Biology subjects

Tan, H.

Publications and source records attributed to Tan, H..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Proteome profiling of plant Cajal bodies uncovers kingdom-specific components

Insight into the molecular composition of cellular microenvironments is essential for understanding their function. Membraneless organelles (MLO), such as the nuclear Cajal body (CB), play fundamental roles in eukaryotic cells, but their very nature poses a challenge to biochemical isolation. Despite the relevance of the CB for cell function in plants and animals, its proteome remains to be profiled. Here, we purify plant CBs using a combination of biochemical isolation and fluorescence-activated particle sorting (FAPS) and define their proteome by mass spectrometry. Our work identifies 15 and 95 proteins uniquely localized and enriched in CBs, respectively, of which 22 are also labelled by CB markers in proximity labelling assays. CB proteins are preferentially involved in RNA processing, chromatin organization, and gene expression. While a partial overlap with known animal CB proteins exists, our data reveal a distinct, kingdom-specific CB proteome. These results provide the first comprehensive proteome of CBs and establishes a foundation for dissecting the molecular functions of this organelle across multicellular lineages.

plant biology↗

bioRxiv2025

Intravital Multimodal Imaging of Human Cortical Organoids for Chronic Stroke Treatment in Mice

Chronic stroke leads to enduring neurological deficits and remains a major clinical challenge. Human induced pluripotent stem cell (hiPSC)-derived cortical organoids (COs) offer promise for regenerative therapies, yet their application in chronic stroke remains understudied, in part due to limited tools for monitoring graft in vivo. Here, we present a Multimodal Imaging Platform for Organoid Tracking (MIPOT), which integrates MRI, bioluminescence imaging, light microscopy, and two-photon fluorescence microscopy to noninvasively track transplanted COs in the post-stroke brain over time. Using MIPOT, we verified precise delivery of COs into cleaned stroke cavities, observed progressive declines in viability within the first 10 days, and introduced labeling methods for functional in vivo tracking of COs at subcellular resolution. By 4 weeks post-transplantation, histological analysis revealed survival of COs in the cerebral cortex. Notably, COs engrafted into the hippocampus displayed enhanced maturation, underscoring the role of local microenvironments in graft integration. MIPOT enables dynamic, noninvasive evaluation of hiPSC COs in chronic stroke, providing a foundation for mechanistic studies and translational development of organoid-based therapies.

neuroscience↗

bioRxiv2025

Characterizing the frequency-specific and spatiotemporal dynamics of β-γ Phase-Amplitude Coupling in Parkinson's disease

Cross-frequency coupling (CFC) has been proposed to facilitate neural information transfer across spatial and temporal scales. Phase-amplitude coupling (PAC), a type of CFC in which the amplitude of a faster brain oscillation is coupled to the phase of a slower brain oscillation, is implicated in various higher-order cognitive functions and was shown to be pathologically altered in neurological and psychiatric disease. In Parkinsons disease (PD), the coupling between gamma amplitude (50-150 Hz) to beta phase (13-35 Hz) is exaggerated. Enhanced {beta}-{gamma} PAC was found in the subthalamic nucleus and various cortical sources and shown to be responsive to dopaminergic therapy and deep brain stimulation (DBS). Therefore, exaggerated {beta}-{gamma} PAC has been proposed to be a disease marker and a potential target for brain circuit interventions. Despite these promising findings, a significant knowledge gap remains, as the spatial and frequency-specific dynamics of {beta}-{gamma} PAC and its association with motor symptoms and therapy remain elusive. To address this knowledge gap, we employed high-density electroencephalography (EEG) with advanced source localisation techniques for PD patients at rest. We highlight three key findings: (1) a frequency-specific increase in high {beta} (23-35 Hz)-{gamma} PAC within and between sources of the cortical motor network, (2) a link between elevated high {beta}-{gamma} PAC and bradykinesia and rigidity when OFF medication, but not tremor, and (3) a medication-induced reduction in high {beta}-{gamma} PAC in the supplementary motor area correlating with clinical improvement. Altogether, this study provides novel insights into the pathophysiology of PD as an oscillopathy and identifies high {beta}-{gamma} PAC as a potential marker of Parkinsonian symptoms and treatment effects. This has important implications for invasive as well as non-invasive therapeutic strategies as high {beta}-{gamma} PAC targeting might hold greater promise than targeting {beta}-{gamma} PAC per se.

neuroscience↗

bioRxiv2025

SLFN11 Loss-Induced Chemoresistance is Associated with Overexpression of Glycerophospholipid Biosynthesis in Ewing Sarcoma.

Ewing sarcoma (EWS) is an aggressive cancer in adolescents and young adults with frequent relapse rates and poor outcomes in recurrent or metastatic cases. Schlafen family member 11 (SLFN11) gene is associated with the sensitivity to DNA-damaging agents (DDAs). The knockout of SLFN11 is associated with acquired chemoresistance in both cell lines and preclinical models. Here, we aimed to elucidate the metabolic underpinnings of SLFN11-loss associated chemoresistance in patient derived cell lines of EWS. Our integrated transcriptomic and metabolomic analyses revealed downregulation of mitochondrial glycerol-3-phosphate dehydrogenase 2 (GPD2) gene, which was accompanied by the upregulation of glycerophospholipid (GPL) biosynthesis pathway. Further, therapeutic targeting of lipid synthesis with the glycerol-3-phosphate acyltransferase 1 (GPAT1) inhibitor (FSG67) enhanced the efficacy of the DDA (SN-38) in SLFN11-/- cells. These findings indicate that SLFN11 loss-mediated chemoresistance can be targeted by blocking GPL biosynthesis in addition to DDA administration.

cancer biology↗

bioRxiv2025

Beyond beta: Aperiodic broadband power reflects Parkinson's disease severity - a multicenter study

Parkinsons disease is linked to increased beta oscillations in the subthalamic nucleus, which correlate with motor symptoms. However, findings across studies have varied. Our standardized analysis of multicenter datasets shows that small sample sizes contributed to these discrepancies--a challenge we address by pooling datasets into one large cohort (n=119). Moving beyond beta power, we disentangled spectral components reflecting distinct neural processes. Combining aperiodic offset, low beta, and low gamma oscillations explained significantly more variance in symptom severity than beta alone. Moreover, interhemispheric within-patient analyses showed that, unlike beta oscillations, aperiodic broadband power-likely reflecting spiking activity-was increased in the more affected hemisphere. These findings identify aperiodic broadband power as a potential biomarker for adaptive deep brain stimulation and provide novel insights into the relationship between subthalamic hyperactivity and motor symptoms in human Parkinsons disease.

neuroscience↗

bioRxiv2025

The role of STN beta oscillations on lower extremity muscle activity in Parkinsonian stepping

Freezing of gait (FOG) is a devastating symptom of Parkinsons disease (PD) often resulting in disabling falls and loss of independence. It affects half of patients, yet current therapeutic strategies are insufficient, and the underlying neural mechanisms remain poorly understood. This study investigated beta oscillation dynamics in the STN during different locomotor states, while examining the effects of levodopa. In particular, it aimed to identify pathological activity by analysing the relationship between the STN and lower limb muscles during stepping. Local field potentials (LFP) in the STN and muscle activity (EMG) of the gastrocnemius and peroneus longus were recorded in 14 PD patients during standing and stepping, ON and OFF levodopa. Levodopa reduced stepping variability, implying improved stepping abilities. Distinct STN beta patterns were observed between stepping and standing, with lower high-beta and higher low-beta during stepping compared to standing, suggesting a distinct role of these frequency bands in motor control during postural and movement states. Levodopa reduced low-beta but increased high-beta activity, highlighting a potential physiological function of high-beta in the STN during standing and stepping. In addition, step-phase specific effects of levodopa included reduced broad-beta band activity in the STN and lower limb muscles during the late-stance and pushing-off phase of the contralateral leg when ON medication. Further analyses suggest that pathological STN activity amplifies muscle activation around movement initiation, potentially reducing the ability of the patient to move freely. These findings offer insight for developing phase-specific stimulation strategies targeting STN beta oscillations during gait.

neuroscience↗

bioRxiv2024

Egocentric perception of walking environments using an interactive vision-language system

Large language models can provide a more detailed contextual understanding of a scene beyond what computer vision alone can provide, which have implications for robotics and embodied intelligence. In this study, we developed a novel multimodal vision-language system for egocentric visual perception, with an initial focus on real-world walking environments. We trained a number of state-of-the-art transformer-based vision-language models that use causal language modelling on our custom dataset of 43,055 image-text pairs for few-shot image captioning. We then designed a new speech synthesis model and a user interface to convert the generated image captions into speech for audio feedback to users. Our system also uniquely allows for feedforward user prompts to personalize the generated image captions. Our system is able to generate detailed captions with an average length of 10 words while achieving a high ROUGE-L score of 43.9% and a low word error rate of 28.1% with an end-to-end processing time of 2.2 seconds. Overall, our new multimodal vision-language system can generate accurate and detailed descriptions of natural scenes, which can be further augmented by user prompts. This innovative feature allows our image captions to be personalized to the individual and immediate needs and preferences of the user, thus optimizing the closed-loop interactions between the human and generative AI models for understanding and navigating of real-world environments.

neuroscience↗

bioRxiv2024

Beta burst characteristics and coupling within the sensorimotor cortical-subthalamic nucleus circuit in Parkinson's disease

BackgroundBursts of exaggerated subthalamic nucleus (STN) beta activity contribute to clinical impairments in Parkinsons disease (PD). Few studies have explored the characteristics and coupling of bursts across the sensorimotor cortical-STN circuit. ObjectiveWe sought to (1) establish the characteristics of sensorimotor cortical and STN bursts during naturalistic behaviours, and (2) determine the predictability of STN bursts from motor cortical recordings. MethodsWe analysed 1,478 hours of wirelessly streamed bilateral sensorimotor cortical and STN recordings from 5 PD patients. ResultsSTN bursts were longer than cortical bursts and had shorter inter-burst intervals. Long bursts (>200ms) in both structures displayed temporal overlap (>30%), with an estimated cortico-STN conduction delay of 8ms. Furthermore, approximately 27% of all STN bursts were preceded by a cortical burst. ConclusionCortical beta bursts tend to precede STN beta bursts, with short delays. However, subcortical mechanisms are also likely to contribute to STN burst initiation and propagation.

neuroscience↗

bioRxiv2024

Target-specific design of drug-like PPI inhibitors via hot-spot-guided generative deep learning

Protein-protein interactions (PPIs) are vital therapeutic targets. However, the large and flat PPI interfaces pose challenges for the development of small-molecule inhibitors. Traditional computer-aided drug design approaches typically rely on pre-existing libraries or expert knowledge, limiting the exploration of novel chemical spaces needed for effective PPI inhibition. To overcome these limitations, we introduce Hot2Mol, a deep learning framework for the de novo design of drug-like, target-specific PPI inhibitors. Hot2Mol generates small molecules by mimicking the pharmacophoric features of hot-spot residues, enabling precise targeting of PPI interfaces without the need for bioactive ligands. The framework integrates three key components: a conditional transformer for pharmacophore-guided, drug-likeness-constrained molecular generation; an E(n)-equivariant graph neural network for accurate alignment with PPI hot-spot pharmacophores; and a variational autoencoder for generating novel and diverse molecular structures. Experimental evaluations demonstrate that Hot2Mol outperforms baseline models across multiple metrics, including docking affinities, drug-likenesses, synthetic accessibility, validity, uniqueness, and novelty. Furthermore, molecular dynamics simulations confirm the good binding stability of the generated molecules. Case studies underscore Hot2Mols ability to design high-affinity and selective PPI inhibitors, demonstrating its potential to accelerate rational PPI drug discovery.

bioinformatics↗

bioRxiv2024

Comparative proteomics of biofilm development in Pseudoalteromonas tunicata discovers a distinct family of Ca2+-dependent adhesins

The marine bacterium, Pseudoalteromonas tunicata, is a useful model for studying mechanisms of biofilm development due to its ability to colonize and form biofilms on a variety of marine and eukaryotic host-associated surfaces. However, the pathways responsible for P. tunicata biofilm formation are still incompletely understood, in part due to a lack of functional information for a large proportion of its proteome. Here, we used comparative shotgun proteomics to examine P. tunicata biofilm development throughout the planktonic phase to three stages of biofilm development at 24, 48, and 72 h. Proteomic analysis identified 232 proteins that were up-regulated during different stages of biofilm development, including many hypothetical proteins as well as proteins known to be important for P. tunicata biofilm development such as the autocidal enzyme AlpP, violacein proteins, S-layer protein SLR4, and various pili proteins. We further investigated the top identified biofilm-associated protein (Bap), a previously uncharacterized 1600 amino acid protein (EAR30327), which we designated as "BapP". Based on AlphaFold modeling and genomic context analysis, we predicted BapP as a distinct Ca2+-dependent biofilm adhesin. Consistent with this prediction, a {Delta}bapP knockout mutant was defective in forming both pellicle and surface-associated biofilms, which was rescued by re-insertion of bapP into the genome. Similar to mechanisms of RTX adhesins, BapP-mediated biofilm formation was influenced by Ca2+ levels, and BapP is likely exported by a type 1 secretion system. Ultimately, our work not only provides a useful proteomic dataset for studying biofilm development in an ecologically relevant organism, but it also adds to our knowledge of bacterial adhesin diversity, emphasizing Bap-like proteins as widespread determinants of biofilm formation in bacteria.

microbiology↗

bioRxiv2024

Thalamo-frontal connectivity patterns in Tourette Syndrome: Insights from combined intracranial DBS and EEG recordings

Thalamic deep brain stimulation (DBS) has shown clinical improvement for patients with treatment-refractory Tourette Syndrome (TS). Advancing DBS for TS requires identifying reliable electrophysiological markers. Recognising TS as a network disorder, we investigated thalamo-cortical oscillatory connectivity by combining local field potential (LFP) recordings from the DBS thalamic target region using the PerceptTM PC neurostimulator with high-density EEG in eight male TS patients (aged 27 to 38) while stimulation was off. We identified a spatially and spectrally distinct oscillatory network connecting the medial thalamus and frontal regions in the alpha band (8-12 Hz), with connectivity strength negatively correlated with TS symptom severity. Moreover, reduced thalamo-frontal alpha connectivity before tic onset, localised in sensorimotor regions and the inferior parietal cortex, suggests its direct role in tic generation. Importantly, associations with symptoms and pre-tic dynamics were specific to connectivity patterns and not evident in the pure power spectra. These findings underscore the importance of investigating electrophysiological oscillatory connectivity to characterise pathological network connections in TS, potentially guiding stimulation-based interventions and future research on closed-loop DBS for TS.

neuroscience↗

bioRxiv2024

Contrasting patterns of specificity and transfer in human odor discrimination learning

Practice enhances olfactory performance. However, laboratory studies to date suggest that olfactory learning is largely restricted to the trained odors, posing a significant challenge for training-based rehabilitation therapies for olfactory loss. In this study, we introduce various types of odors to olfactory discrimination training, conducted unilaterally. We demonstrate contrasting patterns of specificity and transfer of learning, independent of adaptation and task difficulty. Individuals trained with odor mixtures of different ratios show long-term perceptual gains that completely transfer to the untrained nostril and effectively generalize to untrained mixtures dissimilar in structure and odor quality from the trained ones. Conversely, those trained with odor enantiomers show no transfer of learning across nostrils or to unrelated enantiomers, replicating our earlier findings (Feng & Zhou, 2019). Our observations indicate that concentration ratio and chirality represent distinct olfactory attributes. Furthermore, discrimination learning occurs at different stages of olfactory processing, depending on which attribute is task-relevant. These findings open up new avenues to enhance the effectiveness of olfactory training.

neuroscience↗

bioRxiv2024

Deep contrastive learning enables genome-wide virtual screening

Numerous protein-coding genes are associated with human diseases, yet approximately 90% of them lack targeted therapeutic intervention. While conventional computational methods, such as molecular docking, have facilitated the discovery of potential hit compounds, the development of genome-wide virtual screening against the expansive chemical space remains a formidable challenge. Here we introduce DrugCLIP, a novel framework that combines contrastive learning and dense retrieval to achieve rapid and accurate virtual screening. Compared to traditional docking methods, DrugCLIP improves the speed of virtual screening by up to seven orders of magnitude. In terms of performance, DrugCLIP not only surpasses docking and other deep learning-based methods across two standard benchmark datasets, but also demonstrates high efficacy in wet-lab experiments. Specifically, DrugCLIP successfully identified agonists with < 100 nM affinities for 5HT2AR, a key target in psychiatric diseases. For another target NET, whose structure is newly solved and not included in the training set, our method achieved a hit rate of 15%, with 12 diverse molecules exhibiting affinities better than bupropion. Additionally, two chemically novel inhibitors were validated by structure determination with Cryo-EM. Finally, a novel potential drug target TRIP12, with no experimental structures and inhibitors for reference, was used to challenge DrugCLIP. DrugCLIP achieved a hit rate of 17.5% by screening a pocket identified on an AlphaFold2-predicted structure, verified with multi-cycle SPR assays. Molecules with the highest affinities also showed a dose-dependent inhibition to the enzymatic function of TRIP12. Building on this foundation, we present the results of a pioneering trillion-scale genome-wide virtual screening, encompassing approximately 10,000 AlphaFold2 predicted proteins within the human genome and 500 million molecules from the ZINC and Enamine REAL database. This work provides an innovative perspective on drug discovery in the post-AlphaFold era, where comprehensive targeting of all disease-related proteins is within reach.

bioinformatics↗

bioRxiv2024

Evoked resonant neural activity outperforms spectral markers in decoding sleep from the subthalamic nucleus

BackgroundDeep brain stimulation is a treatment for advanced Parkinsons disease and currently tuned to target motor symptoms during daytime. Parkinsons disease is associated with multiple nocturnal symptoms such as akinesia, insomnia and sleep fragmentation which may require adjustments of stimulation during sleep for best treatment outcome. ObjectivesThere is a need for a robust biomarker to guide stimulation titration across sleep stages. This study aimed to investigate whether evoked resonant neural activity (ERNA) is modulated during sleep. MethodsWe recorded local field potentials from the subthalamic nucleus of four Parkinsons patients with externalised electrodes while applying single stimulation pulses to investigate the effect of sleep on ERNA. ResultsWe found that ERNA features change with wakefulness and sleep stages, and are correlated with canonical frequency bands and heart rate. We further evaluated the performance of machine learning models in classifying non-REM sleep versus wakefulness and found that ERNA amplitude outperforms all spectral markers. ConclusionsGiven the heterogeneity of spectral features during sleep, their susceptibility to movement artefacts and superior classification accuracy of models using ERNA features, this study paves the way for ERNA as a marker for automatic stimulation titration during sleep and improved patient care.

neuroscience↗

bioRxiv2024

RAB12-LRRK2 Complex Suppresses Primary Ciliogenesis and Regulates Centrosome Homeostasis in Astrocytes

Leucine-rich repeat kinase 2 (LRRK2) phosphorylates a subset of RAB GTPases, and the phosphorylation levels are elevated by Parkinsons disease (PD)-linked mutations of LRRK2. However, the precise function of the specific RAB GTPase targeted by LRRK2 signaling in the brain remains to be elucidated. Here, we identify RAB12 as a robust LRRK2 substrate in the mouse brains through phosphoproteomics profiling and solve the structure of RAB12-LRRK2 protein complex through Cryo-EM analysis. Mechanistically, RAB12 cooperates with LRRK2 to inhibit primary ciliogenesis and regulate centrosome homeostasis in astrocytes through enhancing the phosphorylation of RAB10 and recruiting Rab interacting lysosomal protein like 1 (RILPL1), while the functions of RAB12 require a direct interaction with LRRK2 and LRRK2 kinase activity. Furthermore, the ciliary deficits and centrosome alteration caused by the PD-linked LRRK2-G2019S mutation are prevented by the deletion of Rab12 in astrocytes. Thus, our study reveals a physiological function of the RAB12-LRRK2 complex in regulating ciliogenesis and centrosome homeostasis. The RAB12-LRRK2 structure offers a guidance in the therapeutic development of PD by targeting the RAB12-LRRK2 interaction.

neuroscience↗

bioRxiv2024

Prediction of pathological subthalamic nucleus beta burst occurrence in Parkinson's disease

The cortico-basal ganglia network in Parkinsons disease (PD) is characterized by the emergence of transient episodes of exaggerated beta frequency oscillatory synchrony known as bursts. Although it is well established that bursts of prolonged duration associate closely with motor impairments, the mechanisms leading to burst initiation remain poorly understood. Crucially, it is unclear whether there are features of basal ganglia activity which reliably predict burst onset. Current adaptive Deep Brain Stimulation (aDBS) approaches can only reactively deliver stimulation following burst detection and are unable to stimulate proactively to prevent burst onset. The discovery of predictive biomarkers could allow for such proactive stimulation, thereby offering potential for improvements in therapeutic efficacy. Here, using deep learning, we show that the timing of subthalamic nucleus (STN) beta bursts can be accurately predicted up to 60 ms prior to onset. Furthermore, we highlight that a dip in the beta amplitude - which is likely to be indicative of a phase reset of oscillatory populations occurring between 80-100 ms prior to burst onset - is a predictive biomarker for burst occurrence. These findings demonstrate proof-of-principle for the feasibility of beta burst prediction for DBS and provide insights into the mechanisms of burst initiation.

neuroscience↗

bioRxiv2024

Investigating the Triple Code Model in Numerical Cognition Using Stereotactic Electroencephalography

The ability to conceptualize numerical quantities is an essential human trait. According to the "Triple Code Model" (TCM) in numerical cognition, distinct neural substrates encode the processing of visual, auditory, and nonsymbolic numerical representations. While our contemporary understanding of human number cognition has benefited greatly from advances in clinical imaging, limited studies have investigated the intracranial electrophysiological correlates of number processing. In this study, 13 subjects undergoing stereotactic electroencephalography for epilepsy participated in a number recognition task. Drawing upon postulates of the TCM, we presented subjects with numerical stimuli varying in representation type (symbolic vs. non-symbolic) and mode of stimuli delivery (visual vs. auditory). Time-frequency spectrograms were dimensionally reduced with principal component analysis and passed into a linear support vector machine classification algorithm to identify regions associated with number perception compared to inter-trial periods. Across representation formats, the highest classification accuracy was observed in the bilateral parietal lobes. Auditory (spoken and beeps) and visual (Arabic) number formats preferentially engaged the superior temporal cortices and the frontoparietal regions, respectively. The left parietal cortex was found to have the highest classification for number dots. Notably, the putamen exhibited robust classification accuracies in response to numerical stimuli. Analyses of spectral feature maps revealed that non-gamma frequency below 30 Hz held greater than chance classification value and could be potentially used to characterize format specific number representations. Taken together, our findings obtained from intracranial recordings provide further support and expand on the TCM model for numerical cognition.

neuroscience↗

bioRxiv2024

Distal Protein-Protein Interactions Contribute to SARS-CoV-2 Main Protease Substrate Binding and Nirmatrelvir Resistance

SARS-CoV-2 main protease, Mpro, is responsible for the processing of the viral polyproteins into individual proteins, including the protease itself. Mpro is a key target of anti-COVID-19 therapeutics such as nirmatrelvir (the active component of Paxlovid). Resistance mutants identified clinically and in viral passage assays contain a combination of active site mutations (e.g. E166V, E166A, L167F), which reduce inhibitor binding and enzymatic activity, and non-active site mutations (e.g. P252L, T21I, L50F), which restore the fitness of viral replication. Although the mechanism of resistance for the active site mutations is apparent, the role of the non-active site mutations in fitness rescue remains elusive. In this study, we use the model system of a Mpro triple mutant (L50F/E166A/L167F) that confers not only nirmatrelvir drug resistance but also a similar fitness of replication compared to the wild-type both in vitro and in vivo. By comparing peptide and full-length Mpro protein as substrates, we demonstrate that the binding of Mpro substrate involves more than residues in the active site. In particular, L50F and other non-active site mutations can enhance the Mpro dimer-dimer interactions and help place the nsp5-6 substrate at the enzyme catalytic center. The structural and enzymatic activity data of Mpro L50F, L50F/E166A/L167F, and others underscore the importance of considering the whole substrate protein in studying Mpro and substrate interactions, and offers important insights into Mpro function, resistance development, and inhibitor design.

molecular biology↗