- Top of page
- Supporting Information
Primary graft dysfunction (PGD) is the most common cause of early death after lung transplantation . PGD is characterized by hypoxemia and radiographic infiltrates occurring in the allograft within 72 h of transplantation . Ten to thirty percent of all subjects receiving lung transplantation develop PGD , which is associated with both short- and long-term morbidity and mortality [2, 4].
Though the mechanisms of PGD remain incompletely understood, donor  and other factors have been implicated in PGD risk . mRNA expression profiling is an ideal tool for identifying key pathways that are involved in the pathogenesis of complex syndromes, such as acute lung injury . We sought to define pathways important in PGD development by comparing changes in donor lung gene expression before transplant with those after reperfusion, using a multicenter cohort study design. We used a gene set enrichment approach employing known biological pathways to capture changes seen in many genes within a cellular pathway in PGD subjects . We hypothesized that specific pathways could be identified that are differentially expressed during the transplant procedure in patients who develop clinically significant PGD.
- Top of page
- Supporting Information
One hundred eight subjects were enrolled and 106 subjects were transplanted. Twenty-three subjects, 21.7% (95% CI: 14.3, 30.8), developed Grade 3 PGD within 72 h. Baseline demographics between PGD cases and matched controls are summarized in Table 1. There were no statistically significant differences in donor age, gender, race, cause of death, smoking history, bronchoscopic findings, ischemic time and preservation fluid type or recipient-related factors such as age, gender, race, pulmonary artery pressure and transplant type. Additionally, we were unable to demonstrate statistically significant differences in intraoperative interventions, including cardiopulmonary bypass (CPB) use or time. As would be expected, postoperative need for mechanical ventilation differed significantly between PGD and non-PGD controls (Table 1).
Table 1. Baseline demographics
| ||PGD (n = 23)||No PGD (n = 23)||p-value|
|Age||36.0 (16.32)||39.7 (12.85)||0.409|
|Female||5 (21.7)||10 (43.5)||0.116|
|Race|| || ||0.641|
|Asian||0||1 (4.3)|| |
|African American||4 (17.4)||4 (17.4)|| |
|Caucasian||15 (65.2)||34 (73.9)|| |
|Unknown/not reported||4 (17.4)||1 (4.3)|| |
|Cause of death|| || ||0.916|
|Anoxia||3 (13.0)||3 (13.0)|| |
|Cerebrovascular||10 (43.5)||11 (47.8)|| |
|Head trauma||7 (30.4)||5 (21.7)|| |
|Other||3 (13.0)||4 (17.4)|| |
|Nonsmoker||13 (56.5)||14 (60.9)||0.765|
|Normal bronchoscopy||19 (82.6)||18 (78.3)||>0.999|
|Total ischemic time||447 (222)||378 (201)||0.273|
|Age||56.4 (8.79)||55.4 (10.27)||0.724|
|Female||5 (21.7)||8 (34.8)||0.326|
|Race|| || ||0.234|
|African American||3 (13.0)||0|| |
|Caucasian||20 (87.0)||21 (91.3)|| |
|Unknown/not reported||0||2 (8.7)|| |
|Diagnosis|| || ||>0.999|
|Bronchiectasis/CF||4 (17.4)||4 (17.4)|| |
|COPD/LAM||5 (21.7)||5 (21.7)|| |
|Pulmonary fibrosis||12 (52.2)||12 (52.2)|| |
|Other||2 (8.7)||2 (8.7)|| |
|Bilateral transplant||16 (69.6)||10 (43.5)||0.078|
|PASP||56.9 (25.36)||60.8 (33.03)||0.671|
|CPB needed||14 (60.9)||8 (34.8)||0.077|
|CPB duration||260.8 (89.01)||201.1 (91.3)||0.156|
|24 h post-op||20 (87.0)||11 (47.8)||0.005|
|48 h post-op||14 (60.9)||3 (13.0)||<0.001|
|72 h post-op||10 (43.5)||3 (13.0)||0.022|
Pathway differences in mRNA expression preprocurement to postreperfusion between PGD cases and controls are presented in Table 2. A total of 362 gene sets were upregulated in recipients who developed PGD of which eight gene sets met significance with a familywise-error rate (FWER) of less than 0.05 (Figure 2). A complete list of gene sets that did not meet our cutoff can be found in the supplement (Table S1). The pathway with the highest normalized enrichment score (NES), a standardized metric that accounts for differences in gene set size and in correlations between gene sets and the expression dataset, was the KEGG nucleotide binding oligomerization domain like receptor (NLR) inflammasome pathway (Figure 2A; NES 2.44; FWER adjusted p < 0.001). The second highest pathway was the KEGG toll-like receptor (TLR) signaling pathway (Figure 2B; NES 2.22; FWER adjusted p < 0.001). Six other pathways met our FWER cutoff and included: BIOCARTA IL-1 receptor (IL1R) pathway (NES 2.19; FWER adjusted p = 0.001), REACTOME myeloid differentiation primary response gene 88 (MYD88) (NES 2.16; FWER adjusted p = 0.001), BIOCARTA NFkB activation by nontypeable Haemophilus influenzae (NTHI) signaling pathway (NES 2.16; FWER adjusted p = 0.001), REACTOME-activated TLR4 signaling pathway (NES 2.10; FWER adjusted p = 0.008), REACTOME TLR 9 cascade (NES 2.07; FWER adjusted p = 0.018) and BIOCARTA toll pathway (NES 2.06; FWER adjusted p = 0.018). Individual enrichment plots are presented in the supplement (Figure S1). Each of these pathways has been described as either activating or affecting innate immune function. Further analysis of the individual transcripts from these pathways based on the rank metric score, a measure of an individual transcript's correlation with the PGD phenotype, demonstrated significant overlap within the NLR and TLR pathways. The highest ranked individual transcripts include IL1β (1.162), NLRP3 (1.135), IL1A (0.952), IL6 (0.931), CCL4 (0.842), TLR6 (0.805), TLR1 (0.796), TNFAIP3 (0.792) and TLR4 (0.677). The enriched individual transcripts from the eight pathways meeting the FWER of 0.05 are presented in Table 3 and the complete ranked gene list for positively correlated transcripts is presented in the supplement (Table S2).
Table 2. Genes set enrichment analysis
|Name||Source||NES||NOM p-val||FDR q-val||FWER p-val|
|NOD-like receptor signaling pathway||KEGG||2.443||<0.001||<0.001||<0.001|
|TOLL-like receptor signaling pathway||KEGG||2.222||<0.001||<0.001||<0.001|
|Activated TLR4 signaling||REACTOME||2.103||<0.001||0.0014||0.008|
Figure 2. Most significantly enriched gene sets. The top portion of the plot shows the running enrichment score (ES) for the gene set from decreasing values of the rank list. The score at the peak of the plot (the score furthest from 0.0) is the ES for the overall gene set. The middle portion of the plot shows where the members of the gene set appear in the ranked list of genes. The bottom portion of the plot shows the value of the ranking metric as the list of ranked genes decreases in value. The ranking metric measures an individual transcript's correlation with the PGD phenotype.
Download figure to PowerPoint
Table 3. Significantly enriched transcripts in GSEA pathways
|NLR signaling pathway||TLR signaling pathway||IL1R pathway||MYD88 cascade||NTHI pathway||Activated TLR4 signaling||TLR9cascade||Toll pathway|
|CXCL1||0.539||IL8||0.558||IRAK2||0.410|| || ||DUSP1||0.229||LY96||0.259||PIK3C3||0.248||EIF2AK2||0.282|
|CXCL2||0.524||MAP2K6||0.542||TNF||0.360|| || ||SMAD3||0.214|| || || || ||LY96||0.259|
|CASP5||0.518||PIK3CG||0.464||NFKBIA||0.309|| || || || || || || || ||IKBKB||0.238|
|CARDS||0.413||TLR8||0.453|| || || || || || || || || || ||CD14||0.200|
|CCL7||0.413||TNF||0.360|| || || || || || || || || || || || |
|CASP1||0.394||TLR5||0.345|| || || || || || || || || || || || |
|BIRC2||0.394||IRAK4||0.341|| || || || || || || || || || || || |
|TNF||0.360||FOS||0.336|| || || || || || || || || || || || |
|BIRC3||0.358||IFNAR1||0.321|| || || || || || || || || || || || |
|NFKBIA||0.309||TBK1||0.320|| || || || || || || || || || || || |
|CASP8||0.299||NFKBIA||0.309|| || || || || || || || || || || || |
|IKBKB||0.238||CASP8||0.299|| || || || || || || || || || || || |
|MAPK10||0.218||LY96||0.259|| || || || || || || || || || || || |
|CCL2||0.199||RAC1||0.258|| || || || || || || || || || || || |
|ERBB2IP||0.196||STAT1||0.254|| || || || || || || || || || || || |
|XIAP||0.189||PIK3CA||0.240|| || || || || || || || || || || || |
|NOD2||0.187||IKBKB||0.238|| || || || || || || || || || || || |
|MAPK11||0.170||PIK3CD||0.232|| || || || || || || || || || || || |
|CCL11||0.164||MAPK10||0.218|| || || || || || || || || || || || |
| || ||PIK3R5||0.200|| || || || || || || || || || || || |
| || ||CD 14||0.200|| || || || || || || || || || || || |
- Top of page
- Supporting Information
In this study, we have defined key pathways involved in the development of PGD, using GSEA. Like most complex traits, the PGD phenotype likely manifests with multiple alterations in gene expression highlighted by fewer dominant pathways. The strength of GSEA lies in its ability to utilize biological information (e.g. published information about well-characterized biological pathways) to guide analysis so that multiple changes in individual genes acting as part of a network within a background of profound physiologic perturbation, as seen in transplantation, can be analyzed with an interpretable result . Using this methodology, our results suggest inflammasome and innate immune-mediated processes are actively involved in the pathophysiology of PGD, when also taking into account changes seen during transplantation in control subjects.
Inflammasomes are proinflammatory macromolecular complexes that activate caspase-1, which results in IL-1β activation . These complexes are part of an increasingly recognized stereotyped innate immunologic response to tissue damage. This threat is recognized in the host by pattern recognition receptors that are either localized in the cell membrane (e.g. TLRs) or in the cytoplasm (e.g. NLRs). Though the specific pathways of inflammasome activation are incompletely understood, danger- and pathogen-associated molecular pattern (DAMPs and PAMPs) recognition plays an important role . Stimuli for activation of these pathways include bacterial and viral pathogens , lysosomal disruption , neutrophil or mitochondria derived reactive oxygen species (ROS) [16, 17] and cell apoptosis [17, 18]. Therefore, our findings may indicate that response to either pathogens (likely donor derived) or response to cell and tissue damage signals are key in PGD pathogenesis and warrant further investigation.
We have identified IL-1β, the major inflammasome-regulated effector cytokine, as being significantly correlated to several pathways potentially involved in PGD pathogenesis. IL-1β has consistently been demonstrated as a key mediator in other types of acute lung injury, and recent investigations by our group have implicated genetic variation in this pathway to associate with differential acute respiratory distress syndrome (ARDS) risk [12, 19-24]. It is possible that a subset of the donor population had subclinical lung injury present at the time of procurement that was augmented by ischemia and reperfusion leading to clinical PGD. Both direct (pneumonia, aspiration, hyperoxia, pulmonary contusion and reperfusion) and indirect (trauma, sepsis and transfusion) causes of ALI are recognized in critically ill patients [25, 26]. Several of these exposures are present in organ donors and are significant contributors to organ unsuitability for transplant . Among organs used for transplant with these exposures, PGD may reflect organ injury which may have progressed to ALI had the donors not been transplanted . Furthermore, brain death directly causes neurogenic pulmonary edema and ALI. Diffuse organ inflammatory responses associated with brain death impair lung function after implant and contribute to PGD [31-33]. Alternately, IL-1 signaling in response to inflammasome activation may represent a pathophysiological amplification of the immune response after reperfusion, perhaps as a result of different levels of tissue damage occurring during organ preservation.
Current methods of organ preservation rely on hypothermia and specially formulated perfusates to decrease metabolic injury. It is recognized that all non-enzymatic and enzymatic processes are reduced by 1.5–3-fold per 10°C temperature decreases from baseline and cold preservation has been the foundation for organ preservation . Recent evidence suggests that hypothermia in the presence of oxygen can result in ROS-mediated hypothermic injury . Among solid organs, the lung is unique because it is stored inflated and does not experience similar degrees of hypoxia [28-30]. Further, evidence in a rodent model demonstrates oxygen dependent dose related lung injury with increasing percentage of oxygen in the gas mixture used for inflation [28, 31]. Thus, the injury seen in PGD may be consequent to oxidant stress with resultant DAMP-regulated inflammasome activation, which may be a target for future ex vivo lung perfusion strategies. Alternatively, hidden donor microbial pathogens alone or in combination with subclinical organ injury may amplify ischemia/reperfusion injury to the extent that recipient antioxidant potential is overwhelmed.
There are several limitations of this study. First, our study design relied on the evaluation of cells from BALF at two time points (donor in situ and postreperfusion). While BALF has the advantage of sampling multiple cell types from a larger section of lung than lung biopsies, this methodology may introduce a sampling bias as cells from this source would be expected to be comprised mostly of alveolar macrophages, neutrophils, B cells, T cells and epithelial cells . Additionally, this mixed cell population would be exclusively donor derived preprocurement and, likewise, donor alveolar cells would be most likely to contribute to gene expression measured after 1 h postreperfusion . This sampling method was specifically chosen to highlight early pathways important in PGD pathogenesis. However, we acknowledge that there may be parenchymal and vascular contributions that may have been present in lung biopsy samples and not BAL, for example. Despite this limitation, our results are consistent with previous reports that TLR signaling pathways are important in lung injury . Second, it is not possible to establish functional causation with our study design. However, our results serve to prioritize inflammasomes, and innate immune activation and modulation, for future study in PGD. Third, given the limited number of samples used for analysis it is possible that important changes in gene expression in single genes may have been overlooked and we may have been unable to demonstrate statistical differences in baseline demographics known to affect PGD. Fourth, our decision to use the FWER, a more conservative correction for multiple hypothesis testing than the false discovery rate (FDR), limited the number of pathways we considered significant. This conservative approach potentially excluded pathways important in PGD pathogenesis; however, we list the full results in the supplemental material (Table S1). Finally, our study lacks an external validation step. Unfortunately, there are no existent external datasets that can be used for replication; for this reason we have adopted a conservative multiple test correction.
In summary, we have demonstrated an association between inflammasome and innate immune activation and PGD. The effects of these pathways suggest importance of the inflammasome and innate immune signaling in PGD pathogenesis. This suggests a complex mechanism of injury initiation and modulation and therefore should stimulate further inquiry into the role of tissue damage from storage, as well as novel methods of identifying donor pathogens, such as donor microbiome analyses.
- Top of page
- Supporting Information
Additional Supporting Information may be found in the online version of this article at the publisher's web-site.
Table S1: Expanded gene set enrichment analysis. NES, normalized enrichment score (accounts for differences in gene set size and in correlations between gene sets and the expression dataset to allow for comparisons across gene sets); NOM p-val, nominal p value (estimates the statistical significance of the enrichment score for a single gene set); FDR, false discovery rate (estimated probability that a gene set with a given NES represents a false positive finding); FWER, familywise-error rate (conservative correction that seeks to ensure that the list of reported results does not include even a single false-positive gene set).
Table S2: Ranked gene list of PGD positively correlated transcripts.
Figure S1: All significantly enriched gene sets meeting FWER corrected cutoff. The top portion of the plot shows the running enrichment score (ES) for the gene set from decreasing values of the rank list. The score at the peak of the plot (the score furthest from 0.0) is the ES for the overall gene set. The middle portion of the plot shows where the members of the gene set appear in the ranked list of genes. The bottom portion of the plot shows the value of the ranking metric as the list of ranked genes decreases in value. The ranking metric measures an individual transcript's correlation with the PGD phenotype.
Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.