The bacterium Acidithiobacillus ferrooxidans
populates extremely acidic (pH 1-3) environmental conditions and
is an important component of a consortium of microorganisms used
in a variety of bioleaching operations involved in copper recovery
in Chile and other parts of the world. It is also thought to be
a contributor to acid mine drainage (AMD). It uses sulfur and iron
as energy sources, fixes carbon and nitrogen and is resistant to
high levels of many toxic metals.
The multiple challenges of its extreme
environment coupled with its unusual metabolism make it an excellent
model for studies of basic biochemistry, physiology and genome evolution.
It is also important to understand its role in mineral recovery, the
formation of AMD and biogeochemical recycling of iron, sulfur, carbon
and nitrogen in extreme acid environments.
Recently, a complete genome sequence
of A. ferrooxidans was made publicly available by The Institute
for Genome Research (TIGR) and we have annotated this sequence identifying
about 3000 possible genes in a 3 megabase genome. We have been able
to assign putative functions to about 70% of the genes with various
levels of confidence. Our group has used bioinformatic procedures
to build several preliminary metabolic models including those for
sulfur assimilation, nitrogen fixation, iron uptake and homeostasis,
biofilm formation, quorum sensing and hydrogen utilization. Aspects
of several of these models have been subjected to experimental validation
supporting the preliminary bioinformatic predictions and, in one case,
yielding an unexpected insight into the novel properties of the genetic
regulator Fur. This finding has significant implications for understanding
the global regulation of iron uptake and homeostasis. The annotation
has also provided information to design oligonucleotides representing
every gene for the production of microarray chips and, in an international
collaboration, the first preliminary microarray results are available
in which a comparison has been made of genes over-expressed in A.
ferrooxidans grown in either iron or sulfur. These results, once
confirmed, will contribute significantly to our understanding of the
organism.
In the present project we propose to
continue our bioinformatic analysis in order to improve and extend
our existing annotation of the A. ferrooxidans genome, publishing
the results in collaboration with TIGR and providing an interactive,
publicly available data base for the microorganism. The extended annotation
will be used to deepen our knowledge of our preliminary metabolic
models and to generate additional metabolic models including those
involved in nucleotide and nucleoside metabolism, cofactor and vitamin
metabolism, lipid and fatty acid biosynthesis, CO2 fixation, central
carbon metabolism, metabolism of complex carbohydrates and iron and
sulfur oxidation. Metabolic models and genome organization can then
be compared to those in other organisms including, but not limited
to, Leptospirillum and Ferroplasma that also live
in extreme acid conditions and share features of unusual physiology
with A. ferrooxidans. It is anticipated that the availability
of sequenced organisms that can be used for such comparisons will
grow substantially in the upcoming years providing additional information
that can be used not only for unraveling the physiology of A.
ferrooxidans but also as aid in understanding microbial autotrophy
in extreme environmental conditions.
Aspects of these models will be subjected
to experimental validation using real time PCR, enzymatic assays,
gene expression studies, complementation of heterologous mutants,
microarray analysis and molecular modelling. Information recovered
from these studies will be fed back to enhance the construction of
the metabolic models. We propose to place a special emphasis on experimental
investigations into the role of Fur in order to understand, at the
molecular level, its role in iron metabolism and to explore the metabolic
and regulatory pathways that integrate the uptake of iron as a nutrient
versus its use as an energy source. The ability to oxidize iron is
a signature of A. ferrooxidans and we have a unique opportunity
to make a contribution to understanding its underlying mechanism.
At the completion of the project we
posit that knowledge from the proposed integration of bioinformatic
analysis, experimental validation and microarray prediction will provide
an initial framework to help explain a significant proportion of the
metabolism of A. ferroxidans and could suggest genetic regulatory
pathways by which it integrates various metabolic capacities such
as iron and sulfur metabolism or nitrogen and carbon dioxide fixation.
By deconvoluting metabolic potential we hope to reveal new kinds of
information that not only connect vast amounts of data, but also capture
usable knowledge in the form of biologically valid relations that
can be applied to biotechnological applications such as biomining.
