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Most genome annotation protocols combine ab initio predictions with transcription and homology analyses to produce reliable gene predictions but they often fail to detect many actual genes. Alternative approaches involving more sensitive homology recognition methods are playing an increasingly important role in the next stage of gene discovery. The hunt for new genes is far from over.
Chronic pulmonary aspergillosis involves the formation of a fungal ball (aspergilloma) in lung cavities. Pseudomonas aeruginosa commonly co-colonizes these lesions; however, the in vivo mechanisms underlying its persistence are unknown. Using a multi-omics approach on resected aspergillomas, we defined the genomic, transcriptional, and metabolic adaptations of P. aeruginosa within this polymicrobial niche. We reconstructed high-quality P. aeruginosa genomes and identified a conserved core genome, along with accessory genes for secondary metabolism, virulence, and antimicrobial resistance. Phylogenomics revealed heterogeneous evolutionary paths among co-colonizing strains. Metatranscriptomics showed stark physiological heterogeneity, from metabolically aggressive to stress-adapted states. High expression of phenazine, quorum-sensing (PQS), siderophore, and secretion-system operons was corroborated by metabolomic detection of phenazine-1-carboxylic acid and 2-heptylquinolin-4(1H)-one, confirming active bacterial antagonism in vivo. Concurrent Aspergillus fumigatus transcriptomics revealed the activation of oxidative stress responses, secondary metabolism (eg fumagillin), and iron scavenging, demonstrating reciprocal competition. Host transcriptomics revealed patient-specific immune signatures that correlated with the metabolic activity of the co-colonizers. This work provides an integrated systems-level analysis of the tri-kingdom aspergilloma ecosystem. P. aeruginosa persistence is driven by genomic plasticity and context-dependent expression of competitive pathways, shaped within a chronic inflammatory environment. These findings redefine aspergillomas as active polymicrobial consortia, establishing a framework for targeting resilient microbial communities in chronic lung disease.
Changes in gene expression during mouse myoblast differentiation were monitored by DNA microarray hybridisation. Four days after the onset of differentiation 2.37% of the genes increased in activity from a value of zero, whereas during the same time 1.68% of total genes had decreased expression. During the first 24 h of differentiation an average of 700 000 CpG sites per haploid genome were demethylated. Maximal loss of DNA methylation is attained after 2 days of differentiation, followed by a gradual remethylation. The highest demethylation is observed in highly repeated DNA sequences, followed by single copy sequences. When DNA replication is inhibited by aphidicolin or L-mimosine this genome-wide demethylation is still observed. During the first 3 h of differentiation there is an increase in the number of hemimethylated CpG sites, which disappear rapidly during the course of genome-wide hypomethylation. Transfection of cells with an antisense morpholino oligonucleotide to 5-methylcytosine DNA glycosylase (G/T mismatch DNA glycosylase) decreases both the activity of the enzyme and genome-wide demethylation. It is concluded that the genome-wide loss of DNA methylation in differentiating mouse myoblasts occurs in part by formation of hemimethylated CpG sites, which can serve as the substrate for 5-methylcytosine-DNA glycosylase.
Using a maskless photolithography method, we produced DNA oligonucleotide microarrays with probe sequences tiled throughout the genome of the plant Arabidopsis thaliana. RNA expression was determined for the complete nuclear, mitochondrial, and chloroplast genomes by tiling 5 million 36-mer probes. These probes were hybridized to labeled mRNA isolated from liquid grown T87 cells, an undifferentiated Arabidopsis cell culture line. Transcripts were detected from at least 60% of the nearly 26,330 annotated genes, which included 151 predicted genes that were not identified previously by a similar genome-wide hybridization study on four different cell lines. In comparison with previously published results with 25-mer tiling arrays produced by chromium masking-based photolithography technique, 36-mer oligonucleotide probes were found to be more useful in identifying intron-exon boundaries. Using two-dimensional HPLC tandem mass spectrometry, a small-scale proteomic analysis was performed with the same cells. A large amount of strongly hybridizing RNA was found in regions "antisense" to known genes. Similarity of antisense activities between the 25-mer and 36-mer data sets suggests that it is a reproducible and inherent property of the experiments. Transcription activities were also detected for many of the intergenic regions and the small RNAs, including tRNA, small nuclear RNA, small nucleolar RNA, and microRNA. Expression of tRNAs correlates with genome-wide amino acid usage.
MOTIVATION: A considerable amount of human intervention is currently required to produce high-quality fingerprint-based physical maps for genomic studies. RESULTS: An algorithm has been developed and implemented to automatically order fingerprinted clones within contigs. The resulting software, named CORAL (Clone ORdering ALgorithm), has been tested on maps that have previously been manually edited and on maps derived from in silico simulations. The fingerprint map and DNA sequence of the human genome has provided an additional test to CORAL. Measurements suggest that CORAL performs significantly better than the software currently used by most laboratories to order fingerprinted clones at throughputs far exceeding those that can be achieved manually.
SUMMARY: GeneCruiser is a web service allowing users to annotate their genomic data by mapping microarray feature identifiers to gene identifiers from databases, such as UniGene, while providing links to web resources, such as the UCSC Genome Browser. It relies on a regularly updated database that retrieves and indexes the mappings between microarray probes and genomic databases. Genes are identified using the Life Sciences Identifier standard. AVAILABILITY: GeneCruiser is freely available in the following forms: Web service and Web application, http://www.genecruiser.org; GenePattern, GeneCruiser access has been integrated into our microarray analysis platform, GenePattern. http://www.genepattern.org.
PlasmoDB (http://PlasmoDB.org) is the official database of the Plasmodium falciparum genome sequencing consortium. This resource incorporates finished and draft genome sequence data and annotation emerging from Plasmodium sequencing projects. PlasmoDB currently houses information from five parasite species and provides tools for cross-species comparisons. Sequence information is also integrated with other genomic-scale data emerging from the Plasmodium research community, including gene expression analysis from EST, SAGE and microarray projects. The relational schemas used to build PlasmoDB [Genomics Unified Schema (GUS) and RNA Abundance Database (RAD)] employ a highly structured format to accommodate the diverse data types generated by sequence and expression projects. A variety of tools allow researchers to formulate complex, biologically based queries of the database. A version of the database is also available on CD-ROM (Plasmodium GenePlot), facilitating access to the data in situations where Internet access is difficult (e.g. by malaria researchers working in the field). The goal of PlasmoDB is to enhance utilization of the vast quantities of data emerging from genome-scale projects by the global malaria research community.
The analysis of gene expression in tissues, cells, and biologic systems has evolved in the last decade from the analysis of a selected set of genes to an efficient high throughput whole-genome screening approach of potentially all genes expressed in a tissue or cell sample. Development of sophisticated methodologies such as microarray technology allows an open-ended survey to identify comprehensively the fraction of genes that are differentially expressed between samples and define the samples' unique biology. This discovery-based research provides the opportunity to characterize either new genes with unknown function or genes not previously known to be involved in a biologic process. The latter category may hold surprises that sometimes urge us to redirect our thinking. Here, we review the impact of large-scale gene expression profiling by DNA-microarray technology on basic and clinical aspects of immunology.
This chapter focuses on the promising post-genomic technologies being used for discovery of new, safer, and better cancer drugs and drug targets. Since cancer is largely a disease of the cell, usually involving unrestricted cell proliferation as a result of heritable genetic changes such as mutation, this chapter will focus on cell-centric technologies and their utility in addressing major questions in cancer biology. Recent advances in cell-based technology, including phenotypic assays, image-based readouts, primary tumor cell growth and maintenance in vitro, gene and small molecule delivery tools, and automated systems for cell manipulation, provide a novel means to understand the etiology and mechanisms of cancer as never before. In addition to the abundant tool sophistication, many aspects of cancer can be emulated and monitored in cell systems, which makes them ideal vehicles for exploitation to discover new targets and drugs. This chapter will first handle nomenclature and provide a context for a "good drug target" within the framework of the human genome, then overview functional genomic gene-based library screening approaches with specific applications to cancer target discovery. Second, small molecule screening applications will be handled, with an emphasis on the new paradigm of massively parallel screening and resultant multidimensional dataset analysis approaches to identify drug candidates, assign mechanism of action, and address problems in deriving selective and safe chemical entities.
The sequence of the human genome is providing researchers with the scaffold upon which genes are built. The definition of the boundaries of the genes themselves and of their complex architecture requires a mapping of the transcriptome to the genome. A methodology was developed for generating a detailed transcriptome map and for reconstituting transcripts by using the genome as a template. As a demonstration of the potential of this method, the structure of the human Toll-like receptor (TLR) genes was reevaluated. For all TLR genes for which a genomic sequence was available (i.e., all except TLR10), novel features of the gene structure were discovered. These features include multiple alternative polyadenylation sites, additional exons or splice variants, and overlaps with other genes. These findings have implications for the analysis of TLR gene expression and for the diversity of the proteins encoded by these genes.
MOTIVATION: To compare entire genomes from different species, biologists increasingly need alignment methods that are efficient enough to handle long sequences, and accurate enough to correctly align the conserved biological features between distant species. The two main classes of pairwise alignments are global alignment, where one string is transformed into the other, and local alignment, where all locations of similarity between the two strings are returned. Global alignments are less prone to demonstrating false homology as each letter of one sequence is constrained to being aligned to only one letter of the other. Local alignments, on the other hand, can cope with rearrangements between non-syntenic, orthologous sequences by identifying similar regions in sequences; this, however, comes at the expense of a higher false positive rate due to the inability of local aligners to take into account overall conservation maps. RESULTS: In this paper we introduce the notion of glocal alignment, a combination of global and local methods, where one creates a map that transforms one sequence into the other while allowing for rearrangement events. We present Shuffle-LAGAN, a glocal alignment algorithm that is based on the CHAOS local alignment algorithm and the LAGAN global aligner, and is able to align long genomic sequences. To test Shuffle-LAGAN we split the mouse genome into BAC-sized pieces, and aligned these pieces to the human genome. We demonstrate that Shuffle-LAGAN compares favorably in terms of sensitivity and specificity with standard local and global aligners. From the alignments we conclude that about 9% of human/mouse homology may be attributed to small rearrangements, 63% of which are duplications.
In the genetic code, UGA serves as a stop signal and a selenocysteine codon, but no computational methods for identifying its coding function are available. Consequently, most selenoprotein genes are misannotated. We identified selenoprotein genes in sequenced mammalian genomes by methods that rely on identification of selenocysteine insertion RNA structures, the coding potential of UGA codons, and the presence of cysteine-containing homologs. The human selenoproteome consists of 25 selenoproteins.
The emerging technology of microarray analysis allows the establishment of molecular portraits of prostate cancer and the discovery of novel genes involved in the carcinogenesis process. Many novel genes have already been identified using this technique, and functional analyses of these genes are currently being tested. The combination of microarray analysis with other recently developed high-throughput techniques, such as proteomics, tissue arrays, and gene promoter-methylation, especially using tissue microdissection methods, will provide us with more comprehensive insights into how prostate cancer develops and responds to gene-targeted therapies. Animal models of prostate cancer are being characterized by high throughput techniques to better define the similarities and differences between those models and the human disease, and to determine whether particular models may be useful for specific targeted therapies in pre-clinical studies. Although profiling of mRNA expression provides important information of gene expression, the development of proteomic technologies will allow for an even more precise global insight into cellular signaling and structural alterations during prostate carcinogenesis. Not only will the "omic" revolution change basic science, but it will lead to a new era of molecular medicine.
The identification and exploitation of biomarkers that may predict response to anti-cancer treatments has the capacity to revolutionize the way that patients with cancer are treated. In breast cancer, the estrogen receptor (ER) and the progesterone receptor (PgR) are known to have a significant predictive value in determining sensitivity to endocrine therapies. Tumor expression of ER or PgR is known to affect clinical outcome and this information is often used to determine a patient's optimal treatment regimen. However, the measurement of ER and PgR alone is more complex than originally thought and the impact of the recently identified isoforms of ER (ERalpha and ERbeta) and PgR (PgRA and PgRB), as well as several variant and mutant forms, upon the choice of treatment remains unclear. Therefore, ER and PgR expression alone are unlikely to determine a patient's optimal treatment regimen, particularly when the amount of 'cross-talk' between different pathways, such as the epidermal growth factor receptor pathway, is considered. In order to account for the complex cell-signaling environment that occurs in breast cancer, multifactorial techniques are needed to analyze tumor biomarker expression. The recent advances in genomic- or proteomic-based approaches has enabled molecular portraits of breast cancers to be painted, allowing biomarkers of response and prognosis to be identified and characterized more accurately than before. In the future, patients could be treated according to the molecular portrait of their tumor biomarker expression, maximizing the therapeutic benefit that each patient receives.
The draft sequence of several complete protozoan genomes is now available and genome projects are ongoing for a number of other species. Different strategies are being implemented to identify and annotate protein coding and RNA genes in these genomes, as well as study their genomic architecture. Since the genomes vary greatly in size, GC-content, nucleotide composition, and degree of repetitiveness, genome structure is often a factor in choosing the methodology utilised for annotation. In addition, the approach taken is dictated, to a greater or lesser extent, by the particular reasons for carrying out genome-wide analyses and the level of funding available for projects. Nevertheless, these projects have provided a plethora of material that will aid in understanding the biology and evolution of these parasites, as well as identifying new targets that can be used to design urgently required drug treatments for the diseases they cause.
In selenoproteins, incorporation of the amino acid selenocysteine is specified by the UGA codon, usually a stop signal. The alternative decoding of UGA is conferred by an mRNA structure, the SECIS element, located in the 3'-untranslated region of the selenoprotein mRNA. Because of the non-standard use of the UGA codon, current computational gene prediction methods are unable to identify selenoproteins in the sequence of the eukaryotic genomes. Here we describe a method to predict selenoproteins in genomic sequences, which relies on the prediction of SECIS elements in coordination with the prediction of genes in which the strong codon bias characteristic of protein coding regions extends beyond a TGA codon interrupting the open reading frame. We applied the method to the Drosophila melanogaster genome, and predicted four potential selenoprotein genes. One of them belongs to a known family of selenoproteins, and we have tested experimentally two other predictions with positive results. Finally, we have characterized the expression pattern of these two novel selenoprotein genes.
There is an urgent need to develop novel classes of antibiotics to counter the threat of the spread of multiply resistant bacterial pathogens. The availability of the complete genome sequence of many pathogenic microbes provides information on every potential drug target and is an invaluable resource in the search for novel compounds. Here, we review the approaches being taken to exploit the genome databases through a combination of bioinformatics, transcriptional analysis, and a further understanding of the molecular basis of the disease process. The emphasis is changing from compound screening to target hunting, as the latter offers flexible ways to design and optimize the next generation of broad-spectrum antibiotics.
Positional cloning represents one of the most successful paradigm shifts in identifying the underlying patho-mechanisms in human disease. While traditional discovery tools focused on identifying defects at the tissue or cellular level, positional cloning identifies the damaged region of the genome as the preliminary step. While a large number of inherited single gene disorders have been mapped using this approach, a bottleneck still exists in combing through the genomic interval, often millions of nucleotides in length, to identify the nucleotide changes which result in a defective protein and subsequent disease. Along with the recent unravelling of the human genetic code, the development of massively parallel tools, such as microarrays, represent an equally important step forward in unraveling pathogenic genome dysfunctions. There are many emerging variants on microarray technology, such as expression arrays, exon arrays, array-based comparative genomic hybridization and sequencing arrays. Several of these platforms, if used properly, can accelerate the positional cloning process. The proper use of the platform is driven by knowledge of the underlying molecular defect being searched for and the operating characteristics of the array. The resultant insight forms the basis for improved molecular diagnostics and novel therapeutic targets.