Biology subjects

Wu, L.

Publications and source records attributed to Wu, L..

At least 181 records · Page 10Linked to original sources

bioRxiv2023

Losartan rewires ovarian cancer tumor-immune microenvironment and suppresses IGF-1 to amplify chemo-immunotherapy sensitivity

Ovarian cancer (OvCa) is the most lethal of the gynecologic malignancies. Immune checkpoint inhibitors, which have revolutionized the treatment of multiple malignancies, have had limited efficacy in OvCa patients. Here, using syngeneic OvCa models and genetic and pharmacologic perturbations, we discovered that losartan - a widely prescribed anti-hypertensive drug - exhibits dual effects on both the tumor microenvironment and cancer cells to sensitize OvCa to chemo-immunotherapy. Specifically, losartan treatment i) reprograms the tumor microenvironment leading to increased vascular perfusion, and thus enhances drug delivery and immune effector cell intratumoral infiltration and function; and ii) rewires the OvCa cells by suppressing the IGF-1 signaling, resulting in enhanced chemosensitivity. As a result of the combined tumor and stromal effects, losartan treatment enhances the efficacy of chemo-immunotherapy in OvCa models. The safety and low cost (less than $1-2/day) of losartan warrant rapid translation of our findings to patients with OvCa.

cancer biology↗

bioRxiv2023

Structural and mechanistic characterization of bifunctional heparan sulfate N-deacetylase-N-sulfotransferase 1

Heparan sulfate (HS) polysaccharides are major constituents of the extracellular matrix, involved in myriad structural and signaling processes. Mature HS polysaccharides contain complex, non-templated patterns of sulfation and epimerization, which mediate interactions with diverse protein partners. Complex HS modifications form around initial clusters of glucosamine-N-sulfate (GlcNS) on nascent polysaccharide chains, but the mechanistic basis underpinning incorporation of the GlcNS modification itself into HS remains unclear. We have determined cryo-electron microscopy structures of human N-deacetylase-N-sulfotransferase (NDST)1, the bifunctional enzyme responsible for initial GlcNS modification of HS. Our structures reveal the architecture of both NDST1 deacetylase and sulfotransferase catalytic domains, alongside a previously unreported non-catalytic N-terminal domain. Surprisingly, the two catalytic domains of NDST1 adopt an unusual back-to-back topology that limits direct cooperativity. Binding analyses, aided by novel activity modulating nanobodies, suggest that sulfotransferase domain substrate anchoring initiates the NDST1 catalytic cycle, providing a plausible mechanism for cooperativity despite spatial domain separation. Our data shed light on key determinants of NDST1 activity, and describe tools to probe NDST1 function in vitro and in vivo.

biochemistry↗

bioRxiv2023

A highly connected river network continuum is not resilient to non-point source pollution from anthropogenic activity

Urbanization significantly impacts the community structure of aquatic organisms and poses a major threat to river biodiversity. However, the extent to which urbanization is linked to the homogenization of microbial communities and the underlying mechanisms remains poorly understood. In this study, we investigated the bacterial and archaeal communities from cities and neighboring natural rivers across river network located in the Qinling Mountains, Northwest China, and further investigated the alpha and beta diversity patterns and the mechanisms influenced by urbanization. We found that the influx of urban non-point source pollutants created a eutrophic condition, and enhanced the urban river microbial populations. Meanwhile, the rapid urbanization tends to decrease the overall habitat heterogeneity, and imposed stronger homogeneous selection and caused microbial communities biotic homogenization. The mechanisms of biotic homogenization can be attributed to modulating generalist/specialist species and invasion of nonnative species. For instance, the urban river had a greater proportion of fast-growing bacteria, algae, nitrifiers, PAH-degrading bacteria, pathogens, fecal bacteria and antibiotic-resistant bacteria than natural river ecosystems. Overall, urbanization leads to a more uniform river biosphere, causing the extinction of unique local species and a subsequent decrease in the regional species pool.

microbiology↗

bioRxiv2023

Shining Light on Osteoarthritis: Spatially Offset Raman Spectroscopy as a Window into Cartilage Health

Articular cartilage is a complex tissue, and early detection of osteoarthritis (OA) is crucial for effective treatment. However, current imaging modalities lack molecular specificity and primarily detect late-stage changes. In this study, we propose the use of Spatially Offset Raman Spectroscopy (SORS) for non-invasive, depth-dependent, and molecular-specific diagnostics of articular cartilage. We demonstrate the potential of SORS to penetrate deep layers of cartilage, providing a comprehensive understanding of disease progression. Our SORS measurements were characterized and validated through mechanical and histological techniques, revealing strong correlations between spectroscopic measurements and both Youngs modulus and depth of cartilage damage. By longitudinally monitoring enzymatically degraded condyles, we further developed a depth-dependent damage-tracking method. Our analysis revealed distinct components related to sample depth and glycosaminoglycan (GAG) changes, offering a comprehensive picture of cartilage health. Collectively, these findings highlight the potential of SORS as a valuable tool for enhancing OA management and improving patient outcomes.

bioengineering↗

bioRxiv2023

A spatially constrained independent component analysis jointly informed by structural and functional connectivity

There are a growing number of neuroimaging studies motivating joint structural and functional brain connectivity. Brain connectivity of different modalities provides insight into brain functional organization by leveraging complementary information, especially for brain disorders such as schizophrenia. In this paper, we propose a multi-modal independent component analysis (ICA) model that utilizes information from both structural and functional brain connectivity guided by spatial maps to estimate intrinsic connectivity networks (ICNs). Structural connectivity is estimated through whole-brain tractography on diffusion-weighted MRI (dMRI), while functional connectivity is derived from resting-state functional MRI (rs-fMRI). The proposed structural-functional connectivity and spatially constrained ICA (sfCICA) model estimates ICNs at the subject level using a multi-objective optimization framework. We evaluated our model using synthetic and real datasets (including dMRI and rs-fMRI from 149 schizophrenia patients and 162 controls). Multi-modal ICNs revealed enhanced functional coupling between ICNs with higher structural connectivity, improved modularity, and network distinction, particularly in schizophrenia. Statistical analysis of group differences showed more significant differences in the proposed model compared to the unimodal model. In summary, the sfCICA model showed benefits from being jointly informed by structural and functional connectivity. These findings suggest advantages in simultaneously learning effectively and enhancing connectivity estimates using structural connectivity.

bioengineering↗

bioRxiv2023

Large-scale lensless microscopy with fast acquisition and region-wise focusing

The imaging field of view (FOV) of lensless microscope is consistent with the size of image sensor in use, enabling the observation of sample areas larger than 20 mm2. Combined with high-performance and even super-resolution phase retrieval algorithms, micron and sub-micron resolution can be achieved, ultimately realizing wide-field and high-resolution imaging performance simultaneously. However, high-throughput lensless imaging poses significant challenges in terms of rapid data acquisition and large-scale phase retrieval. Additionally, when observing biological samples over a large FOV, the focus plane often exhibits inconsistency among different regions, necessitating further parameter calibration. In this study, we propose a fast acquisition and efficient reconstruction strategy for coherent lensless imaging based on a multi-height imaging model. Multiple measurements are manually modulated using an axial translation stage and continuously captured by an image sensor, facilitating rapid data acquisition within seconds and requiring no hardware synchronization. The efficiency and accuracy of phase retrieval are enhanced through precise parameter calibration algorithms, as well as techniques such as region-wise parallel computing and region-wise auto-focusing. Experimental results demonstrate 7.4x5.5 mm2 FOV and 1.55 m half-pitch resolution imaging of human skin and lung tumor sections with region-wise focusing, requiring only an approximate 0.5-s acquisition time and 44-s reconstruction time. Furthermore, by incorporating the pixel super-resolution principle, the 1.10 m half-pitch imaging resolution is demonstrated in full-FOV peripheral blood smears without additional data required, beneficial to the identification of hollow shape and segmentation of blood cells.

bioengineering↗

bioRxiv2023

Identification of factors that prevent ribosome stalling during early elongation

Protein synthesis is performed by the ribosome and a host of highly conserved elongation factors. Elongation factor P (EF-P) prevents ribosome stalling at difficult-to-translate sequences, particularly polyproline tracts. In bacteria, phenotypes associated with efp deletion range from modest to lethal, suggesting that some species encode an additional translation factor that has similar function to EF-P. Here we identify YfmR as a translation factor that is essential in the absence of EF-P in B. subtilis. YfmR is an ABCF ATPase that is closely related to both Uup and EttA, ABCFs that bind the ribosomal E-site and are conserved in more than 50% of bacterial genomes. We show that YfmR associates with actively translating ribosomes and that depleting YfmR from {Delta}efp cells causes severe ribosome stalling at a polyproline tract in vivo. YfmR depletion from {Delta}efp cells was lethal, and caused reduced levels of actively translating ribosomes. Our results therefore identify YfmR as an important translation factor that is essential in B. subtilis in the absence of EF-P. SignificanceTranslation is one of the most ancient and energetically demanding processes that occurs in the cell. Ribosomes constitute more than 60% of cellular mass in actively growing cells, and ribosomes are a major target of antimicrobials and chemotherapeutics. Here, we identify YfmR as a translation factor that is essential in the absence of EF-P. YfmR is a member of the ABCF family of ATPases whose role in translation is only beginning to be understood. Given the broad distribution of ABCFs from bacteria to fungi, we expect our results to have implications for understanding translation elongation in diverse organisms.

biochemistry↗

bioRxiv2023

Spatial Metabolome Lipidome and Glycome from a Single brain Section

Metabolites, lipids, and glycans are fundamental biomolecules involved in complex biological systems. They are metabolically channeled through a myriad of pathways and molecular processes that define the physiology and pathology of an organism. Here, we present a blueprint for the simultaneous analysis of spatial metabolome, lipidome, and glycome from a single tissue section using mass spectrometry imaging. Complimenting an original experimental protocol, our workflow includes a computational framework called Spatial Augmented Multiomics Interface (Sami) that offers multiomics integration, high dimensionality clustering, spatial anatomical mapping with matched multiomics features, and metabolic pathway enrichment to providing unprecedented insights into the spatial distribution and interaction of these biomolecules in mammalian tissue biology.

systems biology↗

bioRxiv2023

Voluntary alcohol intake alters the motivation to seek intravenous oxycodone and neuronal activation during the reinstatement of oxycodone and sucrose seeking

Opioid-alcohol polysubstance use is prevalent and worsens treatment outcomes. Here we assessed whether co-consumption of oxycodone and alcohol would influence intake of one another, demand for oxycodone, and the neurocircuitry underlying cue-primed reinstatement of oxycodone-seeking. Male and female rats underwent oxycodone intravenous self-administration (IVSA) with access to either alcohol (20% v/v) and water or only water immediately after the IVSA session. Next, economic demand for intravenous oxycodone was assessed while access to alcohol and/or water continued. Control rats self-administered sucrose followed by access to alcohol and/or water. Rats underwent extinction training and brains were processed for c-fos mRNA expression immediately following a cue-primed reinstatement test. While both sexes decreased oxycodone intake if they had access to alcohol, and decreased alcohol intake if they had access to oxycodone, female oxycodone+alcohol rats exhibited decreased demand elasticity for intravenous oxycodone and increased cue-primed reinstatement while male rats did not. Spontaneous withdrawal signs were correlated with oxycodone intake while alcohol intake was correlated with anxiety-like behavior. Alcohol consumption increased the number of basolateral and central amygdala neurons activated during sucrose and oxycodone reinstatement and the number of ventral and dorsal striatum neurons engaged by sucrose reinstatement. Nucleus accumbens shell dopamine 1 receptor containing neurons displayed activation patterns consistent with oxycodone reinstatement. Thus, alcohol alters the motivation to seek oxycodone in a sex-dependent manner and alters the neural circuitry engaged by cue-primed reinstatement of sucrose and oxycodone-seeking.

neuroscience↗

bioRxiv2023

Tumor-Associated Macrophages Expand Chemoresistant, Ovarian Cancer Stem-Like Cells

The persistence of ovarian cancer stem-like cells (OvCSCs) after chemotherapy resistance has been implicated in relapse. However, the ability of these relatively quiescent cells to produce the robust tumor regrowth necessary for relapse remains an enigma. Since normal stem cells exist in a niche, and tumor-associated macrophages (TAMs) are the highest abundance immune cell within ovarian tumors, we hypothesized that TAMs may influence OvCSC proliferation. To test this, we optimized OvCSC enrichment by sphere culture and in vitro polarization of monocytes to a TAM-like M2 phenotype. Using cocultures that permitted the exchange of only soluble factors, we found that M2 macrophages increased the proliferation of sphere cells. Longer-term exposure (5-7 days) to soluble TAM factors led to retention of some stem cell features by OvCSCs but loss of others, suggesting that TAMs may support an intermediate stemness phenotype in OvCSCs. Although TAM coculture decreased the percentage of OvCSCs surviving chemotherapy, it increased the overall number. We therefore sought to determine the influence of this interaction on chemotherapy efficacy in vivo and found that inhibiting macrophages improved chemotherapy response. Comparing the gene expression changes in OvCSCs cocultured with TAMs to publicly available patient data identified 34 genes upregulated in OvCSCs by exposure to soluble TAM factors whose expression correlates with outcome. Overall, these data suggest that TAMs may influence OvCSC proliferation and impact therapeutic response.

cancer biology↗

bioRxiv2023

A framework for Frizzled-G protein coupling and implications to the Wnt-PCP signaling pathways

The ten Frizzled receptors (FZDs) are essential in Wnt signaling and play important roles in embryonic development and tumorigenesis. Among these, FZD6 is closely associated with lens development. Understanding FZD activation mechanism is key to unlock these emerging targets. Here we present the cryo-EM structures of FZD6 and FZD3 which are known to relay non-canonical Wnt-PCP (planar cell polarity) signaling pathways as well as FZD1 in their G protein-coupled (active) and G protein-free (inactive) states, respectively. Comparison of the three inactive/active pairs unveiled a shared activation framework among all ten FZDs. Mutagenesis along with imaging and functional analysis on the human lens epithelial tissues suggested potential crosstalk between G-protein binding and Wnt-PCP signaling pathways. Together, this study provides an integrated understanding of FZD structure and function, and lays the foundation for developing therapeutic modulators to activate or inhibit FZD signaling for a range of disorders including cancers and cataracts.

biochemistry↗

bioRxiv2023

Learning Complete Protein Representation by Deep Coupling of Sequence and Structure

Learning effective representations is crucial for understanding proteins and their biological functions. Recent advancements in language models and graph neural networks have enabled protein models to leverage primary or tertiary structure information to learn representations. However, the lack of practical methods to deeply co-model the relationships between protein sequences and structures has led to suboptimal embeddings. In this work, we propose CoupleNet, a network that couples protein sequence and structure to obtain informative protein representations. CoupleNet incorporates multiple levels of features in proteins, including the residue identities and positions for sequences, as well as geometric representations for tertiary structures. We construct two types of graphs to model the extracted sequential features and structural geometries, achieving completeness on these graphs, respectively, and perform convolution on nodes and edges simultaneously to obtain superior embeddings. Experimental results on a range of tasks, such as protein fold classification and function prediction, demonstrate that our proposed model outperforms the state-of-the-art methods by large margins.

bioinformatics↗

bioRxiv2023

Dendrimeric DNA Coordinate Barcoding Design for Spatial RNA Sequencing

Spatially resolved transcriptomic technologies show promise in revealing complex pathophysiological processes, but developing sensitive, high-resolution, and cost-effective methodology is challenging. Here, we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq). This technology combined dendrimeric nano-substrates with microfluidic coordinate barcoding to generate high-density spatial DNA arrays with deterministically combinatorial barcodes in a resolution-flexible and cost-effective manner ([~]$0.5/mm2). Decoder-seq achieved high RNA capture efficiency, [~]68.9% that of in situ sequencing, and enhanced the detection of lowly expressed genes by [~]five-fold compared to 10x Visium. Decoder-seq visualized a spatial single-cell atlas of mouse hippocampus at near-cellular resolution (15 m) and revealed dendrite-enriched mRNAs. Application to renal cancers dissected the heterogeneous tumor microenvironment of two subtypes, and identified spatial gradient expressed genes with the potential in predicting tumor prognosis and progression. Decoder-seq is compatible with sensitivity, resolution, and cost, making spatial transcriptomic analysis accessible to wider biomedical applications and researchers.

genomics↗

bioRxiv2023

Disease associated human TCR characterization by deep-learning framework TCR-DeepInsight

T cell function is defined by both T cell receptors (TCR) and T cell gene expression (GEX). Although single-cell technology enables the simultaneous capture of TCR and GEX information, the lack of a reference atlas and computational tools hinders our ability to uncover the fundamental TCR usage rules and to efficiently characterize disease-associated TCRs (dTCR). Here, through the collection of million-scale single-cell GEX-TCR reference atlas comprising 20 diverse disease conditions, we revealed the intrinsic features of TCR-MHC (Major Histocompatibility Complex) restriction in CD4/CD8 lineages. We observed the higher coherence for TCR/{beta} chains in memory T cells, and detected widely-existing public TCR/{beta} pairs across individuals. Building upon the reference atlas, we introduced TCR- DeepInsight, a deep-learning framework featuring a disease specificity scoring system that enables the characterization of dTCR clusters with similar GEX-TCR. Our study provides a valuable tool for researchers to analyze single-cell GEX-TCR data and identify dTCRs comprehensively and robustly.

bioinformatics↗

bioRxiv2023

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium. The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.

ecology↗

bioRxiv2023

SpatialCTD: a large-scale TME spatial transcriptomic dataset to evaluate cell type deconvolution for immuno-oncology

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular resolution. The task of cell type deconvolution has been introduced to disentangle discrete cell types from such multi-cellular spots. However, existing datasets for cell type deconvolution are limited in scale, predominantly encompassing data on mice, and are not designed for human immuno-oncology. In order to overcome these limitations and promote comprehensive investigation of cell type deconvolution for human immuno-oncology, we introduce a large-scale spatial transcriptomic dataset named SO_SCPLOWPATIALC_SCPLOWCTD, encompassing 1.8 million cells from the human tumor microenvironment across the lung, kidney, and liver. Distinct from existing approaches that primarily depend on single-cell RNA sequencing data as a reference without incorporating spatial information, we introduce Graph Neural Network-based method (i.e., GNNDO_SCPLOWECONVOLVERC_SCPLOW) that effectively utilize the spatial information from reference samples, and extensive experiments show that GNNDO_SCPLOWECONVOLVERC_SCPLOW often outperforms existing state-of-the-art methods by a substantial margin, without requiring single-cell RNA-seq data. To enable comprehensive evaluations on spatial transcriptomics data from flexible protocols, we provide an online tool capable of converting spatial transcriptomic data from other platforms (e.g., 10x Visium, MERFISH and sci-Space) into pseudo spots, featuring adjustable spot size. The SO_SCPLOWPATIALC_SCPLOWCTD dataset and GNNDO_SCPLOWECONVOLVERC_SCPLOW implementation are available at https://github.com/OmicsML/SpatialCTD, and the online converter tool can be accessed at https://omicsml.github.io/SpatialCTD/.

bioinformatics↗

bioRxiv2023

Modeling cross-cell type cis-regulatory patterns via hierarchical deep neural network and gene expression prediction

Linking cis-regulatory sequences to target genes has been a long-standing challenge. In this study, we introduce CREaTor, an attention-based deep neural network designed to model cis-regulatory patterns for genomic elements up to 2Mb from target genes. Coupled with a training strategy that predicts gene expression from flanking candidate cis-regulatory elements (cCREs), CREaTor can model cell type-specific cis-regulatory patterns in new cell types without prior knowledge of cCRE-gene interactions or additional training. The zero-shot modeling capability, combined with the use of RNA-seq and ChIP-seq data only, allows for the readily generalization of CREaTor to a broad range of cell types. Evaluation reveals that CREaTor outperforms existing methods in capturing cCRE-gene interactions across various distance ranges in held-out cell types. Further analysis indicates that the superior performance of CREaTor can be attributed to its capacity to model regulatory interactions at multiple levels, including the higher-order genome organizations that govern cCRE activities as well as cCRE-gene interactions. Collectively, our findings highlight CREaTor as a powerful tool for systematically investigating cis-regulatory programs across various cell types, both in normal developmental processes and disease-associated contexts.

genetics↗

bioRxiv2023

Widespread readthrough events in plants reveal unprecedented plasticity of stop codons

Stop codon readthrough (SCR), the decoding of a stop codon as a sense codon by the ribosome, has important biological implications but remains largely uncharacterized in plants. Here, we identified 1,009 SCR events in two monocots (maize, rice) and two dicots (soybean, Arabidopsis) using a proteogenomic strategy with 80 customized databases. SCR transcripts were mostly significantly shorter and had fewer components than non-SCR transcripts in two monocot plants, although these differences were not as significant in the dicots. Mass spectrometry evidence revealed that all three stop codons involved in SCR events could be recoded as 20 standard amino acids, some of which were also supported by suppressor transfer RNA analysis. In addition, we observed multiple functional signals in the C-terminal extensions of 34 maize SCR proteins, and characterized the structural and subcellular localization changes in the extended protein of BASIC TRANSCRIPTION FACTOR 3. Overall, our study not only demonstrates that SCR events are widespread in plants but also reveals the unprecedented recoding plasticity of stop codons, which provides important new insights into the flexibility of genetic decoding.

plant biology↗