Abstract
An ensemble of genetic networks that describe how the model fungal system, Neurospora crassa, utilizes quinic acid (QA) as a sole carbon source has been identified previously. A genetic network for QA metabolism involves the genes, qa-1F and qa-1S, that encode a transcriptional activator and repressor, respectively and structural genes, qa-2, qa-3, qa-4, qa-x, and qa-y. By a series of 4 separate and independent, model-guided, microarray experiments a total of 50 genes are identified as QA-responsive and hypothesized to be under QA-1F control and/or the control of a second QA-responsive transcription factor (NCU03643) both in the fungal binuclear Zn(II)2Cys6 cluster family. QA-1F regulation is not sufficient to explain the quantitative variation in expression profiles of the 50 QA-responsive genes. QA-responsive genes include genes with products in 8 mutually connected metabolic pathways with 7 of them one step removed from the tricarboxylic (TCA) Cycle and with 7 of them one step removed from glycolysis: (1) starch and sucrose metabolism; (2) glycolysis/glucanogenesis; (3) TCA Cycle; (4) butanoate metabolism; (5) pyruvate metabolism; (6) aromatic amino acid and QA metabolism; (7) valine, leucine, and isoleucine degradation; and (8) transport of sugars and amino acids. Gene products both in aromatic amino acid and QA metabolism and transport show an immediate response to shift to QA, while genes with products in the remaining 7 metabolic modules generally show a delayed response to shift to QA. The additional QA-responsive cutinase transcription factor-1β (NCU03643) is found to have a delayed response to shift to QA. The series of microarray experiments are used to expand the previously identified genetic network describing the qa gene cluster to include all 50 QA-responsive genes including the second transcription factor (NCU03643). These studies illustrate new methodologies from systems biology to guide model-driven discoveries about a core metabolic network involving carbon and amino acid metabolism in N. crassa.
Highlights
Systems biology provides a new paradigm to understand complex traits, such as carbon metabolism [1,2,3,4,5,6], homeostasis [7,8], development [7], response to environmental change [9], longevity [10], the clock [11], and life itself [12]
Is the control experiment needed in identifying QAresponsive genes?
Work on other systems, such as S. cerevisiae, has identified enzymes that are key to determining the flow through metabolic modules in Figure 8 [14]
Summary
Systems biology provides a new paradigm to understand complex traits, such as carbon metabolism [1,2,3,4,5,6], homeostasis [7,8], development [7], response to environmental change [9], longevity [10], the clock [11], and life itself [12] This new approach has a number of common elements [1,3,13] including viewing living systems as biochemical and regulatory networks, measuring a system-wide response with genomic approaches as in RNA and protein profiling [14,15], and cycling through a rationalized discovery process to identify the true underlying network explaining a complex trait of interest [1,3,16].
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