整合分析多组学数据筛选疾病靶点的精准医学策略

随着高通量测序技术的不断发展与完善,对于不同层次和类型的生物组学数据的获取及分析方法也日趋成熟与完善。基于单组学数据的疾病研究已经发现了诸多新的疾病相关因子,而整合多组学数据研究疾病靶点的工作方兴未艾。生命体是一个复杂的调控系统,疾病的发生与发展涉及基因变异、表观遗传改变、基因表达异常以及信号通路紊乱等诸多层次的复杂调控机制,利用单一组学数据分析致病因子的局限性愈发显著。通过对多种层次和来源的高通量组学数据的整合分析,系统地研究临床发病机理、确定最佳疾病靶点已经成为精准医学研究的重要发展方向,将为疾病研究提供新的思路,并对疾病的早期诊断、个体化治疗和指导用药等提供新的理论依据。本文详细介绍了基因组、转录组和表观组等系统组学研究在疾病靶点筛选方面出现的新技术手段和研究进展,并对它们之间的整合分析新策略和优势进行了讨论。     

Molecular network strategy in multi-omics and mass spectrometry imaging

Human physiological activities and pathological changes arise from the coordinated interactions of multiple molecules. Mass spectrometry (MS)-based multi-omics and MS imaging (MSI)-based spatial omics are powerful methods used to investigate molecular information related to the phenotype of interest from homogenated or sliced samples, including the qualitative, relative quantitative and spatial distributions. Molecular network strategy provides efficient methods to help us understand and mine the biological patterns behind the phenotypic data. It illustrates and combines various relationships between molecules, and further performs the molecule identification and biological interpretation. Here, we describe the recent advances of network-based analysis and its applications for different biological processes, such as, obesity, central nervous system diseases, and environmental toxicology.     

A survey on biomarker identification based on molecular networks

Background: Identifying biomarkers for accurate diagnosis and prognosis of diseases is important for the prevention of disease development. The molecular networks that describe the functional relationships among molecules provide a global view of the complex biological systems. With the molecular networks, the molecular mechanisms underlying diseases can be unveiled, which helps identify biomarkers in a systematic way. Results: In this survey, we report the recent progress on identifying biomarkers based on the topology of molecular networks, and we categorize those biomarkers into three groups, including node biomarkers, edge biomarkers and network biomarkers. These distinct types of biomarkers can be detected under different conditions depending on the data available. Conclusions: The biomarkers identified based on molecular networks can provide more accurate diagnosis and prognosis. The pros and cons of different types of biomarkers as well as future directions to improve the methods for identifying biomarkers are also discussed.     

Understanding biological functions through molecular networks

The completion of genome sequences and subsequent high-throughput mapping of molecular networks have allowed us to study biology from the network perspective. Experimental, statistical and mathematical modeling approaches have been employed to study the structure, function and dynamics of molecular networks, and begin to reveal important links of various network properties to the functions of the biological systems. In agreement with these functional links, evolutionary selection of a network is apparently based on the function, rather than directly on the structure of the network. Dynamic modularity is one of the prominent features of molecular networks. Taking advantage of such a feature may simplify network-based biological studies through construction of process-specific modular networks and provide functional and mechanistic insights linking genotypic variations to complex traits or diseases, which is likely to be a key approach in the next wave of understanding complex human diseases. With the development of ready-to-use network analysis and modeling tools the networks approaches will be infused into everyday biological research in the near future.     

A systems-level approach for metabolic engineering of yeast cell factories

The generation of novel yeast cell factories for production of high-value industrial biotechnological products relies on three metabolic engineering principles: design, construction, and analysis. In the last two decades, strong efforts have been put on developing faster and more efficient strategies and/or technologies for each one of these principles. For design and construction, three major strategies are described in this review: (1) rational metabolic engineering; (2) inverse metabolic engineering; and (3) evolutionary strategies. Independent of the selected strategy, the process of designing yeast strains involves five decision points: (1) choice of product, (2) choice of chassis, (3) identification of target genes, (4) regulating the expression level of target genes, and (5) network balancing of the target genes. At the construction level, several molecular biology tools have been developed through the concept of synthetic biology and applied for the generation of novel, engineered yeast strains. For comprehensive and quantitative analysis of constructed strains, systems biology tools are commonly used and using a multi-omics approach. Key information about the biological system can be revealed, for example, identification of genetic regulatory mechanisms and competitive pathways, thereby assisting the in silico design of metabolic engineering strategies for improving strain performance. Examples on how systems and synthetic biology brought yeast metabolic engineering closer to industrial biotechnology are described in this review, and these examples should demonstrate the potential of a systems-level approach for fast and efficient generation of yeast cell factories.     

Bayesian Network Reconstruction Using Systems Genetics Data: Comparison of MCMC Methods

Reconstructing biological networks using high-throughput technologies has the potential to produce condition-specific interactomes. But are these reconstructed networks a reliable source of biological interactions? Do some network inference methods offer dramatically improved performance on certain types of networks? To facilitate the use of network inference methods in systems biology, we report a large-scale simulation study comparing the ability of Markov chain Monte Carlo (MCMC) samplers to reverse engineer Bayesian networks. The MCMC samplers we investigated included foundational and state-of-the-art Metropolis–Hastings and Gibbs sampling approaches, as well as novel samplers we have designed. To enable a comprehensive comparison, we simulated gene expression and genetics data from known network structures under a range of biologically plausible scenarios. We examine the overall quality of network inference via different methods, as well as how their performance is affected by network characteristics. Our simulations reveal that network size, edge density, and strength of gene-to-gene signaling are major parameters that differentiate the performance of various samplers. Specifically, more recent samplers including our novel methods outperform traditional samplers for highly interconnected large networks with strong gene-to-gene signaling. Our newly developed samplers show comparable or superior performance to the top existing methods. Moreover, this performance gain is strongest in networks with biologically oriented topology, which indicates that our novel samplers are suitable for inferring biological networks. The performance of MCMC samplers in this simulation framework can guide the choice of methods for network reconstruction using systems genetics data.     

Advances in quantitative proteomics

Large-scale protein quantification has become a major proteomics application in many areas of biological and medical research. During the past years, different techniques have been developed, including gel-based such as differential in-gel electrophoresis (DIGE) and liquid chromatography-based such as isotope labeling and label-free quantification. These quantitative proteomics tools hold significant promise for biomarker discovery, diagnostic and therapeutic applications. They are also important for research in functional genomics and systems biology towards basic understanding of molecular networks and pathway interactions. In this review, we summarize current technologies in quantitative proteomics and discuss recent applications of the technologies.     

Network medicine

To more effectively target complex diseases like cancer, diabetes and schizophrenia, we may need to rethink our strategies for drug development and the selection of molecular targets for pharmacological treatments. Here, we discuss the potential use of protein signaling networks as the targets for new therapeutic intervention. We argue that by targeting the architecture of aberrant signaling networks associated with cancer and other diseases new therapeutic strategies can be implemented. Transforming medicine into a network driven endeavour will require quantitative measurements of cell signaling processes; we will describe how this may be performed and combined with new algorithms to predict the trajectories taken by a cellular system either in time or through disease states. We term this approach, network medicine.     

Application of network methods for understanding evolutionary dynamics in discrete habitats

In populations occupying discrete habitat patches, gene flow between habitat patches may form an intricate population structure. In such structures, the evolutionary dynamics resulting from interaction of gene‐flow patterns with other evolutionary forces may be exceedingly complex. Several models describing gene flow between discrete habitat patches have been presented in the population‐genetics literature; however, these models have usually addressed relatively simple settings of habitable patches and have stopped short of providing general methodologies for addressing nontrivial gene‐flow patterns. In the last decades, network theory – a branch of discrete mathematics concerned with complex interactions between discrete elements – has been applied to address several problems in population genetics by modelling gene flow between habitat patches using networks. Here, we present the idea and concepts of modelling complex gene flows in discrete habitats using networks. Our goal is to raise awareness to existing network theory applications in molecular ecology studies, as well as to outline the current and potential contribution of network methods to the understanding of evolutionary dynamics in discrete habitats. We review the main branches of network theory that have been, or that we believe potentially could be, applied to population genetics and molecular ecology research. We address applications to theoretical modelling and to empirical population‐genetic studies, and we highlight future directions for extending the integration of network science with molecular ecology.     

Yeast proteomics and protein microarrays

Our understanding of biological processes as well as human diseases has improved greatly thanks to studies on model organisms such as yeast. The power of scientific approaches with yeast lies in its relatively simple genome, its facile classical and molecular genetics, as well as the evolutionary conservation of many basic biological mechanisms. However, even in this simple model organism, systems biology studies, especially proteomic studies had been an intimidating task. During the past decade, powerful high-throughput technologies in proteomic research have been developed for yeast including protein microarray technology. The protein microarray technology allows the interrogation of protein–protein, protein–DNA, protein–small molecule interaction networks as well as post-translational modification networks in a large-scale, high-throughput manner. With this technology, many groundbreaking findings have been established in studies with the budding yeast Saccharomyces cerevisiae, most of which could have been unachievable with traditional approaches. Discovery of these networks has profound impact on explicating biological processes with a proteomic point of view, which may lead to a better understanding of normal biological phenomena as well as various human diseases.     

From protein interaction networks to novel therapeutic strategies

Cellular mechanisms that sustain health or contribute to disease emerge mostly from the complex interplay among various molecular entities. To understand the underlying relationships between genotype, environment and phenotype, one has to consider the intricate and nonsequential interaction patterns formed between the different sets of cellular players. Biological networks capture a variety of molecular interactions and thus provide an excellent opportunity to consider physiological characteristics of individual molecules within their cellular context. In particular, the concept of network biology and its applications contributed largely to recent advances in biomedical research. In this review, we show (i) how biological networks, i.e., protein-protein interaction networks, facilitate the understanding of pathogenic mechanisms that trigger the onset and progression of diseases and (ii) how this knowledge can be translated into effective diagnostic and therapeutic strategies. In particular, we focus on the impact of network pharmacological concepts that go beyond the classical view on individual drugs and targets aiming for combinational therapies with improved clinical efficacy and reduced safety risks.     

Microfluidic tools for developmental studies of small model organisms –nematodes,fruit flies,and zebrafish

Studying the genetics of development with small model organisms such as the zebrafish (Danio Rerio), the fruit fly (Drosophila melanogaster), and the soil-dwelling nematode (Caenorhabditis elegans), provide unique opportunities for understanding related processes and diseases in humans. These model organisms also have potential for use in drug discovery and toxicity-screening applications. There have been sweeping developments in microfabrication and microfluidic technologies for manipulating and imaging small objects, including small model organisms, which allow high-throughput quantitative biological studies. Here, we review recent progress in microfluidic tools able to manipulate small organisms and project future directions and applications of these techniques and technologies.     

Interpretation of network-based integration from multi-omics longitudinal data

Multi-omics integration is key to fully understand complex biological processes in an holistic manner. Furthermore, multi-omics combined with new longitudinal experimental design can unreveal dynamic relationships between omics layers and identify key players or interactions in system development or complex phenotypes. However, integration methods have to address various experimental designs and do not guarantee interpretable biological results. The new challenge of multi-omics integration is to solve interpretation and unlock the hidden knowledge within the multi-omics data. In this paper, we go beyond integration and propose a generic approach to face the interpretation problem. From multi-omics longitudinal data, this approach builds and explores hybrid multi-omics networks composed of both inferred and known relationships within and between omics layers. With smart node labelling and propagation analysis, this approach predicts regulation mechanisms and multi-omics functional modules. We applied the method on 3 case studies with various multi-omics designs and identified new multi-layer interactions involved in key biological functions that could not be revealed with single omics analysis. Moreover, we highlighted interplay in the kinetics that could help identify novel biological mechanisms. This method is available as an R package netOmics to readily suit any application.