Abstract. Patricia G. Melloy*

Transcription

1 Laboratory Exercise Using an International p53 Mutation Database as a Foundation for an Online Laboratory in an Upper Level Undergraduate Biology Class ws Patricia G. Melloy* From the Department of Biological and Allied Health Sciences, Fairleigh Dickinson University, Madison, New Jersey Abstract A two-part laboratory exercise was developed to enhance classroom instruction on the significance of p53 mutations in cancer development. Students were asked to mine key information from an international database of p53 genetic changes related to cancer, the IARC TP53 database. Using this database, students designed several data mining activities to look at the changes in the p53 gene from a number of perspectives, including potential cancer-causing agents leading to particular changes and the prevalence of certain p53 variations in certain cancers. In addition, students gained a global perspective on cancer prevalence in different parts of the world. Students learned how to use the database in the first part of the exercise, and then used that knowledge to search particular cancers and cancercausing agents of their choosing in the second part of the exercise. Students also connected the information gathered from the p53 exercise to a previous laboratory exercise looking at risk factors for cancer development. The goal of the experience was to increase student knowledge of the link between p53 genetic variation and cancer. Students also were able to walk a similar path through the website as a cancer researcher using the database to enhance bench work-based experiments with complementary largescale database p53 variation information. VC 2014 by The International Union of Biochemistry and Molecular Biology, 43(1):28 32, Keywords: laboratory exercise; cancer; molecular biology; bioinformatics Introduction Pedagogical Rationale for Using this Online Laboratory Since the human genome project, bioinformatics has increasingly become a larger part of biological research and drug discovery. As a result, bioinformatics familiarity and skills have become a necessary part of the biology major, and many biology programs are working to include bioinformatics at many different levels of the curriculum [1 3]. Many examples of bioinformatics-based laboratory exercises can be found in the literature. For example, ws Additional Supporting Information may be found in the online version of this article. *Address for correspondence to: Department of Biological and Allied Health Sciences, Fairleigh Dickinson University, Madison, New Jersey, USA. pmelloy@fdu.edu Received 16 July 2014; Accepted 1 October 2014 DOI /bmb Published online 14 November 2014 in Wiley Online Library (wileyonlinelibrary.com) there are laboratory exercises to introduce students to topics such as how genetic changes affect protein structure and can lead to disease [4, 5]. It is important that biology students see the use of computers and data mining as going hand-in-hand with typical bench work-based science. Many researchers are increasingly working with large datasets, and educators do not want students to be intimidated by this large amount of information, but instead know how to navigate a database to find the information that they need. Another practical reason for using online-based laboratory exercises is limited resources in an undergraduate laboratory. In particular for a class like the Cell Biology of Cancer, a primarily undergraduate-based institution may not have the resources needed to look at animal models of cancer or do extensive tissue culture work on cancerous cell lines. Using an online-based laboratory, using the same databases mined by cancer researchers, helps students see the extent of information available on genetic changes in cancer, and how conducting a database search can help guide the direction of research in the laboratory. 28 Biochemistry and Molecular Biology Education

2 Cell Biology of Cancer Course The Cell Biology of Cancer course at Fairleigh Dickinson University is geared toward senior biology majors who have already taken both Cell Biology and Genetics. Typically, the class is capped at 16 students. The course covers four major topic areas including: an introduction to oncogenes and tumor suppressors, cell cycle control and apoptosis, growth factor pathways associated with cancer, and advanced cancer characteristics along with cancer treatment. The main goals of the class include increasing student knowledge and understanding of major genes and cellular pathways playing a role in cancer. Students also learn how to locate, comprehend, and analyze scientific information related to cancer research, as well as how to apply that information. Student critical thinking skills and communication skills are also targeted in various assessments. The lecture class has an associated laboratory course. Laboratory topics include looking at normal versus cancerous cells, gel electrophoresis to detect genetic mutations associated with cancer (from commercially available kits), and laboratory exercises using cell division cycle cdc budding yeast mutants. Wet labs are supplemented by online laboratory exercises to expand upon topics covered in lecture. The p53 mutation database exercise not only enhances student understanding of the p53 genetic pathway and its link to cancer, but also asks students to analyze scientific information on p53 found in a database used by actual cancer researchers. Students apply their critical thinking skills to compare results from database queries of different cancers, exposure to different carcinogens, and p53 mutation prevalence in cancers occurring in different populations. What is p53? Cancerous cells are known to display many common characteristics, including the ability to sidestep cellular pathways that may prevent uncontrolled cell growth [6]. Tumor suppressor genes are known to prevent this uncontrolled cell growth. A few tumor suppressor genes, such as p53 (also known as TP53 in humans) have been heavily studied by cancer researchers over the past thirty years [7]. The p53 gene has been reported as the gene most often mutated in human cancers [8]. In normal cells, p53 is activated in response to DNA damage or certain other kinds of cellular changes, and acting as a transcription factor, promotes expression of the cyclin-dependent kinase inhibitor p21. If DNA damage is too severe, p53 can also activate apoptosis [7, 9]. Much information has been gathered on the location of p53 mutations and the occurrence of certain mutations in different cancers and upon exposure to different cancercausing agents [10, 11]. The International Agency for Research on Cancer has gathered information on p53 mutations all in one location, and setup a user-friendly website to search through the thousands of mutations and the circumstances under which they are found [11]. This two-part laboratory exercise provides students a way to sample a small amount of this information and draw comparisons and suggest broader implications using this information. Methods The database used for the laboratory exercise was the International Agency for Research on Cancer TP53 mutation database ( version R17 from November Since 1989, this database has been gathering information on all p53 mutations reported in the scientific literature. Currently, the IARC website has information on over 28,000 somatic mutations in p53 [11]. More information on p53 was found by observing the slideshow link under database resources on the database homepage. Instructions for completing Part One and Part Two of the laboratory exercise can be found in the Supporting Information. In general, one 2.5 hr laboratory period was required for each part of the exercise. If necessary, discussion of the database results can be expanded into an additional laboratory period. Students needed access to a computer with an updated web browser to conduct the laboratory exercises. No additional software was needed to view the database. Results Student Preparation This laboratory exercise was typically performed near the end of the semester. However, students will be prepared to do the exercise as long as they have been introduced to a few key concepts such as: mutations, tumor suppressors, carcinogens, and cancer progression. Also, students should understand the link between a mutation and its impact on the functional domains of the expressed protein. The slideshow available on the International Agency for Research on Cancer (IARC) p53 website ( slideshow link under database resources ) also provided additional information on p53 such as: other p53 family members, p53 experiments conducted using mouse models, p53 structure and function, p53 pathway and its targets, common p53 mutation patterns, carcinogen fingerprints, and database tools. Recommended Procedure Shown in Fig. 1 are the key steps in the laboratory exercise. The full student guides for Part One and Part Two can be found in the Supporting Information. In Part One (Fig. 1), students examined p53 somatic mutation distribution under certain parameters entered in the initial search page. For data mining activity #1, students followed a series of steps to select for the p53 Melloy 29

3 Biochemistry and Molecular Biology Education FIG 1 Student data mining activities using the IARC TP53 Database. Part One of the laboratory exercise involved four data mining activities to become familiar with the IARC p53 database. Activities #1 and #2 included looking at p53 mutation distribution for liver cancer in general and liver cancer linked to aflatoxin exposure, respectively. Mutation type, mutation effect, and codon distribution were compared between the two experiments. Activity #3 covered p53 prevalence in cancers reported in the United States, while Activity #4 included an investigation of the overall p53 mutation distribution in the entire database. Asterisks indicate places where students select a cancer of choice, followed by the cancer of choice with a particular carcinogen, and finally a country of choice when they conduct Part Two of the laboratory exercise, a modified version of Part One. mutation distribution in liver cancer. A screen shot of the website search page used for the first exercise in shown in Fig. 2. The database search revealed p53 mutation distribution characteristics such as the type of mutation seen (missense, nonsense, deletion, etc.), the mutation effect (type of DNA alteration), and the codon distribution. To understand the significance of the codon numbers presented in the distribution graph, students referred back to a figure in the handout indicating the codon ranges corresponding to particular protein domains. This figure was also available in the slideshow found on the IARC p53 database. Data mining activity #2 was a modification of the first experiment, in which students searched liver cancer again, but this time restricted their search to liver cancer related to exposure to a particular carcinogen, aflatoxin. Students studied the particular p53 mutation distribution (mutation type, mutation effect, and codon distribution) found in this subset of liver cancers. Students then performed data mining activity #3, using a different type of search. Students were investigating the prevalence of p53 mutation in different cancers reported in the United States population. The top three cancers with the highest p53 mutation prevalence reported were recorded by the students. The concluding activity involved looking at the p53 mutation distribution in the entire database for all cancers, not just liver cancer. In Part Two, students worked with a more open-ended format. For data mining activity #1, students chose a particular cancer of interest to them, and searched for p53 mutation distribution associated with that particular cancer. For data mining activity #2, students continued to work with the same cancer, but now added the search criterion of only looking at those cancers linked to exposure to a carcinogen of their choosing. For both of these activities, students were encouraged to first look for particular cancers and particular carcinogens of interest using a web search engine before using the p53 database. Finally, data mining activity #3 involved investigating the p53 mutation prevalence in cancers reported in another country besides the United States. Typical student results can also be found in the Supporting Information as a part of the keys included for Part One and Part Two. Discussion Summary This article described the use of a two-part laboratory exercise exploring the p53 mutation database. In the first part, students recorded the p53 mutation distribution found in liver cancer, and compared these results to the p53 mutation distribution for only liver cancers related to aflatoxin exposure. Students also used the website to determine the overall prevalence of p53 mutation in cancers reported in the United States. In the second part, students had the opportunity to design their own database queries, looking at p53 mutation distribution for a cancer of their choosing, as well as a subset of those cancers related to 30 p53 Online Laboratory Exercise for Biology Elective Course

4 FIG 2 Typical screen shot of first search terms selected for data mining activity #1. Shown is a typical view of the website when a student selects the search terms necessary for an analysis of p53 somatic mutations associated with liver cancer. The next step in the procedure will be to click on the mutation distribution selection. The source of the screen shot is the IARC TP53 Database (p53.iarc.fr). Used with permission from Dr. Magali Olivier. exposure to a particular carcinogen. Students also looked at p53 mutation prevalence in cancers reported in another country besides the United States. This laboratory exercise was used for three different semesters of Cell Biology of Cancer over the past seven years. Because of small changes to the organization of the website over the years, the step by step instructions for the students were updated every time the exercise was used. Other changes included expanding the discussion question portion of part two to include comparisons among the search results from parts one and two. In addition, students connected information gathered on p53 mutation prevalence in cancers reported in different countries back to an earlier laboratory exercise looking at the overall genetic and environmental factors linked to an increased risk of developing certain cancers. This activity, called faces of cancer, can be found on the National Institutes of Health Office of Science Education webpage [12]. All students were able to complete the exercises successfully. The students worked either in pairs or individually. Working in pairs definitely helped the students who were less adept at following the directions precisely or who were not as confident in using the website. Students, in general, grasped the change in codon distribution as seen among the different data mining exercises most readily. Other mutation distribution details, such as mutation type and mutation effect changes, required an understanding of the nature of mutations as covered in a Genetics course. Students were assessed on their knowledge of the website information on the third laboratory exam for the course, as well as being required to have completed the laboratory exercises and a summary of the purpose of the Melloy 31

5 Biochemistry and Molecular Biology Education exercises in their laboratory notebooks. As a part of the laboratory exam, students were asked to answer in a free response question how the results obtained from the database search could be used to inform cancer research experiments performed in the laboratory. Extending the Activities To promote further discussion of the p53 laboratory exercise, a number of directions can be pursued. For example, each student can be asked to choose a different cancer and carcinogen for part two of the exercise, and then individually present his or her results to the rest of the class. Students can also share their data with each other, and the entire class can analyze the p53 mutation distributions (mutation type, mutation effect, and codon distribution) found associated with different cancers and different carcinogens researched by the individual students. Students can compare p53 mutation distribution information in each particular cancer with the distribution results reported for the entire database. Students could also present their data and analyze the class data for p53 cancer prevalence in cancers reported in different countries. Another extension of this assignment would be the concept of carcinogen fingerprints, or particular patterns of p53 mutation associated with exposure to certain carcinogens (described in detail in slideshow associated with the IARC p53 website) [11]. In part one of this exercise, students examined the p53 mutation pattern reported associated with exposure to aflatoxin, a carcinogen known to have a fingerprint [7, 10, 11]. Other carcinogens with known fingerprints could be studied using the database. Future studies using the IARC p53 website might also involve examining the impact of p53 mutation on particular domains of the p53 protein. One will notice during the exercise that the p53 DNA binding domain is most commonly mutated [11]. Students can discuss how this change impacts p53 function. Other bioinformatics tools, such as using a protein data bank [13], could be used to study the structural changes in the p53 protein expressed after particular p53 mutations occur. Use in Other Science Courses Although this two-part laboratory exercise was designed for a Cell Biology of Cancer laboratory class, it could also be used in upper level undergraduate classes such as Molecular Biology, Biochemistry, or Genetics. Parts of the laboratory exercise might also be appropriate for a sophomore-level Cell Biology class, such as the first part of the exercise where particular search parameters are already defined for the students. Acknowledgement Author thanks L. Khreisat for reading this manuscript. References [1] Honts, J. E. (2003) Evolving strategies for the incorporation of bioinformatics within the undergraduate cell biology curriculum. Cell Biol. Educ. 2, [2] Boyle, J. A. (2004) Bioinformatics in undergraduate education. Biochem. Mol. Biol. Educ. 32, [3] Furge, L. L., Stevens-Truss, R., Moore, D. B., and Langeland, J. A. (2009) Vertical and horizontal integration of bioinformatics education. Biochem. Mol. Biol. Educ. 37, [4] Bednarski, A. E., Elgin, S. C. R., and Pakrasi, H. B. (2005) An Inquiry into protein structure and genetic disease: Introducing undergraduates to bioinformatics in a large introductory course. Cell Biol. Educ. 4, [5] Medin, C. L. and Nolin, K. L. (2011) A linked series of laboratory exercises in molecular biology utilizing bioinformatics and GFP. Biochem. Mol. Biol. Educ. 39, [6] Hanahan, D. and Weinberg, R. A. (2011) Hallmarks of cancer: The next generation. Cell 144, [7] Weinberg, R. A. (2014) The Biology of Cancer, 2nd ed., Garland Science, New York. [8] Vogelstein, B., Sur, S., and Prives, C. (2010) p53: The most frequently altered gene in human cancers. Nat. Educ. 3, 6. [9] Alberts, B., Bray, D., Hopkin, K., Johnson, A., Lewis, J., Raff, M., Roberts, K., and Walter, P. (2010) Essential Cell Biology, 3rd ed., Garland Science, New York. [10] Hollstein, M., Sidransky, D., Vogelstein, B., and Harris, C. C. (1991) p53 Mutations in human cancers. Science 253, [11] Petitjean, A., Mathe, E., Kato, S., Ishioka, C., Tavtigian, S. V., Hainaut, P., and Olivier, M. (2007) Impact of mutant p53 functional properties on TP53 mutation patterns and tumor phenotype: Lessons from recent developments in the IARC TP53 database. Hum. Mutat. 28, [12] National Institutes of Health Office of Science Education. (1999) Activity 1 The faces of cancer. Available at: supplements/nih1/cancer/guide/activity1-1.htm (Last accessed July 2014). [13] Berman, H. M., Hendrick, K., and Nakamura, H. (2003) Announcing the worldwide protein data bank. Nat. Struct. Biol. 10, p53 Online Laboratory Exercise for Biology Elective Course