Biology subjects

Lu, H.-Y.

Publications and source records attributed to Lu, H.-Y..

13 recordsLinked to original sources

bioRxiv2026

Dynamic Regulation of Atg4 Protease and Autophagy by Dithiothreitol and Iron in Alternaria alternata

Autophagy is a critical cellular process regulated by Atg proteins, yet its modulation by redox-active compounds and iron remains incompletely understood. Here, we investigated the effects of dithiothreitol (DTT) and iron on autophagy and on AaAtg4 protease activity in the plant-pathogenic fungus Alternaria alternata. Using GFP-tagged AaAtg8, fluorescence microscopy and proteolysis assays revealed that DTT markedly enhanced autophagic vacuole formation and GFP release, indicating increased autophagic flux. Western blot analyses confirmed that DTT promoted AaAtg8 lipidation, while co-treatment with hydrogen peroxide (H2O2) suppressed this modification. AaAtg4 was constitutively active and could process AaAtg8 regardless of DTT supplementation, whereas moderate DTT concentrations elevated AaAtg4 protein abundance and phosphorylation. Bimolecular fluorescence complementation assays demonstrated that DTT, but not iron, facilitated AaAtg4-AaAtg8 interactions and vacuolar localization, whereas H2O2 counteracted these effects. Notably, combined DTT and H2O2 sustained autophagy at a low but stable level, suggesting a redox balance in autophagic regulation. Iron supplementation selectively destabilized AaAtg8 and modulated AaAtg4 phosphorylation in a concentration-dependent manner, without altering autophagy or protease activity. Collectively, these findings demonstrate that DTT enhances autophagy primarily by promoting AaAtg8 lipidation, AaAtg4 phosphorylation, and AaAtg4-AaAtg8 complex formation, while exerting minimal influence on AaAtg4 protease activity. In contrast, ion regulates autophagy flux through its effects on AaAtg4 phosphorylation and AaAtg8 stability, without significantly altering AaAtg4 protease activity, AaAtg8 lipidation, or AaAtg4-AaAtg8 interactions. Together, this work underscores the intricate interplay between redox signaling, nutrient cues, and autophagy regulation in A. alternata. IMPORTANCEThis study provides critical new insights into how redox-active compounds and iron modulate autophagy in the plant-pathogenic fungus Alternaria alternata, a pathogen of agricultural relevance. By dissecting the distinct roles of DTT, hydrogen peroxide, and iron in regulating AaAtg8 lipidation, AaAtg4 phosphorylation, and AaAtg4-AaAtg8 interactions, our findings reveal that autophagy is not simply a constitutive process but is finely tuned by redox balance and nutrient cues. This work advances the fundamental understanding of autophagy regulation in filamentous fungi, highlights the interplay between oxidative stress and protease activity, and establishes a framework for exploring how environmental factors shape fungal pathogenicity. Ultimately, these insights may inform novel strategies to mitigate crop fungal diseases by targeting autophagic pathways.

microbiology↗

bioRxiv2026

Distinct tasks engage a shared neural subspace in human hippocampus and anterior cingulate cortex

A hallmark of human cognitive flexibility is the ability to perform a wide range of unrelated tasks. While much is known about how neural circuits support individual tasks, less is understood about how these circuits support multiple tasks. Are neural representations largely task-specific, or do they retain a shared structure across distinct behaviors? To investigate this question, we recorded neural population activity from the hippocampus and anterior cingulate cortex, two regions linked to generalization and cognitive control, in eleven human patients performing three distinct tasks. Using dimensionality reduction, we estimated the neural subspace associated with each task and compared subspace geometry across tasks. We found that task-related subspaces were not independent: across tasks, approximately half of the subspace dimensions were shared, and these were primarily the dimensions containing most of the shared neural covariance. These findings indicate that neural population activity in these regions is not purely task-specific. This suggests a stable, low-dimensional circuit structure may persist even across unrelated behaviors, potentially providing a common substrate for flexible cognition.

neuroscience↗

bioRxiv2026

Decoding Smell from Receptor Structure

Olfaction enables animals to detect and discriminate a vast array of chemicals, yet how odorant receptors (ORs) encode ligand selectivity remains unclear. Although recent advances in protein structure prediction have expanded access to OR structures, linking these to function at scale has been challenging. Here, we combined AlphaFold3-predicted receptor structures with protein language model embeddings and in vivo pS6-IP-Seq measurements of olfactory sensory neuron activation across a chemically diverse odor panel to train a deep learning model of OR-ligand interactions. The resulting framework predicts receptor responses, organizes receptors by functional similarity independent of sequence, and identifies structural determinants of ligand selectivity. These findings establish a structure-based map of OR function and provide a foundation for predictive and interpretable models of olfactory coding.

molecular biology↗

bioRxiv2025

Evolutionary diversity and function of odorant receptors in birds

Abstract: Odorant receptors (ORs) form among the largest gene families in vertebrates; most mammals have hundreds of intact OR genes. Although birds display diverse behavior and ecology, they were long thought to rely minimally on olfaction. We reexamine this idea by using genomic data to uncover diverse and variable OR repertoires in birds, including the nocturnal kiwi (Apteryx mantelli), which possesses the largest number of ORs known (3,750). We reveal that widespread gene conversion is shaping the evolution of bird ORs, reflecting a novel mechanism for generating functional diversity. We show that ORs are expressed in avian olfactory sensory neurons and show responses to defined, ecologically relevant odorants. Our results demonstrate the importance and functional mechanisms underlying olfaction in birds.

evolutionary biology↗

bioRxiv2025

Distinct roles of neuronal phenotypes during neurofeedback adaptation

Learning adaptation allows the brain to refine motor patterns in response to changing environments rapidly. While population-level neural dynamics and single-neuron activity in motor learning have been widely studied, the contributions of individual neuron types remain poorly understood. Here, we employed a brain-machine interface (BMI) task with perturbations of varying difficulty to investigate single-neuron dynamics underlying neurofeedback adaptation in two rhesus macaques. Cortical neurons were classified based on waveform shape into narrow waveform (NW) and broad waveform (BW) categories, representing putative inhibitory interneurons and excitatory pyramidal neurons, respectively. Compared to BW neurons, NW neurons were more active and more strongly involved in the learning process. Moreover, task difficulty modulated neural responsiveness and coordination within both neuron groups, highlighting differential neuron engagement during motor learning. Our findings provide novel insights into single-neuron mechanisms underlying neurofeedback adaptation and emphasize the distinct functional roles of neuronal phenotypes in rapid learning processes. Author SummaryUnderstanding how the brain adapts to changes is crucial for improving treatments for neurological disorders and enhancing brain-machine interface (BMI) technology, which allows control of external devices using neural signals. In our study, we investigated how different types of brain cells contribute to the adaptation process when individuals encounter unexpected challenges during neurofeedback tasks. We discovered that one type of neuron was notably more active and played a more engaged role in rapidly adjusting neural activity compared to another type. These neurons demonstrated stronger coordination in their activity and showed greater responsiveness as the task difficulty increased. Our findings highlight the distinct roles of specific neurons in quickly adapting to neurofeedback tasks, offering insights that could enhance therapies for movement disorders and improve the precision and reliability of brain-controlled prosthetics.

neuroscience↗

bioRxiv2025

Population-level constraints on single neuron tuning changes during behavioral adaptation

In neurofeedback training, select neurons - of the billions that comprise the brain - are able to adapt their activity to produce a desired behavior, even though the feedback signal is low-dimensional. However, the extent that individual neurons are able to alter their activity within the context of the neurofeedback population is unclear. Using a brain-computer interface in monkeys (n=2; Macaca mulatta), we tested how the tuning of individual neurons changed in response to various feedback perturbations. Overall, we found that neurons that co-varied their activity the most with the population and those that contributed most to the behavior prior to the perturbation were most resistant to change- regardless of perturbation type. The degree to which individual neurons counteracted the applied perturbation explained daily differences in how well neurons collectively produced adapted behavior. Our work provides important insight into how population-level constraints impact the ability of individual neurons to adapt and support behavioral flexibility.

bioengineering↗

bioRxiv2025

A high-quality chromosome-level genome of an undescribed Meretrix species using Nanopore and Hi-C technologies

Meretrix is a commercially valuable bivalve genus in Asia, but only one reference genome has hindered comprehensive genetic studies and germplasm resource evaluation. In this study, we present three reference genomes of Meretrix species: Meretrix sp. MF1, Meretrix sp. MT1, and Meretrix lamarckii JML1. Meretrix sp. MF1 was assembled at the chromosome level using Nanopore sequencing and Hi-C technologies, whereas Meretrix sp. MT1 and Meretrix lamarckii were assembled as scaffold-level assemblies. The chromosome-level genome of Meretrix sp. MF1 consists of 36 contigs, including 19 chromosomes and 17 scaffolds, with a total length of 883.3 Mb and a scaffold N50 of 46.87 Mb. Notably, the genome of Meretrix sp. MF1, a putative novel species, exhibits an Average Nucleotide Identity (ANI) of 94.33% with its closest relative, Meretrix lamarckii. These genomic resources not only provide a crucial foundation for genetic research on Meretrix but also contribute to the development of effective conservation strategies for its sustainable management.

genomics↗

bioRxiv2025

Long-term neuron tracking reveals balance of stability and plasticity in functional properties

Neural stability is essential for executing learned motor behaviors while plasticity provides the flexibility needed to adapt to new tasks and environments. Although low-dimensional neural population dynamics exhibit long-term stability, the extent to which individual neurons retain their functional properties over time and balance the need for both stability and plasticity remains an open question. Tracking individual neurons across multiple recording sessions is crucial to addressing this question, yet conventional methods face challenges such as electrode drift, waveform variability, and large inter-electrode distances that limit the number of channels a neuron is observed on. Here, we introduce a waveform-based neuron tracking method optimized for standard microelectrode arrays, enabling the identification of the same neurons across sessions without relying on spatial overlap, a strategy commonly leveraged with high-density electrode arrays. We apply this method to assess the longitudinal stability of multiple neural properties, including firing rates, inter-spike intervals, tuning properties, and spike-field interactions. Our findings reveal that while spike waveform properties remain stable, certain functional properties such as ISI and tuning can exhibit gradual shifts, suggesting a balance between neural stability and plasticity. Understanding the persistence of individual neural signals provides insight into learning and adaptation while advancing the study of neural stability and plasticity over extended timescales. Beyond basic neuroscience, this framework has potential to enhance the long-term reliability of brain-machine interfaces and closed-loop deep brain stimulation systems that rely on chronic neural sensing.

neuroscience↗

bioRxiv2024

Enolase-1 is essential for neutrophil recruitment during acute inflammation

Enolase-1 (ENO1) is a moonlighting protein with multiple functions. When expressed on the cell surface, ENO1 binds plasminogen (PLG) and promotes cell migration by facilitating plasmin (PLM)-mediated extracellular matrix degradation. Here, we observed that inflammatory stimulation significantly upregulated ENO1 expression on the neutrophil surface, both in vitro and in vivo. An anti-ENO1 monoclonal antibody (mAb), 7E5, which blocks the ENO1-PLG interaction, effectively suppressed neutrophil transmigration. In mouse models of acute inflammation, including lipopolysaccharide (LPS)-induced lung injury and necrotic cell challenge, 7E5 treatment markedly reduced neutrophil recruitment and neutrophil extracellular trap (NET) formation. Moreover, 7E5 neutralized the immunostimulatory activity of soluble ENO1, which was significantly elevated in circulation during acute inflammation. These findings highlight ENO1 as a key regulator of inflammation and neutrophil infiltration. Targeting ENO1 with antibodies could be a promising strategy to mitigate tissue damage caused by excessive neutrophilic inflammation.

immunology↗

bioRxiv2024

Volitional Regulation and Transferable Patterns of Midbrain Oscillations

Dopaminergic brain areas are crucial for cognition and their dysregulation is linked to neuropsychiatric disorders typically treated with pharmacological interventions. These treatments often have side effects and variable effectiveness, underscoring the need for alternatives. We introduce the first demonstration of neurofeedback using local field potentials (LFP) from the ventral tegmental area (VTA). This approach leverages the real-time temporal resolution of LFP and ability to target deep brain. In our study, non-human primates learned to regulate VTA beta power using a customized normalized metric to stably quantify VTA LFP signal modulation. The subjects demonstrated flexible and specific control with different strategies for specific frequency bands, revealing new insights into the plasticity of VTA neurons contributing to oscillatory activity that is functionally relevant to many aspects of cognition. Excitingly, the subjects showed transferable patterns, a key criterion for clinical applications beyond training settings. This work provides a foundation for neurofeedback-based treatments, which may be a promising alternative to conventional approaches and open new avenues for understanding and managing neuropsychiatric disorders. Significance statementThis study demonstrates, for the first time, that neurofeedback using local field potentials (LFP) from the ventral tegmental area (VTA) is feasible in non-human primates. By leveraging the temporal resolution and ability to target deep brain regions, this approach provides a novel way to modulate brain activity linked to dopamine-related functions. The findings reveal that subjects can flexibly control VTA LFP signals and transfer learned strategies to new settings, offering potential for developing neurofeedback-based treatments. This research opens new avenues for managing neuropsychiatric disorders, presenting an alternative to traditional pharmacological interventions that often have side effects and limited effectiveness. The study highlights the plasticity of VTA neurons and their relevance to cognition and mood regulation.

neuroscience↗

bioRxiv2024

Neural population variance explains adaptation differences during learning

Variability, a ubiquitous feature of neural activity, plays an integral role in behavior. However, establishing a causal relationship between neural signals and behavior is difficult. By defining a mathematical mapping between neural spiking activity and behavior, we investigate the role of spiking variability in adaptation during a brain-computer interface (BCI) behavior in male rhesus macaques (Macaca mulatta, n=2). Recent BCI evidence demonstrates that creating novel neural patterns is harder than repurposing existing patterns to respond to changes in external input. However, what limits the ability to repurpose, or adapt, patterns under different magnitudes of change is less well-characterized. Here, we present evidence that variance in neural spiking activity reveals differences in learnability between easy and hard adaptation conditions and across sessions. Furthermore, our study illuminates the limitations in neural changes underlying behavior within a neurofeedback paradigm. Significance StatementVariability in neural activity is a major driver of behavioral variability, though it is unclear how variability is balanced with stable neural activity as new behaviors become more practiced. By using a brain-computer interface methodology, we define a mathematical mapping between neural spiking activity and a behavioral control signal. Through thoughtful manipulation of this mapping, we incite the subjects (rhesus macaques) to learn and adapt neural activity to regain behavioral proficiency. We find that metrics of neural population variability are differentially modulated depending on difficulty of the imposed manipulation. Our exciting results provide important implications for brain-computer interface applications as well as our understanding of learning and adaptation more broadly. Our work represents an important step forwards towards understanding population neural dynamics in this critical component of behavior.

bioengineering↗

bioRxiv2023

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes. Author SummaryMulti-modal measurements have become an emerging trend in recent years due to the capability of studying brain dynamics at disparate scales. However, an integrative framework to systematically capture the multi-scale nonlinear dynamics in brain networks is lacking. A major challenge for creating a cohesive model is a mismatch in the timescale and subsequent sampling rate of the dynamics for disparate modalities. In this work, we introduce a deep learning-based approach to characterize brain communications between regions and scales. By modeling the continuous dynamics of hidden states using the neural network-based ordinary differential equations, the requirement of downsampling the faster sampling signals is discarded, thus preventing from losing dynamics information. Another advantageous feature of the proposed method is flexibility. An adaptable framework to bridge the gap between scales is necessary. Depending on the neural recording modalities utilized in the experiment, any suitable pair of well-established models can be plugged into the proposed multi-scale modeling framework. Thus, this method can provide insight into the brain computations of multi-scale brain activity.

neuroscience↗

bioRxiv2020

Enteroendocrine cells sense bacterial tryptophan catabolites to activate enteric and vagal neuronal pathways

The intestinal epithelium senses nutritional and microbial stimuli using epithelial sensory enteroendocrine cells (EECs). EECs can communicate nutritional information to the nervous system, but similar mechanisms for microbial information are unknown. Using in vivo real-time measurements of EEC and nervous system activity in zebrafish, we discovered that the bacteria Edwardsiella tarda specifically activates EECs through the receptor transient receptor potential ankyrin A1 (Trpa1) and increases intestinal motility in an EEC-dependent manner. Microbial, pharmacological, or optogenetic activation of Trpa1+EECs directly stimulates vagal sensory ganglia and activates cholinergic enteric neurons through 5-HT. We identified a subset of indole derivatives of tryptophan catabolism produced by E. tarda and other gut microbes that potently activates zebrafish EEC Trpa1 signaling and also directly stimulates human and mouse Trpa1 and intestinal 5-HT secretion. These results establish a molecular pathway by which EECs regulate enteric and vagal neuronal pathways in response to specific microbial signals.

physiology↗