Biology subjects

Tang, W.

Publications and source records attributed to Tang, W..

At least 73 records · Page 4Linked to original sources

bioRxiv2024

Characterization of ribosome stalling and no-go mRNA decay stimulated by the Fragile X protein, FMRP

Loss of functional fragile X mental retardation protein (FMRP) causes fragile X syndrome (FXS) and is the leading monogenic cause of autism spectrum disorders and intellectual disability. FMRP is most notably a translational repressor and is thought to inhibit translation elongation by stalling ribosomes as FMRP-bound polyribosomes from brain tissue are resistant to puromycin and nuclease treatment. Here, we present data showing that the C-terminal non-canonical RNA-binding domain of FMRP is essential and sufficient to induce puromycin-resistant mRNA*ribosome complexes. Given that stalled ribosomes can stimulate ribosome collisions and no-go mRNA decay (NGD), we tested the ability of FMRP to drive NGD of its target transcripts in neuroblastoma cells. Indeed, FMRP and ribosomal proteins, but not PABPC1, were enriched in isolated nuclease-resistant disomes compared to controls. Using siRNA knockdown and RNA-seq, we identified 16 putative FMRP-mediated NGD substrates, many of which encode proteins involved in neuronal development and function. Increased mRNA stability of the putative substrates was also observed when either FMRP was depleted or NGD was prevented via RNAi. Taken together, these data support that FMRP stalls ribosomes and can stimulate NGD of a select set of transcripts in cells, revealing an unappreciated role of FMRP that would be misregulated in FXS.

biochemistry↗

bioRxiv2023

Thermo-Responsive Polymers Targeting Inflammation in Murine Colitis

Targeting the site of inflammation is an ideal approach for treating inflammatory bowel disease (IBD). Inflammation targeting enables maximal drug-on-target effects while minimizing off-target side effects. Negatively charged drug carriers have been shown to facilitate drug delivery to inflamed colon mucosa after local administration. To modulate the negative charges and integrate responsiveness to stimuli, here we describe thermo-responsive, inflammation-targeting (TRIT) hydrogels based on functionalized poly(N-isopropylacrylamide-co-methacrylic acid) (PNIPAM-MAA). We show that both chemical modification types and polymer molecular weights affect the resultant microgels adhesion to the inflamed colon in dextran sulfate sodium (DSS)-induced murine colitis in vivo. Further, we quantified the correlations between microgels adhesion and colitis severity for individual mice, demonstrating that the microgels adhesion correlated directly with weight loss percentage in DSS-treated mice. By exploiting charge-mediated interaction and thermo-responsiveness, TRIT hydrogels represent a promising strategy to target inflamed colon mucosa as a drug delivery platform for colonic IBD treatment. TeaserThis study developed thermo-responsive, inflammation-targeting (TRIT) hydrogels that harness charge-mediated interaction and sol-to-gel transition to target inflamed colon mucosa as a new approach for treating inflammatory bowel disease.

bioengineering↗

bioRxiv2023

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↗

bioRxiv2023

The proteomic toolbox for identification, quantification, and characterization of polyclonal antibodies

Recent advances in proteomics and mass spectrometry facilitated the in-depth characterization of monoclonal antibodies and enabled innovative approaches for the quantification of polyclonal antibodies generated against numerous antigens. Human respiratory syncytial virus (RSV) is a contagious respiratory pathogen often manifested as a common cold infection in adults and more serious symptoms in infants and the elderly population. Here, we used a reference IgG1{kappa} monoclonal antibody NISTmAb 8671 and its affinity interaction with an RSV fusion glycoprotein F as a model to develop the proteomic toolbox for identification, quantification, and characterization of polyclonal antibodies. Our toolbox integrated a variety of proteomic and mass spectrometry approaches for accurate mass measurements of antibody fragments, antibody digestion with the complimentary proteases (trypsin, asparaginase, and proalanase), immunoaffinity enrichments, and bottom-up or middle-down proteomics. We measured absolute concentrations of anti-RSV antibody isotypes and subclasses in 69 serum samples of healthy individuals and revealed IgG1 (2,580 ng/mL), IgA1 (280 ng/mL), and IgM (180 ng/mL) as the most abundant isotypes. Interestingly, we also identified the presence of IgG2 (74 ng/ml), IgG4 (4.9 ng/mL) and IgA2 (5.5 ng/mL) antibodies. Interactome measurements detected the consistent co-precipitation of C1q complement complexes. Repertoire profiling of the variable regions of polyclonal antibodies revealed the frequent use of IGHV3 subgroup genes, while IGHV5-51 was the most abundant single gene of the anti-RSV polyclonal antibody response. The presented toolbox will facilitate the in-depth characterization of polyclonal antibodies and pave the way to quantitative approaches in serological studies and precision immunology.

biochemistry↗

bioRxiv2023

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↗

bioRxiv2023

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↗

bioRxiv2023

imply: improving cell-type deconvolution accuracy using personalized reference profiles

Real-world clinical samples are often admixtures of signal mosaics from multiple pure cell types. Using computational tools, bulk transcriptomics can be deconvoluted to solve for the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, which ignores person-to-person heterogeneity. Here we present imply, a novel algorithm to deconvolute cell type proportions using personalized reference panels. imply can borrow information across repeatedly measured samples for each subject, and obtain precise cell type proportion estimations. Simulation studies demonstrate reduced bias in cell type abundance estimation compared with existing methods. Real data analyses on large longitudinal consortia show more realistic deconvolution results that align with biological facts. Our results suggest that disparities in cell type proportions are associated with several disease phenotypes in type 1 diabetes and Parkin-sons disease. Our proposed tool imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/.

bioinformatics↗

bioRxiv2023

magpie: a power evaluation method for differential RNA methylation analysis in N6-methyladenosine sequencing

MotivationRecently, novel biotechnologies to quantify RNA modifications became an increasingly popular choice for researchers who study epitran-scriptome. When studying RNA methylations such as N6-methyladenosine (m6A), researchers need to make several decisions in its experimental design, especially the sample size and a proper statistical power. Due to the complexity and high-throughput nature of m6A sequencing measurements, methods for power calculation and study design are still currently unavailable. ResultsWe propose a statistical power assessment tool, magpie, for power calculation and experimental design for epitranscriptome studies using m6A sequencing data. Our simulation-based power assessment tool will borrow information from real pilot data, and inspect various influential factors including sample size, sequencing depth, effect size, and basal expression ranges. We integrate two modules in magpie: (i) a flexible and realistic simulator module to synthesize m6A sequencing data based on real data; and (ii) a power assessment module to examine a set of comprehensive evaluation metrics. AvailabilityThe proposed power assessment tool magpie is publicly available as a R/Bioconductor package at: https://bioconductor.org/packages/magpie/.

bioinformatics↗

bioRxiv2023

Population genomic analyses reveal extensive genomic regions within selective sweeps associated with adaptation and demographic history of a wheat fungal pathogen

Plant pathogens can quickly evolve in response to the changing host and environment, resulting in destructive epidemics. The fungus Puccinia striiformis f. sp. tritici (Pst), causing wheat stripe rust disease worldwide, is one such pathogen. In the arms race between pathogens and hosts, changes in host populations exerted continuous selective forces on the dynamics of pathogens. However, the footprints of these selection forces and the demography of Pst remain poorly explored, limited our understanding the evolutionary processes that shape the spread and evolution of pathogens. In this study, we revealed several features of worldwide Pst populations through population genomic analyses. There might be limited gene flow between Pst populations from China and other countries. A slower rate of linkage disequilibrium decay was detected comparing to rust fungi with known sexual reproduction. Furthermore, we detected extensive hard and soft sweeps associated with Pst adaptation. Genes within the selective sweeps were enriched in secreted proteins and effectors and showed functions related to pathogenicity or virulence, temperature tolerance, and fungicide resistance implying that Chinese Pst populations suffered positive selection pressures from host and abiotic factors. Moreover, demographic history indicated Pst populations experienced strong bottlenecks at the beginning of the wheat domestication around 10,000 years ago and during modern agriculture around 100 years ago, suggesting that the crop domestication and breeding programs could continuously contribute to the decline of pathogen effective population sizes. Our results provided insights into the evolution of the Pst genome and highlighted the role of modern agriculture on pathogen demography. Author SummaryWheat stripe rust, as a pathogen of wheat, is capable of inducing significant yield reduction on a global scale. Traditional epidemiological investigations have elucidated the causative agent behind this disease, the wheat stripe rust pathogen, as an organism exhibiting swift adaptive evolution in response to its environment. However, the precise mechanisms underpinning its adaptive strategies and the ensuing alterations in population dynamics remain relatively unexplored. In our research, we have undertaken an analysis encompassing wheat stripe rust samples throughout the world. Employing population genomics methods, we have sought to discern the genomic footprints corresponding to the adaptive process within the pathogen. These genomic footprints have illuminated the operation of positive selection pressures caused by both the host plant and the ecological context. Concurrently, it have provided insights into the population size that the wheat stripe rust pathogen has undergone over millennia. Notably, its change in population size revealed the demographic history associated with wheat domestication.

microbiology↗

bioRxiv2023

PP2A Dephosphorylates Phytochrome-Interacting Factor 3 to Modulate Photomorphogenesis in Arabidopsis

The phytochrome (phy) family of sensory photoreceptors modulates developmental programs in response to ambient light. phys control gene expression in part by directly interacting with the bHLH class of transcription factors, phytochrome-interacting factors (PIFs), and inducing their rapid phosphorylation and degradation. Several kinases have been shown to phosphorylate PIFs and promote their degradation. However, the phosphatases that dephosphorylate PIFs are less investigated. Here we describe the identification of four regulatory subunits of Arabidopsis protein phosphatase 2A (PP2A) family (B, B{beta}, B and B{beta}) that interacted with PIF3 in yeast-two-hybrid, in vitro and in vivo assays. The pp2ab{beta} and b{beta}/b{beta} displayed short hypocotyls, while the overexpression of the B subunits induced longer hypocotyls compared to wild type under red light. The light-induced degradation of PIF3 was faster in b{beta}/b{beta} quadruple mutant compared to wild type. Consistently, immunoprecipitated PP2A A and B subunits directly dephosphorylated PIF3-MYC in vitro. RNA-seq analyses showed that B and B{beta} alter global gene expression in response to red light. PIFs (PIF1, PIF3, PIF4 and PIF5) are epistatic to these four B subunits in regulating hypocotyl elongation under red light. Collectively, these data show an essential function of PP2A in dephosphorylating PIF3 to modulate photomorphogenesis in Arabidopsis.

developmental biology↗

bioRxiv2023

A single cell transcriptomic fingerprint of stressed premature, imbalanced differentiation of embryonic stem cells.

Cultured naive pluripotent ESC differentiate into first lineage, XEN or second lineage, formative pluripotency. Hyperosmotic stress (sorbitol), like retinoic acid, decreases naive pluripotency and increases XEN in two ESC lines, as reported by bulk and scRNAseq, analyzed by UMAP. Sorbitol overrides pluripotency in two ESC lines as reported by bulk and scRNAseq, analyzed by UMAP. UMAP analyzed the effects of 5 stimuli - three stressed (200-300mM sorbitol with leukemia inhibitory factor +LIF) and two unstressed (+LIF, normal stemness-NS and -LIF, normal differentiation-ND). Sorbitol and RA decrease naive pluripotency and increase subpopulations of 2-cell embryo-like and XEN sub-lineages; primitive, parietal, and visceral endoderm (VE). Between the naive pluripotency and primitive endoderm clusters is a stress-induced cluster with transient intermediate cells with higher LIF receptor signaling, with increased Stat3, Klf4, and Tbx3 expression. Sorbitol, like RA, also suppresses formative pluripotency, increasing lineage imbalance. Although bulk RNAseq and gene ontology group analyses suggest that stress induces head organizer and placental markers, scRNAseq reveals few cells. But VE and placental markers/cells were in adjacent clusters, like recent reports. UMAPs show that dose-dependent stress overrides stemness to force premature lineage imbalance. Hyperosmotic stress induces lineage imbalance, and other toxicological stresses, like drugs with RA, may cause lineage imbalance, resulting in miscarriages or birth defects.

developmental biology↗

bioRxiv2023

Effect of Intermittent Pneumatic Compression on Preventing Deep Vein Thrombosis Using Microfluidic Vein Chip

Deep vein thrombosis (DVT) is a common disease often occurring in the lower limb veins of bedridden patients. Intermittent pneumatic compression (IPC) has been considered an effective approach to solve this problem. In our previous research, 264 patients were randomly treated either with IPC for one or eight hours per day. The incidence of severe venous thrombosis was significantly lowered in the IPC group than in the control group. However, it is still a chanllenge to real-time monitoring the blood flow and thrombus formation process during IPC operation periods. Here we made a microfluidic vein chip with valves embedded in a flexible channel that can mimic the compression of vessels by IPC contractrion. Driven by inlet blood velocity obtained clinally, numerical simulation were conducted to identify the shear stress and laminar pressure distribution in the vein. The obtained results showed that the thrombus formation can be reduced with Higher compressive pressure and smaller time interval. 24s interval time and 40mmHg maximum contractive pressure was considered to be the most appropriate parameters for DVT prevention. This vein chip offers a new approach in vitro to observe the working mechanism of IPC device, offering important data for its further clinical standard regulation.

bioengineering↗

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

SERPINB3 induces the basal-like/squamous subtype and enhances disease progression in pancreatic cancer

Pancreatic cancer is a heterogeneous disease with distinct subtypes. Here, we investigated candidate driver genes of the highly aggressive basal-like/squamous molecular subtype of pancreatic ductal adenocarcinoma (PDAC). Integrative transcriptomic analyses identified the upregulated serine/cysteine protease inhibitor, SERPINB3 (squamous cell carcinoma antigen 1, SCCA1) in basal-like/squamous PDAC using discovery and validation approaches. Upregulation of SERPINB3 associated with decreased patient survival and a transcriptome profile indicative of the basal-like/squamous subtype. In human PDAC cell lines, SERPINB3 transgene expression enhanced their invasion capability. Moreover, upregulated expression of SERPINB3 in AsPC-1 cells resulted in enhanced lung metastasis in an orthotopic xenograft model. Molecular analysis of the primary tumor xenografts indicated activation of pathways related to metastasis, increased oxidative damage, and angiogenesis when SERPINB3 was upregulated. Furthermore, metabolomic analysis, using patient cohorts and PDAC cell lines showed a distinct metabolic pattern closely associated with both SERPINB3 and the basal-like/squamous subtype, which included upregulation of carnitine/acylcarnitine, amino acid, glutathione, and purine metabolic pathways, and glycolysis. Further RNA-seq and metabolomic analyses indicated that SERPINB3 may potentially induce the basal-like/squamous subtype and metabolic reprogramming through MYC activation. Taken together, our findings identified SERPINB3 as a candidate marker gene for the basal-like/squamous subtype, which may contribute to the disease aggressiveness in this subtype of PDAC. Abbreviations8-Hydroxy-2-deoxyguanosine (8-OHdG), Diaminobenzene (DAB), Gene Set Enrichment Analysis (GSEA), Human Metabolome Technologies, Inc. (HMT), Immunohistochemistry (IHC), Ingenuity pathway analysis (IPA), Pancreatic ductal adenocarcinoma (PDAC), Serine/Cysteine Proteinase Inhibitor Family B Member 3 (SERPINB3) Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/534766v1_ufig1.gif" ALT="Figure 1"> View larger version (50K): org.highwire.dtl.DTLVardef@8209f5org.highwire.dtl.DTLVardef@15be96dorg.highwire.dtl.DTLVardef@13a0c6corg.highwire.dtl.DTLVardef@58b2f_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LISERPINB3 is upregulated in basal-like/squamous PDAC and associates with decreased patient survival C_LIO_LISERPINB3 promotes differentiation into the basal-like/squamous subtype and enhances invasion and metastasis of PDAC C_LIO_LISERPINB3 induces metabolic reprogramming and MYC activation and a metabolic signature indicative of basal-like/squamous PDAC C_LI

cancer biology↗

bioRxiv2023

Modelling capture efficiency of single cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

Gene expression is characterised by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data is prone to technical variability, including low and variable capture efficiency of transcripts from individual cells. Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful in application to both allele and non-allele specific scRNA-seq data.

bioinformatics↗

bioRxiv2023

A Metabolomics Study of Thrombosis after Cardiac Surgery in Children with Congenital Heart Disease

ObjectiveThrombosis is a major complication after cardiac surgery in children with congenital heart disease (CHD). The mechanisms underlying thrombosis development remain poorly understood. We aimed to identify novel circulating metabolites before cardiac surgery that are associated with the development of thrombosis after surgery in children with CHD. Approach and resultsAll blood samples were drawn right before surgical incision and after the induction of anesthesia, and plasma was separated immediately under 4 {degrees}C. Untargeted metabolomic data were measured by Metabolon in plasma from children (age range: 0 days-18 years) with CHD undergoing cardiac surgery. The primary outcome was thrombosis within 30 days of surgery or before discharge. Associations of individual metabolites with thrombosis were assessed with logistic regression with false discovery rate (FDR) correction for multiple comparison and adjustment for clinical characteristics; elastic net regression was used to select a prediction model. Out of 1,115 metabolites measured in samples from 203 children, 776 met the quality control criteria. In total, 26 children (12.8%) developed thrombosis. Among the 776 metabolites, 195 were significantly associated with thrombosis (FDR q-value < 0.05). The top three metabolites showing the strongest associations with thrombosis were eicosapentaenoate, steroid monosulfate C19H28O6S, and formiminoglutamate (FDR = 0.01 for all). Pathway analysis showed that the pathways of nicotinate and nicotinamide metabolism (P = 0.0007) and glycerophospholipid metabolism (P = 0.002) were enriched and had significant impacts on the development of thrombosis. In elastic net regression analysis, the area under the receiver operating-characteristic curve of a prediction model for thrombosis was 0.94 in the training sample (70% of the total sample) and 0.84 in the testing sample (the remaining 30%). ConclusionWe have identified promising novel metabolites and metabolic pathways associated with thrombosis. Future studies are warranted to confirm the findings and examine the mechanistic pathways to thrombosis.

physiology↗

bioRxiv2023

Antibody-mediated cell entry of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enters host cells by first engaging its cellular receptor angiotensin converting enzyme 2 (ACE2) to induce conformational changes in the virus-encoded spike protein and fusion between the viral and target cell membranes. We report here that certain monoclonal neutralizing antibodies against distinct epitopic regions of the receptor-binding domain of the spike can replace ACE2 to serve as a receptor and efficiently support membrane fusion and viral infectivity. These receptor-like antibodies can function in the form of a complex of their soluble immunoglobulin G with Fc-gamma receptor I, a chimera of their antigen-binding fragment with the transmembrane domain of ACE2 or a membrane-bound B cell receptor, indicating that ACE2 and its specific interactions with the spike protein are dispensable for SARS-CoV-2 entry. These results suggest that antibody responses against SARS-CoV-2 may expand the viral tropism to otherwise nonpermissive cell types; they have important implications for viral transmission and pathogenesis.

microbiology↗

bioRxiv2023

The Alfin-like transcription factor GmAlfin09 regulates endoplasmic reticulum stress in soybean via a peroxidase GmPRDX6

Soybean is a valuable oil crop cultivated throughout the world, but also highly susceptible to environmental stressors. The development of approaches to enhancing soybean stress resistance is thus vital to improving yields. In prior studies, Alfin has been shown to serve as an epigenetic regulator of plant growth and development. No studies of Alfin have yet been reported in soybean, however. In this study, the endoplasmic reticulum (ER) stress- and reactive oxygen species (ROS)-related transcription factor GmAlfin09 was identified. Screening of genes co-expressed with GmAlfin09 unexpectedly led to the identification of the peroxidase GmPRDX6. Further analyses revealed that both GmAlfin09 and GmPRDX6 were responsive to ER stress, with GmPRDX6 localizing to the ER under stress. Promoter binding experiments confirmed the ability of GmAlfin09 to directly bind the GmPRDX6 promoter. When GmAlfin09 and GmPRDX6 were overexpressed in soybean, enhanced ER stress resistance and decreased ROS levels were observed. Together, these findings suggest that GmAlfin09 can promote the upregulation of GmPRDX6, which subsequently localizes to the ER, reduced ROS levels, promotes ER homeostasis, and helps ensure the normal growth of soybean even under ER stress. This study highlights a novel genetic target for the future molecular breeding of stress-resistant soybean cultivars. HIGHLIGHTGmAlfin09 can increase the expression of GmPRDX6 to reduce ROS level under ER stress.

plant biology↗