Biology subjects

Zong, Y.

Publications and source records attributed to Zong, Y..

At least 19 recordsLinked to original sources

bioRxiv2026

CXCL13-CXCR5 Signaling in CD8⁺ T Cell Recruitment and Lymphoid Immune Organization in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) exhibits heterogeneity in immune infiltration and clinical outcomes, but the mechanisms governing recruitment and organization of tumor-reactive CD8 T cells remain incompletely defined. We investigated the role of the CXCL13-CXCR5 axis in shaping CD8 T cell recruitment, differentiation, and immune organization in high-risk, non-metastatic ccRCC. Human tumor, plasma, and matched adjacent kidney specimens were analyzed using ELISA, quantitative PCR, migration assays, multiplex immunofluorescence, single-cell RNA sequencing, spatial transcriptomics, and a syngeneic mouse model. CXCL13 was among the most upregulated chemokines in ccRCC relative to matched normal kidney and was embedded within a CD8 T cell-associated inflammatory transcriptional program. In transwell and microphysiological system (MPS) assays, CXCL13 promoted CD8 T cell migration, enriched CXCR5 cells among migrating CD8 T cells and showed reduced migration after CXCL13 or CXCR5 blockade. Single-cell analyses identified CXCR5 expression within stem-like CD8 T cell states associated with TCF7 and IL7R, whereas CXCL13 associated with later cytotoxic/exhausted states along a continuous differentiation landscape. Spatial transcriptomics demonstrated that stem-like CD8 T cells localized within structured lymphoid aggregates enriched for B cells, coordinated CXCL13/CXCR5 expression, and signaling programs. In vivo, tumor-derived CXCL13 suppressed tumor growth, increased intratumoral CD8 T cell infiltration, and enriched CXCR5TCF1CD8 stem-like T cells. In human tumors, higher CXCL13 expression correlated with increased CXCR5CD8 T cell infiltration and improved recurrence-free survival. These findings identify CXCL13 as a regulator of immune recruitment and niche organization and support the CXCL13-CXCR5 axis as a biomarker and possible therapeutic target in ccRCC.

cancer biology↗

bioRxiv2026

Single-cell Landscape of T Cell Heterogeneity in Kawasaki Disease: STAT3/JAK Axis Regulates the Lineage Differentiation Bias of Th17 Cells

BackgroundKawasaki disease (KD) is a pediatric systemic vasculitis in which T-cell-mediated immune responses play a pivotal role. However, the precise dynamic evolution of T-cell subsets during disease progression remains poorly understood. MethodsSingle-cell RNA sequencing (scRNA-seq) was employed to perform high-resolution annotation of peripheral blood mononuclear cells (PBMCs) from healthy controls and KD patients, both pre- and post- IVIG treatment. T-cell developmental trajectories were reconstructed via Monocle3-based pseudotime analysis. Furthermore, the functional significance of the significant pathway was validated in a CAWS-induced KD murine model. ResultsA high-resolution single-cell landscape identified 13 distinct T-cell subtypes. Pseudotime analysis revealed a significant lineage commitment of CD4+ T cells toward a Th17 phenotype during the acute phase of KD, synchronized with the transcriptional upregulation of the STAT3/JAK signaling axis. Animal experiments further demonstrated that pharmacological inhibition of this pathway substantially attenuated inflammatory infiltration in the cardiac vasculature of KD mice. ConclusionThis study identifies the STAT3/JAK-mediated Th17 differentiation bias as a potential regulatory program associated with acute inflammation in Kawasaki disease, thereby highlighting the STAT3/JAK axis as a potential therapeutic target.

bioinformatics↗

bioRxiv2025

Deep Learning Prediction of Intact N-/O-Glycopeptide Tandem Mass Spectra Enhances Glycoproteomics

Protein glycosylation, a post-translational modification involving the attachment of glycans to proteins, plays critical roles in numerous physiological and pathological cellular functions. Characterization of protein glycosylation is among one of the most challenging problems in proteomics due to the high heterogeneity of glycosites and glycan structures. Recently, deep learning has been adopted to predict N-glycopeptide tandem mass (MS/MS) spectra and exhibited a promising effect in N-glycoproteomics. However, current deep learning frameworks struggle to accurately predict O-glycopeptide MS/MS spectra due to the complexity of O-glycopeptides and the limited availability of training data. In this study, we introduce DeepGPO, a deep learning framework for the prediction of both N- and O-glycopeptide MS/MS spectra. The DeepGPO incorporates a Transformer module alongside two graph neural network (GNN) modules specifically designed for handling branched glycans. To address the issue of data scarcity in O-glycoproteomics datasets, various training methods are adopted in DeepGPO, such as the introduction of training weights for different MS/MS spectra and the adoption of pre-training strategies. DeepGPO exhibits accurate prediction of N- and O-glycopeptides MS/MS spectra. With the predicted MS/MS, glycosylation sites can be localized even in the absence of site-determining ions, for instance using higher-energy collisional dissociation (HCD) for the localization of O-glycosylation. We also explored the possibility of differentiating O-glycosites and N-glycosites using the predicted MS/MS spectra. DeepGPO primarily addresses mono-glycosylated peptides and is capable of handling doubly glycosylated peptides, but it currently cannot process peptides with three or more glycan modifications due to limited training data and increased spectral complexity. We anticipate that DeepGPO will inspire future advancements in glycoproteomics research.

bioinformatics↗

bioRxiv2025

Noninvasive ultrasound targeted modulation of calcium influx in splenic immunocytes potentiates antineoplastic immunity attenuating hepatocellular carcinoma proliferation

The spleen, as the largest immune organ, plays a pivotal role in modulating immune responses, particularly in the context of carcinogenesis and tumor progression. Non-pharmacological manipulation, particularly splenic ultrasound stimulation (SUS), has demonstrated significant immunomodulatory efficacy in alleviating chronic inflammatory diseases, suggesting its potential to revitalize splenic immunocompetence suppressing tumor proliferation, yet remains underexplored. This study applied low-frequency pulsed focused ultrasound (FUS) noninvasively stimulating the spleen (FUS sti. spleen) to investigate the efficacy in enhancing antitumor immunity and suppressing hepatocellular carcinoma (HCC). The results showed that FUS sti. spleen significantly suppressed tumor proliferation, achieving a suppression rate of >70% for H22-HCC and >83% for Hepa1-6-HCC, along with significantly prolonged survival. Comprehensive flow cytometry, single-cell RNA sequencing (scRNA-seq) and cytokine analyses demonstrated that SUS profoundly reshaped the splenic and intratumoral immune landscape, specifically activating cytotoxic CD8+ T cells and NK cells while suppressing immunosuppressive cell populations. Mechanistically, FUS facilitated calcium influx in splenic immunocytes, activating multiple signaling pathways, such as TNF, NF{kappa}B, MAPK, HIF-1, and ErbB, thereby counteracting tumor-driven immunosuppressive polarization while potentiating robust immune activation that impedes malignant progression and neoplastic proliferation. Leveraging above insights, we developed spleen-targeted nanodroplets encapsulating bioavailable calcium ions (STNDs@Ca{superscript 2}), which, upon FUS stimulation, undergo cavitation-mediated controlled release of Ca{superscript 2}, further amplifying immunocyte activation and tumor suppression, achieving a remarkable H22-HCC suppression rate of over 90%. This study highlights the therapeutic potential of ultrasound-mediated splenic immunomodulation, both as a standalone intervention and in synergy with STNDs@Ca{superscript 2}, as a novel and noninvasive strategy for cancer immunotherapy.

biophysics↗

bioRxiv2024

Marine medaka responds differently to dioxin compared with its close freshwater relative, the Japanese medaka: the AHR molecular mechanism

The global water pollution now calls for precise risk assessment of chemicals, e.g., dioxins and the emerging dioxin-like compounds (DLCs). The freshwater and marine medaka have been widely implemented in the toxicity testing, and perhaps give mechanistic information for comparative biology. The question that will they report equal results due to their close phylogenetic relation has been raised, therefore, we explored their physiological and molecular responses to dioxin. As the mediator of the dioxin toxicity, the aryl hydrocarbon receptor (AHR) of marine medaka (Oryzias melastigma) has not been functionally characterized and might be species-specific. In terms of sensitivity to dioxin--2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), the EC50 values of omeAHR1a (0.16{+/-}0.12 nM), omeAHR1b (2.96{+/-}2.96 nM), omeAHR2a (0.44{+/-}0.30 nM), and omeAHR2b (9.00{+/-}6.88 nM) exhibit marked variations. The omeAHR2a and omeAHR1a display heightened sensitivity compared to the freshwater Japanese medaka (Oryzias latipes) counterparts olaAHR2a and olaAHR1a, respectively. The results indicate the in vitro sensitivity of AHR among species can vary by one or two orders of magnitude. Physiologically, the marine medaka (LC50: 1.64 ng/L (95% CI: 1.05-2.55 ng/L)) also exhibits a pronounced sensitivity to TCDD than Japanese medaka (LC50: 3.42 ng/L (95% CI: 1.37-6.48 ng/L), aligning with the in vitro AHR sensitivity. Further mechanistic investigations using additional ligands and computational modeling reveal that: 1) most of omeAHR2a, olaAHR2a, dreAHR2, and hsaAHR interact with ligands in the affinity order of TCDD > PCB126 > BNF > indole, mirroring their AHR transactivation potency, but the docking poses and dynamics can vary; 2) one AHR subforms high sensitivity to dioxin--TCDD may extend to DLCs but not to other types of ligands. These insights underscore the difference of AHR biology among species even the close relative species, and finger out the necessity for meticulous consideration when evaluating the toxicity of compounds and when extending predictive toxicity assessments to more species.

ecology↗

bioRxiv2024

The role of PilU in the surface behaviors of Pseudomonas aeruginosa

In Pseudomonas aeruginosa, the dynamic activity of type IV pilus (TFP) is essential for various bacterial behaviors. While PilU is considered a homolog of the TFP disassembling motor PilT, its specific roles remain unclear. Using pilus visualization and single-cell tracking techniques, we characterized TFP dynamics and surface behaviors in wild-type and {Delta}pilU mutants. We found that {Delta}pilU cells displayed increased TFP numbers but reduced cell movement and delayed microcolony formation. Interestingly, beyond affecting the twitching motility, {Delta}pilU cells formed a thick multilayered colony edge on semi-solid surfaces, slowing colony expansion. Cell-cell collision responses changed from touch-turn dominance in wild-type to touch-upright dominance in {Delta}pilU, affecting colony morphology and expansion. These findings expand our understanding of PilUs physiological roles and provide potential targets for developing strategies to control P. aeruginosa biofilm formation and virulence. IMPORTANCEThe dynamic function of TFP can help cells to search appropriate spatial positions and perceive external environments, and is crucial for a variety of cell behaviors including cell movement, pathogenesis, natural transformation and biofilm formation. Beyond the current picture of PilU in generating high retraction forces through a PilT-dependent manner, this study revealed more roles of PilU in cell surface behaviors including single cell behavior like twitching and multicellular behaviors such as microcolony formation and colony expansion. These findings expand our understanding on the physiological roles of PilU in the surface behaviors of P. aeruginosa, and provide us insight in developing TFP-based methods to control the pathogenicity and biofilm formation of P. aeruginosa.

microbiology↗

bioRxiv2024

AI-enabled Alkaline-resistant Evolution of Protein to Apply in Mass Production

Artificial intelligence (AI) models have been used to study the compositional regularities of proteins in nature, enabling it to assist in protein design to improve the efficiency of protein engineering and reduce manufacturing cost. However, in industrial settings, proteins are often required to work in extreme environments where they are relatively scarce or even non-existent in nature. Since such proteins are almost absent in the training datasets, it is uncertain whether AI model possesses the capability of evolving the protein to adapt extreme conditions. Antibodies are crucial components of affinity chromatography, and they are hoped to remain active at the extreme environments where most proteins cannot tolerate. In this study, we applied an advanced large language model (LLM), the Pro-PRIME model, to improve the alkali resistance of a representative antibody, a VHH antibody capable of binding to growth hormone. Through two rounds of design, we ensured that the selected mutant has enhanced functionality, including higher thermal stability, extreme pH resistance and stronger affinity, thereby validating the generalized capability of the LLM in meeting specific demands. To the best of our knowledge, this is the first LLM-designed protein product, which is successfully applied in mass production.

bioinformatics↗

bioRxiv2024

Unraveling the impact of host genetics and factors on the urinary microbiome in a young population

The significance of the urinary microbiome in maintaining health and contributing to disease development is increasingly recognized. However, a comprehensive understanding of this microbiome and its influencing factors remains elusive. Utilizing whole metagenomic and whole-genome sequencing, along with detailed metadata, we characterized the urinary microbiome and its influencing factors in a cohort of 1579 Chinese individuals. Our findings unveil the distinctiveness of the urinary microbiome from other four body sites, delineating five unique urotypes dominated by Gardnerella vaginalis, Sphingobium fluviale, Lactobacillus iners, Variovorax sp PDC80 and Acinetobacter junii, respectively. We identified 108 host factors significantly influencing the urinary microbiome, collectively explaining 12.92% of the variance in microbial composition. Notably, gender-related factors, including sex hormones, emerged as key determinants in defining urotype groups, microbial composition and pathways, with the urinary microbiome exhibiting strong predictive ability for gender (AUC=0.843). Furthermore, we discovered 43 genome-wide significant associations between host genetic loci and specific urinary bacteria, Acinetobacter in particular, linked to 8 host loci (p < 5x10-8). These associations were also modulated by gender and sex hormone levels. In summary, our study provides novel insights into the impact of host genetics and other factors on the urinary microbiome, shedding light on its implications for host health and disease.

microbiology↗

bioRxiv2024

Multiple Omics Find New Cecal Microbial Features Associated with Feed Efficiency in Ducks

As the global population continues to grow exponentially, the competition for resources between livestock and humans has become increasingly intense. Breeding efficient animal breeds, fully utilizing feed resources, and reducing environmental damage are major challenges facing the livestock industry. To address these issues, enhancing the feed utilization efficiency in the poultry industry is crucial. Recent studies have elucidated the pivotal role of gut microbiota in modulating the feeding behavior of their host organisms. Thus, we used metagenomics, transcriptomics, and metabolomics to explore how the intestinal microbiome affects the feed utilization efficiency in ducks. Our metagenomic analysis revealed a significant up-regulation of Elusimicrobiota at the phylum level within the high residual feed intake (HRFI) group, in comparison to the low residual feed intake (LRFI) group. Additionally, functional analysis using Clusters of Orthologous Groups of proteins (COG) indicated prominent disparities in the category of secondary metabolites biosynthesis, transport, and catabolism between the HRFI and LRFI groups. Furthermore, our metabolomics investigation identified an upregulated expression of the secondary metabolite 15-deoxy-{Delta}12,14-prostaglandin J2 (15d-PGJ2) in the HRFI group compared to the LRFI group. Liver transcriptome analysis identified prostaglandin-endoperoxide synthase 2 (PTGS2) as a key hub gene, exerting significant regulatory influence within the arachidonic acid pathway. Notably, the metabolite 15d-PGJ2 is a terminal product in the metabolic pathway of arachidonic acid. The correlation analysis between the cecal microbiota and differential metabolites revealed a significant negative correlation between Elusimicrobiota and the metabolite 15d-PGJ2. In summary, we assumed that the intestinal microbiome Elusimicrobiota regulates the expression of the PTGS2 gene, consequently inducing variations in PTGS2 efficiency between the HRFI and LRFI groups, ultimately leading to diverse residual feed intake levels in ducks. IMPORTANCEThis investigation utilizes metabolomics to elucidate the interplay between genes and microbiome communities. We present evidence of disparities in the composition of microbial consortia among ducks RFI, alongside identification of pivotal genes within the liver that potentially modulate RFI. These results provide novel perspectives on the processes through which the cecum and liver influence RFI.

microbiology↗

bioRxiv2024

Unraveling the gut microbiota of Tibetan chickens: insights into highland adaptation and ecological advantages

Tibetan animals have several unique advantages owing to the harsh ecological conditions under which they live. Tibetan mammals have been studied extensively. However, understanding of the advantages and underlying mechanisms of the representative high-latitude bird, the Tibetan chicken (Gallus gallus, TC), remains limited. The gut microbiota of animals has been conclusively shown to be closely related to both host health and host environmental adaptation. This study aimed to explore the relationships between the cecal microbiome and the advantages of TCs based on comparisons among three populations: native TCs residing on the plateau, domestic TCs living in the plain, and one native plain species. Metatranscriptomic sequencing revealed a significant enrichment of active Bacteroidetes but a loss of active Firmicutes in native TCs. Additionally, the upregulated expression of genes in the cecal microbiome of native TCs showed enriched pathways related to energy metabolism, glycan metabolism, and the immune response. Furthermore, the expression of genes involved in the biosynthesis of short-chain fatty acids (SCFAs) and secondary bile acids (SBAs) was upregulated in the cecal microbiome of native TCs. Data from targeted metabolomics further confirmed elevated levels of certain SCFAs and SBAs in the cecum of native TCs. Based on the multi-omics association analysis, we proposed that the higher ratio of active Bacteroidetes/Firmicutes may be attributed to the efficient energy metabolism and stronger immunological activity of native TCs. Our findings provide a better understanding of the interactions between gut microbiota and highland adaptation, and novel insights into the mechanisms by which Tibetan chickens adapt to the plateau hypoxic environment. IMPORTANCEThe composition and function of the active cecal microbiome were significantly different between the plateau Tibetan chicken population and the plain chicken population. Higher expression genes related to energy metabolism and immune response were found in the cecal microbiome of the plateau Tibetan chicken population. The cecal microbiome in the plateau Tibetan chicken population exhibited higher biosynthesis of short-chain fatty and secondary bile acids, resulting in higher cecal content of these metabolites. The active Bacteroidetes/Firmicutes ratio in the cecal microbiome may contribute to the high-altitude adaptive advantage of the plateau Tibetan chicken population.

microbiology↗

bioRxiv2024

Deep Learning Prediction of Glycopeptide Tandem Mass Spectra Powers Glycoproteomics

Protein glycosylation plays a significant role in numerous physiological and pathological cellular functions. Glycoproteomics based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) studies the protein glycosylation on a proteome-wide scale to get combinational information on glycosylation site, glycosylation level and glycan structure. However, the current sequence searching-based methods for glycoproteomics often fall short in glycan structure determination due to the limited occurrence of structure-determining ions. While spectral searching methods can utilize fragment intensity information to facilitate the identification of glycopeptides, its application is hindered by the difficulties in spectral library construction. In this work, we present DeepGP, a hybrid deep learning framework based on Transformer and graph neural network (GNN), for the prediction of MS/MS spectra and retention time of glycopeptides. Two GNN modules are utilized to capture the branched glycan structure and predict glycan ions intensity, respectively. Additionally, a pre-training strategy is implemented to alleviate the insufficiency of glycoproteomics data. Testing on multiple biological datasets, we demonstrate that DeepGP can predict MS/MS spectra and retention time of glycopeptides closely aligning with the experimental results. Comprehensive benchmarking of DeepGP on synthetic and biological datasets validates its effectiveness in distinguishing similar glycoforms. Remarkably, DeepGP can differentiate isomeric glycopeptides using MS/MS spectra without diagnostic ions. Based on various decoy methods, we demonstrated that DeepGP in combination with database searching can significantly increase the detection sensitivity of glycopeptides. We outlook that DeepGP can inspire extensive future work in glycoproteomics.

bioinformatics↗

bioRxiv2023

A flexible, modular and versatile functional part assembly toolkit for gene cluster engineering in Streptomyces

Streptomyces has enormous potential to produce novel natural products (NPs) as it harbors a huge reservoir of uncharacterized and silent natural product biosynthetic gene clusters (BGCs). However, the lack of efficient gene cluster engineering strategies has hampered the pace of new drug discovery. Here, we developed an easy-to-use, highly flexible DNA assembly toolkit for gene cluster engineering. The DNA assembly toolkit are compatible with various DNA assembling approaches including Biobrick, Golden Gate, CATCH, yeast homologous recombination-based DNA assembly and homing endonuclease-mediated assembly. This compatibility offers great flexibility in handling multiple genetic parts or refactoring large gene clusters. To demonstrate the utility of this toolkit, we quantified a library of modular regulatory parts, and engineered a gene cluster (act) using characterized promoters that led to increased production. Overall, this work provides a powerful part assembly toolkit that can be used for natural product discovery and optimization in Streptomyces.

synthetic biology↗

bioRxiv2023

Multimodal engineering of extracellular vesicles for efficient intracellular protein delivery

Extracellular vesicles (EVs) are promising tools to transfer macromolecular therapeutic molecules to recipient cells, however, efficient functional intracellular protein delivery by EVs remains challenging. Here, we have developed novel and versatile systems that leverage selected molecular tools to engineer EVs for robust cytosolic protein delivery both in vitro and in vivo. These systems, termed VSV-G plus EV-sorting Domain-Intein-Cargo (VEDIC) and VSV-G-Foldon-Intein-Cargo (VFIC), exploit an engineered mini-intein (intein) protein with self-cleavage activity to link cargo to an EV-sorting domain and release it from the EV membrane inside the EV lumen. In addition, we utilize the fusogenic protein VSV-G to facilitate endosomal escape and cargo release from the endosomal system to the cytosol of recipient cells. Importantly, we demonstrate that the combination of the self-cleavage intein, fusogenic protein and EV-sorting domain are indispensable for efficient functional intracellular delivery of cargo proteins by engineered EVs. As such, nearly 100% recombination and close to 80% genome editing efficiency in reporter cells were observed by EV-transferred Cre recombinase and Cas9/sgRNA RNPs, respectively. Moreover, EV-mediated Cre delivery by VEDIC or VFIC engineered EVs resulted in significant in vivo recombination in Cre-LoxP R26-LSL-tdTomato reporter mice following both local and systemic injections. Finally, we applied these systems for improved treatment of LPS-induced systemic inflammation by delivering a super-repressor of NF-B activity. Altogether, this study describes a platform by which EVs can be utilized as a vehicle for the efficient intracellular delivery of macromolecular therapeutics for treatments of disease. Graphic summary: Development of VEDIC and VFIC systems for high-efficiency intracellular protein delivery in vitro and in vivo.Intein in tripartite fusion protein (EV-sorting Domain-Intein-Cargo) performs C-terminal cleavage during the process of EV-biogenesis, resulting in enriched free cargo proteins inside of vesicles. Together with fusogenic protein, VSV-G, these engineered EVs achieve high-efficiency intracellular delivery of cargo protein (Cre and super repressor of NF-B) or protein complex (Cas9/sgRNA RNPs) both in reporter cells and in mice models. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=142 SRC="FIGDIR/small/535834v4_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@d0da37org.highwire.dtl.DTLVardef@1a6514corg.highwire.dtl.DTLVardef@235c9aorg.highwire.dtl.DTLVardef@194bbfa_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

DCBLD1 modulates angiogenesis by regulating of the VEGFR-2 endocytosis in endothelial cells

ObjectiveUnwanted angiogenesis is involved in the progression of various malignant tumors and cardiovascular diseases, and the factors that regulate angiogenesis are potential therapeutic targets. We tested the hypothesis that DCBLD1 (Discoidin, CUB, and LCCL domain-containing protein 1) is a co-receptor of VEGFR-2 and modulates angiogenesis in endothelial cells(ECs). Approach and ResultsA carotid artery ligation model and retinal angiogenesis assay were used to study angiogenesis using globe knockout or EC-specific conditional DCBLD1 knockout mice in vivo. Immunoblotting, immunofluorescence staining, plasma-membrane subfraction isolation, Co-immunoprecipitation and mass-spectrum assay were performed to clarify the molecular mechanisms.Loss of DCBLD1 impaired VEGF response and inhibited VEGF-induced EC proliferation and migration. DCBLD1 deletion interfered with adult and developmental angiogenesis. Mechanistically, DCBLD1 bound to VEGFR-2 and regulated the formation of VEGFR-2 complex with negative regulators: protein tyrosine phosphatases, E3 ubiquitin ligases(Nedd4 and c-Cbl), and also DCBLD1 knockdown promoted lysosome-mediated VEGFR-2 degradation in ECs. ConclusionsThese findings demonstrated the essential role of endothelial DCBLD1 in regulating VEGF signaling and provided evidence that DCBLD1 promotes VEGF-induced angiogenesis by limiting the dephosphorylation, ubiquitination, and lysosome degradation after VEGFR-2 endocytosis. We proposed that endothelial DCBLD1 is a potential therapeutic target for ischemic cardiovascular diseases by the modulation of angiogenesis through regulating of the VEGFR-2 endocytosis.

molecular biology↗

bioRxiv2022

scMinerva: a GCN-featured Interpretable Framework for Single-cell Multi-omics Integration with Random Walk on Heterogeneous Graph

Single-cell multi-omics is a rapidly growing field in biomedicine, where multiple biological contents, such as the epigenome, genome, and transcriptome, can be measured simultaneously. Despite its potential, the integrated analysis and prediction of cellular states based on this complex multi-omics data pose significant challenges due to data sparsity, high noise, and computational overhead. To address these challenges, we developed scMinerva, an unsupervised framework for single-cell multi-omics integrated analysis. The learned embeddings from the multi-omics data enable accurate integrated classification of cell types and stages. Specifically, we construct a heterogeneous graph from multiple omics and propose a novel biased random walk algorithm omics2vec, which can learn the heterogeneous biological graph in a way that balances both local and global network structures. scMinerva successfully outperforms existing unsupervised methods on various simulated and real-world datasets when fine-tuned by very few labels. Additionally, scMinerva demonstrates strong label efficiency, is robust to fluctuation in data quality, allows one omics to compensate for weakness in others and could effectively classify cells with different annotation granularities. Furthermore, we showcase scMinervas ability to accurately provide prospective biomarkers and predict cell differentiation trends for COVID-19-infected cells, through the joint analysis of multi-omics data.

bioinformatics↗

bioRxiv2022

Identification of Microbial Flora and Isolation of Functional Strains Related to the Degradation of Domestic Waste

A large amount of food waste is currently generated due to rapid global population growth, which results in environmental pollution and economical burdens for waste disposal. Composting is one of the most environmentally friendly and cost-effective methods for food waste treatment. This study aims to give insight into microbial community structure and functional strains of household food waste compost, which might optimize the composting process and help increase food waste treatment efficiency. The microbial community is characterized via 16S rRNA sequencing. The dominant microbes are assigned to the three phyla of Firmicutes, Proteobacteria and Bacteroidetes, and the main fungi are assigned to the phylum of Ascomycota. We isolated some functional microbes from the compost microbiota, including Alcaligenes faecalis, Paenochrobactrum glaciei, Lysinibacillus fusiformis, and Proteus terrae. Moreover, we detected lipase and cellulase activities in the compost liquid. However, the measured lipase and cellulase activity is low, and further enhancement of the lipase activity in the food waste compost might increase waste treatment ability. Therefore, we isolated a cellulase and lipase harboring Bacillus cereus strain from municipal a wastewater treatment plant. The B. cereus strain have high-level lipase activity, which could be used for food waste treatment in the future.

microbiology↗

bioRxiv2022

B cells are addicted to immunoglobulin production in the ER

Resting B cell dependence on the B cell antigen receptor (BCR) has been attributed solely to its signaling competence. We have recently shown that the presence of the BCR is vital for endoplasmic reticulum (ER) homeostasis and mitochondrial function both in primary B cells and Burkitt lymphoma (BL) cell lines. Unexpectedly, in BL Ramos cells this role has been shown to be independent of BCR signals from the cell membrane. Now, we provide evidence that also in mouse resting B cells ER-resident immunoglobulin (Ig) heavy chains control ER homeostasis, calcium storage and mitochondrial homeostasis through ER-mitochondria contacts. These findings demonstrate that BCR expression shapes cellular fitness already at the stage of assembly in the ER and uncover a new layer of B cell dependence on the production of Ig heavy chains in the ER. Sensing protein expression in the ER, with counterselection of compromised cells, might well operate also in other differentiated cells.

immunology↗

bioRxiv2022

conST: an interpretable multi-modal contrastive learning framework for spatial transcriptomics

MotivationSpatially resolved transcriptomics (SRT) shows its impressive power in yielding biological insights into neuroscience, disease study, and even plant biology. However, current methods do not sufficiently explore the expressiveness of the multi-modal SRT data, leaving a large room for improvement of performance. Moreover, the current deep learning based methods lack interpretability due to the "black box" nature, impeding its further applications in the areas that require explanation. ResultsWe propose conST, a powerful and flexible SRT data analysis framework utilizing contrastive learning techniques. conST can learn low-dimensional embeddings by effectively integrating multi-modal SRT data, i.e. gene expression, spatial information, and morphology (if applicable). The learned embeddings can be then used for various downstream tasks, including clustering, trajectory and pseudotime inference, cell-to-cell interaction, etc. Extensive experiments in various datasets have been conducted to demonstrate the effectiveness and robustness of the proposed conST, achieving up to 10% improvement in clustering ARI in the commonly used benchmark dataset. We also show that the learned embedding can be used in complicated scenarios, such as predicting cancer progression by analyzing the tumour microenvironment and cell-to-cell interaction (CCI) of breast cancer. Our framework is interpretable in that it is able to find the correlated spots that support the clustering, which matches the CCI interaction pairs as well, providing more confidence to clinicians when making clinical decisions.

bioinformatics↗