Biology subjects

Guo, X.

Publications and source records attributed to Guo, X..

At least 109 records · Page 6Linked to original sources

bioRxiv2025

Neural entrainment to pitch changes of auditory targets in noise

Neural entrainment to certain acoustic features can predict speech-in-noise perception, but these features are difficult to separate. We measured neural responses to both natural speech-in-noise and stimuli (auditory figure-ground) that simulate speech-in-noise without any acoustic or linguistic confound such as stress contour and semantics. The figure-ground stimulus is formed by multiple temporally coherent pure-tone components embedded in a random tone cloud. Previous work has shown that discrimination of dynamic figure-ground based on the fundamental frequency (F0) of natural speech predicts speech-in-noise recognition independent of hearing and age. In this study, we compared the brain substrate for the figure-ground analysis based on the F0 contour and a statistically similar 1/f contour with speech-in-noise. We used the temporal response function to predict the electroencephalography responses to the frequency trajectories of the auditory targets. We demonstrate that the brain significantly tracked the pitch changes in both AFG conditions (F0 and 1/F tracking) and a sentence-in-noise condition (F0 tracking) at similar latencies, but at similar magnitudes only when tracking the F0 contour. The pitch-tracking accuracy was consistently high across the delta and theta bands for the AFG condition but not for speech. Sensor-space analysis revealed that speech-in-noise performance correlated with the positive peak amplitude of the F0 figure-ground at 100 ms. Source-space analysis revealed bilateral temporal lobe and hippocampal generators, and strong tracking in the superior parietal lobe for auditory figures and natural speech. In conclusion, our findings demonstrate that the human brain reliably tracks the F0 trajectory of both speech and a non-linguistic figure in noise, with speech tracking showing reduced accuracy in the theta band compared to figure-ground tracking. Despite the difference in prediction accuracy, we reveal striking similarities in neural entrainment patterns and source locations between the two paradigms. These results suggest that neural entrainment engages high-level cortical mechanisms independent of linguistic content. Furthermore, we show that TRF peak amplitude serves as a potential biomarker for speech-in-noise ability, highlighting possible clinical applications.

neuroscience↗

bioRxiv2025

A computational workflow for assessing drug effects on temporal signaling dynamics reveals robustness in stimulus-specific NFκB signaling

Single-cell studies of signal transduction have revealed complex temporal dynamics that determine downstream biological function. For example, the stimulus-specific dynamics of the transcription factor NF{kappa}B specify stimulus-specific gene expression programs, and loss of specificity leads to disease. Thus, it is intriguing to consider drugs that may restore signaling specificity in disease contexts, or reduce activity but maintain signaling specificity to avoid unwanted side effects. However, while steady-state dose-response relationships have been the focus of pharmacological studies, there are no established methods for quantifying drug impact on stimulus-response signaling dynamics. Here we evaluated how drug treatments affect the stimulus-specificity of NF{kappa}B activation dynamics and its ability to accurately code ligand identity and dose. Specifically, we simulated the dynamic NF{kappa}B trajectories in response to 15 stimuli representing various immune threats under treatment of 10 representative drugs across 20 dosage levels. To quantify the effects on coding capacity, we introduced a Stimulus Response Specificity (SRS) score and a stimulus confusion score. We constructed stimulus confusion maps by employing epsilon network clustering in the trajectory space and in various dimensionally reduced spaces: canonical polyadic decomposition (CPD), functional principal component analysis (fPCA), and NF{kappa}B signaling codons (i.e. established, informative dynamic features). Our results indicated that the SRS score and the stimulus confusion map based on signaling codons are best-suited to quantify stimulus-specific NF{kappa}B dynamics confusion under pharmacological perturbations. Using these tools we found that temporal coding capacity of the NF{kappa}B signaling network is generally robust to a variety of pharmacological perturbations, thereby enabling the targeting of stimulus-specific dynamics without causing broad side-effects.

pharmacology and toxicology↗

bioRxiv2025

Macrophages maintain signaling fidelity in response to ligand mixtures

As immune sentinel cells, macrophages are required to respond specifically to diverse immune threats and initiate appropriate immune responses. This stimulus-response specificity (SRS) is in part encoded in the signaling dynamics of the NF{kappa}B transcription factor. While experimental stimulus-response studies have typically focused on single defined ligands, in physiological contexts cells are exposed to multi-ligand mixtures. It remains unclear how macrophages combine multi-ligand information and whether they are able to maintain SRS in such complex exposure conditions. Here, we leveraged an established mathematical model that captures the heterogeneous single-cell NF{kappa}B responses of macrophage populations to extend experimental studies with systematic simulations of complex mixtures containing up to five ligands. Live-cell microscopy experiments for some conditions validated model predictions but revealed a discrepancy when TLR3 and TLR9 are stimulated. Refining the model suggested that the observed but unexpected ligand antagonism arises from a limited capacity for endosomal transport which is required for responses to CpG and pIC. With the updated model, we systematically analyzed SRS across all combinatorial-ligand conditions and employed three ways of quantifying SRS involving trajectory decomposition into informative trajectory features or machine learning. Our findings show that macrophages most effectively distinguish single-ligand stimuli, and distinguishability declines as more ligands are combined. However, even in complex combinatorial conditions, macrophages still maintain statistically significant distinguishability. These results indicate a robustness of innate immune response specificity: even in the context of complex exposure conditions, the NF{kappa}B temporal signaling code of macrophages can still classify immune threats to direct an appropriate response. Significance ([≤]120)Macrophages sense diverse pathogens within complex environments and respond appropriately. Experimental studies have found that the NF{kappa}B pathway responds with stimulus-specific dynamics when macrophages are exposed to single ligand stimuli. However, it remains unclear complex contexts might erode this stimulus-specificity. Here we systematically studies NF{kappa}B responses using a mathematical model that provides simulations of the heterogeneous population of single cell responses. We show that although the model is parameterized to single ligand data it can predict the responses to multi-ligand mixtures. Indeed, model validation uncovered signaling antagonism between two ligands and the underlying mechanism. Importantly, we found that NF{kappa}B signaling dynamics distinguish ligands within multi-ligand mixtures indicating a robustness of the NF{kappa}B temporal code that was not previously appreciated.

systems biology↗

bioRxiv2025

circASbase：A Comprehensive Database of Alternative Splicing Events in circRNAs

Despite extensive studies highlight the critical roles of alternative splicing in generating mature circRNA isoforms and enhancing their function diversity, a significant gap remains in the availability of dedicated databases for circRNA alternative splicing events. To bridge this gap, we developed circASbase, a pioneering and comprehensive database that catalogues 452,129 alternative splicing events in 884,047 full-length circRNAs from 581 samples across 13 species, and provides rich annotations to facilitate understanding the splicing regulation of circRNAs. Our findings reveal substantial differences between circRNAs and linear transcripts regarding the distribution and occurrence of alternative splicing events, highlighting the unique regulatory landscape of circRNAs. These unique splicing events result in functional differences of circRNAs by affecting IRES sites, m6A sites, ORFs, protein features, miRNA targets, and more. In summary, circASbase not only covers the urgent need of the research community for data repositories, but also represents a significant advancement in our understanding of circRNA biology. With its user-friendly interfaces and web-based visualization tools, circASbase is poised to become an indispensable resource for researchers exploring the regulatory mechanisms and functional roles of alternative splicing events in circRNAs. This database will continuously drive new insights and discoveries in the field, setting the stage for further advancements in circRNA research. circASbase is available at http://reprod.njmu.edu.cn/cgi-bin/circASbase/

bioinformatics↗

bioRxiv2025

Reconstructing Noisy Gene Regulation Dynamics UsingExtrinsic-Noise-Driven Neural Stochastic Differential Equations

Proper regulation of cell signaling and gene expression is crucial for maintaining cellular function, development, and adaptation to environmental changes. Reaction dynamics in cell populations is often noisy because of (i) inherent stochasticity of intracellular biochemical reactions ("intrinsic noise") and (ii) heterogeneity of cellular states across different cells that are influenced by external factors ("extrinsic noise"). In this work, we introduce an extrinsic-noise-driven neural stochastic differential equation (END-nSDE) framework that utilizes the Wasserstein distance to accurately reconstruct SDEs from trajectory data from a heterogeneous population of cells (extrinsic noise). We demonstrate the effectiveness of our approach using both simulated and experimental data from three different systems in cell biology: (i) circadian rhythms, (ii) RPA-DNA binding dynamics, and (iii) NF{kappa}B signaling process. Our END-nSDE reconstruction method can model how cellular heterogeneity (extrinsic noise) modulates reaction dynamics in the presence of intrinsic noise. It also outperforms existing time-series analysis methods such as recurrent neural networks (RNNs) and long short-term memory networks (LSTMs). By inferring cellular heterogeneities from data, our END-nSDE reconstruction method can reproduce noisy dynamics observed in experiments. In summary, the reconstruction method we propose offers a useful surrogate modeling approach for complex biophysical processes, where high-fidelity mechanistic models may be impractical.

molecular biology↗

bioRxiv2025

CHIEF: An Attention-based Ensemble Learning Framework for Functional Protein Design

Protein de novo design is a longstanding challenge in biological field due to the multifaceted physicochemical properties and complexities of proteins. Several deep learning models have emerged for protein de novo design. However, each model exhibits distinct strengths and limitations because of the differences in training strategies or neural network architectures. Therefore, improving the effectiveness of protein design, particularly for functional protein, requires further improvement. To tackle this challenge, we developed CHIEF (Chimera Ensemble Inverse Folding), an attention-based ensemble framework that integrates five pretrained base models (ProteinMPNN-vanilla, ProteinMPNN-soluble, ESM-IF, Frame2seq and PiFold) to leverage their complementary strengths and mitigate limitations. Compared with the base models, CHIEF significantly improved the sequence recovery rate by 16.6-28.0%, while reducing the prediction perplexity by 22.7-34.6%. CHIEF also exhibited superior protein-designing capacity in variable sequence- and structure-based metrics and in large and complex proteins. CHIEF dynamically captured the semantic information from base models in a context-dependent manner and was minimally affected by the ablation of each base model. More importantly, CHIEF demonstrates real-world applicability to design functional malate dehydrogenase (MDH), achieving a 100% success rate. In summary, our study develops an ensemble deep learning model that improves the efficacy of protein sequence design, and will be a valuable platform for protein engineering and drug development.

bioengineering↗

bioRxiv2025

Multimodal brain cell atlas across the adult macaque lifespan

High-throughput single-cell omics of non-human primate tissues present a remarkable opportunity to study primate brain aging. Here, we introduce a transcriptomic and chromatin accessibility landscape of 1,985,317 cells from eight brain regions of 13 cynomolgus female monkeys spanning adult lifespan including exceptionally old individuals up to 29-years old. This dataset uncovers dynamic molecular changes in critical brain functions such as synaptic communication and axon myelination, exhibiting a high degree of cell type and brain region specificity. We identify the multicellular networks of the pons and medulla as a previously unrecognized hotspot for aging. Furthermore, comparative analyses with human neurodegeneration datasets highlight both shared and distinct mechanisms contributing to aging and disease. In addition, we uncover transcription factors implicated in monkey brain aging and pinpoint aging-regulated loci linked to longevity and neurodegeneration. This spatiotemporal atlas will advance our understanding of primate brain aging and its broader implications for health and disease.

neuroscience↗

bioRxiv2025

The first chromosomal-level genome assembly of an allotetraploid oyster

Tetraploid oysters are used to cross with diploids to produce triploid oysters that have become an important part of the oyster aquaculture industry worldwide. Although most tetraploid oysters are artificially induced autotetraploids, allotetraploids can be produced between closely related species, providing new opportunities for polyploid breeding and studying genome interactions. Using PacBio sequencing, Illumina sequencing, and high-throughput chromosome conformation capture scaffolding, we assembled the first high-quality genome assembly of an artificially induced allotetraploid between the Pacific oyster Crassostrea gigas and Portuguese oyster Crassostrea angulata. The assembled genome is 1.23 Gb, with a contig N50 of 2.56 Mb and a scaffold N50 of 57.22 Mb, and anchored to 20 chromosomes. The assembly contains 58,330 protein-coding genes, 98.34% of which are functionally annotated. The heterozygosity and the ratio of repetitive sequences is 5.50% and 46.43%, respectively. This chromosomal-level genome assembly of an allotetraploid oyster provides a valuable genetic resource for studying genome biology of molluscs and for advanced breeding of polyploids that are critical for the oyster aquaculture industry.

genomics↗

bioRxiv2025

Cholesterol efflux protein, ABCA1, supports anti-cancer functions of myeloid immune cells

Although immune therapy has seen significant advances, the majority of breast and other solid tumors do not respond or quickly develop de novo resistance. One factor driving resistance is highly immune suppressive myeloid cells (MCs) such as macrophages. Previous work has established clinical links between cholesterol and cancer outcome, and that MC function can be regulated through disruption in cholesterol metabolism. Thus, we screened for proteins that were expressed in MCs, involved in cholesterol homeostasis and whose expression was associated with survival; we identify the cholesterol efflux protein ABCA1. Preclinical studies revealed that ABCA1 activity resulted in increased anti-cancer functions of macrophages: enhanced tumor infiltration, decreased angiogenic potential, reduced efferocytosis, and improved support of CD8+ T cell activity. Mechanistically, different AKT isoforms are involved, through both PI3K dependent and independent mechanisms. Assessment of human blood and breast tumors revealed correlations between ABCA1 in macrophages and angiogenic potential, VEGFA, and CD8 T cell abundance and activity, highlighting the clinical relevance of our findings. The culmination of the effects of ABCA1 on MC function were demonstrated through increased tumor growth and metastasis in mice with MC specific knockout of ABCA1. Therefore, modulating ABCA1 activity within MCs may represent a novel approach to immune therapy.

cancer biology↗

bioRxiv2025

DNA G-quadruplex Profiling Reveals Functional and Mechanistic Role of G-quadruplexes in Skeletal Muscle Stem Cells

DNA G-quadruplexes (G4s) are non-canonical secondary structures formed in guanine-rich DNA sequences and play important roles in modulating biological processes through a variety of gene regulatory mechanisms. Emerging G4 profiling permits global mapping of endogenous G4 formation. Here in this study, we map the G4 landscapes in adult skeletal muscle stem cells (MuSCs) which are essential for injury induced muscle regeneration. Throughout the myogenic lineage progression of MuSCs from quiescent to activated and further differentiated cells, we uncover dynamic endogenous G4 formation with a pronounced G4 induction when MuSCs become activated and proliferating. We further demonstrate that the G4 induction promotes MuSC activation thus the regeneration process. Mechanistically, we found that promoter associated G4s regulate gene transcription through facilitating chromatin looping. Furthermore, we found that G4 sites are enriched for transcription factor (TF) binding events in activated MuSCs; MAX binds to G4 structures to synergistically facilitate chromatin looping and gene transcription thus promoting MuSC activation and regeneration. The above uncovered global regulatory functions/mechanisms are further dissected on the paradigm of Ccne1 promoter, demonstrating Ccne1 is a bona fide G4/MAX regulatory target in activated MuSCs. Altogether, our findings for the first time demonstrate the prevalent and dynamic formation of G4s in adult MuSCs and the mechanistic role of G4s in modulating gene expression and MuSC activation/proliferation.

molecular biology↗

bioRxiv2025

IQI: Individualized Quantitative trait loci (QTL) Index

There is currently no standard efficient method to precisely detecting and characterizing the difference of quantitative trait loci (QTL) effect among groups and/or populations at individual level. The main reason is that the traditional global regression based QTL analyses can only derive an overall effect of genetic variants on the quantitative trait in a specific group. It is a big challenge to obtain the individual-level QTL effect, which is necessary for accurate testing and precise characterization. Here, we first proposed an individualized QTL index (IQI) that represents the degree of regulation between genetic variants and the trait for each individual. Based on IQI, Differential QTL (DQ) analysis framework was presented to accurately detect the case-control differences in more detail. In addition, IQI can be a new feature/biomarker applied to various accurate bioinformatics analysis, such as differential analysis, disease prediction, and survival analysis. Overall, IQI affords a new perspective on pathogenic mechanisms and will facilitate the study of genetic regulation more detailedly and precisely.

bioinformatics↗

bioRxiv2025

Kinase-Inhibitor Binding Affinity Prediction with Pretrained Graph Encoder and Language Model

MotivationThe accurate prediction of inhibitor-kinase binding affinity is crucial in drug discovery and medical applications, especially in the treatment of diseases such as cancer. Existing methods for predicting inhibitor-kinase affinity still face challenges including insufficient data expression, limited feature extraction, and low performance. Despite the progress made through artificial intelligence (AI) methods, especially deep learning technology, many current methods fail to capture the intricate interactions between kinases and inhibitors. Therefore, it is necessary to develop more advanced methods to solve the existing problems in inhibitor-kinase binding prediction. ResultsThis study proposed Kinhibit, a novel framework for inhibitor-kinase binding affinity predictor. Kinhibit integrates self-supervised pre-trained molecular encoders and protein language models (ESM-S) to extract features effectively. Kinhibit also employed a feature fusion approach to optimize the fusion of inhibitor and kinase features. Experimental results demonstrate the superiority of this method, achieving an accuracy of 92.6% in inhibitor prediction tasks of three MAPK signaling pathway kinases: Raf protein kinase (RAF), Mitogen-activated protein kinase kinase (MEK), and Extracellular Signal-Regulated Kinase (ERK). Furthermore, the framework achieves an impressive accuracy of 93.4% on a dataset containing over 200 kinases. This study provides promising and effective tools for drug screening and biological sciences.

bioinformatics↗

bioRxiv2025

Unified high-resolution immune cell fraction estimation in blood tissue from birth to old age

BackgroundBlood is composed of many different immune cell-types, whose proportions vary throughout life. If not accounted for, these variations can seriously cause confounding or hamper interpretation of DNAm-based biomarkers. Although cell-type deconvolution methods can address this challenge for cord and adult blood, there is currently no method that can be applied to blood tissue from other age groups, including infants and children. ResultsHere we construct and extensively validate a DNAm reference panel, called UniLIFE, for 19 immune cell-types, applicable to blood tissue of any age. We use UniLIFE to delineate the dynamics of immune-cell fractions from birth to old age, and to infer disease associated immune cell fraction variations in newborns, infants, children and adults. In a prospective longitudinal study of type-1 diabetes in infants and children, UniLIFE identifies differentially methylated positions that precede type-1 diabetes diagnosis and that map to diabetes related signaling pathways. In contrast to previous studies, we are able to validate these biomarker associations in an independent DNAm dataset comprising purified monocytes from monozygotic twins discordant for type-1 diabetes, but not in lymphocytes, highlighting the importance of epigenetic changes in the innate immune system in the development of type-1 diabetes. ConclusionsIn summary, we present a life course immune-cell estimator for blood tissue of any age to help improve the identification and interpretation of blood-based DNAm biomarkers for any age groups and specially for longitudinal studies that include infants and children. The UniLIFE DNAm reference panel and algorithms to estimate cell-type fractions and perform cell-type deconvolution are available from our EpiDISH Bioconductor R-package: https://bioconductor.org/packages/release/bioc/html/EpiDISH.html

bioinformatics↗

bioRxiv2025

Epigenetic clocks and inflammaging: pitfalls caused by ignoring cell-type heterogeneity

Epigenetic clocks are machine learning predictors of chronological age that have consistently been shown to be also informative of biological age measures, such as all-cause mortality. A recent study has argued against the use of machine learning methods for building epigenetic clocks, on grounds that these predictors do not enrich for features that correlate with disease. In particular, it argues that standard epigenetic clocks cant measure inflammaging, an age-related trait, proposing an ad-hoc feature rectification strategy and an inflammation clock that predicts age-acceleration in inflammatory diseases. Here we demonstrate that this inflammation clock only captures an increase in the neutrophil to lymphocyte ratio associated with inflammatory diseases like rheumatoid arthritis, and that it fails to capture the shift from naive to mature lymphocytes, which is a key feature of inflammaging. As such, their clock does not measure inflammaging, and is subsumed and outperformed by an ordinary cell-type deconvolution algorithm. Our analysis underscores the critical importance for epigenetic clock and epigenome-wide association studies to always estimate cell-type fractions in the tissue of consideration, and to adjust for its variation, in order to disentangle effects due to changing cell-type composition from DNA methylation changes that happen in specific cell-types.

bioinformatics↗

bioRxiv2025

DUSP12 promotes cell cycle progression and protects cells from cell death by regulating ZPR9

Protein phosphatases are critical for regulating cell signaling, cell cycle, and cell fate decisions, and their dysregulation leads to an array of human diseases like cancer. The dual specificity phosphatases (DUSPs) have emerged as important factors driving tumorigenesis and cancer therapy resistance. DUSP12 is a poorly characterized atypical DUSP widely conserved throughout evolution. Although no direct substrate has been firmly established, DUSP12 that has been implicated in protecting cells from stress, regulating ribosomal biogenesis, and modulating cellular DNA content. In this study, we used affinity- and proximity-based biochemical purification approaches coupled to mass spectrometry to identify the zinc finger protein ZPR9 as a novel DUSP12 interactor, which was validated by in-cell and in-vitro IP assays. Interestingly, ZPR9 binds to the unique zinc-binding domain of DUSP12, which previous reports indicated was important for many of DUSP12s functions within the cell. Prior studies had implicated ZPR9 as a modulator of apoptosis, but it remained unclear if and how ZPR9 participated in the cell cycle and, more so, how it promoted cell death. Using mass spectrometry analyses, we found that overexpression of DUSP12 promoted de-phosphorylation of ZPR9 at Ser143. Overexpression of ZPR9, but not Ser143 phosphomimetic and phosphorylation-deficient mutants, led to an increase in pre-metaphase mitotic defects while knockdown of DUSP12 also showed mitotic defects in metaphase. Furthermore, knockdown of DUSP12 promoted, while knockdown of ZPR9 suppressed, stress-induced apoptosis. Our results support a model where DUSP12 protects cells from stress-induced apoptosis by promoting de-phosphorylation of ZPR9.

cell biology↗

bioRxiv2025

MISO Regulates Mitochondria Dynamics and mtDNA Homeostasis by Establishing Inner Membrane Subdomains

Mitochondrial dynamics and mtDNA homeostasis are essential for numerous biological processes and have recently been linked through distinct subdomains termed small MTFP1-enriched mitochondria (SMEM). However, the molecular mechanisms governing this connection remain unclear. Here, we identified MISO (Mitochondrial Inner membrane Subdomain Organizer), a novel protein that regulates mitochondrial dynamics and drives SMEM formation in response to inner mitochondrial membrane (IMM) stresses. We demonstrate that MISO regulates mitochondrial dynamics in both Drosophila stem cells in vivo and mammalian cells in vitro. Mechanistic study showed that MISO promotes mitochondrial fission while inhibiting fusion. Notably, MISO induces SMEM formation and facilitates their peripheral fission, which regulates the lysosomal degradation of mtDNA. Meanwhile, MISO knock-out completely abolishes SMEM generation, indicating that MISO is both necessary and sufficient for SMEM biogenesis. Furthermore, SMEM functionality requires MISO-dependent recruitment of MTFP1 and subsequent engagement of downstream effectors, including the FIS1-DRP1 fission machinery. IMM stresses, including damages in mtDNA, OXPHOS complexes, and cristae, stabilize the normally short-lived MISO protein, thereby triggering SMEM assembly. Furthermore, MISO-orchestrated SMEM formation depends on its C-terminal domain, likely mediated by oligomerization. Together, our work elucidates a molecular mechanism by which IMM stresses affect mitochondrial dynamics and mtDNA homeostasis.

cell biology↗

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

Osteoporosis with different causation exhibits different changes in bone tissue mineral and organic matrix

Osteoporosis (OP) which is a common skeletal disease with different causation is prevalent in aging population. Postmenopause women generally suffer from OP with bone loss due to estrogen deficiency. Diabetes are also associated with OP by complex metabolic mechanisms. Bone qualities of OP caused by aging were compared with the ovariectomy (OVX) model and the Type 2 diabetic model using Sprague-Dawley (SD) rats in our study. Combining with micro-computed tomography (-CT) and solid-state NMR (SSNMR) methods, this research studied bone changes in SD rats from tissue level to the molecular level. The studies revealed bone loss was most significant for cancellous bones, but not for cortical bones in OP rats. However, at the molecular level, the content of HAP in cortical bone increased with aging, contributing to the brittleness of the bone. Triglyceride, as a senescence maker of osteocyte in cortical bone, was also identified to be closely associated with OP in aging and OVX rats, but not in diabetic rats. This research suggests differing bone qualities in molecular level of OP with various causes more objectively reflect the bone tissue reconstruction rather than bone loss in -CT analysis.

biochemistry↗