Exploring the Genetic Basis for Behavior. Instructor s Notes

Exploring the Genetic Basis for Behavior Instructor s Notes Introduction This lab was designed for our 300-level Advanced Genetics course taken by juniors and seniors majoring in Biology or Biochemistry. It is a two part exercise; either part could be conducted separately and the two labs do not have to be taught concurrently. Lab 1: Mating Frequency Prelab Preparation for Instructors Consult the Bean Beetle Handbook for detailed information on bean beetle culture, handling techniques, and tips for how to identify the two sexes. A large supply of virgin Callosobruchus maculatus adults are needed to conduct this exercise. Several cultures with actively laying females need to be available three to four weeks prior to the start of the experiment. Beans with a single egg were removed and transferred to a well of a tissue culture plate and allowed to develop to adults. Not all individuals developed into adults and it is a good idea to pick more eggs than you anticipate needing. Prelab Assignment for Students Drosophila melanogaster is used as our starting point. Students have worked with fruit flies in a past pre-requisite course and are familiar with their role as a model genetic system. To explore the question of the cost of sexual reproduction, students are assigned a reading, The Sinister Side of Sex (M. Brookes. 2001. Fly: The unsung hero of 20 th Century Science. HarperCollins Publishers Inc.) to prepare for the first lab. This chapter introduces students to some of the costs of sex in fruit flies and some of the genes that have been characterized to play a role in mating. It provides a starting point for students to consider what questions to ask about the bean beetle by extrapolating from the fruit fly. Several videos are available for introducing mating and courtship in D. melanogaster. Courtship http://www.youtube.com/watch?v=svv-oo1qa8m http://www.youtube.com/watch?v=bik2d1mc10&feature=results_main&playnext=1&list=ple71bfcdc7238dca2 Courtship set to Barry White http://www.youtube.com/watch?v=zxxqq2zjvma Courtship song http://www.youtube.com/watch?v=dmgc39zdjta http://www.youtube.com/watch?v=ujhj6scup1y Abnormal courtship http://www.youtube.com/watch?v=d1iwzrp8ara

Experimental Design A common theme was for students to vary the number of mating partners for one of the sexes and measure the lifespan after a period of mating. Some groups also suggested counting the number of eggs laid to measure fecundity. For example, individual females could be mated to no, one or five males over the course of a week in a small Petri plate with mung beans. After one week, males were removed, and the female was monitored daily to measure her lifespan. Additionally, the number of eggs the female lays was determined on a daily basis by removing beans with visible eggs. Conversely, the experiment could be conducted with individual males and varying numbers of female partners. Other courtship behaviors may be suggested for study. One group wanted to test to determine if the bean beetles had a courtship song. This area proved to be difficult to study because we do not have the equipment to record insect song. The following research articles on effects of mating in bean beetles might lead to other related questions and experimental designs: Berg, E. and Maklakov, A. (2012) Sexes suffer from suboptimal lifespan because of genetic conflict in a seed beetle. Proceedings of the Royal Society B. 279: 4296-4302. Brown et al. (2009) Negative phenotypic and genetic associations between copulation duration and longevity in male seed beetles. Heredity 103: 340-345. Crudgington, H.S. and Siva-Jothy, M.T. (2000) Genital damage, kicking and early death. Nature 407: 855-856. Paukku, S and Kotiaho, J. (2005) Cost of reproduction in Callosobruchus maculatus: effects of mating on male longevity and the effect of male mating status on female longevity. Journal of Insect Physiology. 51: 1220-1226. Rönn, J., Katvala, M., Arnqvist, G. (2006) The costs of mating and egg production in Callosobruchus seed beetles. Animal Behaviour 72: 335-342. Rönn, J., Katvala, M., Arnqvist, G. (2007) Coevolution between harmful male genitalia and female resistance in seed beetles. Proceedings of the National Academy of Sciences of the United States of America. 104: 10921 10925. Yamane, T. and Miyatake, T. (2012) Evolutionary correlation between male substances and female remating frequency in a seed beetle. Behavioral Ecology 23: 715-722. Yanagi, S. and Miyatake, T.(2003) Costs of mating and egg production in female Callosobruchus chinensis. Journal of Insect Physiology 49: 823-827. Data Collection After the mating period, students monitor beetles on a daily basis to determine the lifespan. If fecundity is included in the study, students count and remove beans with eggs. This part of the data collection could occur at less frequent intervals (every few days). New beans should be added to replace the beans that were removed.

Data Analysis A t-test was used to determine whether there was any difference in lifespan that correlated with the number of mating partners. Equipment and Supplies For a class of 30 students: Bean beetles cultures (Callosobruchus maculatus) Mung beans (Vigna radiata) 24-well flat bottom tissue culture plates for culturing virgin beetles (Corning Life Sciences, cat. # 353226) Plastic Petri dishes, 60 mm x 15 mm (Fisherbrand Media-Miser, cat. # 08-757-13A) small paint brushes for moving insects (1 for each lab group of 4 students) Dissecting scopes (1 for each lab group of 4 students) Lab 2: Comparative Genetics Prelab Preparation for Instructors The second lab utilizes bioinformatics tools available freely on the internet. The webpages for these tools are updated and will change. It is a good idea to preview sites prior to presenting the lab and update the protocol to reflect recent changes. Updates often provide more resources that could expand the scope of the exercise in the future. Prelab Assignment for Students Again, using D. melanogaster as our jumping off point, students are assigned a review on the genetics of fruit fly mating (Hall, J.C. 1994. The Mating of a Fly. Science 264:1702-1714). The paper provides a table with a list of genes that have been characterized in fruit flies as well as an overview of the field. Students are asked to select genes that might be expected to have homologs in the bean beetle based on the behaviors they observed in Lab 1. Experimental Design This lab allows for the opportunity to use primary literature in conjunction with bioinformatics tools to make decisions about which sequences to use in the analysis. Points to consider are: a. DNA sequence or Protein sequence: The protein sequence is more useful than the DNA sequence when searching for similarity between species. Similar functions would imply conserved amino acid sequences, while the DNA sequence could vary greatly. b. Which sequence or isoform to use? Sequencing projects have dumped a lot of sequence data into Genbank but there may be no experimental data to support the function of these genes. There may be multiple versions of the gene sequence in the database to choose. The GenBank flat file in conjunction with the primary literature can help in deciding the best choice for which sequence to use. After reading the review, students speculate on which fruit fly genes might be expected in the bean beetle genome, based on their observations of the beetles behavior. Some genes have multiple phenotypic effects while others are specific to courtship and mating. There is some freedom to narrow or widen the gene choice, depending on the instructor s preference.

Data collection Unless you have used some of these tools previously in class, students will need some guidance working with websites. A guideline has been provided in the appendix. The D. melanogaster genes can be found NCBI (http://www.ncbi.nlm.nih.gov/). Students search GenBank for the protein sequence (FASTA format) of their D. melanogaster gene of interest. They use that protein sequence to search the Bean Beetle genome (http://www.beanbeetles.org/genome/blast/beetleblast/beetleblast.php) using tblastn. They evaluate their bean beetle sequence matches by the quality scores and alignment to determine if the match is a good candidate. If it is, the scaffold sequence for the top hit can be downloaded (scaffold sequence contains more sequence than the match but represents a contiguous region of genomic DNA). This DNA sequence can be used to perform a blastn against the bean beetle genome to try to extend the sequence and annotate the gene. It can also be used to perform a blastx against GenBank to confirm that it is matching similar genes to the original D. melanogaster sequence. Data Analysis Students evaluate the quality of their blast analyses to determine whether or not they have identified a similar gene in the bean beetle. Not all D. melanogaster genes may be good probes for the bean beetle genome, so some choices may lead to negative results. Equipment and Supplies For a class of 30 students: Laptop computers with internet access (many students bring their own) Appendix Bioinformatics Tools To locate protein sequence for your gene of interest: a. Go to http://www.ncbi.nlm.nih.gov/ b. There is a search bar at the top of the page. Change the default (All Databases) to Protein. Type in the name of the gene of interest followed by Drosophila. c. The search will yield several results (multiple isoforms) and can open a discussion on which sequence to pick. First, be sure the sequence is from Drosophila melanogaster, then look for Full Protein. If Full Protein does not exist, pick the best choice (first isoform or largest size). Select the entry by clicking on the title. You will be brought to the flat file or submission entry for that sequence. d. Flat files contain a lot of useful information but not in the most accessible format. Some translation for the students is necessary. You want to scroll down to the first literature reference associated with this sequence. If it is a primary article specific for the gene of study, it is a good choice. However, if the first reference is for a whole genome project, it is not specific for your gene and you may have a gene prediction. For example, I searched for couch potato Drosophila and received 364 entries with multiple isoforms. When I amended the search to couch potato Drosophila full, I only received one entry. In that entry, the first reference to the primary literature in the flat file was:

AUTHORS TITLE Bellen,H.J., Kooyer,S., D'Evelyn,D. and Pearlman,J. The Drosophila couch potato protein is expressed in nuclei of peripheral neuronal precursors and shows homology to RNA-binding proteins JOURNAL Genes Dev. 6 (11), 2125-2136 (1992) The title to this article provides some meaningful information. The gene name is mentioned and the title indicates that expression studies were performed. The date indicates that it is pregenome sequencing projects (before 2000) and it has less than 10 authors. This entry indicates that this is a good sequence to use because it is based on the characterization of an individual gene. Entries to avoid are the following: AUTHORS Adams,M.D., et al.(almost 100 authors), TITLE The genome sequence of Drosophila melanogaster JOURNAL Science 287 (5461), 2185-2195 (2000) Such an entry indicates no experimental work was performed on the individual gene, but that it is part of a bulk download of genomic sequence. If this reference is the only one associated with the sequence, then the sequence is not the best choice and may be a prediction or a variant. Not every gene in GenBank has the same level of experimental data to support a predicted role. e. Now that the most meaningful and best-supported sequence is selected, go to the top of the flat file, and select FASTA (under the gene name in the title). Hopefully, you see: RecName: Full=Protein couch potato UniProtKB/Swiss-Prot: Q01617.3 GenPept Graphics >gi 48429205 sp Q01617.3 CPO_DROME RecName: Full=Protein couch potato MVKIANYQDLLGSHHQLLIAATAAAAAAAAAEPQLQLQHLLPAAPTTPAVISNPINSIGPINQISSSSHP SNNNQQAVFEKAITISSIAIKRRPTLPQTPASAPQVLSPSPKRQCAAAVSVLPVTVPVPVPVSVPLPVSV PVPVSVKGHPISHTHQIAHTHQISHSHPISHPHHHQLSFAHPTQFAAAVAAHHQQQQQQQAQQQQQAVQQ QQQQAVQQQQVAYAVAASPQLQQQQQQQQHRLAQFNQAAAAALLNQHLQQQHQAQQQQHQAQQQSLAHYG GYQLHRYAPQQQQQHILLSSGSSSSKHNSNNNSNTSAGAASAAVPIATSVAAVPTTGGSLPDSPAHESHS HESNSATASAPTTPSPAGSVTSAAPTATATAAAAGSAAATAAATGTPATSAVSDSNNNLNSSSSSNSNSN AIMENQMALAPLGLSQSMDSVNTASNEEEVRTLFVSGLPMDAKPRELYLLFRAYEGYEGSLLKVTSKNGK TASPVGFVTFHTRAGAEAAKQDLQGVRFDPDMPQTIRLEFAKSNTKVSKPKPQPNTATTASHPALMHPLT GHLGGPFFPGGPELWHHPLAYSAAAAAELPGAAALQHATLVHPALHPQVPTQMTMPPHHQTTAIHPGAAM AHMAAAAAAAAAGGGGGAATAAAAPQSAAATAAAAAAASHHHYLSSPALASPAGSTNNASHPGNPQIAAN

APCSTLFVANLGQFVSEHELKEVFSSHGNSNWLKLLHQ The protein is in the FASTA format appropriate for conducting BLAST analysis. The sequence can be copied into a simple text program and saved. f. Go to http://www.beanbeetles.org/genome/blast/beetleblast/beetleblast.php Paste the FASTA file into the search box. Select program tblastn (to use your protein sequence to search the translated nucleotide database) and select bean beetle database. Then select basic search. Output should look like: TBLASTN 2.2.27+ Reference: Stephen F. Altschul, Thomas L. Madden, Alejandro A. Schäffer, Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997), "Gapped BLAST and PSI-BLAST: a new generation of protein database search programs", Nucleic Acids Res. 25:3389-3402. Database:./db/longContigs.fasta 85,859 sequences; 315,317,553 total letters Query= gi 48429205 sp Q01617.3 CPO_DROME RecName: Full=Protein couch potato Length=738 Sequences producing significant alignments: Score E (Bits) Value scaffold283865 60.8 2e-08 scaffold268507 57.8 2e-07 scaffold225587 39.3 0.087 scaffold50631 33.5 6.6 > gi 48429205 sp Q01617.3 CPO_DROME on scaffold283865 Length=2108 Score = 60.8 bits (146), Expect = 2e-08, Method: Compositional matrix adjust. Identities = 31/41 (76%), Positives = 32/41 (78%), Gaps = 1/41 (2%) Frame = -1 Query 522 MPQTIRLEFAKSNTKVSKPKPQPNTATTASHPALMHPLTGH 562 MPQTIRLEFAKSNTKVSKPK Q A +HP LMHPLTG Sbjct 1049 MPQTIRLEFAKSNTKVSKPKQQATNAAN-THPTLMHPLTGR 930 > gi 48429205 sp Q01617.3 CPO_DROME on scaffold268507 Length=14563 Score = 57.8 bits (138), Expect = 2e-07, Method: Compositional matrix adjust. Identities = 28/53 (53%), Positives = 36/53 (68%), Gaps = 0/53 (0%) Frame = +3 Query 684 GSTNNASHPGNPQIAANAPCSTLFVANLGQFVSEHELKEVFSSHGNSNWLKLL 736 GS+++ G +N PCSTLFVANLGQFVSEHELKE+F+ + + L L Sbjct 6555 GSSSSQPGVGGGMGVSNHPCSTLFVANLGQFVSEHELKEIFARYESRTVLMFL 6713

> gi 48429205 sp Q01617.3 CPO_DROME on scaffold225587 Length=5685 Score = 39.3 bits (90), Expect = 0.087, Method: Compositional matrix adjust. Identities = 18/19 (95%), Positives = 19/19 (100%), Gaps = 0/19 (0%) Frame = +1 Query 475 EGYEGSLLKVTSKNGKTAS 493 +GYEGSLLKVTSKNGKTAS Sbjct 4486 QGYEGSLLKVTSKNGKTAS 4542 > gi 48429205 sp Q01617.3 CPO_DROME on scaffold50631 Length=9815 Score = 33.5 bits (75), Expect = 6.6, Method: Compositional matrix adjust. Identities = 14/35 (40%), Positives = 23/35 (66%), Gaps = 0/35 (0%) Frame = -1 Query 423 MENQMALAPLGLSQSMDSVNTASNEEEVRTLFVSG 457 +E Q L LG+ + +S+ T SNE+ ++ LF+SG Sbjct 1406 LEKQFILLSLGIPREQESLCTLSNEQYLQVLFISG 1302 Lambda K H a alpha 0.316 0.129 0.388 0.792 4.96 Gapped Lambda K H a alpha sigma 0.267 0.0410 0.140 1.90 42.6 43.6 Effective search space used: 58145294310 Database:./db/longContigs.fasta Posted date: Mar 26, 2013 1:46 PM Number of letters in database: 315,317,553 Number of sequences in database: 85,859 Matrix: BLOSUM62 Gap Penalties: Existence: 11, Extension: 1 Neighboring words threshold: 13 Window for multiple hits: 40 g. Students will need to evaluate the quality of their hits based on sequence similarity, length and quality. (For example, four hits are found with couch potato but only two have expected values low enough for further consideration. A good cut off range is an e-values smaller than 10-6 ). Click the sequences in the subject column and click submit to download complete scaffolds. These sequences include data beyond just the area of the hit. Students may want to annotate the sequence region identified in the blast analysis, especially if the scaffold is large. h. Use scaffold sequence to perform a blastn against the bean beetle genome. Can any regions of overlap be identified to extend the sequence?

i. Use scaffold sequence to perform a blastx against GenBank. This analysis can be used to confirm that the quality of the bean beetle sequence. If the sequence is a good candidate for a similar gene, the hits retrieved should list similar functions to the original fruit fly sequence. However, if the sequence was a weak hit, unrelated or unfamiliar function will be seen. j. The sequence quality of the Callosobruchus maculatus genome is variable and there are gaps in the sequence. You may see tracks of Ns (bases that could not be determined). Individual sequence reads are small and it may not be possible to annotate the whole gene. This study was written by M. Ramesh, 2013 (www.beanbeetles.org).