DNA microarrays are powerful and versatile tools for monitoring the expression of tens of thousands of genes simultaneously. This technology has successfully been applied to mon­itor transcriptome regulation in cancer studies (114, 115), the discovery of drug targets (116), and importantly for studying microbial gene expression and regulation under differ­ent growth conditions (117). Similar to the situation in which microprocessors have sped up computation, microarray-based genomic technologies have revolutionized the genetic analysis of biological systems, by moving from assaying gene expression, one gene at a time,

Isolation of RNA and DNA

Hybridization

Whole genome microarray

Data analysis

Figure 15.3 Example of a microarray hybridization experiment using a DNA reference. Depending upon the label used an expressed gene can be differentiated from silent genes based on the spot color.

to the ability to visualize the dynamics of the entire transcriptome of an organism in one hybridization step.

The analysis of gene expression using microarrays involves the steps outlined in Figure 15.3. The first step is the acquisition and purification of RNA of samples grown under differ­ent experimental conditions. Depending on the experimental design this may also involve the isolation of DNA. Methods for simultaneous isolation and purification of both RNA and DNA (118) from the same sample have been developed. After the isolation of nucleic acids from the sample, a fluorescent label must be incorporated into each. In the case of RNA,
labeled dUTPs are incorporated into the sample, as it is reverse transcribed into cDNA. DNA labeling is accomplished using the Klenow polymerase fragment to incorporate the labeled nucleotide. Following this simultaneous hybridization and subsequent laser excita­tion and imaging of each of the dye-labeled samples simultaneously, allows one condition to be compared to the other. Transcribed RNA can be compared to baseline DNA from the same sample allowing the gene expression level to be calculated, relative to the initial sample composition.

15.3 Conclusions

The use of lignocellulosic material as a feedstock for producing fuels and chemicals has enormous potential considering its relative abundance. However, its use continues to be cost prohibitive because of pretreatment and enzyme costs (119). What lacks is a fun­damental understanding of how natural systems efficiently degrade and utilize decaying biomass. Microorganisms of various types are intimately involved in biomass degradation and occasionally are the only biological agents capable of doing so. Gaining a fundamen­tal understanding of biological mechanisms of biomass decay by microbial communities will help us develop cost-effective ways to convert these abundant substrates to fuels and chemicals.

Microorganisms are not alone in nature. Each microorganism involved in biomass degra­dation interacts with both its surroundings and with other organisms. These interactions result in chemical and physical changes that in turn lead to microbial communities that are complex and dynamic but ultimately effective in transforming biomass. Conventional microbiological methods used to evaluate them fail to capture their diversity or biochemical complexity. This is due in part because cultivating microorganisms in the laboratory depends on supplying the right nutrients and growth conditions. Because biomass conversion is a dynamic process there are countless microenvionments that would need to be considered.

New methods for examining the diversity and biochemistry of biomass degradation are emerging. It is now possible to characterize microbial populations using large-scale inte­grative approaches. Rapid advances in the fields of genomics and proteomics have spawned multiple new “omic” subdisciplines as evidenced by the number of recent papers detailing the evolution and molecular basis of microbial communities. Large-scale proteomics-level examination of natural microbial communities is now possible allowing one for not only the functional analysis of enzymes directly involved in biomass conversion but also for the analysis of the processes and interactions involved in community formation. These new technologies will revolutionize the way microbial communities are defined in the future and allow microbiologists to envision and model microbial biomass decay as a set of interacting processes that when combined effectively degrade plant biomass.

Acknowledgment

This work was supported by the US DOE Office of the Biomass Program. The research was also financed as part of the BioEnergy Science Center, a U. S. Department of Energy Bioenergy Research Center supported by the Office of Biological and Environmental Research in the DOE Office of Science.