Using small molecules to study big questions in cellular microbiology

High-throughput screening of small molecules is used extensively in pharmaceutical settings for the purpose of drug discovery. In the case of antimicrobials, this involves the identification of small molecules that are significantly more toxic to the microbe than to the host. Only a small percentage of the small molecules identified in these screens have been studied in sufficient detail to explain the molecular basis of their antimicrobial effect. Rarer still are small molecule screens undertaken with the explicit goal of learning more about the biology of a particular microbe or the mechanism of its interaction with its host. Recent technological advances in small molecule synthesis and high-throughput screening have made such mechanism-directed small molecule approaches a powerful and accessible experimental option. In this article, we provide an overview of the methods and technical requirements and we discuss the potential of small molecule approaches to address important and often otherwise experimentally intractable problems in cellular microbiology.     

Principles and strategies for developing network models in cancer

The flood of genome-wide data generated by high-throughput technologies currently provides biologists with an unprecedented opportunity: to manipulate, query, and reconstruct functional molecular networks of cells. Here, we outline three underlying principles and six strategies to infer network models from genomic data. Then, using cancer as an example, we describe experimental and computational approaches to infer "differential" networks that can identify genes and processes driving disease phenotypes. In conclusion, we discuss how a network-level understanding of cancer can be used to predict drug response and guide therapeutics.     

Harnessing plant viruses in the metagenomics era: from the development of infectious clones to applications

Recent metagenomic studies which focused on virus characterization in the entire plant environment have revealed a remarkable viral diversity in plants. The exponential discovery of viruses also requires the concomitant implementation of high-throughput methods to perform their functional characterization. Despite several limitations, the development of viral infectious clones remains a method of choice to understand virus biology, their role in the phytobiome, and plant resilience. Here, we review the latest approaches for efficient characterization of plant viruses and technical advances built on high-throughput sequencing and synthetic biology to streamline assembly of viral infectious clones. We then discuss the applications of plant viral vectors in fundamental and applied plant research as well as their technical and regulatory limitations, and we propose strategies for their safer field applications.     

Synthetic biology enabling access to designer polyketides

The full potential of polyketide discovery has yet to be reached owing to a lack of suitable technologies and knowledge required to advance engineering of polyketide biosynthesis. Recent investigations on the discovery, enhancement, and non-natural use of these biosynthetic gene clusters via computational biology, metabolic engineering, structural biology, and enzymology-guided approaches have facilitated improved access to designer polyketides. Here, we discuss recent successes in gene cluster discovery, host strain engineering, precursor-directed biosynthesis, combinatorial biosynthesis, polyketide tailoring, and high-throughput synthetic biology, as well as challenges and outlooks for rapidly generating useful target polyketides.     

Application of Bayesian networks on large-scale biological data

The investigation of the interplay between genes, proteins, metabolites and diseases plays a central role in molecular and cellular biology. Whole genome sequencing has made it possible to examine the behavior of all the genes in a genome by high-throughput experimental techniques and to pinpoint molecular interactions on a genome-wide scale, which form the backbone of systems biology. In particular, Bayesian network (BN) is a powerful tool for the ab-initial identification of causal and non-causal relationships between biological factors directly from experimental data. However, scalability is a crucial issue when we try to apply BNs to infer such interactions. In this paper, we not only introduce the Bayesian network formalism and its applications in systems biology, but also review recent technical developments for scaling up or speeding up the structural learning of BNs, which is important for the discovery of causal knowledge from large-scale biological datasets. Specifically, we highlight the basic idea, relative pros and cons of each technique and discuss possible ways to combine different algorithms towards making BN learning more accurate and much faster.     

Plant systems biology: insights,advances and challenges

Plants dwelling at the base of biological food chain are of fundamental significance in providing solutions to some of the most daunting ecological and environmental problems faced by our planet. The reductionist views of molecular biology provide only a partial understanding to the phenotypic knowledge of plants. Systems biology offers a comprehensive view of plant systems, by employing a holistic approach integrating the molecular data at various hierarchical levels. In this  review, we discuss the basics of systems biology including the various ‘omics’ approaches and their integration, the modeling aspects and the tools needed for the plant systems research. A particular emphasis is given to the recent analytical advances, updated published examples of plant systems biology studies and the future trends.     

Data merging for integrated microarray and proteomic analysis.

The functioning of even a simple biological system is much more complicated than the sum of its genes, proteins and metabolites. A premise of systems biology is that molecular profiling will facilitate the discovery and characterization of important disease pathways. However, as multiple levels of effector pathway regulation appear to be the norm rather than the exception, a significant challenge presented by high-throughput genomics and proteomics technologies is the extraction of the biological implications of complex data. Thus, integration of heterogeneous types of data generated from diverse global technology platforms represents the first challenge in developing the necessary foundational databases needed for predictive modelling of cell and tissue responses. Given the apparent difficulty in defining the correspondence between gene expression and protein abundance measured in several systems to date, how do we make sense of these data and design the next experiment? In this review, we highlight current approaches and challenges associated with integration and analysis of heterogeneous data sets, focusing on global analysis obtained from high-throughput technologies.     

Urinary proteomic profiling for diagnostic bladder cancer biomarkers

The ability to detect and monitor bladder cancer in noninvasively obtained urine samples is a major goal. While a number of protein biomarkers have been identified and commercially developed, none have greatly improved the accuracy of sample evaluation over invasive cystoscopy. The ongoing development of high-throughput proteomic profiling technologies will facilitate the identification of molecular signatures that are associated with bladder disease. The appropriate use of these approaches has the potential to provide efficient biomarkers for the early detection and monitoring of recurrent bladder cancer. Identification of disease-associated proteins will also advance our knowledge of tumor biology, which, in turn, will enable development of targeted therapeutics aimed at reducing morbidity from bladder cancer. In this article, we focus on the accumulating proteomic signatures of urine in health and disease, and discuss expected future developments in this field of research.     

Recent breakthroughs in computational structural biology harnessing the power of sequences and structures

Recent advances in computational approaches and their integration into structural biology enable tackling increasingly complex questions. Here, we discuss several key areas, highlighting breakthroughs and remaining challenges. Theoretical modeling has provided tools to accurately predict and design protein structures on a scale currently difficult to achieve using experimental approaches. Molecular Dynamics simulations have become faster and more precise, delivering actionable information inaccessible by current experimental methods. Virtual screening workflows allow a high-throughput approach to discover ligands that bind and modulate protein function, while Machine Learning methods enable the design of proteins with new functionalities. Integrative structural biology combines several of these approaches, pushing the frontiers of structural and functional characterization to ever larger systems, advancing towards a complete understanding of the living cell. These breakthroughs will accelerate and significantly impact diverse areas of science.     

Comparative biology comes into bloom: genomic and genetic comparison of flowering pathways in rice and Arabidopsis

Huge advances in plant biology are possible now that we have the complete genome sequences of several flowering plants. Now, genomes can be comprehensively compared and map-based cloning can be performed more easily. Association study is emerging as a powerful method for the functional identification of genes and molecular genetics has begun to reveal the basis of plant diversity. Taking the flowering pathways as an example, we discuss the potential of several approaches to comparative biology.     

Integrative computational biology for cancer research

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards.     

Breast tumor metastasis: analysis via proteomic profiling

The ability to predict the metastatic behavior of a patient's cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and, to date, they predict individual outcomes poorly. The ongoing development of high-throughput proteomic profiling technologies is opening new avenues for the investigation of cancer and, through application in tissue-based studies and animal models, will facilitate the identification of molecular signatures that are associated with breast tumor cell phenotype. The appropriate use of these approaches has the potential to provide efficient biomarkers, and to improve our knowledge of tumor biology. This, in turn, will enable the development of targeted therapeutics aimed at ameliorating the lethal dissemination of breast cancer. In this review, we focus on the accumulating proteomic signatures of breast tumor progression, particularly those that correlate with the occurrence of distant metastases, and discuss some of the expected future developments in the field.     

Breast tumor metastasis: analysis via proteomic profiling

The ability to predict the metastatic behavior of a patient’s cancer, as well as to detect and eradicate such recurrences, remain major clinical challenges in oncology. While many potential molecular biomarkers have been identified and tested previously, none have greatly improved the accuracy of specimen evaluation over routine histopathological criteria and, to date, they predict individual outcomes poorly. The ongoing development of high-throughput proteomic profiling technologies is opening new avenues for the investigation of cancer and, through application in tissue-based studies and animal models, will facilitate the identification of molecular signatures that are associated with breast tumor cell phenotype. The appropriate use of these approaches has the potential to provide efficient biomarkers, and to improve our knowledge of tumor biology. This, in turn, will enable the development of targeted therapeutics aimed at ameliorating the lethal dissemination of breast cancer. In this review, we focus on the accumulating proteomic signatures of breast tumor progression, particularly those that correlate with the occurrence of distant metastases, and discuss some of the expected future developments in the field.     

植物抗冷分子生物学研究进展（综述）

温度是植物生长的必要条件,然而低温却是限制作物生产的重要因素,为此,各国政府及研究部门一直都把植物低温适应性问题作为一个重要的研究课题．按照低温的不同程度,植物的低温伤害可分为冷害（ｃｈｉｌｌｉｎｇ ｉｎｊｕｒｙ;零上低温对植物的伤害）和冻害（ｆｒｅｅｚｉｎｇ ｉｎｊｕｒｙ;零下低温对植物的伤害）两大类。早期关于植物冷害机理和抗冷机理的研究,是从比较冷敏感（ｃｈｉｌｌｉｎｇ－ｓｅｎｓｉｔｉｖｅ）植物和抗冷（ｃｈｉｌｌｉｎｇ－ｉｎｓｅｎｓｉｔｉｖｅ）植物或比较未经冷驯化的植物（ｎｏｎ－ｃｏｌｄ－ａｃ…     

Fasciola hepatica virulence-associated cysteine peptidases: a systems biology perspective

Fasciola hepatica is a food-borne parasite of animals and humans. It secretes a large family of cysteine peptidases, termed cathepsins, that are important virulence factors. Here, we discuss how advances in molecular technologies have helped to probe the function of liver fluke cathepsins, and consider how evolving systems/molecular biology approaches can continue to inform our understanding of these important parasite enzymes.     

组学技术在核盘菌研究中的应用

核盘菌Sclerotinia sclerotiorum是一种典型的死体营养型植物病原真菌,全球分布且寄主范围广泛,严重危害多种植物,对农业生产造成严重损失。核盘菌研究主要集中在真菌生物学及病理学等方面。近年来,随着高通量分析技术的不断改进,多种组学技术为系统生物学研究提供了平台。文中主要综述利用多种组学研究方法在植物病原真菌核盘菌研究中的应用及研究进展,探讨开展植物病原物及病害发展的系统性研究思路,以期为核盘菌的分子生物学及致病机理等研究提供参考,同时也为其他植物病原物及病害系统研究提供理论依据。     

Genome-wide analysis of DNA methylation patterns

Cytosine methylation is the most common covalent modification of DNA in eukaryotes. DNA methylation has an important role in many aspects of biology, including development and disease. Methylation can be detected using bisulfite conversion, methylation-sensitive restriction enzymes, methyl-binding proteins and anti-methylcytosine antibodies. Combining these techniques with DNA microarrays and high-throughput sequencing has made the mapping of DNA methylation feasible on a genome-wide scale. Here we discuss recent developments and future directions for identifying and mapping methylation, in an effort to help colleagues to identify the approaches that best serve their research interests.