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Cui, X.

Publications and source records attributed to Cui, X..

157 records · Page 9Linked to original sources
bioRxiv2019

Genome analysis and Hi-C assisted assembly of Elaeagnus angustifolia L., a deciduous tree belonging to Elaeagnaceae

Elaeagnus angustifolia L. is a deciduous tree of the Elaeagnaceae family. It is widely used in the study of abiotic stress tolerance in plants and for the improvement of desertification-affected land due to its characteristics of drought resistance, salt tolerance, cold resistance, wind resistance, and other environmental adaptation. Here, we report the complete genome sequencing using the Pacific Biosciences (PacBio) platform and Hi-C assisted assembly of E. angustifolia. A total of 44.27 Gb raw PacBio sequel reads were obtained after filtering out low-quality data, with an average length of 8.64 Kb. And 39.56 Gb clean reads was obtained, with a sequencing coverage of 75x, and Q30 ratio > 95.46%. The 510.71 Mb genomic sequence was mapped to the chromosome, accounting for 96.94% of the total length of the sequence, and the corresponding number of sequences was 269, accounting for 45.83% of the total number of sequences. The genome sequence study of E. angustifolia can be a valuable source for the comparative genome analysis of the Elaeagnaceae family members, and can help to understand the evolutionary response mechanisms of the Elaeagnaceae to drought, salt, cold and wind resistance, and thereby provide effective theoretical support for the improvement of desertification-affected land.

genetics↗
bioRxiv2018

Co-allocation Action of Azospirillum brasilense Inoculation and Seed Priming for Improving Germination and Seedling Growth in Aged Grass Seeds

Germination of seeds during the transportation or after prolonged storage naturally and inevitably decreases because of ageing, but germination potential can be partially restored with seed priming treatments. A novel attempt was made to investigate the effects of combined treatments and to optimize the conditions for naturally aged seeds of tall fescue (Festuca arundinacea Schreb.), orchardgrass (Dactylis glomerata L.) and Russian wild rye (Psathyrostachys juncea (Fisch.) Nevski) using an orthogonal activity level experimental design [factor A: Azospirillum brasilense concentration, factor B: three seed priming treatments (H2O, MgSO4 and H2O2) and factor C: different priming times]. Multivariate regression model analysis was applied to determine the interactive effects of pairwise factors (A and C) and to optimize experimental conditions. The results showed that the mixed treatments positively affected seed germination and seedling growth. The three seed priming treatments were the dominant factors for germination promotion, whereas the bacterial concentration had the largest effect on seedling growth, especially root elongation. The combined results of all determined attributes showed that A. brasilense bio-priming with H2O2 priming constituted the optimal combination. The optimal bacterial concentration of A. brasilense and the time of seed priming were 52.3 x 106 colony forming units (CFU) mL-1 and 17.0 h, respectively. We also discussed the physiological mechanisms that could repair aged seeds: the malondialdehyde content and antioxidant enzyme (superoxide dismutase, peroxidase, catalase and ascorbate peroxidase) activities were affected.

plant biology↗
bioRxiv2018

Leveraging collective regulatory effects of long-range DNA methylations to predict gene expressions and estimate their effects on phenotypes in cancer

DNA methylation of various genomic regions plays an important role in regulating gene expression in diverse biological contexts. However, most genome-wide studies have focused on the effect of 1) methylation in cis, not in trans and 2) a single CpG, not the collective effects of multiple CpGs, on gene expression. In this study, we developed a statistical machine learning model, geneEXPLORER (gene expression prediction by long-range epigenetic regulation), that quantifies the collective effects of both cis- and trans- methylations on gene expression. By applying geneEXPLORER to The Cancer Genome Atlas (TCGA) breast and lung cancer data, we found that most genes are affected by methylations of as much as 10Mb from promoter regions or more, and the long-range methylation explains 50% of the variation in gene expression on average, far greater than cis-methylation. The highly predictive genes are related to breast cancer, especially oncogenes and suppressor genes. Further, the predicted gene expressions could predict clinical phenotypes such as breast tumor status and estrogen receptor status (AUC=0.999, 0.94 respectively) as accurately as the measured gene expression levels. These results suggest that geneEXPLORER provides a means for accurate imputation of gene expression, which can be further used to predict clinical phenotypes.

bioinformatics↗
bioRxiv2018

Insulin-Producing Cells Monitor the Temperature and Compensate for Cold-Induced Sleep in Drosophila

Sleep is regulated by environmental factors including temperature, but the neural circuits that receive the sensory signal and mediate the regulation remain unclear. We examined how cold could influence Drosophila sleep patterns and its neural mechanism. The results showed that Drosophila has more sleep duration, less sleep latency and deeper sleep depth under cold condition. We identified the Insulin-producing Cells (IPCs) can be activated by cold, and receive the cold signal from the 11216 cold-sensing neuron without a direct synaptic connection. Elevation of IPCs sensitivity to cold impairs the sleep-promoting effect of cold while blocking of IPCs enhance the effect mostly on sleep circadian, suggesting that the cold activated IPCs have a compensative role in sleep regulation. Our finding revealed a potential neural circuit that help maintain sleep circadian in detrimental environment and may give new insight to the complicated sleep regulation mechanism.

neuroscience↗
bioRxiv2018

Learning Protein Structural Fingerprints under the Label-Free Supervision of Domain Knowledge

Finding homologous proteins is the indispensable first step in many protein biology studies. Thus, building highly efficient \"search engines\" for protein databases is a highly desired function in protein bioinformatics. As of August 2018, there are more than 140,000 protein structures in PDB, and this number is still increasing rapidly. Such a big number introduces a big challenge for scanning the whole structure database with high speeds and high sensitivities at the same time. Unfortunately, classic sequence alignment tools and pairwise structure alignment tools are either not sensitive enough to remote homologous proteins (with low sequence identities) or not fast enough for the task. Therefore, specifically designed computational methods are required for quickly scanning structure databases for homologous proteins.\n\nHere, we propose a novel ContactLib-DNN method to quickly scan structure databases for homologous proteins. The core idea is to build structure fingerprints for proteins, and to perform alignment-free comparisons with the fingerprints. Specifically, the fingerprints are low-dimensional vectors representing the contact groups within the proteins. Notably, the Cartesian distance between two fingerprint vectors well matches the RMSD between the two corresponding contact groups. This is done by using RMSD as the domain knowledge to supervise the deep neural network learning. When comparing to existing methods, ContactLib-DNN achieves the highest average AUROC of 0.959. Moreover, the best candidate found by ContactLib-DNN has a probability of 70.0% to be a true positive. This is a significant improvement over 56.2%, the best result produced by existing methods.\n\nGitHub: https://github.com/Chenyao2333/contactlib/\n\nIndex Termshomologous proteins, protein structures, remote protein homolog detection, alignment-free comparisons

bioinformatics↗
bioRxiv2018

How do wind speed, release height, seed morphology interact to determine seed dispersal trajectory of Calligonum (Polygonaceae) species

How seed dispersal trajectory shifts with abiotic and biotic factors and what is the relationship between seed dispersal distance and dispersal trajectory are remain unclear. We used wind tunnel and video camera to track the seed dispersal trajectory of 7 Calligonum species with different appendages under the different wind speeds and the release heights. Dispersal trajectories and distances were determined by video analysis and spatial coordinate transformation. Based on perspective principle, 4 modes of trajectories were determined. Wind speed, seed mass and release height were the key factors determining seed dispersal trajectory modes. Release height and wind speed tended to have the strongest explanatory power on seeds with bristles and wings, respectively. Different trajectory modes lead to different dispersal distance, while the same dispersal distance can be the result of different trajectory modes. The proportion of species trajectory modes formed its trajectory spectrum. Wind speed tends to have strong influence on light and low-wind-loading seeds, release height tends to have that on heavy and high-wind-loading seeds. Species with high proportion of horizontal projectile and projectile have high dispersal capacity, vice versa. Therefore, trajectory spectrum of a species reveals its primary dispersal strategies and evolutionary consequences.

ecology↗
bioRxiv2018

Predict plant-derived xenomiRs from plant miRNA sequences using random forest and one-dimensional convolutional neural network models

BackgroundAn increasing number of studies reported that exogenous miRNAs (xenomiRs) can be detected in animal bodies, however, some others reported negative results. Some attributed this divergence to the selective absorption of plant-derived xenomiRs by animals.\n\nResultsHere, we analyzed 166 plant-derived xenomiRs reported in our previous study and 942 non-xenomiRs extracted from miRNA expression profiles of four species of commonly consumed plants. Employing statistics analysis and cluster analysis, our study revealed the potential sequence specificity of plant-derived xenomiRs. Furthermore, a random forest model and a one-dimensional convolutional neural network model were trained using miRNA sequence features and raw miRNA sequences respectively and then employed to predict unlabeled plant miRNAs in miRBase. A total of 241 possible plant-derived xenomiRs were predicted by both models. Finally, the potential functions of these possible plant-derived xenomiRs along with our previously reported ones in human body were analyzed.\n\nConclusionsOur study, for the first time, presents the systematic plant-derived xenomiR sequences analysis and provides evidence for selective absorption of plant miRNA by human body, which could facilitate the future investigation about the mechanisms underlying the transference of plant-derived xenomiR.

bioinformatics↗
bioRxiv2018

ALV-J and REV synergistically activate a new oncogene of KIAA1199 via NF-κB and EGFR signaling regulated by miR-147

The tumorigenesis is the result of the accumulation of multiple oncogenes and tumor suppressor genes changes. Co-infection of avian leucosis virus subgroup J (ALV-J) and reticuloendotheliosis virus (REV), as two oncogenic retroviruses, showed synergistic pathogenic effects characterized by enhanced tumor initiation and progression. The molecular mechanism underlying synergistic effects of ALV-J and REV on the neoplasia remains unclear. Here, we found co-infection of ALV-J and REV enhanced the ability of virus infection, increased viral life cycle, maintained cell survival and enhanced tumor formation. We combined the high-throughput proteomic readout with a large-scale miRNA screening to identify which molecules are involved in the synergism. Our results revealed co-infection of ALV-J and REV activated a latent oncogene of KIAA1199 and inhibited the expression of tumor suppressor miR-147. Further, enhanced KIAA1199, down-regulated miR-147, activated NF-{kappa}B and EGFR were demonstrated in co-infected tissues and tumor. Mechanistically, we showed ALV-J and REV synergistically enhanced KIAA1199 by activation of NF-{kappa}B and EGFR signalling pathway, and the suppression of tumor suppressor miR-147 was contributed to maintain the NF-{kappa}B/KIAA1199/EGFR pathway crosstalk by targeting the 3UTR region sequences of NF-{kappa}B p50 and KIAA1199. Our results contributed to the understanding of the molecular mechanisms of viral synergistic tumorgenesis, which provided the evidence that suggested the synergistic actions of two retroviruses could result in activation of latent pro-oncogenes.\n\nAuthor summaryThe tumorigenesis is the result of the accumulation of multiple oncogenes and tumor suppressor genes changes. Co-infection with ALV-J and REV showed synergistic pathogenic effects characterized by enhanced tumor progression, however, the molecular mechanism on the neoplasia remains unclear. Our results revealed co-infection of ALV-J and REV promotes tumorigenesis by both induction of a latent oncogene of KIAA1199 and suppression of the expression of tumor suppressor miR-147. Mechanistic studies revealed that ALV-J and REV synergistically enhance KIAA1199 by activation of NF-{kappa}B and EGFR signalling pathway, and the suppression of tumor suppressor miR-147 was contributed to maintain the NF-{kappa}B/KIAA1199/EGFR pathway crosstalk by targeting the 3UTR region sequences of NF-{kappa}B p50 and KIAA1199. These results provided the evidence that suggested the synergistic actions of two retroviruses could result in activation of latent pro-oncogenes, indicating the potential preventive target and predictive factor for ALV-J and REV induced tumorigenesis.

molecular biology↗
bioRxiv2018

The Biological Evaluation of Fusidic Acid and Its Hydrogenation Derivative as Antimicrobial and Anti-inflammatory Agents

Fusidic acid (WU-FA-00) is the only commercially available antimicrobial from the fusidane family that has a narrow spectrum of activity against Gram-positive bacteria. Herein, the hydrogenation derivative (WU-FA-01) of fusidic acid was prepared, and both compounds were examined against a panel of six bacterial strains. In addition, their anti-inflammation properties were evaluated using a 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced mouse ear edema model. The results of the antimicrobial assay revealed that both WU-FA-00 and WU-FA-01 displayed a high level of antimicrobial activity against Gram-positive strains. Moreover, killing kinetic studies were performed, and the results were in accordance with the MIC and MBC results. We also demonstrated that the topical application of WU-FA-00 and WU-FA-01 effectively decreased TPA-induced ear edema in a dose-dependent manner. This inhibitory effect was associated with the inhibition of TPA-induced up-regulation of pro-inflammation cytokines IL-1{beta}, TNF- and COX-2. WU-FA-01 significantly suppressed the expression levels of p65, I{kappa}B-, and p-I{kappa}B- in the TPA-induced mouse ear model. Overall, our results showed that WU-FA-00 and WU-FA-01 not only had effective antimicrobial activities in vitro, especially to the Gram-positive bacteria, but also possessed strong anti-inflammatory effects in vivo. These results provide a scientific basis for developing fusidic acid derivatives as antimicrobial and anti-inflammatory agents.

pharmacology and toxicology↗
bioRxiv2018

Bi-clustering interpretation and prediction of correlation between gene expression and protein abundance

Most organisms transcript and protein level only moderately correlate for various reasons, such as regulation of transcription and protein degradation. Better prediction and understanding the correlation between gene expression and protein abundance has been possible by harnessing the matching RNA/protein datasets produced by modern high-throughput RNA-Seq and mass spectrometry methods. In this work, we have utilized some well-studied matching RNA/protein datasets, and explored for the first time a bi-clustering method to cluster genes that have consistent correlation patterns between gene expression and protein abundance. The clustering results have been interpreted from the perspective of both transcriptomic and proteomic features, which show that mRNA half-life, protein half-life and protein structure in concert significantly affect the correlation of gene expression and protein abundance. With these and other carefully selected features, a prediction model based on individual clusters, called Cluster-based Linear prediction Model (CLM), was built and tested on mouse liver mitochondrial, mouse brainstem mitochondrial, Saccharomyces cerevisiae and Danio rerio datasets. CLM could find genes for which protein abundance can be predicted from mRNA data. In summary, based on bi-clustering, feature selection and CLM model, we have established a new and valuable cluster-based protein abundance prediction method.

bioinformatics↗
bioRxiv2017

ZmCOL3, a CCT-domain containing gene affects maize adaptation as a repressor and upstream of ZmCCT

Flowering time is a vital trait to control the adaptation of flowering plants to different environments. CCT-domain containing genes are considered to play an important role in plants flowering. Among 53 maize CCT family genes, 28 of them were located in the flowering time QTL regions and 16 genes were significant associated with flowering time based on candidate gene-based association mapping analysis. Furthermore, a CCT gene named as ZmCOL3 was validated to be a flowering repressor upstream of ZmCCT which is one of the key genes regulating maize flowering. The overexpressed ZmCOL3 could delay flowering time about 4 days whether in long day or short day conditions. The absent of one cytosine in 3UTR and the present of 551bp fragment in promoter regions are likely the causal polymorphisms which may contribute to the maize adaptation from tropical to temperate regions. ZmCOL3 could transactivate ZmCCT transcription or interfere circadian clock to inhibit flowering which was integrated in the modified model of maize photoperiod pathway.\n\nHighlightMaize CCT genes influence flowering time in different latitude environments and one of them named ZmCOL3 is a flowering time repressor which could transactivate ZmCCT transcription to delay flowering.

genetics↗
bioRxiv2016

G protein-coupled estrogen receptor regulates heart rate in zebrafish embryos

Estrogens act by binding to estrogen receptors alpha and beta (ER, ER{beta}), ligand-dependent transcription factors that play crucial roles in sex differentiation, tumor growth and cardiovascular physiology. Estrogens also activate the G protein-coupled estrogen receptor (GPER), however the function of GPER in vivo is less well understood. Here we find that GPER is required to maintain normal heart rate in zebrafish embryos. Acute exposure to estrogens increased heart rate in wildtype and in ER and ER{beta} mutant embryos but not in GPER mutants. Nuclear estrogen receptor signaling remained normal in GPER mutant embryos, however GPER mutant embryos exhibited reduced basal heart rate while heart rate was normal in ER and ER{beta} mutants. We detected GPER transcript in discrete regions of the brain but not in the heart. In the brain, we observed gper expression in cells lacking nuclear estrogen receptor activity, suggesting that GPER acts in the brain to regulate heart rate independently of nuclear estrogen receptor signaling. Our results demonstrate that estradiol plays a previously unappreciated role in the acute modulation of heart rate during zebrafish embryonic development and that GPER functions as an autonomous estrogen receptor in vivo to regulate basal heart rate.

pharmacology and toxicology↗