Biology subjects

Wan, L.

Publications and source records attributed to Wan, L..

4 recordsLinked to original sources

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↗