Biology subjects

Chen, C.

Publications and source records attributed to Chen, C..

At least 253 records · Page 14Linked to original sources

bioRxiv2024

Deciphering single-cell heterogeneity and cellular ecosystem dynamics during prostate cancer progression

Prostate cancer (PC) progresses from benign epithelium through pre-malignant lesions, localized tumors, metastatic dissemination, and castration-resistant stages, with some cases exhibiting phenotype plasticity under therapeutic pressure. However, high-resolution insights into how cell phenotypes evolve across successive stages of PC remain limited. Here, we present the Prostate Cancer Cell Atlas (PCCAT) by integrating [~]710,000 single cells from 197 human samples covering a spectrum of tumor stages. This comprehensive analysis dissects the cellular landscape and characterizes key cell types and molecular features that associate with PC progression and prognosis. In malignant cells, we highlight a distinctive profile denoted by high Major Histocompatibility Complex (MHC) expression, low Androgen Receptor (AR) activity, and enhanced stemness programs associated with enzalutamide resistance. Moreover, we reveal several cell states strongly correlated with PC progression and adverse prognosis, including lineage plasticity-like malignant cells (LPCs), neuroendocrine tumor cells, pericytes, and matrix cancer-associated fibroblasts (mCAFs). Furthermore, we uncover shared cell states that underpin the immune suppressive tumor microenvironment in advanced PC, including activated regulatory T cells, exhausted CD8+ T cells, and SPP1-expressing macrophages. Lastly, we pinpoint a spatial niche composed of mCAFs and SPP1-expressing macrophages localized near the tumor boundary in aggressive PC, which correlates with poor prognosis. Overall, our work provides a valuable resource and offers deeper insights into the diverse cell states, dynamics, and functional characteristics involved in PC progression at single-cell resolution.

cancer biology↗

bioRxiv2024

Multimodal single-cell analyses reveal molecular markers of neuronal senescence in human drug-resistant epilepsy

The histopathological neurons in the brain tissue of drug-resistant epilepsy exhibit aberrant cytoarchitecture and imbalanced synaptic circuit function. However, the gene expression changes of these neurons remain unknown, making it difficult for the diagnosis or to dissect the mechanism of drug-resistant epilepsy. By integrating whole-cell patch clamp recording and single-cell RNA sequencing approaches, we identified a transcriptionally distinct subset of cortical pyramidal neurons. These neurons highly expressed genes CDKN1A (P21), CCL2 and NFKBIA that associated with mTOR pathway, inflammatory response and cellular senescence. We confirmed the expression of senescent marker genes in a subpopulation of cortical pyramidal neurons with enlarged soma size in the brain tissue of drug-resistant epilepsy. We further revealed the expression of senescent cell markers P21, P53, COX2, {gamma}-H2AX, {beta}-Gal and reduction of nuclear integrity marker Lamin B1 in histopathological neurons in the brain tissue of drug-resistant epilepsy patients with different pathologies, but not in control brain tissue with no history of epilepsy. Additionally, chronic, but not acute, epileptic seizures induced senescent markers expression in cortical neurons in mouse models of drug-resistant epilepsy. These results provide important molecular markers for histopathological neurons and new insights into the pathophysiological mechanisms of drug-resistant epilepsy.

neuroscience↗

bioRxiv2024

Centella asiatica Tissue Culture-Derived Extracellular Vesicles: A Multifaceted Approach to Skincare Applications

BackgroundCentella asiatica has long been used as a medicinal herb in traditional Asian medicine. Its wound healing, skin improvement, and neuroprotective properties have been widely studied. Extracellular vesicles (EVs) are secreted by cells and contain bioactive components with therapeutic properties. ObjectivesThis study aims to characterize EVs isolated from C. asiatica tissue culture and investigate their therapeutic properties using in vitro assays and a UVB-induced damage mouse model. MethodsEVs were isolated from C. asiatica tissue culture and characterized by nanoparticle tracking analysis (NTA) and scanning electron microscopy (SEM). Cytotoxicity, anti-oxidation, anti-melanin, and anti-inflammation of the EVs were evaluated by MTT assay, tyrosinase assay and RT-qPCR in chemical or in vitro assays. A UVB-induced photodamage mouse model was established to assess the anti-inflammation effect of EVs in vivo. Gels with or without EVs were applied to the damaged site and skin appearance was observed daily and skin histopathology was analyzed on day 7 by H&E and immunohistochemical staining. ResultsC. asiatica EVs were found to contain high levels of polyphenols and mitigate hydrogen peroxide-induced intracellular ROS. The EVs were further able to reduce intracellular melanin content and tyrosinase activity, and exhibited anti-inflammatory effects by downregulating the expression of pro-inflammatory genes COX2 as well as nitric oxide production. In mice with UVB-induced skin damage, daily application of C. asiatica EV gel reduced skin epidermis thickness and inflammation compared to UVB-only or blank gel at seven days after UV irradiation. ConclusionsThe beneficial effects of C. asiatica EVs on skin quality warrant further studies as promising agents in skin care applications.

pharmacology and toxicology↗

bioRxiv2024

Human stem cell-derived GABAergic interneuron development reveals early emergence of subtype diversity followed by gradual electrochemical maturation

Medial ganglionic eminence-derived inhibitory GABAergic pallial interneurons (MGE-pINs) are essential regulators of cortical circuits; their dysfunction is associated with numerous neurological disorders. We developed human (h) MGE-pINs from pluripotent stem cells for the treatment of drug-resistant epilepsy. Here, we analyzed xenografted hMGE-pINs over the lifespan of host mice using single nuclei RNA sequencing. Comparative transcriptomics against endogenous human brain datasets revealed that 97% of grafted cells developed into somatostatin (SST) and parvalbumin (PVALB) subtypes, including populations that exhibit selective vulnerability in Alzheimers disease. Transplanted hMGE-pINs demonstrated rapid emergence of subclass features, progressing through distinct transcriptional states sequentially involving neuronal migration, synapse organization, and membrane maturation. We present molecular, electrophysiological, and morphological data that collectively confirm the derivation of diverse bona-fide human SST and PVALB subtypes, providing a high-fidelity model to study human MGE-pIN development and functional maturation as well as a compositional atlas for regenerative cell therapy applications.

neuroscience↗

bioRxiv2024

UniSPAC: A Unified Segmentation Framework for Proofreading and Annotation in Connectomics

Reconstructing dense neuronal connections from volume electron microscopy (vEM) images is a critical challenge in neuroscience, driving the development of various automatic neuron segmentation methods1. Although current state-of-the-art automated segmentation methods can achieve high segmentation accuracy, they still require substantial manual proofreading and rely heavily on labeled datasets, which are often scarce, particularly for non-model organisms. Here, we introduce a Unified Segmentation framework for Proofreading and Annotation in Connectomics (UniSPAC) by providing the interactive segmentation model in 2D-level and the neuron tracing model in 3D-level. UniSPAC-2D allows users to correct its segmentation errors through point-based prompts, combining segmentation and proofreading in a single framework. UniSPAC-3D automatically traces neurons segmented by UniSPAC-2D across image slices, significantly reducing human involvement. Furthermore, UniSPAC-2D and UniSPAC-3D models can facilitate the semi-automatic generation of labeled data for new species, eliminating the need for external annotation tools. The fresh annotated data generated during proofreading in turn optimizes the interactive model through an online learning strategy, reducing the labeling effort for novel species over time. UniSPAC outperforms the start-of-the-art Segment Anything Model (SAM) in Drosophila segmentation, achieving 47x higher efficiency, and surpasses ACRLSD in cross-species segmentation on zebra finch data.

neuroscience↗

bioRxiv2024

Evaluation of Brain Age as a Specific Marker of Brain Health

Brain age is widely regarded as a powerful marker of general brain health. Brain age models are typically trained on large datasets to predict chronological age, which may offer advantages in predicting specific health outcomes, much like the success of finetuning large language models for specific applications. However, it is also well-accepted that machine learning models trained to directly predict specific outcomes (i.e., direct models) often outperform those trained on surrogate objectives. Therefore, despite their much larger training data, it is unclear whether brain age models outperform direct models in predicting specific brain health outcomes. Here, we compare large-scale brain age models (pretrained on 53,542 participants) and direct models for predicting specific health outcomes related to Alzheimers Disease (AD) dementia. Using anatomical T1 scans from three continents (N = 1,848), we find that summarizing brain age with a single scalar (i.e., brain age gap) led to poor prediction performance. Using higher-dimensional intermediate representations of brain age models led to better prediction, but was still worse than direct models without finetuning. Using intermediate representations of finetuned brain age models was necessary to achieve similar performance as direct models. Overall, our results do not discount brain age as a useful marker of general brain health, but suggest that using chronological age as a pretraining target might be suboptimal for predicting specific health outcomes.

neuroscience↗

bioRxiv2024

Impaired complement regulation drives chronic lung allograft dysfunction after lung transplantation.

A greater understanding of chronic lung allograft dysfunction (CLAD) pathobiology, the primary cause of mortality after lung transplantation, is needed to improve outcomes. The complement system links innate to adaptive immune responses and is activated early post-lung transplantation to form the C3 convertase, a critical enzyme that cleaves the central complement component C3. We hypothesized that LTx recipients with a genetic predisposition to enhanced complement activation have worse CLAD-free survival mediated through increased adaptive alloimmunity. We interrogated a known functional C3 polymorphism (C3R102G) that increases complement activation through impaired C3 convertase inactivation in two independent LTx recipient cohorts. C3R102G, identified in at least one out of three LTx recipients, was associated with worse CLAD-free survival, particularly in the subset of recipients who developed donor-specific antibodies (DSA). In a mouse orthotopic lung transplantation model, impaired recipient complement regulation resulted in more severe obstructive airway lesions when compared to wildtype controls, despite only moderate differences in graft-infiltrating effector T cells. Impaired complement regulation promoted the intragraft accumulation of memory B cells and antibody-secreting cells, resulting in increased DSA levels. In summary, genetic predisposition to complement activation is associated with B cell activation and worse CLAD-free survival. BRIEF SUMMARYLung transplant recipients genetically predisposed to impaired complement regulation demonstrate worse chronic rejection-free survival. This phenotype is associated with intragraft B cell-activation and donor-specific antibodies.

immunology↗

bioRxiv2024

CLUEY enables knowledge-guided clustering and cell type detection from single-cell omics data

Clustering is a fundamental task in single-cell omics data analysis and can significantly impact downstream analyses and biological interpretations. The standard approach involves grouping cells based on their gene expression profiles, followed by annotating each cluster to a cell type using marker genes. However, the number of cell types detected by different clustering methods can vary substantially due to several factors, including the dimension reduction method used and the choice of parameters of the chosen clustering algorithm. These discrepancies can lead to subjective interpretations in downstream analyses, particularly in manual cell type annotation. To address these challenges, we propose CLUEY, a knowledge-guided framework for cell type detection and clustering of single-cell omics data. CLUEY integrates prior biological knowledge into the clustering process, providing guidance on the optimal number of clusters and enhancing the interpretability of results. We apply CLUEY to both unimodal (e.g. scRNA-seq, scATAC-seq) and multimodal datasets (e.g. CITE-seq, SHARE-seq) and demonstrate its effectiveness in providing biologically meaningful clustering outcomes. These results highlight CLUEY on providing the much-needed guidance in clustering analyses of single-cell omics data. CLUEY package is available from https://github.com/SydneyBioX/CLUEY.

bioinformatics↗

bioRxiv2024

Distinct Binding Modes of Inter-Spike Cross-Linking Suggest a Supplementary Mechanism for SARS-CoV-2 Antibody Neutralization

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its Omicron subvariants drastically amplifies transmissibility, infectivity, and immune escape, mainly due to their resistance to most neutralizing antibodies. Thus, exploring the mechanisms underlying antibody evasion is crucial. Although the full-length native form of antibody, immunoglobulin G (IgG), offers valuable insights into the neutralization, structural investigations primarily focus on the fragment of antigen-binding (Fab). Here, we employ single-particle cryo-electron microscopy (cryo-EM) to characterize a W328-6H2 antibody, in its native IgG form complexed with severe acute respiratory syndrome (SARS), severe acute respiratory syndrome coronavirus 2 wild-type (WT) and Omicron variant BA.1 spike protein (S). Three high-resolution structures reveal that the full-length IgG forms a centered head-to-head dimer of trimer when bound fully stoichiometrically with both SARS and WT S, while adopting a distinct offset configuration with Omicron BA.1 S. Combined with functional assays, our results suggest that, beyond the binding affinity between the RBD epitope and Fab, the higher-order architectures of S trimer and full-length IgG play an additional role in neutralization, elucidate a potential molecular basis of Omicron immune escape, and expand our understanding on antibody evasion mechanisms.

immunology↗

bioRxiv2024

Targeting IL-1/IRAK1/4 signaling in Acute Myeloid Leukemia Stem Cells Following Treatment and Relapse

Therapies for acute myeloid leukemia (AML) face formidable challenges due to relapse, often driven by leukemia stem cells (LSCs). Strategies targeting LSCs hold promise for enhancing outcomes, yet paired comparisons of functionally defined LSCs at diagnosis and relapse remain underexplored. We present transcriptome analyses of functionally defined LSC populations at diagnosis and relapse, revealing significant alterations in IL-1 signaling. Interleukin-1 receptor type I (IL1R1) and interleukin-1 receptor accessory protein (IL1RAP) were notably upregulated in leukemia stem and progenitor cells at both diagnosis and relapse. Knockdown of IL1R1 and IL1RAP reduced the clonogenicity and/or engraftment of primary human AML cells. In leukemic MLL-AF9 mice, Il1r1 knockout reduced LSC frequency and extended survival. To target IL-1 signaling at both diagnosis and relapse, we developed UR241-2, a novel interleukin-1 receptor-associated kinase 1 and 4 (IRAK1/4) inhibitor. UR241-2 robustly suppressed IL-1/IRAK1/4 signaling, including NF-{kappa}B activation and phosphorylation of p65 and p38, following IL-1 stimulation. UR241-2 selectively inhibited LSC clonogenicity in primary human AML cells at both diagnosis and relapse, while sparing normal hematopoietic stem and progenitor cells. It also reduced AML engraftment in leukemic mice. Our findings highlight the therapeutic potential of UR241-2 in targeting IL-1/IRAK1/4 signaling to eradicate LSCs and improve AML outcomes.

cancer biology↗

bioRxiv2024

GCTransNet: 3D mitochondrial instance segmentation based on Global Context Vision Transformers

Mitochondria are double membrane-bound organelles essential for generating energy in eukaryotic cells. Mitochondria can be readily visualized in 3D using Volume Electron Microscopy (vEM), and accurate image segmentation is vital for quantitative analysis of mitochondrial morphology and function. To address the challenge of segmenting small mitochondrial compartments in vEM images, we propose an automated mitochondrial segmentation method called GCTransNet. This method employs grayscale migration technology to preprocess images, effectively reducing intensity distribution differences across EM images. By utilizing 3D Global Context Vision Transformers (GC-ViT) combined with global context self-attention modules and local self-attention modules, GCTransNet precisely models long-range and short-range spatial interactions. The long-range interactions enable the model to capture the global structural relationships within the mitochondrial segmentation network, while the short-range interactions refine local details and boundaries. In our approach, the encoder of the 3D U-Net network, a classical multi-scale learning architecture that retains high-resolution features through skip connections and combines multi-scale features for precise segmentation, is replaced by a 3D GC-ViT. The GC-ViT leverages shifted window-based self-attention, capturing long-range dependencies and offering improved segmentation accuracy compared to traditional U-Net encoders. In the MitoEM mitochondrial segmentation challenge, GCTransNet achieved state-of-the-art results, demonstrating its superiority in automated mitochondrial segmentation. The code and its documentation are publicly available at https://github.com/GanLab123/GCTransNet.

cell biology↗

bioRxiv2024

Reproducible processing of TCGA regulatory networks

BackgroundTechnological advances in sequencing and computation have allowed deep exploration of the molecular basis of diseases. Biological networks have proven to be a useful framework for interrogating omics data and modeling regulatory gene and protein interactions. Large collaborative projects, such as The Cancer Genome Atlas (TCGA), have provided a rich resource for building and validating new computational methods resulting in a plethora of open-source software for downloading, pre-processing, and analyzing those data. However, for an end-to-end analysis of regulatory networks a coherent and reusable workflow is essential to integrate all relevant packages into a robust pipeline. FindingsWe developed tcga-data-nf, a Nextflow workflow that allows users to reproducibly infer regulatory networks from the thousands of samples in TCGA using a single command. The workflow can be divided into three main steps: multi-omics data, such as RNA-seq and methylation, are downloaded, preprocessed, and lastly used to infer regulatory network models with the netZoo software tools. The workflow is powered by the NetworkDataCompanion R package, a standalone collection of functions for managing, mapping, and filtering TCGA data. Here we show how the pipeline can be used to study the differences between colon cancer subtypes that could be explained by epigenetic mechanisms. Lastly, we provide pre-generated networks for the 10 most common cancer types that can be readily accessed. Conclusionstcga-data-nf is a complete yet flexible and extensible framework that enables the reproducible inference and analysis of cancer regulatory networks, bridging a gap in the current universe of software tools.

bioinformatics↗

bioRxiv2024

Using phage display for rational engineering of a higher affinity humanized 3-phosphohistidine-specific antibody

Histidine phosphorylation (pHis) is a non-canonical post-translational modification (PTM) that is historically understudied due to a lack of robust reagents that are required for its investigation, such as high affinity pHis-specific antibodies. Engineering pHis-specific antibodies is very challenging due to the labile nature of the phosphoramidate (P-N) bond and the stringent requirements for selective recognition of the two isoforms, 1-phosphohistidine (1-pHis) and 3-phosphohistidine (3-pHis). Here, we present a strategy for in vitro engineering of antibodies for detection of native 3-pHis targets. Specifically, we humanized the rabbit SC44-8 anti-3-pTza (a stable 3-pHis mimetic) mAb into a scaffold (herein referred to as hSC44) that was suitable for phage display. We then constructed six unique Fab phage-displayed libraries using the hSC44 scaffold and selected high affinity 3-pHis binders. Our selection strategy was carefully designed to enrich antibodies that bound 3-pHis with high affinity and had specificity for 3-pHis versus 3-pTza. hSC44.20N32FL, the best engineered antibody, has an [~]10-fold higher affinity for 3-pHis than the parental hSC44. Eleven new Fab structures, including the first reported antibody-pHis peptide structures were solved by X-ray crystallography. Structural and quantum mechanical calculations provided molecular insights into 3-pHis and 3-pTza discrimination by different hSC44 variants and their affinity increase obtained through in vitro engineering. Furthermore, we demonstrate the utility of these newly developed high-affinity 3-pHis-specific antibodies for recognition of pHis proteins in mammalian cells by immunoblotting and immunofluorescence staining. Overall, our work describes a general method for engineering PTM-specific antibodies and provides a set of novel antibodies for further investigations of the role of 3-pHis in cell biology. Significance StatementHistidine phosphorylation is an elusive PTM whose role in mammalian cell biology is largely unknown due to the lack of robust tools and methods for its analysis. Here we report the development of antibodies with unprecedented affinity and specificity towards 3-pHis and present the first crystal structures of a pHis peptide in complex with an antibody. Finally, we show how these antibodies can be used in standard molecular biology workflows to investigate pHis-dependent biology.

synthetic biology↗

bioRxiv2024

Quantifying Functional Conservation of Human and Mouse Regulatory Elements via FUNCODE

Evolutionary conservation is crucial for understanding genome functions and lays the foundation for using animal models in studying human diseases. However, conventional conservation scores based on DNA sequence evolution do not capture the dynamic biochemical activities of DNA elements, termed functional conservation. Quantifying functional conservation has been limited by the availability of functional genomic data matched across species. To address this, we developed FUNCODE, a framework for characterizing functional conservation through in silico sample matching. Applying FUNCODE to 2,595 uniformly processed datasets from the Encyclopedia of DNA Elements (ENCODE), we generated genome-wide FUNCODE scores for human and mouse regulatory elements, identifying 3.3 million functionally conserved human-mouse element pairs. We demonstrate FUNCODEs diverse applications, including annotating 78,501 novel regulatory elements, transferring 37,968 high-resolution human ENCODE Hi-C loops in immune lineages to mice, identifying conserved functional signals for disease modeling, and enhancing cross-species integration of single-cell omics data.

genomics↗

bioRxiv2024

Orexinergic projections to substantia innominata mediate arousal and analgesia

Understanding neural circuits involved in anesthesia is crucial for improving its safety and efficacy. Hypothalamic orexin neurons (LHAOX), projecting broadly, are essential in regulating arousal and pain. However, the precise targets remain unclear. Here we investigated the orexin projections to the substantia innominata (SI). Combining optogenetics, fiber photometry, and EEG/EMG allowed us to manipulate orexin activities, while simultaneously recording local ligand release and global cortical activities during anesthesia. Brain slice electrophysiology revealed the synaptic connections in the SI, while RNAscope was employed to examine the distribution of orexin receptors and downstream neuronal types. Presynaptic vesicles were identified in the orexin terminals in the SI, where 49.16% of cells expressed OX2R and 6.8% expressed OX1R. Orexin release in the SI was reversibly suppressed by isoflurane. Optogenetic activation of the LHAOX[->]SI circuit significantly increased orexin release and promoted arousal from various anesthesia stages, including reanimation during 0.75% isoflurane (p < 0.0001), prolongation of 3% isoflurane induction (p = 0.0033), and acceleration of emergence from 2% isoflurane (p < 0.0001). Furthermore, activating this circuit induced analgesia to both thermal (p = 0.0074) and inflammatory (p = 0.0127) pain. Patch-clamp recordings revealed that optogenetic activation of orexin terminals in the SI elicited excitatory postsynaptic currents, which were blocked by the OX2R antagonist. SI contains more GABAergic (28.17%) and glutamatergic (11.96%) neurons than cholinergic neurons (4.13%), all of which expressed OX2R. Thus, LHAOX neurons innervate SI neurons to regulate both arousal and pain predominantly through OX2R.

neuroscience↗

bioRxiv2024

Proteomic associations with cognitive variability as measured by the Wisconsin Card Sorting Test in a healthy Thai population: A machine learning approach

Cognitive function is the term for the higher-order mental processes in the brain that gather and process information, and it mirrors brain activity. Cognitive function in adults exhibits variability as a result of genetic and environmental components such as gender, age[1], and lifestyle factors to name a few. Interindividual variability in cognitive trajectories has been observed in community-dwelling older adults across different cognitive domains. Inter-individual variations in cognitive response to identical physical exercise are also evident. This study aimed to explore the association between serum protein expression profiles and one measure of cognitive variability, as measured by the Wisconsin Card Sorting Test (WCST), in a healthy Thai population using a machine learning approach. This study included 199 healthy Thai subjects, ranging in age from 20 to 70 years. Cognitive performance was measured by the WCST, and the WCST % Errors was used to define the lower and higher cognitive ability groups. Serum protein expression profiles were studied by the label-free proteomics method. The Linear Model for Microarray Data (LIMMA) approach in R was utilized to assess differentially expressed proteins (DEPs) between groups; subsequently bioinformatic analysis was performed for the functional enrichment and interaction network analysis of DEPs. A random forest model was built to classify subjects from the lower and higher cognitive ability groups. Cross-validation was used for model performance evaluation. The results showed that, there were 213 DEPs identified between the poor and higher cognition groups, with 155 DEPs being upregulated in the poor cognition group. Those DEPs were significantly enriched in the IL-17 signaling pathway. Furthermore, the analysis of protein-protein interaction (PPI) network revealed that most of the selected DEPs were linked to neuroinflammation-related cognitive impairment. The random forest model achieved a test classification accuracy of 81.5%. The models sensitivity (true positive rate) was estimated to be 65%, and the specificity (true negative rate) was 85.9%. The AUC (0.79) indicates good binary classification performance. The results suggested that a measure of poor WCST performance in healthy Thai subjects might be attributed to higher levels of neuroinflammation.

neuroscience↗

bioRxiv2024

Environmental cues influence timing and location ofconstruction activity in a beaver damming complex

Beavers are famous for building extensive damming complexes1, which can extend over kilometer-scale distances2 and persist for centuries3. Dams have major impacts on their surrounding environment, including effects on the hydrology, geomorphology, and ecosystems of the area4. Current understanding of how the environment in turn affects beavers building activity centers on the single auditory cue of running water, based primarily on captive studies14,15. Observational challenges have limited a more detailed picture of the full feedback loop between beavers and their environment. Here we describe the detailed progress of new construction by 20 beaver colonies in northwest Montana, through field studies using drones to obtain surveys with spatial and temporal resolution each three orders of magnitude finer than typically reported5-13. We show that both the timing and location of beaver construction activity are influenced by environmental factors. Initiation of trail clearing, a stage preceding dam building, was associated with a narrow range of stream flow rates. Beavers preferentially built in locations associated with preexisting canals. These results emphasize the importance of non-dam elements in the beavers construction and its coordination across the colony, and point to environmental feedback processes that may span across years and unrelated colonies.

animal behavior and cognition↗

bioRxiv2024

Elevated Ubiquitin Phosphorylation by PINK1 Contributes to Proteasomal Impairment and Promotes Neurodegeneration

Ubiquitin (Ub), a central regulator of protein turnover, can be phosphorylated by PINK1 (PTEN-induced putative kinase 1) to generate S65-phosphorylated ubiquitin (pUb). Elevated pUb levels have been observed in aged human brains and in Parkinsons disease, but the mechanistic link between pUb elevation and neurodegeneration remains unclear. Here, we demonstrate that pUb elevation is a common feature under neurodegenerative conditions, including Alzheimers disease, aging, and ischemic injury. We show that impaired proteasomal activity leads to the accumulation of sPINK1, the cytosolic form of PINK1 that is normally proteasome-degraded rapidly. This accumulation increases ubiquitin phosphorylation, which then inhibits ubiquitin-dependent proteasomal activity by interfering with both ubiquitin chain elongation and proteasome-substrate interactions. Specific expression of sPINK1 in mouse hippocampal neurons induced progressive pUb accumulation, accompanied by protein aggregation, proteostasis disruption, neuronal injury, neuroinflammation, and cognitive decline. Conversely, pink1 knockout mitigated protein aggregation in both mouse brains and HEK293 cells. Furthermore, the detrimental effects of sPINK1 could be counteracted by co-expressing Ub/S65A phospho-null mutant but exacerbated by over-expressing Ub/S65E phospho-mimic mutant. Together, these findings reveal that pUb elevation, triggered by reduced proteasomal activity, inhibits proteasomal activity and forms a feedforward loop that drives progressive neurodegeneration.

biochemistry↗