Biology subjects

Uchida, N.

Publications and source records attributed to Uchida, N..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Development and IND-enabling studies of a novel Cas9 genome-edited autologous CD34+ cell therapy to induce fetal hemoglobin for sickle cell disease

Sickle cell disease (SCD) is a common severe blood disorder, caused by one major point mutation in the HBB gene. Current pharmacotherapies are only partially effective and potentially curative allogeneic hematopoietic stem cell transplantation (HSCT) is associated with immune toxicities. Genome editing of autologous patient hematopoietic stem cells (HSCs) to reactivate fetal hemoglobin (HbF) in erythroid progeny offers a potentially curative approach to treat SCD and circumvents some problems associated with allogeneic HSCT. Although the FDA has released guidelines for evaluating genome editing risks, it remains unclear how to best to assess the preclinical safety and efficacy of genome-edited cellular drug products to prepare for a clinical trial. Here we describe rigorous pre-clinical characterization and optimization of a therapeutic {gamma}-globin gene promoter editing strategy that supported an investigational new drug (IND) application cleared by the FDA. We compared targets in the {gamma}-globin promoter and BCL11A erythroid-specific enhancer, identified a lead candidate that potently induces HbF, and tested our approach in mobilized CD34+ HSPCs from normal donors and individuals with SCD. We observed efficient editing, induction of HbF to levels predicted to be therapeutic, and reduction of sickling in red blood cells derived from edited HSPCs. With single-cell western and RNA-seq analyses, we defined the heterogeneity and specificity of HbF induction and HBG1/HBG2 transcription. With CHANGE-seq for sensitive and unbiased genome-wide off-target discovery followed by multiplexed targeted sequencing, we did not detect off-target activity in edited HSPCs. Our study provides a blueprint for translating new discoveries on ex vivo genome editing of HSCs towards clinical trials for treating SCD and other blood disorders.

genomics↗

bioRxiv2024

Human striatal progenitor cells that contain inducible safeguards and overexpress BDNF rescue Huntington's disease phenotypes in R6/2 mice

Huntingtons disease (HD) is an autosomal-dominant neurodegenerative disorder characterized by striatal atrophy. Reduced trophic support due to decreased striatal levels of neurotrophins (NTs), mainly brain-derived neurotrophic factor (BDNF), contributes importantly to HD pathogenesis; restoring NTs has significant therapeutic potential. Human pluripotent stem cells (hPSC) offer a scalable platform for NT delivery but has potential safety risks including teratoma formation. We engineered hPSCs to constitutively produce BDNF and contain inducible safeguards to eliminate these cells if safety concerns arise. This study examined the efficacy of intrastriatally transplanted striatal progenitor cells (STRpcs) derived from these hPSCs against HD phenotypes in R6/2 mice. Engrafted STRpcs overexpressing BDNF alleviated motor and cognitive deficits and reduced mutant huntingtin aggregates. Activating the inducible safety switch with rapamycin safely eliminated the engrafted cells. These results demonstrate that BDNF delivery via a novel hPSC-based platform incorporating safety switches could be a safe and effective HD therapeutic.

neuroscience↗

bioRxiv2024

The role of prospective contingency in the control of behavior and dopamine signals during associative learning

Associative learning depends on contingency, the degree to which a stimulus predicts an outcome. Despite its importance, the neural mechanisms linking contingency to behavior remain elusive. Here we examined the dopamine activity in the ventral striatum - a signal implicated in associative learning - in a Pavlovian contingency degradation task in mice. We show that both anticipatory licking and dopamine responses to a conditioned stimulus decreased when additional rewards were delivered uncued, but remained unchanged if additional rewards were cued. These results conflict with contingency-based accounts using a traditional definition of contingency or a novel causal learning model (ANCCR), but can be explained by temporal difference (TD) learning models equipped with an appropriate inter-trial-interval (ITI) state representation. Recurrent neural networks trained within a TD framework develop state representations like our best handcrafted model. Our findings suggest that the TD error can be a measure that describes both contingency and dopaminergic activity.

neuroscience↗

bioRxiv2024

Interpretable deep learning for deconvolutional analysis of neural signals

The widespread adoption of deep learning to build models that capture the dynamics of neural populations is typically based on "black-box" approaches that lack an interpretable link between neural activity and network parameters. Here, we propose to apply algorithm unrolling, a method for interpretable deep learning, to design the architecture of sparse deconvolutional neural networks and obtain a direct interpretation of network weights in relation to stimulus-driven single-neuron activity through a generative model. We characterize our method, referred to as deconvolutional unrolled neural learning (DUNL), and show its versatility by applying it to deconvolve single-trial local signals across multiple brain areas and recording modalities. To exemplify use cases of our decomposition method, we uncover multiplexed salience and reward prediction error signals from midbrain dopamine neurons in an unbiased manner, perform simultaneous event detection and characterization in somatosensory thalamus recordings, and characterize the heterogeneity of neural responses in the piriform cortex and in the striatum during unstructured, naturalistic experiments. Our work leverages the advances in interpretable deep learning to gain a mechanistic understanding of neural activity.

neuroscience↗

bioRxiv2024

An opponent striatal circuit for distributional reinforcement learning

Machine learning research has achieved large performance gains on a wide range of tasks by expanding the learning target from mean rewards to entire probability distributions of rewards -- an approach known as distributional reinforcement learning (RL)1. The mesolimbic dopamine system is thought to underlie RL in the mammalian brain by updating a representation of mean value in the striatum2,3, but little is known about whether, where, and how neurons in this circuit encode information about higher-order moments of reward distributions4. To fill this gap, we used high-density probes (Neuropixels) to acutely record striatal activity from well-trained, water-restricted mice performing a classical conditioning task in which reward mean, reward variance, and stimulus identity were independently manipulated. In contrast to traditional RL accounts, we found robust evidence for abstract encoding of variance in the striatum. Remarkably, chronic ablation of dopamine inputs disorganized these distributional representations in the striatum without interfering with mean value coding. Two-photon calcium imaging and optogenetics revealed that the two major classes of striatal medium spiny neurons -- D1 and D2 MSNs -- contributed to this code by preferentially encoding the right and left tails of the reward distribution, respectively. We synthesize these findings into a new model of the striatum and mesolimbic dopamine that harnesses the opponency between D1 and D2 MSNs5-15 to reap the computational benefits of distributional RL.

neuroscience↗

bioRxiv2023

Tonic dopamine and biases in value learning linked through a biologically inspired reinforcement learning model

A hallmark of various psychiatric disorders is biased future predictions. Here we examined the mechanisms for biased value learning using reinforcement learning models incorporating recent findings on synaptic plasticity and opponent circuit mechanisms in the basal ganglia. We show that variations in tonic dopamine can alter the balance between learning from positive and negative reward prediction errors, leading to biased value predictions. This bias arises from the sigmoidal shapes of the dose-occupancy curves and distinct affinities of D1- and D2-type dopamine receptors: changes in tonic dopamine differentially alters the slope of the dose-occupancy curves of these receptors, thus sensitivities, at baseline dopamine concentrations. We show that this mechanism can explain biased value learning in both mice and humans and may also contribute to symptoms observed in psychiatric disorders. Our model provides a foundation for understanding the basal ganglia circuit and underscores the significance of tonic dopamine in modulating learning processes.

neuroscience↗

bioRxiv2023

Multi-timescale reinforcement learning in the brain

To thrive in complex environments, animals and artificial agents must learn to act adaptively to maximize fitness and rewards. Such adaptive behavior can be learned through reinforcement learning1, a class of algorithms that has been successful at training artificial agents2-6 and at characterizing the firing of dopamine neurons in the midbrain7-9. In classical reinforcement learning, agents discount future rewards exponentially according to a single time scale, controlled by the discount factor. Here, we explore the presence of multiple timescales in biological reinforcement learning. We first show that reinforcement agents learning at a multitude of timescales possess distinct computational benefits. Next, we report that dopamine neurons in mice performing two behavioral tasks encode reward prediction error with a diversity of discount time constants. Our model explains the heterogeneity of temporal discounting in both cue-evoked transient responses and slower timescale fluctuations known as dopamine ramps. Crucially, the measured discount factor of individual neurons is correlated across the two tasks suggesting that it is a cell-specific property. Together, our results provide a new paradigm to understand functional heterogeneity in dopamine neurons, a mechanistic basis for the empirical observation that humans and animals use non-exponential discounts in many situations 10-14, and open new avenues for the design of more efficient reinforcement learning algorithms.

neuroscience↗

bioRxiv2023

Glutamate inputs send prediction error of reward but not negative value of aversive stimuli to dopamine neurons

Midbrain dopamine neurons are thought to signal reward prediction errors (RPEs) but the mechanisms underlying RPE computation, particularly contributions of different neurotransmitters, remain poorly understood. Here we used a genetically-encoded glutamate sensor to examine the pattern of glutamate inputs to dopamine neurons. We found that glutamate inputs exhibit virtually all of the characteristics of RPE, rather than conveying a specific component of RPE computation such as reward or expectation. Notably, while glutamate inputs were transiently inhibited by reward omission, they were excited by aversive stimuli. Opioid analgesics altered dopamine negative responses to aversive stimuli toward more positive responses, while excitatory responses of glutamate inputs remained unchanged. Our findings uncover previously unknown synaptic mechanisms underlying RPE computations; dopamine responses are shaped by both synergistic and competitive interactions between glutamatergic and GABAergic inputs to dopamine neurons depending on valences, with competitive interactions playing a role in responses to aversive stimuli.

neuroscience↗

bioRxiv2023

Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics

Patch foraging presents a ubiquitous decision-making process in which animals decide when to abandon a resource patch of diminishing value to pursue an alternative. We developed a virtual foraging task in which mouse behavior varied systematically with patch value. Mouse behavior could be explained by a model integrating time and rewards antagonistically, scaled by a latent patience state. The model accounted for deviations from predictions of optimal foraging theory. Neural recordings throughout frontal areas revealed encoding of decision variables from the integrator model, most robustly in frontal cortex. Regression modeling followed by unsupervised clustering identified a subset of ramping neurons. These neurons firing rates ramped up gradually (up to tens of seconds), were inhibited by rewards, and were better described as a continuous ramp than a discrete stepping process. Together, these results identify integration via frontal cortex ramping dynamics as a candidate mechanism for solving patch foraging problems.

neuroscience↗

bioRxiv2023

A Hypothalamic Circuit Underlying the Dynamic Control of Social Homeostasis

Social grouping increases survival in many species, including humans1,2. By contrast, social isolation generates an aversive state (loneliness) that motivates social seeking and heightens social interaction upon reunion3-5. The observed rebound in social interaction triggered by isolation suggests a homeostatic process underlying the control of social drive, similar to that observed for physiological needs such as hunger, thirst or sleep3,6. In this study, we assessed social responses in multiple mouse strains and identified the FVB/NJ line as exquisitely sensitive to social isolation. Using FVB/NJ mice, we uncovered two previously uncharacterized neuronal populations in the hypothalamic preoptic nucleus that are activated during social isolation and social rebound and that orchestrate the behavior display of social need and social satiety, respectively. We identified direct connectivity between these two populations of opposite function and with brain areas associated with social behavior, emotional state, reward, and physiological needs, and showed that animals require touch to assess the presence of others and fulfill their social need, thus revealing a brain-wide neural system underlying social homeostasis. These findings offer mechanistic insight into the nature and function of circuits controlling instinctive social need and for the understanding of healthy and diseased brain states associated with social context.

neuroscience↗

bioRxiv2023

Emergence of belief-like representations through reinforcement learning

To behave adaptively, animals must learn to predict future reward, or value. To do this, animals are thought to learn reward predictions using reinforcement learning. However, in contrast to classical models, animals must learn to estimate value using only incomplete state information. Previous work suggests that animals estimate value in partially observable tasks by first forming "beliefs"--optimal Bayesian estimates of the hidden states in the task. Although this is one way to solve the problem of partial observability, it is not the only way, nor is it the most computationally scalable solution in complex, real-world environments. Here we show that a recurrent neural network (RNN) can learn to estimate value directly from observations, generating reward prediction errors that resemble those observed experimentally, without any explicit objective of estimating beliefs. We integrate statistical, functional, and dynamical systems perspectives on beliefs to show that the RNNs learned representation encodes belief information, but only when the RNNs capacity is sufficiently large. These results illustrate how animals can estimate value in tasks without explicitly estimating beliefs, yielding a representation useful for systems with limited capacity. Author SummaryNatural environments are full of uncertainty. For example, just because my fridge had food in it yesterday does not mean it will have food today. Despite such uncertainty, animals can estimate which states and actions are the most valuable. Previous work suggests that animals estimate value using a brain area called the basal ganglia, using a process resembling a reinforcement learning algorithm called TD learning. However, traditional reinforcement learning algorithms cannot accurately estimate value in environments with state uncertainty (e.g., when my fridges contents are unknown). One way around this problem is if agents form "beliefs," a probabilistic estimate of how likely each state is, given any observations so far. However, estimating beliefs is a demanding process that may not be possible for animals in more complex environments. Here we show that an artificial recurrent neural network (RNN) trained with TD learning can estimate value from observations, without explicitly estimating beliefs. The trained RNNs error signals resembled the neural activity of dopamine neurons measured during the same task. Importantly, the RNNs activity resembled beliefs, but only when the RNN had enough capacity. This work illustrates how animals could estimate value in uncertain environments without needing to first form beliefs, which may be useful in environments where computing the true beliefs is too costly.

neuroscience↗

bioRxiv2022

Dynamical management of potential threats regulated by dopamine and direct- and indirect-pathway neurons in the tail of the striatum

Avoiding potential threats before experiencing an actual outcome is critical to prevent a disaster. Here we examined roles of the tail of the striatum (TS) and its dopamine input in threat management. Mice were presented with a potential threat (a moving object) while pursuing rewards. Mice initially failed to obtain rewards, but gradually successfully obtained rewards in later trials. We show that the initial failures depended on dopamine and direct-pathway neurons in TS, and variability in failure rate across trials and individuals was positively correlated with the activity of these neurons. In contrast, indirect-pathway neurons in TS were critical for eventual improvement in reward acquisition, and their activity was positively correlated with successful reward acquisition. These results demonstrate that direct- and indirect-pathway TS neurons promote and suppress threat avoidance, respectively, at different stages, providing a mechanism for overcoming a potential threat while maintaining the threat estimates.

neuroscience↗

bioRxiv2021

Striatal dopamine explains novelty-induced behavioral dynamics and individual variability in threat prediction

Animals exhibit diverse behavioral responses, such as exploration and avoidance, to novel cues in the environment. However, it remains unclear how dopamine neuron-related novelty responses influence behavior. Here, we characterized dynamics of novelty exploration using multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq). Novelty elicits a characteristic sequence of behavior, starting with investigatory approach and culminating in object engagement or avoidance. Dopamine in the tail of striatum (TS) suppresses engagement, and dopamine responses were predictive of individual variability in behavior. Behavioral dynamics and individual variability were explained by a novel reinforcement learning (RL) model of threat prediction, in which behavior arises from a novelty-induced initial threat prediction (akin to "shaping bonus"), and a threat prediction that is learned through dopamine-mediated threat prediction errors. These results uncover an algorithmic similarity between reward- and threat-related dopamine sub-systems. HighlightsO_LINovelty-induced behaviors are analyzed using modern machine-learning methods C_LIO_LINovelty induces risk assessment which develops into engagement or avoidance C_LIO_LIDopamine in the tail of striatum correlates with individual behavioral variability C_LIO_LIReinforcement learning with shaping bonus and uncertainty explains the data C_LI

neuroscience↗

bioRxiv2021

The Boundary-Expressed EPIDERMAL PATTERNING FACTOR-LIKE2 Gene Encoding a Signaling Peptide Promotes Cotyledon Growth during Arabidopsis thaliana Embryogenesis

The shoot organ boundaries have important roles in plant growth and morphogenesis. It has been reported that a gene encoding a cysteine-rich secreted peptide of the EPIDERMAL PATTERNING FACTOR-LIKE (EPFL) family, EPFL2, is expressed in the boundary domain between the two cotyledon primordia of Arabidopsis thaliana embryo. However, its developmental functions remain unknown. This study aimed to analyze the role of EPFL2 during embryogenesis. We found that cotyledon growth was reduced in its loss-of-function mutants, and this phenotype was associated with the reduction of auxin response peaks at the tips of the primordia. The reduced cotyledon size of the mutant embryo recovered in germinating seedlings, indicating the presence of a factor that acted redundantly with EPFL2 to promote cotyledon growth in late embryogenesis. Our analysis indicates that the boundary domain between the cotyledon primordia acts as a signaling center that organizes auxin response peaks and promotes cotyledon growth.

plant biology↗

bioRxiv2020

Gene replacement of α-globin with β-globin restores hemoglobin balance in β-thalassemia-derived hematopoietic stem and progenitor cells

{beta}-thalassemia pathology is not only due to loss of {beta}-globin (HBB), but also erythrotoxic accumulation and aggregation of the {beta}-globin binding partner, -globin (HBA1/2). Here we describe a Cas9/AAV6-mediated genome editing strategy that can replace the entire HBA1 gene with a full-length HBB transgene in {beta}-thalassemia-derived hematopoietic stem and progenitor cells (HSPCs), which is sufficient to normalize {beta}-globin:-globin mRNA and protein ratios and restore functional adult hemoglobin tetramers in patient-derived red blood cells. Edited HSPCs were capable of long-term and bi-lineage hematopoietic reconstitution in mice, establishing proof-of-concept for replacement of HBA1 with HBB as a novel therapeutic strategy for curing {beta}-thalassemia.

molecular biology↗

bioRxiv2020

A gradual backward shift of dopamine responses during associative learning

It has been proposed that the activity of dopamine neurons approximates temporal difference (TD) prediction error, a teaching signal developed in reinforcement learning, a field of machine learning. However, whether this similarity holds true during learning remains elusive. In particular, some TD learning models predict that the error signal gradually shifts backward in time from reward delivery to a reward-predictive cue, but previous experiments failed to observe such a gradual shift in dopamine activity. Here we demonstrate conditions in which such a shift can be detected experimentally. These shared dynamics of TD error and dopamine activity narrow the gap between machine learning theory and biological brains, tightening a long-sought link.

neuroscience↗

bioRxiv2020

Distinct temporal difference error signals in dopamine axons in three regions of the striatum in a decision-making task

Different regions of the striatum regulate different types of behavior. However, how dopamine signals differ across striatal regions and how dopamine regulates different behaviors remain unclear. Here, we compared dopamine axon activity in the ventral, dorsomedial, and dorsolateral striatum, while mice performed in a perceptual and value-based decision task. Surprisingly, dopamine axon activity was similar across all three areas. At a glance, the activity multiplexed different variables such as stimulus-associated values, confidence and reward feedback at different phases of the task. Our modeling demonstrates, however, that these modulations can be inclusively explained by moment-by-moment changes in the expected reward, i.e. the temporal difference error. A major difference between these areas was the overall activity level of reward responses: reward responses in dorsolateral striatum (DLS) were positively shifted, lacking inhibitory responses to negative prediction error. Tenets of habit and skill can be explained by this positively biased dopamine signal in DLS.

neuroscience↗

bioRxiv2020

A super sensitive auxin-inducible degron system with an engineered auxin-TIR1 pair

Auxin-Inducible Degron (AID) technology enables conditional depletion of targeted proteins. However, the applicability of the AID in vertebrate cells has been limited due to cytotoxicity caused by high auxin concentrations. Here, we establish an improved AID system using an engineered orthogonal auxin-TIR1 pair, which exhibits over 1,000 times stronger binding. With ~1,000-fold less auxin concentration, we achieved to generate the AID-based knockout cells in various human and mouse cell lines in a single transfection.

bioengineering↗