Biology subjects

Dong, C.

Publications and source records attributed to Dong, C..

9 recordsLinked to original sources

bioRxiv2018

Spatio-temporal variability of soil nutrients and the responses of growth during growth stages of winter wheat in the north of China

Study on soil nutrient variability and its effect on the growth and development of crop under traditional tillage mode is the foundation to comprehensively implement the precision agriculture policy on the field scale and crop excellent management. In this paper, a winter wheat field of 28.5 hm2 under traditional cultivation model in Tianzhuang town of Huantai county was selected as the research area. Sampling by mesh point method (60x60m), the variation characteristics of soil available nitrogen (AN), available phosphorus (AP) and available potassium (AK) in the before sowing, reviving stage, jointing stage, filling stage of winter wheat were analyzed by the method of geostatistics and GIS. At the same time, Pearson correlation analysis was used to study the response of wheat growth and development to soil nutrient variation. As the growth period goes by, 1) each nutrient showed low-high-low and moderate variability. AN and AK had the highest content at reviving stage, while AP at jointing stage, as turning point. The order of variability of each nutrient was AN>AP>AK. 2) The difference of nutrient variation first increased and then decreased, and showed medium to strong spatial correlation.\n\nThree nutrients in the before sowing stage were strong spatial correlation, and the reviving stage was medium spatial correlation, to the jointing and filling stages, AN was medium spatial correlation, AP and AK were strong spatial correlation. The spatial correlation of each nutrients was the weakest in the reviving stage, and AN was the strongest in the before sowing stage, while AP and AK were strongest in the jointing stage. The spatial correlation of each soil nutrients decreased from before sowing to reviving stage, jointing stage to filling stage, and the spatial correlation increased from reviving stage to jointing stage. 3) Soil nutrient content first increased and then decreased, and the grade of nutrient gradually decreased. 4) The correlation between soil nutrients and wheat growth was gradually increasing. AN had the highest correlation with wheat growth, followed by AK and AP lowest. The effect of soil nutrients on the growth of wheat at the reviving stage was higher than that of the current nutrient, and the growth of wheat at jointing stage was mainly influenced by the current nutrient, while the growth of wheat at the filling period was both influenced by the nutrient content of the last and the current period; the date to supplement fertilizer should be postponed properly. In this study, the soil nutrient dynamics and its influence on the growth of wheat during the winter wheat growth period under the traditional field model were well understood, which could provide a theoretical basis for the precision management of soil nutrients in the northern winter wheat area with relatively uniform planting environment and cultivation management.

scientific communication and education↗

bioRxiv2018

The effects and mechanism of peiminine-induced apoptosis in human hepatocellular carcinoma HepG2 cells

Peiminine is a compound that is isolated from Bolbostemma paniculatum (Maxim) Franquet (Cucurbitaceae family), which has demonstrated antitumor activities. Its precise molecular mechanisms underlying antitumor activity remain elusive. In this study, peiminine-induced apoptosis towards human hepatocellular carcinoma and its molecular mechanisms were investigated. MTT assay was employed to assess anticancer effects of peiminine at concentrations of 2, 4, 6, 8, 10, 12, and 14 g/ml after 24, 48, or 72 h. Nuclear staining and flow cytometry were carried out to further assess apoptosis. Mitochondrial membrane potential evaluation and Western blot analysis were performed to investigate the mechanism of peiminine-induced apoptosis. Peiminine reduced the viability of HepG2 cells in a time- and dose-dependent manner and had an IC50 of 4.58 g/mL at 24h. Flow cytometry assessment indicated that peiminine markedly increased the cell number of apoptotic cells and the mitochondrial membrane potential dose-dependently in HepG2 cells. The results of Western blotting showed the expression of Bcl-2, procaspase-3, procaspase-8, procaspase-9, and PARP1 decreased in HepG2 cells treated with peiminine, while the expression of Bax, caspase-3, caspase-8, caspase-9, and cleaved PARP1 increased. The result suggest taht peiminine can induce apoptosis in human hepatocellular carcinoma HepG2 cells through both extrinsic and intrinsic apoptotic pathways.

cancer biology↗

bioRxiv2018

Identification of Novel Genes and Variations Associated to Glycolytic Potential Based on Pig Model

In livestock, glycolytic potential (GP) is a critical indicator for evaluating the meat quality. To date, two major genes protein kinase AMP-activated {gamma}3 non-catalytic subunit gene (PRKAG3) and phosphorylase kinase catalytic subunit gamma 1(PHKG1), and corresponding cause mutations influencing GP have been confirmed in pigs. Therefore, the aim of this study to identify the novel candidate genes and variations related to GP-related traits using a four-hybrid pig model [Pietrain (P)x Duroc (D)] x[(Landrace) x(Yorkshire)]. We totally constructed six RNA-seq libraries using longissimus dorsi (LD) muscles, and each library contained two higher GP (H) or two lower GP (L) individuals. A total of 525, 698 and 135 differentially expressed genes (DEGs) were identified between H11 vs L11, H9 vs L9, and H5 vs L5 groups using PossionDis method, respectively. Notably, we found 97 non-redundant DEGs were mapped to GP related QTLs from three paired comparison groups. Moreover, 69 DEGs were identified between H (H11, H9 and H5) and L (L11, L9 and L5) groups using NOIseq method. Additionally, 1,076 potential specific SNPs were figured out between H and L groups, and approximately 40 large Indels with a length [≥] 5bp were identified in each sequencing library. In conclusion, our data provide foundation for further confirming the key genes and the functional mutations affecting GP-related traits in pigs, and also pave the way for elucidating the underling molecular regulatory mechanisms of glycogen metabolism in future study. Moreover, this study might provide valuable information for study on human glycogen storage diseases.

genomics↗

bioRxiv2018

Internal Tensile Force and A2 Domain Unfolding of von Willebrand Factor Multimers in Shear Flow

Using Brownian molecular dynamics simulations, we examine the internal dynamics and biomechanical response of von Willebrand Factor (vWF) multimers subject to shear flow. The coarse grain multimer description employed here is based on a monomer model in which the A2 domain of vWF is explicitly represented by a non-linear elastic spring whose mechanical response was fit to experimental force/extension data from vWF monomers. This permits examination of the dynamic behavior of hydrodynamic forces acting on A2 domains as a function of shear rate and multimer length, as well as position of an A2 domain along the multimer contour. Force/position data reveal that collapsed multimers exhibit a force distribution with two peaks, one near each end of the chain; unraveled multimers, however, show a single peak in A2 domain force near the center of multimers. Guided further by experimental data, significant excursions of force acting on a domain are associated with an increasing probability for A2 domain unfolding. Our results suggest that the threshold shear rate required to induce A2 domain unfolding is inversely proportional to multimer length. By examining data for the duration and location of significant force excursions, convincing evidence is advanced that unfolding of A2 domains, and therefore scission of vWF multimers by the size-regulating blood enzyme ADAMTS13, happen preferentially near the center of unraveled multimers.

biophysics↗

bioRxiv2017

miCloud: a plug and play, on-premises bioinformatics cloud, providing seamless integration with Illumina genome sequencers

Benchtop genome sequencers such as the Illumina MiSeq or MiniSeq [1], [2] are revolutionizing genomics research for smaller, independent laboratories, by enabling access to low-cost Next Generation Sequencing (NGS) technology in-house. These benchtop genome sequencing instruments require only standard laboratory equipment, in addition to minimal time for sample preparation. However, post-sequencing bioinformatics data analysis still presents a significant bottleneck, for research laboratories lacking specialized software and technical data analysis skills on their teams. While bioinformatics computes clouds providing solutions following a Software as a Service (SaaS) are available ([3]-[6], review in [7]), currently, there are only a few options which are user-friendly for non-experts while at the same time are also low-cost or free. One primary example is Illumina BaseSpace [8] that is very easy to access by non-experts, and also offers an integrated solution where data are streamed directly from the MiSeq sequencing instrument to the cloud. Once the data is on the BaseSpace cloud, users can access a range of bioinformatics applications with pre-installed algorithms through an intuitive web interface. Nonetheless, BaseSpace can be a costly solution as a yearly subscription depending on whether the user is associated with an academic or private institution, ranges in price from $999 - $4,999. Additional \"iCredits\" [9] might need to be purchased for frequent users that exhaust the base credit allowance as part of the subscription. Considering the reduction of computer hardware cost in recent years, a multi-core Intel Xeon server with 64 GigaByte (GB) of memory and multiple TeraByte (TB) of storage is priced less than the yearly subscription to Basespace [10], and similarly when compared to renting compute cycles from providers such as Amazon Web Services (AWS) [11]. Furthermore, the current generation of laptops usually come with 6-10 GigaBytes (GB) of memory and 1 TeraByte (TB) of storage, providing enough computational capacity to analyze data from small NGS experiments [12] that include only a few samples.

bioinformatics↗

bioRxiv2017

FGF21 regulates melanogenesis in alpaca melanocytes via ERK1/2-Mediated MITF downregulation

Fibroblast growth factor 21 (FGF21) is known as a metabolic regulator to regulate the metabolism of glucose and lipids. However, the underlying mechanism of FGF21 on melanin synthesis remains unknown. Therefore, the current study investigates the effect of FGF21 on melanogenesis in alpaca melanocytes. We transfected the FGF21 into alpaca melanocytes, then detected the melanin contents, protein and mRNA levels of pigmentation-related genes in order to determine the melanogenesis-regulating pathway of FGF21. The results showed that FGF21 overexpression suppressed melanogenesis and decreased the expression of the major target genes termed microphthalmia-associated transcription factor (MITF) and its downstream genes, including tyrosinase (TYR) and tyrosinase-related protein 2 (TRP2). However FGF21 increased the expression of phospho-extracellular signal-regulated kinase (p-Erk1/2). In contrast, FGF21-siRNA, a small interference RNA mediating FGF21 silencing, abolished the inhibition of melanogenesis. Altogether, FGF21 may decrease melanogenesis in alpaca melanocytes via ERK activation and subsequent MITF downregulation, which is then followed by the suppression of melanogenic enzymes and melanin production.

cell biology↗

bioRxiv2017

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models

Translating the vast data generated by genomic platforms into accurate predictions of clinical outcomes is a fundamental challenge in genomic medicine. Many prediction methods face limitations in learning from the high-dimensional profiles generated by these platforms, and rely on experts to hand-select a small number of features for training prediction models. In this paper, we demonstrate how deep learning and Bayesian optimization methods that have been remarkably successful in general high-dimensional prediction tasks can be adapted to the problem of predicting cancer outcomes. We perform an extensive comparison of Bayesian optimized deep survival models and other state of the art machine learning methods for survival analysis, and describe a framework for interpreting deep survival models using a risk backpropagation technique. Finally, we illustrate that deep survival models can successfully transfer information across diseases to improve prognostic accuracy. We provide an open-source software implementation of this framework called SurvivalNet that enables automatic training, evaluation and interpretation of deep survival models.

bioinformatics↗

bioRxiv2017

Fast functional annotation of metagenomic shotgun data by DNA alignment to a microbial gene catalog

BackgroundMetagenomic shotgun sequencing is becoming increasingly popular to study microbes associated with the human body and in environmental samples. A key goal of shotgun metagenomic sequencing is to identify gene functions and metabolic pathways that differ between samples or conditions. However, current methods to identify function in the large number of reads in a high-throughput sequence data file rely on the computationally intensive and low stringency approach of mapping each read to a generic database of proteins or reference microbial genomes.\n\nResultsWe have developed an alternative analysis approach for shotgun metagenomic sequence data utilizing Bowtie2 DNA-DNA alignment of the reads to a database of well annotated genes compiled from human microbiome data. This method is rapid, and provides high stringency matches (>90% DNA sequence identity) of shotgun metagenomics reads to genes with annotated functions. We demonstrate the use of this method with synthetic data, Human Microbiome Project shotgun metagenomic data sets, and data from a study of liver disease. Differentially abundant KEGG gene functions can be detected in these experiments.\n\nConclusionsFunctional annotation of metagenomic shotgun sequence reads can be accomplished by rapid DNA-DNA matching to a custom database of microbial sequences using the Bowtie2 sequence alignment tool. This method can be used for a variety of microbiome studies and allows functional analysis which is otherwise computationally demanding. This rapid annotation method is freely available as a Galaxy workflow within a Docker image.

bioinformatics↗

bioRxiv2016

Accurate prediction of human essential genes using only nucleotide composition and association information

Three groups recently identified essential genes in human cancer cell lines using wet experiments, and these genes are of high values. Herein, we improved the widely used Z curve method by creating a {lambda}-interval Z curve, which considered interval association information. With this method and recursive feature elimination technology, a computational model was developed to predict human gene essentiality. The 5-fold cross-validation test based on our benchmark dataset obtained an area under the receiver operating characteristic curve (AUC) of 0.8814. For the rigorous jackknife test, the AUC score was 0.8854. These results demonstrated that the essentiality of human genes could be reliably reflected by only sequence information. However, previous classifiers in three eukaryotes can gave satisfactory prediction only combining sequence with other features. It is also demonstrated that although the information contributed by interval association is less than adjacent nucleotides, this information can still play an independent role. Integrating the interval information into adjacent ones can significantly improve our classifiers prediction capacity. We re-predicted the benchmark negative dataset by Pheg server (https://cefg.uestc.edu.cn/Pheg), and 118 genes were additionally predicted as essential. Among them, 21 were found to be homologues in mouse essential genes, indicating that at least a part of the 118 genes were indeed essential, however previous experiments overlooked them. As the first available server, Pheg could predict essentiality for anonymous gene sequences of human. It is also hoped the {lambda}-interval Z curve method could be effectively extended to classification issues of other DNA elements.

bioinformatics↗