Biology subjects

Yang, C.

Publications and source records attributed to Yang, C..

At least 415 records · Page 23Linked to original sources

bioRxiv2020

Circadian Rhythm Is Disrupted by ZNF704 in Breast Carcinogenesis

Copy number gain in chromosome 8q21 is considered as the prototype of genetic abnormalities associated with development of breast cancer, yet the oncogenic potential underlying this amplicon in breast carcinogenesis remains to be delineated. We report here that ZNF704, a gene mapped to 8q21, is recurrently amplified in various malignancies including breast cancer. We found that ZNF704 acts as transcription repressor and interacts with the transcription corepressor SIN3A complex. Genome-wide interrogation of the transcriptional targets identifies that the ZNF704/SIN3A complex represses a panel of genes including PER2 that are critically involved in the function of circadian clock. Indeed, ZNF704 overexpression prolongs the period and dampens the amplitude of circadian clock. We showed that ZNF704 promotes the proliferation and invasion of breast cancer cells in vitro and accelerates the growth and metastasis of breast cancer in vivo. Consistently, the level of ZNF704 expression is inversely correlated with that of PER2 in breast carcinomas, and high level of ZNF704 correlates with advanced histological grades, lymph node positivity, and poor prognosis of breast cancer patients, especially those with HER2+ and basal-like subtypes. These results indicate that ZNF704 is an important regulator of circadian clock and a potential driver for breast carcinogenesis.

cancer biology↗

bioRxiv2020

Antibiotics-induced alterations in gut microbiol composition leads to blood-brain barrier permeability increase in rhesus monkeys

Blood-brain barrier (BBB) contributes to maintenance of brain homeostasis. Gut microbiome composition affected BBB development and expression of tight junction proteins in rodents. However, we still do not know if theres any direct effect of gut microbiol composition on BBB permeability and function in normal adult animals. In current study, we determined temporal and spatial change of BBB permeability in rhesus monkeys receiving either oral or intravenous amoxicillin-clavulanic acid (AC), by monitoring CSF/serum albumin ratio (AR) and the volume transfer constant (Ktrans). We showed that oral, but not intravenous AC led to a significant alteration in gut microbiol composition and increase of BBB permeability in all monkeys, especially in thalamus area. Acetic acid and propionic acid might play a pivotal role in this newly found communication between gut and central nervous system. Antibiotics-induced gut microbiol composition change, especially the decreasing of acetic and propionic acid producing phyla and genera, leads to increase of BBB permeability, which may contribute to a variety of neurological and psychological diseases.

neuroscience↗

bioRxiv2019

C-terminal binding proteins 1 and 2 in traumatic brain injury-induced inflammation and their inhibition as an approach for anti-inflammatory treatment

Traumatic brain injury (TBI) induces an acute inflammatory response in the central nervous system that involves both resident and peripheral immune cells. The ensuing chronic neuroinflammation causes cell death and tissue damage and may contribute to neurodegeneration. The molecular mechanisms involved in the maintenance of this chronic inflammation state remain underexplored. C-terminal binding protein (CtBP) 1 and 2 are transcriptional coregulators that repress diverse cellular processes. Unexpectedly, we find that the CtBPs can transactivate a common set of proinflammatory genes both in lipopolysaccharide-activated microglia, astrocytes and macrophages, and in a mouse model of the mild form of TBI. We also find that the expression of these genes is markedly enhanced by a single mild injury in both brain and peripheral blood leukocytes in a severity- and time-dependent manner. Moreover, we were able to demonstrate that specific inhibitors of the CtBPs effectively suppress the expression of the CtBP target genes and thus improve neurological outcome in mice receiving single and repeated mild TBIs. This discovery suggests new avenues for therapeutic modulation of the inflammatory response to brain injury.

neuroscience↗

bioRxiv2019

Golden pompano genome and embryo transcriptome shed light on regulation of minor ZGA gene on heart and vascular development

BACKGROUNDAngiogenesis is closely associated with angiogenesis-dependent diseases including cancers and ocular diseases. Anti-angiogenic therapeutics have been focusing on the (VEGF)/VEGFR signaling axis. However, the clinical resistance, high cost and frequent administration of anti-VEGF drugs make it urgent to discover novel angiogenic pathways.VE-PTP (ptprb) is a novel target with great anti-angiogenic potential. However, it is unclear whether upstream signaling pathways targeting VE-PTP exist in angiogenesis. METHODSWhole genome and embryo transcriptome sequencing were applied to discover the new gene nxhl. Transgenic zebrafish model, morpholino knockdown and small interfering RNA were used to explore the role of nxhl in angiogenesis both in vitro and in vivo. RNA pulldown, RIP and ChIRP-MS were used to identify interactions between RNA and protein. RESULTSWe discovered a novel zebrafish gene nxhl which is a homologue of the conserved gene nxh that co-expressed with some key genes essential for embryo development in vertebrate. Nxhl deletion causes angiogenesis defects in embryo. Moreover, nxhl is essential to mediate effects of angiogenesis in vivo and in vitro, and ptprb depletion duplicates the phenotypes of nxhl deficiency. Importantly, nxhl acts upstream of ptprb and regulates many extreme important ptprb-linked angiogenic genes by targeting VE-PTP (ptprb) through interactions with NCL. Notably, nxhl deletion decreases the phosphorylation of NCL T76 and increases the acetylation of NCL K88, suggesting nxhl may regulate downstream VE-PTP signaling pathways by mediation of NCL posttranslational modification. This is the first description of the interaction between nxhl and NCL, NCL and VE-PTP (ptprb), uncovering a novel nxhl-NCL-VE-PTP signaling pathway on angiogenesis regulation. CONCLUSIONSOur study identifies nxhl controlling angiogenesis by targeting VE-PTP through interactions with NCL, uncovering novel upstream controllers of VE-PTP. This nxhl-NCL-VE-PTP pathway may be a therapeutic target in the treatment of angiogenesis-dependent diseases. Clinical PerspectiveO_ST_ABSWhat Is New?C_ST_ABSO_LIWe report a novel nxhl-NCL-VE-PTP signaling pathway that controls angiogenesis. C_LIO_LIWe for the first time demonstrate that nxhl interacts with NCL which simultaneously binds to VE-PTP that plays key roles on EC adherens junction, integrity and vascular homeostasis. C_LIO_LINxhl also controls some other crucial VE-PTP-linked downstream angiogenic genes (such as Tie2, VEGFaa, VEGFR2, Erbb2, S1pr1 and Hey2) which explain the phenotypes induced by the nxhl deficiency. C_LIO_LIOur study indicates the key role of nxhl on controlling angiogenesis as an upstream regulator of VE-PTP. C_LI What Are the Clinical Implications?O_LIThere are several ongoing researches investigating the utility of VE-PTP or NCL inhibitors on treatment of angiogenesis-dependent diseases including a range of cancers and nonneoplastic diseases, such as AMD, DME, RA and atherosclerosis. C_LIO_LITargeting the nxhl-NCL-VE-PTP pathway may facilitate therapeutic angiogenesis in patients with cancers or ocular diseases such as DME. C_LIO_LIOur study highlights the great potential of nxhl on anti-angiogenic therapeutics by targeting VE-PTP. C_LI

genomics↗

bioRxiv2019

Trans-NanoSim characterizes and simulates nanopore RNA-seq data

We introduce Trans-NanoSim, the first tool that simulates reads with technical and transcriptome-specific features learnt from nanopore RNA-seq data. Through benchmarking on sets of nanopore reads from human and mouse reference transcriptomes, we show the robustness of Trans-NanoSim in capturing the characteristics of nanopore cDNA and direct RNA reads. As a cost-effective alternative to sequencing real transcriptomes, Trans-NanoSim would facilitate the rapid development of analytical tools for nanopore RNA-seq data. Trans-NanoSim is freely accessible at https://github.com/bcgsc/NanoSim

bioinformatics↗

bioRxiv2019

A tissue-specific collaborative mixed model for jointly analyzing multiple tissues in transcriptome-wide association studies

Transcriptome-wide association studies (TWAS) integrate expression quantitative trait loci (eQTLs) studies with genome-wide association studies (GWASs) to prioritize candidate target genes for complex traits. Several statistical methods have been recently proposed to improve the performance of TWAS in gene prioritization by integrating the expression regulatory information imputed from multiple tissues, and made significant achievements in improving the ability to detect gene-trait associations. The major limitation of these methods is that they cannot be used to elucidate the specific functional effects of candidate genes across different tissues. Here, we propose a tissue-specific collaborative mixed model (TisCoMM) for TWAS, leveraging the co-regulation of genetic variations across different tissues explicitly via a unified probabilistic model. TisCoMM not only performs hypothesis testing to prioritize gene-trait associations, but also detects the tissue-specific role of candidate target genes in complex traits. To make use of widely available GWAS summary statistics, we extend TisCoMM to use summary-level data, namely, TisCoMM-S2. Using extensive simulation studies, we show that type I error is controlled at the nominal level, the statistical power of identifying associated genes is greatly improved, and false positive rate (FPR) for non-causal tissues is well controlled at decent levels. We further illustrate the benefits of our methods in applications to summary-level GWAS data of 33 complex traits. Notably, apart from better identifying potential trait-associated genes, we can elucidate the tissue-specific role of candidate target genes. The follow-up pathway analysis from tissue-specific genes for asthma shows that the immune system plays an essential function for asthma development in both thyroid and lung tissues.

bioinformatics↗

bioRxiv2019

FMRP binding to a ranked subset of long genes is revealed by coupled CLIP and TRAP in specific neuronal cell types

Loss of function of the Fragile X Mental Retardation Protein (FMRP) in human Fragile X Syndrome (FXS) and in model organisms results in phenotypes of abnormal neuronal structure and dynamics, synaptic function and connectivity which may contribute to a state of neuronal, circuit and organism hyperexcitability. Previous in vivo identification of FMRP association with specific mRNA targets in mouse brain revealed that FMRP regulates the translation of a large fraction of the synaptic proteome in both pre- and post-synaptic compartments as well as many transcription factors and chromatin modifying proteins. However, it was not previously possible to determine the ratio of FMRP binding to transcript abundance due to the complexity of different neuronal cell types in whole brain. Moreover, it has been difficult to link the translational regulation of specific targets to model phenotypes or human symptoms. For example, loss-of-function of FMRP in the Purkinje cells of the cerebellum results in three cell autonomous phenotypes related to learning and memory, including enhanced mGluR-LTD at parallel fiber synapses, altered dendritic spines and behavioral deficits in a eyeblink-conditioning learning paradigm shared by human FXS patients. The molecular basis for these and related human Fragile X phenotypes is unknown. To address these critical issues we have developed a new mouse model (the Fmr1 cTAG mouse) in which endogenous FMRP can be conditionally tagged for RNA:protein crosslinking and immunoprecipitation (CLIP) identification of the RNAs with which it interacts in vivo. We used the Fmr1 cTAG mouse to quantitatively evaluate FMRP-mRNA association in Purkinje and cerebellar granule neurons which together comprise the parallel-fiber synapse. We calculated a stoichiometrically ranked list of FMRP RNA binding events by normalizing to ribosome-associated transcript abundance determined by TRAP-seq, and now definitively find that FMRP associates with specific sets of mRNAs which differ between the two cell types. In Purkinje cells, many components of the mGluR signaling pathway are FMRP targets including the top-ranked Purkinje cell mRNA Itpr1, encoding the IP3 receptor, the function of which is critical to proper mGluR-dependent synaptic plasticity. In sum, this novel approach provides the first ranked list of FMRP target mRNAs and further reveals that FMRP regulates a specific set of long neural genes related to relevant cell autonomous phenotypes.\n\nHighlightsO_LIWe have created a mouse model in which endogenous FMRP can be conditionally tagged.\nC_LIO_LIUsing tag-specific CLIP we describe ranked and specific sets of in vivo FMRP mRNA targets in two types of neurons.\nC_LIO_LIThis ranking was used to reveal that FMRP regulates mRNAs with long coding sequences.\nC_LIO_LIFMRP mRNA targets in Purkinje cells, including the top-ranked IP3 receptor, are related to cell-autonomous Fragile X phenotypes.\nC_LIO_LIWe have updated our previous list of whole mouse brain FMRP mRNA targets with more replicates, deeper sequencing and improved analysis\nC_LIO_LIThe use of tagged FMRP in less abundant cell populations allowed identification of novel mRNA targets missed in a whole brain analysis\nC_LI

neuroscience↗

bioRxiv2019

MACF1 Facilitates SMAD7 Nuclear Translocation to Drive Bone Formation in Mice

MACF1 is a large crosslinker that contributes to cytoskeleton integrity and cell differentiation. Loss of MACF1 impairs multiple cellular functions in neuron development and epidermal migration, and is the molecular basis for many diseases such as heart failure and Parkinsons disease. MACF1 is highly abundant in bones, however, its involvements in osteogenic differentiation and bone formation are still unknown. In this study, by conditional gene targeting to delete the Macf1 gene specifically in MSCs, we observed ossification retardation and bone loss in MACF1 deficient mice in different developmental stages, which we traced to disorganized cytoskeleton and decreased osteogenic differentiation capability in MSCs. Further, we show that MACF1 interacts and facilitates SMAD7 nuclear translocation to initiate downstream transcription. These findings are hopefully to expand the biological scope of MACF1 in bones, and provide experimental basis for targeting MACF1 in degenerative bone diseases such as osteoporosis.

developmental biology↗

bioRxiv2019

Allicin regulates energy homeostasis through brown adipose tissue

Background/objectivesDisorder of energy homeostasis can lead to a variety of metabolic diseases, especially obesity. Brown adipose tissue (BAT) is a promising potential therapeutic target for the treatment of obesity and related metabolic diseases. Allicin, a main bioactive ingredient in garlic, has multiple biology and pharmacological function. However, the role of Allicin, in the regulation of metabolic organ, especially the role of activation of BAT, has not been well studied. Here, we analyzed the role of Allicin in whole-body metabolism and the activation of BAT.\n\nResultsAllicin had a significant effect in inhibiting body weight gain, decreasing adiposity, maintaining glucose homeostasis, improving insulin resistance, and ameliorating hepatic steatosis in diet-introduced obesity (DIO) mice. Then we find that Allicin can strongly activate brown adipose tissue (BAT). The activation of brown adipocyte treated with Allicin was also confirmed in mouse primary brown adipocytes.\n\nConclusionAllicin can ameliorate obesity through activating brown adipose tissue. Our findings provide a promising therapeutic approach for the treatment of obesity and metabolic disorders.

molecular biology↗

bioRxiv2019

Termin(A)ntor: Polyadenylation Site Prediction using deep learning models

As a widespread RNA processing machinery, alternative polyadenylation plays a crucial role in gene regulation. To help decipher its underlying mechanism and understand its impact, it is desirable to comprehensively profile 3-untranslated region cleavage and associated polyadenylation sites. State-of-the-art polyadenylation site detection tools are known to be influenced by library preparation artefacts or manually selected features. Moreover, recently published machine learning methods have only been tested on pre-constructed datasets, thus lacking validation on experimental data. Here we present Terminitor, the first deep neural network-based profiling pipeline to make predictions from RNA-seq data. We show how Terminitor outperforms competing tools in sensitivity and precision on experimental transcriptome sequencing data, and demonstrate its use with data from short- and long-read sequencing technologies. For species without a good reference transcriptome annotation, Terminitor is still able to pass on the information learnt from a related species and make reasonable predictions. We used Terminitor to showcase how single nucleotide variations can create or destroy polyadenylated cleavage sites in human RNA-seq samples. Author Summary3 cleavage and polyadenylation of pre-mRNA is part of RNA maturation process. One gene can be cleaved at different positions at its 3 end, namely alternatively polyadenylation, thus identifying the correct polyadenylated cleavage site (poly(A) CS) is essential to unveil its role in gene regulation under different physiological and pathological conditions. The current poly(A) CS prediction tools are either heavily influenced by RNA-Seq library preparation artefacts or have only been designed and tested on ad hoc datasets, lacking association with real world applications. In this study, we present a deep learning model, Terminitor, that predicts the probability of a nucleotide sequence containing a poly(A) CS, and validated its performance on human and mouse data. Along with the model, we propose a poly(A) CS profiling pipeline for RNA-seq data. We benchmarked our pipeline against competing tools and achieved higher sensitivity and precision in experimental data. The usage of Terminitor is not limited to genome and transcriptome annotation and we expect it to facilitate the identification of novel isoforms, improve the accuracy of transcript quantification and differential expression analysis, and contribute to the repertoire of reference transcriptome annotation.

bioinformatics↗

bioRxiv2019

Brain-to-brain coupling between instructors and learners discriminates between instructional approaches and predicts learning

The neural mechanisms that support naturalistic learning via effective pedagogical approaches remain elusive. Here we use functional near-infrared spectroscopy to measure brain activity from instructor-learner dyads simultaneously during dynamic conceptual learning. We report that brain-to-brain coupling is correlated with learning outcomes, and, crucially, appears to be driven by specific scaffolding behaviors on the part of the instructors (e.g., asking guiding questions or providing hints). Brain-to-brain coupling enhancement is absent when instructors use an explanation approach (e.g., providing definitions or clarifications). Finally, we find that machine-learning techniques are more successful when decoding instructional approaches (scaffolding vs. explanation) from brain-to-brain coupling data than when using a single-brain method. These findings suggest that brain-to-brain coupling as a pedagogically relevant measure tracks the naturalistic instructional process during instructor-learner interaction throughout constructive engagement, but not information clarification.

neuroscience↗

bioRxiv2019

RNA-Bloom provides lightweight reference-free transcriptome assembly for single cells

We present RNA-Bloom, a de novo RNA-seq assembly algorithm that leverages the rich information content in single-cell transcriptome sequencing (scRNA-seq) data to reconstruct cell-specific isoforms. We benchmark RNA-Blooms performance against leading bulk RNA-seq assembly approaches, and illustrate its utility in detecting cell-specific gene fusion events using sequencing data from HiSeq-4000 and BGISEQ-500 platforms. We expect RNA-Bloom to boost the utility of scRNA-seq data, expanding what is informatically accessible now.

bioinformatics↗

bioRxiv2019

Testing and controlling for horizontal pleiotropy with the probabilistic Mendelian randomization in transcriptome-wide association studies

Integrating association results from both genome-wide association studies (GWASs) and expression quantitative trait locus (eQTL) mapping studies has the potential to shed light on the molecular mechanisms underlying disease etiology. Several statistical methods have been recently developed to integrate GWASs with eQTL studies in the form of transcriptome-wide association studies (TWASs). These existing methods can all be viewed as a form of two sample Mendelian randomization (MR) analysis, which has been widely applied in various GWASs for inferring the causal relationship among complex traits. Unfortunately, most existing TWAS and MR methods make an unrealistic modeling assumption and assume that instrumental variables do not exhibit horizontal pleiotropic effects. However, horizontal pleiotropic effects have been recently discovered to be wide spread across complex traits, and, as we will show here, are also wide spread across gene expression traits. Therefore, not allowing for horizontal pleiotropic effects can be overly restrictive, and, as we will be show here, can lead to a substantial inflation of test statistics and subsequently false discoveries in TWAS applications. Here, we present a probabilistic MR method, which we refer to as PMR-Egger, for testing and controlling for horizontal pleiotropic effects in TWAS applications. PMR-Egger relies on an MR likelihood framework that unifies many existing TWAS and MR methods, accommodates multiple correlated instruments, tests the causal effect of gene on trait in the presence of horizontal pleiotropy, and, with a newly developed parameter expansion version of the expectation maximization algorithm, is scalable to hundreds of thousands of individuals. With extensive simulations, we show that PMR-Egger provides calibrated type I error control for causal effect testing in the presence of horizontal pleiotropic effects, is reasonably robust for various types of horizontal pleiotropic effect mis-specifications, is more powerful than existing MR approaches, and, as a by-product, can directly test for horizontal pleiotropy. We illustrate the benefits of PMR-Egger in applications to 39 diseases and complex traits obtained from three GWASs including the UK Biobank. In these applications, we show how PMR-Egger can lead to new biological discoveries through integrative analysis.

genetics↗

bioRxiv2019

Evaluation of deep-learning-based lncRNA identification tools

Long non-coding RNAs (lncRNAs, length above 200 nt) exert crucial biological roles and have been implicated in cancers1,2. To characterize newly discovered transcripts, one major issue is to distinguish lncRNAs from mRNAs. Since experimental methods are time-consuming and costly, computational methods are preferred for large-scale lncRNA identification. In a recent study, Amin et al.3 evaluated three deep-learning-based lncRNA identification tools (i.e., lncRNAnet4, LncADeep5, and lncFinder6) and concluded \"The LncADeep PR (precision recall) curve is just above the no-skill model and LncADeep showed poor overall performance\". This surprising conclusion is based on the authors use of a non-default setting of LncADeep. Actually, LncADeep has two models, one for full-length transcripts, and the other for transcripts including partial-length. Being aware of the difficulty of assembling full-length transcripts from RNA-seq dataset, LncADeeps default model is for transcripts including partial-length. However, according to the results posted on Amin et al.s website, the authors used LncADeep with full-length model, while they claimed to use the default setting of LncADeep, to identify lncRNAs from GENCODE dataset, which is composed of full- and partial-length transcripts. Thus, in their evaluation, the performance of LncADeep was underestimated. In this correspondence, we have tested LncADeeps default setting (i.e., model for transcripts including partial-length) on the datasets used in Amin et al.3, and LncADeep achieved overall the best performance compared with the other tools results reported by Amin et al.

bioinformatics↗

bioRxiv2019

Trivalent cocktail of de novo designed immunogens enables the robust induction and focusing of functional antibodies in vivo

De novo protein design has been successful in expanding the natural protein repertoire. However, most de novo proteins lack biological function, presenting a major methodological challenge. In vaccinology, the induction of precise antibody responses remains a cornerstone for next-generation vaccines. Here, we present a novel protein design algorithm, termed TopoBuilder, with which we engineered epitope-focused immunogens displaying complex structural motifs. Both in mice and non-human primates, cocktails of three de novo designed immunogens induced robust neutralizing responses against the respiratory syncytial virus. Furthermore, the immunogens refocused pre-existing antibody responses towards defined neutralization epitopes. Overall, our de novo design approach opens the possibility of targeting specific epitopes for vaccine and therapeutic antibody development, and more generally will be applicable to design de novo proteins displaying complex functional motifs.

bioengineering↗

bioRxiv2019

MR-LDP: a two-sample Mendelian randomization for GWAS summary statistics accounting linkage disequilibrium and horizontal pleiotropy

The proliferation of genome-wide association studies (GWAS) has prompted the use of two-sample Mendelian randomization (MR) with genetic variants as instrumental variables (IV) for drawing reliable causal relationships between health risk factors and disease outcomes. However, the unique features of GWAS demand that MR methods account for both linkage disequilibrium (LD) and ubiquitously existing horizontal pleiotropy among complex traits, which is the phenomenon wherein a variant affects the outcome through mechanisms other than exclusively through the exposure. Therefore, statistical methods that fail to consider LD and horizontal pleiotropy can lead to biased estimates and false-positive causal relationships. To overcome these limitations, we propose a probabilistic model for MR analysis to identify the casual effects between risk factors and disease outcomes using GWAS summary statistics in the presence of LD and to properly account for horizontal pleiotropy among genetic variants (MR-LDP). MR-LDP utilizes a computationally efficient parameter-expanded variational Bayes expectation-maximization (PX-VBEM) algorithm to estimate the parameter of interest and further calibrates the evidence lower bound (ELBO) for a likelihood ratio test. We then conducted comprehensive simulation studies to demonstrate the advantages of MR-LDP over the existing methods in terms of both type-I error control and point estimates. Moreover, we used two real exposure-outcome pairs (CAD-CAD and Height-Height; CAD for coronary artery disease) to validate the results from MR-LDP compared with alternative methods, showing that our method is more efficient in using all instrumental variants in LD. By further applying MR-LDP to lipid traits and body mass index (BMI) as risk factors for complex diseases, we identified multiple pairs of significant causal relationships, including a protective effect of high-density lipoprotein cholesterol (HDL-C) on peripheral vascular disease (PVD), and a positive causal effect of body mass index (BMI) on hemorrhoids.

genetics↗

bioRxiv2019

CoMM-S2: a collaborative mixed model using summary statistics in transcriptome-wide association studies

MotivationAlthough genome-wide association studies (GWAS) have deepened our understanding of the genetic architecture of complex traits, the mechanistic links that underlie how genetic variants cause complex traits remains elusive. To advance our understanding of the underlying mechanistic links, various consortia have collected a vast volume of genomic data that enable us to investigate the role that genetic variants play in gene expression regulation. Recently, a collaborative mixed model (CoMM) [42] was proposed to jointly interrogate genome on complex traits by integrating both the GWAS dataset and the expression quantitative trait loci (eQTL) dataset. Although CoMM is a powerful approach that leverages regulatory information while accounting for the uncertainty in using an eQTL dataset, it requires individual-level GWAS data and cannot fully make use of widely available GWAS summary statistics. Therefore, statistically efficient methods that leverages transcriptome information using only summary statistics information from GWAS data are required.\n\nResultsIn this study, we propose a novel probabilistic model, CoMM-S2, to examine the mechanistic role that genetic variants play, by using only GWAS summary statistics instead of individual-level GWAS data. Similar to CoMM which uses individual-level GWAS data, CoMM-S2 combines two models: the first model examines the relationship between gene expression and genotype, while the second model examines the relationship between the phenotype and the predicted gene expression from the first model. Distinct from CoMM, CoMM-S2 requires only GWAS summary statistics. Using both simulation studies and real data analysis, we demonstrate that even though CoMM-S2 utilizes GWAS summary statistics, it has comparable performance as CoMM, which uses individual-level GWAS data.\n\nContactjin.liu@duke-nus.edu.sg\n\nAvailability and implementationThe implement of CoMM-S2 is included in the CoMM package that can be downloaded from https://github.com/gordonliu810822/CoMM.\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2019

Bimodal Regulation of the PRC2 Complex by USP7 Underlies Melanomagenesis

Although overexpression of EZH2, a catalytic subunit of the polycomb repressive complex 2 (PRC2), is an eminent feature of various cancers, the regulation of its abundance and function remains insufficiently understood. We report here that the PRC2 complex is physically associated with ubiquitin-specific protease USP7 in melanoma cells where USP7 acts to deubiquitinate and stabilize EZH2. Interestingly, we found that USP7-catalyzed H2BK120 deubiquitination is a prerequisite for chromatin loading of PRC2 thus H3K27 trimethylation. Genome-wide analysis of the transcriptional targets of the USP7/PRC2 complex identified a cohort of genes including FOXO1 that are involved in cell growth and proliferation. We demonstrated that the USP7/PRC2 complex drives melanoma cell proliferation and tumorigenesis in vitro and in vivo. We showed that the expression of both USP7 and EZH2 elevates during melanoma progression, corresponding to a diminished FOXO1 expression, and the level of the expression of USP7 and EZH2 strongly correlates with histological grades and prognosis of melanoma patients. These results reveal a dual role for USP7 in the regulation of the abundance and function of EZH2, supporting the pursuit of USP7 as a therapeutic target for melanoma.

cell biology↗