Biology subjects

Wan, L.

Publications and source records attributed to Wan, L..

65 records · Page 4Linked to original sources

bioRxiv2020

Automated platform for high-throughput screening of base-modified aptamers for affinity and specificity

Aptamers incorporating chemically modified bases can achieve superior affinity and specificity compared to natural aptamers, but their characterization remains a labor-intensive, low-throughput task. Here we describe the non-natural aptamer array (N2A2) system, in which a minimally modified Illumina MiSeq instrument is used for the high-throughput generation and characterization of large libraries ([~]106) of base-modified DNA aptamer candidates on the basis of both target affinity and specificity. We first demonstrate the capability to screen multiple different base modifications to identify the optimal chemistry for high-affinity target binding. We next use N2A2 to generate aptamers that can maintain excellent specificity even in complex samples, with equally strong target affinity in both buffer and diluted human serum. Given that N2A2 requires only minor mechanical modifications to the MiSeq, we believe N2A2 offers a broadly accessible tool for generating high-quality affinity reagents for diverse applications.

bioengineering↗

bioRxiv2020

Molecular characterization of differences between the tomato immune receptors Fls3 and Fls2

Plants mount defense responses by recognizing indications of pathogen invasion, including microbe-associated molecular patterns (MAMPs). Flagellin from the bacterial pathogen Pseudomonas syringae pv. tomato (Pst) contains two MAMPs, flg22 and flgII-28, that are recognized by tomato receptors Flagellin sensing 2 (Fls2) and Flagellin sensing 3 (Fls3), respectively. It is unknown to what degree each receptor contributes to immunity and if they promote immune responses using the same molecular mechanisms. Characterization of CRISPR/Cas9-generated Fls2 and Fls3 tomato mutants revealed that the two receptors contribute equally to disease resistance both on the leaf surface and in the apoplast. However, striking differences were observed in certain host responses mediated by the two receptors. Compared to Fls2, Fls3 mediated a more sustained production of reactive oxygen species (ROS) and an increase in transcript abundance of 44 tomato genes, with two genes serving as reporters for Fls3. Fls3 had greater in vitro kinase activity and interacted differently with the Pst effector AvrPtoB as compared to Fls2. Using chimeric Fls2/Fls3 proteins, we found that no receptor domain was solely responsible for the Fls3 sustained ROS, suggesting involvement of multiple structural features. This work reveals differences in the immunity outputs between Fls2 and Fls3, suggesting they use distinct molecular mechanisms to activate pattern-triggered immunity in response to flagellin-derived MAMPs.

plant biology↗

bioRxiv2020

Joint Inference of Clonal Structure using Single-cell DNA-Seq and RNA-Seq data

Latest advancements in high-throughput single-cell genome (scDNA) and transcriptome (scRNA) sequencing technologies enabled cell-resolved investigation of tissue clones. However, it remains challenging to cluster and couple single cells for heterogeneous scRNA and scDNA data generated from the same specimen. In this study, we present a computational framework called CC-NMF, which employs a novel Coupled-Clone Non-negative Matrix Factorization technique to jointly infer clonal structure for matched scDNA and scRNA data. CCNMF couples multi-omics single cells by linking copy number and gene expression profiles through their general concordance. We validated CC-NMF using both simulated benchmarks and real-world applications, demon-strating its robustness and accuracy. We analyzed scRNA and scDNA data from an ovarian cancer cell lines mixture, a gastric cancer cell line, as well as a primary gastric cancer, successfully resolving underlying clonal structures and identifying high correlations of coexisting clones between genome and transcriptome. Overall, CCNMF is a coherent computational framework that simultaneously resolves genomic and transcriptomic clonal architecture, facilitating understanding of how cellular gene expression changes along with clonal genome alternations.

bioinformatics↗

bioRxiv2020

Unsupervised Topological Alignment for Single-Cell Multi-Omics Integration

Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging. In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex nonlinear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features. UnionCom software is available at https://github.com/caokai1073/UnionCom.

bioinformatics↗

bioRxiv2019

U1 snRNP regulates cancer cell migration and invasion

Stimulated cells and cancer cells have widespread shortening of mRNA 3-utranslated regions (3UTRs) and switches to shorter mRNA isoforms due to usage of more proximal polyadenylation signals (PASs) in the last exon and in introns. U1 snRNA (U1), vertebrates most abundant non-coding (spliceosomal) small nuclear RNA, silences proximal PASs and its inhibition with antisense morpholino oligonucleotides (U1 AMO) triggers widespread mRNA shortening. Here we show that U1 AMO also modulates cancer cells phenotype, dose-dependently increasing migration and invasion in vitro by up to 500%, whereas U1 over-expression has the opposite effect. In addition to 3UTR length, numerous transcriptome changes that could contribute to this phenotype are observed, including alternative splicing, and mRNA expression levels of proto-oncogenes and tumor suppressors. These findings reveal an unexpected link between U1 regulation and oncogenic and activated cell states, and suggest U1 as a potential target for their modulation.

molecular biology↗

bioRxiv2019

A revised 1.6 Å structure of the GTPase domain of the Parkinson’s disease-associated protein LRRK2 provides insights into mechanisms

Leucine-rich repeat kinase 2 (LRRK2) is a large 286 kDa multi-domain protein whose mutation is a common cause of Parkinsons disease (PD). One of the common sites of familial PD-associated mutations occurs at residue Arg-1441 in the GTPase domain of LRRK2. Previously, we reported that the PD-associated mutation R1441H impairs the catalytic activity of the GTPase domain thereby traps it in a persistently \"on\" state. More recently, we reported that the GTPase domain of LRRK2 exists in a dynamic dimer-monomer equilibrium where GTP binding shifts it to the monomeric conformation while GDP binding shifts it back to the dimeric state. We also reported that all of the PD-associated mutations at Arg-1441, including R1441H, R1441C, and R1441G, impair the nucleotide-dependent dimer-monomer conformational dynamics of the GTPase domain. However, the mechanism of this nucleotide-dependent conformational dynamics and how it is impaired by the mutations at residue Arg-1441 remained unclear. Here, we report a 1.6 [A] crystal structure of the GTPase domain of LRRK2. Our structure has revealed a dynamic switch region that can be differentially regulated by GTP and GDP binding. This nucleotide-dependent regulation is impaired when residue Arg-1441 is substituted with the PD-associated mutations due to the loss of its exquisite interactions consisting of two hydrogen bonds and a {pi}-stacking interaction at the dimer interface.\n\nSignificance StatementMutations in LRRK2 are associated with familial Parkinsons disease, so understanding its mechanism of actions and how they are changed by the disease-associated mutations is important for developing therapeutic strategies. This paper describes an atomic structure of the G-domain of LRRK2 revealing that the conformational dynamics of the switch regions are potentially important for its normal function. It further shows that a disease-associated mutation could lock the G domain in a persistently active-like conformation, thus perturbing its normal function.

biochemistry↗

bioRxiv2019

RobustClone: A robust PCA method of tumor clone and evolution inference from single-cell sequencing data

Single-cell sequencing (SCS) data provide unprecedented insights into intratumoral heterogeneity. With SCS, we can better characterize clonal genotypes and build phylogenetic relationships of tumor cells/clones. However, high technical errors bring much noise into the genetic data, thus limiting the application of evolutionary tools in the large reservoir. To recover the low-dimensional subspace of tumor subpopulations from error-prone SCS data in the presence of corrupted and/or missing elements, we developed an efficient computational framework, termed RobustClone, to recover the true genotypes of subclones based on the low-rank matrix factorization method of extended robust principal component analysis (RPCA) and reconstruct the subclonal evolutionary tree. RobustClone is a model-free method, fast and scalable to large-scale datasets. We conducted a set of systematic evaluations on simulated datasets and demonstrated that RobustClone outperforms state-of-the-art methods, both in accuracy and efficiency. We further validated RobustClone on 2 single-cell SNV and 2 single-cell CNV datasets and demonstrated that RobustClone could recover genotype matrix and infer the subclonal evolution tree accurately under various scenarios. In particular, RobustClone revealed the spatial progression patterns of subclonal evolution on the large-scale 10X Genomics scCNV breast cancer dataset. RobustClone software is available at https://github.com/ucasdp/RobustClone.

bioinformatics↗

bioRxiv2018

Bacillus thuringiensis targets the host intestinal epithelial junctions for successful infection of Caenorhabditis elegans

Pathogenic bacteria use different strategies to infect their hosts including the simultaneous production of pore forming toxins and several virulence factors that help to synergize their pathogenic effects. However, how the pathogenic bacteria are able to complete their life cycle and break out the host intestinal barrier is poorly understood. The infectious cycle of Bacillus thuringiensis (Bt) bacterium in Caenorhabditis elegans is a powerful model system to study the early stages of the infection process. Bt produces Cry pore-forming toxins during the sporulation phase that are key virulence factors involved in Bt pathogenesis. Here we show that during the early stages of infection, the Cry toxins disrupt the midgut epithelial tissue allowing the germination of spores. The vegetative Bt cells then trigger a quorum sensing response that is activated by PlcR regulator resulting in production of different virulence factors, such as the metalloproteinases ColB and Bmp1, that besides Cry toxins are necessary to disrupt the nematode epithelial junctions causing efficient bacterial host infection and dead of the nematode. Overall our work describes a novel mechanism for Bt infection, targeting the epithelial junctions of its host midgut cells.\n\nAuthor summaryThe entomopathogenic bacteria Bacillus thuringiensis (Bt) are used worldwide as biopesticides due to their insecticidal properties. Crystal proteins (Cry) produced by Bt during the sporulation phase of growth are mainly responsible for their insecticidal properties. The infection process of Bt includes three successive steps, virulence, necrotrophic, and sporulation processes. During the virulence process, after ingestion by the susceptible hosts, the Cry toxins form pores in the apical membrane of intestinal cells, inducing favorable conditions for bacterial spore germination. Vegetative bacteria multiply in the host and coordinate their behavior by using the quorum sensor regulator PlcR, which leads to the production of virulence factors allowing the bacteria to kill the host. However, how the bacteria are able to disrupt the host intestinal barrier during the early stages of infection remains unknown. Here we show that Bt employs the nematicidal Cry toxins and additional virulence factors controlled by the PlcR regulon to disrupt the intestinal epithelial junctions of C. elegans at the early stages of infection allowing that Bt bacteria complete its life cycle in the worms. Our work provides new insights into the pathogenesis of Bt, and highlights the importance of breaking down host epithelial junctions for a successful infection, a similar mechanism could be used by other pathogens-host interactions since epithelial junctions are conserved structures from insects to mammals.

microbiology↗

bioRxiv2018

DensityPath: a level-set algorithm to visualize and reconstruct cell developmental trajectories for large-scale single-cell RNAseq data

Cell fates are determined by transition-states which occur during complex biological pro-cesses such as proliferation and differentiation. The advance in single-cell RNA sequencing (scRNAseq) provides the snapshots of single cell transcriptomes, thus offering an essential opportunity to study such complex biological processes. Here, we introduce a novel algorithm, DensityPath, which visualizes and reconstructs the underlying cell developmental trajectories for large-scale scRNAseq data. DensityPath has three merits. Firstly, by adopting the nonlinear dimension reduction algorithm elastic embedding, DensityPath reveals the intrinsic structures of the data. Secondly, by applying the powerful level set clustering method, DensityPath extracts the separate high density clusters of representative cell states (RCSs) from the single cell multimodal density landscape of gene expression space, enabling it to handle the heterogeneous scRNAseq data elegantly and accurately. Thirdly, DensityPath constructs cell state-transition path by finding the geodesic minimum spanning tree of the RCSs on the surface of the density landscape, making it more computationally efficient and accurate for large-scale dataset. The cell state-transition path constructed by DensityPath has the physical interpretation as the minimum-transition-energy (least-cost) path. We demonstrate that DensityPath is capable of identifying complex cell development trajectories with bifurcating and trifurcating branches on the human preimplantation embryos. We demonstrate that DensityPath is robust and has high accuracy of pseudotime calculation and branch assignment on the real scRNAseq as well as simulated datasets.

systems biology↗

bioRxiv2017

Research Synergy and Drug Development: Bright Stars in Neighboring Constellations

Drug discovery and subsequent availability of a new breakthrough therapeutic or cure is a compelling example of societal benefit from research advances. These advances are invariably collaborative, involving the contributions of many scientists to a discovery network in which theory and experiment are built upon. To understand such scientific advances, data mining of public and commercial data sources coupled with network analysis can be used as a digital methodology to assemble and analyze component events in the history of a therapeutic. This methodology is extensible beyond the history of therapeutics and its use more generally supports (i) efficiency in exploring the scientific history of a research advance (ii) documenting and understanding collaboration (iii) portfolio analysis, planning and optimization (iv) communication of the societal value of research. As a proof of principle, we have conducted a case study of five anti-cancer therapeutics. We have linked the work of roughly 237,000 authors in 106,000 scientific publications that capture the research crucial for the development of these five therapeutics. We have enriched the content of networks of these therapeutics by annotating them with information on research awards as well as peer review that preceded these awards. Applying retrospective citation discovery, we have identified a core set of publications cited in the networks of all five therapeutics and additional intersections in combinations of networks as well as awards from the National Institutes of Health that supported this research. Lastly, we have mapped these awards to their cognate peer review panels, identifying another layer of collaborative scientific activity that influenced the research represented in these networks.

scientific communication and education↗

bioRxiv2016

Statistical testing and power analysis for brain-wide association study

The identification of connexel-wise associations, which involves examining functional connectivities between pairwise voxels across the whole brain, is both statistically and computationally challenging. Although such a connexel-wise methodology has recently been adopted by brain-wide association studies (BWAS) to identify connectivity changes in several mental disorders, such as schizophrenia, autism and depression [Cheng et al., 2015a,b, 2016], the multiple correction and power analysis methods designed specifically for connexel-wise analysis are still lacking. Therefore, we herein report the development of a rigorous statistical framework for connexel-wise significance testing based on the Gaussian random field theory. It includes controlling the family-wise error rate (FWER) of multiple hypothesis testings using topological inference methods, and calculating power and sample size for a connexel-wise study. Our theoretical framework can control the false-positive rate accurately, as validated empirically using two resting-state fMRI datasets. Compared with Bonferroni correction and false discovery rate (FDR), it can reduce false-positive rate and increase statistical power by appropriately utilizing the spatial information of fMRI data. Importantly, our method considerably reduces the computational complexity of a permutation-or simulation-based approach, thus, it can efficiently tackle large datasets with ultra-high resolution images. The utility of our method is shown in a case-control study. Our approach can identify altered functional connectivities in a major depression disorder dataset, whereas existing methods failed. A software package is available at https://github.com/weikanggong/BWAS.

neuroscience↗