[show abstract][hide abstract] ABSTRACT: Staphylococcus aureus possesses 16 two-component systems (TCSs), two of which (GraRS and NsaRS) belong to the intramembrane-sensing histidine kinase (IM-HK) family, which is conserved within the firmicutes. NsaRS has recently been documented as being important for nisin resistance in S. aureus. In this study, we present a characterization of NsaRS and reveal that, as with other IM-HK TCSs, it responds to disruptions in the cell envelope. Analysis using a lacZ reporter-gene fusion demonstrated that nsaRS expression is upregulated by a variety of cell-envelope-damaging antibiotics, including phosphomycin, ampicillin, nisin, gramicidin, carbonyl cyanide m-chlorophenylhydrazone and penicillin G. Additionally, we reveal that NsaRS regulates a downstream transporter NsaAB during nisin-induced stress. NsaS mutants also display a 200-fold decreased ability to develop resistance to the cell-wall-targeting antibiotic bacitracin. Microarray analysis reveals that the transcription of 245 genes is altered in an nsaS mutant, with the vast majority being downregulated. Included within this list are genes involved in transport, drug resistance, cell envelope synthesis, transcriptional regulation, amino acid metabolism and virulence. Using inductively coupled plasma-MS we observed a decrease in intracellular divalent metal ions in an nsaS mutant when grown under low abundance conditions. Characterization of cells using electron microscopy reveals that nsaS mutants have alterations in cell envelope structure. Finally, a variety of virulence-related phenotypes are impaired in nsaS mutants, including biofilm formation, resistance to killing by human macrophages and survival in whole human blood. Thus, NsaRS is important in sensing cell damage in S. aureus and functions to reprogram gene expression to modify cell envelope architecture, facilitating adaptation and survival.
[show abstract][hide abstract] ABSTRACT: The human malaria parasite Plasmodium falciparum survives pressures from the host immune system and antimalarial drugs by modifying its genome. Genetic recombination and nucleotide substitution are the two major mechanisms that the parasite employs to generate genome diversity. A better understanding of these mechanisms may provide important information for studying parasite evolution, immune evasion and drug resistance.
Here, we used a high-density tiling array to estimate the genetic recombination rate among 32 progeny of a P. falciparum genetic cross (7G8 × GB4). We detected 638 recombination events and constructed a high-resolution genetic map. Comparing genetic and physical maps, we obtained an overall recombination rate of 9.6 kb per centimorgan and identified 54 candidate recombination hotspots. Similar to centromeres in other organisms, the sequences of P. falciparum centromeres are found in chromosome regions largely devoid of recombination activity. Motifs enriched in hotspots were also identified, including a 12-bp G/C-rich motif with 3-bp periodicity that may interact with a protein containing 11 predicted zinc finger arrays.
These results show that the P. falciparum genome has a high recombination rate, although it also follows the overall rule of meiosis in eukaryotes with an average of approximately one crossover per chromosome per meiosis. GC-rich repetitive motifs identified in the hotspot sequences may play a role in the high recombination rate observed. The lack of recombination activity in centromeric regions is consistent with the observations of reduced recombination near the centromeres of other organisms.
[show abstract][hide abstract] ABSTRACT: The fat body is the main organ of intermediary metabolism in insects and the principal source of hemolymph proteins. As part of our ongoing efforts to understand mosquito fat body physiology and to identify novel targets for insect control, we have conducted a transcriptome analysis of the fat body of Aedes aegypti before and in response to blood feeding.
We created two fat body non-normalized EST libraries, one from mosquito fat bodies non-blood fed (NBF) and another from mosquitoes 24 hrs post-blood meal (PBM). 454 pyrosequencing of the non-normalized libraries resulted in 204,578 useable reads from the NBF sample and 323,474 useable reads from the PBM sample. Alignment of reads to the existing reference Ae. aegypti transcript libraries for analysis of differential expression between NBF and PBM samples revealed 116,912 and 115,051 matches, respectively. De novo assembly of the reads from the NBF sample resulted in 15,456 contigs, and assembly of the reads from the PBM sample resulted in 15,010 contigs. Collectively, 123 novel transcripts were identified within these contigs. Prominently expressed transcripts in the NBF fat body library were represented by transcripts encoding ribosomal proteins. Thirty-five point four percent of all reads in the PBM library were represented by transcripts that encode yolk proteins. The most highly expressed were transcripts encoding members of the cathepsin b, vitellogenin, vitellogenic carboxypeptidase, and vitelline membrane protein families.
The two fat body transcriptomes were considerably different from each other in terms of transcript expression in terms of abundances of transcripts and genes expressed. They reflect the physiological shift of the pre-feeding fat body from a resting state to vitellogenic gene expression after feeding.
PLoS ONE 01/2011; 6(7):e22573. · 3.73 Impact Factor
[show abstract][hide abstract] ABSTRACT: Diclofenac is a non-steroidal anti-inflammatory drug (NSAID) which has been shown to increase the susceptibility of various bacteria to antimicrobials and demonstrated to have broad antimicrobial activity. This study describes transcriptome alterations in S. aureus strain COL grown with diclofenac and characterizes the effects of this NSAID on antibiotic susceptibility in laboratory, clinical and diclofenac reduced-susceptibility (DcRS) S. aureus strains.
Transcriptional alterations in response to growth with diclofenac were measured using S. aureus gene expression microarrays and quantitative real-time PCR. Antimicrobial susceptibility was determined by agar diffusion MICs and gradient plate analysis. Ciprofloxacin accumulation was measured by fluorescence spectrophotometry.
Growth of S. aureus strain COL with 80 μg/ml (0.2 × MIC) of diclofenac resulted in the significant alteration by ≥2-fold of 458 genes. These represented genes encoding proteins for transport and binding, protein and DNA synthesis, and the cell envelope. Notable alterations included the strong down-regulation of antimicrobial efflux pumps including mepRAB and a putative emrAB/qacA-family pump. Diclofenac up-regulated sigB (σB), encoding an alternative sigma factor which has been shown to be important for antimicrobial resistance. Staphylococcus aureus microarray metadatabase (SAMMD) analysis further revealed that 46% of genes differentially-expressed with diclofenac are also σB-regulated. Diclofenac altered S. aureus susceptibility to multiple antibiotics in a strain-dependent manner. Susceptibility increased for ciprofloxacin, ofloxacin and norfloxacin, decreased for oxacillin and vancomycin, and did not change for tetracycline or chloramphenicol. Mutation to DcRS did not affect susceptibility to the above antibiotics. Reduced ciprofloxacin MICs with diclofenac in strain BB255, were not associated with increased drug accumulation.
The results of this study suggest that diclofenac influences antibiotic susceptibility in S. aureus, in part, by altering the expression of regulatory and structural genes associated with cell wall biosynthesis/turnover and transport.
Annals of Clinical Microbiology and Antimicrobials 01/2011; 10:30. · 1.62 Impact Factor
[show abstract][hide abstract] ABSTRACT: Microarray and Next-Gen Sequencing technologies have generated a huge amount of genome scale expression data. Many software tools permit extraction of expression profiles/data for a given gene or a set of genes. It has been established that genes with similar expression profiles are likely to be associated functionally. Unfortunately, there has been no simple online application for mining expression databases for genes with similar profiles. We have developed a method and an associated web tool - Microarray Meta-Miner (http://exon.niaid.nih.gov/MMM/) - to identify genes with expression profiles similar to that of the query gene. Using the microarray meta-data from the ATLAS gene expression database and the NIH Biowulf cluster computing facility, we computed eight different vector similarity metrics (Pearson/Spearman/Kendall correlation coefficients, mutual information, chi-square, Euclidean distance, purity, and cosine similarity) for every gene's expression profile against every other gene's profile. Top scoring hits from the individual metrics were integrated and scored for overlap, generating a matrix of similar expressions. The MMM web tool returns the list of similarly expressed genes for the users query, along with links to annotations, individual expression profiles, and all expression profiles. MMM also retrieves and displays the known interaction data between the similarly expressed genes from the STRING interaction database. The experiment distribution information for the set of similarly expressed genes is also displayed. The current version of MMM supports only human data. Future plans include adding support for other organisms.
[show abstract][hide abstract] ABSTRACT: We used the Staphylococcus aureus microarray meta-database (SAMMD) to compare the transcriptional profiles defined by different experiments targeting the same phenomenon in S. aureus. We specifically examined differences associated with the accessory gene regulator (agr), the staphylococcal accessory regulator (sarA), and growth within a biofilm. We found that in all three cases, there was a striking lack of overlap between the transcriptional profiles. For instance, while all experiments focusing on biofilm formation identified hundreds of differentially expressed genes, only one of these was common to all transcriptomes. Several factors could potentially contribute to this variability including the use of different biofilm models, different growth media, different microarray platforms, and, perhaps most importantly, different strains of S. aureus. The last appeared to be particularly important in the case of the agr and sarA transcriptomes. While these results emphasize the need to introduce some degree of standardization into genome-scale, microarray-based transcriptional profiling experiments, they also demonstrate the need to consider multiple strains of S. aureus in order to avoid any strain-specific bias in the interpretation of results. Our comparisons also illustrate how identification of strain-dependent differences using SAMMD can lead to the development of specific hypotheses that can then be experimentally addressed. Based on this, we have added new features to SAMMD that allow for direct comparisons between transcriptional profiling experiments.
[show abstract][hide abstract] ABSTRACT: Staphylococcus aureus is an important pathogen that forms biofilms. The global regulator sarA is essential for biofilm formation. Since the modulator of sarA (msa) is required for full expression of sarA and regulates several virulence factors, we examined the capacity of the msa mutant to form biofilm.
We found that mutation of msa results in reduced expression of sarA in biofilm and that the msa mutant formed a weak and unstable biofilm. The msa mutant is able to adhere to surfaces and begins to form biofilm but fails to mature indicating that the defect of the msa mutant biofilm is in the accumulation stage but not in primary adhesion.
The msa gene plays an important role in biofilm development which is likely due to its role in modulating the expression of sarA. This finding is significant because it identifies a new gene that plays a role in the development of biofilm.
[show abstract][hide abstract] ABSTRACT: Fusidic acid interferes with the release of elongation factor G (EF-G) after the translocation step of protein synthesis. The objective of this study was to characterize the fusidic acid stimulon of a fusidic acid-susceptible strain of Staphylococcus aureus (SH1000).
S. aureus microarrays and real-time PCR determined transcriptome alterations occurring in SH1000 grown with fusidic acid. The Staphylococcus aureus microarray meta-database (SAMMD) compared and contrasted the SH1000 fusidic stimulon with 89 other S. aureus transcriptional datasets. Fusidic acid gradient analyses with mutant-parent strain pairs were used to identify genes required for intrinsic fusidic acid susceptibility identified during transcriptional analysis.
Many genes altered by fusidic acid challenge are associated with protein synthesis. SAMMD analysis determined that the fusidic acid stimulon has the greatest overlap with the S. aureus cold shock and stringent responses. Six out of nine peptidoglycan hydrolase genes making up the two component YycFG regulon were also up-regulated by fusidic acid, as were a carboxylesterase gene (est) and two putative drug efflux pump genes (emr-qac1 and macA). Genes down-regulated by fusidic acid induction encoded a putative secreted acid phosphatase and a number of protease genes. Roles for the agr operon, the peptidoglycan hydrolase gene isaA and two proteases (htrA1 and htrA2) in the expression of fusidic acid susceptibility were revealed.
The SH1000 fusidic acid stimulon includes genes involved with two stress responses, YycFG-regulated cell wall metabolism, drug efflux, and protein synthesis and turnover.
Journal of Antimicrobial Chemotherapy 10/2008; 62(6):1207-14. · 5.34 Impact Factor
[show abstract][hide abstract] ABSTRACT: Numerous methods are available to compare results of multiple microarray studies. One of the simplest but most effective of these procedures is to examine the overlap of resulting gene lists in a Venn diagram. Venn diagrams are graphical ways of representing interactions among sets to display information that can be read easily. Here we propose a simple but effective web application creating Venn diagrams from two or three gene lists. Each gene in the group list has link to the related information in NCBI's Entrez Nucleotide database. AVAILABILITY: GeneVenn is available for free at http://mcbc.usm.edu/genevenn/
[show abstract][hide abstract] ABSTRACT: Staphylococcus aureus is an important human pathogen, causing a wide variety of diseases ranging from superficial skin infections to severe life threatening infections. S. aureus is one of the leading causes of nosocomial infections. Its ability to resist multiple antibiotics poses a growing public health problem. In order to understand the mechanism of pathogenesis of S. aureus, several global expression profiles have been developed. These transcriptional profiles included regulatory mutants of S. aureus and growth of wild type under different growth conditions. The abundance of these profiles has generated a large amount of data without a uniform annotation system to comprehensively examine them. We report the development of the Staphylococcus aureus Microarray meta-database (SAMMD) which includes data from all the published transcriptional profiles. SAMMD is a web-accessible database that helps users to perform a variety of analysis against and within the existing transcriptional profiles.
SAMMD is hosted and available at http://www.bioinformatics.org/sammd/. Currently there are over 9500 entries for regulated genes, from 67 microarray experiments. SAMMD will help staphylococcal scientists to analyze their expression data and understand it at global level. It will also allow scientists to compare and contrast their transcriptome to that of the other published transcriptomes.
[show abstract][hide abstract] ABSTRACT: Staphylococcus aureus is a human pathogen that causes a wide variety of life-threatening infections using a large number of virulence factors. One of the major global regulators used by S. aureus is the staphylococcal accessory regulator (sarA). We have identified and characterized a new gene (modulator of sarA: msa) that modulates the expression of sarA. Genetic and functional analysis shows that msa has a global effect on gene expression in S. aureus. However, the mechanism of Msa function is still unknown. Function predictions of Msa are complicated by the fact that it does not have a homologous partner in any other organism. This work aims at predicting the structure and function of the Msa protein.
Preliminary sequence analysis showed that Msa is a putative membrane protein. It would therefore be very difficult to purify and crystallize Msa in order to acquire structure information about this protein. We have used several computational tools to predict the physico-chemical properties, secondary structural features, topology, 3D tertiary structure, binding sites, motifs/patterns/domains and cellular location. We have built a consensus that is derived from analysis using different algorithms to predict several structural features. We confirm that Msa is a putative membrane protein with three transmembrane regions. We also predict that Msa has phosphorylation sites and binding sites suggesting functions in signal transduction.
Based on our predictions we hypothesise that Msa is a novel signal transducer that might be involved in the interaction of the S. aureus with its environment.
[show abstract][hide abstract] ABSTRACT: Naturally occurring antimicrobial peptides are currently being explored as potential candidate peptide drugs. Since antimicrobial peptides are part of the innate immune system of every living organism, it is possible to discover new candidate peptides using the available genomic and proteomic data. High throughput computational techniques could also be used to virtually scan the entire peptide space for discovering out new candidate antimicrobial peptides.
We have identified a unique indexing method based on biologically distinct characteristic features of known antimicrobial peptides. Analysis of the entries in the antimicrobial peptide databases, based on our indexing method, using Fourier transformation technique revealed a distinct peak in their power spectrum. We have developed a method to mine the genomic and proteomic data, for the presence of peptides with potential antimicrobial activity, by looking for this distinct peak. We also used the Euclidean metric to rank the potential antimicrobial peptides activity. We have parallelized our method so that virtually any given protein space could be data mined, in search of antimicrobial peptides.
The results show that the Fourier transform based method with the property based coding strategy could be used to scan the peptide space for discovering new potential antimicrobial peptides.
[show abstract][hide abstract] ABSTRACT: Fast Fourier Transforms (FFT) has been extensively used for prediction of DNA sequences. This class of computational problems in bioinformatics are usually highly parallelizable and can be solved efficiently using high performance computing technology. In this paper, a parallel algorithm for prediction of antimicrobial activity using Fourier Transform is presented and implemented in C language and Message Passing Interface (MPI). The performances, such as speedup, efficiency and scalability, of parallel computing are analyzed. The actual performances of the parallel computation, when executed on both SGI supercomputer and Linux cluster, are compared and analyzed. The computation on SGI supercomputer is more efficient because of the fast inter-processors connection and less communication latency. The parallel algorithm designed is scalable and can be used for solving large-scale similar problems with significant improvement of performance.
Proceedings of the International Conference on Parallel and Distributed Processing Techniques and Applications, PDPTA 2005, Las Vegas, Nevada, USA, June 27-30, 2005, Volume 3; 01/2005
[show abstract][hide abstract] ABSTRACT: The post-genomic era has seen a significant increase in the use of computational prediction methods to gain insights into
structure and function of proteins. Prediction tools are used to guide the experimental design to test various hypotheses
about structure and function of known proteins. However, these tools are particularly useful when studying putative protein
sequences with no known function. The genomic era produced a large number of sequences that are described as either hypothetical
proteins or as proteins with unknown function. Current molecular biology techniques are not adequate to efficiently study
this vast reservoir of genetic information. However, computer algorithms can process large amounts of sequence data to predict
structure and function. These knowledge-based computational tools use available experimental data and are regularly updated
to improve their predictive power. The simplest form of function prediction is achieved by comparison of the query sequence
to all available sequences using BLAST. If the query sequence is highly similar to previously characterized proteins, then
it is likely that the query sequence has similar functions. However, if the query sequence does not have any homologous sequence
with known function, then more sophisticated computational tools are necessary to gain insight into structure and function.
Various methods have been developed to search for known domains, motifs, patterns, or profiles. The quality of predictions
is dependent on the type of tools used and is limited to the closeness of the query sequence to known proteins.
In this chapter, we will describe and discuss methods and tools we used to predict structure and function of a putative protein
sequence (Msa) with unknown function. We will address the advantages and limitations of all these approaches by using the
Msa protein from the human pathogen Staphylococcus aureus as a case study. Msa is a novel protein that is involved in regulation of virulence. Since Msa has no known homolog, computational
tools are being used to predict its structure and mechanism of action. These predictions are used to design experiments to
study Msa and explore its use as a therapeutic target to combat antibiotic-resistant infections.