Transcriptome analysis of 
            <i>Escherichia coli</i>
             using high-density oligonucleotide probe arrays

Tjaden, Brian; Saxena, Rini; Stolyar, Sergey; Haynor, David R.; Kolker, Eugene; Rosenow, Carsten

doi:10.1093/nar/gkf505

Cited by 174 publications

(136 citation statements)

References 38 publications

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“…Supporting this, four of the 16 missed RNAs (t44, c0343, c0465, is102) were detected in a single DNA microarray study (Tjaden et al 2002).…”

Section: Functional Analysis Of Clustered Rnas and Cdssmentioning

confidence: 64%

Single-pass classification of all noncoding sequences in a bacterial genome using phylogenetic profiles

Marchais¹,

Naville²,

Bohn³

et al. 2009

Genome Res.

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Identification and characterization of functional elements in the noncoding regions of genomes is an elusive and timeconsuming activity whose output does not keep up with the pace of genome sequencing. Hundreds of bacterial genomes lay unexploited in terms of noncoding sequence analysis, although they may conceal a wide diversity of novel RNA genes, riboswitches, or other regulatory elements. We describe a strategy that exploits the entirety of available bacterial genomes to classify all noncoding elements of a selected reference species in a single pass. This method clusters noncoding elements based on their profile of presence among species. Most noncoding RNAs (ncRNAs) display specific signatures that enable their grouping in distinct clusters, away from sequence conservation noise and other elements such as promoters. We submitted 24 ncRNA candidates from Staphylococcus aureus to experimental validation and confirmed the presence of seven novel small RNAs or riboswitches. Besides offering a powerful method for de novo ncRNA identification, the analysis of phylogenetic profiles opens a new path toward the identification of functional relationships between co-evolving coding and noncoding elements.[Supplemental material is available online at www.genome.org.]In all living organisms, the genome regions located between protein-coding sequences are home to a wide diversity of functional elements that include noncoding RNA (ncRNA) genes, DNA regulatory elements, untranslated regions (UTRs) of genes, transposable and self-replicating elements, and a variety of other transcribed or nontranscribed functional sequences. As these elements are often key players in gene regulation and thus in the global cell interaction network, their systematic identification and characterization has become a major challenge in biology.Computational protocols developed to collect and characterize noncoding elements in genomic sequences rely, to a large extent, on comparative genomics. The most common strategies involve, first, collecting sequences under selective pressure and, second, analyzing the aligned sequences using various classifiers that exploit criteria such as nucleotide composition, folding potential, fold conservation, or covariation between distant positions (Rivas and Eddy 2001;Washietl et al. 2005;Pedersen et al. 2006;Torarinsson et al. 2006). In general, such classifiers are designed to detect structured RNAs among noncoding elements with no further distinction between regulatory elements, repeats, or artifacts produced by sequence comparison algorithms.Comparative genomics entails a significant amount of expert intervention, especially in obtaining the right genome set to optimize the specificity and sensitivity of RNA detection. Although a number of studies have successfully identified ncRNAs in several animal (Missal et al. 2005;Washietl et al. 2007) and microbial genomes (Altuvia 2007), the pace at which such studies are performed and published lags far behind the rate of genome sequence output. Most of the complete...

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“…Supporting this, four of the 16 missed RNAs (t44, c0343, c0465, is102) were detected in a single DNA microarray study (Tjaden et al 2002).…”

Section: Functional Analysis Of Clustered Rnas and Cdssmentioning

confidence: 64%

Single-pass classification of all noncoding sequences in a bacterial genome using phylogenetic profiles

Marchais¹,

Naville²,

Bohn³

et al. 2009

Genome Res.

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“…This means that all H.in¯uenzae genes, con®dently identi®ed in both transcriptomic and proteomic experiments and further characterized below, can now be referred to as conserved hypothetical'. A comparison of the gene expression data for this set and their E.coli orthologs (where available), obtained in our earlier studies (15,16), reaf®rmed that these genes were also expressed in E.coli (Table 1S). In addition, seven of these genes (HI0017, HI0119, HI0315, HI0700, HI0847, HI1053 and HI1349) were predicted by Karlin et al (33) to be highly expressed based on their codon frequencies; for several other genes, high expression was predicted for their E.coli and Vibrio cholerae orthologs (33).…”

Section: Identi®cation Of`hypothetical' Genes Expressed In Hin¯uenzaementioning

confidence: 67%

“…Escherichia coli strain MG1655 gene expression was measured using Affymetrix whole-genome oligonucleotide arrays (15,16). Typically, each E.coli gene and both strands of each intergenic region were assayed with 15 probe pairs of 25mer nucleotide sequences (16).…”

Section: Gene Expression Analysismentioning

confidence: 99%

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Identification and functional analysis of 'hypothetical' genes expressed in Haemophilus influenzae

Kolker¹,

Makarova

Shabalina

et al. 2004

Nucleic Acids Research

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The progress in genome sequencing has led to a rapid accumulation in GenBank submissions of uncharacterized`hypothetical' genes. These genes, which have not been experimentally characterized and whose functions cannot be deduced from simple sequence comparisons alone, now comprise a signi®cant fraction of the public databases. Expression analyses of Haemophilus in¯uenzae cells using a combination of transcriptomic and proteomic approaches resulted in con®dent identi®ca-tion of 54`hypothetical' genes that were expressed in cells under normal growth conditions. In an attempt to understand the functions of these proteins, we used a variety of publicly available analysis tools. Close homologs in other species were detected for each of the 54`hypothetical' genes. For 16 of them, exact functional assignments could be found in one or more public databases. Additionally, we were able to suggest general functional characterization for 27 more genes (comprising~80% total). Findings from this analysis include the identi®cation of a pyruvate-formate lyase-like operon, likely to be expressed not only in H.in¯uenzae but also in several other bacteria. Further, we also observed three genes that are likely to participate in the transport and/or metabolism of sialic acid, an important component of the H.in¯uenzae lipo-oligosaccharide. Accurate functional annotation of uncharacterized genes calls for an integrative approach, combining expression studies with extensive computational analysis and curation, followed by eventual experimental veri®cation of the computational predictions.

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“…The top black line represents the genomic sequence with hatch marks every 100 base pairs, and the gray rectangles oligonucleotide tiling microarrays. Work in Arabidopsis thaliana, Escherichia coli, and Homo sapiens has demonstrated the utility of tiling arrays to map transcript locations (Shoemaker et al, 2001;Tjaden et al, 2002;Yamada et al, 2003;Schadt et al, 2004). In this type of array, both strands of a region of interest are represented on a microarray by a series of end-to-end oligonucleotides.…”

Section: Identification Of Rns-induced H Capsulatum Transcripts Usinmentioning

confidence: 99%

Identification ofHistoplasma capsulatumTranscripts Induced in Response to Reactive Nitrogen Species

Nittler

Hocking-Murray

Foo

et al. 2005

MBoC

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The pathogenic fungus Histoplasma capsulatum escapes innate immune defenses and colonizes host macrophages during infection. After the onset of adaptive immunity, the production of the antimicrobial effector nitric oxide ( ⅐ NO) restricts H. capsulatum replication. However, H. capsulatum can establish persistent infections, indicating that it survives in the host despite exposure to reactive nitrogen species (RNS). To understand how H. capsulatum responds to RNS, we determined the transcriptional profile of H. capsulatum to ⅐ NO-generating compounds using a shotgun genomic microarray. We identified 695 microarray clones that were induced >4-fold upon nitrosative stress. Because our microarray clones were generated from random fragments of genomic DNA, they did not necessarily correspond to H. capsulatum open reading frames. To identify induced genes, we used high-density oligonucleotide tiling arrays to determine the genomic boundaries and coding strand of 153 RNS-induced transcripts. Homologues of these genes in other organisms are involved in iron acquisition, energy production, stress response, protein folding/degradation, DNA repair, and ⅐ NO detoxification. Ectopic expression of one of these genes, a P450 nitric oxide reductase homologue, was sufficient to increase resistance of H. capsulatum to RNS in culture. We propose that H. capsulatum uses the pathways identified here to cope with RNS-induced damage during pathogenesis.

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Transcriptome analysis of Escherichia coli using high-density oligonucleotide probe arrays

Cited by 174 publications

References 38 publications

Single-pass classification of all noncoding sequences in a bacterial genome using phylogenetic profiles

Single-pass classification of all noncoding sequences in a bacterial genome using phylogenetic profiles

Identification and functional analysis of 'hypothetical' genes expressed in Haemophilus influenzae

Identification ofHistoplasma capsulatumTranscripts Induced in Response to Reactive Nitrogen Species

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