UPTEC X 13 027
Examensarbete 30 hp Oktober 2013
Genetic variation in photosensory proteins in Arabidopsis thaliana in relation to altitude
Molecular Biotechnology Programme
Uppsala University School of Engineering
UPTEC X 13 027 Date of issue 2013-10 Author
Genetic variation in photosensory proteins in Arabidopsis thaliana in relation to altitude
Title (Swedish) Abstract
In the Alps there is a large variation in altitude which gives rise to great environmental differences over a short geographical distance. These altitudinal habitat differences include lower temperatures and higher UV radiation with an increase in altitude. Plants are dependent upon light, not only for photosynthesis but also for light cues that control how and when they develop. Two light sensory protein genes of interest in A. thaliana that could be taking part in the adaptation to higher altitude are Phot1 and Phya.
Regions of the genes were sequenced and then they were analyzed to see if there were any visible patterns related to altitude. A germination experiment was done as well, to see if there was any difference in phenotypes; such as time to germinate; between the high and low altitude populations. Phot1 showed patterns correlated to altitude as well as to longitude.
There was very little overall variation in Phya and therefore no patterns correlated to altitude were found.
Adaptation, Phya, Phot1, altitude, germination, light sensory proteins, Arabidopsis thaliana Supervisors
Karl Schmid Christian Lampei
University of Hohenheim, Germany Scientific reviewer
Jon Ågren Uppsala University
Project name Sponsors
English Security Secret until 2014-10
ISSN 1401-2138 Classification
Supplementary bibliographical information Pages
Biology Education Centre Biomedical Center Husargatan 3 Uppsala
Box 592 S-75124 Uppsala Tel +46 (0)18 4710000 Fax +46 (0)18 471 4687
Genetic variation in photosensory proteins in Arabidopsis thaliana in relation to altitude
Tänk dig att du promenerar ner från ett berg. På högsta toppen av berget är det kallt och det växer inga träd och bara enstaka växter . När du går ner för berget blir det varmare, det blir fler växter och du börjar se träd. Om du sedan tänker dig att den här vandringen är en vandring genom tid så har du en idé om vad global uppvärmning kan ge. Det är av detta skäl som det kan vara viktigt för oss att undersöka förändringar som sker i och med höjdförändringar.
Arabidopsis thaliana, även kallad Backtrav på svenska, är en växt som växer på många olika höjder i Alperna och den kan därför användas för att titta på vilka anpassningar som den kan genomgå för att klara av att leva vid olika höjder. Två gener i Backtrav som blev av speciellt intresse för mig var Fototropin 1 och Fytokrom A. Fytokrom A är involverad i många olika processer, allt från groddning till blomning. Fototropin 1 är bland annat involverad i att skydda växten mot hög UV-strålning vilket ökar med höjd. Även ett groddningsexperiment gjordes för att se om det fanns några specifika groddningsmönster i relation till ljusförhållandena.
Det visade sig att det fanns väldigt lite variation inom Fytokrom A genen och därför kan inga slutsatser dras. I Fototropin 1 däremot fanns det variationer som följde ett altitudmönster och därför är det möjligt att det kan finnas en anpassning i den genen till skillnader i höjd.
Groddningsexperimentet visade att det fanns en större variation när fröna gror om de kommer från populationer som växer vid högre höjd.
Examensarbete 30 hp
Civilingenjörsprogrammet Molekylär bioteknik
Uppsala Universitet, oktober 2013
Popul¨arvetenskaplig sammanfattning 3
Table of contents 5
1 Introduction 7
1.1 Background . . . 7
1.2 Arabidopsis thaliana . . . 8
1.3 Phototropin 1 . . . 8
1.4 Phytochrome A . . . 8
2 Materials and Methods 9 2.1 Arabidopsis thaliana populations . . . 9
2.2 DNA extraction . . . 10
2.3 Primer Design and PCR . . . 11
2.4 Data and statistical analysis . . . 12
2.5 Germination experiment . . . 13
3 Results 14 3.1 Phototropin 1 . . . 14
3.2 Phytochrome A . . . 22
3.3 Germination experiment . . . 24
4 Discussion 26 4.1 Phototropin 1 . . . 26
4.2 Phytochrome A . . . 26
4.3 Germination experiment . . . 27
5 Conclusions 28
6 Acknowledgements 28
7 References 29
Phya Phytochrome A gene PHYA Phytochrome A protein Phot1 Phototropin 1 gene PHOT1 Phototropin 1 protein PCR Polymerase chain reaction SNP Single nucleotide polymorphism PCA Principal component analysis PC Principal component
UTR Untranslated region
The large variation in altitude in the Alps gives rise to great differences in climate over small geographical distances. These variations in climate can give rise to adaptations in the plants that are growing at different altitudes. Earlier studies have found that there is variation in several traits affected by altitude (Mend´ez-Vigo et al., 2011; Montesinos-Navarro et al., 2011).
Radiation from the sun increases when the sky is cloudless. However it is common that clouds col- lect over high peaks and thus radiation is decreased (K¨orner, 2007). This means that the variation in radiation is large between cloudy and non-cloudy days and therefore the plants living at these higher altitudes would most likely need to have mechanisms to protect against the occasionally high amounts of radiation. A protein that could be involved in the adaptation to higher radiation is the phototropin 1 protein (PHOT1). PHOT1 is a photoreceptor that is involved in several pathways that help protect against radiation, one such mechanism is the relocation of chloroplasts (Christie, 2007). Chloroplasts are usually located close to the surface of the cells in the leaf, to be as close to the sun as possible to maximize harvesting of sunlight. When radiation is high there is a risk that the chloroplasts may get damaged by the radiation and therefore they relocate to be further into the cell and lowering their risk of getting damaged (Christie, 2007). PHOT1 is also involved in stomata closing in response to radiation, this is to minimize loss of water during hot days (Christie, 2007).
At higher altitudes flowering in Arabidopsis thaliana occurs later in the season (Mend´ez-Vigo et al., 2011). This could be an adaptation to the fact that the snow melts later in the spring at higher altitudes (K¨orner, 2007). Another very important stage in development is the germination which has been found to be adapted to a change in altitude (Montesinos-Navarro et al., 2012). A protein that is well known to be involved in the flowering and germination pathway is the phytochrome A protein (PHYA) which is a photoreceptor in the red and far red spectrum. There is little variation in red and far red light at high and low altitudes. However, plants at different altitudes need to flower at different times and therefore need to react differently to the same light conditions.
This is an investigation into the possibility that there is genetic adaptation to high altitudes in the phytochrome A (Phya) and Phot1 (Phot1 ) genes of Arabidopsis thaliana. Phya is investigated to a lesser extent in this project due to the limited variation to found in this gene. An investigation into the germination of different populations collected from different altitudes is also made to see if there is a possibility that there is an adaptation in the germination mechanism which could be linked to PHYA.
1.2 Arabidopsis thaliana
A. thaliana is a flowering plant that is native to Eurasia and has later spread to the other conti- nents with humans (Hoffmann, 2002). It has several attributes that makes it suitable as a model organism. A. thaliana is small, it has a short generation time and has a large number of offspring.
It has a small genome and it is highly selfing which makes it easy to use in the study of genetics. It became popular as a model organism in the 1980s due to a genetic map being released; which made continued research upon A. thaliana simplified (M¨uller & Grossniklaus, 2010). The publication of the A. thaliana reference genome made it easier to study and there is now an ongoing project called 1001 genomes which aims to sequence 1001 different accessions (http://www.1001genomes.org/).
1.3 Phototropin 1
Phototropin 1 (PHOT1) is a protein that reacts to blue light. It was first discovered to be involved in the phototropism response (movement toward or from light) and has later been found to be involved in several other functions (Briggs et al., 2001). One function that it controls is the opening and closing of stomata. Stomata are pores in the leaves where gas exchange and water evaporation occurs. The closing of the stomata during high light intensity is important to reduce the loss of water (Christie, 2007).
PHOT1 is also involved in is the relocation of chloroplasts. Chloroplasts move depending upon how intensive the light radiation is. When the light radiation is lower the chloroplasts gather horizontally along the leaf to gather more light energy, and when light radiation is higher the chloroplasts gather vertically so that the potential damage from radiation will be less (Christie, 2007). PHOT1 is only involved in the relocation during low light intensities, while it’s close relative PHOT2 is responsible for the avoiding behavior of the chloroplasts (Christie, 2007; Jarillo et al.
1.4 Phytochrome A
Phytochrome A (PHYA) is a light-sensing protein that belongs to the family of phytochrome proteins, all of which receive red and far red light. PHYA is involved in several pathways and plays a role in several of the developmental stages in plants (Li et al., 2011; Kircher et al., 2011).
PHYA is synthesized in the Prform and switches to the Pf r form when it reacts to red light (660 nm) (Li et al., 2011). PHYA can return to its original Pr form when subjected to far red light (730 nm) and also returns to its original state at a steady pace in the dark because of its unstable conformation (Li et al., 2011).
The sensing of red and far red light is important for sensing day length and PHYA is involved in the circadian clock of plants (Srikanth & Schmid, 2011). Day length is a good measure for plants
to use because it is a more reliable indicator of when conditions will remain favorable than for example temperature which can fluctuate quickly. A. thaliana is a long day flowering plant and therefore prefers to flower when the day is longer than the night .
2 Materials and Methods
2.1 Arabidopsis thaliana populations
Thirteen different A. thaliana populations from the Alps in the Southern Tyrol with 2 to 14 accessions from each population were used in this experiment. The altitude range was from 280 meters above sea level to 2350 meters above sea level. The distribution of the populations can be seen in figure 2.1 together with the name and altitude in table 2.1.
Altenburg, Bozen train and Rovero were all collected from places that were disturbed by humans.
Altenburg grew next to a road, Bozen train was collected from close to a train station and Rovero was growing close to a vineyard. The Schnatz population was growing at a high altitude, but in a south facing slope and therefore in a warmer climate than the other high altitude populations.
Figure 2.1: Map of populations positions
Table 2.1: List of populations and their altitudes
Number Population name Altitude (m above sea level) number of accessions
1 Bozen train 280 3
2 Rovero 315 5
3 Castelfeder 390 8
4 Mitterberg 595 7
5 Altenburg 625 7
6 Bozen 730 6
7 Vezzano 880 5
8 Laatsch 1100 10
9 Schnatz 1709 2
10 Voka 1770 4
11 Finail 2200 14
12 Coro 2210 7
13 Vioz 2355 7
2.2 DNA extraction
The leaf material for DNA extraction was collected when plants where sufficiently large. They where then either frozen at -80 ◦C or dried directly on silica gel. The plant material that was originally frozen was later put on silica gel to dry. Approximately 0.05 grams of each dried plant was ground to a fine powder before the DNA extraction.
A CTAB mini preparation protocol was followed based on the method of Saghai-Maroof et. al (1984). 1 ml of 65◦C CTAB extraction buffer (see table 2.2) and a spade tip of sodium disulfate was added to the ground up plant material and mixed. The mixture was then heated in a water bath for 60 minutes while continuously being rocked. 600µl of chloroform/isoamylalcohol mixture was then added to the mixture and rocked back and forth for 5-10 minutes. The eppendorf tube was then centrifugated for 10 minutes at 10000 rotations per minute (rpm). The top layer of liquid was removed with a plastic pipet. 600µl of chloroform/isoamylalcohol was added to the mixture and rocked for 10 minutes then the mixture was centrifugated for another 10 minutes. Isopropanol from the freezer was added to the mixture and was mixed very carefully until the two liquid layers were mixed, it was then set in a freezer at -20◦C for 30 minutes or longer if the following steps could not be done immediatly.
Next the cold mixture was then centrifugated at 5000 rpm for 5 minutes to form a pellet. The liquid was then poured of or pipetted away depending on how well the pellet was sticking to the bottom of the tube. The pellet was washed with 600µl of wash 1 (see table 2.3) and then spun down for 5 minutes at 5000 rpm. The liquid was again removed from the pellet and 600µl wash
2 (see table 2.4) was added. The tube was centrifugated again for 5 minutes at 5000 rpm and the liquid removed from the pellet. The pellet was dried in a vacuspeed machine until completely dry.
The pellet of DNA was then dissolved in 100µl of 10 mM tris buffer pH 8.00.
Table 2.2: CTAB extraction buffer
Stock solutions Final Concentration amount per 100 ml
1 M tris pH 7.5 100 mM 10 ml
5 M NaCl 700 mM 14 ml
0.5 M EDTA pH 8.0 50 mM 10 ml
CTAB 1 % 1 g
ddH2O 65 ml
Table 2.3: Wash 1
Stock solutions Final Concentration amount per 100 ml
EtOH 100% 76 % 76 ml
3 M NaOAc 700 mM 6.67 ml
ddH2O 17.33 ml
Table 2.4: Wash 2
Stock solutions Final Concentration amount per 100 ml
EtOH 100% 76 % 76 ml
7.5 M NH4OAc 10 mM 0.133 ml
ddH2O 23.866 ml
2.3 Primer Design and PCR
Both Phya and Phot1 are large genes 6.6 kb and 9.5 kb respectively. Therefore we chose regions to sequence that were known to be polymorphic in a subset of populations from the same geographic area. In Phya a part of the promoter region was chosen for sequencing. In Phot1 the end at the 3’ region as well as part of the 3’ UTR was chosen for sequencing.
Primers for the PCR reactions were designed using primer blast (http://www.ncbi.nlm.nih.gov/).
The primers were ordered from Metabion and then tested on Col-0 DNA that was available; primer pairs were then chosen for using in PCRs on our target DNA.
The primers that were used in the amplification of phot1 can be seen in table 2.5. The primer pair number 1 was used for most of the amplifications and primer pair 2 was used for the Vioz and Voka populations, which completely failed to be amplified by primer pair 1.
In table 2.6 the primers used for Phya can be seen, these primers were used with great success.
Table 2.5: Primers used for amplification of phot1 Primer Name Sequence
Forward 1 GCTCTGATTCGATGCACGGT
Reverse 1 TGGTTTGTACCCAATTCAAGCC
Forward 2 TGGGCTCTGATTCGATGCAC
Reverse 2 TGGTTTCCATTGTCTTTTGTAGCC
Table 2.6: Primers used for amplification of phyA Primer Name Sequence
In table 2.7 the PCR mix is shown per well, the concentration of the template DNA is only an approximation. The PCR program that was run was a touchdown protocol, where the annealing temperature started at 65◦C and went down in 1 ◦C steps each cycle until 55◦C where it went through 30 cycles. The touchdown protocol is very good to use in cases where the exact annealing temperatures are not known since a range of temperatures are used. The PCR product was checked by running an agarose gel and an approximation of the amount of PCR product could be made from this.
Table 2.7: PCR mix per well
Reactant Final Concentration Volume per well
H2O - 44µl
PCR buffer 1x 10µl
dNTPs 0.15 mM 15µl
MgCl2 2.5 mM 10µl
Forward Primer 0.25 pmol 5µl Reverse Primer 0.25 pmol 5µl
Taq Polymerase 1 U 1µl
Template DNA 1 ng/µl 10µl
2.4 Data and statistical analysis
The data files received from Beckman Coulter-genomics where evaluated by looking at the base- calling and the appearance of the chromatograph in 4Peaks. The sequences were then aligned with ClustalW2 on the internet and then further viewed in Unipro UGENE to look for possible errors that had been missed during the basecalling
Firstly phylogenetic trees were made to see if the accessions would cluster according to popula- tion. The phylogenetic trees were made using Phylip, the alignment data was bootstraped 1000 times and then used for calculating genetic distance with the F84 model. Trees were made with the neighbour joining algorithm and then made into a consensus tree which was plotted in Unipro UGENE.
For the statistical analyses R was used with the package ade and pegas to handle the genetic sequence data. A clustering analysis was then conducted with a clustering algorithm called para- metric model-based inference of population structure (Pritchard et al., 2000). To decide upon the amount of clusters to use in the analysis the find.clusters algorithm in R was run and k (the number of clusters) was chosen to be 2 (Jombart et al. 2010).
A principal component analysis (PCA) was conducted with the alignment data which had been converted into genind object, an object that can store several different genotypes as well as data about which populations the genotypes belong to. The two principal components (PC) explaining most of the variation were plotted against altitude, longitude and latitude to see if there was any correlation.
A Mantel test was performed to investigate if there was a significant correlation between geo- graphic distance and the genetic distance between individuals (Mantel, 1967), the genetic distance was calculated in R with dist.dna and the F84 algorithm was used.
2.5 Germination experiment
A germination experiment was conducted to see if there was any affect of different light conditions on the germination of the different seed populations. Seeds from the different accessions where pooled by population. The seeds where then put on a white filter paper in petri dishes, with 50 seeds on each plate. Water was added with a pipette until the filter paper was moist and the plates where then sealed with parafilm. The plates where then put into a cold chamber for cold treatment for 3-4 days. Three different light conditions where used corresponding to different times in the growing season. Twelve hours corresponds to early spring and late autumn. Fourteen hours corresponds to late spring to early summer and late summer to early autumn. Sixteen hours corresponds to the middle of summer. There were five replicate petri dishes per light setting for each population, the petri dishes were randomized differently for each light setting.
The temperature was kept at a constant 20◦C. Every day the seeds were checked for germination and water was added each day so the seeds would not dry out. The number of germinated seeds was counted each day. A seed was counted as germinated if the cotyledons had sprouted from the seed. After 6 days at 20◦C the experiment was stopped due to mold growing on the petri dishes.
The data was analyzed using R by fitting the cumulative germination data to a log logistic model
of the following form (Ritz, 2005):
y = a
1 + eb(log10t−log10c)
Parameter a is an already known parameter, it is the percentage of seeds germinated on the final day of the experiment. Parameter b is a parameter that is directly proportional to the slope at point t50 which is the time at which half of the seeds have germinated. Parameter b is an important parameter to find since it is proportional to the slope at point t50and this slope gives a measure of how well synchronized the seeds germinate. Parameter c is the point t50 which was not calculated in advance but found when fitting the distribution. t is the time in days of the germination experiment and y is the proportion of seeds germinated at time t. A generalized linear model fit was made with the total number of germinated seeds against the different populations and light conditions, using the glm function in R set to using binomial error distribution and corrected for overdispersion.
3.1 Phototropin 1
The quality of the sanger sequencing was not good for all the sequences and the basecalling was therefore not completely successful and a lot of adjustment had to be done manually. Only 250 bases were of such high quality that I decided to use them. However there was a lot of variation in the sequence, 42 SNPs were found 16 transitions and 26 transversions. There were five places where deletions were found, 3 were single nucleotide deletions. There was a three bp deletion that was only found in the Mitterberg and Rovero populations and a four bp deletion that was mainly found amongst low altitude populations, these deletions can be seen in figure 3.1. In the sequence only a small region was coding, only 3 SNPs were found in this region and all of them gave rise to synonymous mutations. The rest of the mutations were in the 3’ UTR and are therefore non coding but could be involved in regulatory functions (Barett, 2012).
The phylogenetic tree has a good separation between the high and low altitude accession (figure 3.2), as did the clustering analysis (figure 3.3 and 3.4). Four populations (Laatsch335, Vezzano221, Laatsch331 and Fianil8.20) clustered with the low altitude populations though they were collected at high altitude (figure 3.2 and 3.4).
PC1 from the PCA explained 36 % of the variation in the data and was correlated with altitude and longitude (figure 3.5; p-values for the fitted line are 2.19 × 10−11 for altitude and 7.73 × 10−11 for longitude). It can be seen that there is also a correlation between altitude and longitude in figure 3.5 which has a p-value of 2.01 × 109.
In figure 3.6 the genetic distance has been plotted against geographic distance showing that the
genetic distance is correlated with the geographic distance, the mantel test that was conducted with genetic distance and geographic distance had a p-value of 0.001 which is a significant correlation between geographic and genetic distance.
Figure 3.1: A plot of the Phot1 alignment showing the accessions in order of the altitude going from lowest altitude at the left and highest altitude to the right. A: adenine, C: cytosine, T: thymine, G:
guanine, -: gap, others: heterozygote
Figure 3.2: Phylogenetic tree showing how the populations cluster according to altitude difference, the red being the populations growing above 800 m and the purple growing below 800 m, except for the four accessions marked with arrows which should belong amongst the high altitude accessions.
Bootstrap values that are above 50 % are shown, the node where high and low accessions are divided has a bootstrap value of 99.3 %
Figure 3.3: Clustering analysis with k=2, populations are ordered from lowest altitude to highest altitude going from left to right
Figure 3.4: Map showing pie diagrams of how many accessions in each population belong to a certain cluster, color coded according to the clustering diagram in figure 3.3
Figure 3.5: The upper plots show PC1 explaining 36 % variation of the data plotted against altitude and longitude. The slopes of the fitted linear model is 0.005877 and -16.24 respectively and the p values are 2.19×10−11and 7.73×10−11respectively. The third plot shows the correlation between altitude and longitude plotted against each other with a slope of -0.000257 and a p-value of 2.01 × 109
Figure 3.6: Genetic distance of each accession plotted against the geographical distance of each accession. Showing that there is isolation by distance.
3.2 Phytochrome A
The quality of the Phya sequencing was very good and the basecalling of the sequences was very successful, with 446 bp used for the statistical analyses. However there was little variation in the sequences only 13 SNPs 7 transversions and 6 transitions. A 4 bp deletion and a 2 bp deletion were found in some of the accessions. Figure 3.7 shows the alignment of Phya and it can be seen that there is not so very much variation in the sequence and that the variation does not look as if it is correlated to altitude. A quick analysis with making a phylogenetic tree and a PCA showed that there was not enough variation to seperate the different populations from each other and it was not possible to see any geographic or altitude patterns (results not shown).
There is one individual that is an outlier in terms of sequence from all the rest of the individuals, and this is Rovero218 which has a 12 bp deletion in the begining of the sequence and also has a 1 bp insertion at the 420 bp.
Figure 3.7: A plot of the PhyA alignment showing the accessions in order of altitude going from lowest altitude at the left to highest altitude to the right.
3.3 Germination experiment
In figure 3.8 the amount of germination in each population on the final day of the experiment can be seen. Laatsch germinated really badly, however there had been no problems with germination when grown for DNA extraction. Therefore Laatsch was left out from the rest of the data analysis of the experiment. The four populations from the highest altitudes had the highest germination rates except for Rovero which had around 80 % germination. A generalized linear model was fitted to the total amount of germination with populations and light settings as the factors. The population was the only factor that significantly affected the total number of seed germination, with a F-value of 105.1 and a p-value of 1.92 × 10−9. A generalized linear model was also fitted with total number of germination as response and altitude as a factor but the result was not significant (result not shown).
In figure 3.9 the synchronization at t50 is plotted against the altitude, the data from all of the different light settings was used since there was no significant difference between the different light settings, this gave a linear correlation with a p-value of 0.0078. The time to reach t50did not vary much between populations, occurring sometime between day 2 and day 3.
Figure 3.8: Barplot of the average total amount of germinated seeds in each population in each light setting. The populations are ordered according to altitude from lowest altitude at the left.
Figure 3.9: The synchronization scores from all three light settings plotted against altitude with a p-value of 0.0078 and a slope of -0.00166
4.1 Phototropin 1
Large variation was found in the sequenced region of the phototropin1 gene, this is probably due to the 3’ UTR being a region that can tolerate a large amount of mutation without it having a large effect on the gene (Barett, 2012). The PCA that was used on these SNP variations gave a PC that showed correlation with altitude. However, there was a slightly stronger linear correla- tion between longitude and PC1. Due to the landscape and the way the accessions were collected there is a correlation between longitude and altitude which can be seen in figure 3.5. This makes it difficult to say if the correlation to altitude is purely due to there being a migratory pattern visible in the gene or if there is any actual correlation to altitude. The clustering also shows that there is a clear correlation between altitude and the variation in the gene as well as a correlation with longitude as can be seen in figures 3.3 and 3.4. There were a few outliers amongst the high altitude populations and these could be there due to genetic flow occurring between the different populations, which is supported by figure 3.6 showing that there is an isolation by distance pattern (Hutchinson & Templeton, 1999).
Even though the part sequenced was in the 3 UTR it is possible that the mutations can still be functional, the sequence in the 3 UTR can affect the transcription of the gene as well as affecting how quickly the transcript is degraded (Barett, 2012). It is therefore possible that there is some kind of adaptation in this sequence to the altitude. It has been shown previously by Lehman et al.
(2011) that changes in blue light give changes in the transcriptome that can be largely attributed to PHOT1, which means that if there is a mutation affecting the expression levels of PHOT1 in the plant it could have a large effect on expression of other genes during light stress.
Unfortunately there were many of the accessions that were not successfully sequenced due to the difficulties with getting a PCR product. These problems were most likely due to the sequences being very variable and the primers probably not being able to find the target sequence.
For further study of this gene it would be interesting to sample more populations that are at a low altitude but closer to the high altitude populations in this study. This way it would be easier to see if there was a clear pattern to altitude or if the pattern was just a migratory pattern. It would also be of interest to make a study of the phenotypic differences; such as tolerance to UV radiation; of the accessions in comparison with their genotype.
4.2 Phytochrome A
There was very little variation in Phya, it was therefore not possible to find any patterns correlated to altitude. Rovero218 had a 12 bp deletion in the sequence as well as a one bp insert, which none of the other accessions had. This could be due to Rovero218 coming from a highly disturbed
environment next to a vineyard, however none of the other Rovero accessions had this deletion or any other mutations that were of special note.
Since there were differences in the synchronization of germination in the germination experiment this could mean that there is some correlation to the phytochrome genes, although maybe not specifically in Phya. There are four more phytochromes that act to some extent redundantly with PHYA, Phytochrome C has previously been found to have mutations that have adaptive effects for flowering at different altitudes (Mend´ez-Vigo et al., 2011). It could be of interest to sequence all of the phytochrome genes and compare with germination behavior and flowering behavior.
4.3 Germination experiment
The synchronization plots in figure 3.9 show that there is higher synchronization of germination in seeds from a low altitude than seeds from a high altitude. It is probably advantageous for the seeds at a lower altitude to germinate in a synchronized fashion, for example germinating much later than the others could give a disadvantage in the valley since it gets very dry during summer and germinating and flowering quickly in spring before the climate gets dry would be an advantage (EEA report, 2009). However it can also be a disadvantage to germinate too quickly and that is most probably why this pattern of high synchronization is seen in the low altitude populations. In the high altitude populations the synchronization is on average lower and this is probably because there is a greater risk of frost or snow returning after a couple of warm days. It is even possible that the seeds from the high altitude have a bet hedging mechanism for germination where the risk is distributed over time and it could be of interest to investigate this further (Simons, 2011). Tonsor et al.(2012) which was also a study of A. thaliana growing along a climatic gradient suggests that A. thaliana from the Northeast of Spain used in his study show a complex form of bet hedging dependent on two genetically different mechanisms, namely how quickly primary dormancy is lost and how easily temperature dependent dormancy is induced. It is possible that such bet hedging mechanisms will have evolved in other populations as well but would not be detected by a germination experiment only testing for germination at different light conditions.
There was no change in germination rates according to the light setting, only a significant difference between different populations. This points toward light conditions not being so crucial for the germination, however the experiment was conducted at 20 ◦C and it is possible that at different temperatures different effects would be seen. It has been shown by Heschel et al. (2007) that phytochrome mutants react differently depending on which temperature they are grown at, showing that phytochromes not only react upon light conditions but also upon temperature.
The populations Castelfeder, Mitterberg, Altenberg, Bozen, Vezzano and Laatsch all had lower rates of total germination as can be seen in figure 3.8. It is possible that these populations maintain
seed banks which is a risk spreading strategy as well as a strategy to maintain a higher amount of genetic variation in a population over time (Venable & Brown, 1988; Lundemo et al., 2009).
Phot1 has a lot of mutations in the sequenced region, these mutation show a pattern that could possibly be linked to altitude but could also be due to the high altitude populations being collected close to one another. It is difficult to say if the mutations do have any adaptive function since they are in the 3’ UTR, however it is not impossible that they are involved in a regulatory function.
There was very little variation in Phya, and no altitude-related patterns were detected.
Low altitude populations had a higher rate of synchronization than the high altitude populations and this points towards there being some kind of adaptation in the genes controlling germination.
A further investigation into the behavior of germination in these populations could possibly reveal more interesting adaptations and maybe also bet hedging mechanisms.
Firstly I want to thank Karl Schmid for letting me come and work in his group during my thesis.
I want to thank Christian Lampei who supervised me throughout my entire project and always answered my questions whenever I needed guidance. I want to thank Elisabeth Kokai-kota who helped and guided me with my lab work and encouraged me even when things went badly in the lab. I also want to thank the rest of the work group for being so friendly and always helping me whenever I had questions for them. A large thanks to Jon ˚Agren for being my scientific reviewer.
Christie J. 2007 Phototropin blue-light receptors. Annual review of plant biology 58: 21-45
Felsenstein J. 1993. PHYLIP (Phylogeny Inference Package) version 3.6. Distributed by the author. Department of Genetics, University of Washington, Seattle.
Furuya M. 1993. Phytochromes: their molecular species, gene families and functions. Annual review of plant physiology and plant molecular biology 44:617-645
Heschel M. S., Selby J., Butler C., Whitelam G. C., Sharrock R. A., Donohue K. 2007. A new role for phytochromes in temperatur-dependent germination. New Phytologist 174(4): 735-741
Hutchinson D. W., Templeton A. R. 1999. Correlation of pairwise genetic and geographic distance measures: inferring the relative influence of gene flow and drift on the distribution of genetic variability. Evolution 53(6): 1898-1914
Jarillo J., Gabrys H., Capel J., Alonso J. M., Ecker J. R., Cashmore A. R. 2001. Phototropin- related NPL1 controls chloroplast relocation induced by blue light Nature 410(6831):952-954
Jombart T., Devillard S., Balloux F. 2010. Discriminant analysis of principal components: a new method for the analysis of genetically structured populations BMC Genetics 11(94)
Kircher S., Terecskei K., Wolf I., Sipos M., Adam E. 2011. Phytochrome A-specific signaling in Arabidopsis thaliana. Plant signaling & behavior 6(11): 1714-1719
K¨orner C. 2007. The use of altitude in ecological research. Trends in ecology & evolution 22(11): 569-574.
Lehmann P., N¨othen J., Schmidt von Braun S., Bohnsack M. T., Mirus O., Schleiff E. 2010.
Transition of gene expression induced by short-term blue light. Plant biology 13(2): 349-361
Li J., Li G., Wang H., Wang Deng X. 2011. Phytochrome signaling mechanisms. Arabidopsis Book. e0148
Lundemo S., Falahari-Anbaran M., Stenøien H. 2009. Seed banks cause elevated generation times and effective population sizes of Arabidopsis thaliana in northern Europe. Molecular Ecol- ogy 18(13): 2798-2811
Mantel N. 1967. The detection of disease clustering and a generalized regression approach.
Cancer Research 27:209-220
Max Plank institute for developmental biology. 2008. 1001 genomes a catalog of Arabidopsis thaliana genetic variation. http://www.1001genomes.org/ accessed 2013-08-12
M`endez-Vigo B., Pic´o F., Ramiro M., Martinez-Zapater J., Alonso-Blanco C. 2011. Altitudi- nal and climatic adaptation is mediated by flowering traits and FRI, FLC and PHYC genes in Arabidopsis. Plant physiology 157(4): 1942-1955
Montesinos-Navarro A., Wig J., Pic´o F.X., Tonsor S.J. 2011. Arabidopsis thaliana populations show clinal variation in a climatic gradient associated with altitude . The New phytologist 189(1):
Montesinos-Navarro A.,Tonsor S. J., Pic´o F. X. 2012 Clinal variation in seed traits influencing life cycle timing. Evolution 66(11): 3417-3431
M¨uller B., Grossniklaus U. 2010 Model organisms A historical perspective. Journal of Pro- teomics 73(11): 2054-2063
NCBI. Primer-BLAST A tool for finding specific primers. http://www.ncbi.nlm.nih.gov/tools/primer- blast/ accessed 2013-02-05
Okonechnikov K., Golosova O., Fursov M. 2012. Unipro UGENE: a unified bioinformatics toolkit. Bioinformatics 28: 1166-1167
Pritchard J. K., Stephens M., Donnelly P. 2000. Inference of population structure using mul- tilocus genotype data. Genetics 155(2): 945-959
Ritz C., Streibig J. 2005. Bioassay analysis using R. Journal of statistical software 12(5)
R Core Team. 2012. R: A Language and Environment for Statistical Computing. R foundation for statistical computing, Vienna.
Saghai-Maroof M.A., Soliman K.M., Jorgensen R.A., Allard R.W., 1984. Ribosomal DNA spacer-length polymorphisms in barley: Mendelian inheritance, chromosomal location, and popu-
lation dynamics. Procedings of the National Academy of Sciences of the USA 81:8014-8018.
Simons A. M. 2011 Modes of response to environmental change and the elusice empirical evi- dence for bet hedging. Procedings of the royal society B 278(1712): 1601-1609
Srikanth A., Schmid M. 2011. Regulation of flowering time: all roads lead to Rome. Cellular and Molecular Life Sciences 68: 2013-2037
Venable, D.L., Brown J.S. 1988. The selective interactions of dispersal, dormancy, and seed size as adaptations for reducing risk in variable environments. American Naturalist 131: 360-384