Biology subjects

Nguyen, T. T.

Publications and source records attributed to Nguyen, T. T..

5 recordsLinked to original sources

bioRxiv2018

Phosphoproteomics of Highly ABA-Induced1 identifies AT Hook Like10 phosphorylation required for growth regulation during stress

The Clade A protein phosphatase 2C Highly ABA-Induced 1 (HAI1) plays an important role in stress signaling yet little information is available on HAI1-regulated phosphoproteins. Quantitative phosphoproteomics identified phosphopeptides of increased abundance in hai1-2 in unstressed plants and in plants exposed to low water potential (drought) stress. The identity and localization of the phosphoproteins as well as enrichment of specific phosphorylation motifs indicated that these phosphorylation sites may be regulated directly by HAI1 or by HAI1-regulated kinases including Mitogen-Activated Protein Kinases (MPKs), Sucrose-non fermenting Related Kinase 2 (SnRK2s) or Casein Kinases. One of the phosphosites putatively regulated by HAI1 was S313/S314 of AT Hook-Like10 (AHL10), a DNA binding protein of unclear function. HAI1 could directly dephosphorylate AHL10 in vitro and the level of HAI1 expression affected the abundance of phosphorylated AHL10 in vivo. AHL10 S314 phosphorylation was critical for restriction of plant growth under low water potential stress and for regulation of Jasmonic Acid and Auxin-related gene expression as well as expression of developmental regulators including Shootmeristemless (STM). These genes were also mis-regulated in hai1-2. AHL10 S314 phosphorylation was required for AHL10 complexes to form foci within the nucleoplasm, suggesting that S314 phosphorylation may control AHL10 association with the nuclear matrix or with other transcriptional regulators. These data identify a set of HAI1-affected phosphorylation sites, show that HAI1-regulated phosphorylation of AHL10 S314 controls AHL10 function and localization and also indicate that HAI1-AHL10 signaling coordinates growth with stress and defense responses.

plant biology↗

bioRxiv2018

Identifying miRNA-mRNA regulatory relationships in breast cancer with invariant causal prediction

microRNAs (miRNAs) regulate gene expression at the post-transcriptional level and they play an important role in various biological processes in the human body. Therefore, identifying their regulation mechanisms is essential for the diagnostics and therapeutics for a wide range of diseases. There have been a large number of researches which use gene expression profiles to resolve this problem. However, the current methods have their own limitations. Some of them only identify the correlation of miRNA and mRNA expression levels instead of the causal or regulatory relationships while others infer the causality but with a high computational complexity. To overcome these issues, in this study, we propose a method to identify miRNA-mRNA regulatory relationships in breast cancer using the invariant causal prediction. The key idea of invariant causal prediction is that the cause miRNAs of their target mRNAs are the ones which have persistent causal relationships with the target mRNAs across different environments. In this research, we aim to find miRNA targets which are consistent across different breast cancer subtypes. Thus, first of all, we apply the Pam50 method to categorise BRCA samples into different environment\" groups based on different cancer subtypes. Then we use the invariant causal prediction method to find miRNA-mRNA regulatory relationships across subtypes. We validate the results with the miRNA-transfected experimental data and the results show that our method outperforms the state-of-the-art methods. In addition, we also integrate this new method with the Pearson correlation analysis method and Lasso in an ensemble method to take the advantages of these methods. We then validate the results of the ensemble method with the experimentally confirmed data and the ensemble method shows the best performance, even comparing to the proposed causal method. Functional enrichment analyses show that miRNAs in the regulatory relationship predicated by the proposed causal method tend to synergistically regulate target genes, indicating the usefulness of these methods, and the identified miRNA targets could be used in the design of wet-lab experiments to discover the causes of breast cancer.\n\nAuthor summaryCancer is a disease of cells in human body and it causes a high rate of deaths world wide. There has been evidence that non-coding RNAs are key players in the development and progression of cancer. Among the different types of non-coding RNAs, miRNAs, which are short non-coding RNAs, regulate gene expression and play an important role in different biological processes as well as various cancer types. To design better diagnostic and therapeutic plans for cancer patients, we need to know the roles of miRNAs in cancer initialisation and development, and their regulation mechanisms in the human body. In this study, we propose algorithms to identify miRNA-mRNA regulatory relationships in breast cancer. Comparing our methods with existing methods in predicting miRNA targets, our methods show a better performance. The estimated miRNA targets from our methods could be a potential source for further wet-lab experiments to discover the causes of breast cancer.

bioinformatics↗

bioRxiv2018

Antibiotic-induced dysbiosis predicts mortality in an animal model of Clostridium difficile infection

BackgroundAntibiotic disruption of the intestinal microbiota favors colonization by Clostridium difficile. Using a charcoal-based adsorbent to decrease intestinal antibiotic concentrations, we studied the relationship between antibiotic concentrations in feces and the intensity of dysbiosis, and quantified the link between this intensity and mortality.\n\nMethodsWe administered either moxifloxacin (n=70) or clindamycin (n=60) to hamsters by subcutaneous injection from day 1 (D1) to D5, and challenged them with a C. difficile toxigenic strain at D3. Hamsters received various doses of a charcoal-based adsorbent, DAV131A, to modulate intestinal antibiotic concentrations. Gut dysbiosis was evaluated at D0 and D3 using diversity indices determined from 16S rRNA gene profiling. Survival was monitored until D16. We analyzed the relationship between fecal antibiotic concentrations and dysbiosis at the time of C. difficile challenge and studied their capacity to predict subsequent death of the animals.\n\nResultsIncreasing doses of DAV131A reduced fecal concentrations of both antibiotics, lowered dysbiosis and increased survival from 0% to 100%. Mortality was related to the level of dysbiosis (p<10-5 for the change of Shannon index in moxifloxacin-treated animals and p<10-9 in clindamycin-treated animals). The Shannon diversity index and unweighted UniFrac distance best predicted death, with areas under the ROC curve of 0.89 [95%CI, 0.82;0.95] and 0.95 [0.90;0.98], respectively.\n\nConclusionsAltogether, moxifloxacin and clindamycin disrupted the diversity of the intestinal microbiota with a dependency to the DAV131A dose; mortality after C. difficile challenge was related to the intensity of dysbiosis in a similar manner with the two antibiotics.

microbiology↗

bioRxiv2018

A novel, biologically-informed polygenic score reveals role of mesocorticolimbic insulin receptor gene network on impulsivity and addiction

ImportanceActivation of brain insulin receptors occurs on mesocorticolimbic regions, modulating reward sensitivity and inhibitory control. Variations in the functioning of this mechanism likely associate with individual differences in the risk for related psychopathologies (attention-deficit hyperactivity disorder, addiction), an idea that agrees with the high comorbidity between insulin resistant states and psychiatric conditions. While genetic studies comprise an interesting tool to explore neurobiological mechanisms in community samples, the conventional genome-wide association studies and polygenic risk score methodologies completely ignore the fact that genes operate in networks, and code for precise biological functions in specific tissues.\n\nObjectiveWe propose a novel, biologically informed genetic score reflecting the mesocorticolimbic insulin receptor-related gene network, and investigate if it predicts dopamine-related psychopathology (impulsivity and addiction) in community samples.\n\nDesignBirth cohort (Maternal Adversity, Vulnerability and Neurodevelopment, MAVAN) and adult cohort (Study of Addiction, Genes and Environment, SAGE).\n\nSettingGeneral community.\n\nParticipants212 4-year-old children (MAVAN), and 1626 adults (SAGE).\n\nExposureThe biologically informed, mesocorticolimbic specific, insulin receptor polygenic score was created based on levels of co-expression with the insulin receptor in striatum and prefrontal cortex, and calculated in the two samples using the genotype data (Psychip/Psycharray).\n\nMain outcomechildhood impulsivity in the Information Sampling task, and risk for early addiction onset.\n\nResultsThe insulin receptor polygenic score showed improved prediction of childhood impulsivity in boys and risk for early addiction onset in males in comparison to conventional polygenic risk scores for attention-deficit hyperactivity disorder or addiction.\n\nConclusions and relevanceThis novel genomic approach reveals insulin action as a relevant biological process involved in the risk for dopamine-related psychopathology.\n\nKey pointsO_ST_ABSQuestionC_ST_ABSConsidering the modulation of mesocorticolimbic dopaminergic pathways by insulin through the action on its receptors (IR), we investigated if a novel, region specific polygenic score on the IR-related gene network (ePRS-IR) is associated with dopamine-related behaviors (impulsivity and addiction).\n\nFindingsThe ePRS-IR showed improved prediction of childhood impulsivity and risk for early addiction onset in comparison to conventional polygenic risk scores for ADHD or addiction.\n\nMeaningThis novel genomic approach reveals insulin action as a biological process involved in the risk for dopamine-related psychopathology.

neuroscience↗

bioRxiv2018

American Gut: an Open Platform for Citizen-Science Microbiome Research

Although much work has linked the human microbiome to specific phenotypes and lifestyle variables, data from different projects have been challenging to integrate and the extent of microbial and molecular diversity in human stool remains unknown. Using standardized protocols from the Earth Microbiome Project and sample contributions from over 10,000 citizen-scientists, together with an open research network, we compare human microbiome specimens primarily from the USA, UK, and Australia to one another and to environmental samples. Our results show an unexpected range of beta-diversity in human stool microbiomes as compared to environmental samples, demonstrate the utility of procedures for removing the effects of overgrowth during room-temperature shipping for revealing phenotype correlations, uncover new molecules and kinds of molecular communities in the human stool metabolome, and examine emergent associations among the microbiome, metabolome, and the diversity of plants that are consumed (rather than relying on reductive categorical variables such as veganism, which have little or no explanatory power). We also demonstrate the utility of the living data resource and cross-cohort comparison to confirm existing associations between the microbiome and psychiatric illness, and to reveal the extent of microbiome change within one individual during surgery, providing a paradigm for open microbiome research and education.\n\nImportanceWe show that a citizen-science, self-selected cohort shipping samples through the mail at room temperature recaptures many known microbiome results from clinically collected cohorts and reveals new ones. Of particular interest is integrating n=1 study data with the population data, showing that the extent of microbiome change after events such as surgery can exceed differences between distinct environmental biomes, and the effect of diverse plants in the diet which we confirm with untargeted metabolomics on hundreds of samples.

microbiology↗