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Using Metagenomics to Monitor Microbial Communities

Paper Type: Free Essay Subject: Biology
Wordcount: 1547 words Published: 21st May 2018

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Executive summary:

The Advanced Microbial Solutions Company is exploring a new approach to isolate novel organisms or gene from the environment for commercial exploitation. This approach is enabled by metagenomics, a high throughput capacity that can deal with the DNA extraction, sequencing and interpretation of microbial communities. The R&D Department decided to choose a variety of freshwater sediment, marine sand and water samples from Portobello Beach and Duddingston Loch. The project team used marker-gene 16S rRNA to determine the microbial population diversity, especially bacterial and archaeal communities and their relative abundance in these two locations. This pilot study had generated metagenomic data and analysis to ensure that it is feasible to continue this project in the first quarter of 2015. The next stage will be the application research phase where the knowledge from the pilot study will be utilized to investigate specific applications with commercial and environmental potential. The rest of this report will cover the overview of metagenomics and marker gene-based study, the software program used to analyse data, a brief summary of the results, further development and recommendation for the next phase of this project.


Metagenomics is the analysis of genome from environmental samples. The application of metagenomics allows identification of exotic samples which might be a source for the production of novel enzymes, antibiotics and other useful reagents or biomolecules. Environmental samples contain a huge genetic diversity that encompasses microorganisms from the eukarya, bacteria and archaea domains. The presence of 16S rRNA gene in all bacteria with sequence region that are shared among all bacteria but different variable regions among species make it suitable as a marker. Two regions of the 16S rRNA gene, V3 and V4 are used to characterise the microbial communities in the samples by subjected with fingerprinting and Illumina sequencing, respectively. Using16S rRNA marker gene-based study and sequencing can solve a sampling issue for community members whose abundance is uneven. Currently, metagenomes are screened based on either function or sequence. Function-based analysis is a simple method to obtain genes with desired functions without using the sequence of genomic fragments. In this pilot study, sequence-based screening is performed using a next generation sequencer.

Key Findings from the Pilot Study:

The identified species cumulatively represent 90% of all bacteria in each sample. α- rarefaction curves are used to quantify the species richness and diversity. A steep slope of the curve represents a large fraction of the species need to be discovered, while the curve that starts to flatten out signified that a reasonable amount of the DNA in the sample has been sequenced. In Figure 2, the sampling curves of Portobello Beach and Duddingston Loch soared at first and then increased steadily as new species found in the samples are less abundant. It was found that, the species diversity of samples from Portobello Beach is greater than Duddingston Loch.

A comparison of species diversity between habitats is visualised using Non-metric multi-dimensional scaling (NMDS) of Bray-Curtis similarity. Points that are closer to each other indicate highly resemblance metagenomic profile of the samples. Points denoting samples from Portobello Beach-based datasets were located much closer together showing a higher similarity of the profiles than points representing Duddingston Loch-based dataset.

The dominant bacterial taxa in all the samples are Proteobacteria, Actinobacteria and Bacteroidetes. The bacterial communities from the Duddingston Loch samples did not show high species diversity as expected. At first, it was assumed that the significant number of roosting birds and geese in the area will contribute to the microbiological load in the water samples. Temperature, daily tidal range and season appeared to explain some variation in community structure in water and sand samples from Portobello Beach in our study. The bacteria achieved maximum abundance when low tide, particularly, during the warmer month.

Profiling the 16S rRNA gene for microbial studies cannot directly identify the microbes function. Thus, bioinformatics software called PICRUSt (phylogenetic investigation of communities by reconstruction of unobserved states) was used to identify the functional gene of the sample using 16S and reference genomes data. The analysis showed that the organisms still has more common functions among them even though they are derived from different species. This might be due to the horizontal gene transfer that allows distantly related microorganisms to share functions that are missing from closer relatives but particularly widespread in microbe that share a common environment. As a general conclusion, it can be said that the environmental samples from Portobello Beach and Duddingston Loch are characterized by high species richness dominated by a few species.

Further Development and Recommendation:

  1. Community composition

The sample sites are good as both exclude the eukaryote. This is because the metagenomic sequencing of eukaryote’s DNA requires high cost due to their complex genetic material. Additional sample material might be needed for complementary analyses such as FISH (fluorescence in situ hybridization). This additional molecular analyse could facilitate and enhance metagenomic data. In particular, FISH can determine relative and absolute abundance of microbial communities accurately without affecting 16S copy number variation.

  1. Sequencing technology and cost

The small proportion of the costs for metagenomic project will come from sequencing using a next generation sequencer. The decline in the cost of sequencing in the past few years and the expanding of sequencing technologies make it reasonable to use Illumina platform for this project. Illumina sequencer can yield more reads with high accuracy. Besides, it can also detect low abundance microorganisms, especially in environmental samples. These low abundance organisms usually ignored and unexplored in metagenomic analysis. This can cause bias and close the opportunity to discover potential and novel genes in certain organisms. The sequencing cost may be around few hundred dollars while the major portions of cost will derive from sample preparation, other steps of the experiment and data analysis. The sample preparation including barcoding might cost approximately $300 a sample and the data analysis would cost on the order of $2000 or maybe a little higher.

  1. Isolation of enzymes

The isolation of enzymes using sequence-based screening depends on homology searches so it prevents the discovery of entirely new gene sequences. However, it is still an efficient method of isolating enzymes with different levels of similarity from previously identified genes. Usually, functional analysis of metagenome will use the Basic Local Alignment Search Tool (BLAST) to find regions of similarity between biological sequences. However, it is limited by its computational complexity and lack of homologous sequences in reference databases. To overcome this problem, the HMMER software will be used as it is more accurate than BLAST and useful for analyse protein sequence.

In the next stage, the useful genes will be amplified and the new enzymes will be tested. An enrichment metagenomics will also be implemented to increase the number of bacteria with the desired enzyme. By identifying new enzymes and methods for sustainable synthesis, this project will overcome the limited number of available enzymes and offer a huge commercial potential. In terms of economic impact, the company will get the opportunity to access the business market in the United Kingdom and overseas. In addition, global market for industrial enzymes is estimated to be $4.4 million in 2015, a figure that will include sales of enzymes in biological detergents, food processing, dairy industry, textile industry and production of chemicals.

Conclusion and Recommendation:

The pilot study was designed to evaluate metagenomics as an approach to monitor the microbial communities in environmental samples. High throughput metagenomics analysis is applicable to assess the complexity of environmental samples. The data were informative and had provided a guideline for the next phase. The rarefaction analysis leading to the overall conclusion that the microbial communities from the Portobello Beach were much more diverse than those from the Duddingston Loch. This project should continue in the first quarter of 2015 with modify procedures and improvements in metagenomic technique. Further reduction in sequencing costs and advance tools for data analysis make the metagenomics approach feasible for large scale project.


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