Biology subjects

Chen, W.

Publications and source records attributed to Chen, W..

At least 397 records · Page 22Linked to original sources

bioRxiv2021

Assessment of AI-based Protein Structure Prediction for the NLRP3 Target

The recent success of AlphaFold and RoseTTAFold has demonstrated the values of AI methods in predicting highly accurate protein structures. Despite the advances, their roles in the context of small-molecule drug discovery need to be thoroughly explored. In this study, we evaluated whether the AI-based models can lead to reliable three-dimensional structures of protein-ligand complexes. The structure we predicted was NLRP3, a challenging protein target in terms of obtaining the three-dimensional model both experimentally and computationally. The conformation of the binding pockets generated by the AI models were carefully characterized and compared with experimental structures. Further molecular docking results indicated that AI-predicted protein structures combined with molecular dynamics simulations can be useful in small-molecule drug discovery.

biochemistry↗

bioRxiv2021

A temporally resolved, multiplex molecular recorder based on sequential genome editing

DNA is naturally well-suited to serve as a digital medium for in vivo molecular recording. However, DNA-based memory devices described to date are constrained in terms of the number of distinct signals that can be concurrently recorded and/or by a failure to capture the precise order of recorded events1. Here we describe DNA Ticker Tape, a general system for in vivo molecular recording that largely overcomes these limitations. Blank DNA Ticker Tape consists of a tandem array of partial CRISPR-Cas9 target sites, with all but the first site truncated at their 5 ends, and therefore inactive. Signals of interest are coupled to the expression of specific prime editing guide RNAs2. Editing events are insertional, and record the identity of the guide RNA mediating the insertion while also shifting the position of the "write head" by one unit along the tandem array, i.e. sequential genome editing. In this proof-of-concept of DNA Ticker Tape, we demonstrate the recording and decoding of complex event histories or short text messages; evaluate the performance of dozens of orthogonal tapes; and construct "long tape" potentially capable of recording the order of as many as 20 serial events. Finally, we demonstrate how DNA Ticker Tape simplifies the decoding of cell lineage histories.

genomics↗

bioRxiv2021

Multiplex genomic recording of enhancer and signal transduction activity in mammalian cells

Measurements of gene expression and signal transduction activity are conventionally performed with methods that require either the destruction or live imaging of a biological sample within the timeframe of interest. Here we demonstrate an alternative paradigm, termed ENGRAM (ENhancer-driven Genomic Recording of transcriptional Activity in Multiplex), in which the activity and dynamics of multiple transcriptional reporters are stably recorded to DNA. ENGRAM is based on the prime editing-mediated insertion of signal- or enhancer-specific barcodes to a genomically encoded recording unit. We show how this strategy can be used to concurrently genomically record the relative activity of at least hundreds of enhancers with high fidelity, sensitivity and reproducibility. Leveraging synthetic enhancers that are responsive to specific signal transduction pathways, we further demonstrate time- and concentration-dependent genomic recording of Wnt, NF-{kappa}B, and Tet-On activity. Finally, by coupling ENGRAM to sequential genome editing, we show how serially occurring molecular events can potentially be ordered. Looking forward, we envision that multiplex, ENGRAM-based recording of the strength, duration and order of enhancer and signal transduction activities has broad potential for application in functional genomics, developmental biology and neuroscience.

genomics↗

bioRxiv2021

Evaluation of machine learning models for proteoform retention and migration time prediction in top-down mass spectrometry

Reversed-phase liquid chromatography (RPLC) and capillary zone electrophoresis (CZE) are two popular proteoform separation methods in mass spectrometry (MS)-based top-down proteomics. The prediction of proteoform retention time in RPLC and migration time in CZE provides additional information that can increase the accuracy of proteoform identification and quantification. Whereas existing methods for retention and migration time prediction are mainly focused on peptides in bottom-up MS, there is still a lack of methods for the problem in top-down MS. We systematically evaluated 6 models for proteoform retention and/or migration time prediction in top-down MS and showed that the Prosit model achieved a high accuracy (R2 > 0.91) for proteoform retention time prediction and that the Prosit model and a fully connected neural network model obtained a high accuracy (R2 > 0.94) for proteoform migration time prediction.

bioinformatics↗

bioRxiv2021

Qualitative and quantitative top-down proteomics of human colorectal cancer cell lines identified 23000 proteoforms and revealed drastic proteoform-level differences between metastatic and non-metastatic cancer cells

Understanding cancer metastasis at the proteoform level is crucial for discovering new protein biomarkers for cancer diagnosis and drug development. Proteins are the primary effectors of function in biology and proteoforms from the same gene can have drastically different biological functions. Here, we present the first qualitative and quantitative top-down proteomics (TDP) study of a pair of isogenic human metastatic and non-metastatic colorectal cancer (CRC) cell lines (SW480 and SW620). This study pursues a global view of human CRC proteome before and after metastasis in a proteoform specific manner. We identified 23,319 proteoforms of 2,297 genes from the CRC cell lines using capillary zone electrophoresis-tandem mass spectrometry (CZE-MS/MS), representing nearly one order of magnitude improvement in the number of proteoform identifications from human cell lines compared to literature data. We identified 111 proteoforms containing single amino acid variants (SAAVs) using a proteogenomic approach and revealed drastic differences between the metastatic and non-metastatic cell lines regarding SAAVs profiles. Quantitative TDP analysis unveiled statistically significant differences in proteoform abundance between the SW480 and SW620 cell lines on a proteome scale for the first time. Ingenuity Pathway Analysis (IPA) disclosed that many differentially expressed genes at the proteoform level had diversified functions and were closely related to cancer. Our study represents a milestone in TDP towards the definition of human proteome in a proteoform specific manner, which will transform basic and translational biomedical research. For TOC only O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=186 SRC="FIGDIR/small/466093v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@3ee5faorg.highwire.dtl.DTLVardef@16cae5forg.highwire.dtl.DTLVardef@2c0bd0org.highwire.dtl.DTLVardef@1bb9530_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2021

High-resolution spatiotemporal transcriptomic maps of developing Drosophila embryos and larvae

Drosophila has long been a successful model organism in multiple fields such as genetics and developmental biology. Drosophila genome is relatively smaller and less redundant, yet largely conserved with mammals, making it a productive model in studies of embryogenesis, cell signaling, disease mechanisms, etc. Spatial gene expression pattern is critical for understanding of complex signaling pathways and cell-cell interactions, whereas temporal gene expression changes need to be tracked during highly dynamic activities such as tissue development and disease progression. Systematic studies in Drosophila as a whole are still impeded by lack of these spatiotemporal transcriptomic information. Drosophila embryos and tissues are of relatively small size, limiting the application of current technologies to comprehensively resolve their spatiotemporal gene expression patterns. Here, utilizing SpaTial Enhanced REsolution Omics-sequencing (Stereo-seq), we dissected the spatiotemporal transcriptomic changes of developing Drosophila with high resolution and sensitivity. Our data recapitulated the spatial transcriptomes of embryonic and larval development in Drosophila. With these data, we identified known and previously undetected subregions in several tissues during development, and revealed known and potential gene regulatory networks of transcription factors within their topographic background. We further demonstrated that Stereo-seq data can be used for 3D reconstruction of Drosophila embryo spatial transcriptomes. Our data provides Drosophila research community with useful resources of spatiotemporally resolved transcriptomic information across developmental stages.

developmental biology↗

bioRxiv2021

High Resolution Nanostructure with Two-stages of Exponential Energy Dissipation at the Ultrathin Osteochondral Interface Tissue of Human Knee Joint

Cartilage adheres to subchondral bone via a specific osteochondral interface tissue where forces are transferred from soft cartilage to hard bone without fatigue damage over a lifetime of load cycles. However, the fine structure and mechanical properties of osteochondral interface tissue remain unclear. Here, we identified an ultrathin [~]20-30 m calcified region with two-layered micro-nano structures of osteochondral interface tissue in human knee joint, which exhibited characteristic biomolecular compositions and complex nanocrystals assembly. Within this region, an exponential increase of modulus (3 orders of magnitude) was conducive to the force transmission which was verified by finite element simulations. The nanoscale heterogeneity of hydroxyapatite, along with enrichment of elastic-responsive protein-titin which is usually present in muscle, endowed the osteochondral tissue with excellent energy dissipation and fatigue resistance properties. Our results provide potential design for high-performance interface materials for osteochondral interface regeneration and functional coatings.

bioengineering↗

bioRxiv2021

CAR modulates plasma membrane nano-organization and immune signaling downstream of RALF1-FERONIA ligand-receptor signaling system

O_LIThe receptor-like kinase (RLK) FERONIA (FER) senses peptide ligands in the plasma membrane (PM), modulates plant growth and development, and integrates biotic and abiotic stress signaling for downstream adaptive response. However, the molecular interplay of these diverse processes is largely unknown. C_LIO_LIHere, we show that FER, the receptor of Rapid Alkalinization Factor 1 (RALF1), physically interacts with C2 domain ABA-related (CAR) proteins to control the nano-organization of the PM in Arabidopsis. C_LIO_LIDuring this process, the RALF1-FER pathway upregulates CAR protein translation, and then more CAR proteins are recruited to PM. This acts as a rapid feedforward loop to stabilize PM liquid-ordered phase. FER interacts with and phosphorylates CARs, and reducing their lipid-binding ability, which breaks the feedback regulation CAR activity at latter time point. C_LIO_LISimilar to FER mutant, pentuple car14569 mutants inhibit flg22-induced FLS2-BAK1 immune complex formation, which depends on the integrity of nanodomains. O_LITogether, we propose that the FER-CAR module controls the formation of the PM nano-organization during RALF signaling through a self-contained amplifying loop including both positive and negative feedbacks. C_LI C_LI

plant biology↗

bioRxiv2021

Oxalic acid binds to gustatory receptor Gr23a and inhibits feeding in the brown planthopper

Plants produce diverse secondary compounds as natural protection against microbial and insect attack. Most of these compounds, including bitters and acids, are sensed by insect gustatory receptors (Grs). Acids are potentially toxic to insects, but there are few reports on sour compounds as ligands of insect Grs. Here, using two different heterologous expression systems, the insect Sf9 cell line and the mammalian HEK293T cell line, we started from crude extracts of rice (Oryza sativa) and successfully identified oxalic acid (OA) as a ligand of NlGr23a, a Gr in the brown planthopper Nilaparvata lugens. The antifeedant effect of OA on the brown planthopper was dose dependent, and NlGr23a is essential for OAs antifeedant activity in both artificial diets and rice plants. NlGr23a is also indispensable for tarsal OA sensing. To our knowledge, OA is the first identified ligand starting from plant crude extracts and the first known strong acid for insect Grs. These findings on rice-planthopper interactions will be of broad interest for pest control in agriculture and also for better understanding of how insects select host plants. Research organismNilaparvata lugens

ecology↗

bioRxiv2021

A bacterial Argonaute with efficient DNA and RNA cleavage activity guided by small DNA and RNA

Argonaute proteins are widespread in prokaryotes and eukaryotes. Most prokaryotic Argonaute proteins (pAgos) use 5P-gDNA to target complementary DNA. However, more and more studies on the properties of pAgos make their functions more diversified. Previously reported pAgos only possess several forms of high activity in all eight cleavage patterns, which limits their practical applications. Here, we described a unique pAgo from Marinitoga hydrogenitolerans (MhAgo) with eight cleavage activities. MhAgo can utilize all four types of guides (5OH-gDNA, 5P-gDNA, 5OH-gRNA, and 5P-gRNA) for ssDNA and RNA cleavage. Further studies demonstrated that MhAgo had high activities with 16-21 nt guides and no obvious preferences for the 5-end nucleotides of 5OH-guides. Unexpectedly, MhAgo had different preferences for the 5-end nucleotides of 5P-guides depending on the types of targets. Although the specificity of MhAgo was related to the types of guides, single mismatches in the central and 3-supplementary regions of guides greatly reduced the cleavage efficiency. Additionally, the electrophoretic mobility shift assay (EMSA) demonstrated MhAgo had the weakest affinity for 5P-gRNA:tRNA duplex, which was consistent with its cleavage efficiency. In conclusion, MhAgo is highly active under a wide range of conditions and can be used for programmable endonucleolytic cleavage of both ssDNA and RNA substrates. The abundant biochemical characteristics of MhAgo broaden our understanding of pAgos and expand the potential application in nucleic acids manipulations.

molecular biology↗

bioRxiv2021

Serine/arginine-rich splicing factor 7 plays oncogenic roles through specific regulation of m6A RNA modification

Serine/Arginine-Rich Splicing Factor 7 (SRSF7), which is previously recognized as a splicing factor, has been revealed to play oncogenic roles in multiple cancers. However, the mechanisms underlying its oncogenic roles have not been well addressed. Here, based on N6-methyladenosine (m6A) co-methylation network analysis across diverse cell lines, we found SRSF7 positively correlated with glioblastoma cell-specific m6A methylation. We then proved SRSF7 is a novel m6A regulator that specifically facilitates the m6A methylation near its binding sites on the mRNAs involved in cell proliferation and migration through recruiting methyltransferase complex. Moreover, SRSF7 promotes the proliferation and migration of glioblastoma cells largely dependent on the m6A methyltransferase. The two single-nucleotide m6A sites on PBK are regulated by SRSF7 and partially mediate the effects of SRSF7 on glioblastoma cells through recognition by IGF2BP2. Together, our discovery revealed a novel role of SRSF7 in regulating m6A and timely confirmed the existence and functional importance of RNA binding protein (RBP) mediated specific regulation of m6A.

molecular biology↗

bioRxiv2021

Automated skull stripping in mouse fMRI analysis using 3D U-Net

Skull stripping is an initial and critical step in the pipeline of mouse fMRI analysis. Manual labeling of the brain usually suffers from intra- and inter-rater variability and is highly time-consuming. Hence, an automatic and efficient skull-stripping method is in high demand for mouse fMRI studies. In this study, we investigated a 3D U-Net based method for automatic brain extraction in mouse fMRI studies. Two U-Net models were separately trained on T2-weighted anatomical images and T2*-weighted functional images. The trained models were tested on both interior and exterior datasets. The 3D U-Net models yielded a higher accuracy in brain extraction from both T2-weighted images (Dice > 0.984, Jaccard index > 0.968 and Hausdorff distance < 7.7) and T2*-weighted images (Dice > 0.964, Jaccard index > 0.931 and Hausdorff distance < 3.3), compared with the two widely used mouse skull-stripping methods (RATS and SHERM). The resting-state fMRI results using automatic segmentation with the 3D U-Net models are identical to those obtained by manual segmentation for both the seed-based and group independent component analysis. These results demonstrate that the 3D U-Net based method can replace manual brain extraction in mouse fMRI analysis.

neuroscience↗

bioRxiv2021

Uncovering expression signatures of synergistic drug response using an ensemble of explainable AI models

Complex machine learning models are poised to revolutionize the treatment of diseases like acute myeloid leukemia (AML) by helping physicians choose optimal combinations of anti-cancer drugs based on molecular features. While accurate predictions are important, it is equally important to be able to learn about the underlying molecular basis of anti-cancer drug synergy. Explainable AI (XAI) offers a promising new route for data-driven cancer pharmacology, combining highly accurate models with interpretable insights into model decisions. Due to the highly correlated, high-dimensional nature of cancer transcriptomic data, however, we find that existing XAI approaches are suboptimal when applied naively to large transcriptomic datasets. We show how a novel approach based on model ensembling helps to increase the quality of explanations. We then use our method to demonstrate that a hematopoietic differentiation signature underlies synergy for a variety of anti-AML drug combinations.

cancer biology↗

bioRxiv2021

SARS-CoV-2 causes human BBB injury and neuroinflammation indirectly in a linked organ chip platform

COVID-19 is a multi-system disease affecting many organs outside of the lungs, and patients generally develop varying degrees of neurological symptoms. Whereas, the pathogenesis underlying these neurological manifestations remains elusive. Although in vitro models and animal models are widely used in studies of SARS-CoV-2 infection, human organ models that can reflect the pathological alterations in a multi-organ context are still lacking. In this study, we propose a new strategy to probe the effects of SARS-CoV-2 on human brains in a linked alveolus-BBB organ chip platform. The new multi-organ platform allows to recapitulate the essential features of human alveolar-capillary barrier and blood-brain barrier in a microfluidic condition by co-culturing the organ-specific cells. The results reveal direct SARS-CoV-2 exposure has no obvious effects on BBB chip alone. While, infusion of endothelial medium from infected alveolus chips can cause BBB dysfunction and neuroinflammation on the linked chip platform, including brain endothelium disruption, glial cell activation and inflammatory cytokines release. These new findings suggest that SARS-CoV-2 could induce neuropathological alterations, which might not result from direct viral infection through hematogenous route, but rather likely from systemic inflammation following lung infection. This work provides a new strategy to study the virus-host interaction and neuropathology at an organ-organ context, which is not easily obtained by other in vitro models. This will facilitate to understand the neurological pathogenesis in SARS-CoV-2 and accelerate the development of new therapeutics. SUMMARYO_LIA linked human alveolus-BBB chip platform is established to explore the influences of SARS-CoV-2 on human brains in an organ-organ context. C_LIO_LISARS-CoV-2 infection could induce BBB injury and neuroinflammation. C_LIO_LIThe neuropathological changes are caused by SARS-CoV-2 indirectly, which might be mediated by systemic inflammation following lung infection, but probably not by direct viral neuroinvasion. C_LI

bioengineering↗

bioRxiv2021

CoCoRV: a rare variant analysis framework using publicly available genotype summary counts to prioritize germline disease-predisposition genes

Sequencing cases without matched healthy controls hinders prioritization of germline disease-predisposition genes. To circumvent this problem, genotype summary counts from public data sets can serve as controls. However, systematic inflation and false positives can arise if confounding factors are not addressed. We propose a new framework, consistent summary counts based rare variant burden test (CoCoRV), to address these challenges. CoCoRV has consistent variant quality control and filtering, ethnicity-stratified rare variant association test, accurate estimation of inflation factors, powerful FDR control, and can detect rare variants in high linkage disequilibrium. When we applied CoCoRV to pediatric cancer cohorts, the top genes identified were cancer-predisposition genes. We also applied CoCoRV to identify disease-predisposition genes in adult brain tumors and amyotrophic lateral sclerosis. Given that potential confounding factors were well controlled after applying the framework, CoCoRV provides a cost-effective solution to prioritizing disease-risk genes enriched with rare pathogenic variants.

genetics↗

bioRxiv2021

Integrative Analysis of Spatial Transcriptome with Single-cell Transcriptome and Single-cell Epigenome in Mouse Lungs after Immunization

Immunological memory is key to productive adaptive immunity. An unbiased, high throughput gene expression profiling of tissue-resident memory T cells at their precise anatomical locations within the lung is fundamental to understanding lung immunity, but such spatial information has yet to be characterized. In this study, using a well-established Klebsiella pneumoniae infection model, we performed an integrative analysis of spatial transcriptome with single-cell RNA-seq and single-cell ATAC-seq on lung cells from mice after immunization using the 10x Genomics Chromium and Visium platform. We employed several deconvolution algorithms and established an optimized deconvolution pipeline to accurately decipher specific cell-type composition by anatomic location. We identified and located 12 major cell types by scRNA-seq and spatial transcriptomic analysis. Integrating scATAC-seq data from the same cells processed in parallel with scRNA-seq, we found epigenomic profiles provide more robust cell type identification, especially for lineage-specific T helper cells. When combining all three data modalities, we observed a dynamic change in the location of T helper cells as well as their corresponding chemokines for chemotaxis. Furthermore, cell-cell communication analysis of spatial transcriptome provided evidence of lineage-specific T helper cells receiving designated cytokine signaling. In summary, our first-in-class study demonstrated the power of multi-omics analysis to uncover intrinsic spatial- and cell-type-dependent molecular mechanisms of lung immunity. Our data provides a rich research resource of single cell multi-omics data as a reference for understanding spatial dynamics of lung immunization.

bioinformatics↗

bioRxiv2021

Reassortment Network of Influenza A Virus

Influenza A virus (IAV) genomes are composed of eight single-stranded RNA segments. Genetic exchange through reassortment of the segmented genomes often endows IAVs with new genetic characteristics, which may affect transmissibility and pathogenicity of the viruses. However, a comprehensive understanding of the reassortment history of IAVs remains lacking. To this end, we assembled 40296 whole-genome sequences of IAVs for analysis. Using a new clustering method based on Mean Pairwise Distances in the phylogenetic trees, we classified each segment of IAVs into clades. Correspondingly, reassortment events among IAVs were detected by checking the segment clade compositions of related genomes under specific environment factors and time period. We systematically identified 1927 possible reassortment events of IAVs and constructed their reassortment network. Interestingly, minimum spanning tree of the reassortment network reproved that swine act as an intermediate host in the reassortment history of IAVs between avian species and human. Moreover, reassortment patterns among related subtypes constructed in this study are consistent with previous studies. Taken together, our genome-wide reassorment analysis of all the IAVs offers an overview of the leaping evolution of the virus and a comprehensive network representing the relationships of IAVs. AvailabilitySource code freely available for download at https://github.com/ElevenStr/IAVreassortment, implemented in python. Lead Contactbioren@163.com

evolutionary biology↗

bioRxiv2021

The Macromolecular MR Spectrum in Healthy Aging

PurposeMobile macromolecules (MMs) from amino acids, cytosolic proteins and mobile lipids contribute a significant spectral background underlying the metabolite signals in the MR spectrum. A recent consensus recommends that MM contributions should be removed or included in modeling basis sets for determination of metabolite concentrations and/or metabolite ratios. The purpose of this study was to acquire the MM spectrum from healthy participants at a range of ages, and to investigate changes in the signals with age and sex groups. MethodsInversion time (TI) series were acquired to determine an optimal inversion time to null the metabolite signals. Experiments were carried out using a single adiabatic hyperbolic-secant inversion pulse. After the preliminary experiment, 102 volunteers (49M/53F) between 20 and 69 years were recruited for in vivo data acquisition in the centrum semiovale (CSO) and posterior cingulate cortex (PCC). The protocol consisted of a T1-weighted MPRAGE for structural images, followed by PRESS localization using a voxel size of 30 x 26 x 26 mm3 with pre-inversion (TR/TI 2000/600 ms) and CHESS water suppression. Metabolite-nulled spectra were modeled using a reduced basis set (NAA, Cr, Cho, Glu) and a flexible spline baseline (0.1 ppm knot spacing) followed by subtraction of the modeled metabolite signals to yield a clean MM spectrum, using the Osprey software. Pearsons correlation coefficient was calculated between integrals and age for the 14 MM signals between 0.9-4.2 ppm. One-way ANOVA was performed to determine differences between age groups. An independent t-test was carried out to determine differences between sexes. Relationships between brain tissues with age and sex groups were also measured. ResultsMM spectra were successfully acquired in 99 (CSO) and 96 (PCC) of 102 subjects. No significant correlations were seen between age and MM integrals. One-way ANOVA also suggested no age-group differences for any MM peak (all p > 0.004). No differences were observed between sex groups. The voxels were segmented as 80 {+/-} 4% white matter, 18 {+/-} 4% gray matter, and 2 {+/-} 1% CSF for CSO and 28 {+/-} 4% white matter, 61 {+/-} 4% gray matter and 11 {+/-} 1% CSF for PCC. WM and GM showed a significant (p < 0.05) negative linear association with age in the WM-predominant CSO (R = -0.29) and GM-predominant PCC regions (R = -0.57) respectively while CSF increased significantly with age in both regions. ConclusionOur findings indicate that the MM spectrum is stable across a large age range and between sexes, suggesting a pre-defined MM basis function can be used for linear combination modeling of metabolite data from different age and sex groups. HighlightsO_LIA large publicly available MM-aging dataset is presented. C_LIO_LIMacromolecule signals do not change with age between 20 and 70. C_LIO_LIThere is no sex difference for macromolecule integrals. C_LI

bioengineering↗