© 2005 Nature Publishing Group
A network-based analysis of systemic inflammation
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
Oligonucleotide and complementary DNA microarrays are being
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
network knowledge-base approach to analyse genome-wide tran-
scriptional responses in the context of known functional inter-
relationships among proteins, small molecules and phenotypes.
This approach was used to analyse changes in blood leukocyte
gene expression patterns in human subjects receiving an inflam-
matory stimulus (bacterial endotoxin). We explore the known
genome-wide interaction network to identify significant func-
tional 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
onblood leukocytes andother tissues ofcritically injuredpatients, in
order to better elucidate the mechanisms underlying systemic
inflammatory responses9. This approach cannot 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 well-
defined inflammatory challenge.
Gene expression in whole blood leukocytes was determined
immediately before and at 2, 4, 6, 9 and 24h after the intravenous
administration of bacterial endotoxin to four healthy human sub-
jects. Four additional subjects were studied under identical con-
ditions but without endotoxin administration. The infusion of
endotoxin activates innate immune responses and presents with
proinflammatory phase and a subsequent counterregulatory phase,
with resolution of virtually all clinical perturbations within 24h.
K-means cluster and principal component analyses were first used
to visualize the overall response to endotoxin administration. Figure
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 endo-
toxin, whereas no significant changes were observed in control
subjects (estimated false discovery rate ,0.1%). Of the 5,093 probe
returning to baseline by 24h (see bins 0–4). In contrast, a smaller
number of probe sets were induced by 2h (bins 5, 6), and the
returning to baseline by 24h (bins 7–9).
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 per-
turbed biological networks underlying this complex clinical pheno-
biology, derived from basic and clinical research.
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
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 ahigh degreeof connectivity. On average, individual genes have
11.5 interaction partners (median 4.0), of which 7.2 represent direct
physical interactions (median 3.0). Every gene interaction in the
network is supported by published information. For example, the
1Department 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, and5Department 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
Vol 437|13 October 2005|doi:10.1038/nature03985
© 2005 Nature Publishing Group
immediate neighbourhood for the RELA gene (NF-kB p65, Sup-
plementary Fig. 1) 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
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 proto-
typical inflammatory cell containing 292 representative genes and
detailing all direct interactions in our database (Fig. 2). Closer
inspection of the temporal response reveals the fine structure of
dynamic changes in RNA abundance by highlighting the transient
expression of several secreted proinflammatory cytokines and che-
mokines (TNFSF2 (TNF), IL1A, IL1B, CXCL1 (GROa), CXCL2
(GRO-b), CCL2 (MCP-1), CXCL8 (IL-8) and CXCL10) reached a
maximum 2–4h 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 (NFKB1, NFKB2, RELA and RELB) reached
The time period 4–6h afterendotoxin 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 (STAT genes), and the
cAMP-response element-binding protein (CREB) and CCAAT/
enhancer binding protein (CEBP) gene families. Transcription fac-
tors limiting the innate immune response included suppressor of
(4–6h) in increased mRNA abundance of secreted and membrane-
associated proteins that limit the inflammatory response, including
IL1RAP, IL1R2, IL10 and TNFRSF1A. Together, these data compre-
hensively document the temporal modulation of genes controlling
the innate immune response in a human model that progresses
from an acute proinflammatory phase to unencumbered counter-
regulation, 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 connec-
tions 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 ident-
ified by a statistical likelihood calculation (P , 0.0001) were merged
to represent the biological processes.
Our global representation of the inflammatory response to endo-
toxin, shown in Fig. 3a, comprises a networkof 1,556 genes and their
interactions. This network consists of a subset of 1,214 genes (78%)
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
data alone (Fig. 1). Simultaneous survey and evaluation of the sub-
network 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 (a)
increased expression of components of the superoxide-producing
phagocyte NADPH-oxidase system, a multicomponent enzyme
important for host defence11, (b) decreased expression of the major
histocompatibility (MHC) II complex, consistent with reduced
antigen presentation following endotoxin stimulation12,13, (c)
decreased expression of the TCP1 ring complex required for folding
of cytoskeletal proteins, (d) increased expression in the family of
tubulin-A microtubule genes, (e) suppressed expression of several
cell-cycle regulation, and (f) reduced integrin-a and -b chain
expression, affecting cell–cell and cell–matrix adhesion (see Sup-
plementary Methods 2, containing Supplementary Fig. 3a–f).
Further, significant decreases in messenger RNA abundance were
Figure 1 | Gene expression profiles of circulating leukocytes in response to
bacterial endotoxin infusion. Samples from eight healthy volunteers were
tested at baseline (0h) and 2, 4, 6, 9 and 24h after intravenous
administration of endotoxin (four subjects) or vehicle (four subjects).
a, Significant (false discovery rate of ,0.1%) probe sets (5,093) were
of the significant probe sets at the indicated times after endotoxin
NATURE|Vol 437|13 October 2005
© 2005 Nature Publishing Group
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
changes in apparent expression over 24h, identifying nodes and
interactions. b, Temporal changes in apparent expression. The response to
cell-wide response, propagating and resolving over time.
NATURE|Vol 437|13 October 2005
© 2005 Nature Publishing Group
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 (red,
increased; blue, decreased expression). b, Selected regions of the network,
highlighting several groups of genes. Group 1, mitochondrial respiratory
chain complex I (NDUF genes). Group 2, mitochondrial respiratory chain
complex III (UQCR genes). Group 3, ATP synthase complex (ATP5 genes).
Group 4, pyruvate dehydrogenase complex. Group 5, mitochondrial
permeability transition pore complex. Group 6, elongation initiation factor
complex (EIF3 genes). Group 7, ribosomal proteins (RPL, RPS genes).
Group 8, COP9 signallosome (COPS genes). Group 9, proteasome (PSM
NATURE|Vol 437|13 October 2005
© 2005 Nature Publishing Group
observed in the mitochondrial respiratory chain complexes I–V
(NDUF in Fig. 3b (Group 1), UQCR (Group 2) and ATP synthase
dehydrogenase complex (PDH, Fig. 3b, Group 4), which generates,
via acetyl-CoA and the tricarboxylic acid (TCA) cycle, reduced
coenzymes required for ATP synthesis during mitochondrial oxi-
dative phosphorylation. A concomitant increase in expression of
pyruvate dehydrogenase kinase-3 (PDK3), an inhibitor of PDH, was
observed. Expression of the voltage dependent anion channel
(VDAC) and adenine nucleotide translocator (SLC25A5), com-
ponents of the mitochondrial permeability transition pore (MPTP)
antagonistic benzodiazepine receptor (BZRP) was increased (Fig. 3b,
Group 5). MPTP activation has previously been considered an early
event in apoptosis, leading to mitochondrial membrane depolariz-
ation and release of cytochrome c. However, recent reports have
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 activesecretorycells, leukocytes devotea substantial amount of
energy expenditure to protein synthesis16. In concert with the
suppression of modules participating in energy production,
(EIF3 in Fig. 3b, Group 6), a large number of ribosomal proteins
(RPS, RPL genes in Fig. 3b, Group 7), the RNA polymerase II
complex, and also in the functional modules of the ATP/ubiquitin-
dependent protein degradation pathway, the COP9 signallosome
(Fig. 3b, Group 8) and the proteasome (PSM genes in Fig. 3b, Group
Here we use a knowledge-based network analysis to reveal con-
certed dysregulation of functional modules in mitochondrial bioe-
nergetics, proteinsynthesis and proteindegradation inhuman blood
mation. These findings document a reprioritization of the transcrip-
tionalregulatory programmeinleukocytes inresponsetoendotoxin.
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 chal-
lenges, as observed during innate immune system tolerance.
Human disease phenotypes are manifested by the malfunctioning
of multiple functional modules interrelated in physiological regulat-
ory systems. The overwhelming diversity of possible genome-wide
interactions and gene expression patterns limit effective learning
from experimental data alone. Network analyses using comprehen-
sive 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.
Adecrease in high-energy substrates has been observed inthe muscle
injury and endotoxemia17–19. In addition, direct disruption of mito-
chondrial complexes by cellular-stress-derived mediators (for ex-
ample, reactive oxygen species) has been suggested for necrotic cell
death in animal models of sepsis20and 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 confir-
mation 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
systemic inflammation show similar perturbations of the functional
modules identified here as do otherwise healthy, endotoxin-chal-
Human endotoxin model. Eight healthy male and female subjects between 18
and 40years of age provided written informed consent. Details of endotoxin
were intravenously administered either NIH Clinical Center Reference Endo-
toxin, (CC-RE-Lot 2) at a dose of 2ngkg21body weight (n ¼ 4, one female and
threemales)or0.9%sodiumchloride(n ¼ 4,onefemaleandthreemales)overa
5-minperiod. Bloodsamples were collectedbefore endotoxin infusion (0h)and
2, 4, 6, 9 and 24h after infusion.
Blood sampling. Blood was collected and lysis buffer (bicarbonate-buffered
in H2O) was added at a ratio of 20:1 (lysis buffer:blood). Samples were then
incubated at room temperature until erythrocyte lysis was complete (,5–
7min). Leukocytes were recovered by centrifugation (400g, 48C) and washed
once in ice-cold phosphate buffered saline. Leukocyte pellets were then resus-
pended in 8ml RLT buffer (Qiagen) 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 2708C until RNA extraction was required.
Leukocyte RNA isolation. Total cellular RNA was isolated from the leukocyte
pellets using a commercial kit (RNeasy, Qiagen). Purity was confirmed by
Bioanalyser, Agilent Inc).
cRNA synthesis and chip hybridization. cRNA synthesis was performed using
4mg total cellular RNA, hybridized onto Hu133A and Hu133B oligonucleotide
arrays (Affymetrix), 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
Hu133Barrays were analysed. Normalizationwas performed using dChip22, and
expression level was modelled using the perfect match only model. Probe sets
identified as absent on all arrays (using MicroArray Suite v5, Affymetrix) were
notincludedinfurtheranalysis.Probesetssignificantly perturbedafter bacterial
endotoxin administration were identified using significance analysis of micro-
arrays (SAM) (multiclass response)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
no temporal changes of the probe sets reached the significant level (false
discovery rate ,0.1%). A total of 3,714 unique genes were identified from
More information is at http://www.gluegrant.org/.
Verification of genes showing significant transcriptional changes. An
additional six healthy subjects (one female and five males) were administered
2ngkg21(body weight) endotoxin, and blood samples were collected before
(0h) and after (2 and 6h) endotoxin infusion. At either 2 or 6h, ,88% of the
discovery rate ,1%). See Supplementary Methods 1.
Development of the observed interactome of mammalian genes. The Inge-
nuity Pathways Knowledge Base (KB) is the largest curated database of
previously published findings on mammalian biology from the public literature
rat were first identified frompeer-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 (for example, natural
language processing and high-throughput screening).
interactions between mammalian orthologues. For example, the knowledge of
functional interactions (for example, phosphorylation) was combined with
knowledge of protein functions (for example, kinase activity) to algorithmically
infer a direct biochemical interaction. Every resulting gene interaction has
supporting literature findings available online. More information is in Sup-
plementary Methods 2.
Identification of significant pathways in biological processes. The following
steps were used: (1) Genes identified as significant from the experimental data
having direct interaction(s) with other genes in the database. (2) 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
NATURE|Vol 437|13 October 2005
© 2005 Nature Publishing Group
had a maximum of 35 genes. (3) Pathways of highly interconnected genes were
identified by statistical likelihood using the following equation:
Score ¼ 2log10 12
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(n,k) is the binomial
coefficient. (4) Pathways with a score greater than 4 (P , 0.0001) 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.
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Supplementary Information is linked to the online version of the paper at
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 (to R.G.T.).
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.
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
10Affiliations 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.
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.
19Department of Surgery, University of Washington Harborview Medical Center, Seattle,
NATURE|Vol 437|13 October 2005
© 2005 Nature Publishing Group Download full-text
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 (2005) doi:10.1038/nature03985
In this Letter, the affiliations of authors participating in the Inflam-
mation 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
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,
Sine ´ad 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 (2005) doi:10.1038/nature03983
The name of Keith O’Neill was accidentally omitted from the
published author list. He is at the first affiliation in the address list.
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 (2005)
In the labelling of Fig. 1 of this Letter, the spelling of ‘D.semicelatum’
in the text.
†Inflammation and Host Response to Injury Large Scale
David G. Camp3, George Casella4, Irshad H. Chaudry5,
Mashkoor A. Choudhry5, Charles Cooper6, Asit De1,
Constance Elson7, Bradley Freeman8, Richard L. Gamelli9,
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.
Northwest National Laboratory, Richland, Washington 99352, USA.4Department of
Statistics, University of Florida, Gainesville, Florida 32611, USA.
Surgery, University of Alabama School of Medicine, Birmingham, Alabama 35294,
Medical School, Cambridge, Massachusetts 02139, USA.7Department of Biostatistics,
Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
St. Louis, Missouri 63110, USA.
School of Medicine, Maywood, Illinois 60153, USA.
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.
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,
School, Boston, Massachusetts 02115, USA.17Department of Surgery, University of
Texas Houston Health Science Center, Houston Medical School, Houston, Texas
Harvard Medical School, Boston, Massachusetts 02114, USA.
Medical Science, University of Michigan Medical School, Ann Arbor, Michigan 48109,
Illinois 60611, USA.21Department of Biostatistics, University of Washington, Seattle,
Washington 98195, USA.
General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA.
23Department of Medicine, Massachusetts General Hospital, Harvard Medical School,
Boston, Massachusetts 02129, USA.
6Department of Molecular Biology, Massachusetts General Hospital, Harvard
8Department of Surgery, Washington University School of Medicine,
9Department of Surgery, Loyola University Stritch
10Department of Surgery,
12Department of Surgery, University of Texas
16Department of Surgery, Brigham and Women’s Hospital, Harvard Medical
18Department of Molecular Biology, Massachusetts General Hospital,
20Department of Surgery, Northwestern University Medical School, Chicago,
22Center for Engineering in Medicine, Massachusetts
ERRATA & CORRIGENDA
NATURE|Vol 438|1 December 2005