The Human Transcription Factors

Abstract

Transcription factors (TFs) recognize specific DNA sequences to control chromatin and transcription, forming a complex system that guides expression of the genome. Despite keen interest in understanding how TFs control gene expression, it remains challenging to determine how the precise genomic binding sites of TFs are specified and how TF binding ultimately relates to regulation of transcription. This review considers how TFs are identified and functionally characterized, principally through the lens of a catalog of over 1,600 likely human TFs and binding motifs for two-thirds of them. Major classes of human TFs differ markedly in their evolutionary trajectories and expression patterns, underscoring distinct functions. TFs likewise underlie many different aspects of human physiology, disease, and variation, highlighting the importance of continued effort to understand TF-mediated gene regulation.

Figure 1.. The human transcription factor repertoire.

A. Schematic of a prototypical TF. B. Number of TFs and motif status for each DBD family. Inset displays the distribution of the number of C2H2-ZF domains for classes of effector domains (KRAB, SCAN or BTB domains); “Classic” indicates the related and highly conserved SP, KLF, EGR, GLI GLIS, ZIC and WT proteins. C. DBD configurations of human TFs. In the network diagram, edge width reflects the number of TFs with each combination of DBDs. D. Number of auxiliary (non DNA-binding) domains (from Interpro) present in TFs, broken down by DBD family.

Figure 2.. DNA binding specificities of the human transcription factors.

A. Heatmap showing similarity of human TF DNA binding motifs. Representative motif(s) were selected for each TF from the set of motifs directly determined by a high throughput in vitro assay. Pairwise motif similarities were calculated using MoSBAT energy scores (Lambert et al., 2016) and arranged by hierarchical clustering using Pearson dissimilarity and average linkage. B. Motif diversity within each family, as measured by the number of clusters supported by the optimal silhouette value (Lovmar et al., 2005). C. Detailed view of representative motifs for Nuclear Hormone Receptors, displayed on a phylogram according to DBD sequence similarity using motifStack (Ou et al., 2018).

Figure 3.. Orthologs and paralogs of the human transcription factors.

A. Presence and absence of human TF orthologs across eukaryotic species. Amino acid percent identity is plotted for the most similar non-human TF gene in 32 eukaryotic species (from Ensembl Compara database (Herrero et al., 2016)). TFs are ordered first by conservation level (approximated gene age), based on similarity to expected conservation patterns for each of the clades plotted. B. Left, number of human TF-TF paralog pairs that diverged in each clade shown; Right, proportion of all human paralog pairs from each clade that are a TF-TF pair.

Figure 4.. Functional properties of the human transcription factors.

A. RNA-seq gene expression profiles for 1,554 human TFs across 37 human tissues (from the Human Tissue Atlas version 17 (Uhlen et al., 2015)), normalized by row and column. Tissues and TFs are arranged using hierarchical clustering by Pearson Correlation. Mean Expression Level indicates the mean pre-normalization mRNA expression level of each TF (in TPM) across all tissues in which the TF was expressed (TPM ≥ 1). B. TF gene set over-representation for human disease phenotypes (Kohler et al., 2014). Y-axis indicates the significance of the size of the intersection between the set of human TFs and the indicated gene set. Values indicate the number of TFs in the gene set. C. Diseases with GWAS signal (P<5×10⁻⁸) located proximal to TF-encoding genes. Loci containing multiple variants were restricted to the single most strongly associated variant, and subsequently expanded to incorporate variants in strong linkage disequilibrium (LD) (r²>0.8) with this variant using Plink (Purcell et al., 2007). The full set of genetic variants and sources for each disease are provided in Tables S3 and S4. Each resulting variant was assigned to its nearest gene, creating a gene set for each disease. For each gene set, the significance of its overlap with the list of human TFs was estimated using the hypergeometric distribution. P-values were corrected using Bonferroni’s method. Values indicate the number of TF-encoding loci associated with the given disease.