Biology subjects

Zeng, W.

Publications and source records attributed to Zeng, W..

7 recordsLinked to original sources

bioRxiv2018

Bioluminscent Mycobacterium ulcerans, a tool to study host-pathogen interactions in a murine tail model of Buruli ulcer

Buruli ulcer is a neglected tropical disease caused by infection with Mycobacterium ulcerans. In this study we used a previously reported strain of M. ulcerans, genetically engineered to constitutively produce bioluminescence, to follow the progression of Buruli ulcer in mice using an in-vivo imaging (IVIS(R)) system. We aimed to characterize a mouse tail infection model for pathogenesis, as well as for pre-clinical vaccine and drug development research for Buruli ulcer. Immune parameters, such as antibody titers and cytokine levels, were determined throughout the course of the infection and histology specimens were examined for comparison with human pathology. Nine out of ten (90%) BALB/c mice infected subcutaneously with 105 M. ulcerans JKD8049 (containing pMV306 hsp16+luxG13) exhibited light emission from the site of infection over the course of the experiment indicating M. ulcerans growth in-vivo. Five out of ten (50%) animals developed clinical signs of disease. Antibody titers were overall low and their onset was late, as measured by responses to both heterogenous (bacterial whole cell lysate) and single antigen (Hsp18) targets. IFN-{gamma}, and IL-10 are reported to play a vital role in host control of Buruli ulcer and these cytokines were elevated in animals with pathology. For mice with advanced pathology, histology revealed clusters of acid-fast bacilli within subcutaneous tissue 300-400 m beneath the epidermis of the tail, with macrophage infiltration and granuloma-formation resembling human Buruli ulcer. This study has shown the utility of using bioluminescent M. ulcerans and IVIS(R) in a mouse tail infection model to study Buruli ulcer infection.\n\nAuthor summaryBuruli ulcer is one of the so called neglected tropical diseases. It is an infectious disease, mainly occurring in West Africa but also in Australia. It manifests as skin lesion and ulcer. Up to date, the way of transmission is inadequately understood. Also, there is no vaccine to protect against the disease. Buruli ulcer is treatable with a course of antibiotics that need to be given for the duration of two months. More laboratory research is needed to elucidate the mechanism of transmission, develop a vaccine and improve and shorten antibiotic therapy. For this, animal (mouse) models of disease are used. The aim of this study was to refine and improve the mouse tail infection model of Buruli ulcer. For this, we used a genetically modified Mycobacterium ulcerans strain that emits light. After infection of animals, light emitted from the bacteria was read out with an in-vivo imaging (IVIS) camera. This allowed us to monitor the location of bacteria in the living animal over time without the need to kill the animal. We also measured parameters of the immune system such as antibodies and cytokines as a baseline for future studies into immunology, vaccine development and pathology of Buruli ulcer. We successfully improved and characterized the mouse tail infection model in Buruli ulcer with the use of modern technology using light emitting bacteria and the IVIS camera.

microbiology↗

bioRxiv2018

Aerobic removal of microcystin-LR by a novel native effective bacterial community designated as YFMCD4 isolated from Lake Taihu

Microcystins (MCs) are a group of monocyclic heptapeptide hepatotoxins produced by species of cyanobacteria. MC-LR is the most toxic and frequently detected MCs variant in water, which poses a great threat to the natural ecosystem and public health. Its important to seek environment-friendly and cost-efficient methods to remove MC-LR. To investigate the MC-degrading capacities of a novel indigenous bacterial community designated as YFMCD4 and the influence of environmental factors including various temperatures, MC concentrations and pH on the MC-degrading activities, the concentration of MC-LR was measured by high performance liquid chromatography. In addition, the MC-degrading mechanism containing the degradation pathway and products of YFMCD4 was studied using HPLC coupled with an ultra-high resolution LTQ Orbitrap Velos Pro ETD mass spectrometry equipped with electrospray ionization interface. The data showed MC-LR can be removed at the maximum rate of 0.5 {micro}g/(ml{middle dot}h) by YFMCD4 containing Alcaligenes faecalis and Stenotrophomonas acidaminiohila. The MC-degrading rates of YFMCD4 were significantly affected by different temperatures, pH and MC-LR concentrations. Two intermediates of a tetrapeptide and Adda appeared in the degradation process. These results illustrate that the novel bacterial community YFMCD4 can remove MC-LR effectively and completely, which indicates YFMCD4 possesses a significant potential to be used in bioremediation of water bodies contaminated by MC-LR.

ecology↗

bioRxiv2018

Integrating distal and proximal information to predict gene expression via a densely con-nected convolutional neural network

MotivationInteractions among such cis-regulatory elements as enhancers and promoters are main driving forces shaping context-specific chromatin structure and gene expression. Although there have been computational methods for predicting gene expression from genomic and epigenomic information, most of them overlook long-range enhancer-promoter interactions, due to the difficulty in precisely linking regulatory enhancers to target genes. Recently, a novel high-throughput experimental approach named HiChIP has been developed and generating comprehensive data on high-resolution interactions between promoters and distal enhancers. On the other hand, plenty of studies have suggested that deep learning achieves state-of-the-art performance in epigenomic signal prediction, and thus promoting the understanding of regulatory elements. In consideration of these two factors, we integrate proximal promoter sequences and HiChIP distal enhancer-promoter interactions to accurately model gene expression.\n\nResultsWe propose DeepExpression, a densely connected convolutional neural network to predict gene expression using both promoter sequences and enhancer-promoter interactions. We demonstrate that our model consistently outperforms baseline methods not only in the classification of binary gene expression status but also in the regression of continuous gene expression levels, in both cross-validation experiments and cross-cell lines predictions. We show that sequential promoter information is more informative than experimental enhancer information while enhancer-promoter interactions are most beneficial from those within {+/-}100 kbp around the TSS of a gene. We finally visualize motifs in both promoter and enhancer regions and show the match of identified sequence signatures and known motifs. We expect to see a wide spectrum of applications using HiChIP data in deciphering the mechanism of gene regulation.\n\nAvailabilityDeepExpression is freely available at https://github.com/wanwenzeng/DeepExpression.\n\nContactruijiang@tsinghua.edu.cn, ywang@amss.ac.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Selecting precise reference normal tissue samples for cancer research using a deep learning approach

BackgroundNormal tissue samples are often employed as a control for understanding disease mechanisms, however, collecting matched normal tissues from patients is difficult in many instances. In cancer research, for example, the open cancer resources such as TCGA and TARGET do not provide matched tissue samples for every cancer or cancer subtype. The recent GTEx project has profiled samples from healthy individuals, providing an excellent resource for this field, yet the feasibility of using GTEx samples as the reference remains unanswered.\n\nMethodsWe analyze RNA-Seq data processed from the same computational pipeline and systematically evaluate GTEx as a potential reference resource. We use those cancers that have adjacent normal tissues in TCGA as a benchmark for the evaluation. To correlate tumor samples and normal samples, we explore top varying genes, reduced features from principal component analysis, and encoded features from an autoencoder neural network. We first evaluate whether these methods can identify the correct tissue of origin from GTEx for a given cancer and then seek to answer whether disease expression signatures are consistent between those derived from TCGA and from GTEx.\n\nResultsAmong 32 TCGA cancers, 18 cancers have less than 10 matched adjacent normal tissue samples. Among three methods, autoencoder performed the best in predicting tissue of origin, with 12 of 14 cancers correctly predicted. The reason for misclassification of two cancers is that none of normal samples from GTEx correlate well with any tumor samples in these cancers. This suggests that GTEx has matched tissues for the majority cancers, but not all. While using autoencoder to select proper normal samples for disease signature creation, we found that disease signatures derived from normal samples selected via an autoencoder from GTEx are consistent with those derived from adjacent samples from TCGA in many cases. Interestingly, choosing top 50 mostly correlated samples regardless of tissue type performed reasonably well or even better in some cancers.\n\nConclusionsOur findings demonstrate that samples from GTEx can serve as reference normal samples for cancers, especially those do not have available adjacent tissue samples. A deep-learning based approach holds promise to select proper normal samples.

bioinformatics↗

bioRxiv2018

Integrative analysis of single cell genomics data by coupled nonnegative matrix factorizations

When different types of functional genomics data are generated on single cells from different samples of cells from the same heterogeneous population, the clustering of cells in the different samples should be coupled. We formulate this \"coupled clustering\" problem as an optimization problem, and propose the method of coupled nonnegative matrix factorizations (coupled NMF) for its solution. The method is illustrated by the integrative analysis of single cell RNA-seq and single cell ATAC-seq data.\n\nSignificance StatementsBiological samples are often heterogeneous mixtures of different types of cells. Suppose we have two single cell data sets, each providing information on a different cellular feature and generated on a different sample from this mixture. Then, the clustering of cells in the two samples should be coupled as both clusterings are reflecting the underlying cell types in the same mixture. This \"coupled clustering\" problem is a new problem not covered by existing clustering methods. In this paper we develop an approach for its solution based the coupling of two nonnegative matrix factorizations. The method should be useful for integrative single cell genomics analysis tasks such as the joint analysis of single cell RNA-seq and single cell ATAC-seq data.

bioinformatics↗

bioRxiv2017

Common Piezo1 allele in African populations causes xerocytosis and attenuates Plasmodium infection

Hereditary xerocytosis (HX) is thought to be a rare genetic condition characterized by red blood cell (RBC) dehydration with mild hemolysis. Gain-of-function (GOF) mutations in mechanosensitive Piezo1 ion channels are identified in HX patients. RBC dehydration is linked to reduced Plasmodium infection rates in vitro. We engineered a Piezo1 mouse model of HX and show that Plasmodium infection fails to cause experimental cerebral malaria in these mice. Furthermore, we identified a novel GOF human Piezo1 variant, E756del, present in a third of African population. Remarkably, RBCs from individuals carrying this allele are dehydrated and protected against Plasmodium infection in vitro. The presence of an HX-causing Piezo1 mutation at such high frequencies in African population is surprising, and suggests an association with malaria resistance.

physiology↗

bioRxiv2017

The Effect Of Nipped-B-Like (Nipbl) Haploinsufficiency On Genome-Wide Cohesin Binding And Target Gene Expression: Modeling Cornelia de Lange Syndrome

Cornelia de Lange Syndrome (CdLS) is a multisystem developmental disorder frequently associated with heterozygous loss-of-function mutations of Nipped-B-like (NIPBL), the human homolog of Drosophila Nipped-B. NIPBL loads cohesin onto chromatin. Cohesin mediates sister chromatid cohesion important for mitosis, but is also increasingly recognized as a regulator of gene expression. In CdLS patient cells and animal models, the presence of multiple gene expression changes with little or no sister chromatid cohesion defect suggests that disruption of gene regulation underlies this disorder. However, the effect of NIPBL haploinsufficiency on cohesin binding, and how this relates to the clinical presentation of CdLS, has not been fully investigated. Nipbl haploinsufficiency causes CdLS-like phenotype in mice. We examined genome-wide cohesin binding and its relationship to gene expression using mouse embryonic fibroblasts (MEFs) from Nipbl +/- mice that recapitulate the CdLS phenotype. We found a global decrease in cohesin binding, including at CCCTC-binding factor (CTCF) binding sites and repeat regions. Cohesin-bound genes were found to be enriched for histone H3 lysine 4 trimethylation (H3K4me3) at their promoters; were disproportionately downregulated in Nipbl mutant MEFs; and displayed evidence of reduced promoter-enhancer interaction. The results suggest that gene activation is the primary cohesin function sensitive to Nipbl reduction. Over 50% of significantly dysregulated transcripts in mutant MEFs come from cohesin target genes, including genes involved in adipogenesis that have been implicated in contributing to the CdLS phenotype. Thus, decreased cohesin binding at the gene regions directly contributes to disease-specific expression changes. Taken together, our Nipbl haploinsufficiency model allows us to analyze the dosage effect of cohesin loading on CdLS development.

developmental biology↗