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Identifying novel cell cycle proteins in Apicomplexa parasites through co-expression decision analysis.

ABSTRACT: Hypothetical proteins comprise roughly half of the predicted gene complement of Toxoplasma gondii and Plasmodium falciparum and represent the largest class of uniquely functioning proteins in these parasites. Following the idea that functional relationships can be informed by the timing of gene expression, we devised a strategy to identify the core set of apicomplexan cell division cycling genes with important roles in parasite division, which includes many uncharacterized proteins. We assembled an expanded list of orthologs from the T. gondii and P. falciparum genome sequences (2781 putative orthologs), compared their mRNA profiles during synchronous replication, and sorted the resulting set of dual cell cycle regulated orthologs (744 total) into protein pairs conserved across many eukaryotic families versus those unique to the Apicomplexa. The analysis identified more than 100 ortholog gene pairs with unknown function in T. gondii and P. falciparum that displayed co-conserved mRNA abundance, dynamics of cyclical expression and similar peak timing that spanned the complete division cycle in each parasite. The unknown cyclical mRNAs encoded a diverse set of proteins with a wide range of mass and showed a remarkable conservation in the internal organization of ordered versus disordered structural domains. A representative sample of cyclical unknown genes (16 total) was epitope tagged in T. gondii tachyzoites yielding the discovery of new protein constituents of the parasite inner membrane complex, key mitotic structures and invasion organelles. These results demonstrate the utility of using gene expression timing and dynamic profile to identify proteins with unique roles in Apicomplexa biology.

SUBMITTER: Butler CL

PROVIDER: S-EPMC4026381 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Identifying novel cell cycle proteins in Apicomplexa parasites through co-expression decision analysis.

Butler Carrie L CL Lucas Olivier O Wuchty Stefan S Xue Bin B Uversky Vladimir N VN White Michael M

PloS one 20140519 5

Hypothetical proteins comprise roughly half of the predicted gene complement of Toxoplasma gondii and Plasmodium falciparum and represent the largest class of uniquely functioning proteins in these parasites. Following the idea that functional relationships can be informed by the timing of gene expression, we devised a strategy to identify the core set of apicomplexan cell division cycling genes with important roles in parasite division, which includes many uncharacterized proteins. We assembled ...[more]

PMID: 24841368

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Project description:Plasmodium and Toxoplasma are parasites of major medical importance that belong to the Apicomplexa phylum of protozoa. These parasites transform into various stages during their life cycle and express a specific set of proteins at each stage. Although still little is known of how gene expression is controlled in Apicomplexa, histone modifications, particularly acetylation, are emerging as key regulators of parasite differentiation and stage conversion. Here, we investigated the anti-Apicomplexa effect of FR235222, a histone deacetylase (HDAC) inhibitor. We show that FR235222 is active against a variety of Apicomplexa genera, including Plasmodium and Toxoplasma, and is more potent than other HDACi such as TSA and the clinically relevant compound, pyrimethamine. We identify TgHDAC3 as the target of FR235222 in Toxoplasma tachyzoites and demonstrate the crucial role of the conserved and Apicomplexa HDAC-specific residue TgHDAC3 T99 in the inhibitory activity of the drug. We also show that FR235222 induces differentiation of the tachyzoite (replicative) into the bradyzoite (non replicative) stage. Additionally, via its anti-TgHDAC3 activity, FR235222 influences the expression of ~370 genes, a third of which are stage-specifically expressed. These results identify FR235222 as a potent HDAC inhibitor of Apicomplexa, and establish HDAC3 as a central regulator of gene expression and stage conversion in Toxoplasma and likely other Apicomplexa. Freshly released tachyzoites were needle-passed, and filtered using a 3-Âµm nucleopore membrane. Parasites were resuspended into fresh DMEM supplemented with 10% (v/v) FBS and 25 mM HEPES buffer pH7.2. Parasites were incubated in the presence of FR235222 (40 nM) or DMSO (0.1%) for 4 h at 37Â°C with 5% CO2. For ChIP-chip experiments freshly released tachyzoites (~5 x 109 at ~12 x 107 parasites/mL) were fixed for 15 min in 1% formaldehyde. Increase in AcH4 signals was verified by immunoblot to verify that FR235222 treatment was effective. To prepare chromatin samples, fixed parasites were lysed in MNase buffer (0.32 M Sucrose, 50 mM Tris-HCl pH7.8, 4 mM MgCl2, 3 mM CaCl2, 100 mM NaCl, 0.25% (v/v) NP40, 5% (v/v) glycerol, protease inhibitor EDTA-free cocktail (Roche)) and DNA was digested for 4 min at 37Â°C by MNase (2 units/mL). Digestion was stopped with 20 mM EDTA and chromatin was recovered in the soluble fraction after centrifugation at 10,000 g at 4Â°C; this constituted the S1 fractions. Pelleted materials were resuspended in dialysis buffer (1mM Tris-HCl pH7.8, 0.2 mM EDTA) containing 1 mM PMSF and protease inhibitor cocktail (RocheÂ®) and dialyzed overnight at 4Â°C against the same solution. Then dialyzed materials were centrifuged and supernatant were harvested; this constitutes the S2 fractions. For chromatin immunoprecipitations, fractions S1 and S2 were pooled and DNA quality was verified by electrophoresis on 2% agarose gels; oligonucleosome ladder of 100-1000 bp were obtained. The histone-DNA complexes were immunoprecipitated with anti-acetyl histone H4 (UpstateÂ®, catalog # 06-866) antibodies according to NimbleGenâs protocol (http://www.genomecenter.ucdavis.edu/expression_analysis/documents).

Project description:BackgroundWith the large amount of pharmacological and biological knowledge available in literature, finding novel drug indications for existing drugs using in silico approaches has become increasingly feasible. Typical literature-based approaches generate new hypotheses in the form of protein-protein interactions networks by means of linking concepts based on their cooccurrences within abstracts. However, this kind of approaches tends to generate too many hypotheses, and identifying new drug indications from large networks can be a time-consuming process.MethodologyIn this work, we developed a method that acquires the necessary facts from literature and knowledge bases, and identifies new drug indications through automated reasoning. This is achieved by encoding the molecular effects caused by drug-target interactions and links to various diseases and drug mechanism as domain knowledge in AnsProlog, a declarative language that is useful for automated reasoning, including reasoning with incomplete information. Unlike other literature-based approaches, our approach is more fine-grained, especially in identifying indirect relationships for drug indications.Conclusion/significanceTo evaluate the capability of our approach in inferring novel drug indications, we applied our method to 943 drugs from DrugBank and asked if any of these drugs have potential anti-cancer activities based on information on their targets and molecular interaction types alone. A total of 507 drugs were found to have the potential to be used for cancer treatments. Among the potential anti-cancer drugs, 67 out of 81 drugs (a recall of 82.7%) are indeed known cancer drugs. In addition, 144 out of 289 drugs (a recall of 49.8%) are non-cancer drugs that are currently tested in clinical trials for cancer treatments. These results suggest that our method is able to infer drug indications (original or alternative) based on their molecular targets and interactions alone and has the potential to discover novel drug indications for existing drugs.

Dataset Information

Identifying novel cell cycle proteins in Apicomplexa parasites through co-expression decision analysis.

Publications

Identifying novel cell cycle proteins in Apicomplexa parasites through co-expression decision analysis.

Similar Datasets

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