Optimization of Microbiome DNA Yield Extracted from Meconium and Feces of Preterm Neonates to be applied in 16S rRNA Next-Gen Sequencing
Abstract
The composition of the intestinal microbiome of neonates can be identified from meconium and feces by Next-Generation Sequencing (NGS) technology. However, the yield of microbiome DNA of meconium and feces has its own challenges due to the consistency and the high content of PCR inhibitors in these samples. This study aims to optimize the yield of microbiome DNA from meconium sample and feces of pre-term neonates. The DNA yield was obtained by applying certain optimized parameters, i.e., considering the replication and condition of the sample, using a particular kit for DNA extraction, and modifying the DNA elution of the column purification. The genomic DNA obtained was quantified and confirmed using Polymerase Chain Reaction. Results showed that the best DNA yield was achieved by replicating the number of samples twice in the pre-extraction stage, working on fresh meconium and feces samples instead, and suspended the sample in ddH2O prior to extraction process as observed on agarose gel visualization with UV trans-illuminator, as well as in quantitative measurement by a nano spectrophotometer. The best extraction process was using MP Biomedical FastDNA Spin Kit for Soil, in addition to the use of an elution buffer in a smaller volume, resulting in a higher concentration and purity of DNA. In conclusion, we were able to obtain an optimized yet reliable DNA yields, especially from meconium, which fulfilled the quality and quantity requirement for further sequencing process of microbiome.
References
a review’, ICAN: Infant, Child, & Adolescent Nutrition. 2011. 3(5):291–5. doi: 10.1177/1941406411421629.
2. HuJ, Nomura Y, Bashir A, Fernandez-Hernandez H, Itzkowitz S, Pei Z, et al. Diversified microbiota
of meconium is affected by maternal diabetes status. PLoS ONE 8:e78257. 2013. doi: 10.1371/journal.
pone.0078257
3. Browne PD and Cabana MD. Microbiota: in health and disease: from pregnancy to childhood. Wageningen
Academic Publishers. 2017. Jurnal Ilmu Kefarmasian Vol 19, 2021 Indonesia 182
4. Oktaviyani D, Alawiyyah RZ, Nusaiba P, Malik A. a review: composition of neonatal meconium microbiota
and its role for potential probiotic. Pharmaceutical Science and Research (PSR). 2021. 8(1):15-29.
5. Stinson LF, Keelan JA, and Payne MS. Comparison of meconium DNA extraction methods for use in
microbiome studies’, Frontiers in Microbiology. 2018. p. 1–14. doi: 10.3389/fmicb.2018.00270.
6. Ardissone AN, Cruz DM, Davis-Richardson AG, Rechcigl KT, Li N, Drew JC, et al. Meconium
microbiome analysis identifies bacteria correlated with premature birth’, PLoS ONE. 2014. 9(3). doi: 10.1371/
journal.pone.0090784.
7. Jiménez E, Marín ML, Martín R, Odriozola JM, Olivares M, Xaus J, et al. Is meconium from healthy newborns actually sterile? Res. Microbiol. 2008. 159:187–193.
8. Gosalbes MJ, Llop S, Valles Y, Moya A, Ballester F, and Francino MP. Meconium microbiota types
dominated by lactic acid or enteric bacteria are differentially associated with maternal eczema and
respiratory problems in infants. Clin. Exp. Allergy 43. 2013. p. 198–211. doi: 10.1111/cea.12063
9. Del Chierico F, Vernocchi P, Petrucca A, Paci P, Fuentes S, Pratico G, et al. Phylogenetic and metabolic tracking
of gut microbiota during perinatal development. PLoS ONE 10:e0137347. 2015. doi: 10.1371/journal. pone.0137347
10. Collado MC, Rautava S, Aakko J, Isolauri E, and Salminen S. Human gut colonisation may be initiated
in utero by distinct microbial communities in the placenta and amniotic fluid. Sci. Rep. 6:23129. 2016.
doi: 10.1038/srep23129
11. Schrader C, Schielke A, Ellerbroek L, Johne R. PCR inhibitors - occurrence, properties and removal’,
Journal of Applied Microbiology, 113(5). 2012. p.1014–1026. doi: 10.1111/j.1365-2672.2012.05384.x.
12. Albertsen M, Karst SM, Ziegler AS, Kirkegaard RH, Nielsen PH. Back to basics – the influence of DNA
extraction and primer choice on phylogenetic analysis of activated sludge communities’, PLoS One. 2015. p.
1–15. doi: 10.1371/journal.pone.0132783.
13. Jonathan KJT, Ong G, Prasetyaningsih FA, Amandito R, Rohsiswatmo R, Malik A. Clinical characteristics
influence cultivable-bacteria composition in the meconium of Indonesian neonates. Heliyon 6. 2020.
ttps://doi.org/10.1016/j.heliyon.2020.e05576
14. Videnska P, Smerkova K, Zwinsova B, Popovici V, Micenkova L, Sedlar K, et al. Stool sampling and
DNA isolation kits affect DNA quality and bacterial composition following 16S rRNA gene sequencing
using MiSeq Illumina platform’, Scientific Reports. 2019. 9(1):1–14. doi: 10.1038/s41598-019-49520-3.
15. Bürgmann H, Pesaro M, Widmer F, and Zeyer JA. strategy for optimizing quality and quantity of DNA
extracted from soil. J. Microbiol. Methods 45. 2001.p. 7–20.
16. Bollmann-Giolai A, Giolai M, Heavens D, Macaulay I, Malone J, Clark MD. A low-cost pipeline for soil
microbiome profiling. MicrobiologyOpen. 2020. 9:e1133. https://doi.org/10.1002/mbo3.1133
17. Wu WK, Chen CC, Panyod S, Chen RA, Wu MS, Sheen LY, et al. ‘Optimization of fecal sample processing for
microbiome study — The journey from bathroom to bench’. Journal of the Formosan Medical Association.
2018. doi:10.1016/j.jfma.2018.02.005
18. Panek M, Paljetak HC, Barešić A, Perić M, Matijašić M, Lojkić I, et al. Methodology challenges in studying
human gut microbiota – effects of collection, storage, DNA extraction and next generation sequencing
technologies. Sci. Rep. 2018. 8, 5143.
19. Qiagen. n.d. Considerations for isolation and
quantification of both genomic DNA and plasmid DNA. Diambil dari: https://www.qiagen.com/us/
service-and-support/learning-hub/molecular-biologymethods/dna/. Diakses 03 April 2020
20. Thermo Fischer. n.d. Detergents for cells lysis and protein extraction. Diambil dari: https://www.
thermofisher.com/id/en/home/life-science/proteinbiology/protein-biology-learning-center/proteinbiology-
resource-library/pierce-protein-methods/detergents-cell-lysis-protein-extraction.html. Diakses 03 April 2020.
21. New England Biolabs, Inc. Monarch ® Genomic DNA Purification Kit Table of Contents. 2020. pp. 1–24.
Diambil dari: https://www.neb.com/-/media/nebus/files/manuals/manualt3010.pdf?rev=358a9c46ee4141
809ebfddacfddadd9b&hash=7220E4FECD910D7F0E1690DAD01735E8. Diakses 06 April 2020.
22. Bychinski A. and Wieczorek D. The eff ects of decreasing elution volumes on plasmid DNA concentration and
yield using Eluator™ Vacuum Elution Device’. Promega Corporation Web site. 2009. Diambil dari:
https://worldwide.promega.com/resources/pubhub/the-effects-of-decreasing-elution-volumes-on-plasmiddna-
concentration-and-yield/. Diakses 07 April 2020.
23. Pachchigar KP, Khunt A, Hetal Bhilocha. DNA quantification. ICAR Sponsored summer school on
Allele mining in crops: Methods and Utility, 18th July-7th August, 2016.
24. Thermo Scientific. Interpreting Nanodrop (Spectrophotometric) Results. 2016. Diambil
dari: https://tools.thermofisher.com/content/sfs/manuals/3091-NanoDrop-One-Help-UG-en.pdf. Diakses 04 April 2020.
25. Genetic Education. n.d. A complete guide for analysing and interpreting gel electrophoresis results. Diambil
dari: https://geneticeducation.co.in/a-complete-guidefor-analysing-and-interpreting-gel-electrophoresisresults/.
Diakses 07 April 2020
26. Shenghe C, Wei S, Zhaoxi Z, Jingyang L, Minjie D, Haiyan S. A weird DNA band in PCR and its cause.
Journal of Plant Science and Molecular Breeding. 2016. 5(1):2. doi: 10.7243/2050-2389-5-2.
27. Suzuki MT and Giovannoni SJ. Bias caused by template annealing in the amplifi cation of mixtures
of 16S rRNA genes by PCR’, Journal Applied and Environmental Microbiology. 1996. Doi: 10.1128/
aem.62.2.625-630.1996
28. Chauhan T. A complete guide for analysing and interpreting gel electrophoresis results. 2018. Diambil
dari: https://geneticeducation.co.in/a-complete-guidefor-analysing-and-interpreting-gel-electrophoresisresults/.
Diakses 07 April 2020.
29. Zhang M, Sun H, Fei Z, Zhan F, Gong X and Gao S, “Fastq_clean: An optimized pipeline to clean the
Illumina sequencing data with quality control,” 2014 IEEE International Conference on Bioinformatics and
Biomedicine (BIBM), 2014, pp. 44-48, doi: 10.1109/BIBM.2014.6999309.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Licencing
All articles in Jurnal Ilmu Kefarmasian Indonesia are an open-access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License which permits unrestricted non-commercial used, distribution and reproduction in any medium.
This licence applies to Author(s) and Public Reader means that the users mays :
- SHARE:
copy and redistribute the article in any medium or format - ADAPT:
remix, transform, and build upon the article (eg.: to produce a new research work and, possibly, a new publication) - ALIKE:
If you remix, transform, or build upon the article, you must distribute your contributions under the same license as the original. - NO ADDITIONAL RESTRICTIONS:
You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
It does however mean that when you use it you must:
- ATTRIBUTION: You must give appropriate credit to both the Author(s) and the journal, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
You may not:
- NONCOMMERCIAL: You may not use the article for commercial purposes.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.