Transcript Character-based DNA barcoding for identifying conservation

Character-based DNA barcoding
for identifying conservation units in
Odonates
J. Rach1, R. DeSalle2, I.N. Sarkar2,
B. Schierwater1,2 & H. Hadrys1, 3
1ITZ-
Ecology & Evolution, TiHo Hannover,
Germany
2Division of Invertebrate Zoology, American
Museum of Natural History, New York,
USA
3Dept. Ecology & Evolutionary Biology, Yale
University, New Haven, USA
Thank you to:
• DAWB (CBOL)/DIMACS
• Sandra Giere
• Antonia Wargel
• Janne Timm
• Linn Groeneveld
• Nadine Habekost
• Kai Kamm
• DFG & BMBF
Character-based DNA barcoding:
A rapid and reliable method
for the identification of
conservation units in
dragonflies
Contents
1.
Introduction:
- Why barcoding dragonflies?
- Why character-based DNA barcoding?
- Which genetic marker is appropriate?
2.
Methods
- Character-based DNA barcoding
3.
Case studies
I. Species identification
II. Discrimination of conservation units
4.
Conclusions & Future prospects
1. Introduction: Why barcoding dragonflies?
Odonata (demonstrator system):
-
Small insect order
-
Model organisms for ecology and evolution
-
Wide range of habitat specificity
(generalists / specialists)
-
Fast respond to environmental
changes
1. Introduction: Why barcoding dragonflies?
- Prime indicators for all types of fresh water ecosystems
Terrestrial
Aquatic
Increasing importance for conservation management
1. Introduction: Why barcoding dragonflies?
Identification through phenotypic traits is difficult:
- Wing veneation: requires a lot of
experience
- Colours: Bright colours of males
fade quickly after death; females
of same genus inconspiciuous
- Ecological and behavioural patterns:
difficult and time-consuming
- Larvae: discrimination often
impossible
♀
♀
1. Introduction: Why barcoding dragonflies?
Rapid and reliable identification of dragonflies
valuable for conservation management:
If phenotypic traits do not serve  Need of genetic
approaches!
How to get DNA non-invasive:
Exuvia
Middle leg
(Hadrys et al. 1992)
1. Introduction: Why character-based DNA barcoding?
Distance approaches can be misleading:
-
High intraspecific genetic variability (e.g.
geographical clusters) can hinder assignment of
unknown samples to their species
-
Distances between species often lower than within
species
-
Thresholds cannot be defined (might lead to
overestimated biodiversity)
1. Introduction: Why character-based DNA barcoding?
Diagnostic characters useful for DNA barcoding:
Identification at any taxonomic level
Species (n) 123
A (100)
A
B (100)
T
Population (n) 110
B1 (25)
A
B2 (25)
C
B3 (25)
A
B4 (25)
G
123
T
T
T
T
234
C
A
200
C
C
T
A
Character-based
DNA barcodes for
species
350
G
T
234
A
A
A
A
310
T
C
G
T
350
T
T
T
T
and single
populations
1. Introduction: Which genetic marker is appropriate?
CO1 (cytochrome c oxidase 1) supposed to be
appropriate for DNA barcoding of most animal groups:
Has not been applied for Odonates before:
 Search for conserved primer sequences
 Optimization of PCR conditions
 Test for suitability
1. Introduction: Which genetic marker is appropriate?
ND1 (NADH dehydrogenase subunit 1) is a suitable
marker:
- Sequences easy to obtain and analyse
- Detection of geographical patterns
- Identification of conservation units
Namibia Naukluft
Namibia Okavango
Namibia Naukluft
Namibia Okavango
SŸ
d afrika
Ostafrika
SŸ
d afrika
Ostafrika
Cryptic speciation in
Trithemis stictica
2. Methods: Character-based DNA barcoding
1. Standard Methods
-
PCR with gene specific primers
-
Sequencing (MegaBACE 500)
-
Alignment (MUSCLE)
-
NJ tree based on Kimura-2-parameter (K2P)
distances (PAUP)
2. Methods: Character-based DNA barcoding
2. Establishment of character-based DNA barcodes:
-
Search for diagnostic characters by application of
CAOS algorithm
-
Development of perl scripts to assist further
analyses
-
Selection of nucleotide positions for final DNA
barcodes by eye
2. Methods: Character-based DNA barcoding
2. Establishment of character-based DNA barcodes:
0
1
1
0
1
0
I. Phylogenetic Tree
Nucleotide Position
Taxa 90 108 153 171
A
C
T
A
T
B
T
T
T
A
C
T
A
A
A
NODE GROUP POS STATE CONF
0
0
90
C
1
0
0
171
T
1
0
1
90
T
1
0
1
171
A
1
1
0
108
A
1
1
0
153
T
1
1
1
108
T
1
1
1
153
A
1
II. Search for
characteristic attributes
with CAOS algorithm
III. Find unique combinations of
character states
2. Methods: Character-based DNA barcoding
Types of characteristic attributes (CAs):
- Pure (Pu): Exist in all elements of a group but not in
alternate group
- Private (Pr): Only present in some members of a
group but absent from alternate group
- Simple (s): At a single nucleotide position
- Compound (c): combination of states
 sPu and sPr CAs shared by at least 80% of
members of a group were used (Filtered by
diagViewer)
2. Methods: Character-based DNA barcoding
Analyses were assisted by a set of perl scripts:
1.
“BarcodeFilter”: sorts out non-relevant nodes
Nodes within
species cluster are
not relevant for
barcoding species
2. Methods: Character-based DNA barcoding
2.
“BarcodeMaker”: Convertion of “diagViewerattributes file” into tab delimited file importable
to Microsoft Excel:
NODE
GROUP
0
0
19
20
21
0
1
15
1
T [1.00]
15
0
A [0.89]
16
0
T [1.00]
16
1
C [1.00]
17
1
17
0
18
1
18
0
G [0.81]
A [0.95]
G [1.00]
A [1.00]
22
2. Methods: Character-based DNA barcoding
3.
“BarcodeHistMaker”:Counting numbers of CAs at
each nucleotide position (selection of sequence
fragment with highest number of CAs:
45
1 *
17
4 ****
18
4 ****
19
9 *********
20
9 *********
21
22
23
16 ****************
1 *
7 *******
24
20 ********************
25
19 *******************
26
40
35
N um b e r o f C A s
16
30
25
20
15
4 ****
10
5
0
0
50
100
150
200
250
Nucleotide position
300
350
400
450
500
Case studies (Study I)
Case Study I: Species identification
842 ND1 sequences (65 species)
-
Suitability of ND1 for DNA barcoding
-
Applicability of the CAOS algorithm for
character-based DNA barcoding
Case studies (Study I)
NJ tree based on K2P distances:
Overview tree:
ND1 sequence of one
individual of each
species
Overlap of species
cluster
Case studies (Study I)
Results: Character-based DNA barcodes
Species (n) 201 207 213 225 243 255 273 285 294 298 306 318 324
Ce (43)
G
A
T
T
T
T
C
G
G
G
A
T
A
Cs (14)
A
T
A
T
A
T
T
G
A
G
T
T
A
A(0.67)
G(0.33)
T
T
C
C
T
T
A
T
A
T
A
G
Ot (7)
T
T
T
T
C
T
A
A
A
G
T
T
A
Ob (9)
T
A
T
C
T
T
A
T
A
G
T
T
A
Ocoe (24)
T
A
T
T
T
T
T
T
T
A
C
C
A
Oc (34)
T
A
T
T
T
T
A
T
T
G
T
C
G
Oj (47)
T
A
T
T
T
C
A
C
T
A
T
T
A
Tk (19)
A
T
A
A
A
T
T
T
T
G
C
A
T
Nf (9)
Unique combinations of character states at
13 selected nucleotide position
Case studies (Study I)
NJ tree based on K2P distances:
Overview tree:
ND1 sequence of one
individual of each
species
Overlap of species
cluster
Case studies (Study I)
Results: Character-based DNA barcodes
Species (n) 201 207 213 225 243 255 273 285 294 298 306 318 324
Aeelel (1)
A
T
A
T
T
T
A
T
T
G
T
T
G
Aeelus (1)
A
T
A
T
T
T
A
T
T
G
T
T
G
Aj (1)
T
A
A
T
T
T
A
T
T
G
T
T
G
Ap (1)
T
A
A
T
T
T
A
T
T
G
T
T
A
Ai (85)
T
A
A
T
T
T
A
T
T
G
T
T
A
As (23)
T
A
A
T
T
T
A
T
T
G
T
T
A
Aecy (1)
T
T
A
T
T
T
T
A
T
G
T
T
G
Aegr (1)
T
T
A
T
T
T
T
A
T
G
T
T
G
Family Aeshnidae: Combination of
character states shared by two or more
species
 Additional analysis with CAOS algorithm
Case studies (Study I)
Results: Character-based DNA barcodes for Aeshnids
213
216
222
228
231
246
264
273
276
282
Ai (85)
A
T
G
A
A
A
T
A
T
T
As (23)
A
C
A
G
G
A
T
A
T
Aeelel (1)
A
T
A
A
A
G
T
A
Aeelus (1)
A
T
A
A
A
G
T
Anaiso (1)
G
T
A
A
A
A
Brpr (1)
A
A
G
A
A
Aj (1)
A
T
G
A
Ap (1)
A
T
G
Anatri (1)
A
T
Aeri (1)
A
Aegr (1)
Aecy (1)
Taxa/ ()=n
294
324
366
428
437
443
T
T
A
T
A
T
T
T
T
T
A
T
A
T
T
T
T
T
T
G
T
A
T
A
A
T
T
T
A
G
T
A
T
A
T
T
C
T
T
T
A
T
A
C
A
A
T
C
T
G
A
T
A
T
A
C
A
A
G
T
A
T
T
T
T
G
T
A
C
T
A
A
A
T
A
T
T
T
T
A
T
G
T
C
A
A
A
A
A
A
T
A
A
G
A
T
A
T
T
T
A
A
A
A
T
A
T
T
G
T
A
T
G
T
T
A
A
G
A
G
A
T
T
T
T
A
T
G
C
A
T
A
A
A
G
A
G
A
T
T
T
T
A
T
G
T
A
T
A
285
G
Ae (19)
A
T
A
A
A
A
T
T
T
T
T
G
(AX1)
T
A
C
T
Gu (9)
G
A
A
A
A
A
T
T
T
T
T
A
G
T
G
T
T
Gyvill (1)
G
T
A
T
G
A
T
T
T
A
G
A
G
T
G
T
T
Gyma (1)
A
A
A
A
A
A
T
C
T
T
T
A
G
T
A
T
A
Corad (1)
G
T
A
A
A
A
G
T
T
T
T
A
A
T
A
T
A
Corpe (1)
G
C
A
A
A
A
T
C
T
T
A
A
A
T
A
T
A
 better resolution
- Search for diagnostic
characters within
whole ND1 fragment
Case studies (Study I)
NJ tree based on K2P distances:
Overview tree:
ND1 sequence of one
individual of each
species
Overlap of species
cluster
Case studies (Study I)
Results: Character-based DNA barcodes
Species (n)
201
207
213
225
243
cs (20)
G
T
T
T
T
cv (5)
G
T
T
T
T
255
273
285
G (0,8)
A (0,2)
A (0,6)
G (0,4)
T (0,8)
C (0,2)
C (0,6)
T (0,4)
C (0,8)
G (0,2)
G (0,6)
C (0,4)
294
298
306
T
A
T
T
A
T
318
T (0,8)
A (0,2)
A (0,6)
T (0,4)
324
A
A
- Combination of character states shared by several
individuals of Calopteryx splendens (cs) and of
Calopteryx virgo (cv)
- No diagnostic characters found through additional
analysis with CAOS algorithm
Hybridisation
Wrong identification
Recent radiation
Case studies (Study I)
Summary: Case study I
-
60 of 65 species distinguishable through
unique combinations of character states
within ND1 fragment
-
ND1 suitable
-
Diagnostic characters easily found by
application of the CAOS algorithm
Case studies (Study II)
Case Study II: Discrimination of conservation units
Subset of Case study I; 122 ND1 sequences
(9 species)
+
101 CO1 sequences (same 9 species)
-
Suitability of CO1 for DNA barcoding
-
Ability of both markers to discrimininate
conservation units
Case studies (Study II)
NJ trees based on K2P distances
CO1
ND1
ND1
Case studies (Study II)
Results: Character-based DNA barcodes
Species / (no. individuals=n)
Paragomphus genei (n=6)
Crocothemis sanginolenta (n=9)
Trithemis stictica (n=14)
Coryphagrion grandis (n=5)
Pseudagrion bicoerulans (n=22)
Chlorocnemis abotti (n=15)
Orthetrum julia falsum (n=12)
Orthetrum trinacria (n=5)
Crocothemis erythreae (n=13)
105 111 162 174 180 192 207 260 263 272 279
T
A
T
A
C
T
A
T
C
C
A
G
A
C
T
T
T
T
T
C
A
A
T
A
T
A
C
T
T
T
C
A
A
T
G
C
A
C
A
C
C
T
A
G
A
A
T
A
T
G
A
C
T
G
A
A
A
T
A
C
A
A
C
C
A
A
A
A
T
A
T
T
A
T
C
A
A
A
T
A
C
C
A
T
T
G
A
G
G
A
T
T
T
T
A
T
C
A
A
unique combinations of character states at 11
selected nucleotide positions of CO1 fragment
 CO1 also suitable
Case studies (Study II)
Results: Identification of populations
Population/ (no. Individuals=n)
Orthetrum julia falsum / Oj32 (n=7)
Orthetrum julia falsum / Oj16 (n=5)
CO1
X
21 210
A
T
G
A
Population/ (no. Individuals=n)
Orthetrum julia falsum /16 (n=5)
Orthetrum julia falsum /32 (n=7)
258
C
T
ND1
Population/ (no. Individuals=n)
Coryphagrion grandis /19 (n=9)
Coryphagrion grandis /22 (n=6)
 Combination of CO1 and ND1 to improve
identification success
214
T
C
Case studies (Study II)
Results: Identification of conservation units
Population/ (no. Individuals=n)
Pseudagrion bicoerulans / Pb77 (n=6)
Pseudagrion bicoerulans / Pb78 (n=7)
Pseudagrion bicoerulans / Pb79 (n=4)
Pseudagrion bicoerulans / Pb113 (n=5)
Population/ (no. Individuals=n)
Pseudagrion bicoerulans /77 (n=6)
Pseudagrion bicoerulans /78 (n=6)
Pseudagrion bicoerulans /79 (n=4)
Pseudagrion bicoerulans /113 (n=5)
27
C
C
T
C
30
T
T
C
T
33
A
G
T
A
42
A
A
C
A
45 138 171 234 273 309 318
G
A
C
T
T
T
C
A
G
T
C
T
C
T
A
C
T
T
A
T
C
G
A
T
T
T
T
C
111 112 145 168 180 195 210 237 258 294 372 433
A
T
G
T
G
G
A
T
A
T
A
C
A
T
G
C
A
A
G
T
A
C
G
T
T
A
A
T
T
G
G
G
A
C
T
T
A
T
G
C
G
G
A
T
G
T
A
T
Pb77
Pb113
Pb78
Pb79
CO1
ND1
Case studies (Study II)
Results: Identification of cryptic species
Population/ (no. Individuals=n)
Trithemis stictica / Tst119 (n=8)
Trithemis stictica / Tst128 (n=6)
159 247 295 312 319 324 336 378 408 414 432
T
C
C
A
T
T
G
C
T
C
T
C
T
T
G
C
A
A
T
C
A
C
Population/ (no. Individuals=n)
Trithemis stictica /128 (n=6)
Trithemis stictica /119 (n=6)*
Trithemis stictica /118+94 (n=15)
102 113 121 138 166 169 177 192 217 231 255 258
A
T
A
A
A
C
A
G
T
A
G
T
A
C
G
T
A
T
A
A
C
A
G
T
G
G
G
T
T
T
G
G
T
G
A
C
* One individual of Tst119 shares a combination of character
states with 6 individuals of Tst128
Kwando
Populations:Popa Falls
Tst94:
Tst118:
Zebra River
Tst128
Tst119
Tst118
Tst119:
Pb128:
Tst94 = Kenya
CO1
ND1
Case studies (Study II)
Summary: Case study II
-
All nine species distinguishable through unique
combinations of character states within ND1
and CO1 fragments
-
Both markers suitable
-
Character-based DNA barcodes established for
conservation units of several species
Conclusions
Character-based approaches are:
Rapid
-
Application of CAOS algorithm
-
Assignment of samples through a few nucleotide
positions
Reliable
-
Discrete characters
-
Combination of ND1 and CO1 increases success
-
DNA barcodes for several conservation units
Conclusions
Character-based DNA barcoding:
A rapid and reliable method for
the identification of conservation
units in dragonflies !
Future Prospects
Next steps:
-
More species
-
More individuals of some species
-
Development of data base
-
Character-based DNA barcodes for genera
-
Application of character-based DNA barcodes

Identification of adults, exuvia and
larvae

Long-time monitoring
Thank you !!