Biology subjects

Shang, X.

Publications and source records attributed to Shang, X..

47 records · Page 3Linked to original sources

bioRxiv2021

5-methylcytosine modification by Plasmodium NSUN2 stabilizes mRNA and mediates the development of gametocytes

5-methylcytosine (m5C) is an important epitranscriptomic modification involved in mRNA stability and translation efficiency in various biological processes. However, it remains unclear if m5C modification contributes to the dynamic regulation of the transcriptome during the developmental cycles of Plasmodium parasites. Here, we characterize the landscape of m5C mRNA modifications at single nucleotide resolution in the asexual replication stages and gametocyte sexual stages of rodent (P. yoelii) and human (P. falciparum) malaria parasites. While different representations of m5C-modified mRNAs are associated with the different stages, the abundance of the m5C marker is strikingly enhanced in the transcriptomes of gametocytes. Our results show that m5C modifications confer stability to the Plasmodium transcripts and that a Plasmodium ortholog of NSUN2 is a major mRNA m5C methyltransferase in malaria parasites. Upon knock-out of P. yoelii nsun2 (pynsun2), marked reductions of m5C modification were observed in a panel of gametocytogenesis-associated transcripts. These reductions correlated with impaired gametocyte production in rodent and human malaria parasites. Restoration of the nsun2 gene in the knock-out parasites rescued the gametocyte production phenotype as well as m5C modification of the gametocytogenesis-associated transcripts. Together with the mRNA m5C profiles for two species of Plasmodium, our findings demonstrate a major role for NSUN2-mediated m5C modifications in mRNA transcript stability and sexual differentiation in malaria parasites. SignificanceModifications of RNA including methylations of cytosine (m5C) and adenosine (m6A) have important roles in RNA metabolism, cellular responses to stress, and biological processes of differentiation and development. Here, we report on the profiles of m5C mRNA modifications in malaria parasites that infect rodents (Plasmodium yoelii) and humans (Plasmodium falciparum). These parasites have genes that encode homologs of human and plant NSUN2 methyltransferases (m5C "writers"). We show that one of these homologs, termed PyNSUN2, stabilizes mRNA transcripts in P. yoelii and mediates m5C-associated development of the parasite sexual stages (gametocytes). Further research on m5C and other epitranscriptomic modifications may yield new insights into molecular pathways of gametocyte development and mosquito infectivity that can be exploited to interrupt malaria transmission.

microbiology↗

bioRxiv2021

A novel framework for single-cell Hi-C clustering based on graph-convolution-based imputation and two-phase-based feature extraction

The three-dimensional genome structure plays a key role in cellular function and gene regulation. Singlecell Hi-C technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. However, few methods are well designed for single-cell Hi-C clustering, because of high sparsity, noise and heterogeneity of single-cell Hi-C data. In this manuscript, we propose a novel framework, named ScHiC-Rep, for singlecell Hi-C data representation and clustering. ScHiC-Rep mainly contains two parts: data imputation and feature extraction. In the imputation part, a novel imputation workflow is proposed, including graph convolution-based, random walk with restart-based and genomic neighbor-based imputation. In the feature extraction part, a two-phase feature extraction method is proposed, including linear phase for chromosome level and non-linear phase for cell level feature extraction. The evaluation results show that the proposed framework outperforms existing state-of-the-art approaches on both human and mouse datasets.

bioinformatics↗

bioRxiv2021

Efficient and scalable integration of single-cell data using domain-adversarial and variational approximation

Single-cell data provides us new ways of discovering biological truth at the level of individual cells, such as identification of cellular sub-populations and cell development. With the development of single-cell sequencing technologies, a key analytical challenge is to integrate these data sets to uncover biological insights. Here, we developed a domain-adversarial and variational approximation framework, DAVAE, to integrate multiple single-cell data across samples, technologies and modalities without any post hoc data processing. We fit normalized gene expression into a non-linear model, which transforms a latent variable of a lower-dimension into expression space with a non-linear function, a KL regularizier and a domain-adversarial regularizer. Results on five real data integration applications demonstrated the effectiveness and scalability of DAVAE in batch-effect removing, transfer learning, and cell type predictions for multiple single-cell data sets across samples, technologies and modalities. DAVAE was implemented in the toolkit package "scbean" in the pypi repository, and the source code can be also freely accessible at https://github.com/jhu99/scbean.

bioinformatics↗

bioRxiv2020

Genetic determinants of wheat resistance to common root rot (spot blotch) and Fusarium crown rot

Due to soil changes, high density planting, and the use of straw-returning methods, wheat common root rot (spot blotch), Fusarium crown rot (FCR), and sharp eyespot (sheath blight) have become severe threats to global wheat production. Only a few wheat genotypes show moderate resistance to these root and crown rot fungal diseases, and the genetic determinants of wheat resistance to these devastating diseases are poorly understood. This review summarizes recent results of genetic studies of wheat resistance to common root rot, Fusarium crown rot, and sharp eyespot. Wheat germplasm with relatively higher resistance are highlighted and genetic loci controlling the resistance to each disease are summarized.

genetics↗

bioRxiv2020

Dampened PI3K/AKT signaling contributes to cancer resistance of the naked mole rat

Mammalian species have a dramatically different susceptibility to cancer. However, how cancer-resistant species resist oncogenic transformation is not fully understood. Here, we performed a comprehensive analysis of oncogene-induced transcriptional changes in the fibroblasts of a cancer-prone species, the mouse, and three cancer-resistant species, the human, the blind mole rat, and the naked mole rat. We report that multiple cellular processes are more refractory to oncogene-induced transcriptional changes in blind mole-rat, naked mole-rat, or human cells compared to mouse cells, such as cell division, cell adhesion, extracellular matrix organization, and metabolism. Strikingly, naked mole rat cells are more resistant to Ras-induced transcriptional changes compared to the other three species. As a mechanism, we found that critical genes in the PI3K pathway including Akt1 and Pik3ca are downregulated in naked mole rat cells. Activating the PI3K/AKT pathway in the naked mole rat cells renders them susceptible to tumorigenic transformation. This study provides multiple new insights into anti-cancer mechanisms in cancer-resistant species of mammals. Significance statementAnimal species differ greatly in their cancer susceptibility. Cancer rates in the mouse range from 50-90%, while two other rodent species, the naked mole rat and the blind mole rat have only a few cancer cases ever reported. Here we examined the mechanisms responsible these differences by comparing changes in transcription patters in response to oncoproteins in the mouse, naked mole rat, blind mole rat and human cells. The most striking finding was that the naked mole rat cells were resistant to transcriptional changes induced by oncogenic Ras. We found that pathways downstream of Ras were naturally attenuated in the naked mole rat. This finding identifies a novel mechanism that evolved to provide tumor resistance to the naked mole rat.

cancer biology↗

bioRxiv2019

A Clostridia-rich microbiota contributes to increased excretion of bile acids in diarrhea-predominant irritable bowel syndrome

ObjectiveAn excess of fecal bile acids (BAs) is thought to be one of the mechanisms for diarrhea-predominant irritable bowel syndrome (IBS-D). However, the factors causing excessive BA excretion remains unclear. Given the importance of gut microbiota in BA metabolism, we hypothesized that gut dysbiosis might contribute to excessive BA excretion in IBS-D.\n\nDesignMetabolomic and metagenomic analyses were performed of specimens from 290 IBS-D patients and 89 healthy volunteers. By transplanting human microbiota and manipulating specific microbiome species in mice, the effects of microbiota on host BA metabolism were assessed at metabolic, genetic and protein levels. Effects of individual and mixed BAs on enterohepatic feedback pathways were also tested in vitro and in vivo.\n\nResultsTotal fecal BAs were excessively excreted in 24.5% of IBS-D patients. Their fecal metagenomes showed increased abundances of Clostridia and BA-transforming genes (hdhA and bais). The increases of Clostridia bacteria (e.g. C. scindens) were positively associated with the levels of fecal BAs and serum 7-hydroxy-4-cholesten-3-one (C4), while being negatively correlated with serum fibroblast growth factor 19 (FGF19). Both Clostridia-rich human microbiota and C. scindens enhanced levels of serum C4 and hepatic conjugated BAs in mice recipients and reduced ileal FGF19 expression. Inhibition of Clostridium species by vancomycin yielded opposite findings. Clostridia-derived BAs (e.g. conjugated and free ursodeoxycholic acid) significantly suppressed intestinal FGF19 expression.\n\nConclusionThe Clostridia-rich microbiota contributes to excessive BA excretion in IBS-D patients. This study provided the basis for more precise clinical diagnosis and management for IBS-D.

microbiology↗

bioRxiv2019

A Novel Neoantigen Discovery Approach based on Chromatin High Order Conformation: Mapping the Neoantigen to 3D Genome

The high-throughput sequencing technology has yielded reliable and ultra-fast sequencing for DNA and RNA. For tumor cells of cancer patients, when combining the results of DNA and RNA sequencing, one can identify potential neoantigens that stimulate immune response of the T cells. However, when the somatic mutations are abundant, it is computationally challenging to efficiently prioritize the identified neoantigen candidates according to their ability of activating the T cell immuno-response. Numerous prioritization or prediction approaches have been proposed to address this issue but none of them considers the original DNA loci of the neoantigens from the perspective of 3D genome. Here we retrospect the DNA origins of the immune-positive and non-negative neoantigens in the context of 3D genome and discovered that 1) DNA loci of the immuno-positive neoantigens tend to cluster genome-wise. 2) DNA loci of the immuno-positive neoantigens tend to belong to active chromosomal compartment (compartment A) in some chromosomes. 3). DNA loci of the immuno-positive neoantigens tend to locate at specific regions in the 3D genome. We believe that the 3D genome information will help more precise neoantigen prioritization and discovery and eventually benefit precision and personalized medicine in cancer immunotherapy.

bioinformatics↗

bioRxiv2019

Integrating multi-network topology for gene function prediction using deep neural networks

MotivationThe emerging of abundant biological networks, which benefit from the development of advanced high-throughput techniques, contribute to describing and modeling complex internal interactions among biological entities such as genes and proteins. Multiple networks provide rich information for inferring the function of genes or proteins. To extract functional patterns of genes based on multiple heterogeneous networks, network embedding-based methods, aiming to capture non-linear and low-dimensional feature representation based on network biology, have recently achieved remarkable performance in gene function prediction. However, existing methods mainly do not consider the shared information among different networks during the feature learning process. Thus, we propose a novel multi-networks embedding-based function prediction method based on semi-supervised autoencoder and feature convolution neural network, named DeepMNE-CNN, which captures complex topological structures of multi-networks and takes the correlation among multi-networks into account. ResultsWe design a novel semi-supervised autoencoder method to integrate multiple networks and generate a low-dimensional feature representation. Then we utilize a convolutional neural network based on the integrated feature embedding to annotate unlabeled gene functions. We test our method on both yeast and human dataset and compare with four state-of-the-art methods. The results demonstrate the superior performance of our method over four state-of-the-art algorithms. From the future explorations, we find that semi-supervised autoencoder based multi-networks integration method and CNN-based feature learning methods both contribute to the task of function prediction. AvailabilityDeepMNE-CNN is freely available at https://github.com/xuehansheng/DeepMNE-CNN

bioinformatics↗

bioRxiv2018

Identifying Emerging Phenomenon in Plant Long Temporal Phenotyping Experiments

The rapid improvement of phenotyping capability, accuracy, and throughput have greatly increased the volume and diversity of phenomics data. A remaining challenge is an efficient way to identify phenotypic patterns to improve our understanding of the quantitative variation of complex phenotypes, and to attribute gene functions. To address this challenge, we developed a new algorithm to identify emerging phenomena from large-scale temporal plant phenotyping experiments. An emerging phenomenon is defined as a group of genotypes who exhibit a coherent phenotype pattern during a relatively short time. Emerging phenomena are highly transient and diverse, and are dependent in complex ways on both environmental conditions and development. Identifying emerging phenomena may help biologists to examine potential relationships among phenotypes and genotypes in a genetically diverse population and to associate such relationships with the change of environments or development. We present an emerging phenomenon identification tool called Temporal Emerging Phenomenon Finder (TEP-Finder). Using large-scale longitudinal phenomics data as input, TEP-Finder first encodes the complicated phenotypic patterns into a dynamic phenotype network. Then, emerging phenomena in different temporal scales are identified from dynamic phenotype network using a maximal clique based approach. Meanwhile, a directed acyclic network of emerging phenomena is composed to model the relationships among the emerging phenomena. The experiment that compares TEP-Finder with two state-of-art algorithms shows that the emerging phenomena identified by TEP-Finder are more functionally specific, robust, and biologically significant. The source code, manual, and sample data of TEP-Finder are all available at: http://phenomics.uky.edu/TEP-Finder/.

bioinformatics↗

bioRxiv2018

Combining Gene Ontology with Deep Neural Networks to Enhance the Clustering of Single Cell RNA-Seq Data

BackgroundSingle cell RNA sequencing (scRNA-seq) is applied to assay the individual transcriptomes of large numbers of cells. The gene expression at single-cell level provides an opportunity for better understanding of cell function and new discoveries in biomedical areas. To ensure that the single-cell based gene expression data are interpreted appropriately, it is crucial to develop new computational methods.\n\nResultsIn this article, we try to construct the structure of neural networks based on the prior knowledge of Gene Ontology (GO). By integrating GO with both unsupervised and supervised models, two novel methods are proposed, named GOAE (Gene Ontology AutoEncoder) and GONN (Gene Ontology Neural Network) respectively, for clustering of scRNA-seq data.\n\nConclusionsThe evaluation results show that the proposed models outperform some state-of-the-art approaches. Furthermore, incorporating with GO, we provide an opportunity to interpret the underlying biological mechanism behind the neural network-based model.

bioinformatics↗

bioRxiv2018

A learning-based framework for miRNA-disease association prediction using neural networks

MotivationA microRNA (miRNA) is a type of non-coding RNA, which plays important roles in many bio-logical processes. Lots of studies have shown that miRNAs were implicated in human diseases, indicating that miRNAs might be potential biomarkers for various types of diseases. Therefore, it is important to reveal the relationships between miRNAs and diseases/phenotypes.\n\nResultsWe proposed a novel learning-based framework, MDA-CNN, for miRNA-disease association identification. The model first captures richer interaction features between diseases and miRNAs based on a three-layer network with an additional gene layer. An auto-encoder is then employed to identify the essential feature combination for each pair of miRNA and disease automatically. A final label is given by a convolutional neural network taking the reduced feature representation as input. The evaluation results showed that the proposed framework outperformed some state-of-the-art approaches in a large margin on both tasks of miRNA-disease associations prediction and miRNA-phenotype associations prediction.\n\nAvailabilityThe software will be available after the manuscript is accepted.\n\nContactjiajiepeng@nwpu.edu.cn;zywei@fudan.edu.cn;shang@nwpu.edu.cn\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗