Biology subjects

Deng, S.

Publications and source records attributed to Deng, S..

80 records · Page 5Linked to original sources

bioRxiv2020

Excessive Polyamine Generation in Keratinocytes Promotes Self-RNA Endosomal Sensing by Dendritic Cells in Psoriasis

The mechanisms underlying tissue-specific chronic inflammation are elusive. Here we report that mice lacking Protein Phosphatase 6 in keratinocytes are predisposed to psoriasis-like skin inflammation, with an inordinate urea cycle and enhanced oxidative phosphorylation that supports hyperproliferation. This phenotype is mediated by increased Arginase-1 production resulting from CCAAT/enhancer-binding protein beta activation. Single-cell RNA-seq of the psoriatic epidermis revealed that the rate-limiting enzyme for Arginine biosynthesis, Argininosuccinate synthetase 1, maintains the Arginine pool, which is indispensable for immune responses. Accumulated polyamines branched from the urea cycle promote endosomal Tlr7-dependent self-RNA sensing by myeloid dendritic cells. This process is achieved with the assistance of an RNA-binding peptide that originates from the heterogeneous nuclear ribonucleoprotein A1, a probable autoantigen in psoriasis. Finally, targeting urea cycle wiring with an arginase inhibitor markedly improved skin inflammation in murine and non-human primate models of psoriasis. Our findings suggest that urea cycle alteration and excessive polyamine production by psoriatic keratinocytes promote self-RNA sensing by dendritic cells, which links the hyperproliferation of stationary cells with innate-immune activation in an auto-inflammatory condition.

immunology↗

bioRxiv2020

Genome wide association study reveals plant loci controlling heritability of the rhizosphere microbiome

Host genetics has recently been shown to be a driver of plant microbiome composition. However, identifying the underlying genetic loci controlling microbial selection remains challenging. Genome wide association studies (GWAS) represent a potentially powerful, unbiased method to identify microbes sensitive to host genotype, and to connect them with the genetic loci that influence their colonization. Here, we conducted a population-level microbiome analysis of the rhizospheres of 200 sorghum genotypes. Using 16S rRNA amplicon sequencing, we identify rhizosphere-associated bacteria exhibiting heritable associations with plant genotype, and identify significant overlap between these lineages and heritable taxa recently identified in maize. Furthermore, we demonstrate that GWAS can identify host loci that correlate with the abundance of specific subsets of the rhizosphere microbiome. Finally, we demonstrate that these results can be used to predict rhizosphere microbiome structure for an independent panel of sorghum genotypes based solely on knowledge of host genotypic information.

plant biology↗

bioRxiv2019

A statistical method for joint estimation of cis-eQTLs and parent-of-orign effects using an orthogonal framework with RNA-seq data

In the past few years extensive studies have been put on the analysis of genome function, especially on expression quantitative trait loci (eQTL) which offered promise for characterization of the functional sequencing variation and for the understanding of the basic processes of gene regulation. However, most studies of eQTL mapping have not implemented models that allow for the non-equivalence of parental alleles as so-called parent-of-origin effects (POEs); thus, the number and effects of imprinted genes remain important open questions. Imprinting is a type of POE that the expression of certain genes depends on their allelic parent-of-origin which are important contributors to phenotypic variations, such as diabetes and many cancer types. Besides, multi-collinearity is an important issue arising from modeling multiple genetic effects. To address these challenges, we proposed a statistical framework to test the main allelic effects of the candidate eQTLs along with the POE with an orthogonal model for RNA sequencing (RNA-seq) data. Using simulations, we demonstrated the desirable power and Type I error of the orthogonal model which also achieved accurate estimation of the genetic effects and over-dispersion of the RNA-seq data. These methods were applied to an existing HapMap project trio dataset to validate the reported imprinted genes and to discovery novel imprinted genes. Using the orthogonal method, we validated existing imprinting genes and discovered two novel imprinting genes with significant dominance effect.\n\nAuthor SummaryIn the past decades, an unprecedented wealth of knowledge has been accumulated for understanding variations in human DNA level. However, this DNA-level knowledge has not been sufficiently translated to understanding the mechanisms of human diseases. Gene expression quantitative trait locus (eQTL) mapping is one of the most promising approaches to fill this gap, which aims to explore the genetic basis of gene expression. Genomic imprinting is an important epigenetic phenomenon which is an important contributor to phenotypic variation in human complex diseases and may explain some of the \"hidden\" heritable variability. Many imprinting genes are known to play important roles in human complex diseases such as diabetes, breast cancer and obesity. However, traditional eQTL mapping approaches does not allow for the detection of imprinting which is usually involved in gene expression imbalance. In this study, we have for the first time demonstrated the orthogonal statistical model can be applied to eQTL mapping for RNA sequencing (RNA-seq) data. We showed by simulated and real data that the orthogonal model outperformed the usual functional model for detecting main effects in most cases, which addressed the issue of confounding between the dominance and additive effects. Application of the statistical model to the HapMap data resulted in discovery of some potential eQTLs with imprinting effects and dominance effects on expression of RB1 and IGF1R genes.\n\nIn summary, we developed a comprehensive framework for modeling imprinting effect for eQTL mapping, by decomposing the effects to multiple genetic components. This study is providing new insights into statistical modeling of eQTL mapping with RNA-seq data which allows for uncorrelated parameter estimation of genetic effects, covariates and over-dispersion parameter.

genetics↗

bioRxiv2019

Evolutionary decomposition of genetic effects

Genic functions have long been confounded by pleiotropic mutational effects. To understand such genetic effects, we examine HAP4, a well-studied transcription factor in Saccharomyces cerevisiae that functions by forming a tetramer with HAP2, HAP3, and HAP5. Deletion of HAP4 results in highly pleiotropic gene expression responses, some of which are clustered in related cellular processes (clustered effects) while most are distributed randomly across diverse cellular processes (distributed effects). Strikingly, the distributed effects that account for much of HAP4 pleiotropy tend to be non-heritable in a population, suggesting they have little evolutionary consequences. Indeed, these effects are poorly conserved in closely related yeasts. We further show substantial overlaps of clustered effects, but not distributed effects, among the four genes encoding the HAP2/3/4/5 tetramer. This pattern holds for other biochemically characterized yeast protein complexes or metabolic pathways. Examination of a set of cell morphological traits of the deletion lines yields consistent results. Hence, only some gene deletion effects support related biochemical understandings with the rest being pleiotropic and evolutionarily decoupled from the genes normal functions.

genetics↗

bioRxiv2018

Distinct CED-10/Rac1 Domains Confer Context-Specific Functions in Neuronal Development

Rac GTPases act as master switches to coordinate multiple interweaved signaling pathways. A major function for Rac GTPases is to control neurite development by influencing downstream effector molecules and pathways. In Caenorhabditis elegans, the Rac proteins CED-10, RAC-2 and MIG-2 act in parallel to control axon outgrowth and guidance. Here, we have identified a single glycine residue in the CED-10/Rac1 Switch 1 region that confers a non-redundant function in axon outgrowth but not guidance. Mutation of this glycine to glutamic acid (G30E) reduces GTP binding and inhibits axon outgrowth but does not affect other canonical CED-10 functions. This demonstrates previously unappreciated domain-specific functions within the CED-10 protein. Further, we reveal that when CED-10 function is diminished, the adaptor protein NAB-1 (Neurabin) and its interacting partner SYD-1 (Rho-GAP-like protein) can act as inhibitors of axon outgrowth. Together, we reveal that specific domains and residues within Rac GTPases can confer context-dependent functions during animal development.

neuroscience↗

bioRxiv2018

Dynamics of the sex ratio in Tetrahymena thermophila

Sex is often hailed as one of the major successes in evolution, and in sexual organisms the maintenance of proper sex ratio is crucial. As a large unicellular eukaryotic lineage, ciliates exhibit tremendous variation in mating systems, especially the number of sexes and the mechanism of sex determination (SD), and yet how the populations maintain proper sex ratio is poorly understood. Here Tetrahymena thermophila, a ciliate with seven mating types (sexes) and probabilistic SD mechanism, is analyzed from the standpoint of population genetics. It is found based on a newly developed population genetics model that there are plenty of opportunities for both the co-existence of all seven sexes and the fixation of a single sex, pending on several factors, including the strength of natural selection. To test the validity of predictions, five experimental populations of T. thermophila were maintained in the laboratory so that the factors that can influence the dynamics of sex ratio could be controlled and measured. Furthermore, whole-genome sequencing was employed to examine the impact of newly arisen mutations. Overall, it is found that the experimental observations highly support theoretical predictions. It is expected that the newly established theoretical framework is applicable in principle to other multi-sex organisms to bring more insight into the understanding of the maintenance of multiple sexes in a natural population.

evolutionary biology↗

bioRxiv2017

TNER: A Novel Bayesian Background Error Suppression Method for Mutation Detection in Circulating Tumor DNA

The use of ultra-deep, next generation sequencing of circulating tumor DNA (ctDNA) holds great promise for early detection of cancer as well as a tool for monitoring disease progression and therapeutic responses. However, the low abundance of ctDNA in the bloodstream coupled with technical errors introduced during library construction and sequencing complicates mutation detection. To achieve high accuracy of variant calling via better distinguishing low frequency ctDNA mutations from background errors, we introduce TNER (Tri-Nucleotide Error Reducer), a novel background error suppression method that provides a robust estimation of background noise to reduce sequencing errors. It significantly enhances the specificity for downstream ctDNA mutation detection without sacrificing sensitivity. Results on both simulated and real healthy subjects data demonstrate that the proposed algorithm consistently outperforms a current, state of the art, position-specific error polishing model, particularly when the sample size of healthy subjects is small. TNER is publicly available at https://github.com/ctDNA/TNER.

bioinformatics↗

bioRxiv2017

Identification of Cancer-associated Metabolic Vulnerabilities by Modeling Multi-objective Optimality in Metabolism

Computational modeling of the genome-wide metabolic network is essential for designing new therapeutics targeting cancer-associated metabolic disorder, which is a hallmark of human malignancies. However, previous studies generally assumed that metabolic fluxes of cancer cells are subjected to the maximization of biomass production, despite the wide existence of trade-offs among multiple metabolic objectives. To address this issue, we developed a multi-objective model of cancer metabolism with algorithms depicting approximate Pareto surfaces and incorporating multiple omics datasets. To validate this approach, we built individualized models for NCI-60 cancer cell lines, and accurately predicted cell growth rates and other biological consequences of metabolic perturbations in these cells. By analyzing the landscape of approximate Pareto surface, we identified a list of metabolic targets essential for cancer cell proliferation and the Warburg effect, and further demonstrated their close association with cancer patient survival. Finally, metabolic targets predicted to be essential for tumor progression were validated by cell-based experiments, confirming this multi-objective modelling method as a novel and effective strategy to identify cancer-associated metabolic vulnerabilities.

systems biology↗