Integrative genome-wide analysis reveals cooperative regulation of alternative splicing by hnRNP proteins

Understanding how RNA binding proteins control the splicing code is fundamental to human biology and disease. Here, we present a comprehensive study to elucidate how heterogeneous nuclear ribonucleoparticle (hnRNP) proteins, among the most abundant RNA binding proteins, coordinate to regulate alternative pre-mRNA splicing (AS) in human cells. Using splicing-sensitive microarrays, crosslinking and immunoprecipitation coupled with high-throughput sequencing (CLIP-seq), and cDNA sequencing, we find that more than half of all AS events are regulated by multiple hnRNP proteins and that some combinations of hnRNP proteins exhibit significant synergy, whereas others act antagonistically. Our analyses reveal position-dependent RNA splicing maps, in vivo consensus binding sites, a surprising level of cross- and autoregulation among hnRNP proteins, and the coordinated regulation by hnRNP proteins of dozens of other RNA binding proteins and genes associated with cancer. Our findings define an unprecedented degree of complexity and compensatory relationships among hnRNP proteins and their splicing targets that likely confer robustness to cells.     

High-throughput flow cytometry-based assay to identify apoptosis-inducing proteins

After sequencing the human genome, the challenge ahead is to systematically analyze the functions and disease relation of the proteins encoded. Here the authors describe the application of a flow cytometry-based high-throughput assay to screen for apoptosis-activating proteins in transiently transfected cells. The assay is based on the detection of activated caspase-3 with a specific antibody, in cells overexpressing proteins tagged C- or N-terminally with yellow fluorescent protein. Fluorescence intensities are measured using a flow cytometer integrated with a high-throughput autosampler. The applicability of this screen has been tested in a pilot screen with 200 proteins. The candidate proteins were all verified in an independent microscopy-based nuclear fragmentation assay, finally resulting in the identification of 6 apoptosis inducers.     

Measuring and analyzing tissue specificity of human genes and protein complexes

Proteins and their interactions are essential for the survival of each human cell. Knowledge of their tissue occurrence is important for understanding biological processes. Therefore, we analyzed microarray and high-throughput RNA-sequencing data to identify tissue-specific and universally expressed genes. Gene expression data were used to investigate the presence of proteins, protein interactions and protein complexes in different tissues. Our comparison shows that the detection of tissue-specific genes and proteins strongly depends on the applied measurement technique. We found that microarrays are less sensitive for low expressed genes than high-throughput sequencing. Functional analyses based on microarray data are thus biased towards high expressed genes. This also means that previous biological findings based on microarrays might have to be re-examined using high-throughput sequencing results.     

Automated high-content live animal drug screening using C. elegans expressing the aggregation prone serpin α1-antitrypsin Z

The development of preclinical models amenable to live animal bioactive compound screening is an attractive approach to discovering effective pharmacological therapies for disorders caused by misfolded and aggregation-prone proteins. In general, however, live animal drug screening is labor and resource intensive, and has been hampered by the lack of robust assay designs and high throughput work-flows. Based on their small size, tissue transparency and ease of cultivation, the use of C. elegans should obviate many of the technical impediments associated with live animal drug screening. Moreover, their genetic tractability and accomplished record for providing insights into the molecular and cellular basis of human disease, should make C. elegans an ideal model system for in vivo drug discovery campaigns. The goal of this study was to determine whether C. elegans could be adapted to high-throughput and high-content drug screening strategies analogous to those developed for cell-based systems. Using transgenic animals expressing fluorescently-tagged proteins, we first developed a high-quality, high-throughput work-flow utilizing an automated fluorescence microscopy platform with integrated image acquisition and data analysis modules to qualitatively assess different biological processes including, growth, tissue development, cell viability and autophagy. We next adapted this technology to conduct a small molecule screen and identified compounds that altered the intracellular accumulation of the human aggregation prone mutant that causes liver disease in α1-antitrypsin deficiency. This study provides powerful validation for advancement in preclinical drug discovery campaigns by screening live C. elegans modeling α1-antitrypsin deficiency and other complex disease phenotypes on high-content imaging platforms.     

Identification of protein modifications using MS/MS de novo sequencing and the OpenSea alignment algorithm

Algorithms that can robustly identify post-translational protein modifications from mass spectrometry data are needed for data-mining and furthering biological interpretations. In this study, we determined that a mass-based alignment algorithm (OpenSea) for de novo sequencing results could identify post-translationally modified peptides in a high-throughput environment. A complex digest of proteins from human cataractous lens, a tissue containing a high abundance of modified proteins, was analyzed using two-dimensional liquid chromatography, and data was collected on both high and low mass accuracy instruments. The data were analyzed using automated de novo sequencing followed by OpenSea mass-based sequence alignment. A total of 80 modifications were detected, 36 of which were previously unreported in the lens. This demonstrates the potential to identify large numbers of known and previously unknown protein modifications in a given tissue using automated data processing algorithms such as OpenSea.     

New ligation-independent cloning vectors compatible with a high-throughput platform for parallel construct expression evaluation using baculovirus-infected insect cells

Biomedical research has undergone a major shift in emphasis over the past decade from characterizing the genomes of organisms to characterizing their proteomes. The high-throughput approaches that were successfully applied to sequencing of genomes, such as miniaturization and automation, have been adapted for high-throughput cloning and protein production. High-throughput platforms allow for a multi-construct, multi-parallel approach to expression optimization and construct evaluation. We describe here a series of baculovirus transfer and expression vectors that contain ligation-independent cloning regions originally designed for use in high-throughput Escherichia coli expression evaluation. These new vectors allow for parallel cloning of the same gene construct into a variety of baculovirus or E. coli expression vectors. A high-throughput platform for construct expression evaluation in baculovirus-infected insect cells was developed to utilize these vectors. Data from baculovirus infection expression trials for multiple constructs of two target protein systems relevant to the study of human diseases are presented. The target proteins exhibit a wide variation in behavior and illustrate the benefit of investigating multiple cell types, fusion partners and secretion signals in optimization of constructs and conditions for eukaryotic protein production.     

高通量测序数据的基因组拷贝数变异检测方法综述

拷贝数变异是指基因组中发生大片段的DNA序列的拷贝数增加或者减少。根据现有的研究可知,拷贝数变异是多种人类疾病的成因,与其发生与发展机制密切相关。高通量测序技术的出现为拷贝数变异检测提供了技术支持,在人类疾病研究、临床诊疗等领域,高通量测序技术已经成为主流的拷贝数变异检测技术。虽然不断有新的基于高通量测序技术的算法和软件被人们开发出来,但是准确率仍然不理想。本文全面地综述基于高通量测序数据的拷贝数变异检测方法,包括基于reads深度的方法、基于双末端映射的方法、基于拆分read的方法、基于从头拼接的方法以及基于上述4种方法的组合方法,深入探讨了每类不同方法的原理,代表性的软件工具以及每类方法适用的数据以及优缺点等,并展望未来的发展方向。     

单细胞转录组测序在药物成瘾研究中的应用

药物成瘾是复杂的中枢神经系统疾病，相关基础与临床研究均证实药物成瘾的神经机制及神经环路在成瘾行为形成的不同阶段逐渐发生改变。利用全基因组关联研究、全基因组测序、全外显子测序或高通量转录组测序等技术的组学研究对包括药物成瘾在内的精神疾病遗传的脆弱性进行了深入研究。上述单核苷酸多态性检测技术或测序技术主要预测疾病的遗传风险位点。然而，许多中枢神经系统疾病的发生与环境因素密切相关，而且在疾病发展的不同阶段，相关基因的表达存在脑区特异性的细胞异质性信息。因此，传统研究对发病机制的解释存在一定的局限性。单细胞转录组测序技术是针对单个细胞进行转录水平的测定，规避了传统测序对细胞群体平均转录水平检测的缺点，可以定量描述细胞异质性。近年来，单细胞转录测序技术在神经精神科学研究中的应用逐渐受到关注，本文总结了该技术在神经科学研究中的重要应用，并以药物成瘾为例，重点阐述说明其在中枢神经系统疾病中的应用价值。     

Commercial high-throughput sequencing and its applications in DNA analysis

DNA sequences can be used for the analysis of genetic variation and gene function. The high-throughput sequencing techniques that have been developed over the past three years can read as many as one billion bases per run, and are far less expensive than the traditional Sanger sequencing method. Therefore, the high-throughput sequencing has been applied extensively to genomic analyses, such as screening for mutations, construction of genomic methylation maps, and the study of DNA-protein interactions. Although they have only been available for a short period, high-throughput sequencing techniques are profoundly affecting many of the life sciences, and are opening out new potential avenues of research. With the highly-developed commercial high-throughput sequencing platforms, each laboratory has the opportunity to explore this research field. Therefore, in this paper, we have focused on commercially-popular high-throughput sequencing techniques and the ways in which they have been applied over the past three years.     

Proteomics for identifying mechanisms and biomarkers of drug resistance in cancer

A major problem in chemotherapy of cancer patients is drug resistance as well as unpredictable response to treatment. During chemotherapy, multiple alterations of genetics and epigenetics that contribute to chemoresistance take place, eventually impacting on disease outcome. A more complex picture of the mechanisms of drug resistance is now emerging through application of high-throughput proteomics technology. We have entered an exciting time where proteomics are being applied to characterize the mechanisms of drug resistance, and to identify biomarkers for predicting response to chemotherapy, thereby leading to personalized therapeutic strategies of cancer patients. Comparative proteomics have identified a large number of differentially expressed proteins associated with chemoresistance. Although roles and mechanisms of such proteins in chemoresistance need to be further proved, at least some of them may be potential biomarkers for predicting chemotherapeutic response. Herein, we review the recent advancements on proteomic investigation of chemoresistance in human cancer, and emphasize putative biomarkers for predicting chemotherapeutic response and possible mechanisms of chemoresistance identified through proteomic approaches. Suggested avenues for future work are discussed.     

Prediction of personalized drugs based on genetic variations provided by DNA sequencing technologies

Recent reports of death and illness caused by adverse drug reactions have boosted rational drug design research. It has been shown through sequencing of the entire human genome that human genetic variations play a key role in adverse reactions to drugs as well as in differences in the effectiveness of drug treatments. The advent of high-throughput DNA sequencing technologies with bioinformatics of system biology have allowed the easy identification of genetic variations and all other pharmacogenetic variants in a single assay, thus permitting truly personalized drug treatment. This would be particularly valuable for many patients with chronic diseases who must take many medications concurrently. In this review, we have focused on pharmacogenomics for the prediction of variable drug responses between individuals with relevant genetic variations through new DNA sequencing technologies and provided directions for personalized drug therapy in the future.     

Targeted Genome-Wide Enrichment of Functional Regions

Only a small fraction of large genomes such as that of the human contains the functional regions such as the exons, promoters, and polyA sites. A platform technique for selective enrichment of functional genomic regions will enable several next-generation sequencing applications that include the discovery of causal mutations for disease and drug response. Here, we describe a powerful platform technique, termed “functional genomic fingerprinting” (FGF), for the multiplexed genomewide isolation and analysis of targeted regions such as the exome, promoterome, or exon splice enhancers. The technique employs a fixed part of a uniquely designed Fixed-Randomized primer, while the randomized part contains all the possible sequence permutations. The Fixed-Randomized primers bind with full sequence complementarity at multiple sites where the fixed sequence (such as the splice signals) occurs within the genome, and multiplex amplify many regions bounded by the fixed sequences (e.g., exons). Notably, validation of this technique using cardiac myosin binding protein-C (MYBPC3) gene as an example strongly supports the application and efficacy of this method. Further, assisted by genomewide computational analyses of such sequences, the FGF technique may provide a unique platform for high-throughput sample production and analysis of targeted genomic regions by the next-generation sequencing techniques, with powerful applications in discovering disease and drug response genes.     

A genome-scale, constraint-based approach to systems biology of human metabolism

The recent sequencing and annotation of the human genome enables a new era in biomedicine that will be based on an interdisciplinary, systemic approach to the elucidation and treatment of human disease. Reconstruction of genome-scale metabolic networks is an important part of this approach since networks represent the integration of diverse biological data such as genome annotations, high-throughput data, and legacy biochemical knowledge. This article will describe Homo sapiens Recon 1, a functionally tested, genome-scale reconstruction of human cellular metabolism, and its capabilities for facilitating the understanding of physiological and disease metabolic states.     

Single-molecule detection as an approach to rapid DNA sequencing.

Current sequencing technologies are insufficient to cope with large-scale projects such as sequencing the human genome and genomes of model organisms. In addition, as genetic lesions associated with specific human diseases are identified, DNA sequencing will be used increasingly for clinical applications. Thus, new approaches are needed to combine high-throughput with accuracy for both research and diagnostic purposes. A novel technology based on detection of individual fluorescent nucleotides in a flowing sample stream is under development.     

Some remarks on protein attribute prediction and pseudo amino acid composition

With the accomplishment of human genome sequencing, the number of sequence-known proteins has increased explosively. In contrast, the pace is much slower in determining their biological attributes. As a consequence, the gap between sequence-known proteins and attribute-known proteins has become increasingly large. The unbalanced situation, which has critically limited our ability to timely utilize the newly discovered proteins for basic research and drug development, has called for developing computational methods or high-throughput automated tools for fast and reliably identifying various attributes of uncharacterized proteins based on their sequence information alone. Actually, during the last two decades or so, many methods in this regard have been established in hope to bridge such a gap. In the course of developing these methods, the following things were often needed to consider: (1) benchmark dataset construction, (2) protein sample formulation, (3) operating algorithm (or engine), (4) anticipated accuracy, and (5) web-server establishment. In this review, we are to discuss each of the five procedures, with a special focus on the introduction of pseudo amino acid composition (PseAAC), its different modes and applications as well as its recent development, particularly in how to use the general formulation of PseAAC to reflect the core and essential features that are deeply hidden in complicated protein sequences.     

RNA干扰文库及其在功能基因组学研究中的应用

随着人类基因组大规模测序的完成,下一步的挑战是了解每一个基因的功能 . RNA 干扰文库为大规模基因功能筛选提供了可能 . 虽然用于线虫等模式生物的 RNAi 文库,已经证明是大规模基因功能筛选的有效方法,但这些文库不能用于高等动物的细胞 . 自 2003 年以来,用于人的细胞和哺乳动物细胞的 RNAi 文库取得了突破,相继出现构建已知基因 RNAi 文库和构建随机 RNAi 文库的报道,并成功地应用于大规模基因功能的筛选 . RNAi 文库作为一种简单、高效、大规模、高通量的功能基因组学研究的工具,将在基因功能研究、发现新的药物靶基因、发现疾病相关基因等方面有广阔的应用前景 .     

Protein and peptide array analysis of autoimmune disease

Molecular cloning, sequencing of the human genome, and other major advances in biomedical research have contributed substantially to our understanding of autoimmune disease. Nevertheless, to date, such advances have failed to reveal the etiology of or yield curative therapies for autoimmune disease. New approaches are needed. Proteomics, the large-scale study of expression and function of proteins that compose our tissues and mediate disease, represents a powerful and promising strategy. We developed protein and peptide arrays to profile autoantibody responses in autoimmune disease. Protein and peptide array analysis of autoimmune samples is revealing human and pathogen proteins involved in initiation and perpetuation of autoimmunity. Proteomic determination of autoantibody profiles can be utilized for diagnosis, prognostication, and guiding tolerizing therapy for autoimmune disease.