Biology subjects

Dang, T.

Publications and source records attributed to Dang, T..

31 records · Page 2Linked to original sources

bioRxiv2022

A small H2O-soluble ingredient of royal jelly lower cholesterol levels in liver cells by suppressing squalene epoxidase

Excessive cholesterol in the liver is harmful for our health and may cause many diseases, such as fatty liver disease. Many studies in human and animal models have reported that royal jelly (RJ) can be used to treat atherosclerosis. However, the real mechanisms behind this action is unclear. In this study, we investigated the effectivity of RJ on gene expression of squalene epoxidase (SE) a major enzyme involved in cholesterol biosynthesis in HepG2 cells. We found that the expression of SE was decreased in response to RJ treatment. We also found that the origin of the RJ affected its strength. To find out the active ingredient of RJ in cholesterol suppression, we separated RJ into two parts based on the molecular weights using ultrafiltration membrane. We found that the fraction < 10kDa from RJ had comparable effect on SE expression, especially its water-soluble part. Taken together, we think RJ suppresses cholesterol by decreasing SE gene expression in liver. The active ingredient of RJ in this action is < 10kDa in water-soluble form.

cell biology↗

bioRxiv2022

Arbuscular mycorrhizal fungal composition across US citrus orchards, management strategies, and disease severity spectrum

Arbuscular mycorrhizal fungi (AMF) remain understudied in perennial cropping systems. Citrus is a globally grown fruit tree and under threat by the pandemic Huanglongbing (HLB) disease. Here, we assessed in what capacity geographical location, management strategies and disease affect AMF citrus root communities. Root samples were collected from 88 trees in ten orchards located in the two major citrus producing states in the US. Orchards were selected based on conventional or organic practices in California and based on HLB symptom severity in Florida. We used AMF-specific amplicon sequencing primers to capture community composition and diversity. Taxa names were assigned based on a phylogenetic analysis that comprised a backbone of AMF references sequences from Mycobank and virtual taxa from the MaarjAM database. AMF were detected in 78% of citrus root samples with taxa belonging to six known (Dominikia, Funneliformis, Glomus, Rhizophagus, Sclerocystis, Septoglomus) and unknown Glomeraceae genera. Geographical location affected AMF community composition but not richness, whereas management practice and disease influenced both richness and composition. Our approach indicated that perennial agroecosystems share a set of AMF generalist and specialist taxa. Some taxa could improve environmental fitness and be exploited for agricultural purposes.

microbiology↗

bioRxiv2021

De Novo Pyrimidine Synthesis is a Targetable Vulnerability in IDH Mutant Glioma

Mutations affecting isocitrate dehydrogenase (IDH) enzymes are prevalent in glioma, leukemia, and other cancers. Although mutant IDH inhibitors are effective against leukemia, they appear less active in aggressive glioma, underscoring the need for alternative treatment strategies. Through a chemical synthetic lethality screen, we discovered that IDH1 mutant glioma cells are hypersensitive to drugs targeting enzymes in the de novo pyrimidine nucleotide synthesis pathway, including dihydroorotate dehydrogenase (DHODH). We developed a genetically engineered mouse model of mutant IDH1-driven astrocytoma and used it and multiple patient-derived models to show that the brain-penetrant DHODH inhibitor BAY 2402234 displays monotherapy efficacy against IDH mutant gliomas. Mechanistically, this vulnerability selectively applies to de novo pyrimidine, but not purine, synthesis because glioma cells engage disparate programs to produce these nucleotide species and because IDH oncogenes increase DNA damage upon nucleotide pool imbalance. Our work outlines a tumor-selective, biomarker-guided therapeutic strategy that is poised for clinical translation.

cancer biology↗

bioRxiv2021

Molecular Basis of Antibiotic Self-Resistance in a Bee Larvae Pathogen

Paenibacillus larvae, the causative agent of the devastating honey-bee disease American Foulbrood, produces the cationic polyketide-peptide hybrid paenilamicin that displays high antibacterial and antifungal activity. Its biosynthetic gene cluster contains a gene coding for the N-acetyltransferase PamZ. We show that PamZ acts as self-resistance factor in P. larvae by deactivation of paenilamicin. Using tandem MS, NMR spectroscopy and synthetic diastereomers, we identified the N-terminal amino group of the agmatinamic acid as the N-acetylation site. These findings highlight the pharmacophore region of paenilamicin, which we very recently identified as a new ribosome inhibitor. Here, we further elucidated the crystal structure of PamZ:acetyl-CoA complex at 1.34 [A] resolution. An unusual tandem-domain architecture provides a well-defined substrate-binding groove decorated with negatively-charged residues to specifically attract the cationic paenilamicin. Our results will help to understand the mode of action of paenilamicin and its role in pathogenicity of P. larvae to fight American Foulbrood.

biochemistry↗

bioRxiv2021

Stochastic variational variable selection for high-dimensional microbiome data

BackgroundThe rapid and accurate identification of a minimal-size core set of representative microbial species plays an important role in the clustering of microbial community data and interpretation of clustering results. However, the huge dimensionality of microbial metagenomics datasets is a major challenge for the existing methods such as Dirichlet multinomial mixture (DMM) models. In the framework of the existing methods, the computational burden of identifying a small number of representative species from a large number of observed species remains a challenge. ResultsWe proposed a novel framework to improve the performance of the widely used DMM approach by combining three ideas: (i) we extended the finite DMM model to an infinite case by considering Dirichlet process mixtures and estimating the number of clusters as a random variables; (ii) we proposed an indicator variable to identify representative operational taxonomic units that substantially contribute to the differentiation among clusters; and (iii) to address the computational burden of high-dimensional microbiome data, we proposed a stochastic variational inference, which approximates the posterior distribution using a controllable distribution called variational distribution, and stochastic optimization algorithms for fast computation. Using the proposed method, stochastic variational variable selection (SVVS), we analyzed the root microbiome data collected in our soybean field experiment, the human gut microbiome data from three published datasets of large-scale case-control studies and the healthy human microbiome data from the Human Microbiome Project. ConclusionsSVVS demonstrated a better performance and significantly faster computation than those of the existing methods in all cases of testing datasets. In particular, SVVS is the only method that can analyze massive high-dimensional microbial data with more than 50,000 microbial species and 1,000 samples. Furthermore, recent microbiome studies have suggested that selection of the microbial species used as a core set is important.

bioinformatics↗

bioRxiv2020

Detecting significant components of microbiomes by random forest with forward variable selection and phylogenetics

BackgroundRandom forest (RF) captures complex feature patterns that differentiate groups of samples and is rapidly being adopted in microbiome studies. However, a major challenge is the high dimensionality of microbiome datasets. They include thousands of species or molecular functions of particular biological interest. This high dimensionality significantly reduces the power of random forest approaches for identifying true differences. The widely used Boruta algorithm iteratively removes features that are proved by a statistical test to be less relevant than random probes. ResultWe developed a massively parallel forward variable selection algorithm and coupled it with the RF classifier to maximize the predictive performance. The forward variable selection algorithm adds new variable to a set of selected variables as far as the prespecified criterion of predictive power is improved. At each step, the parameters of random forest are optimized. We demonstrated the performance of the proposed approach, which we named RF-FVS, by analyzing two published datasets from large-scale case-control studies: (i) 16S rRNA gene amplicon data for Clostridioides difficile infection (CDI) and (ii) shotgun metagenomics data for human colorectal cancer (CRC). The RF-FVS approach further screened the variables that the Boruta algorithm left and improved the accuracy of the random forest classifier from 81% to 99.01% for CDI and from 75.14% to 90.17% for CRC. ConclusionValid variable selection is essential for the analysis of high-dimensional microbiota data. By adopting the Boruta algorithm for pre-screening of the variables, our proposed RF-FVS approach improves the accuracy of random forest significantly with minimum increase of computational burden. The procedure can be used to identify the functional profiles that differentiate samples between different conditions.

bioinformatics↗

bioRxiv2020

Nature of active forces in tissues: how contractile cells can form extensile monolayers

Actomyosin machinery endows cells with contractility at a single cell level. However, at a tissue scale, cells can show either contractile or extensile behaviour based on the direction of pushing or pulling forces due to neighbour interactions or substrate interactions. Previous studies have shown that a monolayer of fibroblasts behaves as a contractile system1 while a monolayer of epithelial cells2,3 or neural crest cells behaves as an extensile system.4 How these two contradictory sources of force generation can coexist has remained unexplained. Through a combination of experiments using MDCK (Madin Darby Canine Kidney) cells, and in-silico modeling, we uncover the mechanism behind this switch in behaviour of epithelial cell monolayers from extensile to contractile as the weakening of intercellular contacts. We find that this switch in active behaviour also promotes the buildup of tension at the cell-substrate interface through an increase in actin stress fibers and higher traction forces. This in turn triggers a mechanotransductive response in vinculin translocation to focal adhesion sites and YAP (Yes-associated protein) transcription factor activation. Our studies also show that differences in extensility and contractility act to sort cells, thus determining a general mechanism for mechanobiological pattern formation during cell competition, morphogenesis and cancer progression.

biophysics↗

bioRxiv2020

Transcriptional heterogeneity of stemness phenotypes in the ovarian epithelium

The ovarian surface epithelium (OSE) is a monolayer of epithelial cells covering the surface of the ovary. During ovulation, the OSE is ruptured to allow release of the oocyte. This wound is quickly repaired, but mechanisms of this repair are poorly understood. The contribution of tissue-resident stem cells in the homeostasis of several epithelial tissues is widely accepted, such as the intestinal epithelium, airway epithelium, and skin, but their involvement in OSE maintenance is unclear. While putative stem cell populations in the OSE have been described, how they are regulated is poorly defined. We show that traits associated with stem cells (stemness) can be increased in OSE following exposure to the cytokine TGFB1, overexpression of the transcription factor Snai1, or deletion of Brca1. By assessing the gene expression profiles of these populations, we show that stemness is often linked to mesenchymal-associated gene expression and higher activation of ERK signalling, but it is not consistently dependent on their activation. Expression profiles of these populations are extremely context specific, suggesting that stemness may not correspond to a single, distinct population, but rather is a heterogenous state that can possibly emerge from diverse environmental cues. Together, these findings support that the OSE may not require distinct stem cell populations for long-term maintenance, and may achieve this through transient dedifferentiation into a stem-like state.

cell biology↗

bioRxiv2020

A Novel Standalone Microfluidic Device for Local Control of Oxygen Tension for Intestinal-Bacteria Interactions

The intestinal environment is unique because it supports the intestinal epithelial cells under a normal oxygen environment and the microbiota under an anoxic environment. Due to importance of understanding the interactions between the epithelium and the microbiota, there is a strong need for developing representative and simple experimental models. Current approaches do not capture the dual-oxygen environment, require external anaerobic chambers, or are complex. Another major limitation is that in the solutions that can mimic the dual-oxygen environment, the oxygenation level of the epithelial cells is not known, raising the question whether the cells are hypoxic. We report standalone microfluidic devices that form a dual-oxygen environment without the use of an external anaerobic chamber or oxygen scavengers to coculture intestinal epithelial and bacterial cells. By changing the thickness of the device cover, the oxygen tension in the chamber could be modulated. We verified the oxygen levels using several tests: microscale oxygen sensitive sensors incorporated within the devices, hypoxic immunostaining of Caco-2 cells, and genetically encoded bacteria. Collectively, these methods monitored oxygen concentrations in devices more comprehensively than previous reports and allowed for control of oxygen tension to match the requirements of both intestinal cells and anaerobic bacteria. Our experimental model is supported by the mathematical model that considers diffusion of oxygen into the top chamber and the cellular oxygen consumption rate. This allowed us to experimentally determine the oxygen consumption rate of the epithelial cells more precisely.

bioengineering↗

bioRxiv2019

Feature-based Molecular Networking in the GNPS Analysis Environment

Molecular networking has become a key method used to visualize and annotate the chemical space in non-targeted mass spectrometry-based experiments. However, distinguishing isomeric compounds and quantitative interpretation are currently limited. Therefore, we created Feature-based Molecular Networking (FBMN) as a new analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure. FBMN leverages feature detection and alignment tools to enhance quantitative analyses and isomer distinction, including from ion-mobility spectrometry experiments, in molecular networks.

bioinformatics↗

bioRxiv2019

Myosin II isoforms play distinct roles in adherens junction biogenesis

Adherens junction (AJ) assembly under force is essential for many biological processes like epithelial monolayer bending, collective cell migration, cell extrusion and wound healing. The acto-myosin cytoskeleton acts as a major force-generator during the de novo formation and remodelling of AJ. Here, we investigated the role of myosinII isoforms in epithelial junction assembly. Myosin IIA (NMIIA) and Myosin IIB (NMIIB) differentially regulate biogenesis of adherens junction through association with distinct actin networks. Analysis of junction dynamics, actin organization, and mechanical forces of control and knockdown cells for myosins revealed that NMIIA provides the mechanical tugging force necessary for cell-cell junction reinforcement and maintenance. NMIIB is involved in E-cadherin clustering, maintenance of a branched actin layer connecting E-cadherin complexes and perijunctional actin fibres leading to the building-up of anisotropic stress. These data reveal unanticipated complementary functions of NMIIA and NMIIB in the biogenesis and integrity of AJ.

cell biology↗

bioRxiv2018

Stochastic Variational Inference of Mixture Models inPhylogenetics

The pattern of molecular evolution varies among gene sites and genes in a genome. By taking into account the complex heterogeneity of evolutionary processes among sites in a genome, Bayesian infinite mixture models of genomic evolution enable robust phylogenetic inference. With large modern data sets, however, the computational burden of Markov chain Monte Carlo sampling techniques becomes prohibitive. Here, we have developed a variational Bayesian procedure to speed up the widely used PhyloBayes MPI program, which deals with the heterogeneity of amino acid profiles. Rather than sampling from the posterior distribution, the procedure approximates the (unknown) posterior distribution using a manageable distribution called the variational distribution. The parameters in the variational distribution are estimated by minimizing Kullback-Leibler divergence. To examine performance, we analyzed three empirical data sets consisting of mitochondrial, plastid-encoded, and nuclear proteins. Our variational method accurately approximated the Bayesian phylogenetic tree, mixture proportions, and the amino acid propensity of each component of the mixture while using orders of magnitude less computational time.

bioinformatics↗

bioRxiv2018

The critical role of ASD-related gene CNTNAP3 in regulating synaptic development and social behavior in mice

Accumulated genetic evidences indicate that the contactin associated protein-like (CNTNAP) family is implicated in autism spectrum disorders (ASD). In this study, we identified genetic mutations in the CNTNAP3 gene from Chinese Han ASD cohorts and Simons Simplex Collections. We found that CNTNAP3 interacted with synaptic adhesion proteins Neuroligin1 and Neuroligin2, as well as scaffolding proteins PSD95 and Gephyrin. Significantly, we found that CNTNAP3 played an opposite role in controlling the development of excitatory and inhibitory synapses in vitro and in vivo, in which ASD mutants exhibited loss-of-function effects. In this study, we showed that Cntnap3-null mice exhibited deficits in social interaction, spatial learning and prominent repetitive behaviors. These evidences elucidate the pivotal role of CNTNAP3 in synapse development and social behaviors, providing the mechanistic insights for ASD.

neuroscience↗