Biology subjects

Lai, X.

Publications and source records attributed to Lai, X..

At least 19 recordsLinked to original sources

bioRxiv2026

Distinct roles for DNA- and RNA-sensing immune pathways in control of genital HSV-2 infection and restriction of neuroinvasion

Early control of viral infection is thought to rely on pattern-recognition receptors (PRRs) that induce interferons (IFNs) and leukocyte recruitment, but how distinct PRRs coordinate mucosal antiviral defense remains unclear. We show that both the DNA-sensing cGAS-STING pathway and the RNA-sensing RLR-MAVS pathway are required for protection upon genital herpes simplex virus type 2 (HSV-2) infection. cGAS deficiency increased infection-induced pathology in both epithelial and submucosal compartments, whereas MAVS deficiency primarily affected the epithelium. Spatial proteomics and regional transcriptomics revealed that cGAS was essential for early epithelial TBK1 activation, expression of IFN-stimulated genes and recruitment and activation of myeloid and lymphoid cells to the epithelium. While MAVS was essential for full TBK1 activation it had limited impact on the induced IFN response. However, MAVS sustained basal epithelial expression of the antiviral factors IFITM1 and 3, which exert antiviral activity against HSV-2. Notably, cGAS deficiency impaired submucosal IFN responses and enabled viral spread into this tissue, enabling infection of intervening neurons and dissemination to the central nervous system. These findings define coordinated, compartment-specific innate defense against infections.

immunology↗

bioRxiv2026

scRGCL: Neighbor-Aware Graph Contrastive Learning for Robust Single-Cell Clustering

Accurate cell type identification is a fundamental step in single-cell RNA sequencing (scRNA-seq) data analysis, providing critical insights into cellular heterogeneity at high resolution. However, the high dimensionality, zero-inflated, and long-tailed distribution of scRNA-seq data pose significant computational challenges for conventional clustering approaches. Although recent deep learning-based methods utilize contrastive learning to joint-learn representations and clustering assignments, they often overlook cluster-level information, leading to suboptimal feature extraction for downstream tasks. To address these limitations, we propose scRGCL, a single-cell clustering method that learns a regularized representation guided by contrastive learning. Specifically, scRGCL captures the cell-type-associated expression structure by clustering similar cells together while ensuring consistency. For each sample, the model performs negative sampling by selecting cells from distinct clusters, thereby ensuring semantic dissimilarity between the target cell and its negative pairs. Moreover, scRGCL introduces a neighbor-aware re-weighting strategy that increases the contribution of samples from clusters closely related to the target. This mechanism prevents cells from the same category from being mistakenly pushed apart, effectively preserving intra-cluster compactness. Extensive experiments on fourteen public datasets demonstrate that scRGCL consistently outperforms state-of-the-art methods, as evidenced by significant improvements in normalized mutual information (NMI) and adjusted rand index (ARI). Moreover, ablation studies confirm that the integration of cluster-aware negative sampling and the neighbor-aware re-weighting module is essential for achieving high-fidelity clustering. By harmonizing cell-level contrast with cluster-level guidance, scRGCL provides a robust and scalable framework that advances the precision of automated cell-type discovery in increasingly complex single-cell landscapes. Key MessagesO_LIscRGCL uses contrastive learning on a regularized representation for single-cell clustering. C_LIO_LIscRGCL outperforms four state-of-the-art methods on 15 datasets. C_LIO_LIscRGCLs cluster-aware negative sampling and the neighbor-aware re-weighting modules are essential for high-fidelity single cell clustering. C_LI

bioinformatics↗

bioRxiv2026

miREA: a network-based tool for microRNA-oriented enrichment analysis

MicroRNAs (miRNAs) regulate gene expression at the post-transcriptional level, yet interpreting their function at the pathway level remains challenging. Existing enrichment analysis tools predominantly adopt network node-centric approaches that focus on gene expression profiles, neglecting the regulatory information encoded in miRNA-gene interactions (MGIs) that constitute network edges. This omission introduces analytical bias and limits biological interpretability, underscoring the need for network edge-based enrichment analysis methods that explicitly incorporate MGIs. Therefore, we present miREA, a network-based tool for miRNA enrichment analysis that leverages MGIs to characterize miRNA function at the pathway level. miREA contains five edge-based enrichment methods that integrate paired miRNA-gene expression and interactome profiles with pathway networks to perform MGI overrepresentation, MGI scoring-based, network topology-aware, and network propagation analyses. Benchmarking across multiple cancer types shows that the edge-based methods outperform node-based methods in improving sensitivity to identify relevant pathways and biological interpretability while maintaining controlled false positive rates. We further demonstrate the utility of miREA in elucidating miRNA-gene-pathway regulatory mechanisms in bladder cancer. miREA is a versatile enrichment analysis tool that provides pathway-level interpretation of human miRNA function and facilitates mechanistic hypothesis generation for experimental validation. HighlightsO_LImiREA uses information in miRNA-gene interactions for miRNA enrichment analysis. C_LIO_LImiREA contains five new edge-based algorithms that integrate expression and interactome profiles with pathway networks to characterize miRNA function. C_LIO_LIThe edge-based methods are more sensitive than node-based methods at identifying cancer-relevant pathways and more effective at identifying cancer genes and miRNAs. C_LIO_LIWe perform systematic analysis of the miREA methods to show their robustness on 16 cancer datasets. C_LIO_LIWe present a case study that demonstrates the potential of miREA to elucidate miRNA-gene-pathway regulatory mechanisms in bladder cancer. C_LI

bioinformatics↗

bioRxiv2026

RingNet: An Interactive Platform for Multi-Modal Data Visualization in Networks

The exponential growth of data in biomedicine has created an urgent need for intuitive visualization tools. These tools should effectively represent complex biological networks and remain accessible to domain experts without extensive computational training. Current network visualization approaches often require specialized programming skills and/or cannot handle the scale and complexity of modern biomedical datasets, which creates significant barriers to biological discovery. We develop RingNet, a web-based interactive visualization tool that integrates computational efficiency with flexible, user-driven exploration. This tool addresses the communitys need to visualize multi-modal datasets within a single, compact network representation, as well as identify patterns of interest in complex data. RingNet uses an R backend for network computation and coordinate optimization. This generates JSON data structures that feed into a JavaScript and HTML frontend, which provides real-time, interactive visualization functions. It offers dynamic layout adjustments, node and edge filtering, and customizable color schemes for representing data. It can export reproducible, publication-ready figures in SVG and PNG formats. In our case studies, we use RingNet to visualize breast cancer patients omics profiles in a gene regulatory network and a cell-to-cell communication network in atopic dermatitis. This demonstrates RingNets ability to reveal biological relationships across multiple data modalities. HighlightsO_LIRingNet enables intuitive, interactive visualization of multi-modal networks without requiring advanced computational or programming expertise. C_LIO_LIRingNet has two functions: one to identify patterns in complex data at the node level, and the other to identify samples with homogeneous and heterogeneous profiles within network nodes. C_LIO_LIRingNet integrates efficient backend computation with real-time, flexible frontend exploration in a single, web-based framework. C_LIO_LIRingNet reveals cross-modal biological relationships and produces reproducible, publication-ready figures. C_LI Graphic abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=75 SRC="FIGDIR/small/700593v4_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@7c79f2org.highwire.dtl.DTLVardef@2a0c73org.highwire.dtl.DTLVardef@97845eorg.highwire.dtl.DTLVardef@1735352_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

EARLY FLOWERING 3 (ELF3): a novel role in integrating environmental stimuli with root stem cell niche maintenance

Maintaining stem cell niche (SCN) homeostasis in the root apical meristem (RAM) is essential for proper root growth and thus for overall plant development. In Arabidopsis thaliana, a group of slowly dividing cells at the SCN center known as the quiescent center (QC) maintain the surrounding stem cells, including the distally located columella stem cells (CSCs) which give rise to the differentiated columella cells. Many actors, including the PLETHORA (PLT) family transcription factors, regulate the QC quiescence and CSC fate. However, little is known about the integration of external and/or internal cues into regulating SCN homeostasis. In this study, we report for the first time the interaction between PLT3 and the thermosensor and circadian clock related transcriptional regulator, EARLY FLOWERING 3 (ELF3), which are both expressed in the root SCN. We show that ELF3 localizes, similar to PLT3, to subcellular condensates and sustains the QC and CSC fate. We demonstrate that ELF3 forms condensates in vitro and in vivo, in the cytoplasm, as well as in the nucleus, where it then co-localizes with PLT3. We reveal that the interaction of ELF3 and PLT3 is mediated by their intrinsically disordered prion-like domains (PrDs). Furthermore, transient expression in human epithelial cells (HEp-2) cells and in Nicotiana benthamiana shows that PHYTOCHROME INTERACTING FACTORS 3 and 4 (PIF3/4) function as nuclear shuttles for ELF3, recruiting it to nuclear condensates, where it co-localizes with PLT3, PIF3, and PIF4. Accordingly, we propose a model where the co-localization and interactions of ELF3, PLT3, PIF3, and PIF4 represent a dynamic mechanism to integrate environmental signals into SCN maintenance and cell fate decisions.

plant biology↗

bioRxiv2025

Nuclear N-Glycosylation Redefines the Glycoscape and Directs Cell Identity

Glycans modify nucleic acids, proteins and lipids in fundamental biological processes, but have long been considered confined to the secretory pathway and cell surface. Here, we challenge this view with evidence that N-glycosylation, a ubiquitous glycan modification, occurs within cell nuclei across tissues and species, exhibiting cell type-dependent abundance and localization patterns. Using a multi-faceted approach, we show that numerous classical membrane and secreted glycoproteins localize to the nucleus with N-glycan modifications, exemplified by the cell adhesion molecule L1CAM. Mechanistically, we show that the N-glycosylated L1CAM is transported to the nucleus by KPNB1, where it transcriptionally modulates neuronal differentiation. Moreover, this nuclear N-glycosylation event contributes to neurodevelopmental disorders and is pharmacologically reversible. These findings revise the cellular geography of N-glycosylation and expand the known routes of protein trafficking, and highlight nuclear N-glycosylation pathways as potential therapeutic entry points for developmental disorders.

cell biology↗

bioRxiv2025

Network-based analysis reveals microRNA regulation of oncogenic pathways in SOX10-depleted uveal melanoma

SOX10 is essential for melanocyte development and maintenance and plays a critical role in uveal melanoma (UM) initiation and progression. While SOX10s transcriptional regulation of protein-coding genes is well characterized, its role on microRNA (miRNA) regulatory landscape in UM remains unexplored. Here, we employed network-based modeling to systematically characterize miRNA regulatory functions following SOX10 depletion in UM. First, we profiled mRNA and miRNA expression levels in SOX10 wild-type and knockdown UM cells. Then, we integrated the transcriptomic data, a UM network, and a Bayesian model to quantify miRNAs regulatory activities and identify key miRNAs. Subsequently, we employed pathway enrichment analysis combined with literature mining to elucidate the functional roles of identified miRNAs through their target genes and associated signaling pathways in UM. We identified 17 miRNAs that show significant changes in regulatory activities following SOX10 knockdown in UM cells. These miRNAs regulate the expression of genes involved in cancer hallmark pathways, including cell cycle progression, mTORC1 signaling, and fatty acid metabolism. Notably, miR-34a, miR-25, miR-186, and miR-211 have tumor-suppressive potential by targeting genes involved in UM progression and metastasis. Our results suggested that SOX10 depletion in UM can activate tumor-suppressive mechanisms through regulating miRNAs.

cancer biology↗

bioRxiv2025

A computational SOX10 network-based selection strategy to identify new drug targets in uveal melanoma

Uveal melanoma (UM) is the most common intraocular malignancy in adults. In contrast to cutaneous melanoma (CM), effective treatment options for metastatic UM are limited. The transcription factor SOX10 is crucial for CM initiation and survival, making it an interesting candidate for new targeted therapies, but its relevance in UM was unclear. We found that SOX10 was widely expressed in UM and essential for proliferation, cell cycle progression, and survival. The effects were partially mediated by SOX10-related genes including MITF, highlighting high addiction of UM to the SOX10-MITF axis. Additionally, SOX10 knockdown induced massive transcriptomic changes. Due to a lack of specific inhibitors of SOX10 and MITF, a computational approach was used to identify druggable targets by curating a UM-specific protein interaction network to search for candidates downregulated upon SOX10 inhibition. Thereby, the E2F transcription factor family was identified and their potential as druggable target candidates in UM was confirmed using the pan-E2F inhibitor HLM006474, resulting in cell cycle arrest and apoptosis. Taken together, SOX10 is crucial for UM survival and SOX10-associated proteins may serve as promising targets for developing new therapeutic strategies in UM.

cancer biology↗

bioRxiv2025

Integrative multi-modal analysis reveals the contribution of noncoding RNAs to post-treatment progression of IDH-mutant astrocytomas

IDH-mutant (IDHmut) astrocytomas typically arise as grade 2-3 tumors, but a subset of them progress to grade 4 after treatment, significantly worsening the prognosis. It is unresolved how noncoding RNAs (ncRNAs) contribute to this tumor progression. To characterize ncRNAs regulatory roles, we profiled and analyzed the coding and noncoding transcriptomes of matched tumor samples before and after progression to grade 4 in IDHmut astrocytoma patients. By integrating our data with public primary and matched tumor cohorts, we found that upregulated protein-coding genes in progressed tumors overlapped with those in primary grade 4 tumors and were linked to cell proliferation. In contrast, downregulated genes differed between primary and post-treatment grade 4 tumors. A large fraction of genes that were downregulated only in the post-treatment setting were associated with decreased cell differentiation. We identified 53 progression-related ncRNAs predicted to regulate 125 differentially expressed genes. Gene regulatory network analysis revealed their involvement in cell cycle control, extracellular matrix (ECM) organization, and neural differentiation. Notably, hsa-let-7b-3p, a tumor suppressor microRNA, showed recurrent hemizygous deletions and downregulation after post-therapy progression. The long noncoding RNA (lncRNA) PVT1 was recurrently gained and upregulated in grade 4 tumors. The lncRNA NEAT1, previously linked to treatment resistance, was especially upregulated post-treatment and had the highest number of predicted targets (n=38), many related to ECM organization. Overall, our findings highlight the role of ncRNAs in the post-treatment progression of IDHmut astrocytomas, offering new insights into mechanisms of their malignancy.

cancer biology↗

bioRxiv2025

ZIPcnv: accurate and efficient inference of copy number variations from shallow whole-genome sequencing

Shallow whole-genome sequencing (sWGS), a rapid and cost-effective sequencing technology, has gradually been widely adopted for CNV analyses. However, with genome-wide coverage of only 0.1-5x, sWGS data exhibit a pronounced zero-inflation phenomenon, whereby a large fraction of loci contains zero sequencing reads. Zero-inflation causes read counts to fluctuate by several-fold between adjacent windows. As a result, random upward blips in coverage can be misinterpreted as copy-number gains (false positives), and true deletions often become indistinguishable from pervasive zero-coverage noise. In addition, existing CNV detection tools developed for sWGS data often struggle to adapt across different CNV sizes. These combined effects severely constrain the accuracy of CNV inference. To address above challenges, we propose ZIPcnv, a novel CNV detection tool specifically designed for sWGS data. First, we apply a large sliding window to smooth the raw read depth signal, which transforms the original zero-inflated statistical characteristics into approximately normal distribution characteristics. We then design a statistical process model that robustly detects persistent shifts under high background noise using a cumulative sum strategy, classifying genomic regions into candidate and non-candidate CNV regions. Finally, dynamic sliding windows are used for one-pass detection of CNVs of varying lengths, with window size adapting to the CNV region size. We evaluated the performance of ZIPcnv on simulated data and 190 real whole-genome sequencing samples. Experimental results show that ZIPcnv consistently outperforms currently popular CNV detection tools. The ZIPcnv source code is freely available at https://github.com/Nevermore233/ZIPcnv.

bioinformatics↗

bioRxiv2025

Mapping satellite glial cell heterogeneity reveals distinct spatial organization and signifies functional diversity in the dorsal root ganglion

Satellite glial cells (SGCs) envelop the somata, axon hillock, and initial axon segment of sensory neurons in the dorsal root ganglia (DRG), playing a critical role in regulating the neuronal microenvironment. While DRG neurons have been extensively studied and classified based on size, molecular markers, and functional characteristics, very little is still known about SGC heterogeneity and its potential implications on sensory processing in the DRG. Single cell transcriptional analyses have proposed the existence of SGC subtypes, yet in situ validation, spatial distribution, and potential functional implications of such subtypes are still largely unexplored. Here, we present the first comprehensive in situ characterization of SGC heterogeneity within the mouse DRG. By integrating single-cell RNA sequencing with immunohistochemistry, in situ hybridization, and advanced imaging techniques, distinct SGC subclusters were identified, validated, and spatially mapped within their native anatomical context. We visually identify four distinct subpopulations: 1) a predominant population of perisomatic SGC sheaths defined by the expression of marker proteins traditionally used to characterize the entire SGC population, including FABP7, KIR4.1, GS, and CX43. 2) OCT6+ SGCs occasionally being found in mosaic perisomatic sheaths, and consistently associated with axonal glomeruli, primarily ensheathing initial segment axon. 3) SCN7A+ SGCs, exhibiting no/low expression of traditional SGC markers and forming specialized homogenous sheaths around non-peptidergic neuron subtypes, implicating their potential role in pruritic (itch-related) conditions. 4) Interferon response gene-expressing SGCs, responding to Herpes Simplex Virus infection, suggesting potential involvement in antiviral protection. Finally, we investigate human DRG and find an inner perisomatic SGC layer surrounded by an outer SGC layer, with traditional and novel markers distinctively distributed between the two layers. Our results provide novel insight into SGC heterogeneity in the DRG and suggests distinct functional properties for such subtypes of relevance for the neuronal microenvironment.

neuroscience↗

bioRxiv2025

Cell type-specific associations with Alzheimer's Disease conserved across racial and ethnic groups

Genomic studies at single-cell resolution have implicated multiple cell types associated with clinical and pathological traits in Alzheimers Disease (AD), but have not examined common features across broad, multi-ethnic populations, and across multiple regions. To bridge this gap, we performed single-nucleus RNA-seq and ATAC-seq profiling of cortical and subcortical brain regions from post-mortem samples across Non-Latin White, African American, and Latin donors (the latter of any race). Using discrete and continuous dissection of molecular programs, we elucidate cell-type-specific glial and neuronal signatures associated with AD across multiple population groups. Notably, we found that multiple microglial (GPNMB+, CD74+, and CR1+ subgroups) and astrocyte (SERPINH1+ and WIF1+ subgroups) signatures are associated with worse clinical and pathological phenotypes across all three population groups. We also report continuous gene expression factors in oligodendrocytes that are not captured by discrete clusters, yet still show strong associations with disease phenotypes. Finally, we observe these discrete cellular identities and continuous gene programs separate cognitively impaired donors into 6 molecularly distinct subgroups that span racial and ethnic population groups. Overall, our study identifies key cell types and gene programs implicated in AD that are shared across population groups, and provides an initial data set that underscores how representative sampling can capture conserved signatures as well as disease heterogeneity, leading to better prioritization of key cell types for further investigation.

neuroscience↗

bioRxiv2025

Multi-Omics and Machine Learning-Based Profiling of Severity Signatures in Mycoplasma Pneumoniae Infection in Children

Mycoplasma pneumoniae pneumonia (MPP) is a common respiratory infection in children, yet the mechanisms driving its progression to severe disease remain poorly understood. This study employs a comprehensive proteomic and metabolomic approach to elucidate severity-related pathways and identify potential biomarkers for improved diagnosis and targeted therapy. By analyzing blood proteomes from 57 pediatric patients with varying MPP severities alongside 10 healthy controls, and integrating multi-omics data from bronchoalveolar lavage fluid (BALF), we uncovered key severity-associated proteins and metabolites linked to inflammatory and metabolic dysregulation. Notably, alterations in L-arginine metabolism, influenced by APAF1 and SERPINB5, were found to modulate the efferocytosis pathway, while ERCC1 and MLH1, crucial components of the Fanconi anemia pathway, were associated with changes in plasmid acid metabolites affecting fatty acid elongation. Machine learning analysis further identified three critical biomarkers--TNFRSF10B, SAT1, and 4-nitrophenol-- that accurately distinguished between mild and severe MPP cases with high sensitivity (0.8) and specificity (1). These findings provide novel insights into the molecular mechanisms underlying severe MPP, highlighting efferocytosis and fatty acid metabolism as key pathways. The identification of severity-specific biomarkers offers a foundation for enhanced diagnostic precision, improved disease stratification, and the development of targeted therapeutic strategies to optimize the management of severe MPP in pediatric patients. HighlightsO_LIIdentified severity signatures in Mycoplasma pneumoniae infection in children. C_LIO_LIConducted the multi-omics analysis of bronchoalveolar lavage fluid (BALF) in children with varying severities of Mycoplasma pneumoniae infection. C_LIO_LIDiscovered biomarkers that effectively distinguish mild from severe Mycoplasma pneumoniae infection. C_LIO_LIFindings offer potential targets to improve the diagnosis and treatment of severe Mycoplasma pneumoniae C_LIO_LIinfection in children. C_LI

microbiology↗

bioRxiv2024

SOX10 and microRNAs: decoding their interplay in regulating melanoma plasticity

Recent studies show that the dysregulation of the transcription factor SOX10 is essential for development and progression of melanoma. MicroRNAs (miRNAs) can regulate the expression of transcription factors at the post-transcriptional level. The interactions between SOX10 and its targeting miRNAs form network motifs such as feedforward and feedback loops. Such motifs can result in non-linear dynamics in gene expression levels, therefore playing a crucial role in regulating tumor proliferation and metastasis as well as tumors responses to therapies. Here, we reviewed and discussed the intricate interplay between SOX10 and miRNAs in melanoma biology including melanogenesis, phenotype switch, and therapy resistance. Additionally, we investigated the gene regulatory interactions in melanoma, identifying crucial network motifs that involve both SOX10 and miRNAs. We also analyzed the expression levels of the components within these motifs. From a control theory perspective, we explained how these dynamics are linked to the phenotypic plasticity of melanoma cells. In summary, we underscored the importance of employing a data-driven network biology approach to elucidate the complex regulatory mechanisms and identify driver network motifs within the melanoma network. This methodology facilitates a deeper understanding of the regulation of SOX10 by miRNAs in melanoma. The insight gained could potentially contribute to the development of miRNA-based treatments for SOX10, thereby enhancing the clinical management of this malignancy.

systems biology↗

bioRxiv2024

Accurate Multiple Sequence Alignment of Ultramassive Genome Sets

With ever increasing sequencing efficiency, there is a pressing need to tackle a presently intractable task of accurate multiple sequence alignment (MSA) for ultramassive genome sets of millions and beyond. Additionally, efficient graph and probabilistic representations for downstream analysis are in dire lack. In light of these challenges, we develop a set of essentially linearly scalable algorithms, including that for constructing directed acyclic graphs, for training tiled profile hidden Markov models and for conducting alignment on such graphs. The power of these algorithms is demonstrated by both significantly improved accuracy and tremendous acceleration of SAR-CoV-2 MSA by three observed to five projected orders of magnitude for genome set sizes ranging from 40,000 to 4 million when compared with widely utilized MAFFT. Future application to other viral species and extension to more complex genomes will prove this algorithm set as a cornerstone for the coming era of ultramassive genome sets.

bioinformatics↗

bioRxiv2024

The integration of network biology and pharmacophore modeling suggests repurposing Clindamycin as an inhibitor of pyroptosis via Caspase-1 blockage in tumor-associated macrophages

BackgroundUveal melanoma (UM) is a highly malignant intraocular tumor with a poor prognosis and response to therapy, including immune checkpoint inhibitors (ICIs), after the onset of liver metastasis. The metastatic microenvironment contains high levels of tumor-associated macrophages (TAMs) that correlate positively with a worse patient prognosis. We hypothesized that one could increase the efficacy of ICIs in UM metastases by immunomodulating UM-associated macrophages. MethodsTo identify potential targets for the immunomodulation, we created a network-based representation of the biology of TAMs and employed (bulk and single-cell) differential gene expression analysis to obtain a regulatory core of UM macrophages-associated genes. We utilized selected targets for pharmacophore-based virtual screening against a library of FDA-approved chemical compounds, followed by refined flexible docking analysis. Finally, we ranked the interactions and selected one novel drug-target combination for in vitro validation. ResultsBased on the generated TAM-specific interaction network (3863 nodes, 9073 edges), we derived a UM macrophages-associated regulatory core (74 nodes, 286 edges). From the regulatory core genes, we selected eight potential targets for pharmacophore-based virtual screening (YBX1, GSTP1, NLRP3, ISG15, MYC, PTGS2, NFKB1, CASP1). Of 266 drug-target interactions screened, we identified the interaction between the antibiotic Clindamycin and Caspase-1 as a priority for experimental validation. Our in vitro validation experiments showed that Clindamycin specifically interferes with activated Caspase-1 and inhibits the secretion of IL-1{beta}, IL-18, and lactate dehydrogenase (LDH) in macrophages after stimulation. Our results suggest that repurposed Clindamycin could reduce pyroptosis in TAMs, a pro-inflammatory form of programmed immune cell death favouring tumor progression. ConclusionWe were able to predict a novel Clindamycin-Caspase-1 interaction that effectively blocks Caspase-1-mediated inflammasome activity and pyroptosis in vitro in macrophages. This interaction is a promising clinical immunomodulator of the tumor microenvironment for improving ICI responsivenss. This work demonstrates the power of combining network-based transcriptomic analysis with pharmacophore-guided screening for de novo drug-target repurposing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=104 SRC="FIGDIR/small/576201v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@310a9aorg.highwire.dtl.DTLVardef@1af0dcaorg.highwire.dtl.DTLVardef@1b272aborg.highwire.dtl.DTLVardef@86572b_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

Modeling Human Spine-Spinal Cord Organogenesis by hPSC-Derived Neuromesodermal Progenitors

Human trunk development, including spine and spinal cord organogenesis, is a coordinated, orderly, and interdependent process with spatiotemporal tissue patterning. However, the underlying cellular and molecular mechanisms remain largely unclear due to the lack of an effective model that can simulate the early development of human body axis. Here, we reported the long-term patterning and dynamic morphogenesis of human trunk through the formation of spine-spinal cord organoids (SSCOs) self-organized from three-dimensional culture of human PSC-derived neuromesodermal progenitors (NMPs). The SSCOs resembled the morphogenetic features of spine and spinal cord along the anterior-posterior axis, and showed the chondro-osteogenic and neural trajectories consistent with developmental dynamics of spine and spinal cord in gestational embryo through single-cell RNA sequencing (scRNA-seq). In addition, we identified a new HMMR+ bipotent cell population with self-renewal ability and neural/mesodermal competence but distinct from NMPs, which may be involved in trunk development and represent an invaluable tool for disease modeling of spine- and spinal cord-related disorders. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/549829v1_ufig1.gif" ALT="Figure 1"> View larger version (80K): org.highwire.dtl.DTLVardef@14bfd30org.highwire.dtl.DTLVardef@1db7fe8org.highwire.dtl.DTLVardef@1aa66a1org.highwire.dtl.DTLVardef@380932_HPS_FORMAT_FIGEXP M_FIG C_FIG

developmental biology↗

bioRxiv2023

SEPALLATA-driven MADS transcription factor tetramerization is required for inner whorl floral organ development

MADS genes encode transcription factors that act as master regulators of plant reproduction and flower development. The SEPALLATA (SEP) subfamily is required for the development of floral organs and plays roles in inflorescence architecture and development of the floral meristem. The SEPALLTAs act as organizers of MADS complexes, forming both heterodimers and heterotetramers in vitro. To date, the MADS complexes characterized in angiosperm floral organ development contain at least one SEPALLATA protein. Whether DNA-binding by SEPALLATA-containing dimeric MADS complexes are sufficient for launching floral organ identity programs, however, is not clear as only defects in floral meristem determinacy were observed in tetramerization impaired SEPALLATA mutants. Here, we used a combination of genome-wide binding studies, high resolution structural studies of the SEP3/AGAMOUS tetramerization domain, structure-based mutagenesis and complementation experiments in sep1 sep2 sep3 and sep1 sep2 sep3 ag-4 plants transformed with versions of SEP3 encoding tetramerization mutants. We demonstrate that while SEP3 heterodimers are able to bind DNA both in vitro and in vivo and recognize the majority of SEP3 wild type binding sites genome-wide, tetramerization is not only required for floral meristem determinacy, but also absolutely required for floral organ identity in the second, third and fourth whorls.

plant biology↗