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1.  Correction: Characterization of the Vaginal Microbiota among Sexual Risk Behavior Groups of Women with Bacterial Vaginosis 
PLoS ONE  2013;8(12):10.1371/annotation/f7674ab1-fbd5-4293-ad2c-d1a795962e8b.
doi:10.1371/annotation/f7674ab1-fbd5-4293-ad2c-d1a795962e8b
PMCID: PMC3858453
2.  Characterization of the Vaginal Microbiota among Sexual Risk Behavior Groups of Women with Bacterial Vaginosis 
PLoS ONE  2013;8(11):e80254.
Background
The pathogenesis of bacterial vaginosis (BV) remains elusive. BV may be more common among women who have sex with women (WSW). The objective of this study was to use 454 pyrosequencing to investigate the vaginal microbiome of WSW, women who have sex with women and men (WSWM), and women who have sex with men (WSM) with BV to determine if there are differences in organism composition between groups that may inform new hypotheses regarding the pathogenesis of BV.
Methods
Vaginal swab specimens from eligible women with BV at the Mississippi State Department of Health STD Clinic were used. After DNA extraction, 454 pyrosequencing of PCR-amplified 16S rRNA gene sequences was performed. Sequence data was classified using the Ribosomal Database Program classifer. Complete linkage clustering analysis was performed to compare bacterial community composition among samples. Differences in operational taxonomic units with an abundance of ≥2% between risk behavior groups were determined. Alpha and beta diversity were measured using Shannon’s Index implemented in QIIME and Unifrac analysis, respectively.
Results
33 WSW, 35 WSWM, and 44 WSM were included. The vaginal bacterial communities of all women clustered into four taxonomic groups with the dominant taxonomic group in each being Lactobacillus, Lachnospiraceae, Prevotella, and Sneathia. Regarding differences in organism composition between risk behavior groups, the abundance of Atopobium (relative ratio (RR)=0.24; 95%CI 0.11-0.54) and Parvimonas (RR=0.33; 95%CI 0.11-0.93) were significantly lower in WSW than WSM, the abundance of Prevotella was significantly higher in WSW than WSWM (RR=1.77; 95%CI 1.10-2.86), and the abundance of Atopobium (RR=0.41; 95%CI 0.18-0.88) was significantly lower in WSWM than WSM. Overall, WSM had the highest diversity of bacterial taxa.
Conclusion
The microbiology of BV among women in different risk behavior groups is heterogeneous. WSM in this study had the highest diversity of bacterial taxa. Additional studies are needed to better understand these differences.
doi:10.1371/journal.pone.0080254
PMCID: PMC3827412  PMID: 24236175
3.  Ultrasonic Incisions Produce Less Inflammatory Mediator Response during Early Healing than Electrosurgical Incisions 
PLoS ONE  2013;8(9):e73032.
As the use of laparoscopic surgery has become more widespread in recent years, the need has increased for minimally-invasive surgical devices that effectively cut and coagulate tissue with reduced tissue trauma. Although electrosurgery (ES) has been used for many generations, newly-developed ultrasonic devices (HARMONIC® Blade, HB) have been shown at a macroscopic level to offer better coagulation with less thermally-induced tissue damage. We sought to understand the differences between ES and HB at a microscopic level by comparing mRNA transcript and protein responses at the 3-day timepoint to incisions made by the devices in subcutaneous fat tissue in a porcine model. Samples were also assessed via histological examination. ES-incised tissue had more than twice as many differentially-expressed genes as HB (2,548 vs 1,264 respectively), and more differentially-expressed proteins (508 vs 432) compared to control (untreated) tissue. Evaluation of molecular functions using Gene Ontology showed that gene expression changes for the energized devices reflected the start of wound healing, including immune response and inflammation, while protein expression showed a slightly earlier stage, with some remnants of hemostasis. For both transcripts and proteins, ES exhibited a greater response than HB, especially in inflammatory mediators. These findings were in qualitative agreement with histological results. This study has shown that transcriptomics and proteomics can monitor the wound healing response following surgery and can differentiate between surgical devices. In agreement with clinical observations, electrosurgery was shown to incur a greater inflammatory immune response than an ultrasonic device during initial iatrogenic wound healing.
doi:10.1371/journal.pone.0073032
PMCID: PMC3776814  PMID: 24058457
4.  An Integrative Genomics Approach for Associating GWAS Information with Triple-Negative Breast Cancer 
Cancer Informatics  2013;12:1-20.
Genome-wide association studies (GWAS) have identified genetic variants associated with an increased risk of developing breast cancer. However, the association of genetic variants and their associated genes with the most aggressive subset of breast cancer, the triple-negative breast cancer (TNBC), remains a central puzzle in molecular epidemiology. The objective of this study was to determine whether genes containing single nucleotide polymorphisms (SNPs) associated with an increased risk of developing breast cancer are connected to and could stratify different subtypes of TNBC. Additionally, we sought to identify molecular pathways and networks involved in TNBC. We performed integrative genomics analysis, combining information from GWAS studies involving over 400,000 cases and over 400,000 controls, with gene expression data derived from 124 breast cancer patients classified as TNBC (at the time of diagnosis) and 142 cancer-free controls. Analysis of GWAS reports produced 500 SNPs mapped to 188 genes. We identified a signature of 159 functionally related SNP-containing genes which were significantly (P <10−5) associated with and stratified TNBC. Additionally, we identified 97 genes which were functionally related to, and had similar patterns of expression profiles, SNP-containing genes. Network modeling and pathway prediction revealed multi-gene pathways including p53, NFkB, BRCA, apoptosis, DNA repair, DNA mismatch, and excision repair pathways enriched for SNPs mapped to genes significantly associated with TNBC. The results provide convincing evidence that integrating GWAS information with gene expression data provides a unified and powerful approach for biomarker discovery in TNBC.
doi:10.4137/CIN.S10413
PMCID: PMC3565545  PMID: 23423317
triple negative breast cancer GWAS gene expression
5.  Transcriptome profile of a bovine respiratory disease pathogen: Mannheimia haemolytica PHL213 
BMC Bioinformatics  2012;13(Suppl 15):S4.
Background
Computational methods for structural gene annotation have propelled gene discovery but face certain drawbacks with regards to prokaryotic genome annotation. Identification of transcriptional start sites, demarcating overlapping gene boundaries, and identifying regulatory elements such as small RNA are not accurate using these approaches. In this study, we re-visit the structural annotation of Mannheimia haemolytica PHL213, a bovine respiratory disease pathogen. M. haemolytica is one of the causative agents of bovine respiratory disease that results in about $3 billion annual losses to the cattle industry. We used RNA-Seq and analyzed the data using freely-available computational methods and resources. The aim was to identify previously unannotated regions of the genome using RNA-Seq based expression profile to complement the existing annotation of this pathogen.
Results
Using the Illumina Genome Analyzer, we generated 9,055,826 reads (average length ~76 bp) and aligned them to the reference genome using Bowtie. The transcribed regions were analyzed using SAMTOOLS and custom Perl scripts in conjunction with BLAST searches and available gene annotation information. The single nucleotide resolution map enabled the identification of 14 novel protein coding regions as well as 44 potential novel sRNA. The basal transcription profile revealed that 2,506 of the 2,837 annotated regions were expressed in vitro, at 95.25% coverage, representing all broad functional gene categories in the genome. The expression profile also helped identify 518 potential operon structures involving 1,086 co-expressed pairs. We also identified 11 proteins with mutated/alternate start codons.
Conclusions
The application of RNA-Seq based transcriptome profiling to structural gene annotation helped correct existing annotation errors and identify potential novel protein coding regions and sRNA. We used computational tools to predict regulatory elements such as promoters and terminators associated with the novel expressed regions for further characterization of these novel functional elements. Our study complements the existing structural annotation of Mannheimia haemolytica PHL213 based on experimental evidence. Given the role of sRNA in virulence gene regulation and stress response, potential novel sRNA described in this study can form the framework for future studies to determine the role of sRNA, if any, in M. haemolytica pathogenesis.
doi:10.1186/1471-2105-13-S15-S4
PMCID: PMC3439734  PMID: 23046475
6.  Integrative Analysis of Response to Tamoxifen Treatment in ER-Positive Breast Cancer Using GWAS Information and Transcription Profiling 
Variable response and resistance to tamoxifen treatment in breast cancer patients remains a major clinical problem. To determine whether genes and biological pathways containing SNPs associated with risk for breast cancer are dysregulated in response to tamoxifen treatment, we performed analysis combining information from 43 genome-wide association studies with gene expression data from 298 ER+ breast cancer patients treated with tamoxifen and 125 ER+ controls. We identified 95 genes which distinguished tamoxifen treated patients from controls. Additionally, we identified 54 genes which stratified tamoxifen treated patients into two distinct groups. We identified biological pathways containing SNPs associated with risk for breast cancer, which were dysregulated in response to tamoxifen treatment. Key pathways identified included the apoptosis, P53, NFkB, DNA repair and cell cycle pathways. Combining GWAS with transcription profiling provides a unified approach for associating GWAS findings with response to drug treatment and identification of potential drug targets.
doi:10.4137/BCBCR.S8652
PMCID: PMC3292850  PMID: 22399860
tamoxifen genome-wide association studies gene expression
7.  RNA-Seq Based Transcriptional Map of Bovine Respiratory Disease Pathogen “Histophilus somni 2336” 
PLoS ONE  2012;7(1):e29435.
Genome structural annotation, i.e., identification and demarcation of the boundaries for all the functional elements in a genome (e.g., genes, non-coding RNAs, proteins and regulatory elements), is a prerequisite for systems level analysis. Current genome annotation programs do not identify all of the functional elements of the genome, especially small non-coding RNAs (sRNAs). Whole genome transcriptome analysis is a complementary method to identify “novel” genes, small RNAs, regulatory regions, and operon structures, thus improving the structural annotation in bacteria. In particular, the identification of non-coding RNAs has revealed their widespread occurrence and functional importance in gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Histophilus somni, one of the causative agents of Bovine Respiratory Disease (BRD) as well as bovine infertility, abortion, septicemia, arthritis, myocarditis, and thrombotic meningoencephalitis. In this study, we report a single nucleotide resolution transcriptome map of H. somni strain 2336 using RNA-Seq method.
The RNA-Seq based transcriptome map identified 94 sRNAs in the H. somni genome of which 82 sRNAs were never predicted or reported in earlier studies. We also identified 38 novel potential protein coding open reading frames that were absent in the current genome annotation. The transcriptome map allowed the identification of 278 operon (total 730 genes) structures in the genome. When compared with the genome sequence of a non-virulent strain 129Pt, a disproportionate number of sRNAs (∼30%) were located in genomic region unique to strain 2336 (∼18% of the total genome). This observation suggests that a number of the newly identified sRNAs in strain 2336 may be involved in strain-specific adaptations.
doi:10.1371/journal.pone.0029435
PMCID: PMC3262788  PMID: 22276113
10.  Proteome and Membrane Fatty Acid Analyses on Oligotropha carboxidovorans OM5 Grown under Chemolithoautotrophic and Heterotrophic Conditions 
PLoS ONE  2011;6(2):e17111.
Oligotropha carboxidovorans OM5 T. (DSM 1227, ATCC 49405) is a chemolithoautotrophic bacterium able to utilize CO and H2 to derive energy for fixation of CO2. Thus, it is capable of growth using syngas, which is a mixture of varying amounts of CO and H2 generated by organic waste gasification. O. carboxidovorans is capable also of heterotrophic growth in standard bacteriologic media. Here we characterize how the O. carboxidovorans proteome adapts to different lifestyles of chemolithoautotrophy and heterotrophy. Fatty acid methyl ester (FAME) analysis of O. carboxidovorans grown with acetate or with syngas showed that the bacterium changes membrane fatty acid composition. Quantitative shotgun proteomic analysis of O. carboxidovorans grown in the presence of acetate and syngas showed production of proteins encoded on the megaplasmid for assimilating CO and H2 as well as proteins encoded on the chromosome that might have contributed to fatty acid and acetate metabolism. We found that adaptation to chemolithoautotrophic growth involved adaptations in cell envelope, oxidative homeostasis, and metabolic pathways such as glyoxylate shunt and amino acid/cofactor biosynthetic enzymes.
doi:10.1371/journal.pone.0017111
PMCID: PMC3046131  PMID: 21386900
11.  AgBase: supporting functional modeling in agricultural organisms 
Nucleic Acids Research  2010;39(Database issue):D497-D506.
AgBase (http://www.agbase.msstate.edu/) provides resources to facilitate modeling of functional genomics data and structural and functional annotation of agriculturally important animal, plant, microbe and parasite genomes. The website is redesigned to improve accessibility and ease of use, including improved search capabilities. Expanded capabilities include new dedicated pages for horse, cat, dog, cotton, rice and soybean. We currently provide 590 240 Gene Ontology (GO) annotations to 105 454 gene products in 64 different species, including GO annotations linked to transcripts represented on agricultural microarrays. For many of these arrays, this provides the only functional annotation available. GO annotations are available for download and we provide comprehensive, species-specific GO annotation files for 18 different organisms. The tools available at AgBase have been expanded and several existing tools improved based upon user feedback. One of seven new tools available at AgBase, GOModeler, supports hypothesis testing from functional genomics data. We host several associated databases and provide genome browsers for three agricultural pathogens. Moreover, we provide comprehensive training resources (including worked examples and tutorials) via links to Educational Resources at the AgBase website.
doi:10.1093/nar/gkq1115
PMCID: PMC3013706  PMID: 21075795
12.  HPIDB - a unified resource for host-pathogen interactions 
BMC Bioinformatics  2010;11(Suppl 6):S16.
Background
Protein-protein interactions (PPIs) play a crucial role in initiating infection in a host-pathogen system. Identification of these PPIs is important for understanding the underlying biological mechanism of infection and identifying putative drug targets. Database resources for studying host-pathogen systems are scarce and are either host specific or dedicated to specific pathogens.
Results
Here we describe "HPIDB” a host-pathogen PPI database, which will serve as a unified resource for host-pathogen interactions. Specifically, HPIDB integrates experimental PPIs from several public databases into a single, non-redundant web accessible resource. The database can be searched with a variety of options such as sequence identifiers, symbol, taxonomy, publication, author, or interaction type. The output is provided in a tab delimited text file format that is compatible with Cytoscape, an open source resource for PPI visualization. HPIDB allows the user to search protein sequences using BLASTP to retrieve homologous host/pathogen sequences. For high-throughput analysis, the user can search multiple protein sequences at a time using BLASTP and obtain results in tabular and sequence alignment formats. The taxonomic categorization of proteins (bacterial, viral, fungi, etc.) involved in PPI enables the user to perform category specific BLASTP searches. In addition, a new tool is introduced, which allows searching for homologous host-pathogen interactions in the HPIDB database.
Conclusions
HPIDB is a unified, comprehensive resource for host-pathogen PPIs. The user interface provides new features and tools helpful for studying host-pathogen interactions. HPIDB can be accessed at http://agbase.msstate.edu/hpi/main.html.
doi:10.1186/1471-2105-11-S6-S16
PMCID: PMC3026363  PMID: 20946599
13.  Identification of novel non-coding small RNAs from Streptococcus pneumoniae TIGR4 using high-resolution genome tiling arrays 
BMC Genomics  2010;11:350.
Background
The identification of non-coding transcripts in human, mouse, and Escherichia coli has revealed their widespread occurrence and functional importance in both eukaryotic and prokaryotic life. In prokaryotes, studies have shown that non-coding transcripts participate in a broad range of cellular functions like gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Streptococcus pneumoniae (pneumococcus), an obligate human respiratory pathogen responsible for significant worldwide morbidity and mortality. Tiling microarrays enable genome wide mRNA profiling as well as identification of novel transcripts at a high-resolution.
Results
Here, we describe a high-resolution transcription map of the S. pneumoniae clinical isolate TIGR4 using genomic tiling arrays. Our results indicate that approximately 66% of the genome is expressed under our experimental conditions. We identified a total of 50 non-coding small RNAs (sRNAs) from the intergenic regions, of which 36 had no predicted function. Half of the identified sRNA sequences were found to be unique to S. pneumoniae genome. We identified eight overrepresented sequence motifs among sRNA sequences that correspond to sRNAs in different functional categories. Tiling arrays also identified approximately 202 operon structures in the genome.
Conclusions
In summary, the pneumococcal operon structures and novel sRNAs identified in this study enhance our understanding of the complexity and extent of the pneumococcal 'expressed' genome. Furthermore, the results of this study open up new avenues of research for understanding the complex RNA regulatory network governing S. pneumoniae physiology and virulence.
doi:10.1186/1471-2164-11-350
PMCID: PMC2887815  PMID: 20525227
14.  An automated proteomic data analysis workflow for mass spectrometry 
BMC Bioinformatics  2009;10(Suppl 11):S17.
Background
Mass spectrometry-based protein identification methods are fundamental to proteomics. Biological experiments are usually performed in replicates and proteomic analyses generate huge datasets which need to be integrated and quantitatively analyzed. The Sequest™ search algorithm is a commonly used algorithm for identifying peptides and proteins from two dimensional liquid chromatography electrospray ionization tandem mass spectrometry (2-D LC ESI MS2) data. A number of proteomic pipelines that facilitate high throughput 'post data acquisition analysis' are described in the literature. However, these pipelines need to be updated to accommodate the rapidly evolving data analysis methods. Here, we describe a proteomic data analysis pipeline that specifically addresses two main issues pertinent to protein identification and differential expression analysis: 1) estimation of the probability of peptide and protein identifications and 2) non-parametric statistics for protein differential expression analysis. Our proteomic analysis workflow analyzes replicate datasets from a single experimental paradigm to generate a list of identified proteins with their probabilities and significant changes in protein expression using parametric and non-parametric statistics.
Results
The input for our workflow is Bioworks™ 3.2 Sequest (or a later version, including cluster) output in XML format. We use a decoy database approach to assign probability to peptide identifications. The user has the option to select "quality thresholds" on peptide identifications based on the P value. We also estimate probability for protein identification. Proteins identified with peptides at a user-specified threshold value from biological experiments are grouped as either control or treatment for further analysis in ProtQuant. ProtQuant utilizes a parametric (ANOVA) method, for calculating differences in protein expression based on the quantitative measure ΣXcorr. Alternatively ProtQuant output can be further processed using non-parametric Monte-Carlo resampling statistics to calculate P values for differential expression. Correction for multiple testing of ANOVA and resampling P values is done using Benjamini and Hochberg's method. The results of these statistical analyses are then combined into a single output file containing a comprehensive protein list with probabilities and differential expression analysis, associated P values, and resampling statistics.
Conclusion
For biologists carrying out proteomics by mass spectrometry, our workflow facilitates automated, easy to use analyses of Bioworks (3.2 or later versions) data. All the methods used in the workflow are peer-reviewed and as such the results of our workflow are compliant with proteomic data submission guidelines to public proteomic data repositories including PRIDE. Our workflow is a necessary intermediate step that is required to link proteomics data to biological knowledge for generating testable hypotheses.
doi:10.1186/1471-2105-10-S11-S17
PMCID: PMC3226188  PMID: 19811682
15.  Facilitating functional annotation of chicken microarray data 
BMC Bioinformatics  2009;10(Suppl 11):S2.
Background
Modeling results from chicken microarray studies is challenging for researchers due to little functional annotation associated with these arrays. The Affymetrix GenChip chicken genome array, one of the biggest arrays that serve as a key research tool for the study of chicken functional genomics, is among the few arrays that link gene products to Gene Ontology (GO). However the GO annotation data presented by Affymetrix is incomplete, for example, they do not show references linked to manually annotated functions. In addition, there is no tool that facilitates microarray researchers to directly retrieve functional annotations for their datasets from the annotated arrays. This costs researchers amount of time in searching multiple GO databases for functional information.
Results
We have improved the breadth of functional annotations of the gene products associated with probesets on the Affymetrix chicken genome array by 45% and the quality of annotation by 14%. We have also identified the most significant diseases and disorders, different types of genes, and known drug targets represented on Affymetrix chicken genome array. To facilitate functional annotation of other arrays and microarray experimental datasets we developed an Array GO Mapper (AGOM) tool to help researchers to quickly retrieve corresponding functional information for their dataset.
Conclusion
Results from this study will directly facilitate annotation of other chicken arrays and microarray experimental datasets. Researchers will be able to quickly model their microarray dataset into more reliable biological functional information by using AGOM tool. The disease, disorders, gene types and drug targets revealed in the study will allow researchers to learn more about how genes function in complex biological systems and may lead to new drug discovery and development of therapies. The GO annotation data generated will be available for public use via AgBase website and will be updated on regular basis.
doi:10.1186/1471-2105-10-S11-S2
PMCID: PMC3226191  PMID: 19811685
16.  Systems Analysis of Chaperone Networks in the Malarial Parasite Plasmodium falciparum 
PLoS Computational Biology  2007;3(9):e168.
Molecular chaperones participate in the maintenance of cellular protein homeostasis, cell growth and differentiation, signal transduction, and development. Although a vast body of information is available regarding individual chaperones, few studies have attempted a systems level analysis of chaperone function. In this paper, we have constructed a chaperone interaction network for the malarial parasite, Plasmodium falciparum. P. falciparum is responsible for several million deaths every year, and understanding the biology of the parasite is a top priority. The parasite regularly experiences heat shock as part of its life cycle, and chaperones have often been implicated in parasite survival and growth. To better understand the participation of chaperones in cellular processes, we created a parasite chaperone network by combining experimental interactome data with in silico analysis. We used interolog mapping to predict protein–protein interactions for parasite chaperones based on the interactions of corresponding human chaperones. This data was then combined with information derived from existing high-throughput yeast two-hybrid assays. Analysis of the network reveals the broad range of functions regulated by chaperones. The network predicts involvement of chaperones in chromatin remodeling, protein trafficking, and cytoadherence. Importantly, it allows us to make predictions regarding the functions of hypothetical proteins based on their interactions. It allows us to make specific predictions about Hsp70–Hsp40 interactions in the parasite and assign functions to members of the Hsp90 and Hsp100 families. Analysis of the network provides a rational basis for the anti-malarial activity of geldanamycin, a well-known Hsp90 inhibitor. Finally, analysis of the network provides a theoretical basis for further experiments designed toward understanding the involvement of this important class of molecules in parasite biology.
Author Summary
Among the infectious diseases affecting humans, the malarial parasite is responsible for a very high number of deaths. About 2 million people die of malaria every year, of which more than a million are children in sub-Saharan Africa. Malaria is caused by a protozoan parasite belonging to the genus Plasmodium, and Plasmodium falciparum is responsible for the most severe form of malaria. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. We have computationally predicted processes governing parasite growth in humans. Recent reports from several labs point to a critical role played by a group of proteins termed molecular chaperones in parasite growth within red cells. We have therefore chosen to provide a balance sheet of all the activities supported by this group of proteins in the parasite. Our systems-level approach provides information on 95 different chaperones in the parasite and also provides insights into their business partners and cellular processes that they might regulate. In addition to predicting a basis for the anti-malarial potential of known drugs such as geldanamycin, our analysis also highlights new proteins that can be used as anti-malarial targets.
doi:10.1371/journal.pcbi.0030168
PMCID: PMC1976336  PMID: 17941702
17.  Systems Analysis of Chaperone Networks in the Malarial Parasite Plasmodium falciparum 
PLoS Computational Biology  2007;3(9):e168.
Molecular chaperones participate in the maintenance of cellular protein homeostasis, cell growth and differentiation, signal transduction, and development. Although a vast body of information is available regarding individual chaperones, few studies have attempted a systems level analysis of chaperone function. In this paper, we have constructed a chaperone interaction network for the malarial parasite, Plasmodium falciparum. P. falciparum is responsible for several million deaths every year, and understanding the biology of the parasite is a top priority. The parasite regularly experiences heat shock as part of its life cycle, and chaperones have often been implicated in parasite survival and growth. To better understand the participation of chaperones in cellular processes, we created a parasite chaperone network by combining experimental interactome data with in silico analysis. We used interolog mapping to predict protein–protein interactions for parasite chaperones based on the interactions of corresponding human chaperones. This data was then combined with information derived from existing high-throughput yeast two-hybrid assays. Analysis of the network reveals the broad range of functions regulated by chaperones. The network predicts involvement of chaperones in chromatin remodeling, protein trafficking, and cytoadherence. Importantly, it allows us to make predictions regarding the functions of hypothetical proteins based on their interactions. It allows us to make specific predictions about Hsp70–Hsp40 interactions in the parasite and assign functions to members of the Hsp90 and Hsp100 families. Analysis of the network provides a rational basis for the anti-malarial activity of geldanamycin, a well-known Hsp90 inhibitor. Finally, analysis of the network provides a theoretical basis for further experiments designed toward understanding the involvement of this important class of molecules in parasite biology.
Author Summary
Among the infectious diseases affecting humans, the malarial parasite is responsible for a very high number of deaths. About 2 million people die of malaria every year, of which more than a million are children in sub-Saharan Africa. Malaria is caused by a protozoan parasite belonging to the genus Plasmodium, and Plasmodium falciparum is responsible for the most severe form of malaria. Due to the increasing incidence of resistance to existing drugs, there is a growing need to discover new and more effective drugs against malaria. We have computationally predicted processes governing parasite growth in humans. Recent reports from several labs point to a critical role played by a group of proteins termed molecular chaperones in parasite growth within red cells. We have therefore chosen to provide a balance sheet of all the activities supported by this group of proteins in the parasite. Our systems-level approach provides information on 95 different chaperones in the parasite and also provides insights into their business partners and cellular processes that they might regulate. In addition to predicting a basis for the anti-malarial potential of known drugs such as geldanamycin, our analysis also highlights new proteins that can be used as anti-malarial targets.
doi:10.1371/journal.pcbi.0030168
PMCID: PMC1976336  PMID: 17941702

Results 1-17 (17)