Biology subjects

Ye, S.

Publications and source records attributed to Ye, S..

85 records · Page 5Linked to original sources

bioRxiv2020

Effects of N, P, K on Yield and Quality of Jujube in the Loess Hilly Region and Its Fertilization Model

The aim of this study is to explore the optimal N, P and K fertilization model suitable for pear-jujube in the mountain of northern Shaanxi in 2016 years. This experiment use 3-factor, saturated optimal design for quadratic fertilization scheme. The effects of different fertilization treatments on the yield and quality of pear-jujube were studied through field experiments. And comprehensive evaluation based on the quality of pear-jujube. The results showed that N1P3K3 has the highest yield, which is 48% higher than the control (CK). The effect of nitrogen, phosphorus, and potassium on yield is potassium fertilizer (positive effect)> phosphate fertilizer (positive effect)> nitrogen fertilizer (negative effect). Phosphate and potassium fertilizers have significant effects on increasing the content of soluble solids. Application of potassium fertilizer alone can significantly increase the content of reduced Vitamins c. The combined application of nitrogen, phosphorus, and potassium reduced the content of reduced Vitamins c. N3P3K1 treatment can significantly increase the total sugar content in fruits. Single application of phosphate and potassium increases the content of organic acids in fruits. Other fertilization treatments have significant effects on reducing the content of organic acids in fruits. The sugar-acid ratio of N3P3K1 is significantly higher than CK, which had an important effect on improving the taste. A high amount of potassium fertilizer has a significant effect on increasing the total flavonoid content in fruits. The interaction of nitrogen and phosphorus will reduce the total flavonoid content. The effect of nitrogen, phosphorus, and potassium on quality is potassium (positive effect)> nitrogen (positive effect)> phosphate (positive effect). Comprehensive analysis, the optimal fertilization amount when the target yield is 23000 [~] 27000 kg{middle dot}hm-2 and the quality score is above 85 is nitrogen (N) 406.93 [~] 499.31 kg{middle dot}hm-2, phosphorus (P2O5) 203.94 [~] 297.08 kg{middle dot}hm-2, and potassium (K2O) 285.47 [~] 322.82 kg{middle dot}hm-2.

plant biology↗

bioRxiv2020

The neural features in the precentral gyrus predict the severity of internet game disorder: results from the multi-voxel pattern analyses

ImportanceFinding the neural features that could predict internet gaming disorder severity is important in finding the targets for potential interventions using brain modulation methods. ObjectiveTo determine whether resting-state neural patterns can predict individual variations of internet gaming disorder by applying machine learning method and further investigate brain regions strongly related to IGD severity. DesignThe diagnostic study lasted from December 1, 2013, to November 20, 2019. The data were analyzed from December 31, 2019, to July 10, 2020. SettingThe resting-state fMRI data were collected at East China Normal University, Shanghai. ParticipantsA convenience sample consisting of 402 college students with diverse IGD severity Main Outcomes and MeasuresThe neural patterns were represented by regional homogeneity (ReHo) and the amplitude of low-frequency fluctuation (ALFF). Predictive model performance was assessed by Pearson correlation coefficient and standard mean squared error between the predicted and true IGD severity. The correlations between IGD severity and topological features (i.e., degree centrality (DC), betweenness centrality (BC), and nodal efficiency (NE)) of consensus highly weighted regions in predictive models were examined. ResultsThe final dataset consists of 402 college students (mean [SD] age, 21.43 [2.44] years; 239 [59.5%] male). The predictive models could significantly predict IGD severity (model based on ReHo: r = 0.11, p(r) = 0.030, SMSE = 3.73, p(SMSE) = 0.033; model based on ALFF: r=0.19, p(r) = 0.002, SMSE = 3.58, p(SMSE) = 0.002). The highly weighted brain regions that contributed to both predictive models were the right precentral gyrus and the left postcentral gyrus. Moreover, the topological properties of the right precentral gyrus were significantly correlated with IGD severity (DC: r = 0.16, p = 0.001; BC: r = 0.14, p = 0.005; NE: r = 0.15, p = 0.003) whereas no significant result was found for the left postcentral gyrus (DC: r = 0.02, p = 0.673; BC: r = 0.04, p = 0.432; NE: r = 0.02, p = 0.664). Conclusions and RelevanceThe machine learning models could significantly predict IGD severity from resting-state neural patterns at the individual level. The predictions of IGD severity deepen our understanding of the neural mechanism of IGD and have implications for clinical diagnosis of IGD. In addition, we propose precentral gyrus as a potential target for physiological treatment interventions for IGD. Key PointsO_ST_ABSQuestionC_ST_ABSCan machine learning algorithms predict internet gaming disorder (IGD) from resting-state neural patterns? FindingsThis diagnostic study collected resting-state fMRI data from 402 subjects with diverse IGD severity. We found that machine learning models based on resting-state neural patterns yielded significant predictions of IGD severity. In addition, the topological neural features of precentral gyrus, which is a consensus highly weighted region, is significantly correlated with IGD severity. MeaningThe study found that IGD is a distinctive disorder and its dependence severity could be predicted by brain features. The precentral gyrus and its connection with other brain regions could be view as targets for potential IGD intervention, especially using brain modulation methods.

neuroscience↗

bioRxiv2020

CRISPR/Cas9-mediated gene editing induces neurological recovery in an A53T-SNCA overexpression rat model of Parkinson's disease

To date, no publicly available disease-modifying therapy for Parkinsons disease has been developed. This can be partly attributed to the absence of techniques for in vivo deletion of the SNCA gene (encoding -synuclein), which is one of the key players in Parkinsons disease pathology. In particular, A53T-mutated SNCA (A53T-SNCA) is one of the most studied familial pathologic mutations in Parkinsons disease. Here we utilized a recently discovered genome editing technique, CRISPR/Cas9, to delete A53T-SNCA in vitro and in vivo. Among various CRISPR/Cas9 systems, SaCas9-KKH with a single guide RNA (sgRNA) targeting A53T-SNCA was packaged into adeno-associated virus. Adeno-associated virus carrying SaCas9-KKH significantly reduced A53T-SNCA levels in A53T-SNCA-overexpressed HEK293T cells, without off-target effects on wild-type SNCA. Furthermore, we tested the techniques in vivo therapeutic potential in a viral A53T-SNCA overexpression rat model of Parkinsons disease. Gene deletion of A53T-SNCA significantly prevented the overexpression of -synuclein, dopaminergic neurodegeneration, and parkinsonian motor symptoms, whereas a negative control without sgRNA did not. Our findings propose CRISPR/Cas9 system as a potential therapeutic tool for A53T-SNCA familial Parkinsons disease.

genetics↗

bioRxiv2020

Kinome Profiling of Gastrointestinal Stromal Tumors Using Multiplexed Inhibitor Beads and Mass Spectrometry Identifies Wee1 as Candidate Therapeutic Target

Management of gastrointestinal stromal tumor (GIST) has been revolutionized by the identification of activating mutations in KIT and PDGFRA, and the clinical application of receptor tyrosine kinase (RTK) inhibitors in the advanced disease setting. Stratification of GIST into molecularly defined subsets provides insight into clinical behavior and response to approved targeted therapies. Although these RTK inhibitors are effective in the majority of GIST, resistance to these agents remains a significant clinical problem. Development of effective treatment strategies for refractory GIST subtypes requires identification of novel targets to provide additional therapeutic options. Global kinome profiling has the potential to identify critical signaling networks and reveal protein kinases that are essential in GIST. Using Multiplexed Inhibitor Beads and Mass Spectrometry, we explored the majority of the kinome in GIST specimens from the three most common molecular subtypes to identify novel kinase targets. Kinome profiling revealed distinct signatures in GIST subtypes and identified kinases that are universally activated in all GIST, as well as kinases that are unique to each subtype. Kinome profiling in combination with loss-of-function assays identified a significant role for the G2-M tyrosine kinase, Wee1, in GIST cell survival. In vitro and in vivo studies revealed significant efficacy of MK-1775 (Wee1 inhibitor) in combination with avapritinib in KIT and PDGFRA-mutant GIST cell lines, and notable efficacy of MK-1775 as a single agent in the PDGFRA-mutant line. These studies provide strong preclinical justification for the use of MK-1775 in GIST.

cancer biology↗

bioRxiv2020

SIFNet: Electromagnetic Source Imaging Framework Using Deep Neural Networks

Electroencephalography (EEG) and magnetoencephalography (MEG) are used to measure brain activity, noninvasively, and are useful tools for brain research and clinical management of brain disorders. Tremendous effort has been made in solving the inverse source imaging problem from EEG/MEG measurements. This is a challenging ill-posed problem, since the number of measurements is much smaller than the number of possible sources in the brain. Various methods have been developed to estimate underlying brain sources from noninvasive EEG/MEG as this can offer insight about the underlying brain electrical activity with significantly improved spatial resolution. In this work, we propose a novel data-driven Source Imaging Framework using deep learning neural networks (SIFNet), where (1) a simulation pipeline is designed to model realistic brain activation and EEG/MEG signals to train generalizable neural networks, (2) and a residual convolutional neural network is trained using the simulated data, capable of estimating source distributions from EEG/MEG recordings. The performance of our proposed SIFNet approach is evaluated in a series of computer simulations, which indicates the excellent performance of SIFNet outperforming conventional weighted minimum norm algorithms that were tested in this work. The SIFNet is further tested by analyzing interictal EEG data recorded in a clinical setting from a focal epilepsy patient. The results of this clinical data analysis indicate accurate localization of epileptogenic activity as validated by the epileptogenic zone clinically determined in this patient. In sum, the proposed SIFNet approach promises to offer an alternative solution to the EEG/MEG inverse source imaging problem, shows promising signs of being robust against measurement noise, and is easy to implement, therefore, being translatable to clinical practice.

bioengineering↗

bioRxiv2020

Prediction of receptorome for human-infecting virome

The virus receptors are key for the viral infection of host cells. Identification of the virus receptors is still challenging at present. Our previous study has shown that human virus receptor proteins have some unique features including high N-glycosylation level, high number of interaction partners and high expression level. Here, a random-forest model was built to identify human virus receptorome from human cell membrane proteins with an accepted accuracy based on the combination of the unique features of human virus receptors and protein sequences. A total of 1380 human cell membrane proteins were predicted to constitute the receptorome of the human-infecting virome. In addition, the combination of the random-forest model with protein-protein interactions between human and viruses predicted in previous studies enabled further prediction of the receptors for 693 human-infecting viruses, such as the Enterovirus, Norovirus and West Nile virus. As far as we know, this study is the first attempt to predict the receptorome for the human-infecting virome and would greatly facilitate the identification of the receptors for viruses.

bioinformatics↗

bioRxiv2019

Microbial Communities Associated with Sustained Anaerobic Reductive Dechlorination of α-, β-, γ-, and δ-Hexachlorocyclohexane Isomers to Monochlorobenzene and Benzene

Intensive historical and worldwide use of the persistent pesticide technical-grade hexachlorocyclohexane (HCH), composed of the active ingredient {gamma}-HCH (called lindane) along with several other HCH isomers, has led to widespread contamination. We derived four anaerobic enrichment cultures from HCH-contaminated soil capable of sustainably dechlorinating each of -, {beta}-, {gamma}-, and {delta}-HCH isomers stoichiometrically and completely to benzene and monochlorobenzene (MCB). For each isomer, the dechlorination rates increased progressively from <3 {micro}M/day to [~]12 {micro}M/day over two years. The molar ratio of benzene to MCB produced was a function of the substrate isomer, and ranged from {beta} (0.77{+/-}0.15), (0.55{+/-}0.09), {gamma} (0.13{+/-}0.02) to {delta} (0.06{+/-}0.02) in accordance with pathway predictions based on prevalence of antiperiplanar geometry. Cultivation with a different HCH isomer resulted in distinct bacterial communities, but similar archaeal communities. Data from 16S rRNA gene amplicon sequencing and quantitative PCR revealed significant increases in the absolute abundance of Pelobacter and Dehalobacter, especially in the -HCH and {delta}-HCH cultures. This study provides the first direct comparison of shifts in anaerobic microbial communities induced by the dechlorination of distinct HCH isomers. It also uncovers candidate microorganisms responsible for the dechlorination of -, {beta}-, {gamma}-, and {delta}-HCH, a key step towards better understanding and monitoring of natural attenuation processes and improving bioremediation technologies for HCH-contaminated sites.

microbiology↗

bioRxiv2019

The methyl cycle is a conserved regulator of biological clocks.

The methyl cycle is a universally conserved metabolic pathway operating in prokaryotes and eukaryotes. In this pathway, the amino acid methionine is used to synthesize S-adenosylmethionine, the methyl donor co-substrate in the methylation of nucleic acids, histone and non-histone proteins and many other molecules within the cell. The methylation of nucleic acids and proteins is the foundation of epigenetic and epitranscriptomic regulations of gene expression, but whether the methyl cycle centrally regulates gene expression and function by controlling the availability of methyl moieties is poorly understood.\n\nFrom cyanobacteria to humans, a circadian clock that involves an exquisitely regulated transcription-translation-feedback loop driving oscillations in gene expression and orchestrating physiology and behavior has been described. We reported previously that inhibition of the methyl cycle in mammalian cells caused the lengthening of the period of these oscillations, suggesting the methyl cycle may indeed act as a central regulator of gene expression, at least in mammals. Here, we investigated whether the methyl cycle, given its universal presence among living beings, regulates the circadian clock in species across the phylogenetic tree of life.\n\nWe reveal a remarkable evolutionary conservation of the link between the methyl cycle and the circadian clock. Moreover, we show that the methyl cycle also regulates the somite segmentation clock, another transcription-translation negative feedback loop-based timing mechanism that orchestrate embryonic development in vertebrates, highlighting the methyl cycle as a master regulator of biological clocks.\n\nSIGNIFICANCE STATEMENTHere we reveal that the methyl cycle, a universal metabolic pathway leading to the synthesis of S-adenosylmethionine, the methyl donor co-substrate in virtually all transmethylation reactions within the cell, is a conserved regulator of biological clocks. These discoveries highlight the methyl cycle as a metabolic hub that regulates gene expression via the availability of methyl moieties for the methylation of nucleic acids, proteins and many other molecules with the cell.

evolutionary biology↗

bioRxiv2019

Proinflammatory and autoimmunogenic gut microbiome in systemic lupus erythematosus

Systemic lupus erythematosus (SLE), characterized by chronic inflammation and multi-organ damage, has been suggested to associate with gut dysbiosis, but knowledge is limited from small sample size and 16s rRNA-based studies. To shed new light on the role of microbiota in SLE development, we analyzed the fecal metagenome of 117 treatment-naive SLE patients and 115 sex- and age-matched healthy controls (HC) by deep-sequencing; in addition, 52 of the aforementioned patients have post-treatment fecal metagenome for comparison. We found significant differences in microbial composition and function between SLE and HC, revealing multiple plausible contributing bacterial species and metabolic pathways in SLE. In-depth SNP-based analysis revealed an oral-microbiome origin for two marker species, strengthening the importance of bacterial translocation in disease development. Lastly, we confirmed experimentally that peptides of SLE-enriched species mimicking autoantigens such as Sm and Fas could trigger autoimmune responses, suggesting a potential causal role of gut microbiota in SLE.

microbiology↗

bioRxiv2018

Id proteins promote a cancer stem cell phenotype in triple negative breast cancer via Robo1-dependent c-Myc activation

Breast cancers display phenotypic and functional heterogeneity and several lines of evidence support the existence of cancer stem cells (CSCs) in certain breast cancers, a minor population of cells capable of tumor initiation and metastatic dissemination. Identifying factors that regulate the CSC phenotype is therefore important for developing strategies to treat metastatic disease. The Inhibitor of Differentiation Protein 1 (Id1) and its closely related family member Inhibitor of Differentiation 3 (Id3) (collectively termed Id) are expressed by a diversity of stem cells and are required for metastatic dissemination in experimental models of breast cancer. In this study, we show that ID1 is expressed in rare neoplastic cells within ER-negative breast cancers. To address the function of Id1 expressing cells within tumors, we developed two independent murine models of Triple Negative Breast Cancer (TNBC) in which a genetic reporter permitted the prospective isolation of Id1+ cells. Id1+ cells are enriched for self-renewal in tumorsphere assays in vitro and for tumor initiation in vivo. Conversely, depletion of Id1 and Id3 in the 4T1 murine model of TNBC demonstrates that Id1/3 are required for cell proliferation and self-renewal in vitro, as well as primary tumor growth and metastatic colonization of the lung in vivo. Using combined bioinformatic analysis, we have defined a novel mechanism of Id protein function via negative regulation of the Roundabout Axon Guidance Receptor Homolog 1 (Robo1) leading to activation of a Myc transcriptional programme.

cancer biology↗

bioRxiv2018

Deep sequencing of primary human lung epithelial cells challenged with H5N1 influenza virus reveals a proviral role for CEACAM1

Current prophylactic and therapeutic strategies targeting human influenza viruses include vaccines and antivirals. Given variable rates of vaccine efficacy and antiviral resistance, alternative strategies are urgently required to improve disease outcomes. Here we describe the use of HiSeq deep sequencing to analyze host gene expression in primary human alveolar epithelial type II cells infected with highly pathogenic avian influenza H5N1 virus. At 24 hours post-infection, 623 host genes were significantly up-regulated, including the cell adhesion molecule CEACAM1. The up-regulation of CEACAM1 was blocked in the presence of the reactive oxygen species inhibitor, apocynin. H5N1 virus infection stimulated significantly higher CEACAM1 protein expression when compared to low pathogenic PR8 H1N1 virus, suggesting a key role for CEACAM1 in influenza virus pathogenicity. Furthermore, silencing of endogenous CEACAM1 resulted in reduced levels of proinflammatory cytokine/chemokine production, as well as reduced levels of virus replication following H5N1 infection. Our study provides evidence for the involvement of CEACAM1 in a clinically relevant model of H5N1 infection and may assist in the development of host-oriented antiviral strategies.

immunology↗

bioRxiv2018

Genomic risk prediction of coronary artery disease in nearly 500,000 adults: implications for early screening and primary prevention

BackgroundCoronary artery disease (CAD) has substantial heritability and a polygenic architecture; however, genomic risk scores have not yet leveraged the totality of genetic information available nor been externally tested at population-scale to show potential utility in primary prevention.\n\nMethodsUsing a meta-analytic approach to combine large-scale genome-wide and targeted genetic association data, we developed a new genomic risk score for CAD (metaGRS), consisting of 1.7 million genetic variants. We externally tested metaGRS, individually and in combination with available conventional risk factors, in 22,242 CAD cases and 460,387 non-cases from UK Biobank.\n\nFindingsIn UK Biobank, a standard deviation increase in metaGRS had a hazard ratio (HR) of 1.71 (95% CI 1.68-1.73) for CAD, greater than any other externally tested genetic risk score. Individuals in the top 20% of the metaGRS distribution had a HR of 4.17 (95% CI 3.97-4.38) compared with those in the bottom 20%. The metaGRS had higher C-index (C=0.623, 95% CI 0.615-0.631) for incident CAD than any of four conventional factors (smoking, diabetes, hypertension, and body mass index), and addition of the metaGRS to a model of conventional risk factors increased C-index by 3.7%. In individuals on lipid-lowering or anti-hypertensive medications at recruitment, metaGRS hazard for incident CAD was significantly but only partially attenuated with HR of 2.83 (95% CI 2.61- 3.07) between the top and bottom 20% of the metaGRS distribution.\n\nInterpretationRecent genetic association studies have yielded enough information to meaningfully stratify individuals using the metaGRS for CAD risk in both early and later life, thus enabling targeted primary intervention in combination with conventional risk factors. The metaGRS effect was partially attenuated by lipid and blood pressure-lowering medication, however other prevention strategies will be required to fully benefit from earlier genomic risk stratification.\n\nFundingNational Health and Medical Research Council of Australia, British Heart Foundation, Australian Heart Foundation.

genetics↗

bioRxiv2017

Species composition and environmental adaptation of indigenous Chinese cattle

Indigenous Chinese cattle combine taurine and indicine origins and occupy a broad range of different environments. By 50K SNP genotyping we found a discontinuous distribution of taurine and indicine cattle ancestries with extremes of less than 10% indicine cattle in the north and more than 90% in the far south and southwest China. Model-based clustering and f4-statistics indicate introgression of both banteng and gayal into southern Chinese cattle while the sporadic yak influence in cattle in or near Tibetan area validate earlier findings of mitochondrial DNA analysis. Geographic patterns of taurine and indicine mitochondrial and Y-chromosomal DNA diversity largely agree with the autosomal cline. The geographic distribution of the genomic admixture of different bovine species is proposed to be the combined effect of prehistoric immigrations, gene flow, major rivers acting as genetic barriers, local breeding objectives and environmental adaptation. Whole-genome scan for genetic differentiation and association analyses with both environmental and morphological covariables are remarkably consistent with previous studies and identify a number of genes implicated in adaptation, which include TNFRSF19, RFX4, SP4 and several coat color genes. We propose indigenous Chinese cattle as a unique and informative resource for gene-level studies of climate adaptation in mammals.

evolutionary biology↗