The constraints of the individual aspects of this study have been set out in the relevant
sections of the preceding chapters, so this section just focuses on the main constraints. These fall
into three distinct categories:
i) constraints in the raw data available, due to the inability to return to the field site;
ii) constraints in the computational power available to analyze the data;
iii) the constraint that is inherent in using DNA data, as DNA represents genetic potential but
does not provide information on which aspects of that genetic potential are in use at any given
time.
The main impact of the first constraint is the fact that it was not possible to obtain some
repeat measurements or samples, in order to test the hypotheses produced as a result of the initial
data analysis, or in order to obtain information on how the chemical conditions, and microbial
community, changed over the course of the season. This could be addressed by future fieldwork
but it was not possible to return to the site during the course of this study because it is so remote
and so it is very difficult, and expensive, to reach.
Additional information that could be obtained without further fieldwork would be to look
at which genes are being expressed in this environment, if it is possible to extract RNA from
samples of the sulfur that remain preserved in the laboratory freezer. This would provide
valuable further insight into which reactions microbes are, in fact, using, given that they have the
genetic capability to carry out several different sulfur redox reactions, by looking at which of the
highly-abundant sulfur redox genes are actually being expressed. The metagenome sequence data
could be used to design primers which specifically target the genes of interest.
The second constraint was mostly a question of access to hardware, but partly an issue of
the bioinformatic skills needed for the task. In this study both of these problems were overcome
by using the MG-RAST pipeline for the initial stages of the analytical process, as these were the
most demanding in terms of computational power. The issue of computational power could be
solved in future by accessing the University of Colorado’s supercomputer (not available at the
time this analysis was carried out) and by further work to write suitable programs to enable the
analysis to be carried out without needing to use the MG-RAST pipeline.
More generally, access to significant computing power, and bioinformatic skills, is likely
to be an issue for many environmental microbiologists are likely to face this issue as the ability
to produce huge quantities of sequence data has expanded more rapidly than the ability of
cost method for laboratories which only do small amounts of such work to access the necessary
hardware, but it is not yet clear how much resource, and for what cost, a typical analysis would
require. The skill gap is also significant. Whilst collaboration with specialist bioinformatic
groups is one option, that is not currently feasible for all. A tremendous service is provided to the
community by freely-available resources such as MG-RAST, but there are trade-offs. In order to
provide this computational resource for free, the MG-RAST pipeline takes steps to reduce the
computational power needed for each metagenome. Perhaps the most significant of these is the
fact that sequences are clustered at 90% amino acid similarity, and then only one sequence of the
cluster is used for functional identification, rather than every sequence being identified
individually. This could reduce the accuracy of the quantification of functions. It is also true that
using such pipelines means surrendering some degree of intellectual control. Nevertheless, it is a
useful resource. In any future metagenome studies that I did, I might still use the MG-RAST
system for initial quality control of the data, but I would then download the NCBI protein nr
database, use the BLAST Executables (installed on a local computer) to BLAST the metagenome
data against the database to assign function to each sequence individually, and I would then write
programs of my own, to collate and quantify the results, supplemented by existing software such
as MEGAN4 (Huson et al., 2011) which can be used to group BLAST outputs by functional
groups using the SEED (Overbeek et al., 2005) or KEGG (Kanehisa et al., 2008) classification
schemes.
In order to maximize the benefit for the sequence data that are being produced, it would
be extremely helpful if these ‘universal’ classification schemes were improved to more
comprehensively cover the full range of functional genes known. The SEED system (Overbeek
It would also benefit this field if there was some consensus on the quality control
measures used, so that datasets from different studies, and different sites, could be directly
compared in order to draw wider conclusions. For example, the critical question of what quality
control cutoff should be used for determining whether a certain sequence is, or is not, the same
gene as a reference sequence is one which remains unanswered. This is in part because the
answer is unlikely to be the same in every case as different genes evolve at different rates, but it
may be possible to reach some agreement on a cutoff to be used, just as in SSU rRNA analysis
there is a general consensus that 97% identity should be considered a ‘species-level’ OTU, even
though it is recognized that this is merely a convention.
The impact of the third constraint is that it is impossible to tell with certainty which of the
genes present in the metagenome are being used by the microbes that are found within the sulfur
deposit. The underlying assumption that is being made in this work is that genes that are present
in high abundance, which must therefore be present in a significant proportion of the microbes
present, are more likely to be in use than those which are present in low abundance, which could
be derived from microbes that are dormant, or dead. A particular issue arises in the case of the
sulfur redox genes possessed by Sulfurovum and Sulfuricurvum as these organisms possess
several different sulfur redox genes within the same genome. It is impossible to tell which of
these genes they are using, and so exactly which sulfur redox reactions they are catalyzing, from
DNA alone. For example, if they are highly abundant in the metagenome because they are using
their sox genes, then as their cell numbers increase, the relative abundance of all their genes will
increase in parallel, whether each gene is being used or not. This limitation can be addressed by
More generally, the use of metatranscriptomic approaches, using RNA extracted from the
environment as the template for cDNA, and then making a shotgun library from this cDNA,
which is then sequenced in the same way as the metagenome DNA, provides a way to gain a
comprehensive overview of all the genes that are being used by an environmental microbial
community at any time. The comparison of metagenomic and metatranscriptomic data from the
same environmental sample provides invaluable information not only about which genes are in
use, but which are not, which adds to our knowledge of the way in which environmental
conditions affect microbial activities (Frias-Lopez et al., 2008, Stewart et al, 2011b).
The emerging field of metaproteomics will add yet another layer of information, as RNA
expression levels may not necessarily accurately predict protein levels, due to differential rates of
RNA and protein turnover, but so far metaproteomic techniques do not provide the same quantity
of information as metagenomic or metatranscriptomic approaches (Ram et al., 2005).
CONCLUSION
The particular focus of this study was to look at how well bioenergetic
calculations of the amount of energy available from different reactions in this particular
environment predicted the type of microbial energy metabolism that was found there. It is, as far
as I know, the most robust and comprehensive investigation of this question that has been
published to date. Clearly it does not deal with all possible aspects that affect habitability, and
how microbes will react to changing conditions, but it does begin to illuminate some aspects of
this, by demonstrating that energetically-favorable niches can remain unfilled. Much more work
is needed to determine the reasons for this, and how widely applicable these findings are to other
will require culturing experiments, while others, such as the relative lack of phototrophs, will
require a molecular approach. The work in this thesis demonstrates how different types of
investigative approaches, in this case geochemical analysis, bioenergetic calculations,
metagenomic analysis and environmentally-relevant culturing experiments, can be used together
to deepen our understanding of microbial ecology, and the ways in which it impacts our
environment.
Microbial cycling of major elements such as carbon, nitrogen, iron and sulfur can have a
significant impact on the environment, both locally, and globally. We do not know the full extent
of microbial biogeochemical cycling on our planet and its climate, nor how this will change in
responding to any change in environmental conditions. These cycles are inter-connected, and
contain multiple interactions, including the possibility of both positive and negative feedback
loops. In trying to understand how our planet is going to respond to the warming climate, and in
particular in trying to understand the impact of further human action, deliberate or otherwise, it is
critically important that we understand better how these microbial biogeochemical cycles work.
Otherwise, any human interventions designed to improve environmental conditions, for example,
to address climate change, could end up making the problem even worse. We are only just
beginning to scratch the surface of this understanding. It is only in the last few years, with the
massive increase in sequence data available via next-generation sequencing technologies, that we
have the tools that enable us to investigate the extent of microbial diversity, and its actions and
impacts. Work on the human microbiome, the area where most work has been done, continues to
show new and unexpected impacts of this microbiome on human wellbeing. It is not unrealistic
to assume that the impact of the planetary microbiome as a whole on the wellbeing of Earth, and
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