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Advanced Age Impairs Intestinal Antimicrobial Peptide Response and Worsens Fecal Microbiome Dysbiosis Following Burn Injury in Mice

Wheatley, Elizabeth G.*; Curtis, Brenda J.*; Hulsebus, Holly J.*,†; Boe, Devin M.*,†,‡; Najarro, Kevin*; Ir, Diana§; Robertson, Charles E.§; Choudhry, Mashkoor A.||; Frank, Daniel N.§,¶; Kovacs, Elizabeth J.*,†,‡,¶

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doi: 10.1097/SHK.0000000000001321
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Abstract

INTRODUCTION

Adults over 65 constitute the aging population, which is rapidly expanding and expected to double in size by 2050. Advanced age is a significant risk factor for numerous costly and debilitating medical conditions, including worsened prognoses following traumatic injury, such as burn (1). Indeed, nearly 20% of all burn patients in the United Sates are over 65 (2), and extensive clinical evidence reveals that advanced age is an independent risk factor for post-burn mortality (3, 4). Many deaths following burn injury involve secondary complications such as pneumonia, acute respiratory distress syndrome (ARDS), or sepsis (5–7). Although the exact mechanism for this age-dependent increase in mortality has not been fully described, there are likely significant contributions made from the gut.

The gut harbors a diverse population of beneficial microbiota, which generate essential metabolites and contribute to the maintenance of both intestinal and systemic health. The composition of the gut microbiome is tightly regulated by crosstalk between microbiota and the local immune and epithelial cells of the gut (8). Shifts in the composition of the gut microbiome (dysbiosis) can worsen intestinal permeability and have been linked to a number of intestinal and extraintestinal diseases, such as inflammatory bowel diseases, psoriasis, and obesity (9). Intestinal permeability allows for bacterial translocation (10), permitting bacteria and their toxic byproducts to enter the lymphatic system and, ultimately, the bloodstream (11). Dysbiosis of the fecal microbiome and bacterial translocation have been previously reported after burn injury (12, 13), and are believed to contribute to the devastating post-burn phenomena of sepsis, global inflammation, and multiorgan failure. Furthermore, gut dysbiosis has also been reported as a consequence of the normal aging process in humans and in animal models of aging, alongside an increase in global inflammation (14, 15). Recently, the notion has been raised that traumatic injury may intensify or accelerate detrimental inflammation during aging (16). Despite these collective findings, it is not known whether aging exacerbates the severity of dysbiosis of the fecal microbiome following burn injury, potentially worsening secondary complications and patient outcomes.

In this study, we used our well-established murine scald burn model to test whether the post-burn shift in intestinal microbiota is more severe in aged versus young mice. 16S rRNA gene sequencing was used to profile fecal bacterial microbiota 24 h following sham or burn injury. In addition, we characterized the gene expression of several antimicrobial peptides (AMPs), and correlated AMP expression with the abundance of certain bacterial genera across treatment groups, highlighting potential targets for future mechanistic studies. Our findings indicate that burn injury elicits dysbiosis of the fecal microbiome and altered ileal AMP expression in an age-dependent fashion, potentially contributing to the poor prognoses observed in elderly burn patients.

MATERIALS AND METHODS

Mice

Balb/cBy mice were obtained from the National Institute of Aging (NIA) Colony (Charles River Laboratories, Wilmington, Mass). Mice were housed in sterile conditions in the University of Colorado Anschutz Medical Campus Vivarium for a minimum of 2 weeks before any experimentation. All experiments were performed between 8 and 10 AM to minimize confounding factors related to circadian rhythms. All protocols were approved by the University of Colorado Denver Institutional Animal Care and Use Committee. The young mice were 5 months of age, and the aged mice were 21 to 22 months of age.

Murine model of scald burn injury

Young and aged female Balb/cBy mice weighing approximately 23 to 25 g were divided randomly into two groups (burn or sham) as previously described (17), with minor modifications. Briefly, mice were anesthetized (100 mg/kg of ketamine and 10 mg/kg of xylazine) (Webster Veterinary, Sterling, Mass) and dorsa were shaved. The mice were then subjected to a 12% TBSA scald burn injury by using a plastic template and exposing the shaved dorsum to 90°C to 92°C water bath for 8 s, resulting in an insensate, full-thickness burn injury, or exposed to a room temperature water bath for sham injury. All mice received 1 mL of saline resuscitation and buprenorphine for pain control by intraperitoneal injection. Cages were placed on warming pads, and mice were continually monitored during anesthesia recovery. At 24 h after burn, fecal pellets were collected from mice externally using a non-invasive sterile catch method. Mice were humanely euthanized using CO2 narcosis followed by exsanguination at 24 h after burn, and ileal tissue was collected. The data presented herein are representative of a minimum of two animal experiments.

Microbiome analysis

DNA was extracted using a QIAamp PowerFecal kit (QIAGEN Inc.) Bacterial profiles were determined by broad-range amplification and sequence analysis of 16S rRNA genes following our previously described methods (18). In brief, amplicons were generated using primers that target approximately 400 base pairs of the V3V4 variable region of the 16S rRNA gene. PCR products were normalized using a SequalPrep kit (Invitrogen, Carlsbad, Calif), pooled, lyophilized, purified and concentrated using a DNA Clean and Concentrator Kit (Zymo, Irvine, Calif). Pooled amplicons were quantified using Qubit Fluorometer 2.0 (Invitrogen). The pool was diluted to 4 nM and denatured with 0.2 N NaOH at room temperature. The denatured DNA was diluted to 15pM and spiked with 25% of the Illumina PhiX control DNA before loading the sequencer. Illumina paired-end sequencing was performed on the Miseq platform with versions v2.4 of the Miseq Control Software and of MiSeq Reporter, using a 600-cycle version 3 reagent kit.

Illumina Miseq paired-end reads were aligned to mouse reference genome mm10 with bowtie2 and matching sequences discarded. As previously described, the remaining nonmouse paired-end sequences were sorted by sample via barcodes in the paired reads with a python script (18). Sorted paired-end sequence data were deposited in the NCBI BioProject repository under accession ID number PRJNA491477. The sorted paired reads were assembled using phrap, and pairs that did not assemble were discarded. Assembled sequence ends were trimmed over a moving window of 5 nucleotides until average quality met or exceeded 20. Trimmed sequences with more than 1 ambiguity or shorter than 350 nt were discarded. Potential chimeras identified with Uchime (usearch6.0.203_i86linux32) (19) using the Schloss Silva reference sequences were removed from subsequent analyses. Assembled sequences were aligned and classified with SINA (1.3.0-r23838) using the 418,497 bacterial sequences in Silva 115NR99 (20) as reference configured to yield the Silva taxonomy. Operational taxonomic units (OTUs) were produced by clustering sequences with identical taxonomic assignments. This process generated 3,772,550 sequences for 35 samples (median of 104,453 sequences/sample; IQR 74,380–140,264). Data from two identical repeat experiments were combined for a total of n = 8 young sham, n = 8 young burn, n = 9 aged sham, and n = 10 aged burn to obtain the 35 samples for analyses.

The Explicet (v2.10.5) (21) and R software packages (22) were used for data display and analysis. Overall differences in microbial community composition between groups were assessed by PERMANOVA tests using Morisita-Horn dissimilarity scores with 106 permutations. Differences in the relative abundances of individual OTUs between groups were assessed by nonparametric Kruskal–Wallis tests and visualized by plotting −log10 of FDR-corrected P values per individual OTU. Standard measures of alpha biodiversity, including richness (the number of OTUs per sample estimated by SChao1), community evenness (the uniformity of OTU distributions estimated by Shannon H/Hmax), and complexity (Shannon diversity, H), were estimated at the rarefaction point of 16,504 sequences through 1,000 resamplings; an ANOVA was performed across the four groups for each diversity index, followed by post hoc Tukey's honest significant difference tests to obtain adjusted P values that assess differences between pairs of groups. The median Goods coverage score was 99.94% at the rarefaction point of 16,504. A heat map correlating the abundance of bacterial genus-level taxa and ileal AMP gene expression was generated using nonparametric Spearman correlation tests, with significance denoted using raw P values.

Quantitative RT-PCR

RNA was extracted from ileum tissue using RNeasy mini kit (Qiagen, Hilden, Germany) and converted to cDNA using iScript (BioRad, Hercules, Calif) according to manufacturer's protocol. Quantitative RT-PCR was performed using TaqMan probes and reagents: Lyz1 (Mm00657323-m1), Lyz2 (Mm01612741-m1), Cramp (Mm00438285-m1), Defa-rs1 (Mm00655850-m1), Reg3γ (Mm00441127-m1), Reg3β (Mm00440616_g1) (Applied Biosystems, Foster City, Calif). PCR plates were run and analyzed by the QuantStudio 3 Real-Time PCR System (Thermo Fisher, Lafayette, Colo). Results were analyzed using the ΔΔCt algorithm, with GAPDH as the endogenous control (cat no. 4352339E, Applied Biosystems). Data are presented as mean fold change ± SEM relative to young sham group (n = 2–6 per group); data were analyzed using Graph Pad Prism 7.03 using a one-way ANOVA with Tukey's multiple comparisons statistical test.

RESULTS

Analysis of fecal microbiome following burn injury in young and aged mice

We began this investigation by using our well-established murine scald burn model using both young and aged mice (17). Twenty-four hours after sham or burn injury, fecal pellets were collected from mice using a non-invasive sterile catch method. Fecal bacterial community profiling using 16S rRNA gene sequencing revealed broad changes in general across all four treatment groups (Fig. 1A). A PERMANOVA test using the Morisita-Horn dissimilarity index revealed significant differences in overall microbiota community composition across all four groups (P = 0.045). Two-way PERMANOVA tests indicated that both treatment group (P = 0.076) and age (P = 0.036) were independently associated with microbiota composition, with no apparent interaction between these main effects. Pairwise PERMANOVA comparisons also revealed differences in overall composition between young sham and young burn groups (P = 0.036), and young sham and aged burn groups (P = 0.038) (Fig. 1A). Pairwise comparisons between young and aged sham groups indicated a moderate age-dependent difference (P = 0.059), and results between aged sham and aged burn were not significant (P = 0.351). Similarly, three common measures of biodiversity differed across the four treatment groups: richness (SChao1; P = 0.04), evenness (Shannon H/Hmax; P = 0.0017), and complexity (Shannon H; P = 0.090) (Fig. 1B). Both age and burn were associated with increased evenness and complexity, whereas richness decreased in aged mice. Pairwise comparisons of richness revealed a significant difference between young burn and aged burn (P = 0.048), and comparisons of evenness showed significant differences between both young sham and young burn (P = 0.036), and young sham and aged burn (P = 0.0008) (Fig. 1B).

Fig. 1
Fig. 1:
Fecal microbiome diversity across all groups.(A) Schematic representation of fecal microbiota composition across young sham, young burn, aged sham, and aged burn groups. Bars represent relative abundances of particular taxa averaged across each group. Taxa with relative abundance <0.5% are collapsed into the “Other” group. * P < 0.05, pairwise PERMANOVA tests. (B) Measures of biodiversity across all four-treatment groups: richness (Schao1), evenness (Shannon H/Hmax), and complexity (Shannon H). * P < 0.05 compared with young burn, # P < 0.001 compared with aged burn. ANOVA with post hoc Tukey's test. (C) Results of Kruskal–Wallis tests of relative abundance across all four-treatment groups. Vertical lines represent log10 transformed, FDR-corrected P values for individual genus-level taxa, which are labeled 1-60 on the x-axis; only taxa with mean relative abundances greater than 0.1% were included in this analysis. Horizontal lines indicate significance thresholds at P < 0.1 and P < 0.05; n = 8–10 per group. (D) Genera significantly different between all groups (P < 0.1) as plotted in (B), where numbers correspond with the OTU on the x-axis of the plot.

A total of 21 individual taxa differed significantly in relative abundance across all four groups (FDR corrected P < 0.05). We visualized these results by plotting the −log10 of FDR corrected P values per genus-level taxa, denoted as OTUs (Fig. 1C). Genera with significant changes across all groups are listed (Fig. 1D). To gain more insight into how each treatment affected individual bacterial taxa, pairwise comparisons between the four treatment groups (age × burn) were performed, and plotted in a similar manner. Given the modest size of each treatment group (n = 8–10), we took note of genera with an FDR-corrected P < 0.1. Comparison of aged versus young sham groups showed a significant age-dependent increase in two distinct genera belonging to the phylum Firmicutes (Fig. 2A). Young burn versus young sham groups revealed that our model did not elicit significant changes in genera abundance at 24 h (Fig. 2B). When measuring aged burn versus aged sham groups, there was an increase in three genera and a decrease in three genera, all of which belong to the phylum Firmicutes (Fig. 2C). Comparison of aged burn versus young burn groups revealed the greatest number of changes in genera abundance, with a burn-dependent decrease in 18 genera, and increase in four genera, belonging to a variety of different phyla (Fig. 2D). These data demonstrate that aging markedly worsens the severity of gut dysbiosis following cutaneous burn injury.

Fig. 2
Fig. 2:
Pairwise comparisons of genus-level abundance.Plots show the directionality of change in relative abundance and strength of association between (A) aged sham versus young sham, (B) young burn versus young sham, (C) aged burn versus aged sham, and (D) aged burn versus young burn. Black = increased abundance in aged or burn, compared with the reference group (young or sham); gray = decreased abundance in aged or burn, compared with the reference group (young or sham); n = 8–10 per group. Taxa with FDR-corrected P values <0.1 are listed, where numbers correspond with the taxa listed on the x-axes of the plots. Horizontal lines indicate significance thresholds at P < 0.1 and P < 0.05.

Analysis of AMP expression in ileum following burn in young and aged mice

The composition of the gut microbiome is influenced by numerous factors, including crosstalk between microbes and the host immune cells. One of the most critical avenues by which the gut regulates both commensal and pathogenic bacteria is through production of AMPs (23). AMPs are secreted as an early phase of the host immune response, and execute a variety of mechanisms to selectively and directly destroy target bacteria. Given that fecal microbiota differed between the treatment groups, we characterized expression levels of AMP-encoding genes in the ilea of young and aged mice subjected to sham or burn using qRT-PCR. Characterization of Lysozyme c-1 (Lyz1) and Lysozyme c-2 (Lyz2) revealed no significant change in expression across treatment groups (Fig. 3). In contrast, Cramp expression increased 8-fold in young, but not aged mice, following burn (P < 0.0001) (Fig. 3). In addition, Defa-rs1 exhibited a 5-fold age-dependent increase (P < 0.05); however, following burn injury, Defa-rs1 levels in aged mice returned to those observed in young sham (Fig. 3). Most notably, burn injury caused a dramatic 20-fold rise in levels of Reg3γ (P < 0.0001) and 16-fold rise in levels of Reg3β (P < 0.0001) in young mice, but elicited no change in expression in aged mice (Fig. 3). These results inspired us to examine the relationship between AMP expression and bacterial genera abundance in greater detail.

Fig. 3
Fig. 3:
Antimicrobial peptide expression in the ileum.Quantitative RT-PCR of anti-microbial peptide (AMP) expression in whole ileum tissue. AMPs analyzed include Lysozyme c-1 (Lyz1), Lysozyme c-2 (Lyz2), alpha defensin-related sequence1 (Defa-rs1), Cathelicidin-related antimicrobial peptide (Cramp), regenerating islet-derived protein 3 beta (Reg3β), and regenerating islet-derived protein 3 gamma (Reg3γ). Results were analyzed using the ΔΔCt algorithm with GAPDH as the endogenous control. Data are presented as mean fold change ± SEM relative to young sham group. n = 2–6 per group; * P < 0.0001 compared with all three groups; + P < 0.05, ++ P < 0.0001 compared with young sham; # P < 0.05, ## P < 0.01, ### P < 0.0001 compared with young burn; ‡‡ P < 0.01 compared with aged burn (one-way ANOVA with Tukey's multiple comparisons test).

The expression of each AMP was correlated with the abundance of individual taxa across all four treatment groups and visualized using a heat map, to investigate possible relationships between AMP expression and microbial diversity (Fig. 4). Thirteen significant correlations were observed (P < 0.05), and all AMPs correlated with at least one genus, except Lyz2. Collectively, these data indicate that burn injury alters AMP expression in the gut in an age-dependent fashion. Moreover, they reveal that there are distinct correlation patterns between specific AMPs and the composition of the microbiota in the context of aging and burn injury.

Fig. 4
Fig. 4:
Correlation of bacterial relative abundance with ileal AMP gene expression.Heat map comparing relative abundance of bacterial taxa to AMP gene expression in the ileum across all treatment groups. Blue = negative correlation, red = positive correlation, white = no correlation; color intensity represents the magnitude of correlation. n = 8–10 per group, + P < 0.05 (nonparametric Spearman correlation test).

DISCUSSION

Clinical evidence demonstrates that advanced age is an independent risk factor for worsened prognoses following burn injury (3, 4). Similarly, previous work using our murine model of burn injury has shown that aged burn-injured mice exhibit increased mortality and heightened systemic inflammation (24), along with elevated and prolonged lung inflammation (17), compared with young burn-injured mice (25). Others have shown that the gut is a potential contributor to worsened prognoses and inflammation following burn injury (10–13, 26). Furthermore, it has been previously reported that advanced age drives changes in both the mouse and human gut, including intestinal dysbiosis (15, 27). Our data indicate that aged sham mice exhibit broad changes in relative abundance of fecal microbiota compared with young sham mice (Fig. 1A). In addition, aged sham mice display significant upregulation of Turicibacter and genera from the Ruminococcaceae family (Fig. 2A), both members of the phylum Firmicutes. Increased relative abundance of these two genera in the gut coincides with other inflammatory conditions, such as arthritic diseases (28), suggesting this increase may be linked to age-dependent elevated global inflammation (29). Previous studies have shown gut dysbiosis at 24 h post-burn in young mice, including an increase in the pathogen-containing family Enterobacteriaceae (13, 26, 30), and a decrease in the Bacteroidetes group S24-7 (13). In contrast, our results failed to show significant changes in relative abundance of any taxa 24 h after burn in young mice (Fig. 2B), but we emphasize moving forward that differences in mouse background strain, sex, size of TBSA injury, and fecal source may affect measured changes in the microbiome. Although our younger mouse group consisted of aforementioned 5-month-old female BALB/c mice, studies from others used male mice, on the C57BL/6 background at 8 to 9 weeks (13) or 10 to 12 weeks of age (30), or CF-1 mice at only 6 to 8 weeks in age (26). Burn sizes ranged from 12.5% (30), 20% (13), and 28% TBSA (26), and the microbiome was profiled from small and large intestinal fecal matter (13, 30), or cecal contents (26). In contrast, our study profiled the microbiome from externally collected fecal pellets. We elected to utilize fecal pellets, as this intestinal material represents the type of fecal sample most easily translated to future studies involving multiple time points, adoptive transfer, and, importantly, human burn patients. Interestingly, the abundance of Enterobacteriaceae decreased in our aged burn mice when compared with young burn, suggesting aging combined with burn injury may affect the abundance of this genera differently than injury alone. We also observed an age-dependent reduction in S24-7 in our burned mice, in contrast to a burn-dependent reduction reported in young mice by Earley et al. (13). Regardless of changes observed in individual genera, the phenomenon of dysbiosis itself is associated with a number of detrimental medical conditions (9). Overall, in our studies, the greatest changes in relative abundance were observed in aged burn mice compared with young burn mice (Fig. 2D), demonstrating that age contributes markedly to the degree of burn-induced gut dysbiosis.

AMPs represent an important means by which the host regulates gut microbial communities (23), and more recently, AMPs have been investigated in models with intestinal dysbiosis like aging and burn. Age-related changes in AMP expression have been reported in ileal tissue from mice (31) in a timeframe consistent with reports of age-dependent gut dysbiosis (15, 31), supporting the concept that host-microbiome crosstalk influences dysbiosis in older subjects (32). Our data indicate a modest age-dependent increase in expression of several AMPs, and in particular, 5-fold higher expression of the defensin-related peptide Defa-rs1 in the ileum of aged versus young sham mice (Fig. 3). AMP expression in the intestines of young mice has also been characterized in a murine model of burn injury showing modest but not significant increases in Reg3β and Reg3γ after burn (33, 34). Interestingly, we observed drastically altered AMP expression in ileum from young mice after burn injury relative to sham injured mice, including a 16-fold increase in Reg3β and 20-fold increase in Reg3γ, as well as an 8-fold elevation in Cramp expression (Fig. 3). Our results suggested that certain AMPs in the gut might increase in response to elevated systemic inflammation, such as in the aged or after traumatic injury. Unexpectedly, however, in addition to causing dysbiosis, burn injury in aged mice lowered the expression of AMPs to levels observed in young shams, indicating that aged animals are unable to mount an appropriate or timely AMP response in the gut following burn. The most robust increase in AMP expression was observed in young burn mice, in the absence of dysbiosis; proposing upregulation of AMPs after burn may serve as a protective mechanism for maintaining homeostasis of the gut microbiome. In support of this, burn studies profiling other organs, such as the skin, demonstrate heightened AMP expression occurs both proximal to and distally from the burn site, and coincides with increased anti-microbicidal activity against several pathogens in humans (35).

There is a degree of target specificity afforded to individual AMPs which allows them to shape bacterial communities, and distinct bacteria may, in turn, influence the expression of AMPs by stimulating pattern-recognition receptor signaling cascades. Thus, correlations between AMP expression and particular constituents of the gut microbiota may provide insight into how dysbiosis, or changes in AMP expression, arise in the context of aging and burn. Subsequent mechanistic studies, some of which have been initiated in the laboratory, will help to temporally resolve the changes we observed in microbiome dysbiosis and AMP expression. Among the AMPs that were differentially expressed in experimental groups in our study, we found that Reg3β and Reg3γ moderately correlated with the genus Clostridium (Fig. 4), which includes the opportunistic pathogen Clostridium difficile, and is associated with increased mortality following burn (36). Interestingly, among the AMPs we characterized, Reg3γ correlated with abundance of the S24-7 group (Fig. 4), which our current results and the work of Earley et al. have linked to burn-mediated gut dysbiosis (13). These data propose that Reg3γ could serve as an attractive target for future mechanistic studies, for allowing investigation of whether loss of this AMP worsens dysbiosis following burn in young mice, and whether restoration of Reg3γ in the ileum of aged mice could reduce the magnitude of microbial dysbiosis to a younger profile. Conversely, it would be of considerable interest to determine whether a fecal transplant might restore AMP expression in the ileum of aged mice subjected to burn injury. In a study by Kuethe et al., fecal transplantation restored colon mucosal integrity after burn injury in mice (37), suggesting restoration of the microbiome may be sufficient to rescue trauma-induced alterations in the gut. This study, in combination with the literature affirming that microbial dysbiosis in the gut negatively impacts systemic health (9), serves to further emphasize how drastically the gut microbiome could influence clinical outcomes.

Overall, our investigation demonstrates for the first time that advanced age elicits a more severe degree of gut microbial dysbiosis following cutaneous burn injury than is manifest in younger mice given comparable injuries. We report the novel finding that changes in ileal AMP expression in response to burn injury are age-dependent. In addition, our work provides correlation patterns in the gut between individual AMPs and selected bacterial taxa in the context of aging and burn injury, contributing valuable knowledge to the field and providing targets for future mechanistic studies, to identify specific molecules contributing to the poor prognoses observed in elderly burn patients.

Acknowledgments

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

REFERENCES

1. Rani M, Schwacha MG. Aging and the pathogenic response to burn. Aging Dis 3 (2):171–180, 2012.
2. Abu-Sittah GS, Chahine FM, Janom H. Management of burns in the elderly. Ann Burns Fire Disasters 29 (4):249–245, 2016.
3. Keck M, Lumenta DB, Andel H, Kamolz LP, Frey M. Burn treatment in the elderly. Burns 35 (8):1071–1079, 2009.
4. Jeschke MG, Pinto R, Costford SR, Amini-Nik S. Threshold age and burn size associated with poor outcomes in the elderly after burn injury. Burns 42 (2):276–281, 2016.
5. Silva L, Garcia L, Oliveira B, Tanita M, Festti J, Cardoso L, Lavado L, Grion C. Acute respiratory distress syndrome in burn patients: incidence and risk factor analysis. Ann Burns Fire Disasters 29 (3):178–182, 2016.
6. Fitzwater J, Purdue GF, Hunt JL, O’Keefe GE. The risk factors and time course of sepsis and organ dysfunction after burn trauma. J Trauma 54 (5):959–966, 2003.
7. Chan CH, Yang SF, Yeh HW, Yeh YT, Wang YH, Teng YH, Yeh CB. Risk of pneumonia in patients with burn injury: a population-based cohort study. Clin Epidemiol 10:1083–1091, 2018.
8. Ostaff MJ, Stange EF, Wehkamp J. Antimicrobial peptides and gut microbiota in homeostasis and pathology. EMBO Mol Med 5 (10):1465–1483, 2013.
9. Frank DN, Zhu W, Sartor RB, Li E. Investigating the biological and clinical significance of human dysbioses. Trends Microbiol 19 (9):427–434, 2011.
10. Magnotti LJ, Deitch EA. Burns, bacterial translocation, gut barrier function, and failure. J Burn Care Rehabil 26 (5):383–391, 2005.
11. Wrba L, Palmer A, Braun CK, Huber-Lang M. Evaluation of gut-blood barrier dysfunction in various models of trauma, hemorrhagic shock, and burn injury. J Trauma Acute Care Surg 83 (5):944–953, 2017.
12. Wang X, Yang J, Tian F, Zhang L, Lei Q, Jiang T, Zhou J, Yuan S, Wang J, Feng Z, et al. Gut microbiota trajectory in patients with severe burn: a time series study. J Crit Care 42:310–316, 2017.
13. Earley ZM, Akhtar S, Green SJ, Naqib A, Khan O, Cannon AR, Hammer AM, Morris NL, Li X, Eberhardt JM, et al. Burn injury alters the intestinal microbiome and increases gut permeability and bacterial translocation. PLoS One 10 (7):e0129996, 2015.
14. Claesson MJ, Cusack S, O'Sullivan O, Greene-Diniz R, de Weerd H, Flannery E, Marchesi JR, Falush D, Dinan T, Fitzgerald G, et al. Composition, variability, and temporal stability of the intestinal microbiota of the elderly. Proc Natl Acad Sci USA 108: (Suppl 1): 4586–91, 2011.
15. Langille MG, Meehan CJ, Koenig JE, Dhanani AS, Rose RA, Howlett SE, Beiko RG. Microbial shifts in the aging mouse gut. Microbiome 2 (1):50, 2014.
16. Sullivan J, Mirbahai L, Lord JM. Major trauma and acceleration of the ageing process. Ageing Res Rev 48:32–39, 2018.
17. Nomellini V, Faunce DE, Gomez CR, Kovacs EJ. An age-associated increase in pulmonary inflammation after burn injury is abrogated by CXCR2 inhibition. J Leukoc Biol 83 (6):1493–1501, 2008.
18. Markle JG, Frank DN, Mortin-Toth S, Robertson CE, Feazel LM, Rolle-Kampczyk U, von Bergen M, McCoy KD, Macpherson AJ, Danska JS. Sex differences in the gut microbiome drive hormone-dependent regulation of autoimmunity. Science 339 (6123):1084–1088, 2013.
19. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27 (16):2194–200, 2011.
20. Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41 (Database issue):D590–D596, 2013.
21. Robertson CE, Harris JK, Wagner BD, Granger D, Browne K, Tatem B, Feazel LM, Park K, Pace NR, Frank DN. Explicet: graphical user interface software for metadata-driven management, analysis and visualization of microbiome data. Bioinformatics 29 (23):3100–3101, 2013.
22. RDC Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2011.
23. Heimlich DR, Harrison A, Mason KM. Host antimicrobial peptides in bacterial homeostasis and pathogenesis of disease. Antibiotics (Basel) 3 (4):645–676, 2014.
24. Kovacs EJ, Grabowski KA, Duffner LA, Plackett TP, Gregory MS. Survival and cell mediated immunity after burn injury in aged mice. J Am Aging Assoc 25 (1):3–9, 2002.
25. Nomellini V, Gomez CR, Gamelli RL, Kovacs EJ. Aging and animal models of systemic insult: trauma, burn, and sepsis. Shock 31 (1):11–20, 2009.
26. Beckmann N, Pugh AM, Caldwell CC. Burn injury alters the intestinal microbiome's taxonomic composition and functional gene expression. PLoS One 13 (10):e0205307, 2018.
27. Claesson MJ, Jeffery IB, Conde S, Power SE, O’Connor EM, Cusack S, Harris HM, Coakley M, Lakshminarayanan B, O'Sullivan O, et al. Gut microbiota composition correlates with diet and health in the elderly. Nature 488 (7410):178–184, 2012.
28. Forbes JD, Van Domselaar G, Bernstein CN. The gut microbiota in immune-mediated inflammatory diseases. Front Microbiol 7:1081, 2016.
29. Singh T, Newman AB. Inflammatory markers in population studies of aging. Ageing Res Rev 10 (3):319–329, 2011.
30. Hammer AM, Khan OM, Morris NL, Li X, Movtchan NV, Cannon AR, Choudhry MA. The effects of alcohol intoxication and burn injury on the expression of claudins and mucins in the small and large intestines. Shock 45 (1):73–81, 2016.
31. Tremblay S, Cote NML, Grenier G, Duclos-Lasnier G, Fortier LC, Ilangumaran S, Menendez A. Ileal antimicrobial peptide expression is dysregulated in old age. Immun Ageing 14:19, 2017.
32. Nagpal R, Mainali R, Ahmadi S, Wang S, Singh R, Kavanagh K, Kitzman DW, Kushugulova A, Marotta F, Yadav H. Gut microbiome and aging: physiological and mechanistic insights. Nutr Healthy Aging 4 (4):267–285, 2018.
33. Hammer AM, Morris NL, Cannon AR, Khan OM, Gagnon RC, Movtchan NV, van Langeveld I, Li X, Gao B, Choudhry MA. Interleukin-22 prevents microbial dysbiosis and promotes intestinal barrier regeneration following acute injury. Shock 48 (6):657–665, 2017.
34. Rendon JL, Li X, Akhtar S, Choudhry MA. Interleukin-22 modulates gut epithelial and immune barrier functions following acute alcohol exposure and burn injury. Shock 39 (1):11–18, 2013.
35. Plichta JK, Holmes CJ, Gamelli RL, Radek KA. Local burn injury promotes defects in the epidermal lipid and antimicrobial peptide barriers in human autograft skin and burn margin: implications for burn wound healing and graft survival. J Burn Care Res 38 (1):e212–e226, 2017.
36. Finnerty CC, Herndon DN, Lee JO, Rodriguez NA, Al-Haj IH, Wurzer P, Calhoun BR, Jeschke MG. Morbidity and mortality in severely burned children with Clostridium difficile-associated diarrhea. Surgery 159 (6):1631–1637, 2016.
37. Kuethe JW, Armocida SM, Midura EF, Rice TC, Hildeman DA, Healy DP, Caldwell CC. Fecal microbiota transplant restores mucosal integrity in a murine model of burn injury. Shock 45 (6):647–652, 2016.
Keywords:

Aging; bacteria; gut; Reg3gamma; trauma; Abbreviations; AMP; antimicrobial peptide; ARDS; acute respiratory distress syndrome; Cramp; Cathelicidin-related antimicrobial peptide; Defa-rs1; alpha defensin-related sequence1; FDR; false discovery rate; Lyz1; Lysozyme c-1; Lyz2; Lysozyme c-2; NCBI; National Center for Biotechnology Information; NIA; the National Institute of Aging; OTUs; operational taxonomic units; PERMANOVA/ANOVA; permutational multivariate/analysis of variance; Reg3β; regenerating islet-derived protein 3 beta; Reg3γ; regenerating islet-derived protein 3 gamma

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