Biology subjects

Louie, K.

Publications and source records attributed to Louie, K..

At least 19 recordsLinked to original sources

bioRxiv2026

Stochastic Decision Policy Is Optimal Under Costly Control

Human and animal decisions are often noisy. This variability is usually attributed to noise that corrupts the decision evidence, a defect that an ideal decision maker would eliminate. Here we ask whether stochastic choice can instead be optimal once the cost of implementing prescribed policies is counted. We treat the decision maker as an ideal observer that maximizes expected gain but pays costs to sharpen its policy away from a random default. Using sequential sampling, a setting in which the optimal policy is precisely defined, we show that this trade-off makes a stochastic policy optimal, replacing the hard decision boundaries of classical theory with soft stopping zones whose softness is set by the price of control. This soft policy aligns with the strategy used in cutting-edge artificial learning agents for robustness. Noise that corrupts the agent's belief, an important confound of noisy behavior, produces superficially similar yet distinctive signatures in behavioral policies. Leveraging this, we recovered the policies of two macaques with low-dimensional recurrent neural networks and found that their behavior carries both a stochastic policy and belief noise. By separating variability in constructing a decision belief from stochasticity in selecting actions, these results establish that stochastic decision policies can themselves be optimal, offering a common principle for understanding behavioral variability in biological and artificial decision makers.

animal behavior and cognition↗

bioRxiv2026

Distinct Strategies for Engineering Bioenergy Traits in Sorghum Drive Divergent Functional Shifts in the Rhizosphere Microbiome

Engineering bioenergy crops for enhanced biomass digestibility and high-value co-products is a promising approach to sustainable biofuel production. However, the impact of these engineered crops on the belowground microbiome remains poorly understood. In this study, we characterized the composition and functional responses of the belowground microbiome associated with transgenic sorghum lines: one with reduced cell wall crosslinking (AT10) and another with elevated protocatechuic acid (PCA) production (Qsub). The lines were grown under field conditions with four irrigation levels, and samples were collected twice during the growing season. We found that the two transgenic types drove a subtle but significant divergence in bacterial communities, both compositionally and functionally. Community-wide functional profiling showed that AT10 rhizospheres were consistently depleted in genes associated with mobile genetic elements, while Qsub rhizospheres were enriched in TonB-dependent transport and vanillate monooxygenase, an enzyme that funnels lignin-derived monomers toward PCA degradation, alongside reduced pectin- and cellulose-degradation genes. Genome-resolved analysis linked these community-level shifts to specific taxa: in Qsub, the enriched MAGs were taxonomically diverse, ranging from genome-reduced Patescibacteria with an episymbiotic lifestyle to taxa enriched in lignin-associated auxiliary activity enzymes. In AT10, the depleted Azospirillaceae MAGs carried unusually high mobile genetic element content, consistent with the community-wide pattern. Collectively, these findings offer new insights into how genetic modifications aimed at improving bioenergy traits shapes the composition and function of the rhizosphere microbiome, with potential downstream consequences for soil carbon cycling.

plant biology↗

bioRxiv2026

The unconventional kinesin Kif26a is required for the guidance of major axon tracts in the developing mouse brain

Kif26a and Kif26b encode a family of unconventional kinesins with emerging roles in neurodevelopment, and mutations in both genes have been implicated in a spectrum of neurodevelopmental disorders. The precise mechanisms by which the Kif26 family orchestrates mammalian brain development, however, remain unclear. In this study, we show that Kif26a and Kif26b are expressed in distinct regions of the developing mouse brain. Using a new allelic series in mice, we demonstrate that Kif26a is required for the guidance of multiple forebrain axon tracts. This requirement is direct and cell autonomous, as cell proliferation, survival, and cortical layering are unaffected in the Kif26a mutants. These guidance defects closely resemble those reported for the Fzd3-Celsr3-Dystroglycan pathway, suggesting that Kif26a may participate within this conserved signaling axis to steer growing axons.

neuroscience↗

bioRxiv2025

Uncovering the hidden diversity and functional roles of root endophytic Streptomyces under drought stress

The genus Streptomyces has consistently been found to be enriched in drought-stressed plant root microbiomes, yet the ecological basis and functional variation underlying this enrichment at the strain and isolate level remain unclear. Using two 16S rRNA sequencing methods with different levels of taxonomic resolution, we confirmed drought-associated enrichment of Streptomyces in field-grown sorghum roots and identified five closely related but distinct ASVs belonging to the genus with variable drought enrichment patterns. From a culture collection of sorghum root endophytes, we selected 12 Streptomyces isolates representing these ASVs for phenotypic and genomic characterization. Whole-genome sequencing revealed substantial variation in gene content, even among closely related isolates, and exometabolomic profiling showed distinct metabolic responses to media supplemented with drought-versus well-watered root tissue. Traits linked to drought survival, including osmotic stress tolerance, siderophore production, and carbon utilization, varied widely among isolates and were not phylogenetically conserved. Using a broader panel of 48 Streptomyces, we demonstrate that drought enrichment (DE) scores, determined through mono-association experiments in gnotobiotic sorghum systems, showed high variability and lacked correlation with plant growth promotion. Pangenome-wide association identified gene clusters involved in osmolyte transport (e.g., proP) and membrane biosynthesis (e.g., fabG) as positively associated with DE, though most associations lacked phylogenetic signal. Collectively, these results demonstrate that Streptomyces enrichment under drought is not a conserved genus-level trait but is instead strain-specific and functionally heterogeneous. Furthermore, enrichment in the root microbiome does not predict beneficial effects on plant growth. This work underscores the need to resolve functional traits at the strain level and highlights the complexity of microbe-host-environment interactions under abiotic stress. ImportanceUnderstanding how beneficial microbes respond to drought is critical for developing microbiome-based strategies to enhance crop resilience. The Streptomyces genus is consistently enriched in drought-stressed root microbiomes, yet our study reveals that this enrichment is not a uniform isolate-level response. Instead, we show that drought enrichment is highly strain-specific and functionally diverse, with key associated traits like osmotic stress tolerance and siderophore production varying independently of phylogeny. Importantly, enrichment does not predict a microbes ability to promote plant growth under stress. By integrating high-resolution taxonomic, genomic, and metabolomic approaches, we uncover the ecological and functional complexity underlying Streptomyces responses to drought. These findings emphasize the importance of resolving microbial function at the strain level and caution against assuming functional traits based on taxonomic identity itself.

microbiology↗

bioRxiv2025

Decisional reference point pathology: a mechanism and marker for major depressive disorder in humans

The decisional reference point, the central mechanism of behavioral economics, conditions our evaluations of reinforcers. It determines whether a given event is experienced as positive or negative. Here we show, for the first time, a significant and pathological elevation of the reference point in patients with depression that correlates with disease severity. We also show that the mechanism for reference point setting is profoundly impaired in patients with depression. These findings link the previously demonstrated treatment of depression by deep brain stimulation to modulation of the reference point in the anterior cingulate cortex and identify pathology in the dynamics of reference point setting as a novel mechanism in the disorder. Finally, these results lay the foundation for a three-minute virtual diagnostic test for depression.

neuroscience↗

bioRxiv2024

Neuronal representation of the decisional reference point in monkeys

The decisional reference point serves as a hidden benchmark for evaluating options in decision-making. Despite extensive behavioral evidence for the existence of the reference point, its neural instantiation remains unclear. To identify reference point encoding at both the single-neuron and population levels, we analyzed neural activity from macaques performing a wealth accumulation task that dissociated objective reward values from the reference point. Across six frontal regions, we found that reference-related signals were broadly distributed at the single-neuron level. However, at the population level, only the ventral bank of the anterior cingulate cortex (vbACC) encoded the reference point significantly. In contrast, the dorsal bank of ACC and dorsolateral prefrontal cortex showed population-level encoding of reference-dependent subjective value signals. The temporal sequence of these signals and their known anatomical connectivity hints at a dedicated neural circuit for reference-dependence, with the vbACC potentially serving as a global source of reference point signal modulating activity in downstream value-encoding regions. SIGNIFICANCEAll experiences are evaluated relative to an internal benchmark called the reference point. Despite its central role in all modern theories of decision-making, its neural basis has remained unclear. In this study, using dense single-neuron recordings from macaque frontal cortex, we found that only the ventral bank of the anterior cingulate cortex encoded the reference point consistently at the population level. We also found population-level reference-dependent value signals representing anticipated and earned rewards in the dorsal bank of the anterior cingulate cortex and the dorsolateral prefrontal cortex, respectively. Both were inversely modulated by the reference point as predicted by theory. These findings together with known anatomical connectivity support the existence of a dedicated neural circuit for reference-dependent valuation.

neuroscience↗

bioRxiv2024

Hyperdiverse, bioactive, and interaction-specific metabolites produced only in co-culture suggest diverse competitors may fuel secondary metabolism of xylarialean fungi

Xylariales is one of the largest and most ecologically diverse fungal orders that is well-known for its chemical diversity. Enhanced secondary metabolism of Xylariales taxa is associated with increased gene duplication and horizontal gene transfer (HGT) of biosynthetic gene clusters (BGCs), especially in generalist taxa with both greater saprotrophic abilities and broader host ranges as foliar endophytic symbionts. Thus, one hypothesis for BGC diversification among more generalist fungi is that diverse competitive interactions--in both their free-living and symbiotic life stages with many hosts--may exert selective pressure for HGT and a diverse metabolic repertoire. Here, we used untargeted metabolomics to examine how competition (pairwise co-cultures) between seven xylarialean fungi influenced their metabolite production. Of the >9,000 total features detected, 6,115 and 2,071 were over-represented in co-cultures vs. monocultures, respectively. For each strain, each additional co-culture interaction resulted in an 11-14-fold increase in metabolite richness compared to monocultures, reflecting the limited amount of metabolite overlap among different co-culture combinations. Phylogenetic relatedness and BGC content did not impact the diversity of metabolites produced in co-culture; however, co-cultures between more ecologically distinct fungi elicited the strongest metabolic response. Overall, the diversity, specificity, and putative bioactivity of metabolites over-represented only in co-culture supports the role of widespread and diverse competitive fungal interactions to drive xylarialean metabolic diversification. Additionally, as fungal-produced plant hormones were only detected in co-culture, our results reveal the potential for in planta interactions among fungal endophytes to influence the host plant. ImportanceSaprotrophic and endophytic xylarialean fungi are among the most prolific producers of bioactive secondary metabolites, with numerous industrial uses as antibiotics, pharmaceuticals, and insecticidal toxins. Fungal secondary metabolites are typically encoded in biosynthetic gene clusters (sets of physically clustered genes), but the products of most clusters are unknown as the genes are not active in typical culture conditions. Co-cultures can help to "turn on" fungal secondary metabolite production, yet factors that can influence co-culture outcomes are largely unknown. Here, we used untargeted metabolomics to assess how differences in genomic content, ecology, and phylogenetic relatedness among seven diverse xylarialean fungal strains impact metabolic production in co-culture. As expected, co-culturing significantly increased metabolite diversity, as well as the abundance of putatively bioactive metabolites. Each new pairwise combination produced different metabolites, indicative of strain-specific responses to competitors. This new information will enable further characterization of the immense biotechnological potential of xylarialean fungi.

microbiology↗

bioRxiv2024

A Tale of Two Environments: Divisive Normalization and the (In)Flexibility of Choice

The Divisive Normalization (DN) function has been described as a "canonical neural computation" in the brain that achieves efficient representations of sensory and choice stimuli. Recent theoretical work indicates that it efficiently encodes a specific class of Pareto-distributed stimuli. Does the brain shift to different encoding functions in other types of environments, or is there evidence for DN encoding in other types of environments? In this paper, using a within-subject choice experiment, we show evidence of the latter. Our subjects made decisions in two distinct choice environments with choice sets either drawn from a Pareto distribution or from a uniform distribution. Our results indicate that subjects choices are better described by a divisive coding strategy in both environments. Moreover, subjects appeared to calibrate a DN function to match, as closely as possible, the actual statistical properties of each environment. These results suggest that the nervous system may be constrained to use divisive representations under all conditions. Significance StatementHow does the frequency with which we encounter different kinds of decision problems affect how the brain represents those problems? Recent empirical findings suggest that we adapt our internal representations to match the environments in which we are making choices. Theoretical work has shown that one form of internal representation, called divisive normalization, provides an optimal adaptation when making choices in a specific class of environments. Using a stylized experimental design, subjects faced two distinct choice environments, each characterized by different statistical properties. Our findings show humans appear to use the same mechanism in both environments, suggesting that a divisive representation may be a fixed feature of human cognition.

neuroscience↗

bioRxiv2024

Altered host pyruvate metabolism fuels and regulates fungal asexual reproduction

Powdery mildew (PM) fungi are widespread pathogens impacting agricultural productivity. As obligate biotrophs, PMs get all nutrients from their host plant. Concurrent with asexual reproduction, the PM Golovinomyces orontii induces host lipid body accumulation in plant mesophyll cells underlying the epidermal cell that houses the fungal feeding structure. Herein, we show a PM-induced shift in plant primary metabolism, to bypass the pyruvate dehydrogenase complex (PDHc), acts in a dose-dependent manner to support host storage lipid production for PM acquisition. Not only are plant PDHc bypass-derived lipids incorporated into spore storage lipids, but they also act in a regulatory capacity to control the number of fungal reproductive structures per colony, ensuring the fitness of spores that are formed. Evolutionarily distant plant and animal obligate biotrophs manipulate host lipid metabolism for their own acquisition and we propose altered host pyruvate metabolism, bypassing the host PDHc bottleneck, as a common strategy to increase host source flux to acetate/acetyl-CoA for obligate microbial lipid acquisition.

plant biology↗

bioRxiv2024

Origins of noise in both improving and degrading decision making

Noise is a fundamental problem for information processing in neural systems. In decision-making, noise is thought to cause stochastic errors in choice. However, little is known about how noise arising from different sources may contribute differently to value coding and choice behaviors. Here, we examine how noise arising early versus late in the decision process differentially impacts context-dependent choice behavior. We find in model simulations that under early noise, contextual information enhances choice accuracy, while under late noise, context degrades choice accuracy. Furthermore, we verify these opposing predictions in experimental human choice behavior. Manipulating early and late noise - by inducing uncertainty in option values and controlling time pressure - produces dissociable positive and negative context effects. These findings reconcile controversial experimental findings in the literature, suggesting a unified mechanism for context-dependent choice. More broadly, these findings highlight how different sources of noise can interact with neural computations to differentially modulate behavior.

neuroscience↗

bioRxiv2023

Dopamine neurons encode a multidimensional probabilistic map of future reward

Learning to predict rewards is a fundamental driver of adaptive behavior. Midbrain dopamine neurons (DANs) play a key role in such learning by signaling reward prediction errors (RPEs) that teach recipient circuits about expected rewards given current circumstances and actions. However, the algorithm that DANs are thought to provide a substrate for, temporal difference (TD) reinforcement learning (RL), learns the mean of temporally discounted expected future rewards, discarding useful information concerning experienced distributions of reward amounts and delays. Here we present time-magnitude RL (TMRL), a multidimensional variant of distributional reinforcement learning that learns the joint distribution of future rewards over time and magnitude using an efficient code that adapts to environmental statistics. In addition, we discovered signatures of TMRL-like computations in the activity of optogenetically identified DANs in mice during a classical conditioning task. Specifically, we found significant diversity in both temporal discounting and tuning for the magnitude of rewards across DANs, features that allow the computation of a two dimensional, probabilistic map of future rewards from just 450ms of neural activity recorded from a population of DANs in response to a reward-predictive cue. In addition, reward time predictions derived from this population code correlated with the timing of anticipatory behavior, suggesting the information is used to guide decisions regarding when to act. Finally, by simulating behavior in a foraging environment, we highlight benefits of access to a joint probability distribution of reward over time and magnitude in the face of dynamic reward landscapes and internal physiological need states. These findings demonstrate surprisingly rich probabilistic reward information that is learned and communicated to DANs, and suggest a simple, local-in-time extension of TD learning algorithms that explains how such information may be acquired and computed.

animal behavior and cognition↗

bioRxiv2023

Cynipid wasps systematically reprogram host metabolism and restructure cell walls in developing galls

Many insects have evolved the ability to manipulate plant growth to generate extraordinary structures called galls in which insect larva can develop while being sheltered within and feeding on the plant. In particular, Cynipid (Hymenoptera: Cynipidae) wasps have evolved to form some of the most morphologically complex galls known and generate an astonishing array of gall shapes, colors, and sizes. However, the biochemical basis underlying these remarkable cellular and developmental transformations remains poorly understood. A key determinant in plant cellular development is the deposition of the cell wall to dictate the physical form and physiological function of newly developing cells, tissues, and organs. However, it is unclear to what degree cell walls are restructured to initiate and support the formation of new gall tissue. Here, we characterize the molecular alterations underlying gall development using a combination of metabolomic, histological, and biochemical techniques to elucidate how leaf cells are reprogrammed to form galls. Strikingly, gall development involves an exceptionally coordinated spatial deposition of lignin and xylan to form de novo gall vasculature. Our results highlight how Cynipid wasps can radically change the metabolite profile and restructure the cell wall to enable the formation of galls, providing new insights into the mechanism of gall induction and the extent to which plants can be entirely reprogrammed to form novel structures and organs.

plant biology↗

bioRxiv2022

Substrate availability and not thermal-acclimation controls microbial temperature sensitivity response to long term warming

Microbes are responsible for cycling carbon (C) through soils, and the predictions of how soil C stocks change with warming are highly sensitive to the assumptions made about the mechanisms controlling the microbial physiology response to climate warming. Two mechanisms, microbial thermal-acclimation and changes in the quantity and quality of substrates available for microbial metabolism have been suggested to explain the long-term warming impact on microbial physiology. Yet studies disentangling these two mechanisms are lacking. To resolve the drivers of changes in microbial physiology in response to long-term warming, we sampled soils from 13- and 28-year old soil warming experiments in different seasons. We performed short-term laboratory incubations across a range of temperatures to measure the relationship between temperature sensitivity of physiology (growth, respiration, carbon use efficiency and extracellular enzyme activity) and the chemical composition of soil organic matter. We observed apparent thermal acclimation in microbial processes important for C cycling, but only when warming had exacerbated the seasonally-induced, already small soil organic matter pools. Irrespective of warming, greater quantity and quality of soil carbon enhanced the extracellular enzymatic pool and its temperature sensitivity. We suggest that fresh litter input into the system seasonally cancels apparent thermal acclimation of C-cycling processes. Our findings reveal that long-term warming has indirectly affected microbial physiology via reduced C availability in this system, implying that earth system models including these negative feedbacks may be best suited to describe long-term warming impact in soils.

microbiology↗

bioRxiv2022

Input-Specific Inhibitory Plasticity Improves Decision Accuracy Under Noise

Inhibitory interneurons regulate excitability, information flow, and plasticity in neural circuits. Inhibitory synapses are also plastic and can be modified by changes in experience or activity, often together with changes to excitatory synapses. However, given the diversity of inhibitory cell types within the cerebral cortex, it is unclear if plasticity is similar for various inhibitory inputs or what the functional significance of inhibitory plasticity might be. Here we examined spike-timing-dependent plasticity of inhibitory synapses from four major subtypes of GABAergic cells onto layer 2/3 pyramidal cells in mouse auditory cortex. The likelihood of inhibitory potentiation varied across cell types, with somatostatin-positive (SST+) interneuron inputs exhibiting the most potentiation on average. A network simulation of perceptual decision-making revealed that plasticity of SST+-like inputs provided robustness from higher input noise levels to maintain decision accuracy. Differential plasticity at specific inhibitory inputs therefore may be important for network function and sensory perception.

neuroscience↗

bioRxiv2022

Characterization and visualization of global metabolomic responses of Brachypodium distachyon to environmental changes

Plant responses to environmental change are mediated via changes in cellular metabolomes. However, <5% of signals obtained from tandem liquid chromatography mass spectrometry (LC-MS/MS) can be identified, limiting our understanding of how different metabolite classes change under biotic/abiotic stress. To address this challenge, we performed untargeted LC-MS/MS of leaves, roots and other organs of Brachypodium distachyon, a model Poaceae species, under 17 different organ-condition combinations, including copper deficiency, heat stress, low phosphate and arbuscular mycorrhizal symbiosis (AMS). We used a combination of information theory-based metrics and machine learning-based identification of metabolite structural classes to assess metabolomic changes. Both leaf and root metabolomes were significantly affected by the growth medium. Leaf metabolomes were more diverse than root metabolomes, but the latter were more specialized and more responsive to environmental change. We also found that one week of copper deficiency shielded the root metabolome, but not the leaf metabolome, from perturbation due to heat stress. Using a recently published deep learning based method for metabolite class predictions, we analyzed the responsiveness of each metabolite class to environmental change, which revealed significant perturbations of various lipid classes and phenylpropanoids such as cinnamic acids and flavonoids. Co-accumulation analysis further identified condition-specific metabolic biomarkers. Finally, to make these results publicly accessible, we developed a novel visualization platform on the Bioanalytical Resource website, where significantly perturbed metabolic classes can be readily visualized. Overall, our study illustrates how emerging chemoinformatic methods can be applied to reveal novel insights into the dynamic plant metabolome and plant stress adaptation.

plant biology↗

bioRxiv2022

Flexible control of representational dynamics in a disinhibition-based model of decision making

Current models utilize two primary circuit motifs to replicate neurobiological decision making. Recurrent gain control implements normalization-driven relative value coding, while recurrent excitation and non-selective pooled inhibition together implement winner-take-all (WTA) dynamics. Despite evidence for concurrent valuation and selection computations in individual brain regions, existing models focus selectively on either normalization or WTA dynamics and how both arise in a single circuit architecture is unknown. Here we show that a novel hybrid motif unifies both normalized representation and WTA competition, with dynamic control of circuit state governed by local disinhibition. In addition to capturing empirical psychometric and chronometric data, the model produces persistent activity consistent with working memory. Furthermore, the biological basis of disinhibition provides a simple mechanism for flexible top-down control of network states, enabling the circuit to capture diverse task-dependent neural dynamics. These results suggest a new biologically plausible mechanism for decision making and emphasize the importance of local disinhibition in neural processing.

neuroscience↗

bioRxiv2022

Context-dependent choice and evaluation in real-world consumer behavior

A body of work spanning neuroscience, economics, and psychology indicates that decisionmaking is context-dependent, which means that the value of an option depends not only on the option in question, but also on the other options in the choice set--or the context. While context effects have been observed primarily in small-scale laboratory studies with tightly constrained, artificially constructed choice sets, it remains to be determined whether these context effects take hold in real-world choice problems, where choice sets are large and decisions driven by rich histories of direct experience. Here, we investigate whether valuations are context-dependent in real-world choice by analyzing a large restaurant rating dataset (Yelp.com) as well as two independent replication datasets which provide complementary operationalizations of restaurant choice. We find that users make fewer ratings-maximizing choices in choice sets with higher-rated options--a hallmark of context-dependent choice-- and that post-choice restaurant ratings also varied systematically with the ratings of unchosen restaurants. Furthermore, in a follow-up laboratory experiment using hypothetical choice sets matched to the real-world data, we find further support for the idea that subjective valuations of restaurants are scaled in accordance with the choice context, providing corroborating evidence for a general mechanistic-level account of these effects. Taken together, our results provide a potent demonstration of context-dependent choice in real-world choice settings, manifesting both in decisions and subjective valuation of options.

neuroscience↗

bioRxiv2021

Asymmetric and adaptive reward coding via normalized reinforcement learning

Learning is widely modeled in psychology, neuroscience, and computer science by prediction error-guided reinforcement learning (RL) algorithms. While standard RL assumes linear reward functions, reward-related neural activity is a saturating, nonlinear function of reward; however, the computational and behavioral implications of nonlinear RL are unknown. Here, we show that nonlinear RL incorporating the canonical divisive normalization computation introduces an intrinsic and tunable asymmetry in prediction error coding. At the behavioral level, this asymmetry explains empirical variability in risk preferences typically attributed to asymmetric learning rates. At the neural level, diversity in asymmetries provides a computational mechanism for recently proposed theories of distributional RL, allowing the brain to learn the full probability distribution of future rewards. This behavioral and computational flexibility argues for an incorporation of biologically valid value functions in computational models of learning and decision-making.

neuroscience↗