The FTO (fat mass and obesity associated) gene codes for a novel member of the non-heme dioxygenase superfamily

Abstract

Background: Genetic variants in the FTO (fat mass and obesity associated) gene have been associated with an increased risk of obesity. However, the function of its protein product has not been experimentally studied and previously reported sequence similarity analyses suggested the absence of homologs in existing protein databases. Here, we present the first detailed computational analysis of the sequence and predicted structure of the protein encoded by FTO.

Results: We performed a sequence similarity search using the human FTO protein as query and then generated a profile using the multiple sequence alignment of the homologous sequences. Profile-to-sequence and profile-to-profile based comparisons identified remote homologs of the non-heme dioxygenase family.

Conclusion: Our analysis suggests that human FTO is a member of the non-heme dioxygenase (Fe(II)- and 2-oxoglutarate-dependent dioxygenases) superfamily. Amino acid conservation patterns support this hypothesis and indicate that both 2-oxoglutarate and iron should be important for FTO function. This computational prediction of the function of FTO should suggest further steps for its experimental characterization and help to formulate hypothesis about the mechanisms by which it relates to obesity in humans.

Figure 1

Computational analysis of the FTO family. Top. Multiple sequence alignment of the FTO family with known members of the non-heme dioxygenase superfamily. Red triangles above the alignment mark the conserved residues involved in iron and 2-oxoglutarate (2OG) binding with numbers indicating their position in AlkB. The numbers within the red box represent sequence insertions that we did not include in the alignment. X-Ray determined secondary structure of AlkB (PDB code 2fdi) [5] and hABH3 (PDB code 2iuw) [4] are shown below their sequences. PHD secondary structure prediction [26] for the FTO family is included below the human FTO sequence. The alignment was produced using a combination of T-COFFEE [17] and profile-to-profile alignment [24], using the structure-based superposition hABH3/AlkB alignment [28] as reference. Finally, the alignment was slightly refined manually. It was represented with the program Belvu [18] with a coloring scheme indicating average BLOSUM62 score (correlated to amino acid conservation) in each alignment column: dark red (greater than 3), violet (between 3 and 1) and light yellow (between 1 and 0.3). The sequences are named with their SwissProt or SpTrembl identifiers. Species abbreviations: Azovi, Azotobacter vinelandii; Caucr, Caulobacter crescentus; Comte, Comamonas testosteroni; Ecoli, Escherichia coli; Human, Homo sapiens; Jansp, Jannaschia sp.; Metfl, Methylobacillus flagellatus; Mouse, Mus musculus; Ocesp, Oceanospirillum sp.; Oryla, Oryzia latipes; Ostlu, Ostreococcus lucimarinus; Ostta, Ostreococcus tauri; Shewo, Shewanella woodyi; Synsp, Synechococcus sp.; Thaps, Thalassiosira pseudonana; Xenla, Xenopus laevis. Additional details about some lanes and FTO close homologous sequences can be found in Table 1. Complementary information, sequences, and alignments are accessible at [29]. Bottom. Structure of AlkB (PDB code 2fdi) indicating with sticks the invariant side chains in non-heme dioxygenases which are also conserved in the FTO family.

Figure 2

Analysis of K/R rich regions in the FTO family. (A) Plot of the percentage of K/R residues in a window of 17 amino acids of human FTO. The sequence fragment indicated from position 2 to 18 includes the maximum (7/17 at position 10). (B) Representation of the multiple (full) sequence alignment of the FTO family. White indicates regions with more than 30% of K+R residues in a window of 17 amino acids along the aligned sequences and red represents gaps in the alignment. The N-terminal region around the predicted bipartite NLS signal of human FTO stands as the only K/R-rich region conserved in fish as well as mammalian sequences. Both plots were generated using the BiasViz java tool [30].