The use of bioinformatics and genome biology to advance our understanding of bioleaching microorganisms

Commercial biomineral recovery (bioleaching, biooxidation) of copper and gold is a cost effect and environmentally friendly alternative for metal recovery from low grade ores. The complete or nearly complete genome sequences of a number of bioleaching bacteria and archaea are publicly available including genomic information derived from an environmental sample. A study of these genomes and predicted metabolic and regulatory pathways is beginning to provide novel and exciting insights into the metabolism of these microorganisms and how they might work synergistically in tank reactors and in heap bioleaching operations to promote the recovery of metals. The objective of this chapter is to provide a brief overview of recent progress in the areas of bioinformatics and genome biology as they relate to bioleaching microorganisms. After a short introduction to the principle themes of bioinformatics, metagenomics and genome biology, we will outline different cases of how these approaches have been exploited to reveal novel information. By deconvoluting metabolic potential through bioinformatic analysis new kinds of information can be revealed that not only connect vast amounts of data, but may also capture usable knowledge in the form of biologically valid relations that can subsequently be applied to biotechnological applications such as biomining. Many questions regarding the biology of microorganisms can normally be addressed by a range of genetic and biochemical experiments. Unfortunately, A. ferrooxidans has proved recalcitrant to standard genetic manipulation and genetic analysis of other bioleaching microorganisms is only recently being developed. There is only one report of transformation in A. ferrooxidans and this may be strain specific and not of general use (Kusano et al., 1992) and transduction is unknown. Only recently have techniques for conjugation been established and these remain difficult to control and are of low efficiency (Liu et al., 2000; reviewed in Holmes &Bonnefoy, 2006). Exacerbating the problem is the difficulty of obtaining sufficient cell mass for many biochemical assays. Given these experimental hurdles, metabolic models derived from bioinformatic analyses offer an especially attractive starting point for unraveling the interesting physiology of bioleaching microorganisms.