neurons must preserve their intrinsic dynamics and physiological function across several length scales. For example, neurons in crustacean central pattern generators generate similar activity patterns despite multiple-fold increases in their size and changes in morphology. This scale invariance hints at regulation mechanisms that compensate for size changes by somehow altering membrane currents. Using conductance-based neuron models, we asked whether simple activity-dependent feedback can maintain intrinsic voltage dynamics in a neuron as its size is varied. Despite relying only on a single sensor that measures time-averaged intracellular calcium as a proxy for activity, we found that this regulation mechanism could regulate conductance densities of ion channels, and was robust to changes in the size of the neuron. By mapping changes in cell size onto perturbations in the space of conductance densities of all channels, we show how robustness to size change coexists with sensitivity to perturbations that alter the ratios of maximum conductances of different ion channel types. Our findings suggest that biological regulation that is optimized for coping with expected perturbations such as size changes will be vulnerable to other kinds of perturbations such as channel deletions.

neuroscience↗

bioRxiv2019

Adult-born hippocampal neurons undergo extended development and are morphologically distinct from neonatally-born neurons

During immature stages, adult-born neurons pass through critical periods for survival and plasticity. It is generally assumed that by 2 months of age adult-born neurons are mature and equivalent to the broader neuronal population, raising questions of how they might contribute to hippocampal function in old age when neurogenesis has declined. However, few have examined adult-born neurons beyond the critical period, or directly compared them to neurons born in infancy. Here, we used a retrovirus to visualize functionally-relevant morphological features of 2- to 24-week-old adult-born neurons in male rats. From 2-7 weeks neurons grew and attained a relatively mature phenotype. However, several features of 7-week-old neurons suggested a later wave of growth: these neurons had larger nuclei, thicker dendrites and more dendritic filopodia than all other groups. Indeed, between 7-24 weeks, adult-born neurons gained additional dendritic branches, grew a 2nd primary dendrite, acquired more mushroom spines and had enlarged mossy fiber presynaptic terminals. Compared to neonatally-born neurons, old adult-born neurons had greater spine density, larger presynaptic terminals, and more putative efferent filopodial contacts onto inhibitory neurons. By integrating rates of cell birth and growth across the lifespan, we estimate that adult neurogenesis ultimately produces half of the cells and the majority of spines in the dentate gyrus. Critically, protracted development contributes to the plasticity of the hippocampus through to the end of life, even after cell production declines. Persistent differences from neonatally-born neurons may additionally endow adult-born neurons with unique functions even after they have matured. SIGNIFICANCE STATEMENTNeurogenesis occurs in the hippocampus throughout adult life and contributes to memory and emotion. It is generally assumed that new neurons have the greatest impact on behavior when they are immature and plastic. However, since neurogenesis declines dramatically with age, it is unclear how they might contribute to behavior later in life when cell proliferation has slowed. Here we find that newborn neurons mature over many months in rats, and end up with distinct morphological features compared to neurons born in infancy. Using a mathematical model, we estimate that a large fraction of neurons is added in adulthood. Moreover, their extended growth produces a reserve of plasticity that persists even after neurogenesis has declined to low rates.

neuroscience↗

bioRxiv2018

Fundamental bounds on learning performance in neural circuits

How does the size of a neural circuit influence its learning performance? Intuitively, we expect the learning capacity of a neural circuit to grow with the number of neurons and synapses. Larger brains tend to be found in species with higher cognitive function and learning ability. Similarly, adding connections and units to artificial neural networks can allow them to solve more complex tasks. However, we show that in a biologically relevant setting where synapses introduce an unavoidable amount of noise, there is an optimal size of network for a given task. Beneath this optimal size, our analysis shows how adding apparently redundant neurons and connections can make tasks more learnable. Therefore large neural circuits can either devote connectivity to generating complex behaviors, or exploit this connectivity to achieve faster and more precise learning of simpler behaviors. Above the optimal network size, the addition of neurons and synaptic connections starts to impede learning performance. This suggests that overall brain size may be constrained by the need to learn efficiently with unreliable synapses, and may explain why some neurological learning deficits are associated with hyperconnectivity. Our analysis is independent of specific learning rules and uncovers fundamental relationships between learning rate, task performance, network size and intrinsic noise in neural circuits.

neuroscience↗

bioRxiv2018

Neural circuit robustness to acute, global physiological perturbations

Neural function depends on underlying physiological processes that are highly sensitive to physical variables such as temperature. However, some robustness to perturbations in these variables manifests at the circuit level, suggesting that circuit properties are organized to tolerate consistent changes in underlying parameters. We show that a crustacean pacemaker circuit is robust to two global perturbations - temperature and pH - that differentially alter circuit properties. Consistent with high variability in underlying circuit parameters, we find that the critical temperatures and pH values where circuit activity breaks down vary widely across animals. Despite variability in critical points the network state transitions at these critical points are consistent, implying that qualitative circuit dynamics are preserved across animals, in spite of high quantitative parameter variability. Surprisingly, robustness perturbations in pH only moderately affect temperature robustness. Thus, robustness to a global perturbation does not necessarily imply sensitivity to other global perturbations.

bioinformatics↗