A predictive model for regulatory sequences directing liver-specific transcription

Abstract

The identification and interpretation of the regulatory signals within the human genome remain among the greatest goals and most difficult challenges in genome analysis. The ability to predict the temporal and spatial control of transcription is likely to require a combination of methods to address the contribution of sequence-specific signals, protein-protein interactions and chromatin structure. We present here a new procedure to identify clusters of transcription factor binding sites characteristic of sequence modules experimentally verified to direct transcription selectively to liver cells. This algorithm is sufficiently specific to identify known regulatory sequences in genes selectively expressed in liver, promising acceleration of experimental promoter analysis. In combination with phylogenetic footprinting, this improvement in the specificity of predictions is sufficient to motivate a scan of the human genome. Potential regulatory modules were identified in orthologous human and rodent genomic sequences containing both known and uncharacterized genes.

Figure 1

Clusters of TF-binding sites that direct liver-specific transcription. The critical factors are HNF-1, HNF-3, HNF-4, and C/EBP. For human genes, the names denote the approved HUGO symbols, otherwise they are given by the corresponding name of the human ortholog, if available. The numbers denote positions relative to the TSS.

Figure 2

Sequence logos for critical liver TF-binding sites. The position-specific information content is plotted in bits along the ordinate. The low total information content of HNF-3 and C/EBP reflects the low binding specificity of these two factors.

Figure 3

Dependence of the numerical values of the LRA coefficients on the number of included positive training sequences. Between 4 and 15 randomly selected positive training sequences were used for the computation of the LRA coefficients. The bars depict the maximum, minimum, and average coefficient values from 15 trials.

Figure 3

Dependence of the numerical values of the LRA coefficients on the number of included positive training sequences. Between 4 and 15 randomly selected positive training sequences were used for the computation of the LRA coefficients. The bars depict the maximum, minimum, and average coefficient values from 15 trials.

Figure 4

Combining the liver model with phylogenetic footprinting for selected sequences. The sequence similarity between human and rodent sequences as determined with DBA (Jareborg et al. 1999) is shown as a solid black line. The position-dependent LMM score of the human sequence centered with respect to a 200-bp window is shown as a broken gray line. Documented regulatory regions, depicted by triangles, are characterized by strong score for both DBA and LMM. Boxes show annotated exons that possess a high level of cross species conservation, reflected by a high DBA score. The position with respect to the human sequence is shown along the abscissa. Fig. 4A addresses CYP7A1 (human accession L13460, rat U01962). Fig. 4B addresses IGF1 (human S85346 [identical with M12659], mouse Y18062).

Figure 5

Combining the liver model with phylogenetic footprinting on a quasi-genomic scale. See text for explanation.