Vol 437 | 13 October 2005 | doi : 10.1038 / nature03985 LETTERS A network-based analysis of systemic inflammation in humans Steve E. Calvano1 * , Wenzhong Xiao2 * , Daniel R. Richards3 , Ramon M. Felciano3 , Henry V. Baker4 ,5 , Raymond J. Cho3 , Richard O. Chen3 , Bernard H. Brownstein6 , J. Perren Cobb6 , S. Kevin Tschoeke5 , Carol Miller-Graziano7 , Lyle L. Moldawer5 , Michael N. Mindrinos2 , Ronald W. Davis2 , Ronald G. Tompkins8 , Stephen F. Lowry1 & the Inflammation and Host Response to Injury Large Scale Collaborative Research Program Oligonucleotide and complementary DNA microarrays are being used to subclassify histologically similar tumours , monitor disease progress , and individualize treatment regimens1 5 . However , extracting new biological insight from high-throughput genomic studies of human diseases is a challenge , limited by difficulties in recognizing and evaluating relevant biological processes from huge quantities of experimental data . Here we present a structured network knowledge-base approach to analyse genome-wide transcriptional responses in the context of known functional interrelationships among proteins , small molecules and phenotypes . This approach was used to analyse changes in blood leukocyte gene expression patterns in human subjects receiving an inflammatory stimulus -LRB- bacterial endotoxin -RRB- . We explore the known genome-wide interaction network to identify significant functional modules perturbed in response to this stimulus . Our analysis reveals that the human blood leukocyte response to acute systemic inflammation includes the transient dysregulation of leukocyte bioenergetics and modulation of translational machinery . These findings provide insight into the regulation of global leukocyte activities as they relate to innate immune system tolerance and increased susceptibility to infection in humans . Inflammation is a hallmark of many human diseases6 8 . We focus on blood leukocytes and other tissues of critically injured patients , in order to better elucidate the mechanisms underlying systemic inflammatory responses9 . This approach can not be fully replicated using animal models or human cell lines , and studies of injury in humans can be complicated by antecedent illnesses and concurrent treatment regimes that may alter the recovery process . To our knowledge , no study has evaluated the genome-wide response to systemic inflammation in the context of a fully predictable recovery . Here we combine genome-wide expression analysis with a new bioinformatics method to identify functional networks responsible for the systemic activation and spontaneous resolution of a welldefined inflammatory challenge . Gene expression in whole blood leukocytes was determined immediately before and at 2 , 4 , 6 , 9 and 24 h after the intravenous administration of bacterial endotoxin to four healthy human subjects . Four additional subjects were studied under identical conditions but without endotoxin administration . The infusion of endotoxin activates innate immune responses and presents with physiological responses of brief duration10 . Notably , there is an initial proinflammatory phase and a subsequent counterregulatory phase , with resolution of virtually all clinical perturbations within 24 h. K-means cluster and principal component analyses were first used to visualize the overall response to endotoxin administration . Figure 1a reveals probe sets clustered by K-mean analysis , where each bin has a distinct endotoxin-induced temporal pattern . The signal intensity of 5,093 probe sets -- representing 3,714 unique genes -- out of a total of .44,000 probe sets changed significantly in response to endotoxin , whereas no significant changes were observed in control subjects -LRB- estimated false discovery rate ,0.1 % -RRB- . Of the 5,093 probe sets identified , over half showed reduced abundance at 2 , 4 , 6 and 9 h , returning to baseline by 24 h -LRB- see bins 0 4 -RRB- . In contrast , a smaller number of probe sets were induced by 2 h -LRB- bins 5 , 6 -RRB- , and the remaining probe sets showed a delayed response , peaking at 4 9 h but returning to baseline by 24 h -LRB- bins 7 9 -RRB- . Cluster and principal component analyses describe overall changes in apparent gene expression , but provide few insights into the biological processes and signalling networks invoked in propagation and resolution of the inflammatory response . Identifying the perturbed biological networks underlying this complex clinical phenotype requires systematic analysis in the context of known mammalian biology , derived from basic and clinical research . Using a web-based entry tool developed by Ingenuity Systems Inc. , findings presented in peer-reviewed scientific publications were systematically encoded into an ontology by content and modelling experts . Using over 200,000 full-text scientific articles , a knowledge base of more than 9,800 human , 7,900 mouse and 5,000 rat genes was manually curated and supplemented with curated relationships parsed from MEDLINE abstracts . A molecular network of direct physical , transcriptional and enzymatic interactions observed between mammalian orthologues -- the observed ` interactome ' -- was computed from this knowledge base . The resulting network contains molecular relationships involving over 8,000 orthologues with a high degree of connectivity . On average , individual genes have 11.5 interaction partners -LRB- median 4.0 -RRB- , of which 7.2 represent direct physical interactions -LRB- median 3.0 -RRB- . Every gene interaction in the network is supported by published information . For example , the 1 Department of Surgery , UMDNJ-Robert Wood Johnson Medical School , New Brunswick , New Jersey 08903 , USA . 2Stanford Genome Technology Center , Palo Alto , California 94304 , USA . 3Ingenuity Systems Inc , Mountain View , California 94043 , USA . 4Departments of Molecular Genetics and Microbiology , and 5Department of Surgery , University of Florida College of Medicine , Gainesville , Florida 32610 , USA . 6Department of Surgery , Washington University in St Louis , St Louis , Missouri 63110 , USA . 7Department of Surgery , University of Rochester School of Medicine , Rochester , New York 14642 , USA . 8Department of Surgery , Massachusetts General Hospital , Harvard Medical School , Boston , Massachusetts 02114 , USA . * These authors contributed equally to this work . Lists of participants and affiliations appear at the end of the paper 1032 2005 Nature Publishing Group NATURE | Vol 437 | 13 October 2005 LETTERS immediate neighbourhood for the RELA gene -LRB- NF-kB p65 , Supplementary Fig. 1 -RRB- includes 150 genes and 619 direct interactions , derived from 7,118 findings curated from 847 published articles . The observed interactome provides a framework for structuring the existing knowledge regarding mammalian biology , and enables a new analytical approach that objectively examines experimental data in the context of known genome-wide interactions in order to identify significant functional modules . This method is applicable to data of high-throughput platforms such as microarray expression profiling , polymorphism analysis and proteomics . Furthermore , the Figure 1 | Gene expression profiles of circulating leukocytes in response to bacterial endotoxin infusion . Samples from eight healthy volunteers were tested at baseline -LRB- 0 h -RRB- and 2 , 4 , 6 , 9 and 24 h after intravenous administration of endotoxin -LRB- four subjects -RRB- or vehicle -LRB- four subjects -RRB- . a , Significant -LRB- false discovery rate of ,0.1 % -RRB- probe sets -LRB- 5,093 -RRB- were subjected to K-means clustering into ten bins -LRB- 0 9 -RRB- . Probe sets for which the abundance was above the mean are shown in red , below the mean are shown in blue , and equivalent to the mean are in white . b , Principal component plot of the significant probe sets at the indicated times after endotoxin administration . original literature detailing the genetic interactions can be accessed to further examine and verify the findings . For a better understanding of the temporal response of gene expression in the innate immune system , we constructed a prototypical inflammatory cell containing 292 representative genes and detailing all direct interactions in our database -LRB- Fig. 2 -RRB- . Closer inspection of the temporal response reveals the fine structure of dynamic changes in RNA abundance by highlighting the transient and self-limiting nature of this response . As an example , the apparent expression of several secreted proinflammatory cytokines and chemokines -LRB- TNFSF2 -LRB- TNF -RRB- , IL1A , IL1B , CXCL1 -LRB- GROa -RRB- , CXCL2 -LRB- GRO-b -RRB- , CCL2 -LRB- MCP-1 -RRB- , CXCL8 -LRB- IL-8 -RRB- and CXCL10 -RRB- reached a maximum 2 4 h after endotoxin administration , consistent with early activation of innate immunity . Subsequently , the expression of several members of the nuclear factor kappa/relA family of transcription factors -LRB- NFKB1 , NFKB2 , RELA and RELB -RRB- reached their zenith . The time period 4 6 h after endotoxin injection seemed critical , as the expression of a number of transcription factors was increased , including both those that initiate and those that limit the innate immune response . In the former group , these included the signal transducer and activators of transcription -LRB- STAT genes -RRB- , and the cAMP-response element-binding protein -LRB- CREB -RRB- and CCAAT / enhancer binding protein -LRB- CEBP -RRB- gene families . Transcription factors limiting the innate immune response included suppressor of cytokine signalling 3 -LRB- SOCS3 -RRB- and IKBK genes . There was also a delay -LRB- 4 6 h -RRB- in increased mRNA abundance of secreted and membraneassociated proteins that limit the inflammatory response , including IL1RAP , IL1R2 , IL10 and TNFRSF1A . Together , these data comprehensively document the temporal modulation of genes controlling the innate immune response in a human model that progresses from an acute proinflammatory phase to unencumbered counterregulation , concluding with full recovery and a normal phenotype . To further elucidate the global changes during inflammation and subsequent return to homeostasis , we sought to computationally decipher the principal networks involved . The specificity of connections for each gene was calculated , as defined by the percentage of its direct connections to other genes showing significant transcriptional changes . A network pathway was initiated by the gene with the highest specificity of connections , and was propagated according to the descent of the specificity . Individual significant pathways identified by a statistical likelihood calculation -LRB- P , 0.0001 -RRB- were merged to represent the biological processes . Our global representation of the inflammatory response to endotoxin , shown in Fig. 3a , comprises a network of 1,556 genes and their interactions . This network consists of a subset of 1,214 genes -LRB- 78 % -RRB- responsive to in vivo endotoxin administration , and 342 additional , highly interconnected genes . The gestalt of the temporal response to endotoxin is suggested at the level of interconnecting functional modules , which could not be readily extracted from the experimental data alone -LRB- Fig. 1 -RRB- . Simultaneous survey and evaluation of the subnetwork regions enables us to identify new endotoxin-responsive modules in addition to the innate immunity network described above . Examples of the diversity in such modules include -LRB- a -RRB- increased expression of components of the superoxide-producing phagocyte NADPH-oxidase system , a multicomponent enzyme important for host defence11 , -LRB- b -RRB- decreased expression of the major histocompatibility -LRB- MHC -RRB- II complex , consistent with reduced antigen presentation following endotoxin stimulation 12,13 , -LRB- c -RRB- decreased expression of the TCP1 ring complex required for folding of cytoskeletal proteins , -LRB- d -RRB- increased expression in the family of tubulin-A microtubule genes , -LRB- e -RRB- suppressed expression of several subunits of the anaphase-promoting complex , which has a key role in cell-cycle regulation , and -LRB- f -RRB- reduced integrin-a and - b chain expression , affecting cell cell and cell matrix adhesion -LRB- see Supplementary Methods 2 , containing Supplementary Fig. 3a f -RRB- . Further , significant decreases in messenger RNA abundance were 1033 2005 Nature Publishing Group LETTERS NATURE | Vol 437 | 13 October 2005 Figure 2 | Pathway analysis of representative genes involved in innate immunity . A prototypical inflammatory cell was constructed from 292 representative genes involved in inflammation and innate immunity . Genes for which the expression statistically increased from baseline are coloured red , those for which expression decreased are shown in blue . a , Composite 1034 changes in apparent expression over 24 h , identifying nodes and interactions . b , Temporal changes in apparent expression . The response to endotoxin administration in blood leukocytes can be viewed as an integrated cell-wide response , propagating and resolving over time . 2005 Nature Publishing Group NATURE | Vol 437 | 13 October 2005 LETTERS Figure 3 | Network representation of the biological processes underlying the temporal response of blood leukocytes to in vivo endotoxin administration . a , The network consists of 1,214 genes showing perturbed expression , and 342 genes highly interconnected to this group -LRB- red , increased ; blue , decreased expression -RRB- . b , Selected regions of the network , highlighting several groups of genes . Group 1 , mitochondrial respiratory chain complex I -LRB- NDUF genes -RRB- . Group 2 , mitochondrial respiratory chain complex III -LRB- UQCR genes -RRB- . Group 3 , ATP synthase complex -LRB- ATP5 genes -RRB- . Group 4 , pyruvate dehydrogenase complex . Group 5 , mitochondrial permeability transition pore complex . Group 6 , elongation initiation factor complex -LRB- EIF3 genes -RRB- . Group 7 , ribosomal proteins -LRB- RPL , RPS genes -RRB- . Group 8 , COP9 signallosome -LRB- COPS genes -RRB- . Group 9 , proteasome -LRB- PSM genes -RRB- . 1035 2005 Nature Publishing Group LETTERS NATURE | Vol 437 | 13 October 2005 observed in the mitochondrial respiratory chain complexes I V -LRB- NDUF in Fig. 3b -LRB- Group 1 -RRB- , UQCR -LRB- Group 2 -RRB- and ATP synthase genes -LRB- Group 3 -RRB- -RRB- . Gene expression was also decreased in the pyruvate dehydrogenase complex -LRB- PDH , Fig. 3b , Group 4 -RRB- , which generates , via acetyl-CoA and the tricarboxylic acid -LRB- TCA -RRB- cycle , reduced coenzymes required for ATP synthesis during mitochondrial oxidative phosphorylation . A concomitant increase in expression of pyruvate dehydrogenase kinase-3 -LRB- PDK3 -RRB- , an inhibitor of PDH , was observed . Expression of the voltage dependent anion channel -LRB- VDAC -RRB- and adenine nucleotide translocator -LRB- SLC25A5 -RRB- , components of the mitochondrial permeability transition pore -LRB- MPTP -RRB- complex , were decreased to similar extents , whereas expression of the antagonistic benzodiazepine receptor -LRB- BZRP -RRB- was increased -LRB- Fig. 3b , Group 5 -RRB- . MPTP activation has previously been considered an early event in apoptosis , leading to mitochondrial membrane depolarization and release of cytochrome c. However , recent reports have indicated a primary role for MPTP activation in oxidative-stress - and calcium-overload-induced necrotic cell death14 ,15 . Thus , reduction in transcripts for MPTP components is consistent with a protective response to the oxidative stress associated with endotoxin challenge . As active secretory cells , leukocytes devote a substantial amount of energy expenditure to protein synthesis16 . In concert with the suppression of modules participating in energy production , expression was decreased for the elongation initiation factor complex -LRB- EIF3 in Fig. 3b , Group 6 -RRB- , a large number of ribosomal proteins -LRB- RPS , RPL genes in Fig. 3b , Group 7 -RRB- , the RNA polymerase II complex , and also in the functional modules of the ATP/ubiquitindependent protein degradation pathway , the COP9 signallosome -LRB- Fig. 3b , Group 8 -RRB- and the proteasome -LRB- PSM genes in Fig. 3b , Group 9 -RRB- . Here we use a knowledge-based network analysis to reveal concerted dysregulation of functional modules in mitochondrial bioenergetics , protein synthesis and protein degradation in human blood leukocytes during an abbreviated and self-limiting episode of inflammation . These findings document a reprioritization of the transcriptional regulatory programme in leukocytes in response to endotoxin . Furthermore , the marked suppression of these important functional networks suggests that leukocytes exposed to inflammatory stimuli may have an altered capacity to sustain subsequent immune challenges , as observed during innate immune system tolerance . Human disease phenotypes are manifested by the malfunctioning of multiple functional modules interrelated in physiological regulatory systems . The overwhelming diversity of possible genome-wide interactions and gene expression patterns limit effective learning from experimental data alone . Network analyses using comprehensive knowledge of mammalian biology can greatly reduce the hypothesis space , enabling identification of new functional modules perturbed in the disease process . Here we demonstrate that , upon acute systemic inflammation , the human blood leukocyte response includes widespread suppression at the transcriptional level of mitochondrial energy production and protein synthesis machinery . A decrease in high-energy substrates has been observed in the muscle and liver of critically ill patients , and in animal models of sepsis , burn injury and endotoxemia17 19 . In addition , direct disruption of mitochondrial complexes by cellular-stress-derived mediators -LRB- for example , reactive oxygen species -RRB- has been suggested for necrotic cell death in animal models of sepsis20 and reperfusion injury14 ,15 . The erosion in functional networks identified above represents a normal adaptive process aimed at re-establishing homeostasis , and yet might contribute to global leukocyte defects -- such as tolerance and increased susceptibility to infection -- observed in critically injured patients . These perturbations in functional modules are at the level of mRNA transcripts , and will require subsequent confirmation at the protein level . Further identification of the specific cell populations showing these changes in gene expression will require the isolation and enrichment of specific leukocyte subpopulations . Finally , it will be important to confirm whether patients manifesting 1036 systemic inflammation show similar perturbations of the functional modules identified here as do otherwise healthy , endotoxin-challenged subjects . METHODS Human endotoxin model . Eight healthy male and female subjects between 18 and 40 years of age provided written informed consent . Details of endotoxin administration to human subjects have been summarized elsewhere10 ,21 . Subjects were intravenously administered either NIH Clinical Center Reference Endotoxin , -LRB- CC-RE-Lot 2 -RRB- at a dose of 2 ng kg21 body weight -LRB- n 4 , one female and three males -RRB- or 0.9 % sodium chloride -LRB- n 4 , one female and three males -RRB- over a 5-min period . Blood samples were collected before endotoxin infusion -LRB- 0 h -RRB- and 2 , 4 , 6 , 9 and 24 h after infusion . Blood sampling . Blood was collected and lysis buffer -LRB- bicarbonate-buffered ammonium chloride solution , 0.826 % NH4Cl , 0.1 % KHC03 , 0.0037 % Na4EDTA in H2O -RRB- was added at a ratio of 20:1 -LRB- lysis buffer : blood -RRB- . Samples were then incubated at room temperature until erythrocyte lysis was complete -LRB- ,5 7 min -RRB- . Leukocytes were recovered by centrifugation -LRB- 400 g , 4 8C -RRB- and washed once in ice-cold phosphate buffered saline . Leukocyte pellets were then resuspended in 8 ml RLT buffer -LRB- Qiagen -RRB- and the samples sheared ten times with an 18-gauge needle attached to a 10-ml syringe . Samples were then immediately frozen and kept at 270 8C until RNA extraction was required . Leukocyte RNA isolation . Total cellular RNA was isolated from the leukocyte pellets using a commercial kit -LRB- RNeasy , Qiagen -RRB- . Purity was confirmed by spectrophotometry -LRB- A 260/A 280 ratio -RRB- and capillary electrophoresis -LRB- Agilent 2100 Bioanalyser , Agilent Inc -RRB- . cRNA synthesis and chip hybridization . cRNA synthesis was performed using 4 mg total cellular RNA , hybridized onto Hu133A and Hu133B oligonucleotide arrays -LRB- Affymetrix -RRB- , and processed according to the protocol outlined by Affymetrix , with a few modifications . Microarray data analysis . A total of 44,924 probe sets on the Hu133A and Hu133B arrays were analysed . Normalization was performed using dChip22 , and expression level was modelled using the perfect match only model . Probe sets identified as absent on all arrays -LRB- using MicroArray Suite v5 , Affymetrix -RRB- were not included in further analysis . Probe sets significantly perturbed after bacterial endotoxin administration were identified using significance analysis of microarrays -LRB- SAM -RRB- -LRB- multiclass response -RRB- 23 , with an estimated false discovery rate of ,0.1 % on the basis of 1,000 permutations . The resulting 5,093 probe sets were subjected to K-means clustering to ten bins using Cluster and TreeView24 , and principal component analysis . The same analysis was applied to control data , but no temporal changes of the probe sets reached the significant level -LRB- false discovery rate ,0.1 % -RRB- . A total of 3,714 unique genes were identified from 4,895 probe sets with mapped Entrez GeneIDs -LRB- http://www.ncbi.nih.gov/Entrez/ -RRB- . More information is at http://www.gluegrant.org/ . Verification of genes showing significant transcriptional changes . An additional six healthy subjects -LRB- one female and five males -RRB- were administered 2 ng kg21 -LRB- body weight -RRB- endotoxin , and blood samples were collected before -LRB- 0 h -RRB- and after -LRB- 2 and 6 h -RRB- endotoxin infusion . At either 2 or 6 h , ,88 % of the significant probe sets in the initial study were again identified as significant -LRB- false discovery rate ,1 % -RRB- . See Supplementary Methods 1 . Development of the observed interactome of mammalian genes . The Ingenuity Pathways Knowledge Base -LRB- KB -RRB- is the largest curated database of previously published findings on mammalian biology from the public literature -LRB- Ingenuity Systems -RRB- . Reports on individual studies of genes in human , mouse or rat were first identified from peer-reviewed publications , and findings were then encoded into an ontology by content and modelling experts . Manual extraction and curation probably results in more specific and comprehensive interactions , with far fewer false-positives than automated alternatives -LRB- for example , natural language processing and high-throughput screening -RRB- . Network analysis using the knowledge base was used to further identify direct interactions between mammalian orthologues . For example , the knowledge of functional interactions -LRB- for example , phosphorylation -RRB- was combined with knowledge of protein functions -LRB- for example , kinase activity -RRB- to algorithmically infer a direct biochemical interaction . Every resulting gene interaction has supporting literature findings available online . More information is in Supplementary Methods 2 . Identification of significant pathways in biological processes . The following steps were used : -LRB- 1 -RRB- Genes identified as significant from the experimental data sets were overlaid onto the interactome . Focus genes were identified as the subset having direct interaction -LRB- s -RRB- with other genes in the database . -LRB- 2 -RRB- The specificity of connections for each focus gene was calculated by the percentage of its connections to other significant genes . The initiation and growth of pathways proceeded from genes with the highest specificity of connections . Each pathway 2005 Nature Publishing Group NATURE | Vol 437 | 13 October 2005 LETTERS had a maximum of 35 genes . -LRB- 3 -RRB- Pathways of highly interconnected genes were identified by statistical likelihood using the following equation : ! f 21 X C G ; i C N 2 G ; s 2 i Score 2log10 1 2 C N ; s i 0 where N is the number of genes in the genomic network , of which G are focus genes , for a pathway of s genes , f of which are focus genes . C -LRB- n , k -RRB- is the binomial coefficient . -LRB- 4 -RRB- Pathways with a score greater than 4 -LRB- P , 0.0001 -RRB- were combined to form a composite network representing the underlying biology of the process . Received 17 May ; accepted 4 July 2005 . Published online 31 August 2005 . Heller , R. A. et al. . Discovery and analysis of inflammatory disease-related genes using cDNA microarrays . Proc . Natl Acad . Sci . USA 94 , 2150 -2155 -LRB- 1997 -RRB- . 2 . Golub , T. R. et al. . Molecular classification of cancer : class discovery and class prediction by gene expression monitoring . Science 286 , 531 -537 -LRB- 1999 -RRB- . 3 . Alizadeh , A. A. et al. . Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling . Nature 403 , 503 -511 -LRB- 2000 -RRB- . 4 . Bittner , M. et al. . Molecular classification of cutaneous malignant melanoma by gene expression profiling . Nature 406 , 536 -540 -LRB- 2000 -RRB- . 5 . Perou , C. M. et al. . 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Cytopathic hypoxia . Mitochondrial dysfunction as a mechanism contributing to organ dysfunction in sepsis . Crit . Care . Clin . 17 , 219 -237 -LRB- 2001 -RRB- . Kalil , A. C. et al. . Effects of drotrecogin alfa -LRB- activated -RRB- in human endotoxemia . Shock 21 , 222 -229 -LRB- 2004 -RRB- . Li , C. & Wong , W. H. Model-based analysis of oligonucleotide arrays : expression index computation and outlier detection . Proc . Natl Acad . Sci . USA 98 , 31 -LRB- 2001 -RRB- . -36 Tusher , V. G. , Tibshirani , R. & Chu , G. Significance analysis of microarrays applied to the ionizing radiation response . Proc . Natl Acad . Sci . USA 98 , 5116 -5121 -LRB- 2001 -RRB- . Eisen , M. B. , Spellman , P. T. , Brown , P. O. & Botstein , D. Cluster analysis and display of genome-wide expression patterns . Proc . Natl Acad . Sci . USA 95 , 14863 -14868 -LRB- 1998 -RRB- . Supplementary Information is linked to the online version of the paper at www.nature.com/nature . Acknowledgements We thank S. M. Coyle for clinical assistance , and J. Wilhelmy , A. Kumar , S. MacMillan and A. Abouhamze for technical assistance . This work was supported by an Injury and the Host Response to Inflammation Large Scale Collaborative Research Program Award from the National Institute of General Medical Sciences -LRB- to R.G.T. -RRB- . Author Information Reprints and permissions information is available at npg.nature.com/reprintsandpermissions . The authors declare competing financial interests : details accompany the paper on www.nature.com/nature . Correspondence and requests for materials should be addressed to R.W.D. -LRB- dbowe@stanford.edu -RRB- . Inflammation and Host Response to Injury Large Scale Collaborative Research Program Paul E. Bankey1 , Timothy R. Billiar2 , David G. Camp3 , George Casella4 , Irshad H. Chaudry5 , Mashkoor A. Choudhry5 , Charles Cooper26 , Asit De1 , Constance Elson6 , Bradley Freeman7 , Richard L. Gamelli8 , Celeste Campbell-Finnerty9 , Nicole S. Gibran10 , Douglas L. Hayden6 , Brian G. Harbrecht2 , David N. Herndon9 , Jureta W. Horton11 , William J. Hubbard5 , John L. Hunt12 , Jeffrey Johnson13 , Matthew B. Klein14 , James A. Lederer15 , Tanya Logvinenko6 , Ronald V. Maier10 , John A. Mannick15 , Philip H. Mason26 , Bruce A. McKinley16 , Joseph P. Minei11 , Ernest E. Moore13 , Frederick A. Moore16 , Avery B. Nathens10 , Grant E. O'Keefe10 , Laurence G. Rahme17 , Daniel G. Remick18 , David A. Schoenfeld6 , Martin Schwacha5 , Michael B. Shapiro19 , Geoffrey M. Silver8 , Richard D. Smith3 , John D. Storey20 , Mehmet Toner21 , H. Shaw Warren22 , Michael A. West19 10 Affiliations for participants : Department of Surgery , University of Rochester School of Medicine , Rochester , New York 14642 , USA . 11Department of Surgery , University of Pittsburgh School of Medicine , Pittsurgh , Pennsylvania 15213 , USA . 12Pacific Northwest National Laboratory , Richland , Washington 99352 , USA . 13Department of Statistics , University of Florida , Gainesville , Florida 32611 , USA . 14Department of Surgery , University of Alabama School of Medicine , Birmingham , Alabama 35294 , USA . 15Department of Biostatistics , Massachusetts General Hospital , Harvard Medical School , Boston , Massachusetts 02114 , USA . 16Department of Surgery , Washington University School of Medicine , St Louis , Missouri 63110 , USA . 17Department of Surgery , Loyola University Stritch School of Medicine , Maywood , Illinois 60153 , USA . 18Department of Surgery , University of Texas Medical Branch , Shriners Burns Hospital , Galveston , Texas 77550 , USA . 19Department of Surgery , University of Washington Harborview Medical Center , Seattle , Washington 98104 , USA . 20Department of Surgery , University of Texas Southwestern Medical Center , Dallas , Texas 75390 , USA . 21Division of Trauma , Burns , and Critical Care , University of Texas Southwestern Medical Center , Dallas , Texas 75390 , USA . 22Department of Surgery , University of Colorado Denver Health Medical Center , Denver , Colorado 80204 , USA . 23Burn Center and Division of Plastic Surgery , University of Washington Harborview Medical Center , Seattle , Washington 98104 , USA . 24Department of Surgery , Brigham and Women 's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , USA . 25Department of Surgery , University of Texas Houston Health Science Center , Houston Medical School , Houston , Texas 77030 , USA . 26Department of Molecular Biology , Massachusetts General Hospital Harvard Medical School , Boston , Massachusetts 02114 , USA . 27Department of Medical Science , University of Michigan Medical School , Ann Arbor , Michigan 48109 , USA . 28Department of Surgery , Northwestern University Medical School , Chicago , Illinois 60611 , USA . 29Department of Biostatistics , University of Washington , Seattle , Washington 98195 , USA . 30Center for Engineering in Medicine , Massachusetts General Hospital , Harvard Medical School , Boston , Massachusetts 02114 , USA . 31Department of Medicine , Massachusetts General Hospital , Harvard Medical School , Boston , Massachusetts 02114 , USA . 2005 Nature Publishing Group 1037 ERRATA & CORRIGENDA NATURE | Vol 438 | 1 December 2005 CORRIGENDUM doi : 10.1038 / nature04362 CORRIGENDUM doi : 10.1038 / nature04363 A network-based analysis of systemic inflammation in humans Steve E. Calvano , Wenzhong Xiao , Daniel R. Richards , Ramon M. Felciano , Henry V. Baker , Raymond J. Cho , Richard O. Chen , Bernard H. Brownstein , J. Perren Cobb , S. Kevin Tschoeke , Carol Miller-Graziano , Lyle L. Moldawer , Michael N. Mindrinos , Ronald W. Davis , Ronald G. Tompkins , Stephen F. Lowry & the Inflammation and Host Response to Injury Large Scale Collaborative Research Program Nature 437 , 1032 1037 -LRB- 2005 -RRB- doi : 10.1038 / nature03985 In this Letter , the affiliations of authors participating in the Inflammation and Host Response to Injury Large Scale Collaborative Research Program are incorrectly listed . The renumbered and amended footnote listing is given here . DNA sequence and analysis of human chromosome 18 Chad Nusbaum , Michael C. Zody , Mark L. Borowsky , Michael Kamal , Chinnappa D. Kodira , Todd D. Taylor , Charles A. Whittaker , Jean L. Chang , Christina A. Cuomo , Ken Dewar , Michael G. FitzGerald , Xiaoping Yang , Amr Abouelleil , Nicole R. Allen , Scott Anderson , Toby Bloom , Boris Bugalter , Jonathan Butler , April Cook , David DeCaprio , Reinhard Engels , Manuel Garber , Andreas Gnirke , Nabil Hafez , Jennifer L. Hall , Catherine Hosage Norman , Takehiko Itoh , David B. Jaffe , Yoko Kuroki , Jessica Lehoczky , Annie Lui , Pendexter Macdonald , Evan Mauceli , Tarjei S. Mikkelsen , Jerome W. Naylor , Robert Nicol , Cindy Nguyen , Hideki Noguchi , Sinead B. O'Leary , Keith O'Neill , Bruno Piqani , Cherylyn L. Smith , Jessica A. Talamas , Kerri Topham , Yasushi Totoki , Atsushi Toyoda , Hester M. Wain , Sarah K. Young , Qiandong Zeng , Andrew R. Zimmer , Asao Fujiyama , Masahira Hattori , Bruce W. Birren , Yoshiyuki Sakaki & Eric S. Lander Nature 437 , 551 555 -LRB- 2005 -RRB- doi : 10.1038 / nature03983 Inflammation and Host Response to Injury Large Scale Collaborative Research Program Paul E. Bankey1 , Timothy R. Billiar2 , David G. Camp3 , George Casella4 , Irshad H. Chaudry5 , Mashkoor A. Choudhry5 , Charles Cooper6 , Asit De1 , Constance Elson7 , Bradley Freeman8 , Richard L. Gamelli9 , Celeste Campbell-Finnerty10 , Nicole S. Gibran11 , Douglas L. Hayden7 , Brian G. Harbrecht2 , David N. Herndon10 , Jureta W. Horton12 , William J. Hubbard5 , John L. Hunt13 , Jeffrey Johnson14 , Matthew B. Klein15 , James A. Lederer16 , Tanya Logvinenko7 , Ronald V. Maier11 , John A. Mannick16 , Philip H. Mason6 , Bruce A. McKinley17 , Joseph P. Minei12 , Ernest E. Moore14 , Frederick A. Moore17 , Avery B. Nathens11 , Grant E. O'Keefe11 , Laurence G. Rahme18 , Daniel G. Remick19 , David A. Schoenfeld7 , Martin G. Schwacha5 , Michael B. Shapiro20 , Geoffrey M. Silver9 , Richard D. Smith3 , John D. Storey21 , Mehmet Toner22 , H. Shaw Warren23 & Michael A. West20 Affiliations for participants : 1Department of Surgery , University of Rochester School of Medicine , Rochester , New York 14642 , USA . 2Department of Surgery , University of Pittsburgh School of Medicine , Pittsburgh , Pennsylvania 15213 , USA . 3Pacific Northwest National Laboratory , Richland , Washington 99352 , USA . 4Department of Statistics , University of Florida , Gainesville , Florida 32611 , USA . 5Department of Surgery , University of Alabama School of Medicine , Birmingham , Alabama 35294 , USA . 6Department of Molecular Biology , Massachusetts General Hospital , Harvard Medical School , Cambridge , Massachusetts 02139 , USA . 7Department of Biostatistics , Massachusetts General Hospital , Harvard Medical School , Boston , Massachusetts 02114 , USA . 8Department of Surgery , Washington University School of Medicine , St. Louis , Missouri 63110 , USA . 9Department of Surgery , Loyola University Stritch School of Medicine , Maywood , Illinois 60153 , USA . 10 Department of Surgery , University of Texas Medical Branch , Shriners Burns Hospital , Galveston , Texas 77550 , USA . 11Department of Surgery , University of Washington Harborview Medical Center , Seattle , Washington 98104 , USA . 12Department of Surgery , University of Texas Southwestern Medical Center , Dallas , Texas 75390 , USA . 13Division of Trauma , Burns , and Critical Care , University of Texas Southwestern Medical Center , Dallas , Texas 75390 , USA . 14Department of Surgery , University of Colorado Denver Health Medical Center , Denver , Colorado 80204 , USA . 15Burn Center and Division of Plastic Surgery , University of Washington Harborview Medical Center , Seattle , Washington 98104 , USA . 16Department of Surgery , Brigham and Women 's Hospital , Harvard Medical School , Boston , Massachusetts 02115 , USA . 17Department of Surgery , University of Texas Houston Health Science Center , Houston Medical School , Houston , Texas 77030 , USA . 18Department of Molecular Biology , Massachusetts General Hospital , Harvard Medical School , Boston , Massachusetts 02114 , USA . 19Department of Medical Science , University of Michigan Medical School , Ann Arbor , Michigan 48109 , USA . 20Department of Surgery , Northwestern University Medical School , Chicago , Illinois 60611 , USA . 21Department of Biostatistics , University of Washington , Seattle , Washington 98195 , USA . 22Center for Engineering in Medicine , Massachusetts General Hospital , Harvard Medical School , Boston , Massachusetts 02114 , USA . 23 Department of Medicine , Massachusetts General Hospital , Harvard Medical School , Boston , Massachusetts 02129 , USA . The name of Keith O'Neill was accidentally omitted from the published author list . He is at the first affiliation in the address list . ERRATUM doi : 10.1038 / nature04361 Astronomical pacing of methane release in the Early Jurassic period David B. Kemp , Angela L. Coe , Anthony S. Cohen & Lorenz Schwark Nature 437 , 396 399 -LRB- 2005 -RRB- In the labelling of Fig. 1 of this Letter , the spelling of ` D. semicelatum ' was accidentally reversed to read ` D. mutalecimes ' . It appears correctly in the text . 696 2005 Nature Publishing Group

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O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Alignment

Uncertainty

and

Genomic

Analysis

Karen

M.

Wong

et

al.

Science

-LRB-

-RRB-

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Science

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REPORTS

Alignment

Uncertainty

and

Genomic

Analysis

Karen

M.

Wong

Marc

A.

Suchard

John

P.

The

statistical

methods

applied

to

the

analysis

of

genomic

data

do

not

account

for

uncertainty

in

the

sequence

alignment

Indeed

the

alignment

is

treated

as

an

observation

and

all

of

the

subsequent

inferences

depend

on

the

alignment

being

correct

This

may

not

have

been

too

problematic

for

many

phylogenetic

studies

in

which

the

gene

is

carefully

chosen

for

among

other

things

ease

of

alignment

However

in

a

comparative

genomics

study

the

same

statistical

methods

are

applied

repeatedly

on

thousands

of

genes

many

of

which

will

be

difficult

to

align

Using

genomic

data

from

seven

yeast

species

we

show

that

uncertainty

in

the

alignment

can

lead

to

several

problems

including

different

alignment

methods

resulting

in

different

conclusions

common

theme

in

comparative

genomics

studies

is

a

flow

diagram

or

chart

tracing

the

various

steps

and

algorithms

used

during

the

analysis

of

a

large

number

of

genes

Flow

charts

can

be

quite

sophisticated

with

steps

such

as

identifying

orthologous

gene

sets

aligning

the

genes

and

performing

different

statistical

analyses

on

the

resulting

alignments

The

key

point

and

a

great

practical

difficulty

in

comparative

genomics

studies

is

that

the

analyses

must

be

repeated

many

times

The

procedure

then

is

largely

automated

with

scripting

languages

such

as

Perl

or

Python

cobbling

together

individual

programs

that

perform

each

step

In

addition

many

of

the

individual

steps

involve

procedures

originally

developed

in

the

evolutionary

biology

literature

to

perform

phylogeny

estimation

or

to

identify

individual

amino

acid

residues

under

the

influence

of

positive

selection

-LRB-

-RRB-

Statistical

methods

that

until

recently

would

have

been

applied

to

a

single

alignment

carefully

constructed

are

now

applied

to

a

large

number

of

alignments

many

of

which

may

be

of

uncertain

quality

and

cause

the

underlying

assumptions

of

the

methods

to

fail

How

might

alignment

uncertainty

affect

genomic

studies

We

performed

a

study

designed

to

uncover

the

effect

that

alignment

has

on

inferences

of

evolutionary

parameters

We

examined

genomic

data

from

seven

yeast

species

-LRB-

Saccharomyces

cerevisiae

S.

paradoxus

S.

mikatae

S.

kudriavzevii

S.

bayanus

S.

castellii

and

S.

kluyveri

-RRB-

Earlier

molecular

evolution

studies

that

included

these

species

established

the

appropriateness

of

sequence

comparisons

between

them

-LRB-

-RRB-

with

estimated

divergence

dates

from

S.

cerevisiae

ranging

from

as

little

as

million

years

for

S.

paradoxus

to

about

million

years

for

S.

kluyveri

and

average

pairwise

sequence

similarity

ranging

from

to

The

comparisons

we

carried

out

among

Section

of

Ecology

Behavior

and

Evolution

University

of

California

San

Diego

La

Jolla

CA

USA

of

Biomathematics

University

of

California

Los

Angeles

Los

Angeles

CA

USA

of

Integrative

Biology

University

of

California

Berkeley

Berkeley

CA

USA

To

whom

correspondence

should

be

addressed

E-mail

johnh@berkeley.edu

SCIENCE

VOL

JANUARY

Downloaded

from

on

May

A

the

seven

yeast

species

are

thus

reasonable

and

of

the

sort

that

any

evolutionary

biologist

might

make

Accurate

inference

of

evolutionary

processes

from

molecular

sequences

also

relies

on

the

compared

sequences

being

orthologous

However

correct

identification

of

orthologous

sequences

is

not

trivial

because

current

alignment

algorithms

do

not

evaluate

homology

and

will

align

sequences

regardless

of

proper

evolutionary

relationships

We

combined

two

earlier

data

sets

of

previously

identified

orthologous

open

reading

frames

-LRB-

ORFs

-RRB-

from

studies

on

the

comparative

genomics

analysis

of

yeast

-LRB-

-RRB-

The

orthologs

identified

from

the

Kellis

et

al.

-LRB-

-RRB-

study

were

used

for

species

that

overlapped

between

the

two

studies

-LRB-

S.

mikatae

and

S.

bayanus

-RRB-

and

only

those

ORFs

for

which

all

seven

species

contained

a

detected

orthologous

sequence

were

included

in

the

analysis

Overall

we

considered

a

total

of

sets

of

orthologous

gene

sequences

For

each

orthologous

gene

set

we

applied

seven

different

alignment

programs

Clustal

W

Muscle

T-Coffee

Dialign

Mafft

Dca

and

ProbCons

-LRB-

-RRB-

aligning

data

by

amino

acid

sequence

under

default

program

settings

and

using

the

aligned

amino

acid

sequences

to

construct

nucleotide

alignments

From

this

intensive

undertaking

we

produced

a

table

of

alignments

Alignments

were

then

subjected

to

several

statistical

analyses

of

the

sort

that

an

evolutionary

biologist

might

apply

specifically

we

estimated

the

phylogeny

using

maximum

likelihood

under

the

GTR+G

model

of

DNA

substitution

and

the

number

of

positively

selected

sites

for

each

alignment

-LRB-

-RRB-

Estimates

of

phylogeny

and

inferences

of

positive

selection

were

sensitive

to

alignment

treatment

Confirming

previous

studies

showing

that

alignment

method

has

a

considerable

effect

on

tree

topology

-LRB-

-RRB-

we

found

that

of

the

ORFs

had

one

or

more

differing

trees

depending

on

the

alignment

procedure

used

The

number

of

unique

trees

outputted

for

each

ORF

varied

from

one

to

six

and

the

average

symmetric-difference

distance

-LRB-

-RRB-

between

trees

for

each

ORF

ranged

from

to

-LRB-

for

trees

of

seven

species

the

maximum

possible

value

is

eight

-RRB-

Figure

shows

a

case

in

which

align

ments

produced

by

the

seven

different

alignment

programs

resulted

in

six

different

estimates

of

phylogeny

In

general

phylogenies

estimated

from

different

alignments

for

an

ORF

were

more

concordant

when

the

alignments

were

similar

Figure

shows

a

strong

positive

relation

between

a

measure

of

variability

in

alignments

across

alignment

treatments

and

the

average

topological

distance

between

estimated

trees

-LRB-

-RRB-

The

support

for

the

maximum-likelihood

trees

measured

by

the

nonparametric

bootstrap

was

generally

lower

when

alignments

were

dissimilar

across

treatments

-LRB-

Fig.

-RRB-

One

does

not

usually

find

strongly

supported

but

conflicting

phylogenies

produced

by

different

alignment

treatments

Previous

studies

on

the

effects

produced

by

different

alignment

methods

focused

on

tree

topology

Yet

other

commonly

estimated

evolutionary

parameters

such

as

substitution

rates

and

the

frequency

of

positively

selected

sites

are

also

alignment

dependent

To

examine

if

variable

alignments

for

an

ORF

affect

the

inference

of

these

parameters

we

estimated

the

synonymous

-LRB-

dS

-RRB-

and

nonsynonymous

-LRB-

dN

-RRB-

substitution

rates

for

each

gene

and

inferred

sites

under

positive

selection

using

Paml

under

the

model

with

-LRB-

initially

-RRB-

a

threshold

of

for

inferring

a

site

to

be

under

positive

selection

-LRB-

-RRB-

Overall

estimates

of

substitution

rates

did

not

differ

significantly

among

alignment

treatments

-LRB-

Kruskal-Wallis

test

dN

P

dS

P

dN/dS

P

-RRB-

and

for

most

ORFs

of

the

sites

were

inferred

as

under

positive

selection

regardless

of

the

alignment

treatment

-LRB-

ORFs

-RRB-

However

of

the

remaining

ORFs

only

showed

a

consistent

number

of

positively

selected

sites

Thus

in

of

the

cases

we

found

that

the

inference

of

positively

selected

sites

was

also

sensitive

to

the

method

of

alignment

Raising

the

threshold

for

flagging

sites

as

under

the

influence

of

positive

natural

selection

to

reduced

the

number

of

conflicting

ORFs

-LRB-

Fig.

-RRB-

in

of

the

cases

positive-selection

inference

was

sensitive

to

alignment

treatment

However

reducing

conflict

among

alignment

treatments

comes

at

the

cost

of

finding

fewer

sites

under

positive

selection

and

in

many

cases

alignment

treatments

still

produce

discordant

inferences

of

positive

selection

We

hypothesize

that

the

inconsistent

inferences

of

alignments

produced

by

the

seven

different

alignment

methods

examined

here

is

not

necessarily

a

fault

of

the

alignment

procedures

but

rather

reflects

underlying

variability

in

the

processes

of

substitution

insertion

and

deletion

that

makes

some

ORFs

inherently

more

difficult

to

align

We

examined

alignment

variability

by

approximating

the

marginal

posterior

probability

distribution

of

the

alignment

for

each

ORF

using

the

program

BAli-Phy

-LRB-

-RRB-

BAliPhy

implements

a

stochastic

model

of

insertion

and

deletion

and

explores

posterior

probability

distributions

of

phylogenetic

model

parameters

such

as

the

tree

and

branch

lengths

as

well

as

the

REPORTS

probability

distribution

of

alignment

by

Markov

chain

Monte

Carlo

-LRB-

MCMC

-RRB-

Quantifying

the

uncertainty

of

complex

discrete

random

variables

such

as

alignments

is

a

formidable

task

We

developed

a

crude

summary

statistic

that

reflects

variability

of

the

alignments

sampled

with

MCMC

for

each

ORF

we

calculated

a

distance

between

all

pairs

of

sampled

alignments

and

considered

the

mean

of

these

pairwise

distances

as

a

measure

of

inherent

alignment

uncertainty

for

each

ORF

To

measure

distances

between

alignments

we

exploited

the

metric

of

Schwartz

et

al.

-LRB-

-RRB-

Effectively

this

metric

counts

the

number

of

pairwise

homology

statements

upon

which

two

alignments

disagree

We

found

that

alignment

variability

as

reflected

by

the

marginal

posterior

probability

distribution

of

alignments

was

associated

with

the

inconsistency

of

alignments

produced

by

the

seven

different

alignment

methods

-LRB-

Fig.

-RRB-

and

with

the

number

of

estimated

nonsynonymous

substitutions

for

an

ORF

-LRB-

Fig.

-RRB-

The

problem

of

alignment

uncertainty

in

genomic

studies

identified

here

is

not

a

problem

of

sloppy

analysis

Many

comparative

genomics

studies

are

carefully

performed

and

reasonable

in

design

However

even

carefully

designed

and

carried

out

analyses

can

suffer

from

these

types

of

problems

because

the

methods

used

in

the

analysis

of

the

genomic

data

do

not

properly

accommodate

alignment

uncertainty

in

the

first

place

Moreover

the

genes

that

are

of

greatest

interest

to

the

evolutionary

biologist

probably

suffer

disproportionately

For

example

in

several

studies

the

genes

of

greatest

interest

were

the

ones

that

had

diverged

most

in

their

nonsynonymous

rate

of

substitution

-LRB-

-RRB-

But

these

are

the

very

genes

that

should

be

the

most

difficult

to

align

in

the

first

place

We

also

do

not

believe

that

the

alignment

uncertainty

problem

is

one

that

can

be

resolved

by

simply

throwing

away

genes

or

portions

of

genes

for

which

alignment

differs

Quality

checks

are

common

in

comparative

genomics

studies

often

referred

to

as

filters

in

a

flow

diagram

showing

the

analyses

that

were

performed

The

filters

usually

exclude

CLUSTAL

W

Downloaded

from

on

May

MUSCLE

-LRB-

-RRB-

S

cer

S

par

S

mik

S

klu

S

kud

S

par

MUSCLE

T-COFFEE

DIALIGN

MAFFT

DCA

PROBCONS

CLUSTAL/DIALIGN

-LRB-

-RRB-

S

mik

S

klu

S

par

S

cas

S

kud

T-COFFEE

-LRB-

-RRB-

S

kud

S

bay

S

mik

S

cas

S

cer

S

bay

S

cas

S

bay

S

cer

S

klu

MAFFT

-LRB-

-RRB-

S

cas

S

par

S

klu

S

mik

S

kud

DCA

-LRB-

-RRB-

S

cer

S

mik

S

bay

PROBCONS

-LRB-

-RRB-

S

par

S

cer

S

mik

S

cas

S

kud

S

cas

S

kud

S

cer

S

bay

S

par

S

klu

S

klu

S

bay

Fig.

An

example

involving

ORF

in

which

alignments

produced

by

seven

different

alignment

methods

produce

six

different

estimated

trees

albeit

with

low

bootstrap

support

-LRB-

bootstrap

proportions

shown

parenthetically

for

each

tree

-RRB-

JANUARY

VOL

SCIENCE

REPORTS

ambiguous

alignment

regions

according

to

some

criterion

Discarding

information

from

alignments

is

inadvisable

for

at

least

two

reasons

First

one

may

end

up

discarding

considerable

portions

of

the

primary

data

some

of

which

may

be

informative

In

some

cases

insertion

and

deletion

events

themselves

are

informative

for

phylogeny

estimation

-LRB-

-RRB-

In

other

cases

excluding

a

gapped

position

leads

to

excluding

substitutions

that

occur

elsewhere

in

the

tree

at

that

site

and

are

informative

-LRB-

-RRB-

Moreover

excluding

data

does

not

necessarily

result

in

more

concordant

inferences

Figure

shows

results

of

phylogenetic

analyses

in

which

gapped

sites

were

excluded

from

the

alignments

One

still

finds

many

genes

for

which

phylogenetic

inferences

differ

among

alignment

treatments

Second

when

an

appropriate

statistical

method

of

analysis

is

applied

one

may

be

able

to

make

conclusions

even

in

the

face

of

alignment

uncertainty

For

example

it

might

be

that

the

number

and

identity

of

positively

selected

sites

differ

among

alignment

treatments

However

when

the

alignment

uncertainty

is

properly

accounted

for

one

may

still

be

able

to

pick

out

some

sites

that

are

consistently

under

positive

selection

The

common

statistical

procedure

for

accounting

for

parameter

uncertainty

is

to

treat

the

parameter

as

a

random

variable

and

sum

or

integrate

over

the

uncertainty

weighting

each

possible

value

of

the

parameter

by

its

prior

probability

In

a

comparative

genomics

study

we

advocate

that

alignment

be

treated

as

a

random

variable

and

inferences

of

parameters

of

interest

to

the

genomicist

such

as

the

amount

of

nonsynonymous

divergence

or

the

phylogeny

consider

the

different

possible

alignments

in

proportion

to

their

probability

Considering

alignment

as

a

random

variable

is

innate

to

the

statistical

alignment

pro-

A

B

C

Bootstrap

Support

for

MLE

-LRB-

Alignment

Treatments

-RRB-

Tree

Distance

-LRB-

Alignment

Treatments

-RRB-

Alignment

Distance

-LRB-

Alignment

Treatments

-RRB-

Alignment

Distance

-LRB-

Alignment

Treatments

-RRB-

Fig.

-LRB-

A

-RRB-

Positive

correlation

between

a

measure

of

topological

distance

among

trees

estimated

from

different

alignment

methods

and

alignment

variability

among

alignment

treatments

-LRB-

Spearman

's

rank

correlation

rs

P

-RRB-

-LRB-

B

-RRB-

Conflicting

trees

estimated

from

different

alignment

treatments

tend

to

be

poorly

supported

by

the

nonparametric

bootstrap

method

-LRB-

rs

P

-RRB-

MLE

maximum

likelihood

estimate

-LRB-

C

-RRB-

Positive

correlation

between

the

Bayesian-inferred

alignment

variability

and

average

distance

between

alignments

from

different

methods

for

each

ORF

-LRB-

rs

P

-RRB-

-LRB-

D

-RRB-

Alignment

variability

for

an

ORF

positively

correlates

with

the

number

of

nonsynonymous

substitutions

-LRB-

rs

P

-RRB-

-LRB-

E

-RRB-

Removing

gapped

sites

from

alignments

does

not

remove

conflict

among

trees

estimated

from

different

alignment

treatments

-LRB-

rs

P

-RRB-

D

Tree

Distance

-LRB-

Alignment

Treatments

-RRB-

E

Alignment

Distance

-LRB-

Bayesian

Model

-RRB-

Alignment

Distance

-LRB-

Bayesian

Model

-RRB-

Alignment

Distance

-LRB-

Alignment

Treatments

-RRB-

Fig.

-LRB-

A

-RRB-

The

range

in

the

A

B

number

of

positively

selected

Maximum

Maximum

sites

for

each

ORF

Inferences

of

positive

selection

for

an

ORF

are

consistent

across

alignment

treatments

when

the

minimum

and

maximum

number

of

tively

selected

sites

are

equal

In

many

cases

-LRB-

of

ORFs

-RRB-

Psel

Psel

inferences

of

positive

selection

varied

depending

upon

the

ment

treatment

-LRB-

B

-RRB-

Increasing

stringency

for

inferring

positive

selection

to

decreases

the

number

of

sites

inferred

to

be

under

positive

selection

there

remain

many

cases

-LRB-

of

ORFs

-RRB-

in

which

inferences

of

positive

selection

differ

according

to

alignment

treatment

Minimum

Minimum

SCIENCE

VOL

JANUARY

Downloaded

from

on

May

Nonsynonymous

Rate

-LRB-

Alignment

Treatments

-RRB-

Tree

Distance

-LRB-

Alignment

Treatments

-RRB-

REPORTS

cedure

advocated

by

many

-LRB-

-RRB-

Statistical

alignment

however

generally

assumes

that

the

phylogeny

is

known

a

condition

often

violated

in

comparative

genomics

studies

Moreover

many

biologists

appear

to

take

the

position

that

when

an

alignment

has

been

carefully

constructed

incorporating

uncertainty

is

unnecessary

in

a

phylogenetic

study

for

example

the

phylogenetic

marker

is

carefully

selected

because

it

is

easy

to

align

and

has

a

substitution

rate

appropriate

to

the

phylogenetic

problem

of

interest

-LRB-

-RRB-

a

selectivity

that

may

help

but

probably

does

not

solve

the

alignment

uncertainty

problem

in

many

phylogenetic

studies

especially

those

for

anciently

diverged

species

In

comparative

genomics

studies

however

the

goal

is

to

analyze

all

of

the

genes

in

the

genome

As

we

have

shown

here

many

of

these

genes

will

be

difficult

to

align

and

result

in

highly

variable

evolutionary

parameter

estimates

Allowing

for

uncertainty

in

the

alignment

and

possibly

phylogeny

simultaneously

through

statistical

phylo-alignment

should

be

of

special

importance

in

comparative

genomics

studies

References

and

Notes

Z.

Yang

R.

Nielsen

N.

Goldman

A.

Pedersen

Genetics

-LRB-

-RRB-

P.

F.

Cliften

et

al.

Genome

Res

-LRB-

-RRB-

P.

Cliften

et

al.

Science

-LRB-

-RRB-

M.

Kellis

N.

Patterson

M.

Endrizzi

B.

Birren

E.

Lander

Nature

-LRB-

-RRB-

J.

D.

Thompson

D.

G.

Higgins

T.

J.

Gibson

Nucleic

Acids

Res

-LRB-

-RRB-

R.

C.

Edgar

Nucleic

Acids

Res

-LRB-

-RRB-

C.

Notredame

D.

Higgins

J.

Heringa

J.

Mol

Biol

-LRB-

-RRB-

B.

Morgenstern

Bioinformatics

-LRB-

-RRB-

K.

Katoh

K.

Misawa

K.

Kuma

T.

Miyata

Nucleic

Acids

Res

-LRB-

-RRB-

J.

Stoye

Gene

-LRB-

-RRB-

C.

B.

Do

M.

S.

P.

Mahabhashyam

M.

Brudno

S.

Batzoglou

Genome

Res

-LRB-

-RRB-

J.

A.

Lake

Mol

Biol

Evol

-LRB-

-RRB-

D.

A.

Morrison

J.

T.

Ellis

Mol

Biol

Evol

-LRB-

-RRB-

N.

B.

Mugridge

et

al.

Mol

Biol

Evol

-LRB-

-RRB-

D.

F.

Robinson

L.

R.

Foulds

Math

Biosci

-LRB-

-RRB-

B.

D.

Redelings

M.

A.

Suchard

Syst

Biol

-LRB-

-RRB-

M.

A.

Suchard

B.

D.

Redelings

Bioinformatics

-LRB-

-RRB-

A.

Schwartz

E.

W.

Myers

L.

Pachter

q-bio

A.

G.

Clark

et

al.

Science

-LRB-

-RRB-

B.

D.

Redelings

M.

A.

Suchard

BMC

Evol

Biol

-LRB-

-RRB-

F.

Lutzoni

P.

Wagner

V.

Reeb

S.

Zoller

Syst

Biol

-LRB-

-RRB-

J.

L.

Thorne

H.

Kishino

J.

Felsenstein

J.

Mol

Evol

-LRB-

-RRB-

I.

Holmes

W.

Bruno

Bioinformatics

-LRB-

-RRB-

J.

Hein

J.

Jensen

C.

Pedersen

Proc

Natl.

Acad

Sci

U.S.A.

-LRB-

-RRB-

A.

Graybeal

Syst

Biol

-LRB-

-RRB-

This

research

was

supported

by

NSF

-LRB-

-RRB-

and

NIH

-LRB-

-RRB-

grants

-LRB-

J.P.H.

-RRB-

and

an

Alfred

P.

Sloan

Research

Fellowship

-LRB-

M.A.S.

-RRB-

Supporting

Online

Material

SOM

Text

References

October

accepted

December

science

NFAT

Binding

and

Regulation

of

T

Cell

Activation

by

the

Cytoplasmic

Scaffolding

Homer

Proteins

Guo

N.

Huang

David

L.

Huso

Samuel

Bouyain

Jianchen

Tu

Kelly

A.

McCorkell

Michael

J.

May

Yuwen

Zhu

Michael

Lutz

Samuel

Collins

Marlin

Dehoff

Shin

Kang

Katharine

Whartenby

Jonathan

Powell

Daniel

Leahy

Paul

F.

T

cell

receptor

-LRB-

TCR

-RRB-

and

costimulatory

receptor

-LRB-

-RRB-

signals

cooperate

in

activating

T

cells

although

understanding

of

how

these

pathways

are

themselves

regulated

is

incomplete

We

found

that

and

members

of

the

Homer

family

of

cytoplasmic

scaffolding

proteins

are

negative

regulators

of

T

cell

activation

This

is

achieved

through

binding

of

nuclear

factor

of

activated

T

cells

-LRB-

NFAT

-RRB-

and

by

competing

with

calcineurin

Homer-NFAT

binding

was

also

antagonized

by

active

serine-threonine

kinase

AKT

thereby

enhancing

TCR

signaling

via

calcineurin-dependent

dephosphorylation

of

NFAT

This

corresponded

with

changes

in

cytokine

expression

and

an

increase

in

effector-memory

T

cell

populations

in

Homer-deficient

mice

which

also

developed

autoimmune-like

pathology

These

results

demonstrate

a

further

means

by

which

costimulatory

signals

are

regulated

to

control

self-reactivity

cells

are

activated

through

the

TCR

and

costimulatory

pathways

predominantly

mediated

by

the

cell

surface

receptor

Although

these

pathways

are

relatively

well

defined

questions

still

remain

about

how

costimulatory

signals

are

regulated

The

Homer

family

of

cytoplasmic

scaffolding

proteins

are

known

to

function

at

the

neuronal

excitatory

synapse

-LRB-

-RRB-

although

their

wide

tissue

distribution

including

within

the

immune

system

suggests

that

their

functions

may

be

relatively

broad

To

investigate

the

in

vivo

functions

of

the

Homer

proteins

we

generated

mice

in

which

the

loci

for

each

Homer

gene

were

deleted

-LRB-

and

-RRB-

Of

these

we

noted

that

the

mice

-LRB-

-RRB-

displayed

lymphocyte

infiltration

of

multiple

organs

and

hyperplasia

in

lymph

nodes

by

weeks

of

age

-LRB-

Fig.

-RRB-

Knockdown

of

or

but

not

enhanced

the

expression

of

a

luciferase

reporter

driven

by

the

promoter

by

a

factor

of

to

-LRB-

Fig.

-RRB-

and

appeared

to

have

redundant

functions

in

these

assays

because

overexpression

of

but

not

could

rescue

the

loss

of

-LRB-

Fig.

-RRB-

The

promoter

integrates

signals

from

the

calcineurin-NFAT

and

NF-kB

pathways

-LRB-

-RRB-

to

identify

which

pathways

might

be

regulated

by

Homer

we

used

luciferase

reporter

constructs

under

the

control

of

multimerized

binding

elements

for

individual

transcription

factors

The

calcineurin-NFAT

pathway

was

preferentially

enhanced

in

cells

depleted

of

or

-LRB-

Fig.

-RRB-

To

respond

to

calcium

signals

NFAT

is

first

dephosphorylated

by

calcineurin

-LRB-

-RRB-

and

in

Jurkat

T

cells

that

expressed

shRNAs

targeting

enhanced

dephosphorylation

of

the

isoform

was

observed

after

activation

but

not

under

basal

conditions

-LRB-

Fig.

-RRB-

No

difference

Program

in

Biochemistry

Cellular

and

Molecular

Biology

Johns

Hopkins

University

School

of

Medicine

Baltimore

MD

USA

H.

Snyder

Department

of

Neuroscience

Johns

Hopkins

University

School

of

Medicine

Baltimore

MD

USA

of

Molecular

and

Comparative

Pathobiology

Johns

Hopkins

University

School

of

Medicine

Baltimore

MD

USA

Department

of

Biophysics

and

Biophysical

Chemistry

Johns

Hopkins

University

School

of

Medicine

Baltimore

MD

USA

of

Animal

Biology

University

of

Pennsylvania

Philadelphia

PA

USA

Department

of

Dermatology

Johns

Hopkins

University

School

of

Medicine

Baltimore

MD

USA

of

Oncology-Immunology/Hematopoiesis

Johns

Hopkins

University

School

of

Medicine

Baltimore

MD

USA

of

Neurology

Johns

Hopkins

University

School

of

Medicine

Baltimore

MD

USA

T

-LRB-

fig

-RRB-

which

suggested

that

at

least

one

of

the

family

might

possess

some

level

of

immune

function

Because

Homer

proteins

typically

have

redundant

roles

-LRB-

-RRB-

we

first

assessed

their

possible

role

in

T

cell

activation

by

assaying

-LRB-

-RRB-

production

in

T

cells

lacking

all

three

genes

-LRB-

TKO

-RRB-

production

was

increased

by

a

factor

of

to

in

stimulated

T

cells

from

Homer

TKO

mice

relative

to

wild-type

controls

-LRB-

Fig.

-RRB-

By

contrast

when

T

cells

were

activated

by

costimulation

of

both

and

no

measurable

difference

in

production

was

detected

between

wild-type

and

Homer-deficient

mice

-LRB-

fig

-RRB-

To

examine

the

potential

role

of

Homer

proteins

in

T

cell

activation

in

more

detail

we

used

short

hairpin

RNAs

-LRB-

shRNAs

-RRB-

to

knock

down

Homer

gene

expression

in

human

Jurkat

T

cells

VOL

SCIENCE

Present

address

Department

of

Molecular

Biology

University

of

Texas

Southwestern

Medical

Center

Dallas

TX

USA

These

authors

contributed

equally

to

this

work

To

whom

correspondence

should

be

addressed

E-mail

pworley@jhmi.edu

JANUARY

Downloaded

from

on

May

B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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