Biology subjects

Hao, X.

Publications and source records attributed to Hao, X..

7 recordsLinked to original sources

bioRxiv2018

The functional genomic circuitry of human glioblastoma stem cells

SummarySuccessful glioblastoma (GBM) therapies have remained elusive due to limitations in understanding mechanisms of growth and survival of the tumorigenic population. Using CRISPR-Cas9 approaches in patient-derived GBM stem cells to interrogate function of the coding genome, we identify diverse actionable pathways responsible for growth that reveal the gene-essential circuitry of GBM stemness. In particular, we describe the Sox developmental transcription factor family; H3K79 methylation by DOT1L; and ufmylation stress responsiveness programs as essential for GBM stemness. Additionally, we find mechanisms of temozolomide resistance and sensitivity that could lead to combination strategies with this standard of care treatment. By reaching beyond static genome analysis of bulk tumors, with a genome wide functional approach, we dive deep into a broad range of biological processes to provide new understanding of GBM growth and treatment resistance.\n\nSignificanceGlioblastoma (GBM) remains an incurable disease despite an increasingly thorough depth of knowledge of the genomic and epigenomic alterations of bulk tumors. Evidence from multiple approaches support that GBM reflects an aberrant developmental hierarchy, with GBM stem cells (GSCs), fueling tumor growth and invasion. The properties of this tumor subpopulation may also in part explain treatment resistance and disease recurrence. Unfortunately, we still have a limited knowledge of the molecular circuitry of these cells and progress has been slow as we have not been able, until recently, to interrogate function at the genome-wide scale. Here, using parallel genome-wide CRISPR-Cas9 screens, we identify the essential genes for GSC growth. Further, by screening in the presence of low and high dose temozolomide, we identify mechanisms of drug resistance and sensitivity. These functional screens in patient derived cells reveal new aspects of GBM biology and identify a diversity of actionable targets such as genes governing stem cell traits, epigenome regulation and the response to stress stimuli.

cancer biology↗

bioRxiv2018

Pleiotropic Mapping and Annotation Selection in Genome-wide Association Studies with Pe-nalized Gaussian Mixture Models

MotivationGenome-wide association studies (GWASs) have identified many genetic loci associated with complex traits. A substantial fraction of these identified loci are associated with multiple traits - a phenomena known as pleiotropy. Identification of pleiotropic associations can help characterize the genetic relationship among complex traits and can facilitate our understanding of disease etiology. Effective pleiotropic association mapping requires the development of statistical methods that can jointly model multiple traits with genome-wide SNPs together.\n\nResultsWe develop a joint modeling method, which we refer to as the integrative MApping of Pleiotropic association (iMAP). iMAP models summary statistics from GWASs, uses a multivariate Gaussian distribution to account for phenotypic correlation, simultaneously infers genome-wide SNP association pattern using mixture modeling, and has the potential to reveal causal relationship between traits. Importantly, iMAP integrates a large number of SNP functional annotations to substantially improve association mapping power, and, with a sparsity-inducing penalty, is capable of selecting informative annotations from a large, potentially noninformative set. To enable scalable inference of iMAP to association studies with hundreds of thousands of individuals and millions of SNPs, we develop an efficient expectation maximization algorithm based on an approximate penalized regression algorithm. With simulations and comparisons to existing methods, we illustrate the benefits of iMAP both in terms of high association mapping power and in terms of accurate estimation of genome-wide SNP association patterns. Finally, we apply iMAP to perform a joint analysis of 48 traits from 31 GWAS consortia together with 40 tissue-specific SNP annotations generated from the Roadmap Project. iMAP is freely available at www.xzlab.org/software.html.

bioinformatics↗

bioRxiv2018

Mutations in efflux pump Rv1258c (Tap) cause resistance to pyrazinamide and other drugs in M. tuberculosis

Although drug resistance in M. tuberculosis is mainly caused by mutations in drug activating enzymes or drug targets, there is increasing interest in possible role of efflux in causing drug resistance. Previously, efflux genes are shown upregulated upon drug exposure or implicated in drug resistance in overexpression studies, but the role of mutations in efflux pumps identified in clinical isolates in causing drug resistance is unknown. Here we investigated the role of mutations in efflux pump Rv1258c (Tap) from clinical isolates in causing drug resistance in M. tuberculosis by constructing point mutations V219A, S292L in Rv1258c in the chromosome of M. tuberculosis and assessed drug susceptibility of the constructed mutants. Interestingly, V219A, S292L point mutations caused clinically relevant drug resistance to pyrazinamide (PZA), isoniazid (INH), and streptomycin (SM), but not to other drugs in M. tuberculosis. While V219A point mutation conferred a low level resistance, the S292L mutation caused a higher level of resistance. Efflux inhibitor piperine inhibited INH and PZA resistance in the S292L mutant but not in the V219A mutant. S292L mutant had higher efflux activity for pyrazinoic acid (the active form of PZA) than the parent strain. We conclude that point mutations in the efflux pump Rv1258c in clinical isolates can confer clinically relevant drug resistance including PZA and could explain some previously unaccounted drug resistance in clinical strains. Future studies need to take efflux mutations into consideration for improved detection of drug resistance in M. tuberculosis and address their role in affecting treatment outcome in vivo.

microbiology↗

bioRxiv2018

UBE3A-mediated p18/LAMTOR1 ubiquitination and degradation regulate mTORC1 activity and synaptic plasticity

Accumulating evidence indicates that the lysosomal Ragulator complex is essential for full activation of the mechanistic target of rapamycin complex 1 (mTORC1). Abnormal mTORC1 activation has been implicated in several developmental neurological disorders, including Angelman syndrome (AS), which is caused by maternal deficiency of the ubiquitin E3 ligase UBE3A. Here we report that Ube3a regulates mTORC1 signaling by targeting p18, a subunit of the Ragulator. Ube3a ubiquinates p18, resulting in its proteasomal degradation, and Ube3a deficiency in hippocampus of AS mice results in increased lysosomal localization of p18 and other members of the Ragulator-Rag complex, such as RagA, and increased mTORC1 activity. P18 down-regulation by siRNA or shRNA in hippocampal CA1 neurons of AS mice reduces elevated mTORC1 activity and improves long-term potentiation (LTP) and dendritic spine maturation. Our results indicate that Ube3a-mediated regulation of p18 and subsequent mTORC1 signaling is critical for typical synaptic plasticity and dendritic spine development.

neuroscience↗

bioRxiv2018

Identifying and exploiting trait-relevant tissues with multiple functional annotations in genome-wide association studies

Genome-wide association studies (GWASs) have identified many disease associated loci, the majority of which have unknown biological functions. Understanding the mechanism underlying trait associations requires identifying trait-relevant tissues and investigating associations in a trait-specific fashion. Here, we extend the widely used linear mixed model to incorporate multiple SNP functional annotations from omics studies with GWAS summary statistics to facilitate the identification of trait-relevant tissues, with which to further construct powerful association tests. Specifically, we rely on a generalized estimating equation based algorithm for parameter inference, a mixture modeling framework for trait-tissue relevance classification, and a weighted sequence kernel association test constructed based on the identified trait-relevant tissues for powerful association analysis. We refer to our analytic procedure as the Scalable Multiple Annotation integration for trait-Relevant Tissue identification and usage (SMART). With extensive simulations, we show how our method can make use of multiple complementary annotations to improve the accuracy for identifying trait-relevant tissues. In addition, our procedure allows us to make use of the inferred trait-relevant tissues, for the first time, to construct more powerful SNP set tests. We apply our method for an in-depth analysis of 43 traits from 28 GWASs using tissue-specific annotations in 105 tissues derived from ENCODE and Roadmap. Our results reveal new trait-tissue relevance, pinpoint important annotations that are informative of trait-tissue relationship, and illustrate how we can use the inferred trait-relevant tissues to construct more powerful association tests in the Wellcome trust case control consortium study.\n\nAuthor SummaryIdentifying trait-relevant tissues is an important step towards understanding disease etiology. Computational methods have been recently developed to integrate SNP functional annotations generated from omics studies to genome-wide association studies (GWASs) to infer trait-relevant tissues. However, two important questions remain to be answered. First, with the increasing number and types of functional annotations nowadays, how do we integrate multiple annotations jointly into GWASs in a trait-specific fashion to take advantage of the complementary information contained in these annotations to optimize the performance of trait-relevant tissue inference? Second, what to do with the inferred trait-relevant tissues? Here, we develop a new statistical method and software to make progress on both fronts. For the first question, we extend the commonly used linear mixed model, with new algorithms and inference strategies, to incorporate multiple annotations in a trait-specific fashion to improve trait-relevant tissue inference accuracy. For the second question, we rely on the close relationship between our proposed method and the widely-used sequence kernel association test, and use the inferred trait-relevant tissues, for the first time, to construct more powerful association tests. We illustrate the benefits of our method through extensive simulations and applications to a wide range of real data sets.

bioinformatics↗

bioRxiv2017

Transcription factor SmWRKY1 positively promote the biosynthesis of tanshinones in Salvia miltiorrhiza

Tanshinones, one group of bioactive diterpenes, were widely used in the treatment of cardiovascular diseases. WRKYs play important roles in plant metabolism, but their regulation mechanism in S. miltiorrhiza remains elusive. In this study, one WRKY transcription factor SmWRKY1 was isolated and characterized from S. miltiorrhiza. Multiple sequence alignment and phylogenetic tree analysis showed SmWRKY1 shared high homology with other plant WRKYs such as CrWRKY1. SmWRKY1 were predominantly expressed in leaves and stems, and was responsive to salicylic acid (SA), methyl jasmonate (MeJA) and nitric oxide (NO) treatment. Subcellular localization analysis found that SmWRKY1 was localized in the nucleus. Over-expression of SmWRKY1 significantly elevated the transcripts of genes involved in MEP pathway especially 1-deoxy-D-xylulose 5-phosphate synthase (SmDXS) and 1-deoxy-D-xylulose 5-phosphate reductoisomerase (SmDXR), resulted in over 6 folds increase in tanshinones production in transgenic lines (up to 13.731mg/g dry weight (DW)) compared with the control lines. Dual-luciferase (Dual-LUC) assay showed that SmWRKY1 can positively regulate SmDXR expression by binding to its promoter. Our work revealed that SmWRKY1 participated in the regulation of tanshinones biosynthesis and acted as a positive regulator through activating SmDXR in the MEP pathway, thus discloses a new insight to further excavate the regulation mechanism of tanshinones biosynthesis.

bioengineering↗

bioRxiv2017

Intrinsic Hippocampal-Caudate Interaction Correlates with Human Navigation

It has been indicated that both egocentric and allocentric representation systems exist in parallel, and combine to support spatial navigation according to the task. Identifying the neuronal mechanisms and functional roles of the interactions between the two systems promises to provide new insights into the organization of human navigation network. Here we combined resting-state fMRI and behavioral tasks to investigate how the core structures of these systems (i.e., hippocampus and caudate) functionally interact, and further examined their behavioral relevance in navigation in healthy young adults (N = 190). We found a slightly positive connectivity between the hippocampus and caudate (especially in good navigators), suggesting an effective hippocampal-caudate cross talk, which may facilitate the functional integration of the two systems. Interestingly, the hippocampal-caudate interaction correlated with better self-reported navigation ability. Moreover, using an extra 3D pointing task in virtual reality, we found that individuals behavioral performance could be largely predicted by the hippocampal-caudate interactions. Overall, our study demonstrated the intrinsic interaction between two representation systems of the navigation network and its functional roles in behaviors, and further study on dynamic interaction would help us understand better their role in normal aging and psychiatric disorders.

neuroscience↗