ENVIRONMENTAL BIOTECHNOLOGY - Lab Report Example

Environmental Biotechnology Microbes play a fundamental role in every process in the biosphere. All plants and animalshave billions of associated microbes that make necessary nutrients, vitamins and metals available to their host. In the environment, microbial communities are essential for the maintenance of a healthy ecosystem. Due to this, we depend on microbes for the bioremediation of contaminants such as crude oil and chemical spills. Their ability to quickly adapt and respond to the changing world around them makes them excellent environmental indicators.

Metagenomic analysis has provided an excellent tool to study these complex microbial communities, and provides us with the means in which to assess the health of a given system (Atlas 2004). The danger posed by hydrocarbon contamination in sea water areas is rapidly increasing because of increase in human activities that need petroleum as a source of energy and because of the increased interest in the exploitation of a quarter of the world oil reservoir in the arctic ocean. Based on Metagenomic analysis of crude oil contaminated beach sample versus uncontaminated beach sample this paper seeks to explain the main statistically significant shifts between the two samples both taxonomically and metabolically, and provide a suggestion the possible reasoning for why these shifts may have occurred.

Observations and ResultsInserted Figures with captionsTaxonomyFigure 1: Taxonomic domain levelFigure 2: Taxonomic Phylum levelFigure 3: Taxonomic Class levelFigure 4: Taxonomic Order levelFigure 5: Taxonomic Family LevelFigure 6: Metabolism Level 1Figure 7: Metabolism level 2Figure 8: Metabolism level 3What are the main statistically significant shifts between the two samples both taxonomically and metabolically? Taxonomically, at 95% confidence interval, there are statistically significant shifts between the two samples such that at the domain level, bacteria, archae, and viruses are present with bacteria concentration being slightly higher in the contaminated sample as compared to the uncontaminated sample.

Similarly, at the phylum level, there are statistically significant shifts in the proportion of protoebacteria concentration with the uncontaminated beach sample having a higher concentration of approximately less than 73.8% as compared to the contaminated sample. Under the same taxonomic level, bacteroidetes concentration in the contaminated sample is higher which is the similar case for planctomyccetes, cyanobacteria, acidobacteria, firmicutes, chloroflex, and cholorobi. On the contrary, there is a considerable shift in firmicules concentration with the contaminated beach sample having a higher concentration.

At class level, there are statistically significant shifts in gammaproteobacteria with the uncontaminated beach sample having a higher proportion of about less than 37.8%. This is the same observation for actinobacteria, betaprotobacteria, and alphaprotobacteria. On the contrary, delatprotobacteria concentration in the contaminated sample is higher than in the uncontaminated sample. This is a similar observation in the proportion of planctomycetacia, flavobacteria, cytophagia, cyanobacteria, bacteroidia, clostridia, sphingobacteria, solibacterres, chlorobia, and bacilli.

At the order level, significant shifts are seen in higher proportions of pseudomonadales, actinomycetales, rhizobiales, alteromonadales, burkholderiales, and chromatiales in the uncontaminated sample as compared to the contaminated sample. On the other hand, conatmianted beach sample has a significantly higher proportion of planctomycetales, flavobacteriales, desulfobacterales, and rhodobacterales. Importantly, at the family level, the proportion of planctomycetaceae, flavobacteriacae, and rhodobacteriacae, is significantly high in the contaminated sample than in the uncontaminated sample.

On the contrary, the proportion of pseodomonadaceae, mycobacteriacae, bradyrhizobiacae, and burkholderiacae is significantly high in the uncontaminated sample than in the contaminated sample. Metabolically, at 95% confidence interval, there are statistically significant shifts between the two beach samples. At metabolism level 1, the proportion of carbohydrates, respiration, and clustering-based subsystems are considerably high in the contaminated sample than in the uncontaminated sample. On the contrary, under the same level of metabolism, DNA metabolism, virulence, disease and defence, and stress response proportions are significantly higher in the uncontaminated sample than in the contaminated sample.

At metabolism level 2, folate and pterines, and one carbon metabolism, are significantly higher in the contaminated sample than in the uncontaminated one. On the other hand, the proportion of resistance to antibiotics and toxic compounds, oxidative stress, and gram-negative cell wall components is higher in the uncontaminated beach sample than in the contaminate sample. In addition, at metabolism level 3, sugar utilization in thermotogales, flagellum, and flagellar motility proportions are higher in contaminated sample than in the uncontaminated sample.

On the contrary, cobalt-zinc-cadmium resistance and lojap proportions are significantly high in the contaminated sample than in the uncontaminated sample. The differences in STAMP profiles for both taxonomy and metabolism At taxonomy level, the main differences between the contaminated STAMP profile and the uncontaminated STAMP profile is that in the uncontaminated one, the concentration of bacteria, archae, and viruses is higher in comparison to the contaminated profile.At metabolism, the STAMP profile for the contaminated sample contains a higher proportion of carbohydrates, respiration, and clustering-based subsystems than the uncontaminated STAMP profile.

Importantly, DNA metabolism, virulence, disease and defence, and stress response proportions are significantly higher in the uncontaminated STAMP profile than in the contaminated STAMP profile. Metabolic measurements have been supported by ATP content in the sand, microbiological determinations, amount of biomass, and the number of heterotrophic bacteria (Leahy 2010). Analysis of nutrients revealed that biodegradation was limited to nitrogen. Importantly, this test demonstrated to be suitable for comparison of the effectiveness of diverse treatments in improving the biodegradation of shores that are oil-contaminated.

Suggest possible reasoning for why these shifts may have occurred Microorganisms that degenerate hydrocarbons in crude oil are generally found in all sorts of ecosystems, including contaminated and uncontaminated water samples from the beach (Zhou 2006). Importantly, their percent in uncontaminated water sites in general constitutes an insignificant percent of the entire community. After a contamination event for example a hydrocarbon pollution event, their percentage rapidly increases (Schadt 2008).

From the above Metagenomic analysis, it is apparent that oxygen cannot be mostly regarded as a limiting factor in most marine environments; however, bio-available phosphorus, nitrogen, and other trace elements could be possible limiting factors to bioremediation in cases of large crude oil spills (Sandaa 2010). This limitation can nonetheless be overcome through addition of necessary catalysts. This can be improved for instance by adding iron, which is frequently limiting within marine environments.

Moreover, low vitamin levels can also enhance bioremediation.Conclusion Because of the considerable reduction in environmental impact of oil contamination by the use of enhanced stimulation, bioremediation should be considered and be adopted as a substitute response method to oil spill for oil cleanups, more so in regions where the use of other methods can be demanding. The adoption of this method is important if we have to enhance better and efficient contaminated water cleaning procedures.

References:Atlas R. (2004). Microbial degradation of petroleum hydrocarbons: an environmental perspective. Microbiol Rev 45: 180–209.Leahy J. (1990). Microbial degradation of hydrocarbons in the environment. Microbiol Rev 54: 305–315.Sandaa R. (2010). Abundance and diversity of Archaea in heavy-metal-contaminated soils. Appl Environ Microb 65: 3293–3297.Schadt C. (2008). Spatial scaling of functional gene diversity across various microbial taxa. New York: John Wiley & Sons.Zhou J. (2006).

DNA recovery from soils of diverse composition. Appl Environ Microb 62: 316–322.