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Metal ions are essential nutrients, yet they can also be toxic if they over-accumulate. Homeostatic mechanisms and detoxification systems therefore precisely control their intracellular levels and distribution. The tools of functional genomics are rapidly accelerating understanding in this field, particularly in the yeast Saccharomyces cerevisiae.
The ability of glucocorticoids (GCs) to kill lymphoid cells led to their inclusion in essentially all chemotherapy protocols for lymphoid malignancies, particularly childhood acute lymphoblastic leukemia (ALL). GCs mediate apoptosis via their cognate receptor and subsequent alterations in gene expression. Previous investigations, including expression profiling studies with subgenome microarrays in model systems, have led to a number of attractive, but conflicting, hypotheses that have never been tested in a clinical setting. Here, we present a comparative whole-genome expression profiling approach using lymphoblasts (purified at 3 time points) from 13 GC-sensitive children undergoing therapy for ALL. For comparisons, expression profiles were generated from an adult patient with ALL, peripheral blood lymphocytes from GC-exposed healthy donors, GC-sensitive and -resistant ALL cell lines, and mouse thymocytes treated with GCs in vivo and in vitro. This generated an essentially complete list of GC-regulated candidate genes in clinical settings and experimental systems, allowing immediate analysis of any gene for its potential significance to GC-induced apoptosis. Our analysis argued against most of the model-based hypotheses and instead identified a small number of novel candidate genes, including PFKFB2, a key regulator of glucose metabolism; ZBTB16, a putative transcription factor; and SNF1LK, a protein kinase implicated in cell-cycle regulation.
The Rho family of small GTPases is essential for morphological changes during normal cell development and migration, as well as during disease states such as cancer. Our goal is to identify novel effectors of Rho proteins using a cell-based assay for Rho activity to perform genome-wide functional screens using double stranded RNA (dsRNAs) interference. We aim to discover genes could cause the cell phenotype changed dramatically. Biologists currently attempt to perform the genome-wide RNAi screening to identify various image phenotypes. RNAi genome-wide screening, however, could easily generate more than a million of images per study, manual analysis is thus prohibitive. Image analysis becomes a bottleneck in realizing high content imaging screens. We propose a two-step segmentation approach to solve this problem. First, we determine the center of a cell using the information in the DNA-channel by segmenting the DNA nuclei and the dissimilarity function is employed to attenuate the over-segmentation problem, then we estimate a rough boundary for each cell using a polygon. Second, we apply fuzzy c-means based multi-threshold segmentation and sharpening technology; for isolation of touching spots, marker-controlled watershed is employed to remove touching cells. Furthermore, Voronoi diagrams are employed to correct the segmentation errors caused by overlapping cells. Image features are extracted for each cell. K-nearest neighbor classifier (KNN) is employed to perform cell phenotype classification. Experimental results indicate that the proposed approach can be used to identify cell phenotypes of RNAi genome-wide screens.
Since efficient and relatively cheap methods were developed for determining biosequences, a lot of biosequence data has been generated. As the main problem in molecular biology is the analysis of the data instead of the data acquisition, part of the study of computational biology is to extract all kinds of meaningful information from the sequences. Computer-assisted methods have become very important in analyzing biosequence data. However, most of the current computer-assisted methods are limited to finding motifs. Genes can be regulated in many ways, including combinations of regulatory elements. This research is aimed at developing a new integrated system for genome-wide gene expression analysis. This research begins with a new motif-finding method, using a new objective function combining multiple well defined components and an improved stochastic iterative sampling strategy. Combinatorial motif analysis is accomplished by constructive induction that analyzes potential motif combinations. We then apply standard inductive learning algorithms to generate hypotheses for different gene behaviors. A genome-wide gene expression analysis demonstrated the value of this novel integrated system.
In this post-genomic era, we need to define gene function on a genome-wide scale for model organisms and humans. The fundamental unit of biological processes is the cell. Among the most powerful tools to assay such processes in the physiological context of intact living cells are fluorescence microscopy and related imaging techniques. To enable these techniques to be applied to functional genomics experiments, fluorescence microscopy is making the transition to a quantitative and high-throughput technology.
Meningococcal disease was first recognised and Neisseria meningitidis isolated as the causative agent over 100 years ago, but despite more than a century of research, attempts to eliminate this distressing illness have so far been thwarted. The main problem lies in the fact that N. meningitidis usually exists as a harmless commensal inhabitant of the human nasopharynx, the pathogenic state being the exception rather than the norm. As man is its only host, the meningococcus is uniquely adapted to this ecological niche and has evolved an array of mechanisms for evading clearance by the human immune response. Progress has been made in combating the disease by developing vaccines that target specific pathogenic serogroups of meningococci. However, a fully comprehensive vaccine that protects against all pathogenic strains is still just beyond reach. The publication of the genome sequences of two meningococcal strains, one each from serogroups A and B and the imminent completion of a third illustrates the extent of the problems to be overcome, namely the vast array of genetic mechanisms for the generation of meningococcal diversity. Fortunately, genome studies also provide new hope for solutions to these problems in the potential for a greater understanding of meningococcal pathogenesis and possibilities for the identification of new vaccine candidates. This review describes some of the approaches that are currently being used to exploit the information from meningococcal genome sequences and seeks to identify future prospects for combating meningococcal disease.
Distinct types of immune responses are required for efficient elimination of different pathogens. Programming of the desired type of immune response by safe nonreplicating vaccines requires suitable vaccine adjuvants. Adjuvants largely determine the magnitude and quality of immune responses specific for the coadministered antigen. Unfortunately, rational vaccine design requiring a rational choice of vaccine adjuvant, is hampered by a lack of knowledge about the mechanism(s) of vaccine adjuvant activity. The current review addresses different critical immunological processes possibly explaining adjuvant functions. In addition, we discuss traditional vaccine adjuvant formulations and their possible mode of action. Finally, we reflect on the latest technologies for the identification of novel adjuvants using molecular analysis of immune activation and functional genomics.
The ability to align pairs of very large molecular sequences is essential for a range of comparative genomic studies. However, given the complexity of genomic sequences, it has been difficult to devise a systematic method that can align - even within the same species - pairs of large sequences. Most existing approaches typically attempt to align nucleotide sequences while ignoring valuable features contained within them, eg they filter out low-complexity regions and retroelements before aligning the sequences. However, features are then added post-alignment for visualisation and analysis purposes. We argue that repetitive elements and other features (such as genes, exons and regulatory elements) should be part of the alignment process. A hierarchical approach that aligns the biologically relevant features before aligning the detailed nucleotide sequences has a number of interesting characteristics: (1) features define 'alignment anchor points' that can guide meaningful nucleotide alignment; (2) features can be weighted; (3) a hierarchical approach would identify only meaningful regions to be aligned; (4) nucleotide sequences can be described as sequences of features and non-features, providing a natural mechanism to divide the sequences for processing; and (5) computational speed is significantly faster than other approaches. In this paper, we describe and discuss a feature-based approach to aligning large genome sequences. We refer to this as 'feature-based sequence alignment'.
The article presents the current knowledge on the microarray technique and its applications in medical sciences and parasitology. The first part of the article is focused on the technical aspects (microarray preparation, different microarray platforms, probes preparation, hybridization and signal detection). The article also describes possible ways of proceeding during laboratory work on organism of which the genome sequence is not known or has been only partially sequenced. The second part of the review describes how microarray technique have been, or possibly will be, used for better understanding parasite life cycles and development, host-parasite relationship, comparative genomics of virulent organisms, develpoment vaccines against the most virulent parasites and host responses to infection.
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Peroxisome proliferator-activated receptors (PPAR) are nuclear hormone receptors that control the expression of genes involved in lipid homeostasis in mammals. We searched for PPAR in sea bass, a marine fish of particular interest to aquaculture, after hypothesizing that the physiological and molecular processes that regulate lipid metabolism in fish are similar to those in mammals. Here, we report the identification of complementary DNA and corresponding genomic sequences that encode three distinct PPAR from sea bass. The sea bass PPAR are the structural homologs of the mammalian PPAR alpha, beta/delta, and gamma isotypes. As revealed by RNase protection, the tissue expression profile of the fish PPAR appears to be very similar to that of the mammalian PPAR homologs. Thus, PPAR alpha is mainly expressed in the liver, PPAR gamma in adipose tissue, and PPAR beta in all tissues tested, with its highest levels in the liver, where it is also the dominant isotype expressed. Like mammalian PPAR, the sea bass isotypes recognize and bind to PPAR response elements of both mammalian and piscine origin, as heterodimers with the 9-cis retinoic acid receptor. Through the coactivator-dependent receptor ligand assay, we also demonstrated that natural FA and synthetic hypolipidemic compounds can act as ligands of the sea bass PPAR alpha and beta isotypes. This suggests that the sea bass PPAR act through similar mechanisms and perform the same critical lipid metabolism functions as mammalian PPAR.
As tools for functional genomics, expression profiling and proteomics provide correlative data, while expression cloning screens can link genes directly to biological function. However, technical limitations of gene transfer, expression, and recovery of candidate genes have limited wider application of genome-wide expression screens. Here we describe the pEYK retroviral vectors, which maintain high titers and robust gene expression while addressing the major bottleneck of expression cloning--efficient candidate gene recovery. By exploiting schemes for enhanced PCR rescue or strategies for direct isolation of proviral DNA as plasmids in bacterial hosts, the pEYK vectors facilitate cDNA isolation from selected cells and enable rapid iteration of screens and genetic reversion analyses to validate gene candidates. These vectors have proven useful to identify genes linked to cell proliferation, senescence and apoptosis.
Mammalian mitochondrial genomes are organized in a conserved and extremely compact manner, encoding molecules that play a vital role in oxidative phosphorylation (OXPHOS) and carry out a number of other important biological functions. A large-scale screening of the normalized mitochondrial gene expression profiles generated from publicly available mammalian serial analysis of gene expression (SAGE) datasets (over 17.7 millions of tags) was performed in this study. Acquired SAGE libraries represent an extensive range of human, mouse, rat, bovine and swine cell and tissue samples (normal and pathological) in a variety of conditions. Using a straightforward in silico algorithm, variations in total mitochondrial gene expression, as well as in the expression of individual genes encoded by mitochondrial genomes are addressed, and common patterns in the species- and tissue-specific mitochondrial gene expression profiles are discussed.
Various new methods have been proposed to predict functional interactions between proteins based on the genomic context of their genes. The types of genomic context that they use are Type I: the fusion of genes; Type II: the conservation of gene-order or co-occurrence of genes in potential operons; and Type III: the co-occurrence of genes across genomes (phylogenetic profiles). Here we compare these types for their coverage, their correlations with various types of functional interaction, and their overlap with homology-based function assignment. We apply the methods to Mycoplasma genitalium, the standard benchmarking genome in computational and experimental genomics. Quantitatively, conservation of gene order is the technique with the highest coverage, applying to 37% of the genes. By combining gene order conservation with gene fusion (6%), the co-occurrence of genes in operons in absence of gene order conservation (8%), and the co-occurrence of genes across genomes (11%), significant context information can be obtained for 50% of the genes (the categories overlap). Qualitatively, we observe that the functional interactions between genes are stronger as the requirements for physical neighborhood on the genome are more stringent, while the fraction of potential false positives decreases. Moreover, only in cases in which gene order is conserved in a substantial fraction of the genomes, in this case six out of twenty-five, does a single type of functional interaction (physical interaction) clearly dominate (>80%). In other cases, complementary function information from homology searches, which is available for most of the genes with significant genomic context, is essential to predict the type of interaction. Using a combination of genomic context and homology searches, new functional features can be predicted for 10% of M. genitalium genes.
A probe-level model for analysis of GeneChip gene-expression data is presented which identified more than 10,000 single-feature polymorphisms (SFP) between two barley genotypes. The method has good sensitivity, as 67% of known single-nucleotide polymorphisms (SNP) were called as SFPs. This method is applicable to all oligonucleotide microarray data, accounts for SNP effects in gene-expression data and represents an efficient and versatile approach for highly parallel marker identification in large genomes.
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Systems level investigation of genomic scale information requires the development of truly integrated databases dealing with heterogeneous data, which can be queried for simple properties of genes or other database objects as well as for complex network level properties, for the analysis and modelling of complex biological processes. Towards that goal, we recently constructed PathSys, a data integration platform for systems biology, which provides dynamic integration over a diverse set of databases [Baitaluk et al. (2006) BMC Bioinformatics 7, 55]. Here we describe a server, BiologicalNetworks, which provides visualization, analysis services and an information management framework over PathSys. The server allows easy retrieval, construction and visualization of complex biological networks, including genome-scale integrated networks of protein-protein, protein-DNA and genetic interactions. Most importantly, BiologicalNetworks addresses the need for systematic presentation and analysis of high-throughput expression data by mapping and analysis of expression profiles of genes or proteins simultaneously on to regulatory, metabolic and cellular networks. BiologicalNetworks Server is available at http://brak.sdsc.edu/pub/BiologicalNetworks.