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Shotgun Sequencing and PCR Based Matabarcoding to Understand Dietary Profile of P.Femoralis

Info: 1812 words (7 pages) Essay
Published: 3rd Nov 2021 in Biology

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Introduction

In this paper scientists took six faecal samples from an area of the Singapore rainforest that is known to be the habitat for a large population of the species P. femoralis and then analysed the samples using molecular techniques such as shotgun sequencing and PCR based metabarcoding of target genes in order to find out more about what the species’ diet consisted of (Srivathsan, et al., 2016), this species is of particular interest as it is endangered (Ceballos & Ehrlich , 2002).

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This study is an attempt to show the role of molecular techniques in studies like this that shorten what would’ve been a much longer study if the scientists were to observe the species in eating in their habitat. P. femoralis was of particular interest as it is now endangered. The group were able to determine that the diets of P. femoralis consisted of 53 different plant species from 33 families (Srivathsan, et al., 2016). One of the techniques used to obtain these findings was shotgun sequencing which is a DNA sequencing technique in which DNA is randomly broken up into segments (R.Staden, 1979), it then uses chain termination method which is the selective reuptake using DNA polymerase (Sanger, et al., 1977).

The first instance of shotgun sequencing being used to sequence a whole genome was in 1981 when the cauliflower mosaic virus was sequenced and the primary structure of the virus was determined (Gardener, et al., 1981). Another DNA sequencing technique used in this paper was PCR based metabarcoding. This technique is done by extracting DNA and then amplifying the amount of DNA by using Polymerase Chain Reaction (PCR) (Ståhlberg, et al., 2017). After the DNA has been amplified a small portion of that DNA is used to create a DNA barcode (Taberlet, et al., 2007), normally between 400-800 base pairs are used but this can vary depending on the size of the genome being sequenced (Savolainen, et al., 2005).

Illumina sequencing was also used in this experiment which is a way of finding the order of base pairs in from a DNA sample (Jeon, et al., 2015) (Bolger, et al., 2014). The implications of this research have the potential to be massive as studies such as these that don’t use any molecular sequencing and instead observe the eating patterns/habits of species can take decades to do and cost millions because they are so labour intensive. Therefore if the cost of analysing samples using shotgun sequencing as was used in this study can be brought down by developments in technology in the coming years then I see no reason why this won’t become the primary way in which studies of this kind are carried out.

Critical Analysis

From this research is that a dietary profile of 53 plant species from 33 families was identified, from which there are 24 plant genera that have observational data associated with them and of these 15 were identified by metagenomics but the metagenomics also found evidence for an extra 36 species (Srivathsan, et al., 2016). This is an excellent outcome because it means this study has potentially identified 36 previously unknown plant species that observational studies wouldn’t have been able to. Using DNA sequencing was more effective in identifying these new plant species because the monkey could be eating in places scientists can’t see in observational studies such as at the top of trees and without metagenomics the 36 new species were unlikely to have been identified.

This type of sequencing isn’t without problems for example, some scientists have argued that the ability of shotgun sequencing to determine how regions in the DNA link isn’t completely accurate (Meyerson, et al., 2010). In addition the method they have used uses short reads and a small number of barcodes therefore they were expecting some errors in the data (Tammi, et al., 2003) (Li & Durbin, 2009).

One of the strengths of this research is that by taking faecal samples is non-invasive and is a much quicker way than observatory methods and in cases where intervention needs to happen quickly to save a species from extinction metagenomics has an important role to play.

A significant limitation of this study is that only 6 faecal samples were taken and there is no detailed explanation for how they ensured that the samples weren’t contaminated and were from 6 different monkeys (Srivathsan, et al., 2016). For example, if the aim of this experiment was to obtain a dietary profile of P.femoralis then if they ended up with 6 samples that were only from 2 monkeys or worse still samples that were from a different organism then their results do not give the full picture and could be missing significant plant species from the dietary profile. In addition compared to other metagenomic studies, 6 samples appears to be quite a small number and the reason for this is not addressed in the paper, a study looking at the effect of small sample sizes in metagenomics created a mock community of bacteria and took 10 samples which they deemed to be a small sample size so it does appear that only 6 samples is lacklustre in comparison to similar studies (Kwak & Park, 2018).

Moreover, figure 2 (Srivathsan, et al., 2016), states that 21 plant species were identified to genus level from metagenomics but metabarcoding found 2 plant species that metagenomics didn’t identify and field observations found 9 species that neither metagenomics nor metabarcoding found. Why is this? And why hasn’t it been addressed in the paper. In addition there is a difference in family identification between metagenomics, metabarcoding and field observations which is concerning as a difference in the metagenomics and field observations is to be expected but such differences between the findings of metabarcoding and metagenomics without explanation is a big limitation of this paper.

In this paper there is significant analysis of the experimental data (Srivathsan, et al., 2016), for example comparing the species found to the Nee Soon Swamp forest list and also to plants known to be in Singapore (Chong, et al., 2009) was an excellent way to corroborate their findings with those of observational studies.

There is a distinct lack in this paper to acknowledge and consider other work in this field and similar studies, although a lot of metagenomic research has been done since this paper was written there is a lack of recognition for papers that explain how metagenomics work (Lane, et al., 1985), shotgun sequencing for example has been around for a long time and as such should have been more heavily referenced (Roach, et al., 1995).

Future research

The aims of future research should be to repeat this type of experiments to show that they are reliable and repeatable. One specific aim of future research could be to use shotgun sequencing and metabarcoding to find pathogens that are a threat to endangered species, for example, one study has been looking into the microbiome of chimpanzee’s (Ogza, et al., 2019), this use of shotgun sequencing could be used as a excellent diagnostic tool for use in wild species to identify pathogens in situ and get help to them much quicker than we normally could.

Use it to find new plant species in areas hard to get samples from e.g. forests/jungles.

References

Bolger, A. M., Lohse, M. & Usadel, B., 2014. Trimmomatic: a flexible trimmer for Illumina sequence data.. Bioinformatics, 30(15), p. 2114–2120.

Ceballos, G. & Ehrlich , P., 2002. Mammal population losses and the extinction crisis.. Science, 296(5569), pp. 904-907.

Chong, K. Y., Tan, H. & Corlett, R. T., 2009. In: A checklist of the total vascular plant flora of Singapore: native, naturalised and cultivated species. . Singapore: Raffles Museum of Biodiversity Research, National University of Singapore, p. 273.

Gardener, R. C. et al., 1981. The complete nucleotide sequence of an infectious clone of cauliflower mosaic virus by M13mp7 shotgun sequencing. Nucleic Acids Research, 9(12), p. 2871–2888.

Jeon, Y.-S.et al., 2015. Improved pipeline for reducing erroneous identification by 16S rRNA sequences using the Illumina MiSeq platform. Journal of Microbiology, 53(1), pp. 60-69.

Kwak, J. & Park, J., 2018. What we can see from very small size sample of metagenomic sequences.. BMC Bioinformatics, 19(339), pp. doi:10.1186/s12859-018-2431-8.

Lane, D. J. et al., 1985. Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proceeding for the National Academy of Sciences of the United States of America, 82(20), pp. 6955-6959.

Li, H. & Durbin, R., 2009. Fast and accurate short read alignment with Burrows-Wheeler transform.. Bioinformatics, 25(14), pp. 1754-1760.

Meyerson, M., Gabriel, S. & Getz, G., 2010. Advances in understanding cancer genomes through second-generation sequencing.. Nature Reviews Genetics, 11(10), p. 685–696.

Ogza, A. T. et al., 2019. Oral microbiome diversity in chimpanzees from Gombe National Park.. Scientific Reportss, 22(9), p. 17354.

R.Staden, 1979. A strategy of DNA sequencing employing computer programs. Nucleic Acids Research, 6(7), pp. 2601-2610.

Roach, J. C., Boysen, C., Wang, K. & Hood, L., 1995. Pairwise end sequencing: a unified approach to genomic mapping and sequencing. Genomics, 26(2), pp. 345-353.

Sanger, F., Nicklen, S. & Coulson, A. R., 1977. DNA sequencing with chain-terminating inhibitors. Proceedings of the National Academy of Sciences of the United States of America, 74(12), pp. 5463-5467.

Savolainen, V. et al., 2005. Towards writing the encyclopaedia of life: an introduction to DNA barcoding. Philosophical Transactions of the Royal Society B, 360(1462), p. 1805–1811.

Srivathsan, A., Ang, A., Vogler, A. P. & Meier, R., 2016. Fecal metagenomics for the simultaneous assessment of diet, parasites and population genetics of an understudied primate. Frontiers in Zoology, 13(17), pp. DOI 10.1186/s12983-016-0150-4.

Ståhlberg, A. et al., 2017. Simple multiplexed PCR-based barcoding of DNA for ultrasensitive mutation detection by next-generation sequencing. Nature Protocols, 12(4), pp. 664-682.

Taberlet, P. et al., 2007. Power and limitations of the chloroplast trn L (UAA) intron for plant DNA barcoding. Nucleic Acids Research, 37(3), p. e14.

Tammi, T. M., Arner, E., Kindlund, E. & Andersson, B., 2003. Correcting errors in shotgun sequences. Nucleic Acids Research, 31(15), pp. 4663-4672.

 

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