Systematic discovery of nonobvious human disease models through orthologous phenotypes Kriston L. McGarya,1, Tae Joo Parka,b,1,JohnO.Woodsa, Hye Ji Chaa, John B. Wallingforda,b, and Edward M. Marcottea,c,2 aCenter for Systems and Synthetic Biology, Institute for Cellular and Molecular Biology, bThe Howard Hughes Medical Institute and Department of Molecular Cell and Developmental Biology, and cDepartment of Chemistry and Biochemistry, University of Texas, Austin, TX 78712 Edited* by William H. Press, University of Texas, Austin, TX, and approved February 26, 2010 (received for review September 6, 2009) Biologists have long used model organisms to study human diseases, tractable models for human disease await discovery, currently hid- particularly when the model bears a close resemblance to the disease. den by differences in the emergent appearance of phenotype in We present a method that quantitatively and systematically identi- diverse model organisms. Although a framework exists for discus- fies nonobvious equivalences between mutant phenotypes in differ- sing complex gene-phenotype relationships across evolution, we ent species, based on overlapping sets of orthologous genes from lack simple—and importantly, quantifiable—methods for discov- human, mouse, yeast, worm, and plant (212,542 gene-phenotype ering new gene-phenotype relationships from existing data. associations). These orthologous phenotypes, or phenologs, predict As a foundation for a quantifiable approach to identifying equiv- unique genes associated with diseases. Our method suggests a yeast alent phenotypes, we introduce the notion of orthologous pheno- model for angiogenesis defects, a worm model for breast cancer, types (phenologs), defined as phenotypes related by the orthology of mouse models of autism, and a plant model for the neural crest de- the associated genes in two organisms. Phenologs are the phenotype- fects associated with Waardenburg syndrome, among others. Using level equivalent of gene orthologs. Two phenotypes are thus said to these models, we show that SOX13 regulates angiogenesis, and that be orthologous if they share a significantly larger set of common SEC23IP is a likely Waardenburg gene. Phenologs reveal functionally orthologous genes than would be expected at random (i.e., are coherent, evolutionarily conserved gene networks—many predating enriched for the same orthologous genes) (Fig. 1B), even if the the plant-animal divergence—capable of identifying candidate dis- phenotypes may appear dissimilar. ease genes. Phenologs, therefore, are evolutionarily conserved outputs aris- ing from disruption of any of a set of conserved genes (Fig. 1B, angiogenesis | bioinformatics | evolution | gene-phenotype associations | green and blue). These outputs manifest as different traits or de- homology fects in different organisms because of the organism-specificroles played by each set of genes. One example, noted above, is the human iochemical and molecular functions of a given protein are retinoblastoma eye cancer and the Caenorhabditis elegans ectopic generally conserved between organisms; this observation is vulvae. These phenotypes are orthologous, as failure of equivalent B — fundamental to biological research. For example, in x-ray crys- genes (the Rb pathway) performing conserved molecular func- — tallography studies, one can often choose the organism from which tions but in different contexts leads to different phenotypes in the the protein is most easily crystallized to facilitate the study of the different organisms (1, 2). protein’s biochemical function. On the other hand, even with a By quantifying the equivalence of mutational phenotypes be- conserved gene, disruption of function may give rise to radically tween different organisms, we demonstrate that orthologous different phenotypic outcomes in different species. For example, phenotypes may be found objectively, and that these phenologs suggest nonobvious models for human disease. We demonstrate mutating the human RB1 gene leads to retinoblastoma, a cancer of fi the retina, yet disrupting the nematode ortholog contributes to the power of this approach by de ning a unique yeast model that effectively predicts vertebrate angiogenesis genes and a plant ectopic vulvae (1, 2). Thus, although a gene’s “molecular” func- model that predicts genes involved in vertebrate craniofacial tions are conserved, the “organism-level” functions need not be. defects that are associated with human congenital malformations. When a conserved gene is mutated, the resulting organism-level phenotype is an emergent property of the system. This bedrock Results and Discussion principle underlying the use of model organisms not only allows us Phenologs are identified by assembling known gene-phenotype to study important aspects of human biology using mice or frogs, associations for two organisms—considering only genes that are but also permits exploration of inherently multicellular processes, orthologous between the two organisms—and searching for such as cancer, using unicellular organisms like yeast. interorganism phenotype pairs with significantly overlapping sets Within this paradigm, once a molecular function has been dis- of genes. Significance is derived from three observations: (i) the covered in one organism, it should be predictable in other organ- total number of orthologs in organism 1 that give rise to phenotype isms: GSK3 homologs in yeast are kinases, and such GSK3 homologs 1; (ii) the total number of orthologs in organism 2 that give rise to in every other organism will generally be kinases. In contrast, the phenotype 2; and (iii) the number of orthologs shared between emergent organism-level phenotypes are far less predictable be- these two sets. Formally, significance of a phenolog is calculated tween organisms, in part because relationships between genes and phenotypes are many-to-many. Manipulation of GSK3 perturbs nutrient and stress signaling in yeast, anteroposterior patterning and Author contributions: K.L.M., T.J.P., J.O.W., J.B.W., and E.M.M. designed research; segmentation in insects, dorsoventral patterning in frogs, and cra- K.L.M., T.J.P., J.O.W., H.J.C., and E.M.M. performed research; K.L.M., T.J.P., J.O.W., niofacial morphogenesis in mice (3–5). Recognizing functionally H.J.C., J.B.W., and E.M.M. analyzed data; and K.L.M., T.J.P., J.O.W., H.J.C., J.B.W., and equivalent organism-level phenotypes between model organisms E.M.M. wrote the paper. can therefore be nonobvious, especially across large evolutionary The authors declare no conflict of interest. distances. *This Direct Submission article had a prearranged editor. However, the ability to recognize equivalent phenotypes be- Freely available online through the PNAS open access option. tweendifferent model organisms isimportant forthe studyof human 1K.L.M. and T.J.P. contributed equally to this work. diseases. Given the success of studies in model systems (genes and 2To whom correspondence should be addressed. E-mail:[email protected]. phenotypes have been associated in model organisms at a far higher This article contains supporting information online at www.pnas.org/cgi/content/full/ rate than for humans) (Fig. 1A), it seems likely that useful and 0910200107/DCSupplemental. 6544–6549 | PNAS | April 6, 2010 | vol. 107 | no. 14 www.pnas.org/cgi/doi/10.1073/pnas.0910200107 Downloaded by guest on September 27, 2021 A 160,000 C Systematic Discovery of Phenologs. To systematically discover mouse 140,000 phenolog relationships, we collected from the literature a set of Human/Worm Ortholog Linkedcancer to breast in humansLinkedmales to more in worms 1,923 human disease-gene associations (8), 74,250 mouse gene- 120,000 ATM / atm-1 Y BRIP1 / dog-1 Y phenotype associations (9), 27,065 C. elegans gene-phenotype KRAS / let-60 Y 4649 orthologs total 100,000 PHB / phb-1 Y PIK3CA /age-1 Y Human Worm associations (10), and 86,383 yeast gene-phenotype associations RAD51 /rad-51 Y breast/ovarian high incidence 80,000 yeast RAD54L /rad-54 Y – ∼ > SLC22A18 / C53B4.3 Y cancer male progeny (11 14). The dataset spans 300 human diseases and 6,000 TSG101 / tsg-1010 Y 60,000 BARD1 /brd-1 Y Y model organism phenotypes. With these data and the sets of worm BRCA1 / brc-1 Y Y CHEK2 / chk-2 Y Y 9313 Number of unique FAM82B / F33H2.6 Y orthologous gene relationships between each pair of organisms 40,000 GCC2 / hcp-1,hcp-2 Y HMG20A,B / W02D9.3 Y HORMAD2,1 / him-3,htp-1,2 Y (15), we quantitatively examined the overlap of each interorgan- gene-phenotype associations 20,000 KIF15 / klp-10,18 Y -6 MRE11A / mre-11 Y p < 7.2x10 fi human PIGA / D2085.6 Y ism phenotype pair, measuring their signi cance (Fig. 2A). To 0 RAD1 / mrt-2 Y RAD21 / coh-1 Y correct for testing multiple hypotheses, we repeated all analyses SEH1L / npp-18 Y SVIL / viln-1 Y 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 TSPO,BZRPL1 / C41G7.3 Y 1,000 times with randomly permuted gene-phenotype associa- Year WDHD1 / F17C11.10 Y tions, then calculated a false-discovery rate (FDR) based upon the Organism 1 Organism 2 observed null distribution of scores (Fig. 2B and Fig. S1). We B observed 154 significant phenologs (5% FDR) between human Orthologous genes Candidate for diseases and yeast mutational phenotypes, 3,755 between human Gene A Gene A’ phenotype 2 and mouse, 147 between mouse and worm, 119 between mouse Gene B Gene B’ and yeast, 206 between yeast and worm, and 9 between human Gene C Gene C’ Gene D Gene D’ and worm (the low number stems from limited mutational data in Candidate for Gene E Gene E’ both species) (Fig. 2C). phenotype 1 Many specific, intuitively obvious, phenologs were revealed by Orthologous this analysis, especially for the comparison of mouse and human Mutations lead Mutations lead phenotypes to phenotype 1 to phenotype 2 phenotypes. Our analysis recapitulates many known mouse models (phenologs) of disease, providing an important positive control for our ap- Fig. 1. Number of unique gene-phenotype associations, identification of proach; Table 1 lists other specific examples of both known and phenologs, and the example of a worm model of breast cancer.