BBSI Research Proposal: Summer 2006

Ben Cohn

Mentor: Leslie Gregg-Jolly Grinnell College Grinnell, Iowa

Mentor: Greg Buck Virginia Commonwealth University Richmond, Virginia

Identifying transcriptional regulatory elements in Cryptosporidium hominis and microarray characterization of gene regulation in different growth stages.

Introduction

With a perhaps overwhelming amount of genomic data available, it is becoming increasingly apparent that bioinformatics-based methods of study are integral to our understanding of an organism’s genetic response to external cues. Especially in the elucidation of transcriptional control elements, it is now no longer sufficient to depend on in vivo experiments as the sole method of discovery, with sequence analysis playing a side role. The two are mutually necessary and the heuristics of motif elucidation have already been established in at least an initiatory capacity (MacIsaac & Fraenkel, 2006).

Its genome recently sequenced (Xu et al., 2004), Cryptosporidium hominis makes an excellent subject for the use of bioinformatics as tool to shed light on its regulatory programming in response to external influences. A Class 2 Bioterrorism agent, Cryptosporidium hominis and its closely related cousin, C. parvum, cause gastroenteritis and severe diarrhea, which may lead to death in immunocompromised, very old or very young individuals (Xu et al., 2004; Abrahamsen et al., 2004). A water-born pathogen, C. hominis is spread fecal-orally, requires only a single host, and is highly resilient to chlorine and other chemical treatment making cleanup of infected water supplies extremely difficult. A worldwide problem, it is believed that cases of Cryptosporidiosis are vastly underreported, especially in developing countries in which clean, drinkable water is not always available. Over the last twenty years, a renewed call has surfaced for a viable treatment for Cryptosporidiosis. Ultimately, it is hoped that a better understanding of the organism and its genetic profile may lead to a way to control this disease.

Methods

Cryptosporidium hominis expression

Previously, I had established a gene set by querying the annotated genome for keywords such as “heat shock” or “hsp.” With this set, I carried out all motif-finding trials. Now, with experimental data available showing which genes are up or downregulated in the sporozoite (vs. oocyst) phase, I can revise and/or update my set. Furthermore, I will use Gene Ontology (GO) classifications to assist in the identification of relevant genes for further study.

Having reestablished the gene set, I will extract the upstream sequence for each gene using BioBIKE (Massar et al., 2005), truncating at 200bp when applicable. Background frequencies, when required, will be calculated with respect to the upstream regions for each gene in C. hominis. I will input this set of upstream sequences, derived from the original gene set and henceforth-called “the data set,” into several motif-discovery programs. MacIsaac et al. agree that no currently available motif-discovery program seems to distinguish itself above the rest, in terms of finding biologically relevant motifs, each finding certain motifs that the others do not. It is therefore in my best interest to triangulate upon the results by using multiple programs. Last summer and during the academic year, I gained experience using the AlignACE program (Hughes et al., 2000), which uses Gibbs-sampling to pick out motifs. Gibbs sampling has shown to an effective tool in identifying both known and novel regulatory sequences in Sacharomyces cerevisae (Hughes, 2000), Escherichia coli (McGuire, 2000) and Plasmodium falciparum (Militello, 2004). Additionally, I will attempt to incorporate data generated from MEME, MDScan, Weeder, and PhyloCon and PhyloNET (Stormo & Wang, 2005), which use an expectation-maximization algorithm, Gibbs-sampling, enumeration, and phylogenetics, respectively. Controls will be run using randomly generated DNA with the same fractional GC content as the experimental set. Having obtained several “best-scoring” motifs, I will examine any clustering present. That is, I will look at their relative chromosomal positions, to see if any interesting grouping patterns emerge. It might be worth comparing clustering in C. hominis that of C. parvum or T. cruzi. For example, SOS-response genes in E. coli demonstrate no obvious clustering behavior (Walker, 1984), whereas orthologous genes in Acinetobacter baylyi demonstrate archipelago-like clustering (Cohn, unpublished), a prevalent trend of its genome (Barbe, 2004). To expedite the process of running multiple trials on various programs by hand, I will be working to create a script that automatically runs and compiles multiple programs in sequence. In this way, I may be more certain of the statistical significance of the data by running more trials.

In addition to motif finding, I will also be conducting microarray experiments to ascertain which genes are variably regulated under heat shock conditions, with respect to stage of the organism. I will challenge sporozoite and oocyst phase organisms with heat shock, ~29-45°C (Militello et al., 2004; Fernandes, 2005), in contrast to room temperature or body temperature conditions, 25 and 37°C, respectively. It is my hope that the results of these experiments will complement and help to verify the bioinformatics-based portion of the investigation by confirming the relevance of the gene set used.

Trypanosoma cruzi expression

Microarray experiments will also be conducted to quantify gene expression of Trypanosoma cruzi at various time points during its life cycle. Hydroxyurea will be used to induce synchrony, effectively “freezing” the organism in a certain stage (Galanti et al., 1994). As related organisms, it will be useful to compare the genetic programming of each with respect to its life cycle.

Implications, Future Considerations

As an organism particularly resistant to conventional means of sterilization, it is especially important to understand by what means Cryptosporidium hominis interacts with and responds to its environment. If we may discover how it expresses critical genes, we may start to develop drugs that target these processes, effectively controlling the organism.

Cited:

Abrahamsen MS, Templeton TJ, Enomoto S, Abrahante JE, Zhu G, Lancto CA, Deng M, Liu C, Widmer G, Tzipori S, Buck GA, Xu P, Bankier AT, Dear PH, Konfortov BA, Spriggs HF, Iyer L, Anantharaman V, Lyy A, Kapur V (2004). Complete Genome Sequence of the Apicomplexan, Cryptosporidium. Science. 304: 441-445.

Barbe V, Vallenet D, Fonknechten N, Kreimeyer A, Oztas S, Labarre L, et al. (2004). Unique features revealed by the genome sequence of Acinetobacter sp. ADP1, a versatile and naturally transformation competent bacterium. Nucleic Acids Research. 32(19): 5766-79.

Cohn BL (2006). Elucidation of transcriptional elements in the DNA damage-inducible response of Acinetobacter baylyi. Grinnell College. Unpublished.

Fernandes M, Silva R, Rössle SC, Bisch PM, Rondinelli E, Ürményi TP (2005). Gene characterization and predicted protein structure of the mitochondrial chaperonin HSP10 of Trypanosoma cruzi. Gene. 349: 135-142.

Galanti N, Dvorak JA, Grenet J, McDaniel JP (1994). Hydroxyurea-induced synchrony of DNA replication in the kinetoplastida. Experimental Cell Res. 214: 225-230.

Hughes JD, Estep PW, Tavazoie S, Church GM (2000). Computational Identification of Cis-regulatory Elements Associated with Groups of Functionally Related Genes in Sacharomyces cerevisae. J. Mol. Biol. 296: 1205-14.

Massar JP, Travers M, Elhai J, Shrager J (2005). BioLingua: a programmable knowledge environment for biologists. Bioinformatics. 21:199-207. (http://dx.doi.org/10.1093/bioinformatics/bth465)

McGuire AM, Hughes JD, Church GM (2000). Conservation of DNA Regulatory Motifs and Discovery of New Motifs in Microbial Genomes. Genome Research. 10: 744-57.

Militello KT, Dodge M, Bethke L, Wirth DF (2004). Identification of regulatory elements in the Plasmodium falciparum genome. Mol & Biochem. Parasitol. 134: 75-88.

Walker GC (Mar. 1984). Mutagenesis and Inducible Responses to Deoxyribonucleic Acid Damage in Escherichia coli. Microbiological Reviews: 60-93.

Stormo GD, Wang T (2005). Identifying the conserved network of cis-regulatory sites of a eukaryotic genome. Proc. Nat. Acad. Sci. 102(48):17400-17405.

Xu P, Widmer G, Wang Y, Ozaki LS, Alves JM, Serrano MG, Puiu D, Manque P, Akiyoshi D, Mackey AJ, Pearson WR, Dear PH, Bankier AT, Peterson DL, Abrahamsen MS, Kapur V, Tzipori S, Buck GA (2004). The Genome of Cryptosporidium hominis. Nature. 431: 1107-1112.