Biology subjects

Wan, C.

Publications and source records attributed to Wan, C..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

MetaTiME: Meta-components of the Tumor Immune Microenvironment

Recent advances in single-cell RNA sequencing have revealed heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can reveal common cell types and states in the tumor microenvironment (TME). We developed a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.

genomics↗

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

Spinal Fusion Properties of Mechanically-Reinforced, Osteomodulatory Chitosan Hydrogels

Lower back pain is a considerable medical problem that will impact 80% of the U.S. population at some point in their life. For the most severe cases, surgical repair is necessary and is associated with costs upwards of $10.2 billion annually in the United States. To alleviate back pain, spine fusions are a common treatment in which two or more vertebrae are biologically fused together often through the use of a graft material. Unfortunately, iliac crest bone autograft, the current gold standard graft material, can yield insufficient fusion and is associated with considerable donor site morbidity and pain as well as limited supply. Therefore, new materials need to be developed in order to better coordinate healing and new bone growth in the affected area to reduce unnecessary patient burden. In order to address this issue, the incorporation of allograft and one of two types of cellulose (i.e., 0CNCs and CNFs) into a dual-crosslinked chitosan hydrogel loaded with bioactive calcium phosphate was investigated. Hydrogels were then tested for both their material and biological properties. Specifically, hydrogel swelling ratio, mass loss, ion release profile, compressive strength, in vitro biocompatibility and osteoinduction as well as in vivo biocompatibility, and effectiveness in a spine fusion model were determined. Cellulose and allograft incorporation significantly improved hydrogel compressive strength and biocompatibility and CNFs were found to be a significantly more biocompatible form of cellulose than 0CNCs. Additionally, through the controlled delivery of osteoinductive simple signaling molecules (i.e., calcium and phosphate ions), DCF-loaded CNF/Chitosan hydrogels were able to induce osteoblast-like activity in murine mesenchymal stem cells. When evaluated in vivo, these hydrogels were found to be non-toxic though the subacute phase (14 days). A 6-week rabbit spine fusion found these materials to achieve near complete fusion when assessed radiographically. This research provides considerable support for the utility of our novel material for spine fusion procedures as well as other future bone applications.

bioengineering↗

bioRxiv2022

Gene mutations and molecular evolution of the chikungunya virus E1 and E2 from 2014 to 2019

Chikungunya virus (CHIKV), a mosquito-borne Alphavirus, is the etiological agent of chikungunya fever. To investigate the prevalence and genetic characteristics of CHIKV in China, we performed a molecular epidemiological study during the 2014-2019 period. Phylogenetic analysis of CHIKV in the 2014-2019 shows that it is currently clustered geographically, which means that CHIKVs local adaptability is gradually strengthening. Focusing on CHIKV adaptive mutation E1-A226V is not obvious in our study. The CHIKV E1 amino acid non-synonymous mutation results revealed the common mutation site M343I. Most mutation sites belong to the American epidemic strains, and the mutation rate is 2.8%. In 31 sequences, 19 non-synonymous mutations were found in CHIKV E1 (total mutation rate 20/499 = 4%), and 37 non-synonymous mutations in CHIKV E2 (total mutation rate 30/425 = 7%). It is worth noting that the mutation of CHIKV E2 has changed more than that of CHIKV E1 between 2014 and 2019. CHIKV E2 screened out common mutation sites, and the results showed that there are five common mutation sites, namely S119G, L182M, G206D, S300N, and A345T. Eighty-three percent of the CHIKV E2 receptor binding domain mutations in the American strains, namely T3I and N6H, may cause immune escape. We constructed a new luciferase immunosorbent assay using CHIKV E2 antigen and mutant E2 antigen to test the serum of the CHIKV infected patients, and the detected samples and dilution ratios were different. The T3I, N6H, S119G, L182M, G206D, S300N, and A345T mutations in CHIKV E2 could explain these differences between patients. IMPORTANCECHIKV originated in Africa more than 500 years ago, with a common lineage dividing into two distinct branches called West Africa (West African, WA) and East/Central/South Africa (East/Central/South African, ECSA). Moreover, the E1-A226V mutation enhanced the replication and transmission capacity of CHIKV in Aedes albopictus. However, it should be noted that E1-A226V does not explain the CHIKV epidemic that has occurred in the Americas in recent years. We analyzed CHIKV samples in the 2014-2019, and the results showed that the mutation of CHIKV E1 protein occurred was not mainly concentrated in E1-A226V, but concentrated in M343I. This also means that CHIKV constantly mutates in the natural environment and is formed under natural conditions. Analysis of the common mutation sites of CHIKV E2 showed that there were seven common mutation sites S119G/L182M/ G206D /S300N/A345T, and the new mutation of CHIKV E2 may affect serological antibody detection.

evolutionary biology↗

bioRxiv2022

Oligomer-to-Monomer Transition Underlies the Chaperone Function of AAGAB in AP1/AP2 Assembly

Assembly of protein complexes is facilitated by assembly chaperones. Alpha and gamma adaptin binding protein (AAGAB) is a chaperone governing the assembly of the heterotetrameric adaptor complexes 1 and 2 (AP1 and AP2) involved in clathrin-mediated membrane trafficking. Here, we found that before AP1/2 binding, AAGAB exists as a homotetramer. AAGAB tetramerization is mediated by its C-terminal domain, which is critical for AAGAB stability and is missing in mutant proteins found in patients with the skin disease punctate palmoplantar keratoderma type 1 (PPKP1). We solved the crystal structure of the tetramerization domain (TD), revealing a dimer of dimer assembly. Interestingly, AAGAB uses the same TD to recognize and stabilize the {gamma} subunit in the AP1 complex and the subunit in the AP2 complex, forming binary complexes containing only one copy of AAGAB. These findings demonstrate a dual role of TD in stabilizing resting AAGAB and binding to substrates, providing a molecular explanation for disease-causing AAGAB mutations. The oligomerization state transition mechanism may also underlie the functions of other assembly chaperones.

biochemistry↗

bioRxiv2020

Data-informed discovery of hydrolytic nanozymes

Nanozyme is a collection of nanomaterials with enzyme-like activity but exhibits higher environmental tolerance and long-term stability than their natural counterparts. Improving the catalytic activity and expanding the category of nanozymes are prerequisites to complement or even supersede natural enzymes. Specifically, a powerful hydrolytic nanozyme is demanded to degrade the unsustainable substance which natural enzymes hardly achieve. However, the development of hydrolytic nanozymes is still hindered by diverse hydrolytic substrates and following complicated mechanisms. Here, we apply two strategies which are informed by data to screen and predict catalytic active sites of MOF (metal-organic framework) based hydrolytic nanozymes. One is to increase the intrinsic activity by finely tuned Lewis acidity of the metal clusters. The other is to adjust the volume density of the active sites by shortening the length of ligands. Finally, we construct a Ce-FMA-MOF-based hydrolytic nanozyme with robust cleavage ability towards phosphate bonds, amide bonds, glycosidic bonds whose energy ascend in order; and even their mixture, biofilms. This work provides a rational methodology to design hydrolytic nanozyme, enriches the diversity of nanozymes, and potentially sheds a light on the evolution of enzyme engineering in the future.

biophysics↗

bioRxiv2020

IRIS-FGM: an integrative single-cell RNA-Seq interpretation system for functional gene module analysis

SummarySingle-cell RNA-Seq (scRNA-Seq) data is useful in discovering cell heterogeneity and signature genes in specific cell populations in cancer and other complex diseases. Specifically, the investigation of functional gene modules (FGM) can help to understand gene interactive networks and complex biological processes. QUBIC2 is recognized as one of the most efficient and effective tools for FGM identification from scRNA-Seq data. However, its limited availability to a C implementation restricted its application to only a few downstream analyses functionalities. We developed an R package named IRIS-FGM (Integrative scRNA-Seq Interpretation System for Functional Gene Module analysis) to support the investigation of FGMs and cell clustering using scRNA-Seq data. Empowered by QUBIC2, IRIS-FGM can effectively identify co-expressed and co-regulated FGMs, predict cell types/clusters, uncover differentially expressed genes, and perform functional enrichment analysis. It is noteworthy that IRIS-FGM can also takes Seurat objects as input, which facilitate easy integration with existing analysis pipeline. Availability and ImplementationIRIS-FGM is implemented in R environment (as of version 3.6) with the source code freely available at https://github.com/OSU-BMBL/IRIS-FGM Contactqin.ma@osumc.edu Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

scFEA: A graph neural network model to estimate cell-wise metabolic flux using single cell RNA-seq data

The metabolic heterogeneity, and metabolic interplay between cells and their microenvironment have been known as significant contributors to disease treatment resistance. However, with the lack of a mature high-throughput single cell metabolomics technology, we are yet to establish systematic understanding of intra-tissue metabolic heterogeneity and cooperation phenomena among cell populations. To mitigate this knowledge gap, we developed a novel computational method, namely scFEA (single cell Flux Estimation Analysis), to infer single cell fluxome from single cell RNA-sequencing (scRNA-seq) data. scFEA is empowered by a comprehensively reconstructed human metabolic map into a factor graph, a novel probabilistic model to leverage the flux balance constraints on scRNA-seq data, and a novel graph neural network based optimization solver. The intricate information cascade from transcriptome to metabolome was captured using multi-layer neural networks to fully capitulate the non-linear dependency between enzymatic gene expressions and reaction rates. We experimentally validated scFEA by generating an scRNA-seq dataset with matched metabolomics data on cells of perturbed oxygen and genetic conditions. Application of scFEA on this dataset demonstrated the consistency between predicted flux and metabolic imbalance with the observed variation of metabolite abundance in the matched metabolomics data. We also applied scFEA on five publicly available scRNA-seq and spatial transcriptomics datasets and identified context and cell group specific metabolic variations. The cell-wise fluxome predicted by scFEA empowers a series of downstream analysis including identification of metabolic modules or cell groups that share common metabolic variations, sensitivity evaluation of enzymes with regards to their impact on the whole metabolic flux, and inference of cell-tissue and cell-cell metabolic communications.

bioinformatics↗

bioRxiv2020

SSMD: A semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data

Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different dataset scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment (TME). SSMD is featured by (i) a novel non-parametric method to discover data set specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (1) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment, (2) diverse experimental platforms of mouse transcriptomics data, (3) small sample size and limited training data source, and (4) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing to state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD. Key pointsO_LIWe provide a novel tissue deconvolution method, namely SSMD, which is specifically designed for mouse data to handle the variations caused by different mouse strain, genetic and phenotypic background, and experimental platforms. C_LIO_LISSMD is capable to detect data set and tissue microenvironment specific cell markers for more than 30 cell types in mouse blood, inflammatory tissue, cancer, and central nervous system. C_LIO_LISSMD achieve much improved performance in estimating relative proportion of the cell types compared with state-of-the-art methods. C_LIO_LIThe semi-supervised setting enables the application of SSMD on transcriptomics, DNA methylation and ATAC-seq data. C_LIO_LIA user friendly R package and a R shiny of SSMD based webserver are also developed. C_LI

bioinformatics↗

bioRxiv2020

RobMixReg: an R package for robust, flexible and high dimensional mixture regression

MotivationMixture regression has been widely used as a statistical model to untangle the latent subgroups of the sample population. Traditional mixture regression faces challenges when dealing with: 1) outliers and versatile regression forms; and 2) the high dimensionality of the predictors. Here, we develop an R package called RobMixReg, which provides comprehensive solutions for robust, flexible as well as high dimensional mixture modeling. Availability and ImplementationRobMixReg R package and associated documentation is available at CRAN: https://CRAN.R-project.org/package=RobMixReg.

bioinformatics↗

bioRxiv2020

Global Landscape of Native Protein Complexes in Synechocystis sp. PCC 6803

Synechocystis sp. PCC 6803 (hereafter: Synechocystis) is a model organism for studying photosynthesis, energy metabolism, and environmental stress. Though known as the first fully sequenced phototrophic organism, Synechocystis still has almost half of its proteome without functional annotations. In this study, we obtained 291 protein complexes, including 24,092 protein-protein interactions (PPIs) among 2062 proteins by using co-fractionation and LC/MS/MS. The additional level of PPIs information not only revealed the roles of photosynthesis in metabolism, cell motility, DNA repair, cell division, and other physiological processes, but also showed how protein functions vary from bacteria to higher plants due to the changed interaction partner. It also allows us to uncover functions of hypothetical proteins, such as Sll0445, Sll0446, S110447 participating in photosynthesis and cell motility, and Sll1334 regulating the expression of fatty acid. Here we presented the most extensive protein interaction data in Synechocystis so far, which might provide critical insights into the fundamental molecular mechanism in Cyanobacterium.

biochemistry↗

bioRxiv2019

Inferring transcriptional regulators through integrative modeling ofpublic chromatin accessibility and ChIP-seq data

We developed Lisa (http://lisa.cistrome.org) to predict the transcriptional regulators (TRs) of differentially expressed or co-expressed gene sets. Based on the input gene sets, Lisa first uses compendia of public histone mark ChIP-seq and chromatin accessibility profiles to construct a chromatin model related to the regulation of these genes. Then using TR ChIP-seq peaks or imputed TR binding sites, Lisa probes the chromatin models using in silico deletion to find the most relevant TRs. Applied to gene sets derived from targeted TF perturbation experiments, Lisa boosted the performance of imputed TR cistromes, and outperformed alternative methods in identifying the perturbed TRs.

bioinformatics↗

bioRxiv2019

A pan-plant protein complex map reveals deep conservation and novel assemblies

Plants are foundational to global ecological and economic systems, yet most plant proteins remain uncharacterized. Protein interaction networks often suggest protein functions and open new avenues to characterize genes and proteins. We therefore systematically determined protein complexes from 13 plant species of scientific and agricultural importance, greatly expanding the known repertoire of stable protein complexes in plants. Using co-fractionation mass spectrometry, we recovered known complexes, confirmed complexes predicted to occur in plants, and identified novel interactions conserved over 1.1 billion years of green plant evolution. Several novel complexes are involved in vernalization and pathogen defense, traits critical to agriculture. We also uncovered plant analogs of animal complexes with distinct molecular assemblies, including a megadalton-scale tRNA multi-synthetase complex. The resulting map offers the first cross-species view of conserved, stable protein assemblies shared across plant cells and provides a mechanistic, biochemical framework for interpreting plant genetics and mutant phenotypes.

systems biology↗

bioRxiv2019

Diagnostic value of circulating tumor DNA as an effective biomarker in cervical cancer: a meta-analysis

The applications of liquid biopsy have attracted much attention in biomedical research in recent years. Circulating cell-free DNA (cfDNA) in the serum may serve as a unique tumor marker in various types of cancer. Circulating tumor DNA (ctDNA) is a type of serum cfDNA found in patients with cancer and contains abundant information regarding tumor characteristics, highlighting its potential diagnostic value in the clinical setting. However, the diagnostic value of cfDNA as a biomarker in cervical cancer remains unclear. Here, we performed a meta-analysis to evaluate the applications of ctDNA as a biomarker in cervical cancer. A systematic literature search was performed using PubMed, Embase, and WANFANG MED ONLINE databases up to March 18, 2019. All literature was analyzed using Meta Disc 1.4 and STATA 14.0 software. Diagnostic measures of accuracy of ctDNA in cervical cancer were pooled and investigated. Fifteen studies comprising 1109 patients with cervical cancer met our inclusion criteria and were subjected to analysis. The pooled sensitivity and specificity were 0.52 (95% confidence interval [CI], 0.33-0.71) and 0.97 (95% CI, 0.91-0.99), respectively. The pooled positive likelihood ratio and negative likelihood ratio were 16.0 (95% CI, 5.5-46.4) and 0.50 (95% CI, 0.33-0.75), respectively. The diagnostic odds ratio was 32 (95% CI, 10-108), and the area under the summary receiver operating characteristic curve was 0.92 (95% CI, 0.90- 0.94). There was no significant publication bias observed. In the included studies, ctDNA showed clear diagnostic value for diagnosing and monitoring cervical cancer. Our meta-analysis suggested that detection of human papilloma virus ctDNA in patients with cervical cancer could be used as a noninvasive early dynamic biomarker of tumors, with high specificity and moderate sensitivity. Further large-scale prospective studies are required to validate the factors that may influence the accuracy of cervical cancer diagnosis and monitoring.

bioinformatics↗

bioRxiv2019

Improving protein function prediction with synthetic feature samples created by generative adversarial networks

Protein function prediction is a challenging but important task in bioinformatics. Many prediction methods have been developed, but are still limited by the bottleneck on training sample quantity. Therefore, it is valuable to develop a data augmentation method that can generate high-quality synthetic samples to further improve the accuracy of prediction methods. In this work, we propose a novel generative adversarial networks-based method, namely FFPred-GAN, to accurately learn the high-dimensional distributions of protein sequence-based biophysical features and also generate high-quality synthetic protein feature samples. The experimental results suggest that the synthetic protein feature samples are successful in improving the prediction accuracy for all three domains of the Gene Ontology through augmentation of the original training protein feature samples.

bioinformatics↗

bioRxiv2018

Bi-clustering based biological and clinical characterization of colorectal cancer in complementary to CMS classification

In light of the marked differences in the intrinsic biological underpinnings and prognostic outcomes among different subtypes, Consensus Molecular Subtype (CMS) classification provides a new taxonomy of colorectal cancer (CRC) solely based on transcriptomics data and has been accepted as a standard rule for CRC stratification. Even though CMS was built on highly cancer relevant features, it suffers from limitations in capturing the promiscuous mechanisms in a clinical setting. There are at least two facts about using transcriptomic data for prognosis prediction: the engagement of genes or pathways that execute the clinical response pathway are highly dynamic and interactive with others; and a predefined patient stratification not only largely decrease the statistical analysis power, but also excludes the fact that clusters of patients that confer similar clinical outcomes may or may not overlap with a pre-defined subgrouping. To enable a flexible and prospective stratified exploration, we here present a novel computational framework based on bi-clustering aiming to identify gene regulatory mechanisms associated with various biological, clinical and drug-resistance features, with full recognition of the transiency of transcriptional regulation and complicacies of patients subgrouping with regards to different biological and clinical settings. Our analysis on multiple large scale CRC transcriptomics data sets using a bi-clustering based formulation suggests that the detected local low rank modules can not only generate new biological understanding coherent to CMS stratification, but also identify predictive markers for prognosis that are general to CRC or CMS dependent, as well as novel alternative drug resistance mechanisms. Our key results include: (1) a comprehensive annotation of the local low rank module landscape of CRC; (2) a mechanistic relationship between different clinical subtypes and outcomes, as well as their characteristic biological underpinnings, visible through a novel consensus map; and (3) a few (novel) resistance mechanisms of Oxaliplatin, 5-Fluorouracil, and the FOLFOX therapy are revealed, some of which are validated on independent datasets.

bioinformatics↗

bioRxiv2018

Using deep maxout neural networks to improve the accuracy of function prediction from protein interaction networks

Protein-protein interaction network data provides valuable information that infers direct links between genes and their biological roles. This information brings a fundamental hypothesis for protein function prediction that interacting proteins tend to have similar functions. With the help of recently-developed network embedding feature generation methods and deep maxout neural networks, it is possible to extract functional representations that encode direct links between protein-protein interactions information and protein function. Our novel method, STRING2GO, successfully adopts deep maxout neural networks to learn functional representations simultaneously encoding both protein-protein interactions and functional predictive information. The experimental results show that STRING2GO outperforms other network embedding-based prediction methods and one benchmark method adopted in a recent large scale protein function prediction competition.

bioinformatics↗

bioRxiv2018

LTMG (Left truncated mixture Gaussian) based modeling of transcriptional regulatory heterogeneities in single cell RNA-seq data - a perspective from the kinetics of mRNA metabolism

A key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a genes diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

bioinformatics↗