A model-driven discovery process, Computing Life, is used to identify an ensemble of genetic networks that describe the biological clock. A clock mechanism involving the genes white-collar-1 and white-collar-2 (wc-1 and wc-2) that encode a transcriptional activator (as well as a blue-light receptor) and an oscillator frequency (frq) that encodes a cyclin that deactivates the activator is used to guide this discovery process through three cycles of microarray experiments. Central to this discovery process is a new methodology for the rational design of a Maximally Informative Next Experiment (MINE), based on the genetic network ensemble. In each experimentation cycle, the MINE approach is used to select the most informative new experiment in order to mine for clock-controlled genes, the outputs of the clock. As much as 25% of the N. crassa transcriptome appears to be under clock-control. Clock outputs include genes with products in DNA metabolism, ribosome biogenesis in RNA metabolism, cell cycle, protein metabolism, transport, carbon metabolism, isoprenoid (including carotenoid) biosynthesis, development, and varied signaling processes. Genes under the transcription factor complex WCC ( = WC-1/WC-2) control were resolved into four classes, circadian only (612 genes), light-responsive only (396), both circadian and light-responsive (328), and neither circadian nor light-responsive (987). In each of three cycles of microarray experiments data support that wc-1 and wc-2 are auto-regulated by WCC. Among 11,000 N. crassa genes a total of 295 genes, including a large fraction of phosphatases/kinases, appear to be under the immediate control of the FRQ oscillator as validated by 4 independent microarray experiments. Ribosomal RNA processing and assembly rather than its transcription appears to be under clock control, suggesting a new mechanism for the post-transcriptional control of clock-controlled genes.

A diverse array of organisms from bacteria to humans may have evolved the ability to tell time in the presence or absence of external environmental cues. In the lowly bread mould, Neurospora crassa, biomolecular reactions involving the white-collar-1 (wc-1), white-collar-2 (wc-2), and frequency (frq) genes and their products constitute building blocks of a biological clock. Here we use genetic network models to explain quantitatively, from a systems perspective, how these building blocks interact, and how a complex trait like clock oscillation emerges from these interactions. We use a recently developed method of genetic network identification to find an ensemble of oscillating network models quantitatively consistent with available RNA and protein profiling data on the N. crassa clock. Predicted key features of the N. crassa clock system are a dynamically frustrated closed feedback loop, cooperativity in frq gene activation, and/or WC-1/WC-2 protein complex deactivation and substantial posttranscriptional enhancement of wc-1 RNA lifetime. Measuring the wc-1 mRNA lifetime provides a critical test of the genetic networks. (1) and are at the heart of a new systems approach to biology (2). The biological clock (3) provides a prototypical and biologically ubiquitous example of how a complex trait can emerge from the interaction of even a small number of gene regulatory elements. In an experimentally well studied example, the filamentous fungus, Neurospora crassa, and the biomolecular reactions involving the white-collar-1, white-collar-2, and frequency genes and their products constitute building blocks of a biological clock (4). A central open question of systems biology is whether these building blocks are necessary and sufficient to define a circuit or genetic network that oscillates, and how, in quantitative detail, such oscillations emerge from the interactions among these building blocks. Here we use a recently developed method of genetic network identification (5) to find an ensemble of oscillating network models, constituted from wc-1, wc-2, and frq and their products, which is quantitatively consistent with available RNA and protein profiling data on the N. crassa biological clock. The use of genetic networks to integrate diverse experimental information and to predict the behavior of a complex trait, such as the biological clock, provides a new paradigm for quantitative genetics at the molecular level (6).Key features of the genetic network that permit oscillations are: (i) the presence of functional wc-1, wc-2, and frq genes, generating protein products WC-1, WC-2, and FRQ, and the white collar complex (WCC) formed by WC-1 and WC-2; (ii) a closed-feedback loop of the biomolecular reactions in the genetic network, with WCC activating the frq gene 3 the activated frq gene producing frq mRNA 3 frq mRNA producing FRQ protein and 3 FRQ protein deactivating WCC; (iii) dynamical frustration arising in the feedback loop because of WCC's stimulation of the production of FRQ, whereas FRQ induces the deactivation o...

Nonhost resistance protects plants against attack by the vast majority of potential pathogens, including phytopathogenic fungi. Despite its high biological importance, the molecular architecture of nonhost resistance has remained largely unexplored. Here, we describe the transcriptional responses of one particular genotype of barley (Hordeum vulgare subsp. vulgare 'Ingrid') to three different pairs of adapted (host) and nonadapted (nonhost) isolates of fungal pathogens, which belong to the genera Blumeria (powdery mildew), Puccinia (rust), and Magnaporthe (blast). Nonhost resistance against each of these pathogens was associated with changes in transcript abundance of distinct sets of nonhost-specific genes, although general (not nonhost-associated) transcriptional responses to the different pathogens overlapped considerably. The powdery mildew- and blast-induced differences in transcript abundance between host and nonhost interactions were significantly correlated with differences between a near-isogenic pair of barley lines that carry either the Mlo wild-type allele or the mutated mlo5 allele, which mediates basal resistance to powdery mildew. Moreover, during the interactions of barley with the different host or nonhost pathogens, similar patterns of overrepresented and underrepresented functional categories of genes were found. The results suggest that nonhost resistance and basal host defense of barley are functionally related and that nonhost resistance to different fungal pathogens is associated with more robust regulation of complex but largely nonoverlapping sets of pathogen-responsive genes involved in similar metabolic or signaling pathways.

To study protein ubiquitination pathways in the interaction of barley (Hordeum vulgare) with the powdery mildew fungus (Blumeria graminis), we measured protein turnover and performed transient-induced gene silencing (TIGS) of ubiquitin and 26S proteasome subunit encoding genes in epidermal cells. Attack by B. graminis hyperdestabilized a novel unstable green fluorescent protein fusion that contains a destabilization domain of a putative barley 1-aminocyclopropane-1-carboxylate synthase, suggesting enhanced protein turnover. Partial depletion of cellular ubiquitin levels by TIGS induced extreme susceptibility of transformed cells toward the appropriate host pathogen B. graminis f. sp hordei, whereas papilla-based resistance to the nonhost pathogen B. graminis f. sp tritici and host resistance mediated by the mlo gene (for mildew resistance locus O) remained unaffected. Cells were rescued from TIGS-induced ubiquitin depletion by synthetic genes encoding wildtype or mutant barley monoubiquitin proteins. The strongest rescue was from a gene encoding a K63R mutant form of ubiquitin blocked in several ubiquitination pathways while still allowing Lys-48-dependent polyubiquitination required for proteasomal protein degradation. Systematic RNA interference of 40 genes encoding all 17 subunits of the proteasome 19S regulatory particle failed to induce hypersusceptibility against B. graminis f. sp hordei. This suggests a role for Lys-48-linked protein polyubiquitination, which is independent from the proteasome pathway, in basal host defense of barley.

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