Recent dermatophyte divergence revealed by comparative and phylogenetic analysis of mitochondrial genomes

<p>Abstract</p> <p>Background</p> <p>Dermatophytes are fungi that cause superficial infections of the skin, hair, and nails. They are the most common agents of fungal infections worldwide. Dermatophytic fungi constitute three genera, <it>Trichophyton</it>, <it>Epidermophyton</it>, and <it>Microsporum</it>, and the evolutionary relationships between these genera are epidemiologically important. Mitochondria are considered to be of monophyletic origin and mitochondrial sequences offer many advantages for phylogenetic studies. However, only one complete dermatophyte mitochondrial genome (<it>E. floccosum</it>) has previously been determined.</p> <p>Results</p> <p>The complete mitochondrial DNA sequences of five dermatophyte species, <it>T. rubrum </it>(26,985 bp), <it>T. mentagrophytes </it>(24,297 bp), <it>T. ajelloi </it>(28,530 bp), <it>M. canis </it>(23,943 bp) and <it>M. nanum </it>(24,105 bp) were determined. These were compared to the <it>E. floccosum </it>sequence. Mitochondrial genomes of all 6 species were found to harbor the same set of genes arranged identical order indicating that these dermatophytes are closely related. Genome size differences were largely due to variable lengths of non-coding intergenic regions and the presence/absence of introns. Phylogenetic analyses based on complete mitochondrial genomes reveals that the divergence of the dermatophyte clade was later than of other groups of pathogenic fungi.</p> <p>Conclusion</p> <p>This is the first systematic comparative genomic study on dermatophytes, a highly conserved and recently-diverged lineage of ascomycota fungi. The data reported here provide a basis for further exploration of interrelationships between dermatophytes and will contribute to the study of mitochondrial evolution in higher fungi.</p

Analysis of the dermatophyte Trichophyton rubrum expressed sequence tags

BACKGROUND: Dermatophytes are the primary causative agent of dermatophytoses, a disease that affects billions of individuals worldwide. Trichophyton rubrum is the most common of the superficial fungi. Although T. rubrum is a recognized pathogen for humans, little is known about how its transcriptional pattern is related to development of the fungus and establishment of disease. It is therefore necessary to identify genes whose expression is relevant to growth, metabolism and virulence of T. rubrum. RESULTS: We generated 10 cDNA libraries covering nearly the entire growth phase and used them to isolate 11,085 unique expressed sequence tags (ESTs), including 3,816 contigs and 7,269 singletons. Comparisons with the GenBank non-redundant (NR) protein database revealed putative functions or matched homologs from other organisms for 7,764 (70%) of the ESTs. The remaining 3,321 (30%) of ESTs were only weakly similar or not similar to known sequences, suggesting that these ESTs represent novel genes. CONCLUSION: The present data provide a comprehensive view of fungal physiological processes including metabolism, sexual and asexual growth cycles, signal transduction and pathogenic mechanisms

The use of global transcriptional analysis to reveal the biological and cellular events involved in distinct development phases of Trichophyton rubrum conidial germination

<p>Abstract</p> <p>Background</p> <p>Conidia are considered to be the primary cause of infections by <it>Trichophyton rubrum</it>.</p> <p>Results</p> <p>We have developed a cDNA microarray containing 10250 ESTs to monitor the transcriptional strategy of conidial germination. A total of 1561 genes that had their expression levels specially altered in the process were obtained and hierarchically clustered with respect to their expression profiles. By functional analysis, we provided a global view of an important biological system related to conidial germination, including characterization of the pattern of gene expression at sequential developmental phases, and changes of gene expression profiles corresponding to morphological transitions. We matched the EST sequences to GO terms in the <it>Saccharomyces </it>Genome Database (SGD). A number of homologues of <it>Saccharomyces cerevisiae </it>genes related to signalling pathways and some important cellular processes were found to be involved in <it>T. rubrum </it>germination. These genes and signalling pathways may play roles in distinct steps, such as activating conidial germination, maintenance of isotropic growth, establishment of cell polarity and morphological transitions.</p> <p>Conclusion</p> <p>Our results may provide insights into molecular mechanisms of conidial germination at the cell level, and may enhance our understanding of regulation of gene expression related to the morphological construction of <it>T. rubrum</it>.</p

A proteogenomic analysis of Shigella flexneri using 2D LC-MALDI TOF/TOF

<p>Abstract</p> <p>Background</p> <p>New strategies for high-throughput sequencing are constantly appearing, leading to a great increase in the number of completely sequenced genomes. Unfortunately, computational genome annotation is out of step with this progress. Thus, the accurate annotation of these genomes has become a bottleneck of knowledge acquisition.</p> <p>Results</p> <p>We exploited a proteogenomic approach to improve conventional genome annotation by integrating proteomic data with genomic information. Using <it>Shigella flexneri </it>2a as a model, we identified total 823 proteins, including 187 hypothetical proteins. Among them, three annotated ORFs were extended upstream through comprehensive analysis against an in-house N-terminal extension database. Two genes, which could not be translated to their full length because of stop codon 'mutations' induced by genome sequencing errors, were revised and annotated as fully functional genes. Above all, seven new ORFs were discovered, which were not predicted in <it>S. flexneri </it>2a str.301 by any other annotation approaches. The transcripts of four novel ORFs were confirmed by RT-PCR assay. Additionally, most of these novel ORFs were overlapping genes, some even nested within the coding region of other known genes.</p> <p>Conclusions</p> <p>Our findings demonstrate that current <it>Shigella </it>genome annotation methods are not perfect and need to be improved. Apart from the validation of predicted genes at the protein level, the additional features of proteogenomic tools include revision of annotation errors and discovery of novel ORFs. The complementary dataset could provide more targets for those interested in <it>Shigella </it>to perform functional studies.</p

Combining blue native polyacrylamide gel electrophoresis with liquid chromatography tandem mass spectrometry as an effective strategy for analyzing potential membrane protein complexes of Mycobacterium bovis bacillus Calmette-Guérin

<p>Abstract</p> <p>Background</p> <p>Tuberculosis is an infectious bacterial disease in humans caused primarily by <it>Mycobacterium tuberculosis</it>, and infects one-third of the world's total population. <it>Mycobacterium bovis </it>bacillus Calmette-Guérin (BCG) vaccine has been widely used to prevent tuberculosis worldwide since 1921. Membrane proteins play important roles in various cellular processes, and the protein-protein interactions involved in these processes may provide further information about molecular organization and cellular pathways. However, membrane proteins are notoriously under-represented by traditional two-dimensional polyacrylamide gel electrophoresis (2-D PAGE) and little is known about mycobacterial membrane and membrane-associated protein complexes. Here we investigated <it>M. bovis </it>BCG by an alternative proteomic strategy coupling blue native PAGE to liquid chromatography tandem mass spectrometry (LC-MS/MS) to characterize potential protein-protein interactions in membrane fractions.</p> <p>Results</p> <p>Using this approach, we analyzed native molecular composition of protein complexes in BCG membrane fractions. As a result, 40 proteins (including 12 integral membrane proteins), which were organized in 9 different gel bands, were unambiguous identified. The proteins identified have been experimentally confirmed using 2-D SDS PAGE. We identified MmpL8 and four neighboring proteins that were involved in lipid transport complexes, and all subunits of ATP synthase complex in their monomeric states. Two phenolpthiocerol synthases and three arabinosyltransferases belonging to individual operons were obtained in different gel bands. Furthermore, two giant multifunctional enzymes, Pks7 and Pks8, and four mycobacterial Hsp family members were determined. Additionally, seven ribosomal proteins involved in polyribosome complex and two subunits of the succinate dehydrogenase complex were also found. Notablely, some proteins with high hydrophobicity or multiple transmembrane helixes were identified well in our work.</p> <p>Conclusions</p> <p>In this study, we utilized LC-MS/MS in combination with blue native PAGE to characterize modular components of multiprotein complexes in BCG membrane fractions. The results demonstrated that the proteomic strategy was a reliable and reproducible tool for analysis of BCG multiprotein complexes. The identification in our study may provide some evidence for further study of BCG protein interaction.</p

Urine Proteome Profiling Predicts Lung Cancer from Control Cases and Other Tumors

Development of noninvasive, reliable biomarkers for lung cancer diagnosis has many clinical benefits knowing that most of lung cancer patients are diagnosed at the late stage. For this purpose, we conducted proteomic analyses of 231 human urine samples in healthy individuals (n = 33), benign pulmonary diseases (n = 40), lung cancer (n = 33), bladder cancer (n = 17), cervical cancer (n = 25), colorectal cancer (n = 22), esophageal cancer (n = 14), and gastric cancer (n = 47) patients collected from multiple medical centers. By random forest modeling, we nominated a list of urine proteins that could separate lung cancers from other cases. With a feature selection algorithm, we selected a panel of five urinary biomarkers (FTL: Ferritin light chain; MAPK1IP1L: Mitogen-Activated Protein Kinase 1 Interacting Protein 1 Like; FGB: Fibrinogen Beta Chain; RAB33B: RAB33B, Member RAS Oncogene Family; RAB15: RAB15, Member RAS Oncogene Family) and established a combinatorial model that can correctly classify the majority of lung cancer cases both in the training set (n = 46) and the test sets (n = 14–47 per set) with an AUC ranging from 0.8747 to 0.9853. A combination of five urinary biomarkers not only discriminates lung cancer patients from control groups but also differentiates lung cancer from other common tumors. The biomarker panel and the predictive model, when validated by more samples in a multi-center setting, may be used as an auxiliary diagnostic tool along with imaging technology for lung cancer detection. Keywords: Lung cancer, Machine learning, Urinary biomarker