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Publication
Journal: Nucleic Acids Research
March/2/2009
Abstract
'miR2Disease', a manually curated database, aims at providing a comprehensive resource of microRNA deregulation in various human diseases. The current version of miR2Disease documents 1939 curated relationships between 299 human microRNAs and 94 human diseases by reviewing more than 600 published papers. Around one-seventh of the microRNA-disease relationships represent the pathogenic roles of deregulated microRNA in human disease. Each entry in the miR2Disease contains detailed information on a microRNA-disease relationship, including a microRNA ID, the disease name, a brief description of the microRNA-disease relationship, an expression pattern of the microRNA, the detection method for microRNA expression, experimentally verified target gene(s) of the microRNA and a literature reference. miR2Disease provides a user-friendly interface for a convenient retrieval of each entry by microRNA ID, disease name, or target gene. In addition, miR2Disease offers a submission page that allows researchers to submit established microRNA-disease relationships that are not documented. Once approved by the submission review committee, the submitted records will be included in the database. miR2Disease is freely available at http://www.miR2Disease.org.
Publication
Journal: Nucleic Acids Research
December/16/2015
Abstract
With the avalanche of biological sequences generated in the post-genomic age, one of the most challenging problems in computational biology is how to effectively formulate the sequence of a biological sample (such as DNA, RNA or protein) with a discrete model or a vector that can effectively reflect its sequence pattern information or capture its key features concerned. Although several web servers and stand-alone tools were developed to address this problem, all these tools, however, can only handle one type of samples. Furthermore, the number of their built-in properties is limited, and hence it is often difficult for users to formulate the biological sequences according to their desired features or properties. In this article, with a much larger number of built-in properties, we are to propose a much more flexible web server called Pse-in-One (http://bioinformatics.hitsz.edu.cn/Pse-in-One/), which can, through its 28 different modes, generate nearly all the possible feature vectors for DNA, RNA and protein sequences. Particularly, it can also generate those feature vectors with the properties defined by users themselves. These feature vectors can be easily combined with machine-learning algorithms to develop computational predictors and analysis methods for various tasks in bioinformatics and system biology. It is anticipated that the Pse-in-One web server will become a very useful tool in computational proteomics, genomics, as well as biological sequence analysis. Moreover, to maximize users' convenience, its stand-alone version can also be downloaded from http://bioinformatics.hitsz.edu.cn/Pse-in-One/download/, and directly run on Windows, Linux, Unix and Mac OS.
Publication
Journal: Chemical Reviews
April/19/2015
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Publication
Journal: Oxidative Medicine and Cellular Longevity
February/18/2014
Abstract
Alzheimer's disease (AD) is the most common neurodegenerative disease that causes dementia in the elderly. Patients with AD suffer a gradual deterioration of memory and other cognitive functions, which eventually leads to a complete incapacity and death. A complicated array of molecular events has been implicated in the pathogenesis of AD. The major pathological characteristics of AD brains are the presence of senile plaques, neurofibrillary tangles, and neuronal loss. Growing evidence has demonstrated that oxidative stress is an important factor contributing to the initiation and progression of AD. However, the mechanisms that lead to the disruption of redox balance and the sources of free radicals remain elusive. The excessive reactive oxygen species may be generated from mechanisms such as mitochondria dysfunction and/or aberrant accumulation of transition metals, while the abnormal accumulation of Abeta and tau proteins appears to promote the redox imbalance. The resulted oxidative stress has been implicated in Abeta- or tau-induced neurotoxicity. In addition, evidence has suggested that oxidative stress may augment the production and aggregation of Abeta and facilitate the phosphorylation and polymerization of tau, thus forming a vicious cycle that promotes the initiation and progression of AD.
Publication
Journal: Seminars in Cancer Biology
September/6/2016
Abstract
Apoptosis or programmed cell death is natural way of removing aged cells from the body. Most of the anti-cancer therapies trigger apoptosis induction and related cell death networks to eliminate malignant cells. However, in cancer, de-regulated apoptotic signaling, particularly the activation of an anti-apoptotic systems, allows cancer cells to escape this program leading to uncontrolled proliferation resulting in tumor survival, therapeutic resistance and recurrence of cancer. This resistance is a complicated phenomenon that emanates from the interactions of various molecules and signaling pathways. In this comprehensive review we discuss the various factors contributing to apoptosis resistance in cancers. The key resistance targets that are discussed include (1) Bcl-2 and Mcl-1 proteins; (2) autophagy processes; (3) necrosis and necroptosis; (4) heat shock protein signaling; (5) the proteasome pathway; (6) epigenetic mechanisms; and (7) aberrant nuclear export signaling. The shortcomings of current therapeutic modalities are highlighted and a broad spectrum strategy using approaches including (a) gossypol; (b) epigallocatechin-3-gallate; (c) UMI-77 (d) triptolide and (e) selinexor that can be used to overcome cell death resistance is presented. This review provides a roadmap for the design of successful anti-cancer strategies that overcome resistance to apoptosis for better therapeutic outcome in patients with cancer.
Publication
Journal: BMC Systems Biology
September/16/2010
Abstract
BACKGROUND
The identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination.
RESULTS
Herein, we devised a computational model to infer potential microRNA-disease associations by prioritizing the entire human microRNAome for diseases of interest. We tested the model on 270 known experimentally verified microRNA-disease associations and achieved an area under the ROC curve of 75.80%. Moreover, we demonstrated that the model is applicable to diseases with which no known microRNAs are associated. The microRNAome-wide prioritization of microRNAs for 1,599 disease phenotypes is publicly released to facilitate future identification of disease-related microRNAs.
CONCLUSIONS
We presented a network-based approach that can infer potential microRNA-disease associations and drive testable hypotheses for the experimental efforts to identify the roles of microRNAs in human diseases.
Publication
Journal: Bioinformatics
May/15/2014
Abstract
BACKGROUND
Owing to its importance in both basic research (such as molecular evolution and protein attribute prediction) and practical application (such as timely modeling the 3D structures of proteins targeted for drug development), protein remote homology detection has attracted a great deal of interest. It is intriguing to note that the profile-based approach is promising and holds high potential in this regard. To further improve protein remote homology detection, a key step is how to find an optimal means to extract the evolutionary information into the profiles.
RESULTS
Here, we propose a novel approach, the so-called profile-based protein representation, to extract the evolutionary information via the frequency profiles. The latter can be calculated from the multiple sequence alignments generated by PSI-BLAST. Three top performing sequence-based kernels (SVM-Ngram, SVM-pairwise and SVM-LA) were combined with the profile-based protein representation. Various tests were conducted on a SCOP benchmark dataset that contains 54 families and 23 superfamilies. The results showed that the new approach is promising, and can obviously improve the performance of the three kernels. Furthermore, our approach can also provide useful insights for studying the features of proteins in various families. It has not escaped our notice that the current approach can be easily combined with the existing sequence-based methods so as to improve their performance as well.
METHODS
For users' convenience, the source code of generating the profile-based proteins and the multiple kernel learning was also provided at http://bioinformatics.hitsz.edu.cn/main/~binliu/remote/
Publication
Journal: Molecular Cell
December/24/2018
Abstract
Ferroptosis is a regulated necrosis process driven by iron-dependent lipid peroxidation. Although ferroptosis and cellular metabolism interplay with one another, whether mitochondria are involved in ferroptosis is under debate. Here, we demonstrate that mitochondria play a crucial role in cysteine-deprivation-induced ferroptosis but not in that induced by inhibiting glutathione peroxidase-4 (GPX4), the most downstream component of the ferroptosis pathway. Mechanistically, cysteine deprivation leads to mitochondrial membrane potential hyperpolarization and lipid peroxide accumulation. Inhibition of mitochondrial TCA cycle or electron transfer chain (ETC) mitigated mitochondrial membrane potential hyperpolarization, lipid peroxide accumulation, and ferroptosis. Blockage of glutaminolysis had the same inhibitory effect, which was counteracted by supplying downstream TCA cycle intermediates. Importantly, loss of function of fumarate hydratase, a tumor suppressor and TCA cycle component, confers resistance to cysteine-deprivation-induced ferroptosis. Collectively, this work demonstrates the crucial role of mitochondria in cysteine-deprivation-induced ferroptosis and implicates ferroptosis in tumor suppression.
Publication
Journal: Nature
January/29/2014
Abstract
The human immunodeficiency virus (HIV)-1 protein Vif has a central role in the neutralization of host innate defences by hijacking cellular proteasomal degradation pathways to subvert the antiviral activity of host restriction factors; however, the underlying mechanism by which Vif achieves this remains unclear. Here we report a crystal structure of the Vif-CBF-β-CUL5-ELOB-ELOC complex. The structure reveals that Vif, by means of two domains, organizes formation of the pentameric complex by interacting with CBF-β, CUL5 and ELOC. The larger domain (α/β domain) of Vif binds to the same side of CBF-β as RUNX1, indicating that Vif and RUNX1 are exclusive for CBF-β binding. Interactions of the smaller domain (α-domain) of Vif with ELOC and CUL5 are cooperative and mimic those of SOCS2 with the latter two proteins. A unique zinc-finger motif of Vif, which is located between the two Vif domains, makes no contacts with the other proteins but stabilizes the conformation of the α-domain, which may be important for Vif-CUL5 interaction. Together, our data reveal the structural basis for Vif hijacking of the CBF-β and CUL5 E3 ligase complex, laying a foundation for rational design of novel anti-HIV drugs.
Publication
Journal: Chemical Reviews
December/12/2016
Publication
Journal: Bioinformatics
August/10/2015
Abstract
In order to develop powerful computational predictors for identifying the biological features or attributes of DNAs, one of the most challenging problems is to find a suitable approach to effectively represent the DNA sequences. To facilitate the studies of DNAs and nucleotides, we developed a Python package called representations of DNAs (repDNA) for generating the widely used features reflecting the physicochemical properties and sequence-order effects of DNAs and nucleotides. There are three feature groups composed of 15 features. The first group calculates three nucleic acid composition features describing the local sequence information by means of kmers; the second group calculates six autocorrelation features describing the level of correlation between two oligonucleotides along a DNA sequence in terms of their specific physicochemical properties; the third group calculates six pseudo nucleotide composition features, which can be used to represent a DNA sequence with a discrete model or vector yet still keep considerable sequence-order information via the physicochemical properties of its constituent oligonucleotides. In addition, these features can be easily calculated based on both the built-in and user-defined properties via using repDNA.
METHODS
The repDNA Python package is freely accessible to the public at http://bioinformatics.hitsz.edu.cn/repDNA/.
BACKGROUND
bliu@insun.hit.edu.cn or kcchou@gordonlifescience.org
BACKGROUND
Supplementary data are available at Bioinformatics online.
Publication
Journal: Bioinformatics
September/21/2016
Abstract
BACKGROUND
Enhancers are of short regulatory DNA elements. They can be bound with proteins (activators) to activate transcription of a gene, and hence play a critical role in promoting gene transcription in eukaryotes. With the avalanche of DNA sequences generated in the post-genomic age, it is a challenging task to develop computational methods for timely identifying enhancers from extremely complicated DNA sequences. Although some efforts have been made in this regard, they were limited at only identifying whether a query DNA element being of an enhancer or not. According to the distinct levels of biological activities and regulatory effects on target genes, however, enhancers should be further classified into strong and weak ones in strength.
RESULTS
In view of this, a two-layer predictor called ' IENHANCER-2L: ' was proposed by formulating DNA elements with the 'pseudo k-tuple nucleotide composition', into which the six DNA local parameters were incorporated. To the best of our knowledge, it is the first computational predictor ever established for identifying not only enhancers, but also their strength. Rigorous cross-validation tests have indicated that IENHANCER-2L: holds very high potential to become a useful tool for genome analysis.
METHODS
For the convenience of most experimental scientists, a web server for the two-layer predictor was established at http://bioinformatics.hitsz.edu.cn/iEnhancer-2L/, by which users can easily get their desired results without the need to go through the mathematical details.
BACKGROUND
bliu@gordonlifescience.org, bliu@insun.hit.edu.cn, xlan@stanford.edu, kcchou@gordonlifescience.org
BACKGROUND
Supplementary data are available at Bioinformatics online.
Publication
Journal: ACS Nano
February/7/2013
Abstract
We describe the development of novel and biocompatible core/shell (α-NaYbF(4):Tm(3+))/CaF(2) nanoparticles that exhibit highly efficient NIR(in)-NIR(out) upconversion (UC) for high contrast and deep bioimaging. When excited at ~980 nm, these nanoparticles emit photoluminescence (PL) peaked at ~800 nm. The quantum yield of this UC PL under low power density excitation (~0.3 W/cm(2)) is 0.6 ± 0.1%. This high UC PL efficiency is realized by suppressing surface quenching effects via heteroepitaxial growth of a biocompatible CaF(2) shell, which results in a 35-fold increase in the intensity of UC PL from the core. Small-animal whole-body UC PL imaging with exceptional contrast (signal-to-background ratio of 310) is shown using BALB/c mice intravenously injected with aqueously dispersed nanoparticles (700 pmol/kg). High-contrast UC PL imaging of deep tissues is also demonstrated, using a nanoparticle-loaded synthetic fibrous mesh wrapped around rat femoral bone and a cuvette with nanoparticle aqueous dispersion covered with a 3.2 cm thick animal tissue (pork).
Publication
Journal: PLoS ONE
March/17/2016
Abstract
Containing about 22 nucleotides, a micro RNA (abbreviated miRNA) is a small non-coding RNA molecule, functioning in transcriptional and post-transcriptional regulation of gene expression. The human genome may encode over 1000 miRNAs. Albeit poorly characterized, miRNAs are widely deemed as important regulators of biological processes. Aberrant expression of miRNAs has been observed in many cancers and other disease states, indicating they are deeply implicated with these diseases, particularly in carcinogenesis. Therefore, it is important for both basic research and miRNA-based therapy to discriminate the real pre-miRNAs from the false ones (such as hairpin sequences with similar stem-loops). Particularly, with the avalanche of RNA sequences generated in the postgenomic age, it is highly desired to develop computational sequence-based methods in this regard. Here two new predictors, called "iMcRNA-PseSSC" and "iMcRNA-ExPseSSC", were proposed for identifying the human pre-microRNAs by incorporating the global or long-range structure-order information using a way quite similar to the pseudo amino acid composition approach. Rigorous cross-validations on a much larger and more stringent newly constructed benchmark dataset showed that the two new predictors (accessible at http://bioinformatics.hitsz.edu.cn/iMcRNA/) outperformed or were highly comparable with the best existing predictors in this area.
Publication
Journal: Cell
October/27/2015
Abstract
In vivo pharmacology and optogenetics hold tremendous promise for dissection of neural circuits, cellular signaling, and manipulating neurophysiological systems in awake, behaving animals. Existing neural interface technologies, such as metal cannulas connected to external drug supplies for pharmacological infusions and tethered fiber optics for optogenetics, are not ideal for minimally invasive, untethered studies on freely behaving animals. Here, we introduce wireless optofluidic neural probes that combine ultrathin, soft microfluidic drug delivery with cellular-scale inorganic light-emitting diode (μ-ILED) arrays. These probes are orders of magnitude smaller than cannulas and allow wireless, programmed spatiotemporal control of fluid delivery and photostimulation. We demonstrate these devices in freely moving animals to modify gene expression, deliver peptide ligands, and provide concurrent photostimulation with antagonist drug delivery to manipulate mesoaccumbens reward-related behavior. The minimally invasive operation of these probes forecasts utility in other organ systems and species, with potential for broad application in biomedical science, engineering, and medicine.
Publication
Journal: PLoS ONE
May/13/2015
Abstract
Playing crucial roles in various cellular processes, such as recognition of specific nucleotide sequences, regulation of transcription, and regulation of gene expression, DNA-binding proteins are essential ingredients for both eukaryotic and prokaryotic proteomes. With the avalanche of protein sequences generated in the postgenomic age, it is a critical challenge to develop automated methods for accurate and rapidly identifying DNA-binding proteins based on their sequence information alone. Here, a novel predictor, called "iDNA-Prot|dis", was established by incorporating the amino acid distance-pair coupling information and the amino acid reduced alphabet profile into the general pseudo amino acid composition (PseAAC) vector. The former can capture the characteristics of DNA-binding proteins so as to enhance its prediction quality, while the latter can reduce the dimension of PseAAC vector so as to speed up its prediction process. It was observed by the rigorous jackknife and independent dataset tests that the new predictor outperformed the existing predictors for the same purpose. As a user-friendly web-server, iDNA-Prot|dis is accessible to the public at http://bioinformatics.hitsz.edu.cn/iDNA-Prot_dis/. Moreover, for the convenience of the vast majority of experimental scientists, a step-by-step protocol guide is provided on how to use the web-server to get their desired results without the need to follow the complicated mathematic equations that are presented in this paper just for the integrity of its developing process. It is anticipated that the iDNA-Prot|dis predictor may become a useful high throughput tool for large-scale analysis of DNA-binding proteins, or at the very least, play a complementary role to the existing predictors in this regard.
Publication
Journal: Nature
May/23/2016
Abstract
The CRISPR-Cas systems, as exemplified by CRISPR-Cas9, are RNA-guided adaptive immune systems used by bacteria and archaea to defend against viral infection. The CRISPR-Cpf1 system, a new class 2 CRISPR-Cas system, mediates robust DNA interference in human cells. Although functionally conserved, Cpf1 and Cas9 differ in many aspects including their guide RNAs and substrate specificity. Here we report the 2.38 Å crystal structure of the CRISPR RNA (crRNA)-bound Lachnospiraceae bacterium ND2006 Cpf1 (LbCpf1). LbCpf1 has a triangle-shaped architecture with a large positively charged channel at the centre. Recognized by the oligonucleotide-binding domain of LbCpf1, the crRNA adopts a highly distorted conformation stabilized by extensive intramolecular interactions and the (Mg(H2O)6)(2+) ion. The oligonucleotide-binding domain also harbours a looped-out helical domain that is important for LbCpf1 substrate binding. Binding of crRNA or crRNA lacking the guide sequence induces marked conformational changes but no oligomerization of LbCpf1. Our study reveals the crRNA recognition mechanism and provides insight into crRNA-guided substrate binding of LbCpf1, establishing a framework for engineering LbCpf1 to improve its efficiency and specificity for genome editing.
Publication
Journal: BMC Bioinformatics
January/25/2006
Abstract
BACKGROUND
Development of robust and efficient methods for analyzing and interpreting high dimension gene expression profiles continues to be a focus in computational biology. The accumulated experiment evidence supports the assumption that genes express and perform their functions in modular fashions in cells. Therefore, there is an open space for development of the timely and relevant computational algorithms that use robust functional expression profiles towards precise classification of complex human diseases at the modular level.
RESULTS
Inspired by the insight that genes act as a module to carry out a highly integrated cellular function, we thus define a low dimension functional expression profile for data reduction. After annotating each individual gene to functional categories defined in a proper gene function classification system such as Gene Ontology applied in this study, we identify those functional categories enriched with differentially expressed genes. For each functional category or functional module, we compute a summary measure (s) for the raw expression values of the annotated genes to capture the overall activity level of the module. In this way, we can treat the gene expressions within a functional module as an integrative data point to replace the multiple values of individual genes. We compare the classification performance of decision trees based on functional expression profiles with the conventional gene expression profiles using four publicly available datasets, which indicates that precise classification of tumour types and improved interpretation can be achieved with the reduced functional expression profiles.
CONCLUSIONS
This modular approach is demonstrated to be a powerful alternative approach to analyzing high dimension microarray data and is robust to high measurement noise and intrinsic biological variance inherent in microarray data. Furthermore, efficient integration with current biological knowledge has facilitated the interpretation of the underlying molecular mechanisms for complex human diseases at the modular level.
Publication
Journal: Human Mutation
January/22/2015
Abstract
Copy number variation (CNV) has been found to play an important role in human disease. Next-generation sequencing technology, including whole-genome sequencing (WGS) and whole-exome sequencing (WES), has become a primary strategy for studying the genetic basis of human disease. Several CNV calling tools have recently been developed on the basis of WES data. However, the comparative performance of these tools using real data remains unclear. An objective evaluation study of these tools in practical research situations would be beneficial. Here, we evaluated four well-known WES-based CNV detection tools (XHMM, CoNIFER, ExomeDepth, and CONTRA) using real data generated in house. After evaluation using six metrics, we found that the sensitive and accurate detection of CNVs in WES data remains challenging despite the many algorithms available. Each algorithm has its own strengths and weaknesses. None of the exome-based CNV calling methods performed well in all situations; in particular, compared with CNVs identified from high coverage WGS data from the same samples, all tools suffered from limited power. Our evaluation provides a comprehensive and objective comparison of several well-known detection tools designed for WES data, which will assist researchers in choosing the most suitable tools for their research needs.
Publication
Journal: Advanced Materials
July/15/2013
Abstract
Uniform polypyrrole (PPy) nanoparticles are fabricated from a facile one-step aqueous dispersion polymerization. Owing to their high photothermal conversion efficiency and photostability compared with the well-known Au nanorods, as well as their good colloidal stability and biocompatibility, the resulting PPy nanoparticles can used as a novel promising photothermal ablation coupling agent for targeted treatment of cancer.
Publication
Journal: Environmental Science & Technology
December/6/2011
Abstract
Bisphenol A (BPA) is an industrial chemical used in the manufacture of polycarbonate plastics and epoxy resins. Due to the potential of this compound to disrupt normal endocrinal functions, concerns over human exposure to BPA have been raised. Although several studies have reported human exposure to BPA in Western nations, little is known about exposure in Asian countries. In this study, we determined total urinary BPA concentrations (free plus conjugated) in 296 urine samples (male/female: 153/143) collected from the general population in seven Asian countries, China, India, Japan, Korea, Kuwait, Malaysia, and Vietnam, using high-performance liquid chromatography (HPLC) tandem mass spectrometry (MS/MS). On the basis of urinary BPA concentrations, we estimated the total daily intake. The results indicated that BPA was detected in 94.3% of the samples analyzed, at concentrations ranging from <0.1 to 30.1 ng/mL. The geometric mean concentration of BPA for the entire sample set from seven countries was 1.20 ng/mL. The highest concentration of BPA was found in samples from Kuwait (median: 3.05 ng/mL, 2.45 μg/g creatinine), followed by Korea (2.17 ng/mL, 2.40 μg/g), India (1.71 ng/mL, 2.09 μg/g), Vietnam (1.18 ng/mL, 1.15 μg/g), China (1.10 ng/mL, 1.38 μg/g), Malaysia (1.06 ng/mL, 2.31 μg/g), and Japan (0.95 ng/mL, 0.58 μg/g). Among the five age groups studied (≤ 19, 20-29, 30-39, 40-49, and ≥ 50 years), the highest median concentration of BPA was found in urine samples from the age group of ≤ 19 years. There was no significant difference in BPA concentrations between genders (male and female) or domicile of residence (rural and urban). The estimated median daily intakes of BPA for the populations in Kuwait, Korea, India, China, Vietnam, Malaysia, and Japan were 5.19, 3.69, 2.90, 2.13, 2.01, 1.80, and 1.61 μg/day, respectively. The estimated daily intake of BPA in the seven Asian countries was significantly lower than the tolerable daily intake recommended by the U.S. Environmental Protection Agency. This is the first study to document the occurrence of and human exposure to BPA in several Asian countries.
Publication
Journal: Molecular Genetics and Genomics
May/30/2016
Abstract
With the rapid growth of RNA sequences generated in the postgenomic age, it is highly desired to develop a flexible method that can generate various kinds of vectors to represent these sequences by focusing on their different features. This is because nearly all the existing machine-learning methods, such as SVM (support vector machine) and KNN (k-nearest neighbor), can only handle vectors but not sequences. To meet the increasing demands and speed up the genome analyses, we have developed a new web server, called "representations of RNA sequences" (repRNA). Compared with the existing methods, repRNA is much more comprehensive, flexible and powerful, as reflected by the following facts: (1) it can generate 11 different modes of feature vectors for users to choose according to their investigation purposes; (2) it allows users to select the features from 22 built-in physicochemical properties and even those defined by users' own; (3) the resultant feature vectors and the secondary structures of the corresponding RNA sequences can be visualized. The repRNA web server is freely accessible to the public at http://bioinformatics.hitsz.edu.cn/repRNA/ .
Publication
Journal: Bioinformatics
July/18/2013
Abstract
BACKGROUND
The development of high-throughput sequencing technologies has enabled novel methods for detecting structural variants (SVs). Current methods are typically based on depth of coverage or pair-end mapping clusters. However, most of these only report an approximate location for each SV, rather than exact breakpoints.
RESULTS
We have developed pair-read informed split mapping (PRISM), a method that identifies SVs and their precise breakpoints from whole-genome resequencing data. PRISM uses a split-alignment approach informed by the mapping of paired-end reads, hence enabling breakpoint identification of multiple SV types, including arbitrary-sized inversions, deletions and tandem duplications. Comparisons to previous datasets and simulation experiments illustrate PRISM's high sensitivity, while PCR validations of PRISM results, including previously uncharacterized variants, indicate an overall precision of ~90%.
BACKGROUND
PRISM is freely available at http://compbio.cs.toronto.edu/prism.
Publication
Journal: Cell Discovery
July/26/2016
Abstract
MicroRNAs have an important role in bone homeostasis. However, the detailed mechanism of microRNA-mediated intercellular communication between bone cells remains elusive. Here, we report that osteoclasts secrete microRNA-enriched exosomes, by which miR-214 is transferred into osteoblasts to inhibit their function. In a coculture system, inhibition of exosome formation and secretion prevented miR-214 transportation. Exosomes specifically recognized osteoblasts through the interaction between ephrinA2 and EphA2. In osteoclast-specific miR-214 transgenic mice, exosomes were secreted into the serum, and miR-214 and ephrinA2 levels were elevated. Therefore, these exosomes have an inhibitory role in osteoblast activity. miR-214 and ephrinA2 levels in serum exosomes from osteoporotic patients and mice were upregulated substantially. These exosomes may significantly inhibit osteoblast activity. Inhibition of exosome secretion via Rab27a small interfering RNA prevented ovariectomized-induced osteoblast dysfunction in vivo. Taken together, these findings suggest that exosome-mediated transfer of microRNA plays an important role in the regulation of osteoblast activity. Circulating miR-214 in exosomes not only represents a biomarker for bone loss but could selectively regulate osteoblast function.
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