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Inferring Nonneutral Evolution from Human-Chimp-Mouse Orthologous Gene Trios

Author(s): Andrew G. Clark, Stephen Glanowski, Rasmus Nielsen, Paul D. Thomas, Anish Kejariwal, Melissa A. Todd, David M. Tanenbaum, Daniel Civello, Fu Lu, Brian Murphy, Steve Ferriera, Gary Wang, Xianqgun Zheng, Thomas J. White, John J. Sninsky, Mark D. Adams and Michele Cargill

Source: Science , Dec. 12, 2003, New Series, Vol. 302, No. 5652 (Dec. 12, 2003), pp. 1960- 1963

Published by: American Association for the Advancement of Science

Stable URL: https://www.jstor.org/stable/3835731

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REPORTS

and LED 8 (541 observed, 456 expected, P < 0.0006}-providing evidence in plants for a link between genome organization and gene regulation.

Together these data provide an organ ex­ pression map, revealing putative localized hor­ mone-response domains and a complex pattern of regulatory genes that could mediate primary developmental cues. These data should help identify candidate genes involved in pattern formation and cell specificity in the root, which is a model for organogenesis. The expression map will also facilitate both computational and experimental methods aimed at decoding regu­ latory mechanisms in the root. Thus, these re­ sults can now be used to explore how the hundreds of different expression patterns they reveal are established and interpreted at the cellular level to generate a complex organ.

References and Notes 1. N. M. Kerk, T. Ceserani, S. L. Tausta, I. M. Sussex, T. M.

Nelson, Plant Physiol. 132, 27 (2003). 2. T. Asano et al., Plant}. 32, 401 (2002).

3. D. Milioni, P. E. Sado, N. J. Stacey, K. Roberts, M. C. Mccann, Plant Cell 14, 2813 (2002).

4. P. J. Roy, J. M. Stuart. J. Lund, S. K. Kim, Nature 418, 975 {2002).

5. H. Jasper et al., Dev. Cell 3, 511 {2002). 6. P. N. Benfey, J. W. Schiefelbein, Trends Genet. 10, 84

(1994). 7. Materials and methods are available as supporting

material on Science Online. 8. J. Sheen, Plant Physiol. 127, 1466 {2001 ). 9. J. Quackenbush, Nature Rev. Genet. 2, 418 {2001).

10. The program Ouster was used in the analysis and down­ loaded from http-/ /rana.lbl.gov/EisenSoflware.htm.

11. M. B. Eisen, P. T. Spellman, P. 0. Brown, D. Botstein, Proc. Natl. Acad. Sci. U.S.A. 95, 14863 {1998).

12. K. Birnbaum et al., unpublished data. 13. T. Berleth, J. Mattsson, Cu". Opin. Plant Biol. 3, 406 (2000). 14. U. Wittstock. B. A. Halkier, Trends Plant Sci. 7, 263 (2002). 15. L. L. Murdock, R. E. Shade, J. Agric. Food Chem. 50,

6605 {2002). 16. B. A. Cohen, R. D. Mitra, J. D. Hughes, G. M. Church,

Nature Genet. 26, 183 {2000). 17. H. Caron et al., Science 291, 1289 (2001). 18. A. P. Mahonen et al., Genes Dev. 14, 2938 {2000). 19. M. Bonke, S. Thitamadee, A. P. Mahonen, M. T.

Hauser, Y. Helariutta, Nature, in press. 20. j. W. Wysocka-Diller, Y. Helariutta, H. Fukaki, J. E.

Malamy, P. N. Benfey, Development 127, 595 {2000). 21. The plant line was generated by the Haseloff labora-

Inferring Nonneutral Evolution from Human-Chimp-Mouse

Orthologous Gene Trios Andrew G. Clark, 1 Stephen Glanowski, 3 Rasmus Nielsen, 2

Paul D. Thomas,4 Anish Kejariwal, 4 Melissa A. Todd,2 David M. Tanenbaum, 5 Daniel Civello, 6 Fu Lu,5 Brian Murphy, 3

Steve Ferriera,3 Gary Wang, 3 Xianqgun Zheng, 5

Thomas J. White, 6 John J. Sninsky,6 Mark D. Adams,5 * Michele Cargill 6 t

Even though human and chimpanzee gene sequences are nearly 99% identica~ se­ quence comparisons can nevertheless be highly infom,ative in identifying biologically important changes that have occurred since our ancestral lineages diverged. We an­ alyzed alignments of 7645 chimpanzee gene sequences to their human and mouse orthologs. These three-species sequence alignments allowed us to identify genes undergoing natural selection along the human and chimp lineage by fitting models that include parameters specifying rates of synonymous and nonsynonymous nucleotide substitution. This evolutionary approach revealed an infom,ative set of genes with significantly different patterns of substitution on the human lineage compared with the chimpanzee and mouse lineages. Partitions of genes into in­ ferred biological classes identified accelerated evolution in several functional class­ es, including olfaction and nuclear transport. In addition to suggesting adaptive physiological differences between chimps and humans, human-accelerated genes are significantly more likely to underlie major known Mendelian disorders.

Although the human genome project will al­ low us to compare our genome to that of other primates and discover features that are uniquely human, there is no guarantee that such features are responsible for any of our unique biological attributes. To identify genes and biological processes that have been most altered by our recent evolutionary di­ vergence from other primates, we need to fit the data to models of sequence divergence that allow us to distinguish between diver-

gence caused by random drift and divergence driven by natural selection. Early observa­ tions of unexpectedly low levels of protein divergence between humans and chimpan­ zees led to the hypothesis that most of the evolutionary changes must have occurred at the level of gene regulation (J). Recently, much more extensive efforts at DNA se­ quencing in nonhuman primates has con­ firmed the very close evolutionary relation­ ship between humans and chimps (2), with an

tory (www.plantsci.cam.ac.uk/Haseloff/Home.html). The lines were obtained through the Arabidopsis In­ formation Resource (www.arabidopsis.org/).

22. Y. Lin, j. Schiefelbein, Development 128, 3697 {2001 ). 23. M. M. Lee, j. Schiefelbein, Ce// 99, 473 (1999). 24. E. Truernit, N. Sauer, Planta 196, 564 (1995). 25. We thank J. Malamy for valuable ideas on the proto­

plasting technique; H. Petri, K. Gordon, and J. Hirst for assistance in cell sorting; H. Dressman and the Duke Microarray Core Facility for assistance with microar­ rays; A. Pekka Mahonen and Y. Helariutta for use of the pWOL::GFP line and M. Cilia and D. Jackson for the pSUCZ::GFP line, both before publication; M. Levesque for valuable discussions; and G. Sena and T. Nawy for photos. This work was supported by NSF grants MCB-020975 (P.N.B. and D.E.S.), DBl-9813360 (D.W.G.), DBl-0211857 (D.W.G.), and a Small Grant for Exploratory Research (P.N.B. and D.E.S.). The NIH supported K.B. with a postdoctoral fellowship grant (5 F32 GM20716-03).

Supporting Online Material www.sciencemag.org/cgi/content/full/302/5652/1956/ DC1 Materials and Methods Figs. S 1 to S3 Tables S1 to S4

4 August 2003; accepted 15 October 2003

average nucleotide divergence of just 1.2% (3-5). The role of protein divergence in caus­ ing morphological, physiological, and behav­ ioral differences between these two species, however, remains unknown.

Here we apply evolutionary tests to iden­ tify genes and pathways from a new collec­ tion of more than 200,000 chimpanzee exonic sequences that show patterns of divergence consistent with natural selection along the human and chimpanzee lineages.

To construct the human-chimp-mouse alignments, we sequenced PCR amplifica­ tions using primers designed to essentially all human exons from one male chimpanzee, resulting in more than 20,000 human-chimp gene alignments spanning 18.5 Mb (6-8). To identify changes that are specific to the di­ vergence in the human lineage, we compared the human-chimp aligned genes to their mouse ortholog. Inference of orthology in­ volved a combination of reciprocal best matches and syntenic evidence between hu­ man and mouse gene annotations (9, 10). This genome-wide set of orthologs under­ went a series of filtering steps to remove ambiguities, orthologs with little sequence data, and genes with suspect annotation ( 6). The filtered ortholog set was compared to

1Molecular Biology and Genetics, 2 Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA 3Applied Biosystems, 45 West Gude Drive, Rockville, MD 20850, USA. 4 Protein Informatics, Cetera Genomics, 850 Lincoln Centre Drive, Foster City, CA 94404, USA scelera Genomics, 45 West Gude Drive, Rockville, MD 20850, USA 6Celera Diagnostics, 1401 Harbor Bay Parkway, Alameda, CA 94502, USA.

*Present address: Department of Genetics, Case Western Reserve University, 10900 Euclid Avenue, Cleveland, OH 44106, USA. tTo whom correspondence should be addressed. E­ mail: [email protected]

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other public sets and found to be highly consistent (11) (table SI). We used the most conservative set of 7645 genes for which we had the highest confidence in orthology and sequence annotation (12) (Database SI).

To identify genes that have undergone adaptive protein evolution, we applied two formal statistical tests that fit models of mo­ lecular evolution at the codon level. Both tests fit models of the nucleotide-substitution process by maximum likelihood (ML) (13), and both include parameters specifying rates of synonymous and nonsynonymous substi­ tution (14- 16). In the first (Model I), we performed a classic test of the null hypothesis of dN/d8 = I in the human lineage (17, 18). The second model is a modification of the method described by Yang and Nielsen (J 6), which allows variation in the dN/d8 ratio among lineages and among sites at the same time. In this method (Model 2), a likelihood ratio test of the hypothesis of no positive selection is performed by comparing the like­ lihood values for two hypotheses. Under the null hypothesis, it is assumed that all sites are either neutral ( d~ d8 = 1) or evolve under negative selection (dN/d8 < I). Under the alternative hypothesis, some of the sites are allowed to evolve with dN > d8 in the human lineage only (Fig. I). We refer to this as Model 2, and to the P-value of neutrality as P 2 ( 6). The test based on Model 2 is not as conservative as the test based on Model I and may tend to detect genes with accelerated amino acid substitution rates in humans even if the average dN/d8 rate is not larger than I.

There were 1547 human genes and 1534 chimp genes, which met the criteria for positive selection (with dN/d8 >I). The neutral null hy­ pothesis of Model I was rejected for 72 genes (0.94% of the tests) at P < 0.001, 414 genes (5.4%) at P < 0.Dl, and 1216 genes (15.9%) at P < 0.05 (12). There were six human genes for which the neutral null hypothesis of Model I was rejected at P < 0.05 and ~ds was greater than I (12). The neutral null hypothesis of Model 2 was rejected for 28 genes (0.38%) at P2 < 0.001, 178 genes (2.3%) at P2 < 0.01, and 667 genes (8.7%) at P 2 < 0.05. The relatively low overlap of these sets reflects the different nature of the tests. Of the 154 7 human genes that ex­ hibited ~ds > I, only 125 also fell into the class of 178 human genes with a P2 < 0.Dl. Similarly, Model 2 can detect cases where a protein has a domain undergoing positive selec­ tion, but the overall di)d8 may not be elevated, and thus would be missed by Model I. For this reason, the remainder of the analysis considers only the Model 2 test results.

Before attempting any biological inference from the results of the statistical tests, it is im­ portant to consider whether attributes like GC content, repeat density, local recombination rate, and segmental duplications might affect the rates and patterns of substitution (19, 20). In principle,

the ML estimation procedure corrects for varia­ tion in base composition; however, if the true substitution rate differs across the genome in a manner that is correlated with GC content, then we should be able to detect this by simple cor­ relation ( 6, 12) (Database S2). The synonymous substitution rate was significantly correlated with the following attributes: GC content (0.164, P < 0.000 I), local recombination rate in cM/Mb (21) (0.100, P < 0.001), and LINE Qong inter­ spersed nuclear element) density (-0.091, P < 0.0001). None of these factors was significantly correlated with either nonsynonymous substitu­ tion rate or P 2-value; however, genes associated with some biological processes, such as olfac­ tion, do show nonrandom associations with genomic location [P < 10-4 , Kolmogorov­ Smimov (K-S) test] and GC content (P < 10-9, K-S test). We also verified that segmental dupli­ cations were not responsible for distortions in the patterns of substitution seen in our tests, mostly because genes with close duplicates were under­ represented in our set because of the requirement for strict human-mouse orthology. Interestingly, the genes with P 2-values <0.05 are overrepre­ sented in the Online Mendelian Inheritance in Man (OMIM) catalog of genes associated with genetic disease (P = 0.009), demonstrating the relevance of interspecific comparisons (ftp.ncbi. nih.gov/repository/OMIM/morbidmap ).

Many of the 7645 genes have been classified into inferred functional categories based on the

Fig. 1. Graphical rep­ resentation of the test of positive selection (Model 2). The null hy- M pothesis (H0 ) assumes all three branches have two classes of amino acid residues:

Ho

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Panther classification system (6, 22). We asked, for the subset of genes in each functional cate­ gory, whether the distribution of P2 values for those genes differed significantly from the P 2 distribution for the full set of 7645 genes (6) (tables S2 and S3). In this way, we can gain insight into higher-order biological processes and molecular functions that may be under se­ lective pressure in a given lineage (Tables I and 2). The statistical tests of significance are valid as formal inferences, and these lead immediately to tentative biological hypotheses, only some of which we describe here.

In the human lineage, genes involved in olfaction show a significant tendency to be under positive selection (P MW < 0.005) (Ta­ ble I and Fig. 2). Nearly all the genes clas­ sified to olfaction are olfactory receptors (ORs). It seems likely that the different life­ styles of chimps and humans might have led to divergent selection pressure on these re­ ceptors. There has been a rapid acceleration ofpseudogene formation in human ORs (23), and the acceleration of apparent amino acid substitution in pseudogenes could potentially lead to a spurious inference of selection. However, we verified that most of the OR genes in our set are bona fide genes (http:// bioinformatics.weizmann.ac.il/HORDE/), in­ dicating that these genes are either undergo­ ing positive selection or are in the process of pseudogenization (24).

p.: dN> ds

,,,"'" H

M Po: dN/d s<1 ~ P1:dN=ds C

those that are neutrally evolving (p,: dN = d5) and those that are under constraint (p0 : dN/d5< 1). The alternative hypothesis (Ha) allows the human lineage to have a subset of sites (p.) with accelerated amino acid substitution (dN > d5).

1.00 • Overall

0.90 □ Olfaction o Developmental Processes

0.80 ■ Amino Acid Catabolism __ t:,.OMIM_

0.70 □ :::

□ □ □

0 ·p

JJ ~ 0.60 la • ""' !: 0.50 ■

·,g 10.40

■

■ u

0.30 ■

0.20

0.10

0.00

0.00 0.05 0.10 0.15

Model 2 P-value

□ □

0.20

□

■

0.25

Fig. 2. P 2-value distributions of selected groups of genes. The plot shows the cumula­ tive fraction of selected bio­ logical processes showing the excess of cases of signif­ icant positive selection in genes for olfaction, amino acid cataboUsm, and Mende­ lian disease genes (OMIM) relative to the overall distri­ bution of genes. The distri­ bution of developmental genes that do not show a significant excess is shown for comparison.

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Several other classes of genes ( amino acid catabolism, developmental processes, reproduc­ tion, neurogenesis, and hearing) show many genes with low P 2 values, although these classes do not show significant P MW values or contain fewer than 20 genes (table SI and Fig. 2). It is possible that individual genes within these cate­ gories account disproportionately for specific

phenotypic effects. For example, 7 (GSTZI, HGD, PAH, ALDH6Al, BCKDHA, PCCB, and HAL) of the 16 genes in the amino acid catab­ olism category have P2 values less than 0.05. A speculative suggestion is that this signal of pos­ itive selection may arise from different dietary habits or pressures in the two lineages. For ex­ ample, branched-chain amino acid catabolism,

Table 1. Biological processes showing the strongest evidence for positive selection. The top panel includes the categories showing the greatest acceleration in human lineage, and the bottom panel includes categories with the greatest acceleration in the chimp lineage.

Biological process Number of PMw (human/

genes* Model 2)*

Categories showing the greatest acceleration in human lineage Olfaction Sensory perception Cell surface receptor-mediated signal transduction Chemosensory perception Nuclear transport G-protein-mediated signaling Signal transduction Cell adhesion Ion transport Intracellular protein traffic Transport Metabolism of cyclic nucleotides Amino acid metabolism Cation transport Developmental processes Hearing

48 146 (98) sos (464)

S4(6) 26

252 (211) 1030(989)

132 237 278 391

20 78

179 542

21

0 0(0.026) 0(0.0386) 0 (0.1157) 0.0003 0.0003 (0.1205) 0.0004 (0.0255) 0.0136 0.0247 0.0257 0.0326 0.0408 0.0454 0.0458 0.0493 0.0494

Categories with the greatest acceleration in the chimp lineage Signal transduction Amino acid metabolism Amino acid transport Cell proliferation and differentiation Cell structure Oncogenesis Cell structure and motility Purine metabolism Skeletal development Mesoderm development Other oncogenesis DNA repair

1030 (989) 0.0004 (0.0255) 78 0.0454 23 0.1015 82 0.3116

174 0.2633 201 0.3132 239 0.2208

35 0.9127 44 0.2876

168 0.5813 39 0.2777 49 0.9363

PMw (chimp/ Model 2)*

0.9184 0.9691 (0.9079)

0.199 (0.0864) 0.9365 (0.7289) 0.2001 0.2526 (0.0773) 0.0276 (0.0092) 0.3718 0.8025 0.8099 0.7199 0.1324 0.0075 0.8486 0.2322 0.9634

0.0276 (0.0092) 0.0075 0.0102 0.0182 0.0233 0.0267 0.0299 0.0423 0.0438 0.0439 0.0469 0.0477

*The number of genes and the PMw values excluding olfactory receptor genes are shown in parentheses.

Table 2. Molecular functions showing the strongest evidence for positive selection. The table includes only human-accelerated categories, because the only categories accelerated in the chimp lineage are chaperones (P = 0.0124), cell adhesion molecules (P = 0.0220), and extracellular matrix (P = 0.0333).

Molecular function

G protein coupled receptor G protein modulator Receptor Ion channel Extracellular matrix Other G protein modulator Extracellular matrix glycoprotein Voltage-gated ion channel Other hydrolase Oxygenase Protein kinase receptor Transporter Ligand-gated ion channel Microtubule binding motor protein Microtubule family cytoskeletal protein

Number of genes*

199 (153) 62

448 134 97(95) 32 44(42) 62 95 46 37

214 45 22 54

PMw (human/ Model 2)*

0 (0.2533) 0.0008 0.0030 0.0043 0.0120 (0.0178) 0.0149 0.0178 (0.0269) 0.0219 0.0260 0.0303 0.0314 0.0338 0.0405 0.0421 0.0467

PMw (chimp/ Model 2)*

0.8689 (0.6776) 0.3776 0.9798 0.8993 0.1482 (0.1593) 0.4441 0.1579 (0.1765) 0.6692 0.4823 0.4792 0.6911 0.1836 0.9503 0.6385 0.2815

*The number of genes and the PMw values excluding olfactory receptor genes are shown in parentheses.

which involves the ALDH6Al, BCKDHA, and PCCB genes, is the primary pathway for energy production from muscle protein under starvation conditions (25). For all seven genes, mutations have been found that result in human metabolic disorders, consistent with the idea that natural selection shifted these genes in a manner that is relevant to reproductive fitness.

Most of the human developmental genes with low P2 values fall into two main cate­ gories: skeletal development (TLL2, ALPL, BMP4, SDC2, MMP20, and MGP) and neu­ rogenesis (NLGN3, SEMA3B, PLXNCl, NTF3, WNT2, WIFI, EPHB6, NEUROGl, and SIM2). In addition, several of the genes with low P 2 values are homeotic transcription factor genes (CDX4, HOXA5, HOXD4, MEOX2, POU2F3, MIXLI, and PHTF), which play key roles in early development. Several genes associated with pregnancy, such as the progesterone receptor (PGR), GNRHR, ·MTNRIA, and PAPPA, appear to exhibit nonneutral divergence between hu­ mans and chimps. PGR is involved not only in maintenance of the uterus, but is also expressed on the cell membrane of sperm, where it may play a role in the acrosome reaction (26), so the physiological basis for the adaptive evolution remains unclear.

Speech is considered to be a defining char­ acteristic of humans. The forkhead-box P2 tran­ scription factor (P2 = 0.0027) has been impli­ cated in speech development, and has previously been identified as undergoing an unusual hu­ man-specific pattern of substitution (27). Several genes involved in the development of hearing also appear to have undergone adaptive evolu­ tion in the human lineage, and we speculate that understanding spoken language may have re­ quired tuning of hearing acuity. The gene with the most significant pattern of human-specific positive selection is alpha tectorin, whose protein product plays a vital role in the tectorial mem­ brane of the inner ear. Single-amino acid poly­ morphisms are associated with familial high­ frequency hearing loss (28), and knockout mice are deaf. These results strongly motivate a de­ tailed assessment of the nature ofhearing differ­ ences between humans and chimpanzees. Other genes involved in hearing that appear to be under human-specific selection include DIAPHI, FOXIl, EYA4, EYAl, and OTOR

The inference of lineage-specific evolution­ ary acceleration requires a phylogenetic tree. By simply adding mouse to our alignments, we went from a directionless pairwise comparison of hu­ man and chimp to having reasonable ability to infer common ancestral state, and lineage-specif­ ic changes. These approaches will gain in both statistical and biological power as additional pri­ mate or other mammalian genomes are se­ quenced, enabling identification of genes that exhibited accelerated amino acid substitution since our most recent common ancestor. Al­ though it is tempting to conclude that this will

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constitute a list of genes that "make us human," one has to take a step back to see the gulf that exists between understanding at this narrowly focused molecular level and at the organismal level. A large number of human genes, when transformed into mutant yeast or Drosophila, can rescue the mutant phenotype, but this does not make these genetically modified organisms any more human. This study has focused only on protein-coding genes, and it will require exami­ nation of regulatory sequences to determine the contribution of regulation of gene expression to the evolutionary divergence between humans and chimps.

Perhaps the best way to understand the rela­ tion between DNA sequence divergence and the differences between human and chimpanzee physiology and morphology is to compare these differences to the variability among humans. Human-chimp DNA sequence divergence is roughly 10 times the divergence between ran­ dom pairs of humans. Contrasts that are under way to place human polymorphism in the con­ text of human-specific divergence further em­ power these models to identify molecular targets of natural selection. Evolutionary analysis will be extended to include comparison of the X chromosome and autosomes, the impact of local recombination rates and GC content, codon-us­ age patterns, and divergence in regulatory se­ quences. Additional insight will be gained by examining sequence divergence in the context of gene-expression differences. The informative­ ness of all these approaches will increase by inclusion of additional mammalian genome se­ quences, and realization of the goal to ascribe functional significance to the complex landscape of our own genome will most effectively be made in the context of our close relatives.

References and Notes 1. M. C. King. A. C. Wilson, Science 188, 107 (1975). 2. Y. Satta, J. Klein, B. Takahata, Mot. Phylogenet. Evol.

14, 259 {2000). 3. F. C. Chen, W.-H. Li, Am. J. Hum. Genet. 68, 444

(2001). 4. I. Ebersberger, D. Metzler, C. Schwarz, S. Paabo, Am.}.

Hum. Genet. 70, 1490 (2002). 5. R. Sakate et al., Genome Res. 13, 1022 (2003). 6. Detailed materials and methods are available as sup­

porting material on Science Online. 7. A total of 201,805 primer pairs were successfully designed

to 23.363 human coding sequences (27.6 Mb). 8. Primer pairs were amplified in 39 female human

individuals (19 African-Americans, 20 Caucasians) and 1 male chimpanzee (4X0033, Southwest Nation­ al Primate Research Center) by a standard PCR and sequencing protocols. Trimmed chimp sequences were BLASTed against human exon sequence (9) to create virtual transcripts.

9. J. C. Venter et al., Science 291, 1304 (2001 ). 10. R. J. Mural et al., Science 296, 1661 (2002). 11. Mouse-human orthologs were downloaded from Na­

tional Center for Biotechnology Information {NCBI) HomoloGene; NCBI Homol_seq_pairs; NCBI Homol­ ogy Map; and Mouse Genome Database, Mouse Ge­ nome Informatics Web Site, The Jackson Laboratory (Bar Harbor, ME).

12. All 7645 alignments in Phylip format (13) and a flatfile of genes and their associated statistics are available at http://panther.celera.com/appleraHCM_ alignments/index.jsp. Sequences have been deposited

in GenBank under accession codes AY398769- AY421703.

13. J. Felsenstein, J. Mot. Evol. 17, 368 (1981). 14. N. Goldman, Z. Yang, Mot. Biol. Evol. 11, 725

(1994). 15. S. V. Muse, B. S. Gaut, Mot. Biol. Evol. 11, 715 (1994). 16. Z. Yang. R. Nielsen, Mot. Biol. Evol. 19, 908 (2002). 17. Z. Yang. R. Nielsen, J. Mot. Evol. 46, 409 (1998). 18. Z. Yang. R. Nielsen, Mot. Biol. Evol. 17, 32 (2000). 19. I. Hellmann et al., Genome Res. 13, 831 (2003). 20. J. A. Bailey et al., Science 297, 1003 (2002). 21. A. Kong et al., Nature Genet. 31,241 (2003). 22. P. D. Thomas et al., Nucleic Acids Res. 31,334 (2003). 23. Y. Gilad, 0. Man, S. Paabo, D. Lancet, Proc. Natl.

Acad. Sci. U.S.A. 100, 3324 (2003). 24. Y. Gilad, C. D. Bustamante, D. Lancet, S. Paabo, Am. J.

Hum. Genet. 73, 489 (2003). 25. H. R. Freund, M. Hanani, Nutrition 18, 287 (2002). 26. S. Gadkar et al., Biol. Reprod. 67, 1327 (2003). 27. W. Enard et al., Nature 418, 869 (2002).

REPORTS

28. S. Naz et al., j. Med. Genet. 40, 360 (2003). 29. The data in this paper were obtained from more

than 18 million sequencing reads obtained from the Cetera Genomics sequencing center in Rock­ ville, MD. We thank J. Duff, C. Gire, M. A. Rydland, C. Forbes, and B. Small for development and main­ tenance of software systems, laboratory informa­ tion management systems, and analysis programs. S. Hannenhalli and S. Levy provided particularly helpful discussions. C. Aquadro, B. Lazzaro, K. Mon­ tooth, T. Schlenke, and P. Wittkopp provided help­ ful comments on the manuscript.

Supporting Online Material www.sciencemag.org/cgi/content/full/302/5652/1960/ DC1 Materials and Methods Tables S1 to S3 Databases S1 and S2

7 July 2003; accepted 24 October 2003

The Proteasome of Mycobacterium tuberculosis Is Required for Resistance to Nitric Oxide

K. Heran Darwin, 1 Sabine Ehrt,1 Jose-Carlos Gutierrez-Ramos, 2 Nadine Weich,2 Carl F. Nathan 1 •3 *

The f roduction of nitric oxide and other reactive nitrogen intermediates (RNI by macrophages helps to control infection by Mycobacterium tuber­ culosis (Mtb). However, the protection is imperfect and infection persists. To identify genes that Mtb requires to resist RNI, we screened 10,100 Mtb transposon mutants for hypersusceptibility to acidified nitrite. We found 12 mutants with insertions in seven genes representing six pathways, including the repair of DNA (uvrB) and the synthesis of a flavin cofactor (fbiC). Five mutants had inse·rtions in proteasome-associated genes. An Mtb mutant deficient in a presumptive proteasomal adenosine triphosphatase was at­ tenuated in mice, and exposure to proteasomal protease inhibitors markedly sensitized wild-type Mtb to RNI. Thus, the mycobacterial proteasome serves as a defense against oxidative or nitrosative stress.

Mtb persistently infects about two billion people. The identification of pathways used by the microbe to resist elimination by the host immune response may suggest new targets for prevention or treatment of tuber­ culosis. During latent infection, the primary residence of Mtb is the macrophage. The antimicrobial arsenal of the activated mac­ rophage includes inducible nitric oxide synthase (iNOS or NOS2) (1). At the acidic pH (:S5.5) prevalent in the phagosome of activated macrophages (2), nitrite, a major oxidation product of NO, is partially pro­ tonated to nitrous acid, which dismutates to form NO and another radical, ·NO 2 (3).

'Department of Microbiology and Immunology, Weill Medical College of Cornell University, New York, NY 10021, USA. 2 Millennium Pharmaceuticals, 75 Sidney Street, Cambridge, MA 02139, USA. 3 Programs in Immunology and Molecular Biology, Weill Graduate School of Medical Sciences of Cornell University, New York, NY 10021, USA.

*To whom correspondence should be addressed. E­ mail: [email protected]

Thus, mildly acidified nitrite is a physio­ logic antimicrobial system. RNI may inflict not only nitrosative but also oxidative in­ jury, such as when NO combines with su­ peroxide from bacterial metabolism to gen­ erate peroxynitrite (4). Reagent NO kills Mtb with a molar potency exceeding that of most antituberculosis drugs (5, 6). In hu­ mans and mice with tuberculosis, macro­ phages in infected tissues and airways ex­ press enzymatically active iNOS (7-9), and mice lacking iNOS cannot control Mtb in­ fection (J 0). Despite the protective effects of RNI, a small number of viable mycobacteria usually persist for the lifetime of the infected host (J J) and sometimes resume growth.

To identify Mtb genes required for re­ sistance against RNI, we screened 10,100 transposon mutants individually for in­ creased sensitivity to nitrite at pH 5.5 [sup­ porting online material (SOM text)]. Twelve mutants were hypersensitive. To quantify their phenotype, bacteria were ex­ posed to pH 5.5 with or without 3 mM

www.sciencemag.org SCIENCE VOL 302 12 DECEMBER 2003 1963

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