A Cross-Genomic Approach for Systematic Mapping of Phenotypic Traits to Genes

Abstract

We present a computational method for de novo identification of gene function using only cross-organismal distribution of phenotypic traits. Our approach assumes that proteins necessary for a set of phenotypic traits are preferentially conserved among organisms that share those traits. This method combines organism-to-phenotype associations,along with phylogenetic profiles,to identify proteins that have high propensities for the query phenotype; it does not require the use of any functional annotations for any proteins. We first present the statistical foundations of this approach and then apply it to a range of phenotypes to assess how its performance depends on the frequency and specificity of the phenotype. Our analysis shows that statistically significant associations are possible as long as the phenotype is neither extremely rare nor extremely common; results on the flagella,pili, thermophily,and respiratory tract tropism phenotypes suggest that reliable associations can be inferred when the phenotype does not arise from many alternate mechanisms.

The increasing number of fully sequenced genomes has made it possible to infer protein function using comparative genome techniques. Most current computational methods assign function to proteins by matching them to other proteins with known function (for review, see Bork et al. 1998); this matching has traditionally relied on sequence homology (Altschul et al. 1990), but nonhomology-based methods have also been introduced recently. The Clusters of Orthologous Groups (COGs) database (http://www.ncbi.nlm.nih.gov/COG/) is a homology-based method that establishes COGs as groups of homologs that are found in at least three major phylogenetic lineages, and enables transfer of functional information from one ortholog to the entire set of proteins within a COG (Tatusov et al. 1997). Phylogenetic profiles (Gaasterland and Ragan 1998; Pellegrini et al. 1999), gene clusters (Overbeek et al. 1999), and gene fusion analysis (Enright et al. 1999; Marcotte et al. 1999; Snel et al. 2000) are methods that can group together proteins that do not necessarily share sequence homology. Phylogenetic profiles describe the presence or absence of proteins in different genomes, and proteins with similar phylogenetic profiles are thought to share similar functions (Pellegrini et al. 1999). Gene cluster analysis (Overbeek et al. 1999; Tamames et al. 2001) infers functional relationships between genes from conservation of chromosomal proximity. Gene fusion analysis (Enright et al. 1999; Marcotte et al. 1999; Snel et al. 2000) identifies proteins that either belong to a protein complex or catalyze consecutive steps in a pathway by looking for corresponding genes that are separate in one organism, but are fused into one sequence in another. For a comparison of these nonhomology techniques see Huynen et al. (2000).

This study introduces an alternative method that infers protein function without requiring any prior functional annotation on any proteins. Instead, the method uses organism-level phenotype annotations and phylogenetic profiles to identify proteins with high propensities for a given phenotype. The method has broad applicability, as there are many well-characterized phenotypes, and phylogenetic profiles can be directly computed from sequenced genomes. A recent work (Levesque et al. 2003) described a related but different approach for predicting protein function on the basis of phenotypic traits, and applied them to identify flagellar proteins; that approach uses various settheoretic algorithms and phylogenetic information in the form of orthologous gene sets obtained from the COGs database. Another recent work (Martin et al. 2003) used clusters of phylogenetic profiles to identify proteins that differentiate Gram-positive from Gram-negative bacterial genomes.

We first present the statistical foundations of our approach. Then, we apply our method to the flagella phenotype to show that our method works better than earlier approaches that use phylogenetic profiles (Pellegrini et al. 1999; Levesque et al. 2003). In addition, we apply our approach on three new phenotypes that have not been tried previously (pili, thermophily, and respiratory tract tropism), and make novel predictions. Our analyses show that reliable associations can be inferred when the phenotype is unlikely to arise from many alternate mechanisms. As opposed to previous approaches, our method has the advantage that it can eliminate annotations that are not statistically significant; additionally, our theoretical analysis shows that phenotypes that are either extremely rare or extremely common do not permit annotations of gene function. These features are critical for general application of the approach to a wide range of phenotypes.

METHODS

Each protein in a reference organism with the phenotype of interest is analyzed by identifying whether it is preferentially conserved among organisms exhibiting the phenotype. For each protein, a BLAST search (Altschul et al. 1990) against the nonredundant (nr) database (http://www.ncbi.nih.gov/BLAST/blast_databases.html) reveals possible homologs, and a genome is considered to contain a homolog when one of its proteins has an alignment to the query protein sequence with e-value below 1.0 e-10, and when the length of the alignment is at least 2/3 the length of the query sequence (the latter requirement is useful for screening out good alignments from shorter motifs).

Once homologs in all genomes are identified, proteins are matched to the phenotype of interest as follows. The extent to which a protein i is associated with a given phenotype f is quantified by a propensity score Φ_f(i):

1

in which T_f is the number of genomes that exhibit phenotype f, N is the total number of genomes, t_i,f is the number of genomes that both exhibit phenotype f and contain homologs to gene i, and n_i is the total number of genomes that contain homologs to gene i.

The hypergeometric distribution is then used to screen out statistically insignificant protein-phenotype associations. For a given gene i, if its homologs are found in a total of n_i genomes, then

2

gives the probability that by random chance alone the gene is found in t genomes exhibiting phenotype f. The probability that a gene is found in at least t_i,f genomes with phenotype f by random chance alone is . Finally, using the conservative Bonferroni correction (Miller Jr. 1991) to account for multiple testing, the probability that some gene i is found in at least t_i,f genomes with phenotype f among a set of X genes is given by

3

in which X is the number of genes in the organism whose genes we are annotating. These P_f(i) values are used for eliminating protein-phenotype associations that are not statistically significant.

Theoretical Limitations

The number of organisms exhibiting a phenotype limits the maximum propensity score. Φ_f(i) is maximized when gene i is found only in the target genomes (i.e., when t_i,f = n_i). Therefore, the maximum propensity for phenotype f is:

4

The number of organisms exhibiting a given phenotype also limits the statistical significance of the results. In particular, P_f(i) is minimized (most significant) when t_i,f = n_i = T_f, and the minimum on the propensity scores for a phenotype f is:

5

Equations 4 and 5 describe a trade-off between statistical significance and propensity when choosing the query phenotype. For a given number of sequenced genomes N, a smaller T_f (i.e., a more rare phenotype) will allow for higher propensity scores but at lower statistical significance limits. Intuitively, a large indicates that phenotype f is too rare or too common, and a small indicates that the phenotype is too common. With 86 genomes and 4000 genes, is <4.0 e-07 when 7<T_f <79(see Fig. 1).

Figure 1.

Relationship between maximum propensity Φ and minimum estimated as a function of the number of organisms exhibiting phenotype f, given that there are N = 86 total genomes and that we are testing X = 4000 genes.

RESULTS

We apply our method to identify proteins associated with flagella, pili, thermophily, and respiratory tropism phenotypes using 86 sequenced genomes (13 archaeabacteria and 73 eubacteria) annotated for these phenotypes (see online Supplemental Material available at www.genome.org for the list of organisms and their phenotype annotations). The phenotype annotations were obtained by reading through all matching PubMed (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db-PubMed) abstracts supplemented by an exhaustive search of relevant online research Web pages; however, it is possible that a few phenotype annotations are missing in our data set. The frequency of each of these phenotypes does not preclude statistically significant associations (see Table 1), and thus, our approach is applicable.

Table 1.

Maximum Propensities and Minimum P_f Values for Flagella, Pili, Thermophily, and Respiratory Tract Tropism Phenotypes

Phenotype f	Tf	Max Propensity Φf*	X	Min Estimated p-Value Pf*
Flagella	40	2.15	4289	7.93e-22
Pili	14	6.14	5565	1.22e-12
Thermophily	15	5.73	2588	1.19e-13
Respiratory Tract Tropism	14	6.14	2240	4.93e-13

The values are computed with N = 86 genomes. X is the number of ORFs in the organism that we are annotating, and is used to apply the Bonferroni correction to the minimum P_f values.

Flagellar Proteins

The many annotated flagellar proteins in Escherichia coli allow us to assess the performance of our method. Additionally, previous work on this phenotype allows us to benchmark and compare our method with other approaches. Table 2 shows the 60 most statistically significant Escherichia coli genes with flagellar propensity scores >1.9(90% of the maximum flagellar propensity 2.15). This list includes 24 known flagellar genes, one putative motility gene (mbhA, b0230), and five nonflagellar genes known to be involved in chemotaxis.

Table 2.

The 60Most Statistically Significant Escherichia coli Genes With Flagellar Propensity Scores Greater Than 1.9 (90% of Max Propensity)

Locus	Gene	Propensity	p value	t/n	Identification
b1077	flgF	2.15	2.950E-11	30/30	+flagellar biosynthesis cell-proximal portion of basal-body rod
b1078	flgG	2.15	2.950E-11	30/30	+flagellar biosynthesis cell-distal portion of basal-body rod
b1939	fliG	2.15	2.950E-11	30/30	+flagellar biosynthesis, component of motor switch and energizing
b0229	fhiA	1.97	5.730E-10	33/36	+polar flagellar assembly protein
b1879	flhA	1.95	8.642E-08	30/33	+flagellar biosynthesis; possible export of flagellar proteins
b1880	flhB	1.97	5.730E-10	33/36	+putative part of export apparatus for flagellar proteins
b1948	fliP	1.97	5.730E-10	33/36	+flagellar biosynthesis protein flip
b1938	fliF	2.15	7.081E-10	28/28	+flagellar biosynthesis; basal-body membrane ring and collar protein
b1074	flgC	2.08	7.231E-10	30/31	+flagellar biosynthesis cell-proximal portion of basal-body rod
b1941	fliI	1.92	4.597E-09	33/37	+flagellum-specific ATPase
b1076	flgE	2.15	1.378E-08	26/26	+flagellar biosynthesis hook protein
b1950	fliR	2.15	1.378E-08	26/26	+flagellar biosynthesis
b1884	cheR	2.01	4.494E-08	29/31	response regulator for chemotaxis; protein glutamate methyltransferase
b1887	cheW	2.15	5.606E-08	25/25	positive regulator of CheA protein activity
b1945	fliM	2.15	5.605E-08	25/25	+flagellar biosynthesis component of motor switch
b1080	flgI	2.15	2.172E-07	24/24	+homolog of Salmonella P-ring of flagella basal body
b1883	cheB	2.00	8.063E-07	27/29	response regulator for chemotaxis (cheA sensor); protein methylesterase
b1888	cheA	2.07	1.113E-06	25/26	sensory transducer kinase between chemo- signal receptors and CheB and CheY
b1079	flgH	2.15	2.863E-06	22/22	+flagellar biosynthesis basal-body outer-membrane L ring protein
b1082	flgK	2.15	9.791E-06	21/21	+flagellar biosynthesis hook-filament junction protein 1
b1890	motA	2.15	3.187E-04	18/18	+proton conductor component of motor; no effect on switching
b0230	mbhA	2.15	9.560E-04	17/17	* putative motility protein
b1889	motB	2.15	9.560E-04	17/17	+enables flagellar motor rotation, linking torque machinery to cell wall
b1924	fliD	2.15	9.560E-04	17/17	+flagellar biosynthesis; filament capping protein; enables filament assembly
b0316	yahB	2.15	2.789E-03	16/16	putative transcriptional regulator LYSR-type
b1659	ydhB	2.15	2.789E-03	16/16	putative transcriptional regulator LYSR-type
b1339	ydaK	1.95	1.062E-02	19/21	putative transcriptional regulator LYSR-type
b0504	ybbS	2.03	1.179E-02	17/18	putative transcriptional regulator LYSR-type
b3105	yhaJ	2.02	3.211E-02	16/17	putative transcriptional regulator LYSR-type
b1925	fliS	2.15	2.789E-03	16/16	+flagellar biosynthesis; repressor of class 3a and 3b operons (RflA activity)
b1946	fliN	2.15	2.789E-03	16/16	+flagellar biosynthesis, component of motor switch and energizing
b0494	tesA	2.04	4.199E-03	18/19	acyl-CoA thioesterase I; also functions as protease I
b0387	yaiI	2.15	7.921E-03	15/15	orf, hypothetical protein
b2213	ada	2.15	7.921E-03	15/15	O6-methylguanine-DNA methyltransferase; transcription activator/repressor
b2681		2.15	7.921E-03	15/15	putative transport protein
b3686	glnG	2.15	7.921E-03	15/15	response regulator for gln
b3687	ibpA	2.15	7.921E-03	15/15	heat shock protein
b3081	ygjL	1.95	1.062E-02	19/21	putative NADPH dehydrogenase
b0898	ycaD	2.03	1.179E-02	17/18	putative transport
b4119	melA	2.03	1.179E-02	17/18	alpha-galactosidase
b1542	ydfI	1.94	2.896E-02	18/20	putative oxidoreductase
b2172	yeiQ	1.94	2.896E-02	18/20	putative oxidoreductase
b4323	uxuB	1.94	2.896E-02	18/20	D-mannonate oxidoreductase
b1521	uxaB	2.02	3.211E-02	16/17	altronate oxidoreductase
b3356	yhfA	1.94	2.896E-02	18/20	orf, hypothetical protein
b3924	fpr	1.94	2.896E-02	18/20	ferredoxin-NADP reductase
b1256	yciD	2.02	3.211E-02	16/17	putative outer membrane protein
b1813	yeaB	2.02	3.211E-02	16/17	orf, hypothetical protein
b2069	yegD	2.02	3.211E-02	16/17	putative heat shock protein
b3775	ppiC	2.02	3.211E-02	16/17	peptidyl-prolyl cis-trans isomerase C (rotamase C)
b0610	rnk	2.15	5.931E-02	13/13	regulator of nucleoside diphosphate kinase
b1075	flgD	2.15	5.931E-02	13/13	+flagellar biosynthesis, initiation of hook assembly
b1083	flgL	2.15	5.931E-02	13/13	+flagellar biosynthesis; hook-filament junction protein
b1688		2.15	5.931E-02	13/13	orf, hypothetical protein
b2922	yggE	2.15	5.931E-02	13/13	putative actin
b3010	yqhC	2.15	5.931E-02	13/13	putative ARAC-type regulatory protein
b3328	hofG	2.15	5.931E-02	13/13	putative general protein secretion protein
b4355	tsr	2.15	5.931E-02	13/13	methyl-accepting chemotaxis protein I, serine sensor receptor
b1073	flgB	2.02	8.488E-02	15/16	+flagellar biosynthesis, cell-proximal portion of basal-body rod

The genes marked with + in the identification column are know flagellar genes. t is the number of flagellar bacteria that contain homologs to the gene, and n is the total number of genomes that contain homologs to the gene. Genes in adjacent shaded rows are paralogs.

The list in Table 2 contains 12 additional known flagellar genes that were not identified using the original phylogenetic profile approach described in Pellegrini et al. (1999). This list also includes all of the genes already identified in that approach except for fliQ, which has a propensity score of 2.15, but is not included in Table 2 because its P_flagella value is not significant. Note that the original phylogenetic profile approach does not use phenotype information and instead transfers functional annotations between proteins with similar profiles.

We also compared our method to the Similarity Measure (Levesque et al. 2003) method. To make direct comparisons, we applied this method to the 86 genomes considered here. At a similarity threshold of 0.65, the least restrictive cutoff used in Levesque et al. (2003), their method identified 12 known flagellar genes; all of them are a subset of the 24 identified by our approach.

Both the Similarity Measure and our method have similar performance when applied to the COGs data with the 21 genomes considered in Levesque et al. (2003); each identifies 29 known flagellar genes, among 34 top scoring genes for the Similarity Measure algorithm and 31 top scoring genes for our method.

More rigorously, the Receiver Operating Characteristic (ROC) curves in Figure 2 compare the sensitivity versus specificity tradeoffs when all three approaches are applied to the 86 genomes considered here. These curves show that our approach consistently produces fewer false positives at each level of sensitivity. It is important to note that the false-positive rates in Figure 2 are upper bounds, because we cannot assume that all flagellar proteins have been annotated (i.e., some of the putative false positives may be flagellar proteins). Figure 2 also shows that propensity scores can be used to improve performance independently of the estimated P values. At high specificity, the ROC curves improve (move closer to the upper, left corner) as we increase the propensity cutoffs from 1 to 1.8. Larger propensity cutoffs increase the number of false negatives, and eventually at cutoffs ≥2.0, the flagella ROC curves begin to worsen.

Figure 2.

Receiver Operating Characteristic (ROC) curves comparing our approach with the approaches of Levesque et al. (2003) and Pellegrini et al. (1999) on the same flagella data set. Each ROC curve for our approach is obtained by keeping all genes with propensity scores greater than a fixed cutoff and varying the P-value cutoffs. The ROC curve for the Levesque et al. (2003) approach is obtained by varying the similarity threshold cutoff. The ROC curve for the Pellegrini et al. (1999) approach is obtained by comparing phylogenetic profiles against the FlgL gene (used in their study) and varying the Manhattan distance cutoff.

Proteins Associated With Pili

Pili are another structural feature of some bacteria for which some of the component proteins are known. Table 3 shows the 40 most statistically significant Pseudomonas aeruginosa proteins with propensity scores >4.5 for organisms that have pili (Sauer et al. 2000). Five of the seven known proteins in this list are known fimbrial biogenesis proteins (pilA, pilN, pilO, pilP, and pilQ); their corresponding Bonferroni corrected P_f values are <0.109, with three of these five having P_f values <0.05.

Table 3.

The 40Most Statistically Significant Pseudomonas aeruginosa Genes With Pili Propensity Scores Greater Than 4.5.

Locus	Gene	Propensity	p value	t/n	Identification
PA0454		5.27	2.87E-07	12/14	conserved hypothetical protein
P3651	cdsA	5.20	6.01E-06	11/13	phosphatidate cytidylyltransferase
PA4525	pilA	6.14	2.42E-05	9/9	type 4 fimbrial precursor PilA
PA0618		6.14	2.42E-05	9/9	probable bacteriophage protein
PA0619		6.14	2.42E-05	9/9	probable bacteriophage protein
PA3020		4.83	2.69E-05	11/14	probable soluble lytic transglycosylase
PA0936		6.14	3.15E-04	8/8	hypothetical protein
PA4512		4.91	1.23E-02	8/10	hypothetical protein
PA4115		5.03	1.18E-03	9/11	conserved hypothetical protein
PA3235		5.46	2.64E-03	8/9	conserved hypothetical protein
PA0209		6.14	3.56E-03	7/7	conserved hypothetical protein
PA0616		6.14	3.56E-03	7/7	hypothetical protein
PA0617		6.14	3.56E-03	7/7	probable bacteriophage protein
PA0622		6.14	3.56E-03	7/7	probable bacteriophage protein
PA0623		6.14	3.56E-03	7/7	probable bacteriophage protein
PA1376	aceK	4.91	1.23E-02	8/10	isocitrate dehydrogenase kinase/phosphatase
PA5043	pilN	4.91	1.23E-02	8/10	type 4 fimbrial biogenesis protein PilN
PA5040	pilQ	4.91	1.23E-02	8/10	type 4 fimbrial biogenesis protein PilQ
PA0289		4.91	1.23E-02	8/10	probable transcriptional regulator
PA1009		4.91	1.23E-02	8/10	hypothetical protein
PA1661		4.91	1.23E-02	8/10	hypothetical protein
PA4476		4.91	1.23E-02	8/10	hypothetical protein
PA4970		4.91	1.23E-02	8/10	conserved hypothetical protein
PA5225		4.91	1.23E-02	8/10	hypothetical protein
PA0461		5.38	2.62E-02	7/8	conserved hypothetical protein
PA0834		5.38	2.62E-02	7/8	conserved hypothetical protein
PA0612		5.38	2.62E-02	7/8	hypothetical protein
PA0628		5.38	2.62E-02	7/8	conserved hypothetical protein
PA4879		5.38	2.62E-02	7/8	conserved hypothetical protein
PA2017		6.14	3.56E-02	6/6	hypothetical protein
PA3209		6.14	3.56E-02	6/6	conserved hypothetical protein
PA5536		6.14	3.56E-02	6/6	conserved hypothetical protein
PA0080		4.78	1.09E-01	7/9	hypothetical protein
PA0502		4.78	1.09E-01	7/9	probable biotin biosynthesis protein bioH
PA1727		4.78	1.09E-01	7/9	conserved hypothetical protein
PA3726		4.78	1.09E-01	7/9	conserved hypothetical protein
PA4605		4.78	1.09E-01	7/9	conserved hypothetical protein
PA4777		4.78	1.09E-01	7/9	probable two-component sensor
PA5041	pilP	4.78	1.09E-01	7/9	type 4 fimbrial biogenesis protein PilP
PA5042	pilO	4.78	1.09E-01	7/9	type 4 fimbrial biogenesis protein PilO

t is the number of organisms with pili that contain homologs to the gene, and n is the total number of genomes that contain homologs to the gene. Genes in adjacent shaded rows are paralogs.

Proteins Associated With Thermophily

Thermoanaerobacter tengcongensis is an anaerobic thermophilic eubacterium whose genome was sequenced recently (Bao et al. 2002). How thermophiles have adapted to survive at high temperatures is not fully understood. Radiation sensitivity studies indicate that thermophiles repair DNA efficiently, but sequencing results suggest that many of their DNA repair genes are still unrecognized because they are too different from those of well-studied organisms (Grogan 1998). Here, we use our method to uncover the 40 most statistically significant T. tengcongensis genes with thermophily propensity scores >3.0 (Table 4). This list includes three DNA repair genes, one of which is reverse gyrase. Reverse gyrase is the only known topoisomerase that induces positive supercoiling in DNA, and hence, improves DNA stability at high temperatures (Forterre et al. 2000). This list also includes nine components of ferredoxin oxidoreductase. Anaerobic metabolism involving ferredoxin oxidoreductase appears to be unique to hyperthermophiles (Kelly and Adams 1994), and oxidoreductases related to hydrogen evolution have been shown recently to be crucial in the central metabolism of hyperthermophiles, replacing dehydrogenases in many key steps of metabolism (Borges et al. 1996). In addition, the recent isolation of a strain of microorganisms from hydrothermal vents that use Fe(III) as the electron acceptor and can grow at 121°, suggests that Fe(III) reduction may be an important process for growing in hydrothermal environments (Kashefi and Lovely 2003). Altogether, Table 4 identifies at least 21 genes that may be associated with thermophily: three DNA repair genes, nine ferredoxin oxidoreductase genes, and nine additional hypothetical genes that currently have unknown function.

Table 4.

The 40Most Statistically Significant T. tengcongensis Proteins With Thermophily Propensity Scores Greater Than 3.0

Locus	Gene	Propensity	p value	t/n	Identification
TTE2470	MesJ4	5.38	2.40E-12	15/16	predicted ATPase of the PP-loop superfamily implicated in cell cycle control
TTE0073	MesJ	5.06	1.65E-11	15/17	predicted ATPase of the PP-loop superfamily implicated in cell cycle control
TTE0285		5.73	9.29E-12	14/14	conserved hypothetical protein
TTE1955	PflA2	4.78	9.78E-11	15/18	Pyruvate-formate lysase-activating enzyme (protein modification & repair)
TTE0474	Gcd14	4.30	1.84E-09	15/20	predicted SAM-dependent methyltransferase involved in tRNA-Met maturation
TTE1745	rgy	5.73	7.71E-09	12/12	Reverse gyrase (DNA replication, recombination, and repair)
TTE1895	SmtA4	5.29	9.63E-08	12/13	SAM-dependent methyltransferases
TTE2198	PorA6	3.58	1.55E-07	15/24	ferredoxin oxidoreductase, alpha subunit (anaerobic metabolism)
TTE1209	PorA3	3.34	1.46E-05	14/24	ferredoxin oxidoreductase, alpha subunit (anaerobic metabolism)
TTE1340	PorA4	3.34	1.46E-05	14/24	ferredoxin oxidoreductase, alpha subunit (anaerobic metabolism)
TTE0961	PorA2	3.39	1.22E-04	13/22	2-oxoacid ferredoxin oxidoreductase, alpha subunit (fermentation)
TTE1354	SpeD	4.01	3.05E-07	14/20	S-adenosylmethionine decarboxylase (polyamine biosynthesis)
TTE1210	PorB2	3.44	3.88E-07	15/25	ferredoxin oxidoreductase, beta subunit (anaerobic metabolism)
TTE1341	PorB3	3.44	3.88E-07	15/25	ferredoxin oxidoreductase, beta subunit (anaerobic metabolism)
TTE1276	Nfo	4.91	6.50E-07	12/14	Endonuclease IV (DNA degradation)
TTE1779	PflX	4.85	1.02E-05	11/13	pyruvate formate lyase activating enzyme (protein modification and repair)
TTE0960	PorB	3.34	1.46E-05	14/24	2-oxoacid ferredoxin oxidoreductase, beta subunit (fermentation)
TTE1571		3.73	2.01E-05	13/20	conserved hypothetical protein
TTE2189		3.24	2.72E-04	13/23	conserved hypothetical protein
TTE2193	PorG3	4.50	4.50E-05	11/14	indolepyruvate ferredoxin oxidoreductase, beta subunit
TTE1537	HypE3	3.09	6.99E-05	14/26	Hydrogenase maturation factor (electron transport)
TTE2532		3.82	1.13E-04	12/18	predicted Zn-dependent hydrolases of beta-lactamase fold
TTE0444		5.73	3.13E-04	8/8	conserved hypothetical protein
TTE1518	LigT	5.73	3.13E-04	8/8	3-5 RNA Ligase (cell envelope: synthesis of murein sacculus & peptidoglycan)
TTE0818	GltB	4.69	1.34E-03	9/11	Glutamate synthase domain 3 (methanogenesis)
TTE1866		3.28	1.58E-03	12/21	conserved hypothetical protein
TTE0714		5.10	2.59E-03	8/9	putative integrase-resolvase (DNA replication, recombination, repair)
TTE2659		5.10	2.59E-03	8/9	putative RecB family exonuclease
TTE0820	GltB3	5.73	3.11E-03	7/7	amidophophoribosyltransferase (purine ribonucleotide synthesis)
TTE1891		5.73	3.11E-03	7/7	MinD P-loop ATPase containing an inserted ferredoxin domain (electron transport)
TTE1892		5.73	3.11E-03	7/7	MinD P-loop ATPase containing an inserted ferredoxin domain (electron transport)
TTE1893		5.73	3.11E-03	7/7	conserved hypothetical protein
TTE1898		5.73	3.11E-03	7/7	predicted methyltransferases
TTE2657		5.73	3.11E-03	7/7	conserved hypothetical protein
TTE0705		4.59	1.20E-02	8/10	putative integrase-resolvase (DNA replication, recombination, repair)
TTE0715		4.59	1.20E-02	8/10	predicted transposase
TTE1551	Dap2	3.97	1.50E-02	9/13	putative acyl-peptide hydrolase
TTE2658		3.97	1.50E-02	9/13	conserved hypothetical protein
TTE2633		5.02	2.26E-02	7/8	conserved hypothetical protein
TTE2194	PorA5	3.37	2.72E-02	10/17	indolepyruvate ferredoxin oxidoreductase, alpha subunit

t is the number of thermophiles that contain homologs to the protein, and n is the total number of genomes that contain homologs to the protein. Genes in adjacent shaded rows are paralogs.

Proteins Associated With Respiratory Tract Tropism

We identified 14 bacteria with respiratory tract tropism, and used this list to compute respiratory tract tropism propensity scores for Streptococcus pneumoniae genes. In this case, there are no genes with statistically significant propensities for the respiratory tract tropism phenotype – none of the top propensity scores have P_f values <2. Perhaps this is because the phenotype description is too general, as bacterial tropism is known to involve a wide variety of mechanisms that include immune evasion, metabolic adaptation, and physical attachment and invasion. The lack of statistically significant associations indicates that respiratory tropism is difficult to study as a single phenotype, at least using our method.

DISCUSSION

We have described an approach that combines organism-to-phenotype associations along with phylogenetic profiles to identify proteins with high propensities for a given phenotype; such an approach can be used to annotate proteins with phenotype information. We validated this approach by demonstrating its ability to identify known flagellar and pili proteins, and then applied it to the identification of proteins associated with thermophily.

Phenotype annotations are usually more general than traditional protein functional annotations; typically, several proteins spanning multiple functional complexes and pathways contribute to a given phenotype, and the same phenotype can be accomplished in more than one way. Correspondingly, we have found that it is insufficient to simply search for proteins that are conserved in a majority of the organisms exhibiting the query phenotype. For example, none of the identified flagellar proteins are conserved in all 40 flagellar genomes, and most of them are conserved in 20 or fewer flagellar genomes. By using propensity scores, our approach is able to match proteins to phenotype without requiring that the proteins be conserved in a majority of the organisms with that phenotype.

Proteins with the same propensity scores can have very different phylogenetic profiles, and therefore, it is unlikely that a single representative protein can be used to match and identify the set of proteins responsible for a phenotype. Figure 3 shows the average Hamming distance between phylogenetic profiles of E. Coli proteins at each flagellar propensity level. The average Hamming distance between the phylogenetic profiles of the proteins with highest flagellar propensity scores is 4.0, whereas proteins with lower propensity scores can have Hamming distances >30. In addition, Figure 4 depicts the hierarchical clustering of the top proteins associated with flagella5 and thermophily (as given in Tables 2 and 4), and shows that the phylogenetic profiles of the top proteins can vary considerably. Hence, even if it is possible to identify a representative protein for a given phenotype (e.g., as in Pellegrini et al. 1999), it is not possible to find all relevant proteins by simply searching for other proteins with similar phylogenetic profiles. Our approach is robust against these large distances between phylogenetic profiles, because it uses propensity scores as opposed to raw phylogenetic profiles.

Figure 3.

Average phylogenetic distances between E. Coli proteins at each flagellar propensity level.

Figure 4.

Hierarchical clustering (average-linkage) of the top proteins associated with flagella and thermophily (see Tables 2 and 4), on the basis of their phylogenetic profiles. Genomes are on the x-axis, and genes are on the y-axis. Gray coloring indicates the presence of a gene in a genome.

An artifact of previous phylogenetic comparison approaches is that distances between phylogenetic profiles are sensitive to the size of the set of background genomes. For example, arbitrarily expanding the set of background genomes usually increases the distances between phylogenetic profiles. In our approach, this scaling relationship is automatically captured by propensity scores, and expanding the set of background genomes will, in general, increase the statistical significance (i.e., lower P_f values) of the top proteins. Follow-up work along these lines should address evolutionary distances between species; it is not obvious how to handle statistical significance in an analytical way, and nonparametric approaches may be more promising in this regard.

These initial results are encouraging, and provide a statistical framework for the general application of the approach to a large class of well-characterized phenotypes. This process might begin by looping through organism phenotype annotations and computing their and scores in order to filter out phenotypes that are too common or too rare, and then match the remaining phenotypes to individual proteins by checking each protein's propensity for that phenotype. With the rapidly increasing pace of whole-genome sequencing, and the commensurate accumulation of novel genes, approaches such as ours can efficiently generate high-yield hypotheses for experimental validation of gene function. In this regard, whole-organism characterization of phenotypic traits may become a central activity in the post-genomic approach to understanding biological networks.