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PubMed2006

Niche metabolism in parasitic protozoa.

Complete or partial genome sequences have recently become available for several medically and evolutionarily important parasitic protozoa. Through the application of bioinformatics complete metabolic repertoires for these parasites can be predicted. For experimentally intractable parasites insight provided by metabolic maps generated in silico has been startling. At its more extreme end, such bioinformatics reckoning facilitated the discovery in some parasites of mitochondria remodelled beyond previous recognition, and the identification of a non-photosynthetic chloroplast relic in malarial parasites. However, for experimentally tractable parasites, mapping of the general metabolic terrain is only a first step in understanding how the parasite modulates its streamlined, yet still often puzzlingly complex, metabolism in order to complete life cycles within host, vector, or environment. This review provides a comparative overview and discussion of metabolic strategies used by several different parasitic protozoa in order to subvert and survive host defences, and illustrates how genomic data contribute to the elucidation of parasite metabolism.

Animals↗

PubMed2001

A cSNP map and database for human chromosome 21.

Single nucleotide polymorphisms (SNPs) are likely to contribute to the study of complex genetic diseases. The genomic sequence of human chromosome 21q was recently completed with 225 annotated genes, thus permitting efficient identification and precise mapping of potential cSNPs by bioinformatics approaches. Here we present a human chromosome 21 (HC21) cSNP database and the first chromosome-specific cSNP map. Potential cSNPs were generated using three approaches: (1) Alignment of the complete HC21 genomic sequence to cognate ESTs and mRNAs. Candidate cSNPs were automatically extracted using a novel program for context-dependent SNP identification that efficiently discriminates between true variation, poor quality sequencing, and paralogous gene alignments. (2) Multiple alignment of all known HC21 genes to all other human database entries. (3) Gene-targeted cSNP discovery. To date we have identified 377 cSNPs averaging ~1 SNP per 1.5 kb of transcribed sequence, covering 65% of known genes in the chromosome. Validation of our bioinformatics approach was demonstrated by a confirmation rate of 78% for the predicted cSNPs, and in total 32% of the cSNPs in our database have been confirmed. The database is publicly available at http://csnp.unige.ch or http://csnp.isb-sib.ch. These SNPs provide a tool to study the contribution of HC21 loci to complex diseases such as bipolar affective disorder and allele-specific contributions to Down syndrome phenotypes.

Base Composition↗

PubMed2002

The Bioperl toolkit: Perl modules for the life sciences.

The Bioperl project is an international open-source collaboration of biologists, bioinformaticians, and computer scientists that has evolved over the past 7 yr into the most comprehensive library of Perl modules available for managing and manipulating life-science information. Bioperl provides an easy-to-use, stable, and consistent programming interface for bioinformatics application programmers. The Bioperl modules have been successfully and repeatedly used to reduce otherwise complex tasks to only a few lines of code. The Bioperl object model has been proven to be flexible enough to support enterprise-level applications such as EnsEMBL, while maintaining an easy learning curve for novice Perl programmers. Bioperl is capable of executing analyses and processing results from programs such as BLAST, ClustalW, or the EMBOSS suite. Interoperation with modules written in Python and Java is supported through the evolving BioCORBA bridge. Bioperl provides access to data stores such as GenBank and SwissProt via a flexible series of sequence input/output modules, and to the emerging common sequence data storage format of the Open Bioinformatics Database Access project. This study describes the overall architecture of the toolkit, the problem domains that it addresses, and gives specific examples of how the toolkit can be used to solve common life-sciences problems. We conclude with a discussion of how the open-source nature of the project has contributed to the development effort.

Algorithms↗

PubMed2003

Identification of glycosylphosphatidylinositol-anchored proteins in Arabidopsis. A proteomic and genomic analysis.

In a recent bioinformatic analysis, we predicted the presence of multiple families of cell surface glycosylphosphatidylinositol (GPI)-anchored proteins (GAPs) in Arabidopsis (G.H.H. Borner, D.J. Sherrier, T.J. Stevens, I.T. Arkin, P. Dupree [2002] Plant Physiol 129: 486-499). A number of publications have since demonstrated the importance of predicted GAPs in diverse physiological processes including root development, cell wall integrity, and adhesion. However, direct experimental evidence for their GPI anchoring is mostly lacking. Here, we present the first, to our knowledge, large-scale proteomic identification of plant GAPs. Triton X-114 phase partitioning and sensitivity to phosphatidylinositol-specific phospholipase C were used to prepare GAP-rich fractions from Arabidopsis callus cells. Two-dimensional fluorescence difference gel electrophoresis and one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis demonstrated the existence of a large number of phospholipase C-sensitive Arabidopsis proteins. Using liquid chromatography-tandem mass spectrometry, 30 GAPs were identified, including six beta-1,3 glucanases, five phytocyanins, four fasciclin-like arabinogalactan proteins, four receptor-like proteins, two Hedgehog-interacting-like proteins, two putative glycerophosphodiesterases, a lipid transfer-like protein, a COBRA-like protein, SKU5, and SKS1. These results validate our previous bioinformatic analysis of the Arabidopsis protein database. Using the confirmed GAPs from the proteomic analysis to train the search algorithm, as well as improved genomic annotation, an updated in silico screen yielded 64 new candidates, raising the total to 248 predicted GAPs in Arabidopsis.

Arabidopsis↗

PubMed2006

Characterization of 43 non-protein-coding mRNA genes in Arabidopsis, including the MIR162a-derived transcripts.

Messenger RNAs that do not contain a long open reading frame (ORF) or non-protein-coding RNAs (npcRNAs) are an emerging novel class of transcripts. Their functions may involve the RNA molecule itself and/or short ORF-encoded peptides. npcRNA genes are difficult to identify using standard gene prediction programs that rely on the presence of relatively long ORFs. Here, we used detailed bioinformatic analyses of expressed sequence tag/cDNA databases to detect a restricted set of npcRNAs in the Arabidopsis (Arabidopsis thaliana) genome and further characterized these transcripts using a combination of bioinformatic and molecular approaches. Compositional analyses revealed strong nucleotide strand asymmetries in the npcRNAs, as well as a biased GC content, suggesting the existence of functional constraints on these RNAs. Thirteen of these transcripts display tissue-specific expression patterns, and three are regulated in conditions affecting root architecture. The npcRNA 78 gene contains the miR162 sequence in an alternative intron and corresponds to the MIR162a locus. Although DICER-LIKE 1 (DCL1) mRNA is known to be regulated by miR162-guided cleavage, its level does not change in a mir162a mutant. Alternative splicing of npcRNA 78 leads to several transcript isoforms, which all accumulate in a dcl1 mutant. This suggests that npcRNA 78 is a genuine substrate of DCL1 and that splicing of this microRNA primary transcript and miR162 processing are competitive nuclear events. Our results provide new insights into Arabidopsis npcRNA biology and the potential roles of these genes.

Alternative Splicing↗

PubMed2003

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification.

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.

Algorithms↗

PubMed2005

The potential of proteomics and peptidomics for allergy and asthma research.

Progress in the field of proteomics, the branch of biology that studies the full set of proteins derived from a given genome, is moving fast. Two-dimensional gel electrophoresis (2DG) separation of complex protein mixtures and the subsequent analysis of isolated protein spots by mass spectrometry allow fast and accurate identification of proteins. The comparison of spots from different samples separated on customized 2D gels allows the detection of punctual differences in their mobility and facilitates tracing back differences in protein expression, presence of isoforms, splice variants and posttranslational modifications by mass spectrometry. In spite of significant analytical challenges owing to the high complexity of the proteome and the challenge deriving from the necessity to process huge amounts of raw data generated by mass spectrometric profiling, proteomics has evolved to an indispensable tool in life sciences. A restricted window of the proteome that consists of peptides and small proteins not easily manageable by conventional gel electrophoresis prompted the development of separation methods based on liquid chromatography. This new research field termed peptidomics already contributed, together with proteomics to enlarge our knowledge about biological processes and supported by sophisticated bioinformatics tools, to the discovery of new diagnostic and therapeutic targets. The technological capabilities of biophysical separation, mass spectrometry and bioinformatics form the basis of discovery programs that aim at mining the proteome starting from microgram amounts of protein extracts derived from body fluids and tissues. Proteomics and peptidomics have a great potential to speed up allergy and asthma research, where disease- and tissue-specific samples are easy to obtain.

Asthma↗

PubMed2007

The modular nature of genetic diseases.

Evidence from many sources suggests that similar phenotypes are begotten by functionally related genes. This is most obvious in the case of genetically heterogeneous diseases such as Fanconi anemia, Bardet-Biedl or Usher syndrome, where the various genes work together in a single biological module. Such modules can be a multiprotein complex, a pathway, or a single cellular or subcellular organelle. This observation suggests a number of hypotheses about the human phenome that are now beginning to be explored. First, there is now good evidence from bioinformatic analyses that human genetic diseases can be clustered on the basis of their phenotypic similarities and that such a clustering represents true biological relationships of the genes involved. Second, one may use such phenotypic similarity to predict and then test for the contribution of apparently unrelated genes to the same functional module. This concept is now being systematically tested for several diseases. Most recently, a systematic yeast two-hybrid screen of all known genes for inherited ataxias indicated that they all form part of a single extended protein-protein interaction network. Third, one can use bioinformatics to make predictions about new genes for diseases that form part of the same phenotype cluster. This is done by starting from the known disease genes and then searching for genes that share one or more functional attributes such as gene expression pattern, coevolution, or gene ontology. Ultimately, one may expect that a modular view of disease genes should help the rapid identification of additional disease genes for multifactorial diseases once the first few contributing genes (or environmental factors) have been reliably identified.

Computational Biology↗

PubMed2004

Pharmacoproteomic approach to the study of drug mode of action, toxicity, and resistance: applications in diabetes and cancer.

Proteomics is a powerful technique for investigating protein expression profiles in biological systems and their modifications in response to stimuli or to particular physiological or pathophysiological conditions. It is therefore a technique of choice for the study of drug mode of action, side-effects, toxicity and resistance. It is also a valuable approach for the discovery of new drug targets. All these proteomic applications to pharmacological issues may be called pharmacoproteomics. The pharmacoproteomic approach could be particularly useful for the identification of molecular alterations implicated in type 2 diabetes and for further characterization of existing or new drugs. In oncology, proteomics is widely used for the identification of tumour-specific protein markers, and pharmacoproteomics is used for the evaluation of chemotherapy, particularly for the characterization of drug-resistance mechanisms. The large amount of data generated by pharmacoproteomic screening requires the use of bioinformatic tools to insure a pertinent interpretation. Herein, we review the applications of pharmacoproteomics to the study of type 2 diabetes and to chemoresistance in different types of cancer and the current state of this technology in these pathologies. We also suggest a number of bioinformatic solutions for proteomic data management.

Animals↗

PubMedDate not supplied

Proteomic analysis for early detection of ovarian cancer: a realistic approach?

Ovarian cancer is a multifaceted disease wherein most women are diagnosed with advanced stage disease. One of the most imperative issues in ovarian cancer is early detection. Biomarkers that allow cancer detection at stage I, a time when the disease is amenable to surgical and chemotherapeutic cure in over 90% of patients, can dramatically alter the horizon for women with this disease. Recent developments in mass spectroscopy and protein chip technology coupled with bioinformatics have been applied to biomarker discovery. The complexity of the proteome is a rich resource from which the patterns can be gleaned; the pattern rather than its component parts is the diagnostic. Serum is a key source of putative protein biomarkers, and, by its nature, can reflect organ-confined events. Pioneering use of mass spectroscopy coupled with bioinformatics has been demonstrated as being capable of distinguishing serum protein pattern signatures of ovarian cancer in patients with early- and late-stage disease. This is a sensitive, precise, and promising tool for which further validation is needed to confirm that ovarian cancer serum protein signature patterns can be a robust biomarker approach for ovarian cancer diagnosis, yielding improved patient outcome and reducing the death and suffering from ovarian cancer.

Biomarkers, Tumor↗

PubMed2006

Biochemical, molecular characterization, and glycoproteomic analyses of alpha(1)-proteinase inhibitor products used for replacement therapy.

BACKGROUND: Isoelectric focusing (IEF) of alpha(1)-proteinase inhibitor (A1PI) shows that commercial products and plasma have different glycoisoform band patterns. Those in Aralast (Grifols Biologicals) reflect an anodal shift of glycoisoforms, which has caused concern. The protein, including glycoproteomic analyses, and structural features of A1PI products were investigated by state-of-the-art techniques. STUDY DESIGN AND METHODS: Batches from Aralast, Prolastin (Bayer), and Zemaira (Aventis Behring LLC) were analyzed by high-resolution IEF and high-performance size-exclusion chromatography (HP-SEC). Preparative separated isoforms from IEF were further purified by chromatography and subjected to mass spectrometry for sequence analyses, peptide mapping, and glycosylation analysis. Deamidation was quantified by enzymatic isoaspartate detection. Multiple sequence alignments and structural bioinformatics analyses were performed. RESULTS: In HP-SEC, Prolastin had the highest aggregate content at approximately 30 percent. Isoforms from all products purified by high-resolution IEF were sequenced with an amino acid coverage of more than 98 percent. Deamidation of Asn116 and Asn314 in A1PI was to found to some extent in all products and confirmed quantitatively by enzymatic analysis. There were no signs of methionine oxidation. Cys232 was found to be cysteinylated in A1PI in Prolastin and Aralast as in plasma, but not in Zemaira. All products showed truncation of the C-terminal lysine. Intact A1PI concentrates contained mainly diantennary, disialylated and smaller amounts of triantennary, trisialylated N-glycans. The percentage of fucosylation was similar in all products. Site-specific glycan analysis revealed bands M6 contained only diantennary glycans, whereas the more acidic bands M4 and M2 also carried triantennary structures. The most acidic isoforms, M2 in Prolastin and Zemaira and M0 in Aralast, additionally exhibited tetraantennary N-glycans. CONCLUSION: Protein chemical characterization of A1PI showed that all A1PI products to some extent differ from A1PI circulating in human plasma. Bioinformatic analysis indicated that removal of C-terminal Lys394 and cysteinylation of Cys232 are unlikely to affect structure and/or function of A1PI but cysteinylation may influence interaction between A1PI and its physiologic ligands. Aralast, Prolastin, and Zemaira contain the same set of N-glycans in the same ratios as those in normal human plasma A1PI. Tri- and tetraantennary structures are responsible for the partitioning into IEF isoforms, with the migration shift of Aralast not being due to any difference in the N-glycosylation, but to the partial loss of the C-terminal lysine.

Chromatography, High Pressure Liquid↗

PubMed2006

Application of surface-enhanced laser desorption/ionization time-of-flight-based serum proteomic array technique for the early diagnosis of prostate cancer.

AIM: To identify the serum biomarkers of prostate cancer (PCa) by protein chip and bioinformatics. METHODS: Serum samples from 83 PCa patients and 95 healthy men were taken from a mass screening in Changchun, China. Protein profiling was carried out using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS). The data of spectra were analyzed using two bioinformatics tools. RESULTS: Eighteen serum differential proteins were identified in the PCa group compared with the control group (P < 0.01). There were four proteins at the higher serum level and 14 proteins at the lower serum level in the PCa group. A decision tree classification algorithm that used an eight-protein mass pattern was developed to correctly classify the samples. A sensitivity of 92.0% and a specificity of 96.7% for the study group were obtained by comparing the PCa and control groups. CONCLUSION: We identified new serum biomarkers of PCa. SELDI-TOF MS coupled with a decision tree classification algorithm will provide a highly accurate and innovative approach for the early diagnosis of PCa.

Aged↗

PubMed2002

Genetic determinants of coxsackievirus B3 pathogenesis.

The development of high throughput genomic and bioinformatic analysis tools, coupled with established molecular techniques, has allowed new insights into the pathogenesis of infectious diseases. In humans, coxasackievirus B3 (CVB3) is the primary etiological agent of viral myocarditis, an inflammatory disease process involving the heart muscle. Early host cellular survival and apoptotic mechanisms during viral infections, as well as immune events, affect myocarditis progression and outcome. Therefore, our laboratory has been keenly interested in infectomics, defined here as the transcriptional events of both virus and host. We first elucidated up- or downregulated transcriptional activities in CVB3-infected hearts by mRNA differential display. Further characterization of these regulated genes including Nip21, IP10, and IGTPase, and study of their role in CVB3-infection are underway. In further dissection of the stages of myocarditis-peak viremia, inflammatory infiltration and tissue repair-we used cDNA microarrays to probe differential gene expression in the myocardium following virus infection. Following virus infection, there are global decreases in metabolic and mitochondrial genes, increases in signaling genes and distinctive patterns in other functional groups. To establish early gene expression profiles in infected cells by themselves, we also used oligonucleotide arrays in an in vitro model of CVB3 infection. Notably, we have found increased expression of transcription factors c-fos and c-jun down-stream of extracellular signal-related kinase, a pathway which is crucial for virus replication and pathogenesis. Our investigations based on gene profiling following CVB3 infection have thus far been fruitful in providing new experimental leads. High throughput genetic analysis has allowed us to simultaneously try on greater than 12,000 potential genetic "glass slippers." Our in vitro experimental plan has enabled us to chart prominent patterns of gene expression, analyzed by novel bioinformatic approaches, and to separate varied and potentially significant gene expression events.

Computational Biology↗

PubMed2003

From genes to proteins: in vitro expression of rickettsial proteins.

The availability of the complete genome sequences of several organisms allows the comparative analysis of genomes, a branch of bioinformatics known as genomics. With this approach, much can be learned about the biology of organisms that are difficult to culture, even when few, if any, of their proteins have been isolated and studied directly. We have focused our interest on Rickettsia conorii, an obligate intracellular bacterium responsible for Mediterranean spotted fever, a disease endemic in southern Europe. While bioinformatic annotation of the complete genome of this bacteria has allowed identification of 1,374 genes, a large number of them remain functionally uncharacterized. The final goal of many experiments in molecular biology is to use biological systems to synthesize the protein encoded by the gene being studied. Because three-dimensional structures are more resilient to evolution and change than amino acid sequences, structure determination of some open reading frames should also exhibit structural similarity to previously described protein families. We have thus initiated a systematic expression and structure determination program for the proteins encoded by rickettsial genes of interest. We have cloned different genes of R. conorii by recombinational cloning (GATEWAY), Invitrogen) a method that uses in vitro site-specific recombination to accomplish a directional cloning of PCR products and the subsequent automatic subcloning of the DNA segment into new vector backbones at high efficiency. The constructions in p-Dest17 yielded several clones able to express recombinant proteins with a C-terminal histidine tag. Expression of corresponding proteins was then performed using a cell-free protein expression system (Rapid Translation System, RTS, Roche Diagnostics). The recombinational cloning approach coupled to RTS provides an approach to rapid optimization of protein expression and is very useful to express rickettsial proteins. Moreover, this system is able to overcome some of the limitations encountered with rickettsial proteins highly toxic for E. coli or insect cells.

Bacterial Proteins↗

PubMed2004

Inactivation of an ABC transporter gene, mcyH, results in loss of microcystin production in the cyanobacterium Microcystis aeruginosa PCC 7806.

The cyanobacterium Microcystis aeruginosa is widely known for its production of the potent hepatotoxin microcystin. Microcystin is synthesized nonribosomally by the thiotemplate function of a large, modular enzyme complex encoded within the 55-kb microcystin synthetase (mcy) gene cluster. Also encoded within the mcy gene cluster is a putative ATP binding cassette (ABC) transporter, McyH. This study details the bioinformatic and mutational analyses of McyH and offers functional predictions for the hypothetical protein. The transporter is putatively comprised of two homodimers, each with an N-terminal hydrophobic domain and a C-terminal ATPase. Phylogenetically, McyH was found to cluster with members of the ABC-A1 subgroup of ABC ATPases, suggesting an export function for the protein. Two mcyH null mutant (DeltamcyH) strains were constructed by partial deletion of the mcyH gene. Microcystin production was completely absent in these strains. While the mcyH deletion had no apparent effect on the transcription of other mcy genes, the complete microcystin biosynthesis enzyme complex could not be detected in DeltamcyH mutant strains. Finally, expression levels of McyH in the wild type and in DeltamcyA, DeltamcyB, and DeltamcyH mutants were investigated by using immunoblotting with an anti-McyH antibody. Expression of McyH was found to be reduced in DeltamcyA and DeltamcyB mutants and completely absent in the DeltamcyH mutant. By virtue of its association with the mcy gene cluster and the bioinformatic and experimental data presented in this study, we predict that McyH functions as a microcystin exporter and is, in addition, intimately associated with the microcystin biosynthesis pathway.

ATP-Binding Cassette Transporters↗

PubMed2003

Recognition of mycobacterial epitopes by T cells across mammalian species and use of a program that predicts human HLA-DR binding peptides to predict bovine epitopes.

Bioinformatics tools have the potential to accelerate research into the design of vaccines and diagnostic tests by exploiting genome sequences. The aim of this study was to assess whether in silico analysis could be combined with in vitro screening methods to rapidly identify peptides that are immunogenic during Mycobacterium bovis infection of cattle. In the first instance the M. bovis-derived protein ESAT-6 was used as a model antigen to describe peptides containing T-cell epitopes that were frequently recognized across mammalian species, including natural hosts for tuberculosis (humans and cattle) and small-animal models of tuberculosis (mice and guinea pigs). Having demonstrated that some peptides could be recognized by T cells from a number of M. bovis-infected hosts, we tested whether a virtual-matrix-based human prediction program (ProPred) could identify peptides that were recognized by T cells from M. bovis-infected cattle. In this study, 73% of the experimentally defined peptides from 10 M. bovis antigens that were recognized by bovine T cells contained motifs predicted by ProPred. Finally, in validating this observation, we showed that three of five peptides from the mycobacterial antigen Rv3019c that were predicted to contain HLA-DR-restricted epitopes were recognized by T cells from M. bovis-infected cattle. The results obtained in this study support the approach of using bioinformatics to increase the efficiency of epitope screening and selection.

Animals↗

PubMed2006

Characterization of Francisella tularensis outer membrane proteins.

Francisella tularensis is a gram-negative coccobacillus that is capable of causing severe, fatal disease in a number of mammalian species, including humans. Little is known about the proteins that are surface exposed on the outer membrane (OM) of F. tularensis, yet identification of such proteins is potentially fundamental to understanding the initial infection process, intracellular survival, virulence, immune evasion and, ultimately, vaccine development. To facilitate the identification of putative F. tularensis outer membrane proteins (OMPs), the genomes of both the type A strain (Schu S4) and type B strain (LVS) were subjected to six bioinformatic analyses for OMP signatures. Compilation of the bioinformatic predictions highlighted 16 putative OMPs, which were cloned and expressed for the generation of polyclonal antisera. Total membranes were extracted from both Schu S4 and LVS by spheroplasting and osmotic lysis, followed by sucrose density gradient centrifugation, which separated OMs from cytoplasmic (inner) membrane and other cellular compartments. Validation of OM separation and enrichment was confirmed by probing sucrose gradient fractions with antibodies to putative OMPs and inner membrane proteins. F. tularensis OMs typically migrated in sucrose gradients between densities of 1.17 and 1.20 g/ml, which differed from densities typically observed for other gram-negative bacteria (1.21 to 1.24 g/ml). Finally, the identities of immunogenic proteins were determined by separation on two-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis and mass spectrometric analysis. This is the first report of a direct method for F. tularensis OM isolation that, in combination with computational predictions, offers a more comprehensive approach for the characterization of F. tularensis OMPs.

Animals↗

PubMed2005

Genomic analysis of anaerobic respiration in the archaeon Halobacterium sp. strain NRC-1: dimethyl sulfoxide and trimethylamine N-oxide as terminal electron acceptors.

We have investigated anaerobic respiration of the archaeal model organism Halobacterium sp. strain NRC-1 by using phenotypic and genetic analysis, bioinformatics, and transcriptome analysis. NRC-1 was found to grow on either dimethyl sulfoxide (DMSO) or trimethylamine N-oxide (TMAO) as the sole terminal electron acceptor, with a doubling time of 1 day. An operon, dmsREABCD, encoding a putative regulatory protein, DmsR, a molybdopterin oxidoreductase of the DMSO reductase family (DmsEABC), and a molecular chaperone (DmsD) was identified by bioinformatics and confirmed as a transcriptional unit by reverse transcriptase PCR analysis. dmsR, dmsA, and dmsD in-frame deletion mutants were individually constructed. Phenotypic analysis demonstrated that dmsR, dmsA, and dmsD are required for anaerobic respiration on DMSO and TMAO. The requirement for dmsR, whose predicted product contains a DNA-binding domain similar to that of the Bat family of activators (COG3413), indicated that it functions as an activator. A cysteine-rich domain was found in the dmsR gene, which may be involved in oxygen sensing. Microarray analysis using a whole-genome 60-mer oligonucleotide array showed that the dms operon is induced during anaerobic respiration. Comparison of dmsR+ and DeltadmsR strains by use of microarrays showed that the induction of the dmsEABCD operon is dependent on a functional dmsR gene, consistent with its action as a transcriptional activator. Our results clearly establish the genes required for anaerobic respiration using DMSO and TMAO in an archaeon for the first time.

Anaerobiosis↗