Biology subjects

Su, S.

Publications and source records attributed to Su, S..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Exosome Trafficking Is a Key Regulator of Adipocyte Thermogenesis

Activation of beige adipocytes enhances energy expenditure and promotes metabolic health, presenting a promising approach for combating obesity and diabetes. As part of this process, thermogenesis, fueled in part by uncoupled mitochondrial respiration, plays a central role in converting calories into thermal energy, thereby preventing their storage as fat. Here, we identify a role for exosome trafficking as an intrinsic regulator of beige adipocyte thermogenesis. Exosomes are small extracellular vesicles that mediate cell-cell and intracellular communication by transporting regulatory cargo, including microRNAs, proteins, and lipids. Using both human cells and mouse models, we show that thermogenic activation of beige adipocytes promotes the rapid release of exosomes enriched in microRNAs known to suppress thermogenic programs. Genetic or pharmacological blockade of exosome secretion attenuates thermogenesis, whereas enhancing exosome release amplifies thermogenic output. Mice deficient in the exosome secretion regulator Rab27a exhibit reduced energy expenditure in response to both cold exposure and {beta}3-adrenergic stimulation. These findings establish exosome trafficking as a key contributor to beige adipocyte thermogenic capacity, highlighting an intracellular mechanism that may be leveraged to enhance energy expenditure and treat obesity-related metabolic diseases. Significance StatementThermogenic adipocytes, including beige fat cells, help maintain energy balance by converting excess nutrients into heat, thereby reducing fat storage and supporting metabolic function. Although these cells are known to promote energy expenditure, the intracellular processes that enable their full thermogenic response are not well defined. Here, we show that exosome secretion is required for beige adipocytes to reach their full thermogenic potential. Blocking exosome release dampens this response, while boosting it amplifies thermogenic output. These findings point to exosome release as an essential part of thermogenic regulation and a potential target for improving metabolic health.

cell biology↗

bioRxiv2025

Transcutaneous Vagus Nerve Stimulation boosts evidence accumulation during perceptual decision-making

The locus coeruleus-norepinephrine (LC-NE) system has been implicated in perceptual decision-making, but its causal contribution and underlying mechanisms in humans remain unclear. Here, we used transcutaneous vagus nerve stimulation (tVNS) to modulate LC-NE activity during a random dot motion task, with stimulation delivered at three distinct time points across groups, each targeting different stages of LC-NE engagement during the task. tVNS reliably increased pupil-linked LC-NE activity across all groups. Notably, early stimulation, at a time when LC-NE activity was still at baseline, elicited a more sustained pupil dilation that extended into the decision phase, resulting in comparable pupil responses during decision-making across groups. For behavior, tVNS selectively improved decision accuracy in contexts characterized by initially low performance, without affecting response times. Drift diffusion modeling revealed that this improvement was specifically associated with increased drift rate, consistent with more efficient evidence accumulation with tVNS. These effects were consistent across groups but most pronounced when tVNS was applied at the early time point. Our results provide causal evidence that tVNS enhances decision-making in a state-dependent manner, likely by stabilizing attentional engagement and facilitating evidence accumulation when endogenous control is suboptimal.

neuroscience↗

bioRxiv2025

stPipe: A flexible and streamlined R/Bioconductor pipeline for preprocessing sequencing-based spatial transcriptomics data

Spatial transcriptomics technology has developed rapidly in recent years, with various sequencing-based platforms such as 10x Visium, Slide-seq and Stereo-seq becoming widely used by researchers. Each platform brings its own set of protocols and customised data analysis pipelines which presents challenges when the goal is to obtain uniformly preprocessed data that is conveniently formatted for downstream analysis. To address the need for simpler, open-source solutions that deal with sequencing-based spatial transcriptomics (sST) data from different platforms, we present stPipe, a comprehensive and modular pipeline for analysing sST data. stPipe handles various analysis steps including (i) data processing from raw paired end FASTQ files to create a spatially resolved gene count matrix; (ii) the collation of relevant quality control metrics during preprocessing to ensure unwanted artefacts can be filtered from further analysis; and (iii) the adoption of standardised data storage containers to allow results to be easily passed on to a wide range of downstream analysis packages tailored to different goals (such as clustering, cell-cell communication analysis and differential expression analysis). stPipe is implemented as an R/Bioconductor package that builds upon functionality in the scPipe software, and offers a flexible preprocessing pipeline that can manage data from all current main-stream sST plaforms. A key use case for stPipe is in methods benchmarking, and we demonstrate how the uniform processing of sST data collected on reference tissue samples from the cadasSTre and SpatialBenchVisium projects is made easier, allowing comparisons between different technology platforms and downstream analysis tools. Our framework thus aims to advance the standardization and optimization of spatial transcriptomics analyses, fostering collaboration and innovation within the research community.

bioinformatics↗

bioRxiv2025

Autism spectrum disorder risk genes have convergent effects on transcription and neuronal firing patterns in primary neurons

Autism spectrum disorder (ASD) is a highly heterogenous neurodevelopmental disorder with numerous genetic risk factors. Notably, a disproportionate number of risk genes encode transcription regulators including transcription factors and proteins that regulate chromatin. Here, we teste the function of nine such ASD-linked transcription regulators by depleting them in primary cultured neurons. We then define the resulting gene expression disruptions using RNA-sequencing and test effects on neuronal firing using multielectrode array recordings. We identify shared gene expression signatures across many ASD risk genes that converge on disruption of critical synaptic genes. Fitting with this, we detect robust disruptions to neuronal firing throughout neuronal maturation. Together, these findings provide evidence that loss of multiple ASD-linked transcriptional regulators disrupts transcription of synaptic genes and has convergent effects on neuronal firing that may contribute to enhanced ASD risk.

neuroscience↗

bioRxiv2025

Comprehensive Infectome Analysis Reveals Diverse Infectious Agents with Zoonotic Potential in Wildlife

Understanding wildlife-pathogen interactions is crucial for mitigating zoonotic risk. Through meta-transcriptomic sequencing we profiled the infectomes of 1,922 samples from 67 mammalian species across China, uncovering a remarkable diversity of viral, bacterial, fungal, and parasitic pathogens. Of the 195 pathogens identified, 62 were novel, including a bi- segmented coronavirus in diseased lesser pandas, which we propose represents a new genus - Zetacoronavirus. The orders Carnivora and Rodentia exhibited the highest pathogen diversity and were implicated in numerous host-jumping events. Comparative analysis of diseased versus healthy animals revealed a trend of higher pathogen loads in the former, with possible differences in tissue tropisms. In total, 48 zoonotic and 17 epizootic pathogens were identified, with frequent cross-species transmission, emphasizing the potential for emerging public health threats. This study highlights the urgent need for wildlife pathogen surveillance to inform proactive disease management strategies.

microbiology↗

bioRxiv2024

Pan-cancer N-glycoproteomic atlas of patient derived xenografts uncovers FAT2 as a therapeutic target for head and neck cancers

Cell surface proteins offer significant cancer therapeutic potential attributable to their accessible membrane localization and central role in cellular signaling. Despite this, their promise remains largely untapped due to the technical challenges inherent to profiling cell surface proteins. Here, we employed N-glycoproteomics to analyze 85 patient-derived xenografts (PDX), constructing Glyco PDXplorer - an in vivo pan-cancer atlas of cancer-derived cell surface proteins. We developed a target discovery pipeline to prioritize proteins with favorable expression profiles for immunotherapeutic targeting and validated FAT2 as a head and neck squamous cancer (HNSC) enriched surface protein with limited expression in normal tissue. Functional studies revealed that FAT2 is essential for HNSC growth and adhesion through regulation of surface architecture and integrin-PI3K signaling. Chimeric antigen receptor (CAR) T cells targeting FAT2 demonstrated potent anti-tumor activity in HNSC models. This work lays the foundation for developing FAT2-targeted therapies and represents a pivotal resource to inform therapeutic target discovery for multiple cancers. HIGHLIGHTSO_LIPan-cancer landscape of cancer-derived cell surface proteins detected in vivo C_LIO_LIDevelopment of a multi-omic discovery pipeline to prioritize proteins with optimal expression profiles as immunotherapy targets C_LIO_LIIdentification and validation of FAT2 as a head and neck squamous cancer enriched surface protein with minimal expression in normal tissues C_LIO_LIFAT2 coordinates cell surface organization, adhesion, growth and survival through the integrin-PI3K-AKT pathway C_LIO_LIFAT2 CAR T cells demonstrate anti-tumour activity in pre-clinical models C_LI

cancer biology↗

bioRxiv2024

Histone variant H2BE controls activity-dependent gene expression and homeostatic scaling

A cells ability to respond and adapt to environmental stimuli relies in part on transcriptional programs controlled by histone proteins. Histones affect transcription through numerous mechanisms including through replacement with variant forms that carry out specific functions. We recently identified the first widely expressed H2B histone variant, H2BE and found that it promotes transcription and is critical for neuronal function and long-term memory. However, how H2BE is regulated by extracellular stimuli and whether it controls activity-dependent transcription and cellular plasticity remain unknown. We used CUT&Tag and RNA-sequencing of primary neurons, single-nucleus sequencing of cortical tissue, and multielectrode array recordings to interrogate the expression of H2BE in response to stimuli and the role of H2BE in activity-dependent gene expression and plasticity. We find that unlike Further, we show that neurons lacking H2BE are unable to mount proper long-term activity-dependent transcriptional responses both in cultured neurons and in animal models. Lastly, we demonstrate that H2BE knockout neurons fail to undergo the electrophysiological changes associated with homeostatic plasticity in neurons after long-term stimulation. In summary, these data demonstrate that H2BE expression is inversely correlated to activity and necessary for long-term activity-dependent responses, revealing the first instance of a histone variant involved in the homeostatic plasticity response in neurons.

genetics↗

bioRxiv2024

Transcutaneous Vagus Nerve Stimulation Boosts Post-Error Accuracy During Perceptual Decision-Making

The locus coeruleus-norepinephrine (LC-NE) system is a well-established regulator of behavior, yet its precise role remains unclear. Animal studies predominantly support a "gain" hypothesis, suggesting that the LC-NE system enhances sensory processing, while human studies have proposed an alternative "urgency" hypothesis, postulating that LC-NE primarily accelerates responses. To address this discrepancy, we administered transcutaneous vagus nerve stimulation (tVNS) in two experiments involving 43 participants. In the first experiment, we showed that 4-second tVNS trains reliably induced greater pupil dilation compared to SHAM condition, indicating increased LC-NE activity. In the second experiment, we applied tVNS during a random dot motion task to assess its impact on perceptual decision-making. Notably, tVNS improved accuracy without affecting reaction times, which appears inconsistent with the "urgency" hypothesis. Drift-diffusion model analyses further supported the "gain" hypothesis, revealing that tVNS increased the drift rate, indicative of enhanced evidence accumulation. Accuracy and drift-rate improvements were especially pronounced following errors and in less proficient participants, who otherwise exhibited post-error declines in these measures under SHAM condition. Our findings suggest that the influence of the LC-NE system adapts to task demands, becoming especially beneficial in challenging contexts. Overall, this study underscores the potential of tVNS as a non-invasive tool to investigate the causal role of the LC-NE system in human behavior.

neuroscience↗

bioRxiv2024

First-male sperm precedence in polyandrous Spodoptera frugiperda allows sterile males induce population suppression

Males respond to intense sperm competition by adapting reproductive strategies to promote fertilization success, which is critical for population reproduction. Thus, investigating the patterns and mechanisms of sperm competition is crucial for the development and application of pest population management techniques. In this study, we analyzed the sperm precedence pattern of a major pest, the fall armyworm, and used this pattern to manage the pest population. First, we found that females had a post-mating response and did not gain direct benefit through multiple mating. Next, in a double mating experiment, we used a molecular marker created by CRISPR/Cas9 to determine that most females use only the sperm of the first male to produce offspring. To further explore the role of fertilizing sperm in sperm competition, we constructed a sterile male line with eupyrene sperm defect by knocking out the B2t gene. Interestingly, two round mating assays showed that first mating with B2t-null males inhibited sperm fertilization from a second wild-type male. In other words, prior mating with B2t-null males significantly reduced the fertility and fecundity of females. Based on this finding, we continued to explore whether sperm-deficient sterile males could be used in the management of FAW populations. Cage experiments and mathematical modeling analyses showed that the release of excess B2t-null males induced population suppression. Our study expands our knowledge of sperm competition patterns in lepidopteran. In addition, our study provides a paradigm to develop and apply genetic control methods based on sperm competition outcome in polyandrous pests. SignificanceSperm competition is essential for maintaining population reproduction. Understanding patterns and mechanisms of sperm competition facilitates the development of appropriate pest genetic control methods. Here, we describe that a globally major pest, the fall armyworm displays the first-male sperm precedence pattern. Interestingly, first mating with B2t-null males, which produces non-functional eupyrene sperm, significantly reduces the fertility and fecundity of females. That means that the ejaculate of the first male, even if its eupyrene sperm are defective, can inhibit sperm fertilization from a second wild-type male. Based on this, the release of excess B2t-null males significantly suppresses FAW populations. These results suggest that future development of genetic control techniques based on targeting nucleated sperm can effectively control FAW populations.

zoology↗

bioRxiv2024

How Error Correction Affects PCR Deduplication: A Survey Based on UMI Datasets of Short Reads

Next-Generation Sequencing (NGS) data is widely utilised for various downstream applications in bioinformatics, and numerous techniques have been developed for PCR-deduplication and error-correction to eliminate bias and errors introduced during the sequencing. This study first-time provides a joint overview of recent advances in PCR-deduplication and error-correction on short reads. In particular, we utilise UMI-based PCR-deduplication strategies and sequencing data to assess the performance of the solely-computational PCR-deduplication approaches and investigate how error correction affects the performance of PCR-deduplication. Our survey and comparative analysis reveal that the deduplicated reads generated by the solely-computational PCR-deduplication and error-correction methods exhibit substantial differences and divergence from the sets of reads obtained by the UMI-based deduplication methods. The existing solely-computational PCR-deduplication and error-correction tools can eliminate some errors but still leave hundreds of thousands of erroneous reads uncorrected. All the error-correction approaches raise thousands or more new sequences after correction which do not have any benefit to the PCR-deduplication process. Upon these discoveries, we offer practical suggestions to enhance the existing computational approaches for improving the quality of short-read sequencing data.

bioinformatics↗

bioRxiv2024

Amino Acid-Dependent Material Properties of Tetrapeptide Condensates

Condensates formed by intrinsically disordered proteins mediate a myriad of cellular processes and are linked to pathological conditions including neurodegeneration. Rules of how different types of amino acids (e.g., {pi}-{pi} pairs) dictate the physical properties of biomolecular condensates are emerging, but our understanding of the roles of different amino acids is far from complete. Here we studied condensates formed by tetrapeptides of the form XXssXX, where X is an amino acid and ss represents a disulfide bond along the backbone. Eight peptides form four types of condensates at different concentrations and pH values: droplets (X = F, L, M, P, V, A); amorphous dense liquids (X = L, M, P, V, A); amorphous aggregates (X = W), and gels (X = I, V, A). The peptides exhibit enormous differences in phase equilibrium and material properties, including a 368-fold range in the threshold concentration for phase separation and a 3856-fold range in viscosity. All-atom molecular dynamics simulations provide physical explanations of these results. The present work also reveals widespread critical behaviors, including critical slowing down manifested by the formation of amorphous dense liquids and critical scaling obeyed by fusion speed, with broad implications for condensate function.

biophysics↗

bioRxiv2024

Turn `noise' to signal: accurately rectify millions of erroneous short reads through graph learning on edit distances

Although the per-base erring rate of NGS is very low at 0.1% to 0.5%, the percentage/probability of erroneous reads in a short-read sequencing dataset can be as high as 10% to 15% or in the number of millions. Correction of these wrongly sequenced reads to retrieve their huge missing value will improve many downstream applications. As current methods correct only some of the errors at the cost of introducing many new errors, we solve this problem by turning erroneous reads into their original states, without bringing up any non-existing reads to keep the data integrity. The novelty of our method is originated in a computable rule translated from PCR erring mechanism that: a rare read is erroneous if it has a neighbouring read of high abundance. With this principle, we construct a graph to link every pair of reads of tiny edit distances to detect a solid part of erroneous reads; then we consider them as training data to learn the erring mechanisms to identify possibly remaining hard-case errors between pairs of high-abundance reads. Compared with state-of-the-art methods on tens of datasets of UMI-based ground truth, our method has made a remarkably better performance under 19 metrics including two entropy metrics that measure noise levels in a dataset. Case studies found that our method can make substantial impact on genome abundance quantification, isoform identification, SNP profiling, and genome editing efficiency estimation. For example, the abundance level of the reference genome of SARS-CoV-2 can be increased by 12% and that of Monkeypox can be boosted by 52.12% after error correction. Moreover, the number of distinct isomiRs is decreased by 31.56%, unveiling there are so many previously identified isomiRs that are actually sequencing errors. Author summaryDetecting short-read sequencing errors and correcting the related erroneous reads is a long-standing problem in bioinformatics. Current error correction algorithms correct only small parts of the errors but simultaneously introduce thousands of non-existing sequences. We present a new method to rectify erroneous reads under 300 bp produced by PCR-involved miRNA-sequencing, small RNA sequencing, or paired-end RNA sequencing, regardless of platform or sample type. Our method is the first kind considering the PCR erring mechanism and machine learning technique to improve sequencing data quality by turning millions of erroneous short reads into their original state without bringing up any non-existing sequences into the read set. Our error correction method can make a significant impact on a wide range of cutting-edge downstream applications. The observations and advantages in the case studies lay down strong evidence to question the accuracies of current downstream research outcomes and open new avenues to conduct downstream analysis whenever short-read data are adopted.

bioinformatics↗

bioRxiv2024

Measuring X inactivation skew for retinal diseases with adaptive nanopore sequencing

X-linked genetic disorders typically affect females less severely than males due to the presence of a second X chromosome not carrying the deleterious variant. However, the phenotypic expression in females is highly variable, which may be explained by an allelic skew in X chromosome inactivation. Accurate measurement of X inactivation skew is crucial to understand and predict disease phenotype in carrier females, with prediction especially relevant for degenerative conditions. We propose a novel approach using nanopore sequencing to quantify skewed X inactivation accurately. By phasing sequence variants and methylation patterns, this single assay reveals the disease variant, X inactivation skew, its directionality, and is applicable to all patients and X-linked variants. Enrichment of X-chromosome reads through adaptive sampling enhances cost-efficiency. Our study includes a cohort of 16 X-linked variant carrier females affected by two X-linked inherited retinal diseases: choroideremia and RPGR-associated retinitis pigmen-tosa. As retinal DNA cannot be readily obtained, we instead determine the skew from peripheral samples (blood, saliva and buccal mucosa), and correlate it to phenotypic outcomes. This revealed a strong correlation between X inactivation skew and disease presentation, confirming the value in performing this assay and its potential as a way to prioritise patients for early intervention, such as gene therapy currently in clinical trials for these conditions. Our method of assessing skewed X inactivation is applicable to all long-read genomic datasets, providing insights into disease risk and severity and aiding in the development of individualised strategies for X-linked variant carrier females.

genomics↗

bioRxiv2024

The molecular basis of pine wilt disease resistance in Pinus massoniana

Pine wilt disease (PWD), caused by the pine wood nematode (PWN) Bursaphelenchus xylophilus, results in significant economic and ecological damage to Pinus forests and plantations worldwide. Pinus massoniana is the primary host of PWD in southern China, but its response to the PWN remains largely unstudied. Previously, we observed PWD in a P. massoniana nursery that contained over 71 commonly used cultivars. Through field phenotyping, we identified two groups of PWN resistant cultivars. Resistant cultivars (RC) exhibited very low PWN carrying amounts (PCA) and had relatively low mortality, and tolerant cultivars (TC) had high PCA but low mortality. In this study, we confirmed via PWN inoculation assays that the resistant and tolerant cultivars had lower mortality rates, 10% and 11%, respectively, than other cultivars (which had a mortality rate of 83.3%). The RC had a PCA that was significantly lower than that of other cultivars, while the TC exhibited a higher PCA. To explore the molecular mechanisms underlying the response of P. massoniana to PWN, the transcriptome and metabolome of the above cultivars were profiled by high throughput sequencing. As no reference genome is available for P. massoniana, we generated a new full-length transcriptome library with iso-seq. Using the transcriptome and metabolome from different P. massoniana cultivars inoculated with PWN, we found three major PWD resistance strategies. 1) The common response strategy involved three important molecular pathways. First, synthesis of indole-3-acetic acid (IAA) and abcisic acid (ABA) were suppressed during PWN invasion, thus suppressing the synthesis of polysaccharides (especially myo-inositol and trehalose) and (-)-riboflavin through ABC transporters. Second, by inhibiting aspartic acid, the synthesis of arginine and proline through APS5 (aspartate aminotransferase 5) were suppressed. Third, by reducing cysteine, GERD (germacrene D synthase) was up-regulated, and sesquiterpenoid and triterpenoid metabolites were accumulated to resist PWN invasion. 2) The most effective resistance strategy involved the accumulation of reactive oxygen species (ROS), which enhanced jasmonic acid (JA) accumulation and highly induced the expression of chitinase, thus improving resistance to PWN. 3) The tolerance strategy involved the induction of phosphatidylcholine, which promoted flavonoid and anthocyanin synthesis via LPIN (phosphatidate phosphatase), LOX2S (lipoxygenase), and LOX1_5 (linoleate 9S-lipoxygenase), thus significantly inhibiting the pathogenicity of the PWN but not the PCA. These results illustrate the molecular mechanisms by which P. massoniana cultivars resist or tolerate PWN and are of great significance in the prevention and control of PWD.

plant biology↗

bioRxiv2023

A Novel Subtype of Myeloproliferative Neoplasms Driven by a MYC-Alarmin Axis

Despite advances in understanding the genetic abnormalities in myeloproliferative neoplasms (MPNs) and the development of JAK2 inhibitors, there is an urgent need to devise new treatment strategies, particularly for triple negative myelofibrosis (MF) patients whose MPNs lack mutations in the JAK2 kinase pathway and have very poor clinical outcomes. Here we report that MYC copy number gain and increased MYC expression frequently occur in triple negative MF, and that MYC-directed activation of S100A9, an alarmin protein that plays pivotal roles in inflammation and innate immunity, is necessary and sufficient to drive development and progression of MF. Notably, the MYC-S100A9 circuit provokes a complex network of inflammatory signaling that involves various hematopoietic cell types in the bone marrow microenvironment. Accordingly, genetic ablation of S100A9 or treatment with small molecules targeting the MYC-S100A9 pathway effectively ameliorates MF phenotypes, highlighting the MYC-alarmin axis as a novel therapeutic vulnerability for this subgroup of MPNs. SIGNIFICANCEThis study establishes that MYC expression is increased in triple negative MPNs via trisomy 8, that a MYC-S100A9 circuit manifest in these cases is sufficient to provoke myelofibrosis and inflammation in diverse hematopoietic cell types in the BM niche, and that the MYC-S100A9 circuit is targetable in triple negative MPN.

cancer biology↗

bioRxiv2023

scPipe: An extended preprocessing pipeline for comprehensive single-cell ATAC-Seq data integration in R/Bioconductor

scPipe is a flexible R/Bioconductor package originally developed to analyse platform-independent single-cell RNA-Seq data. To expand its preprocessing capability to accommodate new single-cell technologies, we further developed scPipe to handle single-cell ATAC-Seq and multi-modal (RNA-Seq and ATAC-Seq) data. After executing multiple data cleaning steps to remove duplicated reads, low abundance features and cells of poor quality, a SingleCellExperiment object is created that contains a sparse count matrix with features of interest in the rows and cells in the columns. Quality control information (e.g. counts per cell, features per cell, total number of fragments, fraction of fragments per peak) and any relevant feature annotations are stored as metadata. We demonstrate that scPipe can efficiently identify "true" cells and provides flexibility for the user to fine-tune the quality control thresholds using various feature and cell-based metrics collected during data preprocessing. Researchers can then take advantage of various downstream single-cell tools available in Bioconductor for further analysis of scATAC-Seq data such as dimensionality reduction, clustering, motif enrichment, differential accessibility and cis-regulatory network analysis. The scPipe package enables a complete beginning-to-end pipeline for single-cell ATAC-Seq and RNA-Seq data analysis in R.

bioinformatics↗

bioRxiv2023

CLOCK Mediates Delay in Cardiac Myofibroblast Formation and Attenuates Progression of Myocardial Fibrosis through Inhibition of Smad 3 Transcriptional Activity

IntroductionMyocardial fibrosis, characterized by excessive extracellular matrix deposition, leading to adverse cardiac remodeling and impaired function. The differentiation of cardiac fibroblasts into myofibroblasts plays a pivotal role in this process. The involvement of the core clock protein CLOCK in the formation of cardiac myofibroblasts and the advancement of myocardial fibrosis is not well understood. This study aims to investigate how CLOCK modulates cardiac myofibroblast differentiation and attenuates myocardial fibrosis by inhibiting Smad3 transcriptional activity. MethodsIn vivo, adeno-associated virus 9 was utilized for overexpression or silencing of the CLOCK gene via mice intravenous injection. In vitro experiments were conducted using primary cultures of cardiac fibroblasts isolated from mouse hearts to examine the expression levels of molecular markers associated with myofibroblast differentiation, such as -smooth muscle actin (-SMA) and collagen I. Additionally, a Smad3 luciferase reporter gene experiment was employed to assess the transcriptional activity of Smad3 under CLOCK regulation. ResultsThe results demonstrate that CLOCK overexpression significantly reduces the expression of -SMA and collagen I in cardiac fibroblasts, indicating a suppression of myofibroblast formation. Conversely, CLOCK knockdown enhances the expression of these fibrotic markers. Interestingly, CLOCK was found to negatively regulate the transcriptional activity of Smad3, suggesting a potential mechanism by which CLOCK exerts its inhibitory effect on cardiac fibrosis. Specifically, CLOCK interacts with Smad3, inhibiting its nuclear translocation and subsequent activation of fibro genic gene expression. ConclusionThese findings highlight the crucial role of CLOCK in the regulation of cardiac myofibroblast formation and the progression of myocardial fibrosis. CLOCK appears to exert an inhibitory effect on fibrotic signaling pathways, partially through the inhibition of Smad3 transcriptional activity. This study provides novel insights into the molecular mechanisms underlying cardiac fibrosis and emphasizes the therapeutic potential of targeting CLOCK-mediated pathways for treating myocardial fibrosis.

molecular biology↗

bioRxiv2023

Multitask Learning of Longitudinal Circulating Biomarkers and Clinical Outcomes: Identification of Optimal Machine-Learning and Deep-Learning Models

Many circulating biomarkers are assessed at different time intervals during clinical studies. Despite of the success of standard joint models in predicting clinical outcomes using low-dimensional longitudinal data (1-2 biomarkers), significant computational challenges are encountered when applying these techniques to high-dimensional biomarker datasets. Modern machine- or deep-learning models show potential for multiple biomarker processes, but systematic evaluations and applications to high-dimensional data in the clinical settings have yet to be reported. We aimed to enhance the scalability of joint modeling and provide guidance on optimal approaches for high-dimensional biomarker data and outcomes. We evaluated multiple deep-learning and machine-learning models using 24 clinical biomarkers and survival data from the SQUIRE trial, a phase 3 randomized clinical trial investigating necitumumab and standard gemcitabine/cisplatin treatment in patients with squamous non-small-cell lung cancer (NSCLC). Overall, we confirmed that longitudinal models enabled more accurate prediction of patients survival compared to those solely based on baseline information. Coupling multivariate functional principal component analysis (MFPCA) with Cox regression (MFPCA-Cox) provided the highest predictive discrimination and accuracy for the NSCLC patients with AUC values of 0.7 - >0.8 at various landmark time points and prediction timeframes, outperforming recent advanced Transformer and convolutional neural network deep-learning algorithms (TransformerJM and Match-Net, respectively). In conclusion, we identified that MFPCA-Cox represents a robust and versatile joint modeling algorithm for high-dimensional biomarker longitudinal data with irregular and missing data, capturing complex relationships within the data, yielding accurate predictions for both longitudinal biomarkers and survival outcomes, and gaining insights into the underlying dynamics.

pharmacology and toxicology↗