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The Proceedings of the American Thoracic Society 2:188-194 (2005)
© 2005 The American Thoracic Society

Functional Genomic Insights into Acute Lung Injury

Role of Ventilators and Mechanical Stress

Stephanie A. Nonas, James H. Finigan, Li Gao and Joe G. N. Garcia

Center for Translational Respiratory Medicine, Division of Pulmonary and Critical Care Medicine, Johns Hopkins University, School of Medicine, Baltimore, Maryland

Correspondence and requests for reprints should be addressed to Joe G. N. Garcia, M.D., Lowell T. Coggeshall Professor of Medicine, University of Chicago Pritzker School of Medicine, 5841 S. Maryland Avenue, W604 Chicago, IL 60637. E-mail: jgarcia{at}medicine.bsd.uchicago.edu

ABSTRACT

Acute lung injury (ALI) is a complex and devastating illness, often occurring in the setting of sepsis and trauma. Despite recent advances in the understanding and treatment of ALI, pathogenic mechanisms and genetic modifiers in ALI remain incompletely understood. Furthermore, there has been increasing interest in the identification of genetic variations that contribute to ALI susceptibility and severity in order to gain unique insights into ALI pathogenesis and to design novel treatment strategies. However, the sporadic nature of ALI and the lack of family-based cohort studies preclude conventional genomic approaches such as linkage mapping (or "positional cloning"). We have used a "candidate gene approach" with extensive gene expression profiling studies in animal (rat, murine, canine) and human models of ALI to identify potential ALI candidate genes associated with sepsis and ventilator-associated lung injury. These studies, when combined with innovative in silico bioinformatics approaches, revealed both novel (pre–B-cell colony enhancing factor, myosin light chain kinase) and previously identified (interleukin 6, macrophage migration inhibitory factor) gene candidates. Subsequent single nucleotide polymorphism discovery and genotyping studies revealed polymorphisms that demonstrate an influence on ALI susceptibility in patients. These studies indicate that the candidate gene approach is a robust strategy to provide novel insights into the genetic basis of ALI, and the identification of potentially novel therapeutic targets.

Key Words: single nucleotide polymorphism • interleukin 6 • pre–B-cell colony enhancing factor

Acute lung injury (ALI) is a common and devastating illness in patients with sepsis, pneumonia, or trauma, and carries an annual mortality rate of 30 to 50% (1). Marked by profound inflammation, increased vascular permeability, and alveolar flooding, the acute respiratory failure that is associated with ALI invariably requires mechanically assisted ventilation. Despite advances in care for patients with ALI, the development of sophisticated hemodynamic monitoring technologies, and new insights into the pathogenesis underlying sepsis and ALI, there remains a significant gap in the full translation of this progress into increased ALI survival (2). Furthermore, improved understanding of ALI at both the molecular and population level has not reconciled the heterogeneity in patient susceptibility to ALI or ALI outcomes. Until recently, studies focused on understanding the molecular basis of complex disorders such as ALI were limited to a gene-by-gene approach. However, the capacity for high-throughput sequencing coupled with the mapping of the human genome heralded additional revolutionary technologic breakthroughs with tools for a large-scale analysis of the genome, including rapid, high-throughput gene expression profiling and genotyping (3, 4). Access to the complete genome sequences of prokaryotes, eukaryotic model organisms, as well as the mouse, rat, and dog has sparked efforts to identify specific gene expression patterns via large-scale microarray analysis that will ultimately help diagnose, prognosticate, guide therapy, or otherwise contribute to our overall understanding of human disease.

In this article, we describe our use of a "candidate gene approach" with extensive gene expression profiling studies in animal (rat, murine, canine) and human models of ALI to identify potential ALI candidate genes associated with sepsis and ventilator-associated lung injury (VALI). These studies, combined with innovative in silico bioinformatics approaches, revealed both novel and previously identified gene candidates. Subsequent single nucleotide polymorphism (SNP) discovery and genotyping studies have revealed polymorphisms that demonstrate an influence on ALI susceptibility in patients. These studies indicate that the candidate gene approach is a robust strategy to provide novel insights into the genetic basis of ALI, and the identification of potentially novel therapeutic targets.

STUDIES IMPLICATING A GENETIC BASIS FOR ALI SUSCEPTIBILITY AND SEVERITY

Despite the evidence from association-based studies suggesting that genetic variation contributes to ALI susceptibility and severity (5–13), the genetic basis of ALI remains incompletely understood. Multiple factors contribute to the difficulty in defining the exact nature of genetic factors relevant to ALI. For example, ALI arises from diverse precipitating factors in a critically ill population exhibiting large phenotypic variance. Incomplete gene penetrance, as is the case with the gene for familial pulmonary hypertension (14), and the strong potential for locus heterogeneity, further complicate the genetic exploration of ALI susceptibility, as do the complex gene–environment interactions that undoubtedly exist. As susceptibility and severity of a complex illness such as ALI are unlikely to be determined by the activity of a single gene, we and other investigators have employed a "candidate gene approach" (Figure 1), a strategy for investigating the genetic basis of complex illnesses such as ALI using unrelated cases and control subjects. In the candidate gene approach, investigators study the association between SNPs in a certain gene or allele and a specific disease by studying the frequency of the target variant allele in a population of affected patients and comparing this with the frequency in control subjects. Using this approach, Marshall and coworkers identified a well known insertion/deletion polymorphism (D) in angiotensin-converting enzyme (ACE) that was associated with a marked increase in mortality in patients with the acute respiratory distress syndrome (ARDS) (8). Noting that the homozygous DD genotype was significantly increased in patients with ARDS compared with ICU control subjects without ARDS, these investigators went on to demonstrate that patients with ARDS who were homozygous for the deletion, and therefore carriers of the ACE DD genotype, were at the highest risk, with a more than fourfold increase in mortality compared with "wild-type" control subjects. This landmark study was the first to demonstrate a positive association between a candidate gene displaying polymorphic variants with the incidence and outcome of ARDS. Several other recent studies used similar techniques to identify polymorphisms in inflammatory markers such as tumor necrosis factor {alpha} (TNF-{alpha}) and interleukin 6 (IL-6), as well as surfactant protein B, thrombospondin 1 (TSP-1), CD14, plasminogen activator inhibitor type 1 (PAI-1), and the CD14-159/toll-like receptor complex that correlate with susceptibility and outcome in patients with severe ALI (5–7, 10, 11, 13, 15). These case–control studies, many of which were hampered by small cohort sizes (typically less than 100 patients), have demonstrated the preliminary applicability of a candidate gene approach to the analysis of ALI with implied associations between the frequency of the target variant allele and ALI.



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  		Figure 1. Overview of the "candidate gene approach." Candidate genes are identified by expression arrays from both human disease and animal models of human disease, or by published association or linkage studies. As depicted, linkage studies are not feasible in patients with ALI. The selection of these candidate genes for further genotyping are validated using knockout (KO) and transgenic technologies and the structure and function of their corresponding gene products evaluated using proteomic methods. Ultimately, genotyping of SNPs in these genes using well phenotyped patient populations will allow the identification of genetic polymorphisms in disease-specific predictor genes that are associated with increased risk or severity of disorders such as ALI.

 
CROSS-SPECIES GENE EXPRESSION PROFILING AND IDENTIFICATION OF NOVEL ALI CANDIDATE GENES

Although these association data clearly suggest a genetic basis for ALI, there is a paucity of strategies with which to evaluate or implicate novel ALI genes. The sporadic nature of ALI precludes conventional genomic approaches such as heritability studies or linkage mapping (or "positional cloning"), strategies that are effective in other multigenic lung disorders, such as asthma, where large families with both affected and unaffected individuals can be examined for loci linked to the trait of interest. Thus, alternative techniques using animal and cell culture models of disease must be used to discover genetic susceptibility loci for ALI and identify new molecular targets. These novel targets can then be confirmed in prospective human and animal studies of ALI. Expression profiling using high-density gene chip arrays and tissue from animal models of disease or human tissues is helpful in identifying potential candidates for subsequent SNP analysis on the basis of either enhanced or reduced expression. Despite these clear advantages, traditional gene array analyses often yield large numbers of potential candidates, and the challenge has been to narrow down such lists to the best possible candidates. One technique that we have used successfully has been to compare gene response patterns across species, using evolutionarily conserved orthologs to identify likely candidate genes.

To help identify genetic determinants that render patients susceptible to the adverse effects of mechanical ventilation, we combined extensive array data from multiple animal models (rat, murine, canine) of VALI and from a cell-culture model of stretch-induced injury, allowing us to build a candidate gene list for use in subsequent SNP and genotyping analyses in patient populations. Three in vivo animal models were used: a rat model of VALI in which rats were exposed to high-volume mechanical ventilation (12 ml/kg) for 5 h, a murine model in which mice were exposed to high-volume mechanical ventilation (15 ml/kg) for 2 h, and a canine model in which dogs underwent saline lavage followed by high-volume mechanical ventilation at 17 ml/kg for 5 h. Control animals for each model were allowed to breathe spontaneously. Lung tissue and bronchoalveolar lavage (BAL) fluid were collected for microarray and protein analyses. In separate experiments, human pulmonary artery endothelial cells were cultured and exposed for 48 h to cyclical mechanical stretch (designed to simulate VALI) and RNA extracted for microarray analysis. Gene expression profiling was performed using the Affymetrix GeneChip Microarray System (Affymetrix, Santa Clara, CA) that we have described previously (16) with semiquantitative RT-PCR, Western blot, and real-time PCR used to validate individual gene expression.

We next employed a novel bioinformatics approach to the generated gene expression data in these VALI models in order to mine for additional candidate genes. First, using a eukaryote ortholog database, we identified a subset of genes which were represented across all species (rat, murine, canine, human) on our Affymetrix chips, narrowing down a potential list of more than 30,000 expressed sequence targets (ESTs) to 3,077 probe IDs which were common to all four systems. Next, we filtered the expression data by selecting those genes with significant (p < 0.05) unidirectional expression in response to mechanical stress across species, thereby further narrowing the list of potential targets to 141 genes. Finally, we excluded all genes that did not achieve at least a 1.35-fold change in expression, a filtering event which resulted in a final list of 69 VALI candidate genes (19). Figure 2 depicts this filtering approach used for the generation of a robust candidate gene list, which included data derived from a total of 25 Affymetrix chips. This analysis confirmed several previously identified ALI/VALI candidate genes including IL-6, IL-1ß (IL-1B), and coagulation factor III and identified several novel unknown candidate genes including the chemokine (C-X-C motif) receptor 4 (CXCR4) and the IL-1 receptor 2 (IL1R2).



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  		Figure 2. Filtering approach using an ortholog database to identify mechanical stress–related genes. Depicted is the rationale for the development of an ortholog database (described in detail in Reference 19) and filtering of the microarray data obtained from more than 50 Affymetrix Chips involved in the response to mechanical ventilation or cyclic stretch across four species (rat, mouse, canine, human).

 
We next expanded our analysis to include gene expression data both from patients with ALI and from murine and canine models of ALI, with the goal of generating a candidate gene list for use in subsequent SNP and genotyping analyses in patient populations (25). Two canine models of ALI were used: a model of unilateral saline lavage-induced lung injury with subsequent high-volume mechanical ventilation (17), and a model of intrabronchially delivered endotoxin (LPS) followed by conventional mechanical ventilation (18). Two murine ALI models were also used: a model of intratracheal LPS alone and a second model with 2 h of high-volume mechanical ventilation (17 ml/kg) as the primary insult as we recently described (18, 19). Control groups for canine and murine models were allowed to ventilate spontaneously. Lung tissue and BAL fluid were collected for microarray and protein analyses and compared with human BAL and serum samples obtained from mechanically ventilated patients with ALI and from healthy control subjects. Using this technique, once again we confirmed altered expression in several reported candidates such as IL-6, PAI-1, and macrophage migration inhibitory factor (MIF) (11, 20–24), as well as several unknown candidates including Pre–B-cell colony enhancing factor (PBEF), myosin light chain kinase (MLCK), aquaporin 1 (AQP1), and IL-1 receptor antagonist (IL-1RA) (7, 12, 25–27) (Table 1). Ontology analysis of the candidate genes demonstrates that the major mechanical stretch-related ALI biological process was blood coagulation with genes in this ontology including fibrinogen A, coagulation factor III, plasminogen activator, urokinase receptor (uPAR), tissue factor, and PAI-1 (Figure 3). Many of these same genes have been implicated elsewhere as potential candidate genes in ALI (13, 28, 29) and in VALI (30, 31). In addition to coagulation, there was prominent representation of genes involved in innate immunity (MIF, CD14, TNF, IL-1RA, C5aR, IL-6). Based on these reports as well as data generated by our cross-species analysis of ALI and VALI, we speculate that mechanical stretch directly upregulates coagulation and inflammatory gene expression in the pulmonary endothelium with an accompanied activation of the coagulation cascade, inflammatory cell recruitment, and the development of ALI, a scenario consistent with clinical reports on the effect of excessive tidal volume ventilation (32). Although provocative, further studies of ALI are obviously needed, especially focusing on the time course analysis of expression pattern of selected candidate genes in response to ALI. Nevertheless, these studies suggest that the candidate gene approach is a robust strategy to provide novel insights into the genetic basis of ALI, and the identification of potentially novel therapeutic targets.


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  		TABLE 1. Implicated and novel ali genes identified by the ortholog approach

 


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  		Figure 3. Lung gene ontologies involved in the response to mechanical stress. We employed MAPPFinder (www.genMAPP.org), a program that uses a relational database to link the thousands of genes in an array dataset with corresponding gene ontologies (GOs), to identify gene ontologies involved in the response to mechanical ventilation or cyclic stretch across four species (rat, mouse, canine, human). Ontology analysis shows biological relationships between genes and gene products, giving experimental gene array expression data a biological context. Here, the blood coagulation ontology was identified as the major mechanical stretch–related ALI biological process, with significant representation of the immune response, cell motility/chemotaxis, and inflammatory response ontologies.

 
PBEF AS AN ALI CANDIDATE GENE VIA EXPRESSION PROFILING

As noted above, cross-species expression profiling revealed extensive expression of genes along specific ontologies (16, 33, 34), and identified a set of previously identified ALI genes (such as the gene encoding IL-6) as well as novel ALI candidate genes (Table 1). Within this list of novel ALI genes is the PBEF gene, one of a handful of genes with extremely high level of expression across the range of ALI models used and in human ALI samples. Whereas we were the first to report that PBEF is significantly upregulated in the lung as well as in models of lung injury (25), the published literature on PBEF is quite sparse (35–37). This gene encodes for a proinflammatory cytokine, originally described for its role in the maturation of B-cell precursors with gene expression up-regulated in amniotic membranes from patients undergoing premature labor, especially with amniotic infections. As this was the first demonstration of PBEF expression in lung tissues, we validated these results by RT-PCR of lung tissue RNA, by real-time PCR, and by immunohistochemistry studies. PBEF protein levels were significantly increased in both BAL fluid and serum of human, murine, and canine ALI models as well as in cytokine- or cyclic stretch–activated lung microvascular endothelium (25). Triple immunohistochemical staining of canine lungs revealed colocalization of increased PBEF expression in lung endothelium, type II alveolar epithelial cells, and infiltrating neutrophils, as well as upregulation of PBEF expression in inflammatory cytokine–stimulated human pulmonary microvascular endothelial cells in vitro (25). These results support PBEF as a potential biomarker in ALI and potentially involved in inflammatory lung processes, a notion supported by recent studies in patients with sepsis which convincingly demonstrate that PBEF inhibits neutrophil apoptosis (25). Although the exact mechanisms by which PBEF participates in ALI are unknown, recent analyses using gene relevance networks and bacterial 2 hybrid assays (S. Q. Ye and coworkers, unpublished data) have suggested potential roles for PBEF in protein degradation, innate immunity, and cell/energy metabolism (Figure 4).



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  		Figure 4. Gene relevance network and bacterial two-hybrid assay evaluation of PBEF. To explore potential mechanisms of PBEF function in cells, we employed two complementary approaches involving gene relevance networks (Pathlinx; see www.hopkinsgenomics.org) and bacterial two-hybrid assay. These studies each used a pairwise comparison of gene expression, cDNA in the case of gene relevance networks and protein in the case of the bacterial two-hybrid assay, to compare PBEF expression with that of other genes in order to identify common pathways of enhanced (or decreased) expression in response to mechanical stress. These analyses revealed common targets at both the gene and protein level, in three prominent pathways: protein degradation, innate immunity, and cell metabolism.

 
ESTABLISHMENT OF A DNA CONSORTIUM OF PATIENTS WITH ALI

To address the potential genetic influences on ALI susceptibility and outcome, we established an ALI Genomic DNA Repository comprised of DNA harvested from patients with sepsis and ALI (currently ~ 550 samples). This collaborative enrollment network, entitled Consortium to Evaluate Lung Edema Genetics (CELEG), details the preliminary genotyping of patients with sepsis-induced ALI, those with sepsis alone, and healthy control subjects. Our data indicate an astonishing ethnic-specific predilection of allelic variants in candidate genes associated with ALI among the African-American patients with ALI and sepsis (12, 22). Much of the genetic variation between individuals lies in differences in SNPs, which are variant forms of genes that occur in at least 1% of the population. These types of polymorphisms influence either the transcriptional regulation of the gene, if the SNP resides within the promoter region, or alter the structure/function of the gene product (i.e., protein) via processes that might include post-translational modification. The underpinning of this approach is that an association between functional variants of a gene and a clinical phenotype may help to identify key pathophysiologic processes during disease and provide genetic factors and potential therapeutic targets. SNPs can be used in epidemiologic association studies to test susceptibilities to common diseases as well as to explain the diversity of clinical manifestations, outcome, and risk of chronicity among patients with a given disease. In our candidate gene approach (Figure 1), once a list of potential candidate genes has been determined using microarray analyses of animal and cell culture models of ALI, further specific studies must be performed to confirm the relevance of these genes in animal models of disease, using traditional transgenic and knockout techniques. Once this is done, potential candidates may then be evaluated in patient populations using mid- and high-throughput genotyping to explore the relevance of the candidates in defining risk factors for and ethnic predilection to ALI. Below, we describe one such analysis.

PBEF AS AN ALI CANDIDATE GENE

As our candidate gene approach identified PBEF as a viable and novel candidate gene and potential biomarker in ALI, we next examined whether common variants in the human PBEF gene might be associated with susceptibility to sepsis-associated ALI. We recently reported direct DNA sequencing of the PBEF gene (25), and identified 11 PBEF SNPs with 2 specific SNPs, T-1001G and C-1543T transversions, in the human PBEF gene promoter (–1 to –3,000 bp) having the highest degree of representation in 12 subjects with ALI. Both PBEF SNPs were genotyped in a case–control population of 271 white subjects with sepsis-associated ALI, sepsis alone, and healthy control subjects, with additional relevant characteristics of the study population available elsewhere (25).The T-1001G SNP was in Hardy-Weinberg equilibrium (p = 0.50) and subjects with ALI had significantly greater frequency of the G variant of T-1001G (30%) compared with healthy control subjects (12%, p<0.001). The frequency of the G variant among subjects with sepsis (25%) was also higher than in healthy control subjects, although not statistically significantly different from that in patients with ALI (p = 0.09) (24). In a univariate analysis, carriers of the G allele had a 2.75-fold increased risk of ALI compared with control subjects (p = 0.002). The second SNP, C-1543T, was also in Hardy-Weinberg equilibrium (p = 0.46), with the T-allele frequency observed in subjects with ALI (20%) significantly lower than the frequency observed in the healthy control group (31%, p = 0.026). Again, the frequency of the T variant among patients with sepsis (24%) was lower than in healthy control subjects, but was not statistically significant (p = 0.136). Haplotype weighted analysis of both the T-1001G and C-1543T SNPs revealed four haplotypes (GT, GC, TT, TC) with the frequency of the GC haplotype greater than twofold higher in the ALI group compared with healthy control subjects, representing a susceptible haplotype. In contrast, the TT haplotype was greater than twofold lower in patients with ALI and represents a protective haplotype. Univariate logistic regression analysis revealed that carriers of the GC haplotype had a 7.71-fold higher risk of ALI (95% confidence interval [CI], 3.01–19.75, p < 0.001) and 4.84-fold higher risk of sepsis (95% CI, 1.97–11.90, p = 0.001), while carriers of the TT haplotype had a 0.84-fold lower risk of ALI, although this difference did not reach significance (25). Multiple logistic regression analysis revealed that after controlling for 12 other risk factors, the G mutant allele remains an independent risk factor for ALI susceptibility. The G allele from T-1001G SNP and the C allele from C-1543T were not independently associated with sepsis or mortality among patients with sepsis or ALI, which may reflect, in part, a limited sample size preventing the detection of a difference between sepsis and ALI groups. Multiple logistic regression analysis using relevant clinical risk factors revealed that, after controlling for age, sex, and other comorbidity factors (cancer, immunosuppression, etc.), the G mutant allele remained an independent risk factor for ALI susceptibility (odds ratio [OR], 2.16; 95% CI, 1.01–4.62) but not for sepsis without ALI (25). As all patients with ALI in this study also had sepsis, further analysis of DNA from patients with ALI from causes other than sepsis may be necessary to distinguish whether the haplotype GC is a risk factor or the haplotype TT a protective factor for ALI itself or rather severe sepsis, which is a frequent cause of ALI.

Preliminary studies addressing the functionality of the T-1001G variant using the luciferase reporter gene assay did not demonstrate a significant role for this variant in gene transcription regulation; however, the T-allele in the C-1543T SNP in the PBEF promoter region was associated with a nearly twofold decrease in the reporter gene expression. This result is consistent with our observations from animal models of ALI, human patients with ALI, and in vitro cell culture experiments, and suggests that higher expression of PBEF is implicated in the pathogenesis of ALI. These results further suggest that genetically determined increased PBEF expression contributes to susceptibility to ALI.

PRELIMINARY IL-6 GENE VARIANT GENOTYPING IN ALI

As mentioned above, our combined model approach confirmed altered expression of several reported candidates (AQP1, MIF, PAI-1), including IL-6 (Table 1). Unlike PBEF, IL-6 is a well recognized ALI candidate gene and ALI biomarker. The elevation and persistence of circulating IL-6 has been associated with increased mortality in critically ill patients with ARDS, sepsis, and trauma, and IL-6 concentrations have been shown to be elevated in the BAL fluid from patients with established severe ALI (38, 39). Functional polymorphisms in the promoter region of the IL-6 gene exist (G174C) with the C allele associated with reduced gene promoter activity, lower circulating IL-6 concentrations, and a lower mortality rate in patients with acute respiratory failure admitted to the ICU (9). In the multi-species ALI studies performed, we noted significant IL-6 gene expression across all species as well as differential region-specific expression in the canine ALI model. We subsequently evaluated eight IL-6 SNPs from white patients with sepsis and ALI as well as healthy control subjects. Associations between IL-6 variants were assessed (Figure 5A). The strength of linkage disequilibrium (LD) between pairs of SNPs was measured as D' using Haploview (http://www.broad.mit.edu/personal/jcbarret/haplo/documentation.php). Regions of strongly associated markers (LD blocks) were inferred from the definition proposed by Gabriel and colleagues (40) as implemented in Haploview. Figure 5B depicts the presence of a protective ALI haplotype, suggesting that the role of IL-6 in ALI is complex and may have a dual role in the temporal response to sepsis and mechanical stress.



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  		Figure 5. Analysis of SNPs of the IL-6 gene. (A) Pairwise linkage disequilibrium (LD) measured by D' in SNPs spanning the IL-6 gene generated in 98 patients with sepsis-induced ALI and (B) haplotype associated with the ALI phenotype. The location of each tested SNP along chromosome 7 is indicated on top. The number in each diamond indicates the magnitude of LD between respective pairs of SNPs. The strength of LD is depicted by progression of color. For example, red squares show strong linkage disequilibrium, pink squares show moderate linkage disequilibrium, and white squares show little or no linkage disequilibrium. Thus, as seen, the eight IL-6 SNPs generate three distinct haplotype blocks. (B) Haplotype frequency estimates and significance level for comparison between ALI and control groups are derived from the three-locus estimated haplotype frequency analyses for three SNPs in the last LD block.

 
SUMMARY

Although the pathogenic and genetic basis of acute lung injury remains incompletely understood, the identification of novel ALI biomarkers holds promise for unique insights. Expression profiling in animal models of ALI/VALI (canine, murine, and rat) and human ALI detected significant expression of PBEF, a gene not previously associated with lung pathophysiology. These results were validated by real-time PCR and immunohistochemistry studies, with PBEF protein levels significantly increased in both BAL fluid and serum of ALI models as well as in cytokine- or cyclic stretch–activated lung microvascular endothelium. Subsequent SNP screening revealed a T-1001G transversion in the human PBEF gene promoter, and genotyping of a well characterized cohort of patients with sepsis-associated ALI and normal control subjects revealed that carriers of the variant G allele had a 2.16-fold higher risk of ALI (95% CI, 1.01–4.62). Together, these results strongly indicate that PBEF is a potential novel biomarker in ALI and demonstrate the successful application of robust genomic technologies in the identification of novel candidate genes in complex lung disease.

The completion of the Human Genome Project, the availability of high-throughput biology and parallel developments in computational analysis have heralded the era of molecular medicine and revolutionized the concept of translational biomedical research. Our studies underscore the powerful potential of using genomic approaches to decipher the genetic basis of complex lung disorders. The candidate gene approach, when coupled to creative bioinformatics approaches and extensive expression profiling, can yield novel and valuable information delineating genetic factors in ALI (Figure 6). Critical care physicians of the future will be armed with high-throughput technologies and phenotyping protocols which will customize care of the ICU patient, improve the survival of patients with critical illness, and herald a new era in critical care medicine.



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  		Figure 6. Genomic schema depicting pathobiologic events in ALI. Mechanical stress activates both alveolar and lung endothelial cells, resulting in induction of coagulation, innate immunity, and inflammatory response genes. Gene activation facilitates inflammatory pathways and increases vascular permeability through paracellular endothelial gaps and ultimately alveolar flooding. Variation in the expression of these genes may help to explain differences in susceptibility to and severity of acute lung injury in the population.

 
FOOTNOTES

Conflict of Interest Statement: None of the authors has a financial relationship with a commercial entity that has an interest in the subject of this manuscript.

(Received in original form January 27, 2005; accepted in final form April 26, 2005)

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