Chapter 20 : Functional Genomics

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As genetic systems for archaea become further developed and implemented, functional genomics tools can be expanded to enable a full systems biology approach to studying archaea. This chapter provides a review of the current status of functional genomics efforts to investigate archaea. In this discussion, functional genomics refers to transcriptional response (transcriptomics), protein inventory and differential abundance (proteomics), and protein structural attributes (structural genomics) examined in the context of entire genomes. Currently, certain archaea are emerging as model systems for functional genomics studies. This realization was sobering given the need for highly sophisticated, analytical, and statistical skills, as well as the significant expense, that are typically part and parcel of functional genomics approaches. As is the case with many facets of archaeal biology, the field of proteomics can be divided into accomplishments achieved with three different groups of archaea: methanogens, halophiles, and organisms that fit into neither category. A proteomics approach to investigate protein levels in cells grown at low (4°C) and optimal temperatures was recently reported. This study represented the first global analysis of proteins involved in cold adaptation. Coupled with useful genetic systems, strategic use of functional genomics approaches will form the basis for complete and accurate genome annotation. Information gained from ongoing and emerging functional genomics efforts focusing on archaea will provide clues and insights that will accelerate the genetic system’s development.

Citation: Jenney, Jr. F, Tachdjian S, Chou C, Kelly R, Adams M. 2007. Functional Genomics, p 434-462. In Cavicchioli R (ed), Archaea. ASM Press, Washington, DC. doi: 10.1128/9781555815516.ch20
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Figure 1

Time-dependent response of to a temperature shift from 80°C to 90°C (Tachdjian and Kelly, unpublished). Histogram (top) and Venn diagram (bottom) represent the number of genes differentially expressed more than 2-fold at each stage of the heat shock. HS, heat shock.

Citation: Jenney, Jr. F, Tachdjian S, Chou C, Kelly R, Adams M. 2007. Functional Genomics, p 434-462. In Cavicchioli R (ed), Archaea. ASM Press, Washington, DC. doi: 10.1128/9781555815516.ch20
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Figure 2

Time-dependent differential expression of ORFs SSO0337 and SSO2887 from subjected to a pH shift. ORFs SSO0337 (A) and SSO2887 (B) were subjected to a pH shift from pH 4.0 to pH 2.0 at 80°C (Tachdjian and Kelly, unpublished). Samples were taken 1 min before pH shift was initiated, then 5, 30, and 60 min after reaching pH 2.0 (lines drawn through points). Significant levels of differential expression occurred at intermediate sampling times, although no substantial changes were observed for the pre-acid shock versus 60-min contrast.

Citation: Jenney, Jr. F, Tachdjian S, Chou C, Kelly R, Adams M. 2007. Functional Genomics, p 434-462. In Cavicchioli R (ed), Archaea. ASM Press, Washington, DC. doi: 10.1128/9781555815516.ch20
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Figure 3

Fluorescence intensities of DNA microarrays. (A) cDNA versus cDNA derived from two independent cultures of cells grown with peptides as the carbon source. (B) cDNA versus cDNA derived from two independent cultures of cells grown with peptides or maltose as the carbon source. In A, the upper and lower diagonal line pairs indicate twofold and fivefold changes in the signal intensities, respectively, while only the lines indicating fivefold changes are given in B. See text for details. Reproduced from the ( ) with permission of the publisher.

Citation: Jenney, Jr. F, Tachdjian S, Chou C, Kelly R, Adams M. 2007. Functional Genomics, p 434-462. In Cavicchioli R (ed), Archaea. ASM Press, Washington, DC. doi: 10.1128/9781555815516.ch20
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Figure 4

Cellular processes involved in the cold shock response of when grown at 72°C rather than at the optimal temperature near 100°C. Modified from the Ph.D. thesis ( ) with permission of the author.

Citation: Jenney, Jr. F, Tachdjian S, Chou C, Kelly R, Adams M. 2007. Functional Genomics, p 434-462. In Cavicchioli R (ed), Archaea. ASM Press, Washington, DC. doi: 10.1128/9781555815516.ch20
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Table 1.

Possible model archaeal systems for functional genomics studies

Citation: Jenney, Jr. F, Tachdjian S, Chou C, Kelly R, Adams M. 2007. Functional Genomics, p 434-462. In Cavicchioli R (ed), Archaea. ASM Press, Washington, DC. doi: 10.1128/9781555815516.ch20
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Table 2.

Archaeal “omics”: proteomic, transcriptomic, and structural genomic studies

Citation: Jenney, Jr. F, Tachdjian S, Chou C, Kelly R, Adams M. 2007. Functional Genomics, p 434-462. In Cavicchioli R (ed), Archaea. ASM Press, Washington, DC. doi: 10.1128/9781555815516.ch20
Generic image for table
Table 3.

Success rates for the various steps in the SG protocol from gene to protein structure ( ) in the Protein Data Bank ( )

Citation: Jenney, Jr. F, Tachdjian S, Chou C, Kelly R, Adams M. 2007. Functional Genomics, p 434-462. In Cavicchioli R (ed), Archaea. ASM Press, Washington, DC. doi: 10.1128/9781555815516.ch20
Generic image for table