Biology subjects

Ma, Q.

Publications and source records attributed to Ma, Q..

At least 199 records · Page 11Linked to original sources

bioRxiv2019

Sarcomeres regulate cardiomyocyte maturation through MRTF-SRF signaling

Cardiomyocyte maturation is essential for robust heart contraction throughout life. The signaling networks governing cardiomyocyte maturation remain poorly defined. Our prior studies established the transcription factor SRF as a key regulator of the assembly of sarcomeres, the contractile unit of cardiomyocytes. Whether sarcomeres regulate other aspects of maturation remains unclear. Here we generated mice with cardiomyocyte specific, mosaic mutation of -actinin-2 (Actn2), a key organizer of sarcomeres, to study its cell-autonomous role in cardiomyocyte maturation. In addition to the expected structural defects, Actn2 mutation triggered dramatic transcriptional dysregulation, which strongly correlated with transcriptional changes observed in SRF-depleted cardiomyocytes. Actn2 mutation increased monomeric actin, which perturbed the nuclear localization of the SRF cofactor MRTFA. Overexpression of a dominant-negative MRTFA mutant was sufficient to recapitulate the transcriptional and morphological defects in Actn2 and Srf mutant cardiomyocytes. Together, we demonstrate that ACTN2-based sarcomere assembly and MRTF-SRF signaling establish a positive feedback loop that promotes cardiomyocyte maturation.

developmental biology↗

bioRxiv2019

In-Depth Characterization and Validation in BRG1-Mutant Lung Cancers Define Novel Catalytic Inhibitors of SWI/SNF Chromatin Remodeling

Members of the ATP-dependent SWI/SNF chromatin remodeling complexes are among the most frequently mutated genes in cancer, suggesting their dysregulation plays a critical role. The synthetic lethality between SWI/SNF catalytic subunits BRM/SMARCA2 and BRG1/SMARCA4 has instigated great interest in targeting BRM. Here we have performed a critical and in-depth investigation of novel dual inhibitors (BRM011 and BRM014) of BRM and BRG1 in order to validate their utility as chemical probes of SWI/SNF catalytic function, while obtaining insights into the therapeutic potential of SWI/SNF inhibition. In corroboration of on-target activity, we discovered compound resistant mutations through pooled screening of BRM variants in BRG1-mut cancer cells. Strikingly, genome-wide transcriptional and chromatin profiling (ATAC-Seq) provided further evidence of pharmacological perturbation of SWI/SNF chromatin remodeling as BRM011 treatment induced specific changes in chromatin accessibility and gene expression similar to genetic depletion of BRM. Finally, these compounds have the capacity to inhibit the growth of tumor-xenografts, yielding important insights into the feasibility of developing BRM/BRG1 ATPase inhibitors for the treatment of BRG1-mut lung cancers. Overall, our studies not only establish the feasibility of inhibiting SWI/SNF catalytic function, providing a framework for SWI/SNF therapeutic targeting, but have also yielded successful elucidation of small-molecule inhibitors that will be of importance in probing SWI/SNF function in various disease contexts.

cancer biology↗

bioRxiv2019

In vivo CRISPR screening identifies RNF20/40 as epigenetic regulators of cardiomyocyte maturation

Between birth and adulthood cardiomyocytes (CMs) undergo dramatic changes in size, ultrastructure, metabolism, and gene expression, in a process collectively referred to as CM maturation. The transcriptional network that coordinates CM maturation is poorly understood, creating a bottleneck for cardiac regenerative medicine. Forward genetic screens are a powerful, unbiased method to gain novel insights into transcriptional networks, yet this approach has rarely been used in vivo in mammals because of high resource demands. Here we utilized somatic mutagenesis to perform the first reported in vivo CRISPR genetic screen within a mammalian heart. We discovered and validated several novel transcriptional regulators of CM maturation. Among them were RNF20 and RNF40, which form a complex that monoubiquitinates H2B on lysine 120. Mechanistic studies indicated that this epigenetic mark controls dynamic changes in gene expression required for CM maturation. These insights into CM maturation will inform efforts in cardiac regenerative medicine. More broadly, our approach will enable unbiased forward genetics across mammalian organ systems.

genetics↗

bioRxiv2019

The usage of six human IGHJ genes follows a particular nonrandom selection: The recombination signal sequence affects the usage frequency of six human IGHJ genes

The formation of the B cell receptor (BCR) heavy chain variable region is derived from the germline V(D)J gene rearrangement according to the \"12/23\" rule and the \"beyond 12/23\" rule. The usage frequency of each V(D)J gene in the peripheral BCR repertoires is related to the initial recombination, self-tolerance selection, and the clonal proliferative response. However, their specific differences and possible mechanisms are still unknown. We analyzed in-frame and out-of-frame BCR-H repertoires from human samples with physiological and various pathological conditions by high-throughput sequencing. Our results showed that IGHJ gene frequency follows a similar pattern where IGHJ4 is used at high frequency (>40%), IGHJ6/IGHJ3/IGHJ5 is used at medium frequencies (10%[~]20%), and IGH2/IGHJ1 is used at low frequency (<4%) under whether physiological or various pathological conditions. Furthermore, analysis of the recombination signal sequences suggested that the conserved nonamer and heptamer and certain 23 bp spacer length may affect the initial IGHD-IGHJ recombination, which results in different frequencies of IGHJ genes among the initial BCR-H repertoire. Based on this \"background repertoire\", we recommend that re-evaluation and further investigation are needed when analyzing the significance and mechanism of IGHJ gene frequency in self-tolerance selection and the clonal proliferative response.

genomics↗

bioRxiv2019

Identification of gut microbiome markers for schizophrenia delineates a potential role of Streptococcus

Emerging evidence has linked the gut microbiota to schizophrenia. However, the functional changes in the gut microbiota and the biological role of individual bacterial species in schizophrenia have not been explored systematically. Here, we characterized the gut microbiota in schizophrenia using shotgun metagenomic sequencing of feces from a discovery cohort of 90 drug-free patients and 81 controls, as well as a validation cohort of 45 patients taking antipsychotics and 45 controls. We screened 83 schizophrenia-associated bacterial species and constructed a classifier comprising 26 microbial biomarkers that distinguished patients from controls with a 0.896 area under the receiver operating characteristics curve (AUC) in the discovery cohort and 0.765 AUC in the validation cohort. Our analysis of fecal metagenomes revealed that schizophrenia-associated gut-brain modules included short-chain fatty acids synthesis, tryptophan metabolism, and synthesis/degradation of neurotransmitters including glutamate, {gamma}-aminobutyric acid, and nitric oxide. The schizophrenia-enriched gut bacterial species include several oral cavity-resident microbes, such as Streptococcus vestibularis. We transplanted Streptococcus vestibularis into the gut of the mice with antibiotic-induced microbiota depletion to explore its functional role. We observed that this microbe transiently inhabited the mouse gut and this was followed by hyperactivity and deficit in social behaviors, accompanied with altered neurotransmitter levels in peripheral tissues. In conclusion, our study identified 26 schizophrenia-associated bacterial species representing potential microbial targets for future treatment, as well as gut-brain modules, some of which may give rise to new microbial metabolites involved in the development of schizophrenia.

microbiology↗

bioRxiv2019

Causal inference for the effect of environmental chemicals on chronic kidney disease

There is evidence from a limited number of statistical and animal studies that suggest that perfluoroalkyl acids (PFAs) are linked to a decline in kidney function. Thus, PFA exposure may be a modifiable risk factor for chronic kidney disease (CKD). As PFA is pervasive throughout our environment, determining its health effects is an important public health concern. We examined cross-sectional data from the 2009-2010 cycle of NHANES using generalized propensity score (GPS) analysis and univariate and multivariate ordinary least squares (OLS) regression to determine the link between urinary PFA concentration and estimated glomerular filtration rate (eGFR). GPS estimation methods used were Hirano-Imbens, additive spline, and a generalized additive model. Each of the statistical models used associated an increase in PFA concentration with a decline in eGFR, though the eGFR fit using the multivariate regression model were consistently higher than from the other four models. We conclude that PFA is a modifiable risk factor for CKD and GPS analysis produces credible results in estimating the effect of chemical exposures on continuous measure of kidney functions such as eGFR.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC=\"FIGDIR/small/769430v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (13K):\norg.highwire.dtl.DTLVardef@19c1773org.highwire.dtl.DTLVardef@c79b8aorg.highwire.dtl.DTLVardef@b4b6c4org.highwire.dtl.DTLVardef@4b0253_HPS_FORMAT_FIGEXP M_FIG Graphical abstract C_FIG

bioinformatics↗

bioRxiv2019

Har-P, a short P-element variant, weaponizes P-transposase to severely impair Drosophila development

Without transposon-silencing Piwi-interacting RNAs (piRNAs), transposition causes an ovarian atrophy syndrome in Drosophila called gonadal dysgenesis (GD). Harwich (Har) strains with P-elements cause severe GD in F1 daughters when Har fathers mate with mothers lacking P-element-piRNAs (i.e. ISO1 strain). To address the mystery of why Har induces severe GD, we bred hybrid Drosophila with Har genomic fragments into the ISO1 background to create HISR-D or HISR-N lines that still cause Dysgenesis or are Non-dysgenic, respectively. In these lines, we discovered a highly truncated P-element variant we named \"Har-P\" as the most frequent de novo insertion. Although HISR-D lines still contain full-length P-elements, HISR-N lines lost functional P-transposase but retained Har-Ps that when crossed back to P-transposase restores GD induction. Finally, we uncovered P-element-piRNA-directed repression on Har-Ps transmitted paternally to suppress somatic transposition. The Drosophila short Har-Ps and full-length P-elements relationship parallels the MITEs/DNA-transposase in plants and SINEs/LINEs in mammals.

molecular biology↗

bioRxiv2019

Functional classification of noncoding RNAs associated with distinct histone modifications by PIRCh-seq

Many long noncoding RNAs (lncRNAs) regulate gene transcription through binding to histone modification complexes. Therefore, a comprehensive study of nuclear RNAs in a histone modification-specific manner is critical to understand their regulatory mechanisms. Here we develop a method named Profiling Interacting RNAs on Chromatin by deep sequencing (PIRCh-seq), in which we profile chromatin-associated transcriptome in 5 different cell types using antibodies recognizing histone H3 and 6 distinct histone modifications associated with active or repressive chromatin states. PIRCh-seq identified chromatin-associated RNAs with substantially less contamination by nascent transcripts, as compared to existing methods. We classified chromatin-enriched lncRNAs into 6 functional groups based on the patterns of their association with specific histone modifications. LncRNAs were enriched with different chromatin modifications in different cell types, suggesting lncRNAs regulation may also be cell type-specific. By integrating profiles of RNA secondary structure and RNA m6A modification, we found that RNA bases which bind to chromatin tend to be more single stranded. We discovered hundreds of allele-specific RNA-chromatin interactions, nominating specific single nucleotide variants that alter RNA association with chromatin. These results provide a unique resource to globally study the functions of chromatin-associated lncRNAs and elucidate the basic mechanisms of chromatin-RNA interaction.

cell biology↗

bioRxiv2019

rSeqTU -- a machine-learning based R package for prediction of bacterial transcription units

A transcription unit (TU) is composed of one or multiple adjacent genes on the same strand that are co-transcribed in mostly prokaryotes. Accurate identification of TUs is a crucial first step to delineate the transcriptional regulatory networks and elucidate the dynamic regulatory mechanisms encoded in various prokaryotic genomes. Many genomic features, e.g., gene intergenic distance, and transcriptomic features including continuous and stable RNA-seq reads count signals, have been collected from a large amount of experimental data and integrated into classification techniques to computationally predict genome-wide TUs. Although some tools and web servers are able to predict TUs based on bacterial RNA-seq data and genome sequences, there is a need to have an improved machine-learning prediction approach and a better comprehensive pipeline handling QC, TU prediction, and TU visualization. To enable users to efficiently perform TU identification on their local computers or high-performance clusters and provide a more accurate prediction, we develop an R package, named rSeqTU. rSeqTU uses a random forest algorithm to select essential features describing TUs and then uses support vector machine (SVM) to build TU prediction models. rSeqTU (available at https://s18692001.github.io/rSeqTU/) has six computational functionalities including read quality control, read mapping, training set generation, random-forest-based feature selection, TU prediction, and TU visualization.

bioinformatics↗

bioRxiv2019

Reproducibility of functional brain alterations in major depressive disorder: evidence from a multisite resting-state functional MRI study with 1,434 individuals

Resting-state functional MRI (R-fMRI) studies have demonstrated widespread alterations in brain function in patients with major depressive disorder (MDD). However, a clear and consistent conclusion regarding a repeatable pattern of MDD-relevant alterations is still limited due to the scarcity of large-sample, multisite datasets. Here, we address this issue by including a large R-fMRI dataset with 1,434 participants (709 patients with MDD and 725 healthy controls) from five centers in China. Individual functional activity maps that represent very local to long-range connections are computed using the amplitude of low-frequency fluctuations, regional homogeneity and distance-related functional connectivity strength. The reproducibility analyses involve different statistical strategies, global signal regression, across-center consistency, clinical variables, and sample size. We observed significant hypoactivity in the orbitofrontal, sensorimotor, and visual cortices and hyperactivity in the frontoparietal cortices in MDD patients compared to the controls. These alterations are not affected by different statistical analysis strategies, global signal regression and medication status and are generally reproducible across centers. However, these between-group differences are partially influenced by the episode status and the age of disease onset in patients, and the brain-clinical variable relationship exhibits poor cross-center reproducibility. Bootstrap analyses reveal that at least 400 subjects in each group are required to replicate significant alterations (an extent threshold of P<.05 and a height threshold of P<.001) at 50% reproducibility. Together, these results highlight reproducible patterns of functional alterations in MDD and relevant influencing factors, which provides crucial guidance for future neuroimaging studies of this disorder.

neuroscience↗

bioRxiv2018

Bi-clustering based biological and clinical characterization of colorectal cancer in complementary to CMS classification

In light of the marked differences in the intrinsic biological underpinnings and prognostic outcomes among different subtypes, Consensus Molecular Subtype (CMS) classification provides a new taxonomy of colorectal cancer (CRC) solely based on transcriptomics data and has been accepted as a standard rule for CRC stratification. Even though CMS was built on highly cancer relevant features, it suffers from limitations in capturing the promiscuous mechanisms in a clinical setting. There are at least two facts about using transcriptomic data for prognosis prediction: the engagement of genes or pathways that execute the clinical response pathway are highly dynamic and interactive with others; and a predefined patient stratification not only largely decrease the statistical analysis power, but also excludes the fact that clusters of patients that confer similar clinical outcomes may or may not overlap with a pre-defined subgrouping. To enable a flexible and prospective stratified exploration, we here present a novel computational framework based on bi-clustering aiming to identify gene regulatory mechanisms associated with various biological, clinical and drug-resistance features, with full recognition of the transiency of transcriptional regulation and complicacies of patients subgrouping with regards to different biological and clinical settings. Our analysis on multiple large scale CRC transcriptomics data sets using a bi-clustering based formulation suggests that the detected local low rank modules can not only generate new biological understanding coherent to CMS stratification, but also identify predictive markers for prognosis that are general to CRC or CMS dependent, as well as novel alternative drug resistance mechanisms. Our key results include: (1) a comprehensive annotation of the local low rank module landscape of CRC; (2) a mechanistic relationship between different clinical subtypes and outcomes, as well as their characteristic biological underpinnings, visible through a novel consensus map; and (3) a few (novel) resistance mechanisms of Oxaliplatin, 5-Fluorouracil, and the FOLFOX therapy are revealed, some of which are validated on independent datasets.

bioinformatics↗

bioRxiv2018

Rootstock effects on scion phenotypes in a ‘Chambourcin’ experimental vineyard

Understanding how root systems modulate shoot system phenotypes is a fundamental question in plant biology and will be useful in developing resilient agricultural crops. Grafting is a common horticultural practice that joins the roots (rootstock) of one plant to the shoot (scion) of another, providing an excellent method for investigating how these two organ systems affect each other. In this study, we use the French-American hybrid grapevine Chambourcin (Vitis L.) as a model to explore the rootstock-scion relationship. We examined leaf shape, ion concentrations, and gene expression in Chambourcin grown own-rooted as well as grafted to three different rootstocks ( SO4, 1103P and 3309C) across two years and three different irrigation treatments. Results described here demonstrate that 1) the largest source of variation in leaf shape stems from the interaction of rootstock by irrigation; 2) leaf position, but also rootstock and rootstock by irrigation interaction, are the primary sources of variation in leaf ion concentrations; and 3) gene expression in scion leaves exhibited significantly different patterns of gene expression from ungrafted vines, and these expression patterns were rootstock-specific. Our work provides an initial description of the subtle and complex effect of grafting on Chambourcin leaf morphology, ionomics and gene expression in grapevine scions. Further work across multiple years, environments and additional phenotypes is required in order to determine how the relationship between the rootstock and the scion can best be leveraged for adapting grapevines to a changing climate.

plant biology↗

bioRxiv2018

Production of very-high-amylose cassava by post-transcriptional silencing of branching enzyme genes

High amylose starch, a desired raw material in the starch industry, can be produced by plants deficient in the function of branching enzymes (BEs). Here we report the production of transgenic cassava plants with starches containing up to 50% amylose due to the constitutive expression of hair-pin dsRNAs targeting the BE1 or BE2 genes. A significant decrease in BE transcripts was confirmed in these transgenic plants by quantitative real-time RT-PCR. The absence of BE1 protein in the BE1-RNAi plant lines (BE1i) and a dramatically lower level of BE2 protein in the BE2-RNAi plant lines (BE2i) were further confirmed by Western blot assays. All transgenic plant lines were grown up in the field, but with reduced biomass production of the above-ground parts and storage roots compared to wild type (WT). Considerably high amylose content in the storage roots of BE2i plant lines was achieved, though not in BE1i plant lines. Storage starch granules of BE1i and BE2i plants had similar morphology as WT, however, the size of BE1i starch granules were bigger than that of WT. Comparisons of amylograms and thermograms of all three sources of storage starches revealed dramatic changes to the pasting properties and a higher melting temperature for BE2i starches. Glucan chain length distribution analysis showed a slight increase in chains of DP>36 in BE1i lines and a dramatic increase in glucan chains between DP 10-20 and DP>40 in BE2i lines, compared to that of WT starch. Furthermore, BE2i starches displayed a B-type X-ray diffraction pattern instead of the A-type pattern found in BE1i and WT starches. Therefore, cassava BE1 and BE2 function differently in storage root starch biosynthesis; silencing of cassava BE1 or BE2 caused various changes to starch physico-chemical properties and amylopectin structure. We also report that remarkably high amylose content in cassava starch has been first obtained in transgenic cassava by silencing of BE2 expression, thus showing a high potential for future industrial utilization.

bioengineering↗

bioRxiv2018

LTMG (Left truncated mixture Gaussian) based modeling of transcriptional regulatory heterogeneities in single cell RNA-seq data - a perspective from the kinetics of mRNA metabolism

A key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a genes diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

bioinformatics↗

bioRxiv2018

Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning Framework

Identification of transcription factor binding sites (TFBSs) and cis-regulatory motifs (motifs for short) from genomics datasets, provides a powerful view of the rules governing the interactions between TFs and DNA. Existing motif prediction methods however, are limited by high false positive rates in TFBSs identification, contributions from non-sequence-specific binding, and complex and indirect binding mechanisms. High throughput next-generation sequencing data provides unprecedented opportunities to overcome these difficulties, as it provides multiple whole-genome scale measurements of TF binding information. Uncovering this information brings new computational and modeling challenges in high-dimensional data mining and heterogeneous data integration. To improve TFBS identification and novel motifs prediction accuracy in the human genome, we developed an advanced computational technique based on deep learning (DL) and high-performance computing, named DESSO. DESSO utilizes deep neural network and binomial distribution to optimize the motif prediction. Our results showed that DESSO outperformed existing tools in predicting distinct motifs from the 690 in vivo ENCODE ChIP-Sequencing (ChIP-Seq) datasets for 161 human TFs in 91 cell lines. We also found that protein-protein interactions (PPIs) are prevalent among human TFs, and a total of 61 potential tethering binding were identified among the 100 TFs in the K562 cell line. To further expand DESSOs deep-learning capabilities, we included DNA shape features and found that (i) shape information has a strong predictive power for TF-DNA binding specificity; and (ii) it aided in identification of the shape motifs recognized by human TFs which in turn contributed to the interpretation of TF-DNA binding in the absence of sequence recognition. DESSO and the analyses it enabled will continue to improve our understanding of how gene expression is controlled by TFs and the complexities of DNA binding. The source code and the predicted motifs and TFBSs from the 690 ENCODE TF ChIP-Seq datasets are freely available at the DESSO web server: http://bmbl.sdstate.edu/DESSO.

bioinformatics↗

bioRxiv2018

The novel lncRNA lnc-NR2F1 is pro-neurogenic and mutated in human neurodevelopmental disorders

Long noncoding RNAs (lncRNAs) have been shown to act as important cell biological regulators including cell fate decisions but are often ignored in human genetics. Combining differential lncRNA expression during neuronal lineage induction with copy number variation morbidity maps of a cohort of children with autism spectrum disorder/intellectual disability versus healthy controls revealed focal genomic mutations affecting several lncRNA candidate loci. Here we find that a t(5:12) chromosomal translocation in a family manifesting neurodevelopmental symptoms disrupts specifically lnc-NR2F1. We further show that lnc-NR2F1 is an evolutionarily conserved lncRNA functionally enhances induced neuronal cell maturation and directly occupies and regulates transcription of neuronal genes including autism-associated genes. Thus, integrating human genetics and functional testing in neuronal lineage induction is a promising approach for discovering candidate lncRNAs involved in neurodevelopmental diseases.

neuroscience↗

bioRxiv2018

QUBIC2: A novel biclustering algorithm for large-scale bulk RNA-sequencing and single-cell RNA-sequencing data analysis

The combination of biclustering and large-scale gene expression data holds a promising potential for inference of the condition specific functional pathways/networks. However, existing biclustering tools do not have satisfied performance on high-resolution RNA-sequencing (RNA-Seq) data, majorly due to the lack of (i) a consideration of high sparsity of RNA-Seq data, e.g., the massive zeros or lowly expressed genes in the data, especially for single-cell RNA-Seq (scRNA-Seq) data, and (ii) an understanding of the underlying transcriptional regulation signals of the observed gene expression values. Here we presented a novel biclustering algorithm namely QUBIC2, for the analysis of large-scale bulk RNA-Seq and scRNA-Seq data. Key novelties of the algorithm include (i) used a truncated model to handle the unreliable quantification of genes with low or moderate expression, (ii) adopted the mixture Gaussian distribution and an information-divergency objective function to capture shared transcriptional regulation signals among a set of genes, (iii) utilized a Core-Dual strategy to identify biclusters and optimize relevant parameters, and (iv) developed a size-based P-value framework to evaluate the statistical significances of all the identified biclusters. Our method validation on comprehensive data sets of bulk and single cell RNA-seq data suggests that QUBIC2 had superior performance in functional modules detection and cell type classification compared with the other five widely-used biclustering tools. In addition, the applications of temporal and spatial data demonstrated that QUBIC2 can derive meaningful biological information from scRNA-Seq data. The source code for QUBIC2 can be freely accessed at https://github.com/maqin2001/qubic2.

bioinformatics↗

bioRxiv2018

Nitric oxide synthase-mediated early nitric oxide-burst alleviates drought-induced oxidative damage in ammonium supplied-rice roots

Ammonium (NH4+) can enhance rice drought tolerance in comparison to nitrate (NO3-). The mechanism underpinning this relationship was investigated based on the time-dependent nitric oxide (NO) production and its protective role in oxidative stress of NH4+-/NO3--supplied rice under drought. An early burst of NO was induced by drought 3h after root NH4+ treatment but not after NO3- treatment. Root oxidative damage induced by drought was significantly higher in NO3- than in NH4+-treatment due to its reactive oxygen species accumulation. Inducing NO production by applying NO donor 3h after NO3- treatment alleviated the oxidative damage, while inhibiting the early NO burst increased root oxidative damage in NH4+ treatment. Application of nitric oxide synthase (NOS) inhibitor N(G)-nitro-L-arginine methyl ester (L-NAME) completely suppressed NO synthesis in roots 3h after NH4+ treatment and aggravated drought-induced oxidative damage, indicating the aggravation of oxidative damage might have resulted from changes in NOS-mediated early NO burst. Drought also increased root antioxidant enzymes activities, which were further induced by NO donor but repressed by NO scavenger and NOS inhibitor in NH4+-treated roots. Thus, the NOS-mediated early NO burst plays an important role in alleviating oxidative damage induced by drought by enhancing antioxidant defenses in NH4+-supplied rice roots.\n\nHighlightNOS-mediated early NO burst plays an important role in alleviating oxidative damage induced by water stress, by enhancing the antioxidant defenses in roots supplemented with NH4+

physiology↗