Embryogenomics: developmental biology meets genomics

Fundamental questions in developmental biology are: what genes are expressed, where and when they are expressed, what is the level of expression and how are these programs changed by the functional and structural alteration of genes? These questions have been addressed by studying one gene at a time, but a new research field that handles many genes in parallel is emerging. The methodology is at the interface of large-scale genomics approaches and developmental biology. Genomics needs developmental biology because one of the goals of genomics – collection and analysis of all genes in an organism – cannot be completed without working on embryonic tissues in which many genes are uniquely expressed. However, developmental biology needs genomics – the high-throughput approaches of genomics generate information about genes and pathways that can give an integrated view of complex processes. This article discusses these new approaches and their applications to mammalian developmental biology.     

Bioinformatics

Bioinformatics is an interdisciplinary field that blends computer science and biostatistics with biological and biomedical sciences such as biochemistry, cell biology, developmental biology, genetics, genomics, and physiology. An important goal of bioinformatics is to facilitate the management, analysis, and interpretation of data from biological experiments and observational studies. The goal of this review is to introduce some of the important concepts in bioinformatics that must be considered when planning and executing a modern biological research study. We review database resources as well as data mining software tools.     

Nine quick tips for pathway enrichment analysis

Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance. The relative abundance of genes pertinent to specific pathways is measured through statistical methods, and associated functional pathways are retrieved from online bioinformatics databases. In the last decade, along with the spread of the internet, higher availability of computational resources made PEA software tools easy to access and to use for bioinformatics practitioners worldwide. Although it became easier to use these tools, it also became easier to make mistakes that could generate inflated or misleading results, especially for beginners and inexperienced computational biologists. With this article, we propose nine quick tips to avoid common mistakes and to out a complete, sound, thorough PEA, which can produce relevant and robust results. We describe our nine guidelines in a simple way, so that they can be understood and used by anyone, including students and beginners. Some tips explain what to do before starting a PEA, others are suggestions of how to correctly generate meaningful results, and some final guidelines indicate some useful steps to properly interpret PEA results. Our nine tips can help users perform better pathway enrichment analyses and eventually contribute to a better understanding of current biology.     

Production of useful secondary metabolites in plants: Functional genomics approaches

The paradigm of biological research has been changed by recent developments in genomics, high-throughput biology, and bioinformatics. Conventional biology often was based on empirical, labor-intensive, and time-consuming methods. In the new paradigm, biological research e is driven by a holistic approach on the basis of rational, automatic, and high-throughput methods. New functional compounds can be discovered by using high-throughput screening systems. Secondary metabolite pathways and the genes involved in those pathways are then determined by studying functional genomics in conjunction with the data-mining tools of bioinformatics. In addition, these advances in metabolic engineering enable researchers to confer new secondary metabolic pathways to crops by transferring three to five, or more, heterologous genes taken from various other species. In the future, engineering for the production of useful compounds will be designed by a set of software tools that allows the user to specify a cell’s genes, proteins, and other molecules, as well as their individual interactions.     

金小蜂基因组学研究前沿

金小蜂不仅是重要的昆虫天敌资源,还是理想的模式生物。2010年1月15日,3种金小蜂(丽蝇蛹集金小蜂Nasonia vitripennis、吉氏金小蜂N.giraulti和长角金小蜂N.longicornis)的基因组在《科学》上发表。这一事件标志着金小蜂研究"后基因组时代"的开始。金小蜂基因组测序完成后,科学家们就利用生物信息学、比较基因组学、功能基因组学等方法,基于基因组平台,在进化遗传学、发育生物学、神经生物学、行为学等领域开展了系列研究,取得了重要进展,国际著名杂志《昆虫分子生物学》和《遗传》还以特刊的形式进行刊载。本文就金小蜂基因组学相关研究取得的进展予以扼要概述,并探讨其研究方向和发展前景。     

Tunable promoters in systems biology

The construction of synthetic promoter libraries has represented a major breakthrough in systems biology, enabling the subtle tuning of enzyme activities. A number of tools are now available that allow the modulation of gene expression and the detection of changes in expression patterns. But, how does one choose the correct promoter and what are the appropriate methods for reading promoter strength? Furthermore, how fine should the tuning of gene expression be for some specific applications and how can the simultaneous and individual tuning of multiple genes be achieved? Some recent studies have helped us to find answers to many of these questions.     

A gentle introduction to SNP analysis: resources and tools

Bioinformatics is the use of informatics tools and techniques in the study of molecular biology, genetic, or clinical data. The field of bioinformatics has expanded tremendously to cope with the large expansion of information generated by the mouse and human genome projects, as newer generations of computers that are much more powerful have emerged in the commercial market. It is now possible to employ the computing hardware and software at hand to generate novel methodologies in order to link data across the different databanks generated by these international projects and derive clinical and biological relevance from all of the information gathered. The ultimate goal would be to develop a computer program that can provide information correlating genes, their single nucleotide polymorphisms (SNPs), and the possible structural and functional effects on the encoded proteins with relation to known information on complex diseases with great ease and speed. Here, the recent developments of available software methods to analyze SNPs in relation to complex diseases are reviewed with emphasis on the type of predictions on protein structure and functions that can be made. The need for further development of comprehensive bioinformatics tools that can cope with information generated by the genomics communities is emphasized.     

Beyond Arabidopsis. Translational biology meets evolutionary developmental biology

Developmental processes shape plant morphologies, which constitute important adaptive traits selected for during evolution. Identifying the genes that act in developmental pathways and determining how they are modified during evolution is the focus of the field of evolutionary developmental biology, or evo-devo. Knowledge of genetic pathways in the plant model Arabidopsis serves as the starting point for investigating how the toolkit of developmental pathways has been used and reused to form different plant body plans. One productive approach is to identify genes in other species that are orthologous to genes known to control developmental pathways in Arabidopsis and then determine what changes have occurred in the protein coding sequence or in the gene's expression to produce an altered morphology. A second approach relies on natural variation among wild populations or crop plants. Natural variation can be exploited to identify quantitative trait loci that underlie important developmental traits and, thus, define those genes that are responsible for adaptive changes. The possibility of applying comparative genomics approaches to Arabidopsis and related species promises profound new insights into the interplay of evolution and development.     

The evolution of developmental mechanisms

Over the past two to three decades, developmental biology has demonstrated that all multicellular organisms in the animal kingdom share many of the same molecular building blocks and many of the same regulatory genetic pathways. Yet we still do not understand how the various organisms use these molecules and pathways to assume all the forms we know today. Evolutionary developmental biology tackles this problem by comparing the development of one organism to another and comparing the genes involved and gene functions to understand what makes one organism different from another. In this review, we revisit a set of seven concepts defined by Lewis Wolpert (fate maps, asymmetric division, induction, competence, positional information, determination, and lateral inhibition) that describe the characters of many developmental systems and supplement them with three additional concepts (developmental genomics, genetic redundancy, and genetic networks). We will discuss examples of comparative developmental studies where these concepts have guided observations on the advent of a developmental novelty. Finally, we identify a set of evolutionary frameworks, such as developmental constraints, cooption, duplication, parallel and convergent evolution, and homoplasy, to adequately describe the evolutionary properties of developmental systems.     

Sphingolipids and membrane biology as determined from genetic models

The importance of sphingolipids in membrane biology was appreciated early in the twentieth century when several human inborn errors of metabolism were linked to defects in sphingolipid degradation. The past two decades have seen an explosion of information linking sphingolipids with cellular processes. Studies have unraveled mechanistic details of the sphingolipid metabolic pathways, and these findings are being exploited in the development of novel therapies, some now in clinical trials. Pioneering work in yeast has laid the foundation for identifying genes encoding the enzymes of the pathways. The advent of the era of genomics and bioinformatics has led to the identification of homologous genes in other species and the subsequent creation of animal knock-out lines for these genes. Discoveries from these efforts have re-kindled interest in the role of sphingolipids in membrane biology. This review highlights some of the recent advances in understanding sphingolipids' roles in membrane biology as determined from genetic models.     

Cell biology, chemogenomics and chemoproteomics

The scientific techniques used in molecular biological research and drug discovery have changed dramatically over the past 10 years due to the influence of genomics, proteomics and bioinformatics. Furthermore, genomics and functional genomics are now merging into a new scientific approach called chemogenomics. Advancements in the study of molecular cell biology are dependent upon "omics" researchers realizing the importance of and using the experimental tools currently available to cell biologists. For example, novel microscopic techniques utilizing advanced computer imaging allow for the examination of live specimens in a fourth dimension, viz., time. Yet, molecular biologists have not taken full advantage of these and other traditional and novel cell biology techniques for the further advancement of genomic and proteomic-oriented research. The application of traditional and novel cellular biological techniques will enhance the science of genomics. The authors hypothesize that a stronger interdisciplinary approach must be taken between cell biology (and its closely related fields) and genomics, proteomics and bio-chemoinformatics. Since there is a lot of confusion regarding many of the "omics" definitions, this article also clarifies some of the basic terminology used in genomics, and related fields. It also reviews the current status and future potential of chemogenomics and its relationship to cell biology. The authors also discuss and expand upon the differences between chemogenomics and the relatively new term--chemoproteomics. We conclude that the advances in cell biology methods and approaches and their adoption by "omics" researchers will allow scientists to maximize our knowledge about life.     

Craniofacial variability and modularity in macaques and mice

Evolutionary developmental biology of primates will be driven largely by the developmental biology of the house mouse. Inferences from how known developmental perturbations produce phenotypic effects in model organisms, such as mice, to how the same perturbations would affect craniofacial form in primates must be informed by comparisons of phenotypic variation and variability in mice and the primate species of interest. We use morphometric methods to compare patterns of cranial variability in homologous datasets obtained for two strains of laboratory mice and rhesus macaques. C57BL/6J represents a common genetic background for transgenic models. A/WySnJ mice exhibit altered facial morphology which results from reduction in the growth of the maxillary process during formation of the face. This is relevant to evolutionary changes in facial prognathism in nonhuman primate and human evolution. Rhesus macaques represent a nonhuman primate about which a great deal of phenotypic and genetic information is available. We find significant similarities in covariation patterns between the C57BL/6J mice and macaques. Among-trait variation in genetic and phenotypic variances are fairly concordant among the three groups, but among-trait variation in developmental stability is not. Finally, analysis of modularity based on phenotypic and genetic correlations did not reveal a consistent pattern in the three groups. We discuss the implications of these results for the study of evolutionary developmental biology of primates and outline a research strategy for integrating mouse genomics and developmental biology into this emerging field.     

And then there were many: MADS goes genomic

During the past decade, MADS-box genes have become known as key regulators in both reproductive and vegetative plant development. Traditional genetics and functional genomics tools are now available to elucidate the expression and function of this complex gene family on a much larger scale. Moreover, comparative analysis of the MADS-box genes in diverse flowering and non-flowering plants, boosted by bioinformatics, contributes to our understanding of how this important gene family has expanded during the evolution of land plants. Therefore, the recent advances in comparative and functional genomics should enable researchers to identify the full range of MADS-box gene functions, which should help us significantly in developing a better understanding of plant development and evolution.     

The challenges for molecular nutrition research 3: comparative nutrigenomics research as a basis for entering the systems level

Human nutrition and metabolism may serve as the paradigm for the complex interplay of the genome with its environment. The concept of nutrigenomics now enables science with new tools and comprehensive analytical techniques to investigate this interaction at all levels of the complexity of the organism. Moreover, nutrigenomics seeks to better define the homeostatic control mechanisms, identify the de-regulation in the early phases of diet-related diseases, and attempts to assess to what extent an individual's sensitizing genotype contributes to the overall health or disease state. In a comparative approach nutrigenomics uses biological systems of increasing complexity from yeast to mammalian models to define the general rules of metabolic and genetic mechanisms in adaptations to the nutritional environment. Powerful information technology, bioinformatics and knowledge management tools as well as new mathematical and computational approaches now make it possible to study these molecular mechanisms at the cellular, organ and whole organism level and take it on to modeling the processes in a "systems biology" approach. This review summarizes some of the concepts of a comparative approach to nutrigenomics research, identifies current lacks and proposes a concerted scientific effort to create the basis for nutritional systems biology.     

A molecular approach to the evolution of vertebrate paired appendages

Over the past few years, genes involved in the ontogenesis of tetrapod limbs have been Isolated and characterized. Some of the developmental mechanisms responsible for the morphogenesis of these complex structures can now be investigated through a new approach. In addition, these genes can serve as tools to re-evaluate some aspects of the long-standing question of the fin-to-limb transition. Comparative molecular developmental biology is providing new insight into the similarities and differences in the morphologies of these homologous structures.     

Proteome signatures—how are they obtained and what do they teach us?

The dawn of a new Proteomics era, just over a decade ago, allowed for large-scale protein profiling studies that have been applied in the identification of distinctive molecular cell signatures. Proteomics provides a powerful approach for identifying and studying these multiple molecular markers in a vast array of biological systems, whether focusing on basic biological research, diagnosis, therapeutics, or systems biology. This is a continuously expanding field that relies on the combination of different methodologies and current advances, both technological and analytical, which have led to an explosion of protein signatures and biomarker candidates. But how are these biological markers obtained? And, most importantly, what can we learn from them? Herein, we briefly overview the currently available approaches for obtaining relevant information at the proteome level, while noting the current and future roles of both traditional and modern proteomics. Moreover, we provide some considerations on how the development of powerful and robust bioinformatics tools will greatly benefit high-throughput proteomics. Such strategies are of the utmost importance in the rapidly emerging field of immunoproteomics, which may play a key role in the identification of antigens with diagnostic and/or therapeutic potential and in the development of new vaccines. Finally, we consider the present limitations in the discovery of new signatures and biomarkers and speculate on how such hurdles may be overcome, while also offering a prospect for the next few years in what could be one of the most significant strategies in translational medicine research.     

Xenopus white papers and resources: folding functional genomics and genetics into the frog

The frog Xenopus has been vital for biomedical science for over 80 years, contributing to diverse fields from cell signaling, cell and developmental biology, to ion channel physiology and toxicology. Its experimentally manipulable oocytes and embryos provide abundant material for molecular and biochemical approaches for a wide range of gene discovery and protein function studies. In recent years, the Xenopus community has invested in key resources for functional genomics, including genome-wide full-length cDNA collections and genome assemblies as well as genetic tools. These assets combine with Xenopus' extensive range of functional assays to create exciting new research avenues with medical as well as basic applications. This review describes how these resources were developed and what new tools are on the horizon.     

Metabolomics technology and bioinformatics

Metabolomics is the global analysis of all or a large number of cellular metabolites. Like other functional genomics research, metabolomics generates large amounts of data. Handling, processing and analysis of this data is a clear challenge and requires specialized mathematical, statistical and bioinformatics tools. Metabolomics needs for bioinformatics span through data and information management, raw analytical data processing, metabolomics standards and ontology, statistical analysis and data mining, data integration and mathematical modelling of metabolic networks within a framework of systems biology. The major approaches in metabolomics, along with the modern analytical tools used for data generation, are reviewed in the context of these specific bioinformatics needs.