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Wen, H.

Publications and source records attributed to Wen, H..

At least 55 records · Page 3Linked to original sources

How do we imagine a speech? A triple network model for situationally simulated inner speech

Inner speech is a silent verbal experience and plays central roles in human consciousness and cognition. Despite impressive studies over the past decades, the neural mechanisms of inner speech remain largely unknown. In this study, we adopted an ecological paradigm called situationally simulated inner speech. Unlike mere imaging speech of words, situationally simulated inner speech involves the dynamic integration of contextual background, episodic and semantic memories, and external events into a coherent structure. We conducted dynamic activation and network analyses on fMRI data, where participants were instructed to engage in inner speech prompted by cue words across 10 different contextual backgrounds. Our seed-based co-activation pattern analyses revealed dynamic involvement of the language network, sensorimotor network, and default mode network in situationally simulated inner speech. Additionally, frame-wise dynamic conditional correlation analysis uncovered four temporal-reoccurring states with distinct functional connectivity patterns among these networks. We proposed a triple network model for deliberate inner speech, including language network for a truncated form of overt speech, sensorimotor network for perceptual simulation and monitoring, and default model network for integration and sense-making processing. HighlightsO_LIIn ten contextual backgrounds, subjects were instructed to perform situationally simulated inner speech based on cue words. C_LIO_LIThe ventral parts of the bilateral somatosensory areas and middle superior temporal gyrus were as centers for seed-based co-activation pattern analyses. C_LIO_LIA triple network model of language network, sensorimotor network, and default mode network was proposed for deliberate inner speech. C_LI

neuroscience↗

SARS-CoV-2 B Epitope-Guided Neoantigen NanoVaccines Enhance Tumor-Specific CD4/CD8 T Cell Immunity Through B Cell Antigen Presentation

Current neoantigen cancer vaccines activate T cell immunity through dendritic cell /macrophage-mediated antigen presentation. It is unclear whether incorporating B cell-mediated antigen presentation into current neoantigen vaccines could enhance CD4/CD8 T cell immunity to improve their anticancer efficacy. We developed a SARS-CoV-2 B cell epitope-guided neoantigen peptide/mRNA cancer nanovaccines (BSARSTNeoAgVax) to improve anticancer efficacy by enhancing tumor-specific CD4/CD8 T cell antitumor immunity through B cell-mediated antigen presentation. BSARSTNeoAgVax crosslinked with B cell receptor, promoted SARS-CoV-2 B cell-mediated antigen presentation to tumor-specific CD4 T cells, increased tumor-specific follicular/non-follicular CD4 T cells, and enhanced B cell-dependent tumor-specific CD8 T cell immunity. BSARSTNeoAgVax achieved superior efficacy in melanoma, pancreatic, and breast cancer models compared to the current neoantigen vaccines. Our study provides a universal platform, SARS-CoV-2 B epitope-guided neoantigen nanovaccines, to improve anticancer efficacy against various cancer types by enhancing CD4/CD8 T cell antitumor immunity through viral-specific B cell-mediated antigen presentation.

immunology↗

Pressorum Sensing: Growth-induced Compression Activates cAMP Signaling in Pseudomonas aeruginosa

Bacteria employ various strategies to coordinate population-level behaviors, with quorum sensing being a well-established mechanism. Here, we report a novel population-level regulatory mechanism in Pseudomonas aeruginosa, which we term pressorum sensing. This mechanism allows bacteria to modulate their collective behavior in response to growth-induced mechanical compression in confined spaces. Using a highly sensitive cAMP biosensor in combination with microfluidics, we demonstrate that when compressive forces reach approximately 30 nN, P. aeruginosa cells rapidly increases intracellular cAMP levels via the Pil-Chp chemosensory system. This response leads to up-regulation of the Type III Secretion System, a key virulence factor. Unlike quorum sensing, which relies on diffusible chemical signals, pressorum sensing utilizes mechanical cues to gauge population density and spatial confinement. In bacterial colonies, this mechanism generates striking spatial patterns of cAMP signaling, including traveling rings that coincide with step-like structures in colony morphology. Our findings reveal a previously unknown link between mechanical compression and bacterial virulence, providing new insights into how P. aeruginosa coordinates population-level responses in confined environments. This work also expands our knowledge of mechanogenetics and opens up new possibilities in synthetic biology and bioengineering applications.

microbiology↗

Single-Cell and Spatial Transcriptomic Analyses Deciphering the Three-Layer Architecture of Human Tuberculosis Granulomas

BackgroundGranulomas (defining tuberculosis histopathological feature) are central to the hosts defense against Mycobacterium tuberculosis, critically influencing patient outcomes. However, knowledge of human granulomas structure and function are incomplete. This study employs single-cell and spatial transcriptomics to dissect human granulomas cellular composition, structure, communication and function from 19 pulmonary, lymphatic and skeletal samples. ResultsOur study identified nine key immune-activated/signaling-active cell clusters. Notably, we delineated a three-layered granuloma structure: a core with macrophages (Macro-c09, Macro-c10) and occasional fibroblasts (Fib-c03); a fibroblast-rich (Fib-c01) periphery; and an immune-infiltrated intermediate layer comprising diverse immune-cells recruited by strong signaling-molecules (SPP1/MIF) from core/periphery cells. This study also shows granuloma heterogeneity across individuals and tissues. ConclusionsBy merging scRNA-seq with ST-seq, we offer an intricate single-cell perspective of granulomas spatial-structure and formation mechanisms, identify signaling-molecules and significantly changed genes as potential targets for host-directed tuberculosis immunotherapy, highlight fibroblasts crucial role in granuloma formation, and provide an important reference/improved understanding of TB.

microbiology↗

A systematic evaluation of dynamic functional connectivity methods using simulation data

Numerous dynamic functional connectivity (dFC) methods have been proposed to study time-resolved network reorganization in rest and task fMRI. However, a comprehensive comparison of their performance is lacking. In this study, we compared the efficacy of seven dFC methods (and their enhanced versions) to track transient network reconfiguration using simulation data. The seven methods include flexible least squares (FLS), dynamic conditional correlation (DCC), general linear Kalman filter (GLKF), multiplication of temporal derivatives (MTD), sliding-window functional connectivity with L1-regularization (SWFC), hidden Markov models (HMM), and hidden semi-Markov models (HSMM). Multiple datasets of non-fMRI-BOLD and fMRI-BOLD signals with predefined covariance structures, signal-to-noise ratio levels, and sojourn time distributions were simulated. We adopted inter-subject analysis to eliminate the effects of signals of non-interest, resulting in enhanced methods: ISSWFC, ISMTD, ISDCC, ISFLS, ISKF, ISHMM, and ISHSMM. Efficacy was defined as the spatiotemporal association between simulated and estimated data. We found that all enhanced dFC methods outperformed their original versions. Efficacies depend on several factors, such as considering the neurovascular effect in simulated data, the covariance structure between two time series, state sojourn distribution, and signal-to-noise ratio levels. These results highlight the importance of selecting appropriate dFC methods in fMRI study.

neuroscience↗

Elasticity and Thermal Stability are Key Determinants of Hearing Rescue by Mini-Protocadherin-15 Proteins

Protocadherin-15 is a core protein component of inner-ear hair-cell tip links pulling on transduction channels essential for hearing and balance. Protocadherin-15 defects can result in non-syndromic deafness or Usher syndrome type 1F (USH1F) with hearing loss, balance deficits, and progressive blindness. Three rationally engineered shortened versions of protocadherin-15 (mini-PCDH15s) amenable for gene therapy have been used to rescue function in USH1F mouse models. Two can successfully or partially rescue hearing, while another one fails. Here we show that despite varying levels of hearing rescue, all three mini-PCDH15 versions can rescue hair-cell mechanotransduction. Negative-stain electron microscopy shows that all three versions form dimers like the wild-type protein, while crystal structures of some engineered fragments show that these can properly fold and bind calcium ions essential for function. In contrast, simulations predict distinct elasticities and nano differential scanning fluorimetry shows differences in melting temperature measurements. Our data suggest that elasticity and thermal stability are key determinants of sustained hearing rescue by mini-PCDH15s.

bioengineering↗

Pandemic Vibrio parahaemolyticus wave succession by gene loss

While spontaneous mutation and gene acquisition are well-established drivers of pathogen adaptation, the role of gene loss remains underexplored. Here, we investigated the emergence and diversification of the pandemic clone (PC) of Vibrio parahaemolyticus through large-scale phylogenomic analysis of 8,684 global isolates. The PC rapidly acquired multiple marker genes and genomic islands, subsequently diverging into successive sublineages mediating independent waves of cross-country transmission, as also observed in V. cholerae. Wave succession in the last two decades was driven by loss of putrescine utilization (Puu) genes, conferring phenotypic advantages for environmental adaptation (enhanced biofilm formation) and human transmission (increased cell adhesion/intestinal colonization, reduced virulence), consistent with the virulence trade-off hypothesis. We identified Puu-gene loss in multiple bacterial genera, with effects on biofilm and adhesion replicated in V. cholerae and Escherichia coli, suggesting convergent evolution and universal phenotypic effects. Our results highlight the indispensable role of gene loss in bacterial pathogen adaptation.

microbiology↗

Emodin: an alveolar macrophage protector in acute pancreatitis induced lung injury

Emodin (EMO), an anthraquinone derivative from roots and leaves of various plants, has been widely used in many inflammatory diseases. Alveolar macrophages (AMs) play a critical role in maintaining alveolar homeostasis in the lung. To investigate the pathophysiological mechanism of AMs in acute pancreatitis-associated lung injury (AP-ALI) and the potential protective therapeutic of EMO for AP-ALI, AMs isolated from AP-ALI mice, murine cell line MH-S and RAW264.7 were pre-treated with EMO to assess its protective roles on regulating inflammation, pyroptosis, and mitophagy of macrophages in AP-ALI. The results showed that 1) in vivo, the relative quantity of AMs was significantly decreased across the time in API-ALI; however, the mitochondria flux presented earlier changes than the resident AMs alteration in our experimental system. EMO pretreatment significantly alleviated the severity of lung injury and improved the damaged alveolar structure, and further reversed the reduction of mitochondria impairment in AMs in mice. 2) In vitro, EMO significantly suppressed LPS/ATP associated NLRP3 inflammasome activation, enhanced mitophagy, and protected mitochondria damage. Furthermore, the data of mitophagy inhibition by 3-MA demonstrated that EMO protective effects were partially via manipulating mitophagy-mitochondria-alveolar macrophage axis. Our data shed light on the comprehensive understanding on EMO therapeutics in AP-ALI.

molecular biology↗

Resolving Paradoxes in Molecular Evolution: The Integrated WF-Haldane (WFH) Model of Genetic Drift

Genetic drift, the random changes in frequencies of neutral variants, is the fundamental force of molecular evolution. Under-estimation of genetic drift is a major cause of mis-conclusions on evolution. However, the standard Wright-Fisher (WF) model of random sampling in population of size N only partially defines genetic drift with 1/N or 1/Ne (Ne being a function of varying N's). In parallel, JBS Haldane proposed the branching process for genetic drift (Haldane 1927), whereby each gene copy is transmitted to K descendants with the mean and variance of E(K) and V(K). Genetic drift in its essence is V(K), i.e., the variance in transmission success, or V(K)/N when averaged over the population. Under the general WF models (i.e., the standard model as well as the many modifications), paradoxes have emerged. They include: i) Most curiously, genetic drift may often become stronger as N becomes larger at the ecological time scale, opposite to the model; ii) Sex chromosomes experience drift differently even with the same normalized N; iii) Genetic drift operates on advantageous mutations depends on V(K) but not on N; iv) Irresolution and paradoxes emerge in multi-copy gene systems, including diploidy, viruses and others, whereby evolution progresses both within and between individuals (Wang, et al. 2024). We now show that the integration of the WF and Haldane (i.e., WFH) model can resolve these paradoxes. Most important, the WFH model can fully define genetic drift in molecular evolution.

evolutionary biology↗

Dual Targeting of STING and PI3Kγ Eliminates Regulatory B Cells to Overcome STING Resistance for Pancreatic Cancer Immunotherapy

The immune suppression in tumors and lymph nodes of pancreatic ductal adenocarcinoma (PDAC), regulated by suppressive myeloid cells and regulatory B (Breg) cells, hinders the effectiveness of immunotherapy. Although STING agonists activate myeloid cells to overcome immune suppression, it expands Breg cells, conferring STING resistance in PDAC. We discovered that blocking PI3K{gamma} during STING activation abolished IRF3 phosphorylation to eliminate Breg cells, while PI3K{gamma} inhibition sustained STING-induced IRF3 phosphorylation to preserve STING function in myeloid cells. Therefore, we developed a dual functional compound SH-273 and its albumin nanoformulation Nano-273, which stimulates STING to activate myeloid cells and inhibits PI3K{gamma} to eliminates Breg cells overcoming STING resistance. Nano-273 achieved systemic antitumor immunity through intravenous administration, which decreases Breg cells and remodels microenvironment in tumors and lymph nodes. Nano-273, combined with anti-PD-1, extended median survival to 200 days in transgenic KPC PDAC mice (KrasG12D-P53R172H-Cre), offering potential for PDAC treatment.

pharmacology and toxicology↗

CentIER: accurate centromere identification for plant genomes with sequence specificity information

Centromere identification is one of the important problems in genomics, providing a foundation for the studies of centromeres in aspects of composition, functionality, evolution, inheritance, and variation. The existing wet-experiment-based method is costly and time-consuming, while the bioinformatic method can only detect tandem repeats losing non-repetitive sequence regions in the centromere. To address these shortcomings, we introduce a new pipeline, CentIER, for the automatic and accurate identification and annotation of centromere regions by taking advantage of the sequence specificity information. CentIER only requires users to input the genomic sequence, and then it can partition the centromeric region from a chromosome, identify tandem repeat monomers, annotate retrotransposons, and ultimately output visualized results. By referencing the experimentally determined centromere regions, it was discovered that the predictive accuracy of centromere recognition by CentIER exceeded 90%. Following the evaluation of CentIERs accuracy, it was applied to investigate the sequence and distribution characteristics of centromeric retrotransposons and tandem repeat sequences of different species, providing insights into these traits in monocotyledonous and dicotyledonous plants.

bioinformatics↗

Inhibition of O-GlcNAc transferase activates type I interferon-dependent antitumor immunity by bridging cGAS-STING pathway

The O-GlcNAc transferase (OGT) is an essential enzyme that mediates protein O-GlcNAcylation, a unique form of posttranslational modification of many nuclear and cytosolic proteins. Recent studies observed increased OGT and O-GlcNAcylation levels in a broad range of human cancer tissues compared to adjacent normal tissues, indicating a universal effect of OGT in promoting tumorigenesis. Here, we show that OGT is essential for tumor growth in immunocompetent hosts by repressing the cyclic GMP-AMP synthase (cGAS)-dependent DNA sensing pathway. We found that deletion of OGT (Ogt-/-) caused a marked reduction in tumor growth in both syngeneic tumor models and a genetic colorectal cancer (CRC) model induced by mutation of the Apc gene (Apcmin). Pharmacological inhibition or genetic deletion of OGT induced a robust genomic instability (GIN), leading to cGAS-dependent production of the type I interferon (IFN-I) and IFN-stimulated genes (ISGs). As a result, deletion of Cgas or Sting from Ogt-/- cancer cells restored tumor growth, and this correlated with impaired CD8+ T cell-mediated antitumor immunity. Mechanistically, we found that OGT-dependent cleavage of host cell factor C1 (HCF-1) is required for the avoidance of GIN and IFN-I production in tumors. In summary, our results identify OGT-mediated genomic stability and activate cGAS-STING pathway as an important tumor cell-intrinsic mechanism to repress antitumor immunity.

cancer biology↗

Reference-guided automatic assembly of genomic tandem repeats with only HiFi and Hi-C data enables population-level analysis

The existing de novo methods of complete genome assembly are not able to generate large-scale pangenomes with complete assemblies due to the shortcomings such as requiring multiple types of sequencing data of high price, requiring large amount of manual curation, and not being able to achieve haplotype-resolved complete assembly of long tandem repeats in most situations. To solve this problem, in this study, we propose a new genome assembly mode called reference-guided assembly which relies on the reference information to recall the reads for complex genomic regions of interest and assembles them in de novo-like way. As a proof-of-concept, we developed an algorithm TRFill which can reassemble or fill the gaps of tandem repeats in chromosome-level assembly in either haploid or diploid way using only HiFi and Hi-C data. The experimental results on human centromeres and tomato subtelomeres show that TRFill successfully improved the completeness and correctness of about two thirds of the tested tandem repeat sequences. Furthermore, TRFill improved the completeness of subtelomeric tandem repeats by 50% in a recently published tomato pangenome, enabling a population-level analysis of the subtelomeric tandem repeats, which found the local law of sequence similarity of tandem repeats providing theoretical basis for reference-guided assembly in turn.

bioinformatics↗

16p11.2 Microduplication is Associated with Lobule-Specific Abnormalities in Cerebellar Structure and Function

The 16p11.2 microduplication (16p11.2dp/+) is associated with several neuropsychiatric disorders including schizophrenia, autism spectrum disorder, bipolar disorder, intellectual disability, and attention deficit/hyperactivity disorder (ADHD). Cerebellar abnormalities have been increasingly implicated in these neuropsychiatric disorders, including those conferred by 16p11.2 microduplication. In 16p11.2dp/+ mouse models, the cerebellum is a site of transcriptional dysregulation, and cerebellar microcephaly has been reported in humans with 16p11.2 microduplication. Despite mounting evidence indicating a role for the cerebellum in neuropsychiatric disorders associated with this CNV, cerebellar cellular structure and cerebellar-dependent behavior in mice with 16p11.2 microduplication remain uncharacterized. To address this, we histologically labeled Purkinje cells (PCs) and molecular layer interneurons (MLIs) in a mouse model of 16p11.2 microduplication. We did not find any structural differences in cerebellar lobule IV/V, nor did we observe impairments in gait or motor coordination, behaviors that are associated with lobule IV/V. In contrast, we discovered a significant increase in calbindin/parvalbumin-positive PCs mislocalized to the granule layer of cerebellar lobule VI in 16p11.2dp/+ mice compared to wild-type (WT) littermates. Additionally, we found a significant decrease in parvalbumin-positive MLIs without a decrease in total DAPI-positive cell counts in lobule VI of 16p11.2dp/+ mice compared to WT littermates. Cerebellar lobule VI is associated with delay eyeblink conditioning, and 16p11.2dp/+ mice are impaired in cerebellum-dependent associative learning on this task. Specifically, 16p11.2dp/+ mice showed deficits in both conditioned response (CR) percentage and CR onset latency relative to WT mice. These results suggest that lobule VI-specific alterations to PC localization and MLI parvalbumin expression in 16p11.2dp/+ mice impair both cerebellar learning and adaptive timing of cerebellar-driven, conditioned responses. Thus, we have identified novel structural and functional alterations in the cerebellum that are associated with 16p11.2 microduplication. Importantly, individuals with schizophrenia and ADHD also show CR acquisition deficits in delay eyeblink conditioning. Together, these data suggest that the behavioral impairments in 16p11.2dp/+ mice resemble impairments seen in neuropsychiatric disorders linked to 16p11.2 microduplication in humans. Further investigation of cerebellar cortex neurons in 16p11.2dp/+ mice may provide insights into the pathogenesis of neuropsychiatric disorders linked to this copy number variant.

neuroscience↗

MEM-GAN: A Pseudo Membrane Generator for Single-cell Imaging in Fluorescent Microscopy

Fluorescent microscopy imaging is vital to capturing single-cell spatial data, characterizing tissue organization and facilitating comprehensive analysis of cellular state. Advancements in fluorescent microscopy imaging technologies have enabled precise downstream cellular analysis, particularly in cell segmentation. Accurate segmentation of individual cells allows better profiling and understanding of cell properties and behaviors. The majority of existing segmentation methods predominantly concentrate on enhancing segmentation algorithms, and their effectiveness strongly relies on the input stained image quality. Factors such as high cellular density, indistinct cell boundaries, and staining artifacts can result in uneven and low-quality staining, particularly causing missing or unclear membrane staining. These artifacts adversely impact the efficacy of the subsequent cell segmentation methods. To tackle this insufficient membrane staining, we propose a novel approach, MO_SCPLOWEMC_SCPLOW-GAN, to generate high-quality membranes for cells with missing or weak membranes. Inspired by advanced style transfer techniques in computer vision, MO_SCPLOWEMC_SCPLOW-GAN styles the content of the cells with missing or weak membranes into cells with integrated membrane staining. Considering the differences in membrane morphology between epithelial/tumor cells and immune cells, MO_SCPLOWEMC_SCPLOW-GAN deals with tumor and immune cells separately, not only enhancing membrane staining for cells with partially weak membrane signals but also generating membranes for cells with only nuclear channels. The proposed MO_SCPLOWEMC_SCPLOW-GAN is evaluated using the publicly available CosMx dataset. Experimental results demonstrate significant improvements in image staining quality, more accurate representation of membrane morphology characteristics, and better performance in downstream segmentation tasks. MO_SCPLOWEMC_SCPLOW-GAN is flexibly adapted and applied to other spatially resolved transcriptomics datasets, such as MERFISH and FISHseq. Our work provides a new perspective on tackling the challenges in cell segmentation from fluorescent microscopy image restoration. The implementation of MO_SCPLOWEMC_SCPLOW-GAN is open-source and available at the github repository https://github.com/OmicsML/Mem-GAN. The interactive webserver-based demo of MO_SCPLOWEMC_SCPLOW-GAN can be accessed at http://omicsml.ai/memgan.

bioinformatics↗

A General Single-Cell Analysis Framework via Conditional Diffusion Generative Models

AO_SCPLOWBSTRACTC_SCPLOWThe fast-growing single-cell analysis community extends the horizon of quantitative analysis to numerous computational tasks. While the tasks hold vastly different targets from each other, existing works typically design specific model frameworks according to the downstream objectives. In this work, we propose a general single-cell analysis framework by unifying common computational tasks as posterior estimation problems. In light of conditional diffusion generative models, we introduce scDiff through the proposed framework and study different conditioning strategies. With data-specific conditions, scDiff achieves competitive performance against state-of-the-art in various benchmarking tasks. In addition, we illustrate the flexibility of scDiff by incorporating prior information through large language models and graph neural networks. Additional few-shot and zero-shot experiments prove the effectiveness of the prior conditioner on scDiff. Our implementation is publicly available at https://github.com/OmicsML/scDiff.

bioinformatics↗

CellPLM: Pre-training of Cell Language Model Beyond Single Cells

The current state-of-the-art single-cell pre-trained models are greatly inspired by the success of large language models. They trained transformers by treating genes as tokens and cells as sentences. However, three fundamental differences between single-cell data and natural language data are overlooked: (1) scRNA-seq data are presented as bag-of-genes instead of sequences of RNAs; (2) Cell-cell relations are more intricate and important than inter-sentence relations; and (3) The quantity of single-cell data is considerably inferior to text data, and they are very noisy. In light of these characteristics, we propose a new pre-trained model CellPLM, which takes cells as tokens and tissues as sentences. In addition, we leverage spatially-resolved transcriptomic data in pre-training to facilitate learning cell-cell relationships and introduce a Gaussian mixture prior distribution as an additional inductive bias to overcome data limitation. CellPLM is the first single-cell pre-trained transformer that encodes cell-cell relations and it consistently outperforms existing pre-trained and non-pre-trained models in diverse downstream tasks, with 100x times higher inference speed compared to existing pre-trained models.

bioinformatics↗

bamSliceR: cross-cohort variant and allelic bias analysis for rare variants and rare diseases

The NCI Genomic Data Commons (GDC) provides controlled access to sequencing data from thousands of subjects, enabling large-scale study of impactful genetic alterations such as simple and complex germline and structural variants. However, efficient analysis requires significant computational resources and expertise, especially when recalling variants from raw sequence reads. We thus developed bamSliceR, an R/Bioconductor package that builds upon the GenomicDataCommons package to extract aligned sequence reads from cross-GDC meta-cohorts, followed by targeted analysis of variants and effects (including transcript-aware variant annotation from transcriptome-aligned GDC RNA data). Here we demonstrate population-scale genomic & transcriptomic analyses with minimal compute burden via bamSliceR, identifying recurrent, clinically relevant sequence and structural variants in the TARGET AML and BEAT-AML cohorts. We then validate results in the (non-GDC) Leucegene cohort, demonstrating how the bamSliceR pipeline can be seamlessly applied to replicate findings in non-GDC cohorts. These variants directly yield clinically impactful and biologically testable hypotheses for mechanistic investigation. bamSliceR has been submitted to the Bioconductor project, where it is presently under review, and is available on GitHub at https://github.com/trichelab/bamSliceR.

bioinformatics↗