Functional Genomics and Network Biology

In recent years, major advances have been made in transcriptomics, metabolomics, proteomics, and particularly population genetics. More than 850 genome-wide association studies (GWAS) were published by the end of 2010, now covering a vast number of diseases and phenotypes and often incorporating tens of thousands of subjects or, in some cases, more than 100 000 subjects, increasing statistical power to identify and replicate contributing loci. Sequencing throughput advances and decreasing costs per sample have allowed the application of genome-wide genotyping and sequencing technology to larger DNA samples as well as transcriptome and epigenome features. This year was marked not only by continuing progress with GWAS, sequencing, and copy number variation studies but also by follow-up of GWAS and other findings with functional studies that uncover new molecular pathways contributing to disease and pave the way for future clinical applications. The American Heart Association Functional Genomics and Translational Biology Interdisciplinary Council (http://www.americanheart.org/presenter.jhtml?identifier=3016539) provides a forum for a multidisciplinary group of volunteers committed to making a substantial contribution to reduce the burden of heart disease and stroke through genetics, genomics, proteomics, and metabolomics research and translational science. With input from the Early Career Committee of the Council on Functional Genomics and Translational Biology, we considered many papers and selected 10 outstanding manuscript groups (15 articles) published in 2010 that reflect these advances, and we briefly summarize each below in chronological order.

The main focus of this thesis is the use of high-throughput sequencing technologies in functional genomics (in particular in the form of ChIP-seq, chromatin immunoprecipitation coupled with sequencing, and RNA-seq) and the study of the structure and regulation of transcriptomes. Some parts of it are of a more methodological nature while others describe the application of these functional genomic tools to address various biological problems. A significant part of the research presented here was conducted as part of the ENCODE (ENCyclopedia Of DNA Elements) Project. The first part of the thesis focuses on the structure and diversity of the human transcriptome. Chapter 1 contains an analysis of the diversity of the human polyadenylated transcriptome based on RNA-seq data generated for the ENCODE Project. Chapter 2 presents a simulation-based examination of the performance of some of the most popular computational tools used to assemble and quantify transcriptomes. Chapter 3 includes a study of variation in gene expression, alternative splicing and allelic expression bias on the single-cell level and on a genome-wide scale in human lymphoblastoid cells; it also brings forward a number of critical to the practice of single-cell RNA-seq measurements methodological considerations. The second part presents several studies applying functional genomic tools to the study of the regulatory biology of organellar genomes, primarily in mammals but also in plants. Chapter 5 contains an analysis of the occupancy of the human mitochondrial genome by TFAM, an important structural and regulatory protein in mitochondria, using ChIP-seq. In Chapter 6, the mitochondrial DNA occupancy of the TFB2M transcriptional regulator, the MTERF termination factor, and the mitochondrial RNA and DNA polymerases is characterized. Chapter 7 consists of an investigation into the curious phenomenon of the physical association of nuclear transcription factors with mitochondrial DNA, based on the diverse collections of transcription factor ChIP-seq datasets generated by the ENCODE, mouseENCODE and modENCODE consortia. In Chapter 8 this line of research is further extended to existing publicly available ChIP-seq datasets in plants and their mitochondrial and plastid genomes. The third part is dedicated to the analytical and experimental practice of ChIP-seq. As part of the ENCODE Project, a set of metrics for assessing the quality of ChIP-seq experiments was developed, and the results of this activity are presented in Chapter 9. These metrics were later used to carry out a global analysis of ChIP-seq quality in the published literature (Chapter 10). In Chapter 11, the development and initial application of an automated robotic ChIP-seq (in which these metrics also played a major role) is presented. The fourth part presents the results of some additional projects the author has been involved in, including the study of the role of the Piwi protein in the transcriptional regulation of transposon expression in Drosophila (Chapter 12), and the use of single-cell RNA-seq to characterize the heterogeneity of gene expression during cellular reprogramming (Chapter 13). The last part of the thesis provides a review of the results of the ENCODE Project and the interpretation of the complexity of the biochemical activity exhibited by mammalian genomes that they have revealed (Chapters 15 and 16), an overview of the expected in the near future technical developments and their impact on the field of functional genomics (Chapter 14), and a discussion of some so far insufficiently explored research areas, the future study of which will, in the opinion of the author, provide deep insights into many fundamental but not yet completely answered questions about the transcriptional biology of eukaryotes and its regulation.

Biophysical, chemical, and nanoscience approaches to the study of nuclear structure and activity have been developing recently and hold considerable promise. A selection of fundamental problems in genome organization and function are reviewed and discussed in the context of these new perspectives and approaches. Advancing these concepts will require coordinated networks of physicists, chemists, and materials scientists collaborating with cell, developmental, and genome biologists.

With the development of bioinformatics, genomics and high-throughput technology provides a lot of life system components ( such as proteins ) and the relationship between them in the data, these complex biological networks contain with rich life knowledge of moving mechanism of system, mining these covert knowledge become the one of the main task in the post genome era. However, currently the research of the biological network diagram structure has focused on static analysis and massive small pattern analysis, there is no effective dynamic analysis models and algorithms to meet the mass of structure dynamic analysis and data mining. In this paper, based on the existing graph analysis of data mining method, through the construction of a set of processing biological network analysis model to dynamic analysis and processing graph structure, improve the mass of data analysis and processing efficiency. The results of the project has wide application prospect, and can be used for biological network analysis, as well as chemistry, Internet and related fields.

In the last two decades, our understanding of the molecular mechanisms within the cell has witnessed a great leap forward. For the most part this is due to the fast innovation of the genomic measurements technologies and wide spread usage of computational methods which enables knowledge extraction from the massive datasets produced by these measurements. A notable example of a field that has substantially benefitted from this progress is cancer patient outcome prediction, in which the aim is to predict patient prognosis from common clinical variables such as tumor size, age or histological parameters. With the application of machine learning methods to gene expression profiles of the tumor a major improvement of the prediction accuracy could be realized. These models are later succeeded by Network based Outcome Predictors (NOP) that consider the cellular wiring diagram of cell in the model to identify stable and relevant markers that can accurately estimate outcome of patients. Problematically, after a decade of research in this area, NOPs did not find extensive application compared to the classical models due to contradicting reports regarding their performance, stability and relevance of markers in the literature. In this thesis, we introduce a new NOP - called FERAL - that alleviates several fundamental issues in state-of-the-art NOPs which prevented these models to reach the optimal prediction performance, stability and marker relevance. We furthermore demonstrate that generic biological networks do not contain sufficiently informative interactions to truly aid NOP. We therefore infer a phenotype-specific network called SyNet which connects pairs of genes that together achieve patient outcome prediction performance beyond what is attainable by individually genes. We show that a NOP that use identical gene expression datasets, yields superior performance merely by considering groups of genes suggested by SyNet. We, moreover, show that model performance is severely reduced if nodes in SyNet are shuffled, which confirms that also the links in SyNet are relevant to outcome prediction. An important limitation of current biological networks is that they are restricted to pairwise interactions. We show that higher order interactions between functional elements in the cell are relevant in outcome prediction. We later introduce a novel genomics method called Multi-Contact 4C (MC-4C) to measure and investigate multi-way interactions between functional elements. In contrast to existing methods, MC-4C exploits long-read 3rd generation sequencing technologies and detects higher order interactions that occur in a region of interest at the level of a single allele. We further devise a well-founded statistical model that is required for significance estimation of observed interactions. UsingMC-4C, we experimentally confirm a 26 years old hypothesis regarding the looping and co-localization of enhancers in the O -globin region in the mouse genome. Additionally, we provide the first experimental explanation for the “vermicelli” phenomenon that was observed through microscopic inspection of cells depleted of WAPL (the element responsible for unwinding of loops in mammalian cells). Therefore, targeted multi-way conformation analysis methods like MC-4C promise to uncover how the multitude of regulatory sequences and genes coordinate their activity in the spatial context of the genome.

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

Genomics is a field of study that is rapidly transforming many areas of biological and biomedical research which has enabled the transition from sequential studies of single genes to more ecological approach, involving the simultaneous study of many components and their interactions with the environment from pathways, through cell tissues to whole organisms and communities. Genomics application areas include clinical diagnostics, agro biotechnology, environmental biotechnology and pharmacogenomics. The focus of most genome research is on the nuclear genome, though mitochondrial genomes have been extremely useful for the identification of fish species and populations. Marine microbial assemblages are diverse and unique and the challenge is to discover what functions are played by these microorganisms. To provide adequate tools for marine biologists, therefore, one important aim will be to develop genomic approaches, such as whole genome sequencing and functional genomics, for key species across the evolutionary tree of marine organisms. Genomics is a highly dynamic research field. Hence, rapid developments in genomics can afford new opportunities for applications in marine environment, particularly in the areas of Fish genome resources conservation and genetic enhancement.

Objective To investigate the fibrosis-associated genes, especially those transforming growth factor-β(TGF-β) associated, expression in the tissue of extensive fibrotic kidney resulting from severe by-dronephrosis. Methods Compared 12 extensive fibrotic kidneys resulting from severe hydronephrosis with 6 normal renal tissues using PCR array. All samples were collected from our hospital from Aug. 2005 to Dec. 2006. The data were treated with △△Ct method. When 2△△Ct≤<0.5 or≥2, the difference was considered sig-nificant. Results A total of 49 differential genes expressed in the fibrotic renal tissues were screened out, of which, 25 were up-regulated and 24 were down-regulated. The differential genes included members and receptors of TGF-β super family and bone morphogenetic protein(BMP) family, target molecule of TGF-β or BMP signal pathway and several regulators. Conclusions Through investigating with PCR array, we certifi-cated the positive effect of Nodal, GSC and IGFI to renal fibrosis. And we suppose that TGF-β play a sec-ondary role in renal fibrosis, while BMP family and Leftyl work in a functional genome in the regulating of renal fibrosis. Key words: hydronephrosis; renal fibrosis; gene chip; functional genomics

Data input module for Birth Defects Systems Manager

BackgroundSince the full genome sequences of Saccharomyces cerevisiae were released in 1996, genome sequences of over 90 fungal species have become publicly available. The heterogeneous formats of genome sequences archived in different sequencing centers hampered the integration of the data for efficient and comprehensive comparative analyses. The Comparative Fungal Genomics Platform (CFGP) was developed to archive these data via a single standardized format that can support multifaceted and integrated analyses of the data. To facilitate efficient data visualization and utilization within and across species based on the architecture of CFGP and associated databases, a new genome browser was needed.ResultsThe Seoul National University Genome Browser (SNUGB) integrates various types of genomic information derived from 98 fungal/oomycete (137 datasets) and 34 plant and animal (38 datasets) species, graphically presents germane features and properties of each genome, and supports comparison between genomes. The SNUGB provides three different forms of the data presentation interface, including diagram, table, and text, and six different display options to support visualization and utilization of the stored information. Information for individual species can be quickly accessed via a new tool named the taxonomy browser. In addition, SNUGB offers four useful data annotation/analysis functions, including 'BLAST annotation.' The modular design of SNUGB makes its adoption to support other comparative genomic platforms easy and facilitates continuous expansion.ConclusionThe SNUGB serves as a powerful platform supporting comparative and functional genomics within the fungal kingdom and also across other kingdoms. All data and functions are available at the web site .

BackgroundPathway analysis has been widely used to gain insight into essential mechanisms of the response to myocardial infarction (MI). Currently, there exist multiple pathway databases that organize molecular datasets and manually curate pathway maps for biological interpretation at varying forms of organization. However, inconsistencies among different databases in pathway descriptions, frequently due to conflicting results in the literature, can generate incorrect interpretations. Furthermore, although pathway analysis software provides detailed images of interactions among molecules, it does not exhibit how pathways interact with one another or with other biological processes under specific conditions.MethodsWe propose a novel method to standardize descriptions of enriched pathways for a set of genes/proteins using Gene Ontology terms. We used this method to examine the relationships among pathways and biological processes for a set of condition-specific genes/proteins, represented as a functional biological pathway-process network. We applied this algorithm to a set of 613 MI-specific proteins we previously identified.ResultsA total of 96 pathways from Biocarta, KEGG, and Reactome, and 448 Gene Ontology Biological Processes were enriched with these 613 proteins. The pathways were represented as Boolean functions of biological processes, delivering an interactive scheme to organize enriched information with an emphasis on involvement of biological processes in pathways. We extracted a network focusing on MI to demonstrate that tyrosine phosphorylation of Signal Transducer and Activator of Transcription (STAT) protein, positive regulation of collagen metabolic process, coagulation, and positive/negative regulation of blood coagulation have immediate impacts on the MI response.ConclusionsOur method organized biological processes and pathways in an unbiased approach to provide an intuitive way to identify biological properties of pathways under specific conditions. Pathways from different databases have similar descriptions yet diverse biological processes, indicating variation in their ability to share similar functional characteristics. The coverages of pathways can be expanded with the incorporation of more biological processes, predicting involvement of protein members in pathways. Further, detailed analyses of the functional biological pathway-process network will allow researchers and scientists to explore critical routes in biological systems in the progression of disease.

The precision medicine paradigm is centered on therapies targeted to particular molecular entities that will elicit an anticipated and controlled therapeutic response. However, genetic alterations in the drug targets themselves or in genes whose products interact with the targets can affect how well a drug actually works for an individual patient. To better understand the effects of targeted therapies in patients, we need software tools capable of simultaneously visualizing patient-specific variations and drug targets in their biological context. This context can be provided using pathways, which are process-oriented representations of biological reactions, or biological networks, which represent pathway-spanning interactions among genes, proteins, and other biological entities. To address this need, we have recently enhanced the Reactome Cytoscape app, ReactomeFIViz, to assist researchers in visualizing and modeling drug and target interactions. ReactomeFIViz integrates drug-target interaction information with high quality manually curated pathways and a genome-wide human functional interaction network. Both the pathways and the functional interaction network are provided by Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of these new features to the visualization of drugs in the contexts of pathways and networks. Complementing previous features in ReactomeFIViz, these new features enable researchers to ask focused questions about targeted therapies, such as drug sensitivity for patients with different mutation profiles, using a pathway or network perspective.

There have been numerous attempts to derive general models for the structure and function of resource delivery networks in biology. Such theories typically predict the quantitative structure of vascular networks across scales. For example, fractal branching models of plant structure predict that the network dimensions within plant stems or leaves should be scale-free. However, very few empirical examples of such networks are available with which to evaluate such hypotheses. Here, we apply recently developed leaf network extraction software to a global leaf dataset. We find that leaf networks are neither entirely scale-free nor governed entirely by a characteristic scale. Indeed, we find many network properties, such as vein length distributions, which are governed by characteristic scales, and other network properties, notably vein diameter distributions, which are typified by power-law behaviour. Our findings suggest that theories of network structure will remain incomplete until they address the multiple constraints on network architecture.

SUMMARYProteins that fail to correctly fold or assemble into oligomeric complexes in the endoplasmic reticulum (ER) are degraded by a ubiquitin and proteasome dependent process known as ER-associated degradation (ERAD). Although many individual components of the ERAD system have been identified, how these proteins are organised into a functional network that coordinates recognition, ubiquitination, and dislocation of substrates across the ER membrane is not well understood. We have investigated the functional organisation of the mammalian ERAD system using a systems-level strategy that integrates proteomics, functional genomics, and the transcriptional response to ER stress. This analysis supports an adaptive organisation for the mammalian ERAD machinery and reveals a number of metazoan-specific genes not previously linked to ERAD.

The Erhualian pig, originally distributed in the Taihu area, is well known for its universally high fertility. Previous studies have found that high ovulation numbers, low embryo mortality and high uterine volumes are important physiological characteristics underlying the high prolificacy of the Erhualian pig. Although candidate genes such as follicle-stimulating hormone β (FSHβ) and several quantitative trait loci (QTLs) on chromosome 2, 6, 7, 8, 12, 13 and 15 have been reported to affect the litter size in the Erhualian pig, the key genes related to high prolificacy remain poorly understood. In this review, we summarize the recent research progress in the physiological and genetic mechanisms underlying the high prolificacy of the Erhualian pig. First we review the role of high ovulation numbers, low embryo mortality rates and high uterine volumes in the formation of the high litter size in the Erhualian pig. Then we summarize candidate genes and QTLs for the high litter size detected by classical strategies, as well as by genomic strategies. Moreover, we describe the methods to investigate the causative genes of the high prolificacy through integrative analysis of multi-omics data including genomics, transcriptomics, proteomics and functional genomics. This review will provide insights to understand the molecular basis of the high prolificacy in the Erhualian pig.