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Pollen DNA barcoding

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Microscopic image of ligularia pollen

Pollen DNA barcoding is the process of identification of pollen donor plant species through the amplification and sequencing of specific, conserved regions of plant DNA. Being able to accurately identify pollen has a wide range of applications though it has been difficult in the past due to the limitations of microscopic identification of pollen[1]. Pollen DNA barcoding is a technique that has grown in popularity due to the decreases in costs associated with "next generation sequencing" (NGS) techniques[2] and is being continually improved in efficiency including through the use of a dual-indexing approach[3].

Identification of pollen through DNA barcoding involves the specific targeting of gene regions that are found in most to all plant species but have high variation between members of different species. The unique sequence of base pairs for each species within these gene regions can be used as an identifying feature.

The applications of pollen DNA barcoding range from forensics, to food safety, to conservation. Each of these fields benefits from the creation of plant barcode reference libraries[4]. These libraries range largely in size and scope of their collections as well as what target region(s) they specialize in.

One of the main challenges of identifying pollen is that it is often collected as a mixture of pollen from several species. Metabarcoding[5] is the process of identifying the individual DNA from a mixed DNA sample and is commonly used in mixed pollen loads found on pollinating animals and in environmental DNA also called eDNA.

Target regions

There have been several different regions of plant DNA that have been used as targets for genetic barcoding including rbcL[4], matK[6], trnH-psbA[7], ITS1[8] and ITS2. A combination of rbcL and matK has been recommended for use in plant DNA barcoding. It has been found that trnL is better for degraded DNA and ITS1 is better for differentiating species within a genus[9].

Advantages

Identifying pollen via microscopy requires a high level of expertise in the pollen characteristics of the specific plants being studied and even with expertise it can be extremely difficult to identify pollen accurately with high taxonomic resolution[1]. The skills required to do DNA barcoding are much more common[10] making the approach easier to adopt. Some of the other major advantages include the savings in time and resources compared to microscopic identification. Identifying pollen is time consuming involving spreading pollen on a slide, staining the pollen to make it more visible, then focusing in on individual pollen grains and identifying them based on size, shape, as well as the shape and number of pores[11]. If a pollen reference library is not available, then pollen has to be collected from wild specimens or from herbarium specimens and is then added to a pollen reference library.

The cryptic plants visited by some pollinators can be difficult to determine[12], by using pollen DNA barcoding researchers can uncover "invisible" interactions between plants and pollinators[13].

Challenges

There are many challenges when it comes to genetic barcoding of pollen. The amplification process of DNA can mean that even small pieces of plant DNA can be detected included those from contaminants to a sample. Strict procedures to prevent contamination are important and can be facilitated by the hardiness of the pollen coat allowing the pollen washed of contaminants without damaging the pollen DNA.

Other challenges include the lack of DNA barcode reference libraries for species and the lack of standardized regions being widely used. This is likely due to the newness of DNA barcoding.

Determining the amount of each contributor to a mixed pollen load can be difficult to determine through the use of DNA barcoding. However, scientists have been able to compare pollen amounts via rank[14].

Alternatives

Innovations in automated microscopy and imagining software offer one potential alternative in the identification of pollen. Through the use of pattern-recognition software, researchers have been developed software that can characterize microscopic pollen images based on texture analyzes compared to reference images[15].

Applications

Butterfly foraging for nectar from a flower in the Chinese Himalayas

Use in pollination networks

Being able to identify pollen is especially important in pollination networks which are made up of all the interactions between plants and the animals that facilitate their pollination[16][17]. Identifying the pollen carried on insects helps scientists understand what plants are being visited by which insects. Insects can also have homologous features making them difficult to identify and are themselves sometimes identified through genetic barcoding[18], usually of the CO1 region[19][20]. Since every insect that visits a flower isn’t necessarily a pollinator[21] (many lack features such as hairs allowing them to carry pollen while others avoid the pollen-laden anthers to steal nectar) pollination networks are made more accurate by including what pollen is being carried by which insects. Some scientists argue that pollination effectiveness (PE), which is measured by studying the germination rates of seeds produced from flowers visited only once by a single insect is the best way to determine pollination by insects[22] though other scientists have used DNA barcoding to determine the genetic origin of pollen found on insects and have argued that this in conjunction with other traits is a good indication of pollination effectivenes[23]. By studying the composition and structure of pollination networks, conservationists can understand the stability of a pollination network and identify which species are most important and which are at most risk to perpetuation[24] leading to pollinator declines[25].

Another advantage of pollen DNA barcoding is that is can be used to determine the source of pollen found on insect museum specimens[26], these records of insect-plant interactions can then be compared to modern-day interactions to see how pollination networks have changed with time[27] due to global warming, land use change, and other factors.

Forensics

Being accurately able to identify pollen found on evidence helps forensic investigators identify which regions evidence originated from based on the plants are endemic to those regions[28]. Atmospheric pollen originating from illegal cannabis farms were successfully detected by scientists[29] which could allow law enforcement officials to narrow down the search areas for illegal farms.

Ancient pollen

Due to the hardy structure of pollen which have evolved to survive being transported sometimes great distances while keeping the internal genetic information, DNA, intact, the origin of pollen found mixed in ancient substrates can sometimes be determined through DNA barcoding.

Food safety

Honeybees carry pollen as well as the nectar used in their production of honey. For food quality and safety concerns it is important to understand the plant providence of human-consumed bee products including honey, royal jelly, and pollen pellets. Investigators can test what plants honeybees foraged on and thus the origin of the nectar used in honey by collecting pollen packets collected from honeybees corbicular loads and then identify the pollen via DNA metabarcoding[30].

References

  1. ^ a b Bell, Karen L.; de Vere, Natasha; Keller, Alexander; Richardson, Rodney T.; Gous, Annemarie; Burgess, Kevin S.; Brosi, Berry J. (2016-04-13). "Pollen DNA barcoding: current applications and future prospects". Genome. 59 (9): 629–640. doi:10.1139/gen-2015-0200. ISSN 0831-2796.
  2. ^ Park, Sang Tae; Kim, Jayoung (2016-11). "Trends in Next-Generation Sequencing and a New Era for Whole Genome Sequencing". International Neurourology Journal. 20 (Suppl 2): S76–83. doi:10.5213/inj.1632742.371. ISSN 2093-4777. PMC 5169091. PMID 27915479. {{cite journal}}: Check date values in: |date= (help)CS1 maint: PMC format (link)
  3. ^ Sickel, Wiebke; Ankenbrand, Markus J.; Grimmer, Gudrun; Holzschuh, Andrea; Härtel, Stephan; Lanzen, Jonathan; Steffan-Dewenter, Ingolf; Keller, Alexander (2015-07-22). "Increased efficiency in identifying mixed pollen samples by meta-barcoding with a dual-indexing approach". BMC Ecology. 15: 20. doi:10.1186/s12898-015-0051-y. ISSN 1472-6785.{{cite journal}}: CS1 maint: unflagged free DOI (link)
  4. ^ a b Bell, Karen L.; Loeffler, Virginia M.; Brosi, Berry J. (2017-03-01). "An rbcL Reference Library to Aid in the Identification of Plant Species Mixtures by DNA Metabarcoding". Applications in Plant Sciences. 5 (3): 1600110. doi:10.3732/apps.1600110.
  5. ^ Cristescu, Melania E. "From barcoding single individuals to metabarcoding biological communities: towards an integrative approach to the study of global biodiversity". Trends in Ecology & Evolution. 29 (10): 566–571. doi:10.1016/j.tree.2014.08.001.
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  16. ^ Bell, Karen L.; Fowler, Julie; Burgess, Kevin S.; Dobbs, Emily K.; Gruenewald, David; Lawley, Brice; Morozumi, Connor; Brosi, Berry J. (2017-06-01). "Applying Pollen DNA Metabarcoding to the Study of Plant–Pollinator Interactions". Applications in Plant Sciences. 5 (6): 1600124. doi:10.3732/apps.1600124.
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  18. ^ Tang, Min; Hardman, Chloe J.; Ji, Yinqiu; Meng, Guanliang; Liu, Shanlin; Tan, Meihua; Yang, Shenzhou; Moss, Ellen D.; Wang, Jiaxin (2015-09-01). "High-throughput monitoring of wild bee diversity and abundance via mitogenomics". Methods in Ecology and Evolution. 6 (9): 1034–1043. doi:10.1111/2041-210x.12416. ISSN 2041-210X.
  19. ^ Hebert, Paul D. N.; Cywinska, Alina; Ball, Shelley L.; deWaard, Jeremy R. (2003-02-07). "Biological identifications through DNA barcodes". Proceedings of the Royal Society of London B: Biological Sciences. 270 (1512): 313–321. doi:10.1098/rspb.2002.2218. ISSN 0962-8452. PMID 12614582.
  20. ^ Rach, Jessica; Bergmann, Tjard; Paknia, Omid; DeSalle, Rob; Schierwater, Bernd; Hadrys, Heike (2017-04-13). "The marker choice: Unexpected resolving power of an unexplored CO1 region for layered DNA barcoding approaches". PLOS ONE. 12 (4): e0174842. doi:10.1371/journal.pone.0174842. ISSN 1932-6203.{{cite journal}}: CS1 maint: unflagged free DOI (link)
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  25. ^ Vamosi, Jana C.; Gong, Yan-Bing; Adamowicz, Sarah J.; Packer, Laurence (April 2017). "Forecasting pollination declines through DNA barcoding: the potential contributions of macroecological and macroevolutionary scales of inquiry". The New Phytologist. 214 (1): 11–18. doi:10.1111/nph.14356. ISSN 1469-8137. PMID 27901268.
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  29. ^ Aboulaich, Nadia; Trigo, M. Mar; Bouziane, Hassan; Cabezudo, Baltasar; Recio, Marta; Kadiri, Mohamed El; Ater, Mohammed. "Variations and origin of the atmospheric pollen of Cannabis detected in the province of Tetouan (NW Morocco): 2008–2010". Science of The Total Environment. 443: 413–419. doi:10.1016/j.scitotenv.2012.10.075.
  30. ^ Galimberti, Andrea; Mattia, Fabrizio De; Bruni, Ilaria; Scaccabarozzi, Daniela; Sandionigi, Anna; Barbuto, Michela; Casiraghi, Maurizio; Labra, Massimo (2014-10-08). "A DNA Barcoding Approach to Characterize Pollen Collected by Honeybees". PLOS ONE. 9 (10): e109363. doi:10.1371/journal.pone.0109363. ISSN 1932-6203.{{cite journal}}: CS1 maint: unflagged free DOI (link)
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