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Studies on various model systems have shown that a relatively small number of transcription factors can set up strikingly complex spatial and temporal patterns of gene expression. This is achieved mainly by means of combinatorial or differential gene regulation, i.e. regulation of a gene by two or more transcription factors simultaneously or under different conditions. While a number of specific molecular details of the mechanisms of combinatorial regulation have emerged, our understanding of the general principles of combinatorial regulation on a genomic scale is still limited. In this work, we approach this problem by using the largest assembled transcriptional regulatory network for yeast. A specific network transformation procedure was used to obtain the co-regulatory network describing the set of all significant associations among transcription factors in regulating common target genes. Analysis of the global properties of the co-regulatory network suggested the presence of two classes of regulatory hubs: (i) those that make many co-regulatory associations, thus serving as integrators of disparate cellular processes; and (ii) those that make few co-regulatory associations, and thereby specifically regulate one or a few major cellular processes. Investigation of the local structure of the co-regulatory network revealed a significantly higher than expected modular organization, which might have emerged as a result of selection by functional constraints. These constraints probably emerge from the need for extensive modular backup and the requirement to integrate transcriptional inputs of multiple distinct functional systems. We then explored the transcriptional control of three major regulatory systems (ubiquitin signaling, protein kinase and transcriptional regulation systems) to understand specific aspects of their upstream control. As a result, we observed that ubiquitin E3 ligases are regulated primarily by unique transcription factors, whereas E1 and E2 enzymes share common transcription factors to a much greater extent. This suggested that the deployment of E3s unique to specific functional contexts may be mediated significantly at the transcriptional level. Likewise, we were able to uncover evidence for much higher upstream transcription control of transcription factors themselves, in comparison to components of other regulatory systems. We believe that the results presented here might provide a framework for testing the role of co-regulatory associations in eukaryotic transcriptional control.

We investigate the conservation of amino acid residue sequences in 21 DNA-binding protein families and study the effects that mutations have on DNA-sequence recognition. The observations are best understood by assigning each protein family to one of three classes: (i) non-specific, where binding is independent of DNA sequence; (ii) highly specific, where binding is specific and all members of the family target the same DNA sequence; and (iii) multi-specific, where binding is also specific, but individual family members target different DNA sequences. Overall, protein residues in contact with the DNA are better conserved than the rest of the protein surface, but there is a complex underlying trend of conservation for individual residue positions. Amino acid residues that interact with the DNA backbone are well conserved across all protein families and provide a core of stabilising contacts for homologous protein-DNA complexes. In contrast, amino acid residues that interact with DNA bases have variable levels of conservation depending on the family classification. In non-specific families, base-contacting residues are well conserved and interactions are always found in the minor groove where there is little discrimination between base types. In highly specific families, base-contacting residues are highly conserved and allow member proteins to recognise the same target sequence. In multi-specific families, base-contacting residues undergo frequent mutations and enable different proteins to recognise distinct target sequences. Finally, we report that interactions with bases in the target sequence often follow (though not always) a universal code of amino acid-base recognition and the effects of amino acid mutations can be most easily understood for these interactions.

Global surveys of genomes measure the usage of essential molecular parts, defined here as protein families, superfamilies or folds, in different organisms. Based on surveys of the first 20 completely sequenced genomes, we observe that the occurrence of these parts follows a power-law distribution. That is, the number of distinct parts (F) with a given genomic occurrence (V) decays as F=aV(-b), with a few parts occurring many times and most occurring infrequently. For a given organism, the distributions of families, superfamilies and folds are nearly identical, and this is reflected in the size of the decay exponent b. Moreover, the exponent varies between different organisms, with those of smaller genomes displaying a steeper decay (i.e. larger b). Clearly, the power law indicates a preference to duplicate genes that encode for molecular parts which are already common. Here, we present a minimal, but biologically meaningful model that accurately describes the observed power law. Although the model performs equally well for all three protein classes, we focus on the occurrence of folds in preference to families and superfamilies. This is because folds are comparatively insensitive to the effects of point mutations that can cause a family member to diverge beyond detectable similarity. In the model, genomes evolve through two basic operations: (i) duplication of existing genes; (ii) net flow of new genes. The flow term is closely related to the exponent b and can accommodate considerable gene loss; however, we demonstrate that the observed data is reproduced best with a net inflow, i.e. with more gene gain than loss. Moreover, we show that prokaryotes have much higher rates of gene acquisition than eukaryotes, probably reflecting lateral transfer. A further natural outcome from our model is an estimation of the fold composition of the initial genome, which potentially relates to the common ancestor for modern organisms. Supplementary material pertaining to this work is available from www.partslist.org/powerlaw., (Copyright 2001 Academic Press.)