Biology subjects

Li, D.

Publications and source records attributed to Li, D..

674 records · Page 38Linked to original sources

bioRxiv2017

Disruption of thyroid endocrine in zebrafish exposed to BDE-209

Polybrominated diphenyl ethers (PBDs) could adversely affect the thyroid endocrine system; previous studies report that BDE-209 has the potential effect on the fish thyroid endocrine system. In this study, we aimed to verify the bioconcentration and metabolism of BDE-209 in zebrafish. One day post-fertilization (dpf) zebrafish embryos were treated with different concentrations of BDE-209 (0, 0.01, 0.1 and 1 mg/L) until 10 dpf. BDE-209 was obviously accumulated in the zebrafish after 10 days exposure, and the metabolic products such as octa-BDE and nona-BDE were detected. After treated with BDE-209, the triiodthyronine (T3) and thyroxine (T4) levels were significantly decreased, suggesting that exposure to BDE-209 could disrupt the thyroid endocrine system in zebrafish. The transcriptional expression of genes involved in the hypothalamic-pituitary-thyroid (HPT) axis was altered. The mRNA expression levels of corticotrophin-releasing hormone (CRH) and thyroid-stimulating hormone (TSH{beta}) were significantly increased. The mRNA expression of pax8 and nkx2.1 which regulate thyroid development and synthesis were also increased. These data indicated that BDE-209 could disrupt the thyroid endocrine system in zebrafish, which could be assessed by hypothalamic-pituitary-thyroid axis.

developmental biology↗

bioRxiv2017

HPCDb: an integrated database of pancreatic cancer

We have established a database of Human Pancreatic Cancer (HPCDb) through effectively mining, extracting, analyzing, and integrating PC-related genes, single-nucleotide polymorphisms (SNPs), and microRNAs (miRNAs), now available online at http://www.pancancer.org/. Data were extracted from established databases, [≥]5 published literature (PubMed), and microarray chips (screening of differentially expressed genes using limma package in R, |log2 fold change (FC)| > 1). Further, protein-protein interactions (PPIs) were investigated through the Human Protein Reference Database. miRNA-target relationships were also identified using the online software TargetScan. Currently, HPCDb contains 3284 genes, 120 miRNAs, 589 SNPs, 10,139 PPIs, and 3904 miRNA-target pairs. The detailed information on PC-related genes (e.g., gene identifier (ID), symbol, synonyms, full name, chip sets, expression alteration, PubMed ID, and PPIs), miRNAs (e.g., accession number, chromosome location, related disease, PubMed ID, and miRNA-target interactions), and SNPs (e.g., SNP ID, allele, gene, PubMed ID, chromosome location, and disease) is presented through user-friendly query interfaces or convenient links to NCBI GEO, NCBI PubMed, NCBI Gene, NCBI dbSNP, and miRBase. Overall, HPCDb provides biologists with relevant information on human PC-related molecules at multiple levels, helping to generate new hypotheses or identify candidate markers.

bioinformatics↗

bioRxiv2017

Necroptosis promotes the Aging of the Male Reproductive System in Mice

Necroptosis is a form of programmed necrotic cell death in mammals that is mediated by a pair of kinases, RIP1 and RIP3, as well as the RIP3 substrate MLKL. We report here that male reproductive organs of both RIP3-and MLKL-knockout mice retain \"youthful\" morphology and function into advanced age, while those of age-matched wild type mice deteriorate. The RIP3 phosphorylation of MLKL, the activation marker of necroptosis, is detected in spermatogonial stem cells in the testes of old but not in young wild type mice. When the testes of young wild type mice are given a local necroptotic stimulus, their reproductive organs showed accelerated aging. Feeding of wild type mice with an RIP1 inhibitor prior to the normal onset of age-related changes in their reproductive organs blocked the appearance of signs of aging. Thus, necroptosis in testes promotes the aging-associated deterioration of the male reproductive system in mice.

cell biology↗

bioRxiv2017

Scalable Multi-Sample Single-Cell Data Analysis by Partition-Assisted Clustering and Multiple Alignments of Networks

Mass cytometry (CyTOF) has greatly expanded the capability of cytometry. It is now easy to generate multiple CyTOF samples in a single study, with each sample containing single-cell measurement on 50 markers for more than hundreds of thousands of cells. Current methods do not adequately address the issues concerning combining multiple samples for subpopulation discovery, and these issues can be quickly and dramatically amplified with increasing number of samples. To overcome this limitation, we developed Partition-Assisted Clustering and Multiple Alignments of Networks (PAC-MAN) for the fast automatic identification of cell populations in CyTOF data closely matching that of expert manual-discovery, and for alignments between subpopulations across samples to define dataset-level cellular states. PAC-MAN is computationally efficient, allowing the management of very large CyTOF datasets, which are increasingly common in clinical studies and cancer studies that monitor various tissue samples for each subject.\n\nAuthor SummaryRecently, the cytometry field has experienced rapid advancement in the development of mass cytometry (CyTOF). CyTOF enables a significant increase in the ability to monitor 50 or more cellular markers for millions of cells at the single-cell level. Initial studies with CyTOF focused on few samples, in which expert manual discovery of cell types were acceptable. As the technology matures, it is now feasible to collect more samples, which enables systematic studies of cell types across multiple samples. However, the statistical and computational issues surrounding multi-sample analysis have not been previously examined in detail. Furthermore, it was not clear how the data analysis could be scaled for hundreds of samples, such as those in clinical studies. In this work, we present a scalable analysis pipeline that is grounded in strong statistical foundation. Partition-Assisted Clustering (PAC) offers fast and accurate clustering and Multiple Alignments of Networks (MAN) utilizes network structures learned from each homogeneous cluster to organize the data into data-set level clusters. PAC-MAN thus enables the analysis of a large CyTOF dataset that was previously too large to be analyzed systematically; this pipeline can be extended to the analysis of similarly large or larger datasets.

bioinformatics↗

bioRxiv2017

The 3D Genome Browser: a web-based browser for visualizing 3D genome organization and long-range chromatin interactions

Recent advent of 3C-based technologies such as Hi-C and ChIA-PET provides us an opportunity to explore chromatin interactions and 3D genome organization in an unprecedented scale and resolution. However, it remains a challenge to visualize chromatin interaction data due to its size and complexity. Here, we introduce the 3D Genome Browser (http://3dgenome.org), which allows users to conveniently explore both publicly available and their own chromatin interaction data. Users can also seamlessly integrate other \"omics\" data sets, such as ChIP-Seq and RNA-Seq for the same genomic region, to gain a complete view of both regulatory landscape and 3D genome structure for any given gene. Finally, our browser provides multiple methods to link distal cis-regulatory elements with their potential target genes, including virtual 4C, ChIA-PET, Capture Hi-C and cross-cell-type correlation of proximal and distal DNA hypersensitive sites, and therefore represents a valuable resource for the study of gene regulation in mammalian genomes.

bioinformatics↗

bioRxiv2016

The origin and evolution of a pandemic lineage of the kiwifruit pathogen Pseudomonas syringae pv. actinidiae

Recurring epidemics of kiwifruit (Actinidia spp.) bleeding canker disease are caused by Pseudomonas syringae pv. actinidiae (Psa), whose emergence coincided with domestication of its host. The most recent pandemic has had a deleterious effect on kiwifruit production worldwide. In order to strengthen understanding of population structure, phylogeography and evolutionary dynamics of Psa, we sampled 746 Pseudomonas isolates from cultivated and wild kiwifruit across six provinces in China, of which 87 were Psa. Of 234 Pseudomonas isolated from wild Actinidia spp. none were identified as Psa. Genome sequencing of fifty isolates and the inclusion of an additional thirty from previous studies show that China is the origin of the recently emerged pandemic lineage. However China harbours only a fraction of global Psa diversity, with greatest diversity found in Korea and Japan. Distinct transmission events were responsible for introduction of the pandemic lineage of Psa into New Zealand, Chile and Europe. Two independent transmission events occurred between China and Korea, and two Japanese isolates from 2014 cluster with New Zealand Psa. Despite high similarity at the level of the core genome and negligible impact of within-lineage recombination, there has been substantial gene gain and loss even within the single clade from which the global pandemic arose.\n\nSIGNIFICANCE STATEMENTBleeding canker disease of kiwifruit caused by Pseudomonas syringae pv. actinidiae (Psa) has come to prominence in the last three decades. Emergence has coincided with domestication of the host plant and provides a rare opportunity to understand ecological and genetic factors affecting the evolutionary origins of Psa. Here, based on genomic analysis of an extensive set of strains sampled from China and augmented by isolates from a global sample, we show, contrary to earlier predictions, that China is not the native home of the pathogen, but is nonetheless the source of the recent global pandemic. Our data identify specific transmission events, substantial genetic diversity and point to non-agricultural plants in either Japan or Korea as home to the source population.

epidemiology↗

bioRxiv2016

The need to include phylogeny in trait-based analyses of community composition

O_LIA growing number of studies incorporate functional trait information to analyse patterns and processes of community assembly. These studies of trait-environment relationships generally ignore phylogenetic relationships among species. When functional traits and the residual variation in species distributions among communities have phylogenetic signal, however, analyses ignoring phylogenetic relationships can decrease estimation accuracy and power, inflate type I error rates, and lead to potentially false conclusions.\nC_LIO_LIUsing simulations, we compared estimation accuracy, statistical power, and type I error rates of linear mixed models (LMM) and phylogenetic linear mixed models (PLMM) designed to test for trait-environment interactions in the distribution of species abundances among sites. We considered the consequences of both phylogenetic signal in traits and phylogenetic signal in the residual variation of species distributions generated by an unmeasured (latent) trait with phylogenetic signal.\nC_LIO_LIWhen there was phylogenetic signal in the residual variation of species among sites, PLMM provided better estimates (closer to the true value) and greater statistical power for testing whether the trait-environment interaction regression coefficient differed from zero. LMM had unacceptably high type I error rates when there was phylogenetic signal in both traits and the residual variation in species distributions. When there was no phylogenetic signal in the residual variation in species distributions, LMM and PLMM had similar performances.\nC_LIO_LILMMs that ignore phylogenetic relationships can lead to poor statistical tests of trait-environment relationships when there is phylogenetic signal in the residual variation of species distributions among sites, such as caused by unmeasured traits. Therefore, phylogenies and PLMMs should be used when studying how functional traits affect species abundances among communities in response to environmental gradients.\nC_LI

ecology↗

bioRxiv2016

Deconstructing cell size control into physiological modules in Escherichia coli

It is generally assumed that the allocation and synthesis of total cellular resources in microorganisms are uniquely determined by the growth conditions. Adaptation to a new physiological state leads to a change in cell size via reallocation of cellular resources. However, it has not been understood how cell size is coordinated with biosynthesis and robustly adapts to physiological states. We show that cell size in Escherichia coli can be predicted for any steady-state condition by projecting all biosynthesis into three measurable variables representing replication initiation, replication-division cycle, and the global biosynthesis rate. These variables can be decoupled by selectively controlling their respective core biosynthesis using CRISPR interference and antibiotics, verifying our predictions that different physiological states can result in the same cell size. We performed extensive growth inhibition experiments, and discovered that cell size at replication initiation per origin, namely the initiation mass or \"unit cell,\" is remarkably invariant under perturbations targeting transcription, translation, ribosome content, replication kinetics, fatty acid and cell-wall synthesis, cell division, and cell shape. Based on this invariance and balanced resource allocation, we explain why the total cell size is the sum of all unit cells. These results provide an overarching framework with quantitative predictive power over cell size in bacteria.

microbiology↗