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 20th 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:
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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
Bellen,H.J., Kooyer,S., D'Evelyn,D. and Pearlman,J.
TITLE
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
Score
(Bits)
Sequences producing significant alignments:
scaffold283865
scaffold268507
scaffold225587
scaffold50631
60.8
57.8
39.3
33.5
E
Value
2e-08
2e-07
0.087
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
Sbjct
1049
MPQTIRLEFAKSNTKVSKPKPQPNTATTASHPALMHPLTGH
MPQTIRLEFAKSNTKVSKPK Q
A
+HP LMHPLTG
MPQTIRLEFAKSNTKVSKPKQQATNAAN-THPTLMHPLTGR
562
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
Sbjct
6555
GSTNNASHPGNPQIAANAPCSTLFVANLGQFVSEHELKEVFSSHGNSNWLKLL
GS+++
G
+N PCSTLFVANLGQFVSEHELKE+F+ + +
L L
GSSSSQPGVGGGMGVSNHPCSTLFVANLGQFVSEHELKEIFARYESRTVLMFL
736
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
Sbjct
4486
EGYEGSLLKVTSKNGKTAS
+GYEGSLLKVTSKNGKTAS
QGYEGSLLKVTSKNGKTAS
493
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
Sbjct
1406
MENQMALAPLGLSQSMDSVNTASNEEEVRTLFVSG
+E Q L LG+ + +S+ T SNE+ ++ LF+SG
LEKQFILLSLGIPREQESLCTLSNEQYLQVLFISG
Lambda
0.316
K
0.129
H
0.388
a
0.792
alpha
4.96
Gapped
Lambda
0.267
K
0.0410
H
0.140
a
1.90
alpha
42.6
457
1302
sigma
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.
j.
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.
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).