Biology subjects

Zhou, H.

Publications and source records attributed to Zhou, H..

At least 163 records · Page 9Linked to original sources

bioRxiv2024

Single-dose avian influenza A(H5N1) Clade 2.3.4.4b hemagglutinin-Matrix-M nanoparticle vaccine induces neutralizing responses in nonhuman primates

With the recent rise in cases of highly pathogenic avian influenza (HPAI) A(H5N1) clade 2.3.4.4b infection in humans and animals, there is an associated increase in the risk of human-to-human transmission. In this study, we characterize recombinant A(H5N1) A/American Wigeon/South Carolina/22/000345-001/2021 (A/AW/SC/2021) clade 2.3.4.4b vaccine. Purified recombinant A/AW/SC/2021 HA trimers upon formulation with Matrix-M adjuvant, saponin-cholesterol-phospholipid icosahedral particles, non-covalently anchored to the vertices of the Matrix-M forming A(H5N1) HA-Matrix-M nanoparticles (H5-MNPs). In naive mice, two intranasal (IN) or intramuscular (IM) doses of A/AW/SC/2021 H5-MNP vaccine induced robust antibody- and cell-mediated immune responses, including neutralizing antibodies against A(H5N1). In non-human primates (NHPs) primed with seasonal influenza vaccine, a single IM or IN dose of the A/AW/SC/2021 H5-MNP vaccine induced geometric mean serum A(H5N1) clade 2.3.4.4b pseudovirus neutralizing titers of 1:1160 and 1:54, respectively; above the generally accepted seroconverting neutralizing titer of 1:40. Immunization with H5-MNP vaccine induced antibody responses against conserved epitopes in the A(H5N1) HA stem, vestigial esterase subdomain, and receptor binding site. This novel A(H5N1) H5-MNP IN and IM vaccine was immunogenic in rodents and NHPs as a potential A(H5N1) pandemic single-dose vaccine.

microbiology↗

bioRxiv2024

Sequence specificity of an essential nuclear localization sequence in Mcm3

Proteins with nuclear localization sequences (NLSs) are directed into the cell nucleus through interactions between the NLS and importin proteins. NLSs are generally short motifs rich in basic amino acids; however, identifying NLSs can be challenging due to the lack of a universally conserved sequence. In this study, we characterized the sequence specificity of an essential and conserved NLS in Mcm3, a subunit of the replicative DNA helicase. Through mutagenesis and AlphaFold 3 (AF3) modeling, we demonstrate that the precise positioning of basic residues within the NLS is critical for nuclear transport of Mcm3 through optimal interactions with importin. Disrupting these interactions impairs the nuclear import of Mcm3, resulting in defective chromatin loading of MCM and poor cell growth. Our results provide a structure-guided framework for predicting and analyzing monopartite NLSs, which, despite lacking a single consensus sequence, retain key characteristics shared between the NLSs of Mcm3 and the SV40 large T antigen. Author SummaryTransporting proteins into and out of the cell nucleus is essential for chromosome-associated activities. Nuclear localization sequences (NLSs), short motifs rich in basic amino acids, are commonly found in nuclear proteins. NLSs work by interacting with importin, a key transport receptor responsible for recognizing and guiding NLS-containing proteins through the nuclear pore complex into the nucleus. Other than being rich in basic amino acids, NLSs generally lack a discernible consensus sequence, raising questions about how they specifically control nuclear transport through their interactions with importins. Through a detailed mutagenesis study of a conserved and essential NLS in Mcm3, a subunit of the replicative DNA helicase, we demonstrate that the use of AlphaFold 3 (AF3), alongside genetic, biochemical, and cell biological analyses, define key contacts between Mcm3s NLS and importin that are required for nuclear import of Mcm3.

microbiology↗

bioRxiv2024

Image-based DNA Sequencing Encoding for Detecting Low-Mosaicism Somatic Mobile Element Insertions

Active LINE-1 (L1), Alu, and SVA mobile elements in the human genome are capable of retrotransposition, resulting in novel mobile element insertions (MEIs) in both germline and somatic tissues. Detecting MEIs through DNA sequencing relies on supporting reads overlapping MEI junctions; however, artifacts from DNA amplification, sequencing, and alignment errors produce numerous false positives. Systematic detection of somatic MEIs, particularly those with low mosaicism, remains a significant challenge. Previous methods had required a high number of supporting reads which limits the detection sensitivity, or human inspections that are susceptible to biases. Here, we developed RetroNet, an algorithm that encodes MEI-supporting sequencing reads into images, and employs a deep neural network to identify somatic MEIs with as few as two reads. Trained on extensive and diverse datasets and benchmarked across various conditions, RetroNet surpasses previous methods and eliminates the need for extensive manual examinations. The RetroNet analysis on the Illumina sequencing of 161x or 195x of a cancer cell line achieved an average precision of 0.885 and recall of 0.579 for detecting somatic L1 insertions that are present in as few as 1.79% of the cells. Additionally, we demonstrated that RetroNet is effective for analyzing highly degraded DNA, such as circulating tumor DNA. RetroNet is applicable to the rapidly generated short-read sequencing data and has the potential to provide further insights into the functional and pathological implications of somatic retrotranspositions.

genomics↗

bioRxiv2024

Aramchol attenuates fibrosis in mouse models of biliary fibrosis and blocks the TGFβ-induced fibroinflammatory mediators in cholangiocytes

BackgroundFibroinflammatory cholangiopathies, such as primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC), are characterized by inflammation and biliary fibrosis, driving disease-related complications. In biliary fibrosis, cholangiocytes activated by transforming growth factor-{beta} (TGF{beta}) release signals that recruit immune cells to drive inflammation and activate hepatic myofibroblasts to deposit the extracellular matrix (ECM). TGF{beta} regulates stearoyl-CoA desaturase (SCD), an enzyme that catalyzes the synthesis of monounsaturated fatty acids, in stimulating fibroinflammatory lipid signaling. However, the role of SCD or its inhibitor, Aramchol, has not been investigated in biliary fibrosis or TGF{beta}-mediated cholangiocyte activation. Method10-16-week-old multi-drug resistance 2 knockout (Mdr2-/-) and 3,5-diethoxycarboncyl-1,4-dihydrocollidine (DDC) diet-fed mice were orally gavaged daily with Aramchol at 12.5 mg/kg/day for 4 and 3 weeks, respectively. Liver and serum were harvested for the assessment of fibrosis and inflammation. Transformed human cholangiocyte cells (H69) and mouse large biliary epithelial cells (MLEs) were used to test the effects of the SCD inhibitor, Aramchol, at varying doses on TGF{beta}-mediated expression of fibroinflammatory signals and were confirmed in PSC-derived cholangiocytes (PSC-Cs) using ELISA, qPCR, and Western blot analyses. ResultsAramchol treatment of Mdr2-/- mice with established biliary fibrosis (treatment) and DDC diet-induced (prevention) models of cholestatic injury and fibrosis demonstrated significant reductions in both measures of ECM synthesis (mRNA expression of ECM components in the liver), collagen content of the liver (picrosirius red staining and hydroxyproline content) and myofibroblast activation (SMA staining). Il6 and Tnfa were also reduced with Aramchol in the liver. RNA-seq analysis of H69 cells showed that Aramchol co-treatment led to significant inhibition of TGF{beta}-induced hepatic fibrosis pathways while upregulating peroxisome proliferator-activated receptor (PPAR) signaling. SCD expression was significantly increased in TGF{beta}-treated H69 cells (2-fold, p<0.05). Aramchol in a dose-dependent manner significantly attenuated the increased expression of the fibrotic marker, plasminogen activator inhibitor-1 (PAI-1/SERPINE1), and hepatic stellate cell-activating genes (VEGFA and PDGFB) in TGF{beta}-activated H69 and MLEs. Aramchol also markedly reduced the expression of the inflammatory cytokine, interleukin 6 (IL6). SCD siRNA knockdown produced similar results in H69 cells. Furthermore, in PSC-Cs, the expressions of SCD, VEGFA and IL6 were significantly reduced with Aramchol. The expression of the anti-fibroinflammatory factors PPAR and -{gamma} were modestly increased in cholangiocyte cell lines with increased expression of PPAR-responsive genes and increased nuclear binding of DNA PPAR response elements with Aramchol co-treatment compared to TGF{beta} only. ConclusionAramchol, an SCD inhibitor, both attenuates and prevents biliary fibrosis in mouse models of cholestatic injury and fibrosis. This effect is partially due to Aramchol inhibiting TGF{beta}-induced fibroinflammatory mediators in cholangiocytes by upregulating PPAR and -{gamma} expression and activity. These findings, along with Aramchols excellent safety profile in clinical trials, provide the rationale for assessing Aramchol in further clinical studies in patients with biliary fibrosis, particularly PSC, where a treatment is desperately needed.

cell biology↗

bioRxiv2024

Dissecting Regulatory Non-Coding Heart Disease GWAS Loci with High-Resolution 3D Chromatin Interactions Reveals Causal Genes with Pathophysiological Relevance to Heart Failure

Heart failure is caused in part by cardiac remodeling processes that include the death of cardiac myocytes and their replacement by cardiac fibroblasts. We hypothesized that these two cell types may harbor epigenetic contexts in which heart disease-associated non-coding SNPs perturb gene expression relevant to disease. Accordingly, we generated high-resolution Hi-C data layered with chromatin accessibility and transcriptomic information to annotate and link putative distal regulatory elements in heart disease-associated loci to gene promoters. Our analysis identified several target genes with established roles in cardiac fibrosis and/or heart disease (GJA1, TBC1D32, CXCL12, IL6R, and FURIN). Perturb-seq in cardiac fibroblasts to knock out putative regulatory elements confirmed regulatory relationships involving GJA1, CXCL12, and FURIN, as gene editing led to changes in transcriptomic signatures associated with fibroblasts in heart failure. Our results demonstrate how integrative multi-omic approaches can delineate pathophysiologically relevant regulatory circuits that connect protein-coding genes to non-coding genetic variants associated with disease.

genomics↗

bioRxiv2024

ExoSloNano: Multi-Modal Nanogold Tags for identi-fication of Macromolecules in Live Cells & Cryo-Electron Tomograms

In situ cryo-Electron Microscopy (cryo-EM) enables the direct interrogation of structure-function relationships by resolving macromolecular structures in their native cellular environment. Tremendous progress in sample preparation, imaging and data processing over the past decade has contributed to the identification and determination of large biomolecular complexes. However, the majority of proteins are of a size that still eludes identification in cellular cryo-EM data, and most proteins exist in low copy numbers. Therefore, novel tools are needed for cryo-EM to identify the vast majority of macromolecules across multiple size scales (from microns to nanometers). Here, we introduce and validate novel nanogold probes that enable the detection of specific proteins using cryo-ET (cryo-Electron Tomography) and resin-embedded correlated light and electron microscopy (CLEM). We demonstrate that these nanogold probes can be introduced into live cells, in a manner that preserves intact molecular networks and cell viability. We use this system to identify both cytoplasmic and nuclear proteins by room temperature EM, and resolve associated structures by cryo-ET. We further employ gold particles of different sizes to enable future multiplexed labeling and structural analysis. By providing high efficiency protein labeling in live cells and molecular specificity within cryo-ET tomograms, we establish a broadly enabling tool that significantly expands the proteome available to electron microscopy.

biophysics↗

bioRxiv2024

Deciphering RNA regulation with a foundation language model

RNA metabolism is tightly regulated by cis-elements and trans-acting factors. Most information guiding such regulation is encoded in RNA sequences. Considering the similarities in semantic and syntactic features between RNAs and human language, we developed LAMAR, a transformer-based foundation language model for RNA regulation, to decipher general rules underlying RNA processing. The model was pretrained on approximately 15 million sequences from both genome and transcriptome of 225 mammals and 1569 viruses, and further fine-tuned with labeled datasets for various tasks. The resulting fine-tuned models outperformed the state-of-the-art methods in predicting mRNA translation efficiency and mRNA half-life, while achieving comparable accuracy to specifically designed methods in predicting splice sites of pre-mRNAs and internal ribosome entry sites. Our results indicated that a single foundation language model is applicable in the comprehensive analysis of different aspects of RNA regulation, providing new insight into the design and optimization of RNA drugs.

bioinformatics↗

bioRxiv2024

Spatial mutual nearest neighbors for spatial transcriptomics data

Mutual nearest neighbors (MNN) is a widely used computational tool to perform batch correction for single-cell RNA-sequencing data. However, in applications such as spatial transcriptomics, it fails to take into account the 2D spatial information. Here, we present spatialMNN, an algorithm that integrates multiple spatial transcriptomic samples and identifies spatial domains. Our approach begins by building a k-Nearest Neighbors (kNN) graph based on the spatial coordinates, prunes noisy edges, and identifies niches to act as anchor points for each sample. Next, we construct a MNN graph across the samples to identify similar niches. Finally, the spatialMNN graph can be partitioned using existing algorithms, such as the Louvain algorithm to predict spatial domains across the tissue samples. We demonstrate the performance of spatialMNN using large datasets, including one with N=36 10x Genomics Visium samples. We also evaluate the computing performance of spatialMNN to other popular spatial clustering methods. Our software package is available at (https://github.com/Pixel-Dream/spatialMNN).

bioinformatics↗

bioRxiv2024

Gut Microbial Impact on Colitis and Colitis-Associated Carcinogenesis in a Primary Sclerosing Cholangitis-IBD Model

Background and AimsPrimary sclerosing cholangitis (PSC) associated inflammatory bowel diseases (IBD) increase colorectal dysplasia and malignancy risk. Current mouse models do not adequately replicate human PSC-IBD, limiting mechanistic understanding and therapeutic development. This study uses Mdr2/Il10 double knockout (DKO) mice to examine microbiota roles in mediating colitis, colitis-associated colorectal dysplasia and hepatobiliary inflammation/fibrosis. GoalDevelop and phenotype a chronic spontaneous PSC-IBD mouse model, emphasizing colitis, colonic dysplasia, hepatobiliary inflammation/ fibrosis and the functional roles of resident microbiota. MethodsWe utilized germ-free (GF) and specific-pathogen-free Mdr2/Il10 DKO, Il10-/- and Mdr2-/- mice to model PSC-IBD. We monitored colonic dysplasia progression, colitis kinetics and severity by lipocalin-2, cytokine measurement, and tissue evaluations of colon and liver. We manipulated the microbiome to assess its functional effects. ResultsDKO mice exhibited age- and region-specific accelerated colitis and spontaneous colonic dysplasia progressing to high-grade invasive adenocarcinomas. Despite aggressive colonic inflammation, DKO mice showed reduced hepatic fibrosis, increased hepatic reparative macrophages, and matrix metalloproteinase activity compared to Mdr2-/- mice. GF DKO had heightened liver inflammation and mortality with absent colitis and colonic dysplasia, reversed with microbial reconstitution from DKO mice. Changes in DKO primary/secondary bile acid profiles mirrored those in PSC-IBD. ConclusionThe Mdr2/Il10 DKO model mirrors key factors in PSC-IBD patients in terms of inflammation and carcinogenesis. We found an important role for the dysbiotic microbiota in DKO mice for disease onset and progression. Targeting microbiota and bile acid metabolism may provide promising strategies for developing effective PSC-IBD therapies.

microbiology↗

bioRxiv2024

PRESSED FLOWER works downstream of ASYMMETRIC LEAVES 2 to affect sepal flatness in Arabidopsis

The development of flattened organs such as leaves and sepals is essential for proper plant function. While much research has focused on leaf flatness, little is known about how sepals achieve flat organ morphology. Previous study has shown that in Arabidopsis an ASYMMETRIC LEAVES 2 (AS2) gene mutation as2-7D causes ectopic AS2 expression on the abaxial sepal epidermis, which leads to growth discoordination between the two sides of sepals, resulting in outgrowth formation on abaxial sepal epidermis and sepal flatness disruption. Here we report that the PRESSED FLOWER (PRS) works downstream of AS2 in affecting sepal flatness. Genetic analysis showed that PRS mutations suppressed the outgrowth formation on the abaxial sepal epidermis in as2-7D mutant. Through tracking the PRS expression dynamics at a cellular resolution throughout the early developmental stages in WT and as2-7D sepals, we found that on the abaxial epidermis of as2-7D sepals, ectopic AS2 expression up-regulated PRS expression, leading to the epidermal outgrowth initiation. AS2 affected PRS activity on multiple levels: AS2 activated PRS expression through direct binding to PRS promoter region; AS2 also physically interacted with PRS. Our study highlights the complex interplay between AS2 and PRS in modulating sepal flatness.

developmental biology↗

bioRxiv2024

Proteomic analysis of papillary thyroid carcinoma in the context of Hashimoto's thyroiditis

ObjectiveTo analyze the proteomic characteristics of papillary thyroid carcinoma in the context of Hashimotos thyroiditis by mass spectrometry and screen the corresponding differential proteins. MethodsPostoperative paraffin specimens of 3 patients with Hashimotos thyroiditis combined with papillary thyroid carcinoma and 3 patients with Hashimotos thyroiditis and benign nodules were collected. The differential proteins were obtained by qualitative and quantitative analysis using Q ExactiveTM mass spectrometer. Functional enrichment cluster analysis and protein interaction analysis were then performed on these differential proteins. Finally, key proteins were screened out and immunohistochemical verification was performed. Results72 up-regulated differentially expressed proteins and 21 down-regulated differentially expressed proteins were screened out. THBS2 and COL12A1 were further identified by bioinformatics analysis. After immunohistochemical verification, it was concluded that COL12A1 can be used as a specific tumor marker. ConclusionThere are differentially expressed proteins between the Hashimotos thyroiditis combined with papillary thyroid carcinoma group and the Hashimotos thyroiditis combined with benign nodules group. The up-regulated expression of COL12A1 can become an auxiliary diagnostic marker for papillary thyroid carcinoma with Hashimotos thyroiditis.

genomics↗

bioRxiv2024

Mechanisms of traditional Chinese medicine in modulating cardiac microvascular endothelial cells in various injury models: A comprehensive systematic review

BackgroundThe structural and functional failure of cardiac microvascular endothelial cells (CMECs) is a primary contributor to coronary microvascular dysfunction (CMD). Traditional Chinese medicine (TCM) has been identified as a potential therapeutic approach for preserving CMECs and mitigating CMD. ObjectiveThis systematic review aims to present the latest evidence on TCM intervention mechanisms in CMECs under diverse injury models. MethodsThis systematic review was performed following the parameters of the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). A comprehensive literature search was conducted using PubMed, Embase, Web of Science, Scopus, China National Knowledge Infrastructure and China Biology Medicine disc. Reference lists of selected articles were reviewed to identify relevant studies. The search was not limited by year and was conducted solely in English. Eligible studies comprised publications describing in vitro studies that presented the latest evidence on TCM intervention mechanisms in CMECs under diverse injury models. ResultsA total of 63 papers were included in this study. According to the cell processing approach, 19 studies on ischemia or hypoxic injury models, 16 studies on Ischemia/reperfusion (I/R) or hypoxia/reoxygenation (H/R) injury models, 10 studies on inflammatory injury models, 5 studies on metabolic injury models, 3 studies on angiotensin II injury models, and 10 studies on other models. TCM exhibits structural and functional intervention capabilities in diverse damage conditions of CMECs. Its mechanism of action involves antioxidant, anti-apoptotic, anti-inflammatory effects, as well as regulation of energy metabolism through signaling pathways such as HIF-1/VEGF, PI3K/AKT, MAPK, and NF-{kappa}B. ConclusionsThe CCM and its constituents modulate CMECs through multiple signaling pathways in response to various injury models, thereby conferring protection on the coronary microcirculation. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/611361v1_ufig1.gif" ALT="Figure 1"> View larger version (75K): org.highwire.dtl.DTLVardef@188068forg.highwire.dtl.DTLVardef@166c434org.highwire.dtl.DTLVardef@1db6c16org.highwire.dtl.DTLVardef@952182_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2024

Deep Generative Optimization of mRNA Codon Sequences for Enhanced Protein Production and Therapeutic Efficacy

Messenger RNA (mRNA) therapeutics show immense promise, but their efficacy is limited by suboptimal protein expression. Here, we present RiboDecode, a deep learning framework that generates mRNA codon sequences for enhanced mRNA translation. RiboDecode introduces several advances, including direct learning from large-scale ribosome profiling data and generative exploration of a large sequence space. In silico analysis demonstrates RiboDecode s robust predictive accuracy for unseen genes and cellular environments. In vitro experiments showed substantial improvements in protein expression, significantly outperforming past methods. In addition, RiboDecode enables mRNA design with consideration of cellular context and demonstrates robust performance across different mRNA formats, including m1{Psi}-modified and circular mRNAs, an important feature for mRNA therapeutics. In vivo mouse studies showed that optimized influenza hemagglutinin mRNAs induce ten times stronger neutralizing antibody responses against influenza virus compared to the unoptimized sequence. In an optic nerve crush model, optimized nerve growth factor mRNAs achieve equivalent neuroprotection of retinal ganglion cells at one-fifth the dose of the unoptimized sequence. Collectively, RiboDecode represents a paradigm shift from rule-based to a data-driven, context-aware approach for mRNA therapeutic applications, enabling the development of more potent and dose-efficient treatments.

bioinformatics↗

bioRxiv2024

Predicting the bacterial host range of plasmid genomes using the language model-based one-class SVM algorithm

The prediction of the plasmid host range is crucial for investigating the dissemination of plasmids and the transfer of resistance and virulence genes mediated by plasmids. Several machine learning-based tools have been developed to predict plasmid host ranges. These tools have been trained and tested based on the bacterial host records of plasmids in related databases. Typically, a plasmid genome in databases such as NCBI is annotated with only one or a few bacterial hosts, which does not encompass all possible hosts. Consequently, existing methods may significantly underestimate the host ranges of mobilizable plasmids. In this work, we propose a novel method named HRPredict, which employs a word vector model to digitally represent the encoded proteins on plasmid genomes. Since it is difficult to confirm which host a particular plasmid definitely cannot enter, we develop a machine learning approach for predicting whether a plasmid can enter a specific bacterium as a no negative samples learning task. Using multiple one-class SVMs that do not require negative samples for training, the HRPredict predicts the host range of plasmids across 45 families, 56 genera, and 56 species. In the benchmark test set, we constructed reliable negative samples for each host taxonomic unit via two indirect methods, and we found that the AUC, F1-score, recall, precision, and accuracy of most taxonomic unit prediction models exceeded 0.9. Among the 13 broad-host-range plasmid types, HRPredict demonstrated greater coverage than HOTSPOT and PlasmidHostFinder, thus successfully predicting the majority of hosts previously reported. Through the feature importance calculation for each SVM model, we found that genes closely related to the plasmid host range are involved in functions such as bacterial adaptability, pathogenicity, and survival. These findings provide significant insight into the mechanisms through which bacteria adjust to diverse environments through plasmids. Impact StatementPlasmids are important vectors for horizontal gene transfer and play a crucial role in regulating bacterial host adaptation to the environment. The spread of plasmid-mediated antibiotic resistance genes and virulence factors is one of the most important public health issues today. Owing to the lack of highly efficient methods for predicting the host range of newly discovered plasmids, especially broad-host-range plasmids, it is difficult to fully elucidate the regulatory role of plasmids in microbial communities and to predict the risk of antibiotic resistance transmission in clinical settings. Existing prediction tools tend to underestimate the host range of mobilizable plasmids. The current paper aims to overcome this limitation. Based on the concept of a "no negative samples learning task," we propose a new plasmid host range prediction method (i.e., HRPredict) that uses an SVM algorithm based on language models. HRPredict may be a powerful tool that will improve biologists understanding of horizontal plasmid transfer and help predict the occurrence and development of bacterial resistance. Data SummaryHRPredict is freely available via https://github.com/FengTaoSMU/HRPredict.

bioinformatics↗

bioRxiv2024

Decoding protein glycosylation by an integrative mass spectrometry-based de novo sequencing strategy

Glycoproteins, representing over 50% of human proteins and most biopharmaceuticals, are crucial for regulating various biological processes. The complexity of multiple glycosylation sites, often leading to incomplete sequence coverage and ambiguous glycan modification profiles. Here, we developed an integrative mass spectrometry-based approach for decoding unknown glycoproteins, which is featured with the combination of deglycosylation-mediated de novo sequencing with glycosylation site characterization. We utilized enzymatic deglycosylation of N-/ O-glycan to achieve comprehensive sequence coverage. Additionally, EThcD fragmentation enables the identification of high-quality long peptides, facilitating precise protein assembly. We subsequently applied this method to de novo sequencing of the highly glycosylated therapeutic fusion protein Etanercept (Enbrel(R)). We also sequenced three new tumor necrosis factor receptor (TNFR): Fc-fusion biologics with largely unknown sequences, unveiling subtle distinctions in the primary sequences. Furthermore, we characterized N- and O-glycosylation modifications of these proteins at subunit, glycopeptide, and glycan levels. This strategy bridges the gap between the de novo sequencing and glycosylation modification, providing complete information of the primary structure and glycosylation modifications for glycoproteins. Notably, our method could be a robust solution for accurate sequencing of the glycoproteins and has practical value not only in basic research but also in the biopharmaceutical industry.

biochemistry↗

bioRxiv2024

Extracellular Vesicle-Associated Neutrophil Elastase Activates Hepatic Stellate Cells and Promotes Liver Fibrogenesis via ERK1/2 Pathway

Liver fibrosis associated with increased mortality is caused by activation of hepatic stellate cells and excessive production and accumulation of extracellular matrix in response to fibrotic insults. It has been shown that in addition to liver inflammation, systemic inflammation also contributes to liver fibrogenesis. A deeper understanding of mechanisms that control liver fibrotic response to intra-and extra-hepatic inflammation is essential to develop novel clinical strategies against this disease. Extracellular vesicles (EV) have been recognized as immune mediators that facilitate activation of hepatic stellate cells. In inflammatory diseases, activated neutrophils release neutrophil elastase (NE) bound to EV, which has been identified as a significant contributor to inflammation by promoting immune cell activation. Here, we aimed to explore the role of inflammation derived plasma EV-associated NE in liver fibrogenesis and its potential mechanisms. We show EV-associated NE induces activation, proliferation and migration of hepatic stellate cells by promoting activation of the ERK1/2 signaling pathway. This effect did not occur through EV without surface NE, and Sivelestat, a NE inhibitor, inhibited activation of the ERK1/2 signaling pathway mediated by EV-associated NE. Moreover, we found plasma EV-associated NE increases deposition of collagen1 and -smooth muscle actin in the liver of a mouse model of liver fibrosis (Mdr2-/-). Notably, this effect does not occur in control mice without preexisting liver disease. These data suggest that EV-associated NE is a pro-fibrogenic factor for hepatic stellate cell activation via the ERK1/2 signaling pathway in pre-existing liver injuries. Inhibition of the plasma EV-associated NE in inflammatory conditions may be a therapeutic target for liver fibrosis in patients with inflammatory diseases.

cell biology↗

bioRxiv2024

Video Foundation Models for Animal Behavior Analysis

Computational approaches leveraging computer vision and machine learning have transformed the quantification of animal behavior from video. However, existing methods often rely on task-specific features or models, which struggle to generalize across diverse datasets and tasks. Recent advances in machine learning, particularly the emergence of vision foundation models, i.e., large-scale models pre-trained on massive, diverse visual repositories, offers a way to tackle these challenges. Here, we investigate the potential of frozen video foundation models across a range of behavior analysis tasks, including classification, retrieval, and localization. We use a single, frozen model to extract general-purpose representations from video data, and perform extensive evaluations on diverse open-sourced animal behavior datasets. Our results demonstrate that features with minimal adaptation from foundation models achieve competitive performance compared to existing methods specifically designed for each dataset, across species, behaviors, and experimental contexts. This highlights the potential of frozen video foundation models as a powerful and accessible backbone for automated behavior analysis, with the ability to accelerate research across diverse fields from neuroscience, to ethology, and to ecology.

animal behavior and cognition↗

bioRxiv2024

Natural Selection in a Synthetic Yeast Endosymbiont Promotes Stable Coexistence

Bacteria engulfment by a higher order host is believed to be the beginning of an evolutionary process that ultimately formed mitochondria. In an effort to experimentally elucidate the early effects of natural selection on bacteria resident in a eukaryotic host, a synthetic endosymbiont model system has been exploited. Here we describe a reproducible series of mutations that were observed after Escherichia coli was passaged within Saccharomyces cerevisiae for >8 passages which led to enhanced coexistence of bacteria within the yeast. These naturally selected mutations, formed by gene acquisition of trans-posable elements in rcsC, cpxA, and idnK, result in both functional and non-functional protein products with phenotypic effects on the bacteria that promote endosymbiont stability.

synthetic biology↗