Biology subjects

Zhang, C.

Publications and source records attributed to Zhang, C..

At least 667 records · Page 37Linked to original sources

bioRxiv2022

The structure of the humanised A33 Fab C226S variant, an immunotherapy candidate for colorectal cancer

Colorectal cancer (CRC) causes the second highest cancer-related deaths worldwide. The human A33 antigen is a validated immunotherapy target, which is homogeneously expressed in 95% cases of primary and metastatic colorectal cancers. In this article, we report the structure of a humanised antigen-binding fragment A33 (A33 Fab), a therapeutic antibody candidate, in two different crystal forms. Insights into the structural features of A33 Fab are provided with a focus on the grafted complementarity-determining regions (CDRs) and the switch linker between the variable and the constant regions. SynopsisThe crystal structure of humanised A33 Fab, targeting colorectal cancer related antigen, was determined in two different space groups.

biophysics↗

bioRxiv2022

scFLUX: a web server for metabolic flux and variation prediction using transcriptomics data

Quantitative assessment of single cell fluxome is critical for understanding the metabolic heterogeneity in diseases. Unfortunately, single cell fluxomics using laboratory approaches is currently infeasible, and none of the current flux estimation tools could achieve single cell resolution. In light of the natural associations between transcriptomic and metabolomic profiles, it remains both a feasible and urgent task to use the available single cell transcriptomics data for prediction of single cell fluxome. We present scFLUX here, which provides an online platform for prediction of metabolic fluxome and variations using transcriptomics data, on individual cell or sample level. This is in contrast to other flux estimation methods that are only able to model the fluxes for cells of pre-defined groups. The scFLUX webserver implements our in-house single cell flux estimation model, namely scFEA, which integrates a novel graph neural network architecture with a factor graph derived from the complex human metabolic network. To the best of our knowledge, scFLUX is the first and only web-based tool dedicated to predicting individual sample-/cell-metabolic fluxome and variations of metabolites using transcriptomics data. scFLUX is available at http://scflux.org/. The stand-alone tools for using scFLUX locally are available at https://github.com/changwn/scFEA.

bioinformatics↗

bioRxiv2022

A Multifunctional Anchor for Multimodal Expansion Microscopy

In situ imaging of biomolecular location with nanoscale resolution enables mapping of the building blocks of life throughout biological systems in normal and disease states. Expansion microscopy (ExM), by physically enlarging specimens in an isotropic fashion, enables nanoimaging on standard light microscopes. Key to ExM is the equipping of different kinds of molecule, with different kinds of anchoring moiety, so they can all be pulled apart by polymer swelling. Here we present a multifunctional anchor, an acrylate epoxide, that enables multiple kinds of molecules (e.g., proteins and RNAs) to be equipped with anchors in a single experimental step. This reagent simplifies ExM protocols and greatly reduces cost (by 2-10 fold for a typical multiplexed ExM experiment) compared to previous strategies for equipping RNAs with anchors. We show that this unified ExM (uniExM) protocol can be used to preserve and visualize RNA transcripts, proteins in biologically relevant ultrastructure, and sets of RNA transcripts in patient-derived xenograft (PDX) cancer tissues, and can support the visualization of other kinds of biomolecular species as well. Thus, uniExM may find many uses in the simple, multimodal nanoscale analysis of cells and tissues.

bioengineering↗

bioRxiv2022

The AI Neuropsychologist: Automatic scoring of memory deficits with deep learning

BackgroundMemory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patients ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinicians experience, motivation and tiredness. MethodsHere, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multi-head convolutional neural network. ResultsThe model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection. ConclusionsOur AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images.

neuroscience↗

bioRxiv2022

A novel attention-based peptide language model for dipeptidyl peptidase-IV inhibitory peptides mining

Mining of anti-diabetic dipeptidyl peptidase IV (DPP-IV) inhibitory peptides (DPP-IV-IPs) is currently a costly and laborious process. Due to the absence of rational peptide design rules, it relies on cumbersome screening of unknown enzyme hydrolysates. Here, we present an enhanced deep learning (DL) model called BERT-DPPIV, specifically designed to classify DPP-IV-IPs and exploring their design rules to discover potent candidates. The end-to-end model utilizes a fine-tuned bidirectional encoder representations (BERT) architecture to extract structural/functional information from input peptides and accurately identify DPP-IV-Ips from input peptides. Experimental results in benchmark dataset showed BERT-DPPIV yielded state-of-the-art accuracy of 0.894, surpassing the 0.797 obtained by sequence-feature model. Furthermore, we leverage the attention mechanism to uncover that our model could recognize restriction enzyme cutting site and specific residues that contribute to the inhibition of DPP-IV. Moreover, guided by BERT-DPPIV, proposed design rules of DPP-IV inhibitory tripeptides and pentapeptides were validated and they can be used to screen potent DPP-IV-IPs.

bioinformatics↗

bioRxiv2022

A Development-Inspired Niche for Homeostatic Human Mini-Intestines

Epithelial organoids derived from intestinal tissue, also referred to as mini-intestines or mini-guts, recapitulate many aspects of the organ in vitro and can be used for biological discovery, personalized medicine, and drug development. Murine intestinal organoids represent a homeostatic system that balances stem cell maintenance within a crypt-like compartment and differentiation within a villus-like compartment1-3. However, this homeostatic balance and spatial organization has not been achieved with human intestinal organoids4. Here, we leverage single cell RNA-seq data (scRNA-seq) and high-resolution imaging to interrogate the developing human intestinal stem cell niche. We identified an EGF-family member, EPIREGULIN (EREG), as uniquely expressed in the developing crypt, and found that EREG can take the place of EGF as an in vitro niche factor. Unlike EGF, which leads to growth of thin-walled cystic organoids, EREG-organoids are spatially resolved into budded and proliferative crypt domains and a differentiated villus-like central lumen. Transcriptomics and epigenomics showed that EREG-organoids are globally similar to the native intestine while EGF-organoids have an altered chromatin landscape, downregulate the master intestinal transcription factor CDX25,6, and ectopically express stomach genes.

developmental biology↗

bioRxiv2022

Loss of Spindly sensitizes EML4-ALK v3 lung cancer cells to HSP90 inhibitors

Heat Shock Protein 90 inhibitors (HSP90i) have shown activity in EML4-ALK+ non-small cell lung cancer (NSCLC) patients but clinical responses have been heterogeneous. It has been suggested that distinct EML4-ALK variants may have a differential impact on the response to HSP90 inhibition. Here, we show that NSCLC cells harboring the most common EML4-ALK variant 1 (v1) or variant 3 (v3) are in fact similarly sensitive to HSP90i. To discover new genetic alterations that could be involved in stratifying sensitivity, we performed a genome-wide CRISPR/Cas9 knockout screen and found that loss of Spindly increases the sensitivity of EML4-ALK v3, but not v1, NSCLC cells to low concentrations of HSP90i from three distinct chemical families. Upon loss of Spindly, prolonged exposure to low concentrations of HSP90i impairs chromosome congression and cellular fitness. Collectively, our data suggest that mutations leading to loss of Spindly in EML4-ALK v3 NSCLC patients may increase sensitivity to low doses of HSP90i.

cancer biology↗

bioRxiv2022

Selective degradation of an ER stress-induced protein by ER-associated degradation mechanism during stress recovery

Unfolded protein response (UPR) is a conserved signaling pathway that is activated by accumulation of misfolded proteins in the endoplasmic reticulum (ER) and stimulates production of ER chaperones to restore ER proteostasis. However, little is known how UPR-induced proteins return to their pre-stress levels upon removal of ER stress. TUNICAMYCIN-INDUCED1 (TIN1) is an Arabidopsis protein that is normally expressed in pollen but is rapidly induced by ER stresses in vegetative tissues. Here we show that the ER stress-induced TIN1 is rapidly degraded in the UPR recovery phase. We found that TIN1 degradation depends on its asparagine-linked glycans and requires both EMS-mutagenized bri1 suppressor 5 (EBS5) and EBS6 for its recruitment to the ER-associated degradation (ERAD) complex. Loss-of-function mutations in Arabidopsis ERAD components greatly stabilize TIN1. Interestingly, two other UPR-induced proteins that are coexpressed with TIN1 remained stable upon removal of ER stress, suggesting that rapid degradation during the stress-recovery phase likely applies to a subset of UPR-induced proteins. Further investigation should uncover the mechanisms by which the ERAD machinery differentially recognizes UPR-induced ER proteins.

biochemistry↗

bioRxiv2022

Pathobionts from chemically disrupted gut microbiota induce insulin-dependent diabetes in mice

BackgroundDysbiotic gut microbiome, genetically predisposed or chemically disrupted, has been linked with insulin-dependent diabetes (IDD) including autoimmune type 1 diabetes (T1D) in both humans and animal models. However, specific IDD-inducing gut bacteria remain to be identified and their casual role in disease development demonstrated via experiments that can fulfill Kochs postulates. ResultsHere, we show that novel gut pathobionts in the Muribaculaceae family, enriched by a low-dose dextran sulfate sodium (DSS) treatment, translocated to the pancreas and caused local inflammation, beta cell destruction and IDD in C57BL/6 mice. Antibiotic removal and transplantation of gut microbiota showed that this low DSS disrupted gut microbiota was both necessary and sufficient to induce IDD. Reduced butyrate content in the gut and decreased gene expression levels of an antimicrobial peptide in the pancreas allowed for the enrichment of members in the Muribaculaceae family in the gut and their translocation to the pancreas. Pure isolate of one such members induced IDD in wildtype germ-free mice on normal diet either alone or in combination with normal gut microbiome after gavaged into stomach and translocated to pancreas. ConclusionThe pathobionts that are chemically enriched in dysbiotic gut microbiota are sufficient to induce insulin-dependent diabetes after translocation to the pancreas. This indicates that IDD can be mainly a microbiome-dependent disease, inspiring the need to search for novel pathobionts for IDD development in humans.

microbiology↗

bioRxiv2022

Structure-based prediction and characterization of photo-crosslinking in native protein-RNA complexes

UV-crosslinking of protein and RNA in direct contacts has been widely used to study protein-RNA complexes despite our poor understanding on the mechanisms of photo-crosslinking. This knowledge gap is due to the challenge to precisely map the crosslink sites in protein and RNA simultaneously in their native sequence and structural contexts. Here we developed PxR3D-map, a computational method to analyze protein-RNA interactions and photo-crosslinking by bridging crosslinked nucleotides and amino acids mapped using different assays with protein-RNA complex structures. PxR3D-map reliably predicts crosslink sites using structural information characterizing protein-RNA interaction interfaces. We found that photo-crosslinking is facilitated by base stacking with not only aromatic residues, but also dipeptide bonds that involve glycine, and distinct mechanisms are utilized by different types of RNA-binding domains. Our work suggests protein-RNA photocrosslinking is highly selective in the cellular environment, which can guide interpretation of data generated by UV-crosslinking-based assays and further technology development.

biophysics↗

bioRxiv2022

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding

Spatially resolved transcriptomics (SRT) provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state. Cell type annotation is a crucial task in the spatial transcriptome analysis of cell and tissue biology. In this study, we propose Spatial-ID, a supervision-based cell typing method, for high-throughput cell-level SRT datasets that integrates transfer learning and spatial embedding. Spatial-ID effectively incorporates the existing knowledge of reference scRNA-seq datasets and the spatial information of SRT datasets. A series of quantitative comparison experiments on public available SRT datasets demonstrate the superiority of Spatial-ID compared with other state-of-the-art methods. Besides, the application of Spatial-ID on a SRT dataset with 3D spatial dimension measured by Stereo-seq shows its advancement on the large field tissues with subcellular spatial resolution.

genomics↗

bioRxiv2022

Accurate inference of genome-wide spatial expression with iSpatial

Spatially resolved transcriptomic analyses can reveal molecular insights underlying tissue structure and context-dependent cell-cell or cell-environment interaction. Due to the current technical limitation, obtaining genome-wide spatial transcriptome at single-cell resolution is challenging. Here we developed a new algorithm named iSpatial to derive spatial pattern of the entire transcriptome by integrating spatial transcriptomic and single-cell RNA-seq datasets. Compared to other existing methods, iSpatial has higher accuracy in predicting gene expression and their spatial distribution. Furthermore, it reduces false-positive and false-negative signals in the original datasets. By testing iSpatial with multiple spatial transcriptomic datasets, we demonstrate its wide applicability to datasets from different tissues and by different techniques. Thus, we innovated a computational approach to reveal spatial organization of the entire transcriptome at single cell resolution without the need of new technology development. With numerous high quality datasets available in the public domain, iSpatial provides a unique way for understanding the structure, function of complex tissues and disease processes. HIGHLIGHTSO_LIiSpatial infers genome-wide spatial gene expression pattern by integrating spatial transcriptomic and scRNA-seq data C_LIO_LIiSpatial outperforms existing approaches in inferring spatial gene expression patterns C_LIO_LIiSpatial reduces false-positive/negative signals of the original spatial transcriptome C_LIO_LIiSpatial is applicable to spatial transcriptomic datasets from different tissues and techniques C_LI

bioinformatics↗

bioRxiv2022

The majority of microorganisms in gas hydrate bearing subseafloor sediments ferment macromolecules

BackgroundGas hydrate-bearing subseafloor sediments harbor a large number of microorganisms. Sedimentary organic matter and upward methane fluids represent two important sources of carbon and energy for deep biosphere. However, which metabolism dominates the deep subseafloor of gas hydrate zone is poorly constrained. Here we studied the microbial communities in gas-hydrate rich sediments up to 49 meters below seafloor recovered by drilling in the South China Sea. We focused on distinct geochemical conditions, and performed metagenomic and metatranscriptomic analyses to characterize microbial diversity and their role in carbon mineralization. ResultsComparative microbial community analysis revealed that samples above and in sulfate-methane interface (SMI) zones clearly distinguish from those below the SMI. Chloroflexota, are most abundant above the SMI, whereas Caldatribacteriota dominate below the SMI. Verrucomicrobiota, Bathyarchaeia and Hadarchaeota were similarly present in both types of sediment. The genomic inventory and transcriptional activity suggest roles in fermentation of macromolecule. By contrast, sulfate reducers and methanogens, organisms that catalyze the consumption or production of commonly observed chemical compounds in sediments are rare. Methanotrophs of the ANME-1 group thrived in or slightly below the current sulfate methane interface. Rare members from Heimdallarchaeia were identified to encode the potential for anaerobic oxidation of short-chain hydrocarbons. ConclusionsThese findings indicate that fermentation of macromolecules is the predominant energy source for microorganisms in deep subseafloor sediments that are experiencing upward methane fluxes.

microbiology↗

bioRxiv2022

Hepatic AMPK activation in response to dynamic REDOX balance is a biomarker of exercise to improve blood glucose control

Antioxidant intervention is considered to inhibit reactive oxygen species (ROS) and alleviates hyperglycemia. Paradoxically, moderate exercise can produce ROS to improve diabetes. The exact redox mechanism of these two different approaches remains largely unclear. Here, by comparing exercise and antioxidants intervention on type 2 diabetic rats, we found moderate exercise upregulated compensatory antioxidant capability and reached a higher level of redox balance in the liver. In contrast, antioxidant intervention achieved a low-level redox balance by inhibiting oxidative stress. Both of these two interventions could promote glycolysis and aerobic oxidation mediated by hepatic AMPK activation, ameliorating diabetes. During exercise, different levels of ROS generated by exercise have differential regulations on the activity and expression of hepatic AMPK. Moderate exercise-derived ROS promoted hepatic AMPK glutathionylation activation. However, excess exercise increased oxidative damage, and inhibited the activity and expression of AMPK. Overall, our results illustrate that both exercise and antioxidant intervention improve blood glucose in diabetes by promoting redox balance, despite the levels of redox balance are different. Moreover, the activation and expression of AMPK could act as a biomarker to reflect the effective treatment range for diabetes. This finding provides theoretical evidence for the precise regulation of diabetes by antioxidants and exercise.

biochemistry↗

bioRxiv2022

habCluster: Identifying Geographical Boundary among Intraspecific Units Using Community Detection Algorithm in R

Conservation management for a species generally rests on intraspecific units, while identification of their geographic boundaries is necessary for the implementation. Intraspecific units can be discriminated using population genetic methods, yet an analytical approach is still lacking for detecting their geographic boundaries. Here, based on landscape connectivity, we present a geographical boundary delineation method, habCluster, using community detection algorithm. Community detection is an algorithm in graph theory used to identify clusters of highly connected nodes within a network. We assume that the habitat raster cells with better connections tend to form a continuous habitat patch than the others, thus making the range of an intraspecific unit. The method is tested on grey wolf (Canis lupus) habitat in Europe and on giant panda (Ailuropoda melanoleuca) habitat in China. The habitat suitability for grey wolves and giant pandas were evaluated using species distribution modeling. Each cell in the habitat suitability index (HSI) raster is treated as a node and directly connected with its eight neighbor cells. The edge weight between nodes is the distance between the center of them weighted by the average of their HSI values. We implement habCluster using R programming language with inline C++ code to speed up the computing. The geographical clusters detected were compared with the HSI maps for both species and with the nature reserves for giant panda. We found the boundaries of the clusters delineated using habCluster could serve as a good indicator of habitat patches, and they match generally well with nature reserves in the giant panda case. habCluster can provide spatial analysis basis for conservation management plans such as monitoring, translocation and reintroduction, as well as for population structure research.

ecology↗

bioRxiv2022

Spermine inhibits PAMP- elicited ROS and Ca2+ burst and reshapes the transcriptional landscape of PTI in Arabidopsis

Polyamines are small polycationic amines which levels increase during defense. Previous studies support the contribution of the polyamine spermine (Spm) to the establishment of the hypersensitive response (HR) during incompatible plant-pathogen interactions. However, the potential contribution of Spm to other layers of defense, and pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) in particular, was not completely established. Here we compared the contribution of Spm and putrescine (Put) to early and late PTI responses. We find that Put and Spm show opposite effects on PAMP-elicited reactive oxygen species (ROS) production, with Put increasing whereas Spm lowering flg22-stimulated ROS burst. Through genetic and pharmacological approaches, we find that the inhibitory effect of Spm on flg22-elicited ROS is independent of polyamine oxidation and EDS1 (ENHANCED DISEASE SUSCEPTIBILITY 1), PAD4 (PHYTOALEXIN DEFICIENT 4), salicylic acid and NPR1 (NONEXPRESSER OF PR GENES 1) defense components but resembles chemical inhibition of RBOHD (RESPIRATORY BURST OXIDASE HOMOLOG D) function. Remarkably, Spm can also suppress ROS elicited by FLS2-independent but RBOHD-dependent pathways, thus pointing to compromised RBOHD function. Consistent with this, we find that Spm dampens flg22-stimulated cytosolic Ca2+ influx necessary for RBOHD function and reshapes the transcriptional landscape of PTI and defense responses against Pseudomonas syringae pv. tomato DC3000. Overall, we provide molecular evidence for the differential contribution of Put and Spm to PTI with an impact on plant defense.

plant biology↗

bioRxiv2022

Two Competing Guilds as a Core Microbiome Signature for Chronic Diseases

Summary ParagraphOver eons of co-evolution, the gut microbiota has become an essential organ for humans1,2. However, it is unclear what core members and their ecological organization ensures the stable provision of this organs essential health-relevant functions to the host. With high quality metagenome-assembled genomes as network nodes, here we identified two competing guilds3 of the most stably and highly connected bacteria that together correlate with a wide range of host health conditions. Genomes in these two guilds kept their ecological relationship unchanged despite experiencing profound abundance changes during a 3-month high fiber intervention and 1-year follow-up in patients with type 2 diabetes (T2DM). The genomes of one guild harbored more genes for plant polysaccharide degradation and butyrate production, while the other guild had more genes for virulence or antibiotic resistance. A Random Forest regression model showed that the abundance distributions of these genomes were associated with 41 out of 43 bio-clinical parameters in the study cohort. With these genomes as reference, Random Forest modeling successfully classified case and control of T2DM, atherosclerotic cardiovascular disease, liver cirrhosis, inflammatory bowel diseases, colorectal cancer, ankylosing spondylitis, schizophrenia, and Parkinsons disease in 12 independent metagenomic datasets from 1,816 participants across ethnicity and geography. This core microbiome signature may serve as a common target for health recovery.

microbiology↗

bioRxiv2022

Improving the RNA velocity approach using long-read single cell sequencing

The concept of RNA velocity has been recently developed that allowed to look at the otherwise static single-cell RNA sequencing data in a dynamic way, which permitted inferences about cell fates. However, the more precise parameters, such as the number of exons/introns, can also be determined using long-read methods. Comparing the numbers of exons and introns allows including more genes for downstream velocity analysis and resolves the precise cell fate. The recently developed concept of "RNA velocity" concerns with dynamic changes in mRNA expression and complements single-cell RNA sequencing (scRNA-seq) data, which are static snapshots of a certain cell state taken at a given time point1. RNA velocity measures the change in mRNA abundance by differentiating the newly transcribed unspliced pre-mRNAs from mature spliced mRNAs. The rapidly developing long-read sequencing technology lends itself for RNA velocity analysis of scRNA-seq data, which was previously performed primarily using second-generation sequencing.

bioinformatics↗