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Citation: Jung, Y.; Cui, H.S.; Lee, E.K.;
Joo, S.Y.; Seo, C.H.; Cho, Y.S. Effects
of Factors Influencing Scar Formation
on the Scar Microbiome in Patients
with Burns. Int. J. Mol. Sci. 2023,24,
15991. https://doi.org/10.3390/
ijms242115991
Academic Editors: Lars-Peter
Kamolz, Marc Jeschke and Sebastian
P. Nischwitz
Received: 21 September 2023
Revised: 29 October 2023
Accepted: 2 November 2023
Published: 6 November 2023
Copyright: © 2023 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
International Journal of
Molecular Sciences
Article
Effects of Factors Influencing Scar Formation on the Scar
Microbiome in Patients with Burns
Yeongyun Jung 1, Hui Song Cui 1, Eun Kyung Lee 1, So Young Joo 2, Cheong Hoon Seo 2and Yoon Soo Cho 2,*
1Burn Institute, Hangang Sacred Heart Hospital, Hallym University College of Medicine,
Seoul 07247, Republic of Korea; jyg1076@hallym.ac.kr (Y.J.); bioeast007@naver.com (H.S.C.);
eunlee0617@gmail.com (E.K.L.)
2Department of Rehabilitation Medicine, Hangang Sacred Heart Hospital, Hallym University College of Medicine,
Seoul 07247, Republic of Korea; anyany98@gmail.com (S.Y.J.); chseomd@gmail.com (C.H.S.)
*Correspondence: yschorm@hallym.ac.kr; Tel.: +82-02-2639-5739; Fax: +82-02-2633-7571
Abstract:
Skin microbiome dysbiosis has deleterious effects, and the factors influencing burn scar
formation, which affects the scar microbiome composition, are unknown. Therefore, we investigated
the effects of various factors influencing scar formation on the scar microbiome composition in
patients with burns. We collected samples from the burn scar center and margin of 40 patients with
burns, subgrouped by factors influencing scar formation. Scar microbiome composition-influencing
factors were analyzed using univariate and multivariate analyses. Skin graft, hospitalization period,
intensive care unit (ICU) admission, burn degree, sex, age, total body surface area burned (TBSA),
time post-injury, transepidermal water loss, the erythrocyte sedimentation rate, and C-reactive protein
levels were identified as factors influencing burn scar microbiome composition. Only TBSA and ICU
admission were associated with significant differences in alpha diversity. Alpha diversity significantly
decreased with an increase in TBSA and was significantly lower in patients admitted to the ICU than
in those not admitted to the ICU. Furthermore, we identified microorganisms associated with various
explanatory variables. Our cross-sectional systems biology study confirmed that various variables
influence the scar microbiome composition in patients with burns, each of which is associated with
various microorganisms. Therefore, these factors should be considered during the application of skin
microbiota for burn scar management.
Keywords:
burn; burn scar; microbial community composition; factors influencing scar formation;
skin microbiome
1. Introduction
Millions of people suffer from postsurgical skin damage, scarring from trauma, or
burns annually [
1
]. Burn injuries cause functional and cosmetic problems by triggering
abnormal wound-healing processes, often leading to raised, erythematous, and itchy
hypertrophic scars [
2
]. These problems can have considerable physical and psychological
effects and substantially reduce patients’ quality of life [
3
,
4
]. Therefore, the management of
burn scars is important.
Notably, patient, injury, and treatment characteristics influence scar formation [
5
].
However, the pathophysiology, taxonomy, and clinical course of post-burn scarring vary [
5
].
Current burn scar treatments include surgical and nonsurgical methods, such as laser
therapy, steroid injections, and compression therapy. However, these treatments have
limited efficacy and often fail to manage symptoms [
6
]. The difficulty in treating morbid
scarring after burns is that it is caused by various risk factors; extensive prospective
studies on these risk factors are limited [
7
]. Consequently, long-term studies are needed to
improve the outcomes of burn scar treatment and patients’ quality of life. Furthermore,
new technologies are required to improve outcomes [6,8].
Int. J. Mol. Sci. 2023,24, 15991. https://doi.org/10.3390/ijms242115991 https://www.mdpi.com/journal/ijms
Int. J. Mol. Sci. 2023,24, 15991 2 of 12
As the human microbiome is associated with human health and disease, considerable
research has been conducted in this field in recent decades. With the advancement in
the understanding of the role of microbial communities, research on disease treatment
through microbiome regulation has been conducted [
9
]. For example, treating Clostridium
difficile infections through fecal microbiome transplantation has achieved a cure rate of
>90.000% [
10
]. However, treating various skin diseases using skin microbial community
control is challenging [
11
]. Strategies for controlling skin microbial communities include
skin microbiome transplantation, bacteriotherapy, and prebiotic stimulation [
11
]. These
approaches have been used to treat acne, atopic dermatitis, and underarm odor and have
achieved positive outcomes [12–14].
Furthermore, some studies have investigated the effect of burn injuries on the skin
microbiome and have reported that burn injuries lead to the emergence of a distinct skin
microbiome in patients with burns, compared with that in controls [
15
–
17
]. However,
burns are understudied relative to other skin disorders, and the mechanism through
which the skin microbiome affects patients with burns remains unknown. Factors that
influence scar formation after burn injury include patient characteristics, such as sex,
age, and skin type, and injury and treatment characteristics, such as burn severity, the
length of hospitalization, and the type of surgery [
5
]. These factors also affect the skin
microbiome [
18
–
22
]. Nevertheless, there is a need to verify which of these factors have a
marked influence on the composition of the skin microbiome to gain a better understanding
of the skin microbiome in patients with burns.
In the present study, we conducted a comprehensive analysis of biological systems
using a substantial patient dataset to identify key clinical factors that sustantially affect the
composition of the skin microbiome in patients with burns. Our findings provide a novel
microbiological perspective on the formation of burn scars, based on an analysis of the skin
microbiome in patients with burns.
2. Results
2.1. Study Cohort
In total, 80 burn scar samples (40 from the central areas of the scars and 40 from the
margin areas of the scars) were collected from 40 patients with burns. 16S rRNA amplicon
sequencing revealed that the scar microbiomes of the samples overall included species
belonging to 19 phyla, 180 families, and 364 genera (Figure 1).
Of the 40 patients, 23 were male, and 17 were female patients. Skin grafting was
performed in 50.000% of the patients. In addition, 25.000% of the participants had a history
of intensive care unit (ICU) admission. Data were collected from patients with various burn
severities: 22.500% had superficial second-degree burns, 32.500% had deep second-degree
burns, and 45.000% had third-degree burns (Supplementary Table S1).
The patients were classified into subgroups to compare their characteristics in more
detail (Table 1and Supplementary Table S2). The patients with superficial second-degree
burns did not undergo skin grafting, 2 of the 13 patients with deep second-degree burns
underwent skin grafting, and all patients with third-degree burns underwent skin grafting.
Compared with the other severity groups (patients with superficial second-degree and
deep second-degree burns), the patients with third-degree burns had a significantly higher
total body surface area burned (TBSA) (p= 0.006), longer hospitalization period in the
Department of Burn Surgery (p= 0.004), and greater scar thickness (p= 0.030).
Int. J. Mol. Sci. 2023,24, 15991 3 of 12
Int.J.Mol.Sci.2023,24,xFORPEERREVIEW3of14
Figure1.Relativemicrobialcomposition,atthephylum(a),family(b),andgenus(c)levels,atthe
burnscarcenterandburnmarginsinpatientswithburns.
Figure 1.
Relative microbial composition, at the phylum (
a
), family (
b
), and genus (
c
) levels, at the
burn scar center and burn margins in patients with burns.
Int. J. Mol. Sci. 2023,24, 15991 4 of 12
Table 1. Biomechanical characteristics of participants and scars according to burn severity.
Variable
Burn Degree Group (Patients with Burns)
Superficial 2nd
Degree (n= 9)
Deep 2nd Degree
(n= 13) 3rd Degree (n= 18) p
Age, years 41.56 ±13.69 46.69 ±11.79 48.06 ±10.93 0.488
Sex Male 5 (55.56%) 8 (61.54%) 10 (55.56%) 0.938
Female 4 (44.44%) 5 (38.46%) 8 (44.44%)
Skin graft Yes 0 (0.00%) 2 (15.38%) 18 (100.00%) <0.001
No 9 (100.00%) 11 (84.62%) 0 (0.00%)
TBSA burned, % 10.67 ±8.72 12.00 ±9.44 27.33 ±16.87 0.006
ESR, mm/H 10.67 ±8.05 10.08 ±7.94 13.00 ±8.34 0.563
CRP, mg/L 0.93 ±0.81 2.16 ±2.93 2.92 ±3.79 0.312
Length of hospitalization, days 15.44 ±11.56 18.46 ±12.57 38.67 ±20.35 0.004
ICU admission Yes 2 (22.22%) 1 (7.69%) 7 (38.89%) 0.138
No 7 (77.78%) 12 (92.31%) 11 (61.11%)
Time after injury, days 73.75 ±51.29 69.46 ±37.66 137.40 ±74.36 0.006
Variables
Burn Degree Group (Scar Samples)
Superficial 2nd
Degree (n= 18)
Deep 2nd Degree
(n= 26) 3rd Degree (n= 36) p
Scar type Center of scar 9 (50.00%) 13 (50.00%) 18 (50.00%) >0.999
Scar margin 9 (50.00%) 13 (50.00%) 18 (50.00%)
Itch NRS 1.67 ±2.47 1.85 ±2.46 2.86 ±3.37 0.453
Thickness, mm 0.02 ±0.03 0.07 ±0.12 0.18 ±0.20 0.030
Melanin, AU 145.20 ±56.39 176.80 ±79.28 148.00 ±61.60 0.352
Erythema, AU 258.70 ±131.20 325.30 ±148.90 305.30 ±143.10 0.305
TEWL, g/m2h10.99 ±3.67 11.60 ±3.67 12.29 ±4.55 0.620
Skin hydration, AU 48.73 ±14.82 44.60 ±13.58 45.59 ±16.33 0.772
Data are expressed as mean
±
standard deviation or n(percentage). The p-value was obtained using Welch’s
t-test and the chi-square test for continuous and categorical variables, respectively. Statistical significance was
set at
p< 0.05.
Abbreviations: TBSA, total body surface area; ESR, erythrocyte sedimentation rate; CRP, C-
reactive protein; ICU, intensive care unit; NRS, numerical rating scale; TEWL, transepidermal water loss; AU,
arbitrary unit.
2.2. Differences in Microbial Composition among Groups (Beta Diversity)
A canonical correspondence analysis (CCA) was used to confirm whether patient and
scar biomechanical characteristics affected the composition of the skin microbial community.
The treatment methods after burn injury (skin graft, the length of hospitalization, and
the length of ICU stay), burn severity (burn degree and TBSA), patient characteristics
(sex and age), the degree of inflammation (erythrocyte sedimentation rate and C-reactive
protein level), and time after burn injury were found to affect the beta diversity of the skin
microbiome composition significantly (Figure 2).
These results suggest that the factors influencing scar formation (patient, injury, and
treatment characteristics) also influence the scar microbiome composition. Except for
transepidermal water loss (TEWL), the other scar biomechanical characteristics did not yield
significant results. Skin hydration, melanin, itch numerical rating scale (NRS), erythema,
and thickness did not significantly affect the scar microbial community. In addition, no
significant differences were observed in the microbiome between the scar center and margin.
2.3. Differences in Scar Microbial Diversity (Alpha Diversity)
Differences in species diversity (alpha diversity) were analyzed in relation to 11 vari-
ables that significantly affected the beta diversity of the skin microbial composition. Con-
tinuous variables, such as age, burn degree, TBSA, the erythrocyte sedimentation rate
(ESR), the C-reactive protein (CRP) level, the length of hospitalization, time after burn
injury, and TEWL, were grouped into quartiles. Regarding the duration of ICU admission,
instead of quartile grouping, the patients were categorically assigned to groups according to
whether they were admitted to the ICU. Nine of the eleven variables yielded no significant
Int. J. Mol. Sci. 2023,24, 15991 5 of 12
differences in phylogenetic diversity (Supplementary Figure S1). Alpha diversity differed
significantly according to ICU admission status and TBSA (Figure 3). Phylogenetic diversity
was significantly (p= 0.005) lower in the patients admitted to ICU than in those not admit-
ted to ICU (Figure 3b). For TBSA, Q1 (lowest TBSA) and Q4 (highest TBSA) did not show
any significant difference (p= 0.122). However, when comparing Q2 and Q4 (p= 0.041),
and Q3 and Q4 (p= 0.027), we found that the higher the TBSA, the lower the phylogenetic
diversity (Figure 3d). These results indicate that TBSA and admission or non-admission to
the ICU could affect the composition and diversity of the scar microbial community.
Int.J.Mol.Sci.2023,24,xFORPEERREVIEW5of14
Figure2.Canonicalcorrespondenceanalysis(CCA)basedonBray–Curtisdissimilarity.Skingraft-
ingwasusedasthegroupingvariable,asityieldedthelowestp-valueintheunivariateanalysis.
Theeffectoftheotherexplanatoryvariableswasalsoincludedinthemodel.Thetableshowsthe
resultsoftheCCAforvariableswithsignificanteffectsidentifiedintheunivariateanalysis.Arrows
indicatetherelationshipbetweenvariables.*0.01<p<0.05,**0.001<p≤0.01,***p≤0.001.ns
indicatesp≥0.05.
Theseresultssuggestthatthefactorsinfluencingscarformation(patient,injury,and
treatmentcharacteristics)alsoinfluencethescarmicrobiomecomposition.Exceptfortran-
sepidermalwaterloss(TEWL),theotherscarbiomechanicalcharacteristicsdidnotyield
significantresults.Skinhydration,melanin,itchnumericalratingscale(NRS),erythema,
andthicknessdidnotsignificantlyaffectthescarmicrobialcommunity.Inaddition,no
significantdifferenceswereobservedinthemicrobiomebetweenthescarcenterandmar-
gin.
2.3.DifferencesinScarMicrobialDiversity(AlphaDiversity)
Differencesinspeciesdiversity(alphadiversity)wereanalyzedinrelationto11var-
iablesthatsignificantlyaffectedthebetadiversityoftheskinmicrobialcomposition.Con-
tinuousvariables,suchasage,burndegree,TBSA,theerythrocytesedimentationrate
(ESR),theC-reactiveprotein(CRP)level,thelengthofhospitalization,timeafterburn
injury,andTEWL,weregroupedintoquartiles.RegardingthedurationofICUadmission,
insteadofquartilegrouping,thepatientswerecategoricallyassignedtogroupsaccording
towhethertheywereadmiedtotheICU.Nineoftheelevenvariablesyieldednosignif-
icantdifferencesinphylogeneticdiversity(SupplementaryFigureS1).Alphadiversitydif-
feredsignificantlyaccordingtoICUadmissionstatusandTBSA(Figure3).Phylogenetic
diversitywassignificantly(p=0.005)lowerinthepatientsadmiedtoICUthaninthose
notadmiedtoICU(Figure3b).ForTBSA,Q1(lowestTBSA)andQ4(highestTBSA)did
notshowanysignificantdifference(p=0.122).However,whencomparingQ2andQ4(p
=0.041),andQ3andQ4(p=0.027),wefoundthatthehighertheTBSA,thelowerthe
phylogeneticdiversity(Figure3d).TheseresultsindicatethatTBSAandadmissionor
non-admissiontotheICUcouldaffectthecompositionanddiversityofthescarmicrobial
community.
Figure 2.
Canonical correspondence analysis (CCA) based on Bray–Curtis dissimilarity. Skin grafting
was used as the grouping variable, as it yielded the lowest p-value in the univariate analysis. The
effect of the other explanatory variables was also included in the model. The table shows the results
of the CCA for variables with significant effects identified in the univariate analysis. Arrows indicate
the relationship between variables. * 0.01 < p< 0.05, ** 0.001 < p
≤
0.01, *** p
≤
0.001. ns indicates
p≥0.05.
Int.J.Mol.Sci.2023,24,xFORPEERREVIEW6of14
Figure3.Comparisonofalphadiversityofskinmicrobiotaaccordingtointensivecareunit(ICU)
admissionandtotalbodysurfacearea(TBSA).(a)Shannonindexand(b)phylogeneticdiversity
accordingtoICUadmission.(c)Shannonindexand(d)phylogeneticdiversityaccordingtoTBSA.
Val uesareexpressedasmeans±standarddeviation.Asterisk(*and***)indicatesstatisticallysig-
nificant(0.01<p<0.05andp≤0.001)differencesbetweengroupsbasedonWelc h’st-testandKrus-
kal–Wallistest.
2.4.RelativeAbundanceofCategoricalVariables,asDeterminedUsingLinearDiscriminant
AnalysisEffectSize
Lineardiscriminantanalysiseffectsize(LEfSe)wasusedtoinvestigatethetaxonomic
differencesinthescarmicrobiomeforcategoricalvariables(LEfSeanalysis,p<0.01,LDA
score>3).AccordingtotheLEfSe,Propionibacteriumwasrelativelymoreabundantin
males,whereasAcinetobacter,Lactobacillus,Bacillus,unclassifiedBacilaceae,Pseudomonas,
unclassifiedEnterobacteriaceae,unclassifiedBartonellaceae,andRhodobacterwererela-
tivelymoreabundantinfemales(Figure4a).Thepatientswhounderwentskingrafting
hadspeciesofrelativelymoregenera(Enhydrobacter,Anaerococcus,Actinomyces,andBdel‐
lovibrio)thanthosewhodidnot,andthepatientswhodidnotundergoskingraftingwere
confirmedtohavemoreunclassifiedActinomycetales(Figure4b).Thepatientsadmied
toICUhadahigherabundanceofDermabacterthanthosewhowerenotadmiedtoICU.
However,thepatientsadmiedtoICUhadalowerabundanceofParacoccus,Chryseobac‐
terium,unclassifiedComamonadaceae,unclassifiedGemellaceae,Abiotrophia,Pantcea,Jan‐
thinobacterium,Arthobacter,andunclassifiedPseudomonadaceae(Figure4c).Nocharac-
teristicgenerawereidentifiedinrelationtoburndegree.
Figure 3.
Comparison of alpha diversity of skin microbiota according to intensive care unit (ICU)
admission and total body surface area (TBSA). (
a
) Shannon index and (
b
) phylogenetic diversity
according to ICU admission. (
c
) Shannon index and (
d
) phylogenetic diversity according to TBSA.
Values are expressed as means
±
standard deviation. Asterisk (* and ***) indicates statistically
significant (0.01 < p< 0.05 and p
≤
0.001) differences between groups based on Welch’s t-test and
Kruskal–Wallis test.
Int. J. Mol. Sci. 2023,24, 15991 6 of 12
2.4. Relative Abundance of Categorical Variables, as Determined Using Linear Discriminant
Analysis Effect Size
Linear discriminant analysis effect size (LEfSe) was used to investigate the taxonomic
differences in the scar microbiome for categorical variables (LEfSe analysis, p< 0.01, LDA
score > 3). According to the LEfSe, Propionibacterium was relatively more abundant in
males, whereas Acinetobacter,Lactobacillus,Bacillus, unclassified Bacilaceae, Pseudomonas,
unclassified Enterobacteriaceae, unclassified Bartonellaceae, and Rhodobacter were relatively
more abundant in females (Figure 4a). The patients who underwent skin grafting had
species of relatively more genera (Enhydrobacter,Anaerococcus,Actinomyces, and Bdellovibrio)
than those who did not, and the patients who did not undergo skin grafting were confirmed
to have more unclassified Actinomycetales (Figure 4b). The patients admitted to ICU had a
higher abundance of Dermabacter than those who were not admitted to ICU. However, the
patients admitted to ICU had a lower abundance of Paracoccus,Chryseobacterium, unclassi-
fied Comamonadaceae, unclassified Gemellaceae, Abiotrophia,Pantcea,Janthinobacterium,
Arthobacter, and unclassified Pseudomonadaceae (Figure 4c). No characteristic genera were
identified in relation to burn degree.
Int.J.Mol.Sci.2023,24,xFORPEERREVIEW7of14
Figure4.Mostdifferentiallyabundanttaxaselectedusinglineardiscriminantanalysiseffectsize
(LEfSe)for(a)sex,(b)skingraft,and(c)intensivecareunit(ICU)admission.
2.5.IdentificationofContinuousVar ia blesandAssociatedMicrobes
Aregressionanalysiswasusedtodeterminetherelationshipbetweenscarmicrobial
communitiesandcontinuousvariables(thelengthofhospitalization,age,TBSA,timeafter
injury,TEWL,ESR,andCRPlevels)(Figure5).Theregressionanalysisofthetop30bac-
terialgeneraandcontinuousvariablesshowednosignificantcorrelationswiththelength
ofhospitalization,age,orTEWL.TBSApositivelycorrelatedwithMethylobacterium(R=
0.291,p=0.009)andnegativelycorrelatedwithParacoccus(R=−0.292,p=0.009).Timeafter
injurypositivelycorrelatedwithAcinetobacter(R=0.370,p=0.001)andKocuria(R=0.351,
p=0.001).ESRpositivelycorrelatedwithBrevundimonas(R=0.369,p=0.001),Chryseobac‐
terium(R=0.328,p=0.003),andFacklamia(R=0.331,p=0.003)andnegativelycorrelated
withPropionibacterium(R=−0.309,p=0.005).TheCRPlevelpositivelycorrelatedwith
Chryseobacterium(R=0.490,p<0.001).TheseresultssuggestthatTBSA,thedurationof
burninjury,ESR,andCRPlevelsaffectskinmicrobialcommunitiesandincreaseorde-
creasetheabundanceofspecificskinmicrobes.
Figure 4.
Most differentially abundant taxa selected using linear discriminant analysis effect size
(LEfSe) for (a) sex, (b) skin graft, and (c) intensive care unit (ICU) admission.
2.5. Identification of Continuous Variables and Associated Microbes
A regression analysis was used to determine the relationship between scar microbial
communities and continuous variables (the length of hospitalization, age, TBSA, time
after injury, TEWL, ESR, and CRP levels) (Figure 5). The regression analysis of the top
30 bacterial genera and continuous variables showed no significant correlations with the
length of hospitalization, age, or TEWL. TBSA positively correlated with Methylobacterium
(R= 0.291, p= 0.009) and negatively correlated with Paracoccus (R=
−
0.292, p= 0.009).
Time after injury positively correlated with Acinetobacter (R= 0.370, p= 0.001) and Kocuria
(R= 0.351, p= 0.001). ESR positively correlated with Brevundimonas (R= 0.369, p= 0.001),
Chryseobacterium (R= 0.328, p= 0.003), and Facklamia (R= 0.331, p= 0.003) and negatively
Int. J. Mol. Sci. 2023,24, 15991 7 of 12
correlated with Propionibacterium (R=
−
0.309, p= 0.005). The CRP level positively correlated
with Chryseobacterium (R= 0.490, p< 0.001). These results suggest that TBSA, the duration
of burn injury, ESR, and CRP levels affect skin microbial communities and increase or
decrease the abundance of specific skin microbes.
Int.J.Mol.Sci.2023,24,xFORPEERREVIEW8of14
Figure5.Correlationmatrixshowssignificantcorrelations(p<0.05)betweenthedifferentmicrobial
taxainthedifferentialabundanceanalysisandthecontinuousvariables.Thecolorcodeandsizeof
thecirclesareρcorrelationcoefficients.
3.Discussion
Thesurvivalrateofpatientswithburnshasimprovedmarkedlyoverthepastfew
decades;however,post-burnpathologicalscarringremainsoneofthebiggestchallenges
inthemanagementofpatientswithburns[23].Pathologicalscarformationafterburns
dependsonseveralvariables,includingpatient,injury,andtreatmentcharacteristics[5].
Theskinmicrobiomeexertsawiderangeofeffectsontheimmunesystem,barrierfunc-
tion,andwound-healingresponseoftheskin,andithasanti-agingandanti-inflammatory
effects[24–26].Burninjuriesaltertheskinmicrobiome,affectingtheseprocesses[15].
Therefore,asthefirststepfromassociationtocausation,theidentificationoftheeffectsof
thevariousrelatedfactorsisimportant.Inthepresentstudy,weaimedtodeterminethe
effectsofvariousfactorsinfluencingscarformationonthescarmicrobiomecomposition
inpatientswithburns.Weperformedasystemsbiologyanalysisofawell-characterized
cohortofpatientswithburns.TheCCAanalysisshowedthatpatient,injury,andtreat-
mentcharacteristicsareimportantvariablesaffectingthescarmicrobialcommunity.The
effectsofspecificfactorsoncommunitydiversityandcompositionwerealsoidentified.
Overthepastfewyears,severalstudiesontheeffectsofburninjuriesonthemicro-
biomehavebeenconducted[27].Burninjuriesreducegutmicrobialdiversityandincrease
intestinalpermeability[28,29].Furthermore,burninjuriesincreasethenumberofpoten-
tiallypathogenicbacteria,leadingtointestinalimbalance[28,29].Skinmicrobiomestudies
havealsobeenconducted[15–17].Burninjuriesarereportedlyassociatedwithamicrobial
communitythatisdistinctfromthatofhealthyskin[15–17].However,conflictingresults
havebeenreportedonskinmicrobiomediversity.AccordingtoLiuetal.,moreopera-
tionaltaxonomicunitswereobservedinpatientswithburnsthanincontrols.Further-
more,Shannon’sevennessindexwashigherinpatientswithburnsthanincontrols[16].
MouseexperimentsconductedbySanjaretal.showedthatcontrolshadahigheralpha
diversity(Chao1,Shannon,Simpson,observedspecies,andphylogeneticdiversityindi-
ces)thanpatientswithburnedskin[17].Theseinconsistentresultscouldbeaributedto
thedifferencesbetweenhumansandmice,andsuchcontrastingresultsmayberelatedto
thelackoflarge-scalestudies.Variou sriskfactorsforburnscarformationareknown;
however,todate,nostudieshavebeenconductedonthecorrelationbetweenthesefactors
andthescarmicrobiome.Therefore,furtherresearchisrequiredtogainabeerunder-
standingofburninjuriesandtheskinmicrobiome.
Figure 5.
Correlation matrix shows significant correlations (p< 0.05) between the different microbial
taxa in the differential abundance analysis and the continuous variables. The color code and size of
the circles are ρcorrelation coefficients.
3. Discussion
The survival rate of patients with burns has improved markedly over the past few
decades; however, post-burn pathological scarring remains one of the biggest challenges
in the management of patients with burns [
23
]. Pathological scar formation after burns
depends on several variables, including patient, injury, and treatment characteristics [
5
].
The skin microbiome exerts a wide range of effects on the immune system, barrier function,
and wound-healing response of the skin, and it has anti-aging and anti-inflammatory
effects [
24
–
26
]. Burn injuries alter the skin microbiome, affecting these processes [
15
].
Therefore, as the first step from association to causation, the identification of the effects of
the various related factors is important. In the present study, we aimed to determine the
effects of various factors influencing scar formation on the scar microbiome composition
in patients with burns. We performed a systems biology analysis of a well-characterized
cohort of patients with burns. The CCA analysis showed that patient, injury, and treatment
characteristics are important variables affecting the scar microbial community. The effects
of specific factors on community diversity and composition were also identified.
Over the past few years, several studies on the effects of burn injuries on the mi-
crobiome have been conducted [
27
]. Burn injuries reduce gut microbial diversity and
increase intestinal permeability [
28
,
29
]. Furthermore, burn injuries increase the number of
potentially pathogenic bacteria, leading to intestinal imbalance [
28
,
29
]. Skin microbiome
studies have also been conducted [
15
–
17
]. Burn injuries are reportedly associated with a
microbial community that is distinct from that of healthy skin [
15
–
17
]. However, conflicting
results have been reported on skin microbiome diversity. According to Liu et al., more
operational taxonomic units were observed in patients with burns than in controls. Further-
more, Shannon’s evenness index was higher in patients with burns than in controls [
16
].
Mouse experiments conducted by Sanjar et al. showed that controls had a higher alpha
diversity (Chao 1, Shannon, Simpson, observed species, and phylogenetic diversity indices)
than patients with burned skin [
17
]. These inconsistent results could be attributed to the
differences between humans and mice, and such contrasting results may be related to the
lack of large-scale studies. Various risk factors for burn scar formation are known; however,
Int. J. Mol. Sci. 2023,24, 15991 8 of 12
to date, no studies have been conducted on the correlation between these factors and the
scar microbiome. Therefore, further research is required to gain a better understanding of
burn injuries and the skin microbiome.
Burns are often classified as major or minor, according to TBSA. Major burns cause
serious problems in the local burn area, as well as in the whole body, because of immune
and inflammatory reactions and metabolic shock [
8
]. They are also known to alter the
human microbiome [
8
]. The present study’s results indicate that TBSA could affect the
alpha diversity of the scar microbiome. According to a previous study involving older
patients with burns, those with a higher TBSA had a reduced gut microbiome diversity
than those with a lower TBSA [
30
]. No reports on the diversity of TBSA and burn scar (skin)
microbial communities have been published. Consistent with the results of previous studies
on the gut microbiome, the present study’s results show that the alpha diversity of the scar
microbiome decreased with a higher TBSA. Taken together, these results suggest that severe
burn injuries lower microbial community diversity in the gut and skin, leading to dysbiotic
conditions. Anaerococcus was more abundant in patients who underwent skin grafting.
Anaerococcus spp. are common skin commensal bacteria; however, these are present in
chronic skin diseases and injuries [
31
,
32
]. Thus, our finding was plausible, because patients
who underwent skin grafting had a higher TBSA and a longer hospitalization period after
injury than those who did not undergo skin grafting.
ICU admission affects the alpha diversity of the scar microbiome. Patients admitted
to ICUs have a lower skin alpha diversity than those who are not [
33
–
35
]. The loss of
diversity in ICU inpatients may be associated with differences in treatment modalities, such
as systemic antibiotic therapy, and long-term exposure to hospital pathogens [
35
]. In our
study, Paracoccus was more abundant in the patients not admitted to the ICU (Figure 4c)
and negatively correlated with TBSA (Figure 4). Paracoccus is less prevalent in chronic
wounds than in healthy skin [
36
]. The patients admitted to the ICU had a significantly
higher TBSA than those who were not (Supplementary Table S1). These results suggest
that Paracoccus plays an important role in shaping normal skin microbial communities.
The scar microbiome exhibits sex-specific differences in composition and alpha diver-
sity [
22
]. In the present study, the phylogenetic diversity tended to be higher in females than in
males, although the differences did not reach statistical significance (
Supplementary Figure S1
).
We also confirmed that the scar microbial community exhibited a lower Acinetobacter abundance
and a higher Propionibacterium abundance in men than in women (Figure 4a). According to
a study by Ying et al., Acinetobacter was significantly less prevalent in males than in females,
whereas the relative abundance of Propionibacterium was significantly higher in males than in fe-
males [
37
], consistent with the findings of the present study. Propionibacterium is a representative
commensal bacterium of the skin microbiome and is thought to confer health benefits through
short-chain fatty acid production [
38
]. Lipophilic bacteria, such as Propionibacterium, prefer envi-
ronments containing abundant moisture and sebum [
37
]. Women have lower sebum levels with
increasing age, whereas men have better sebum maintenance with increasing age [
39
]. The par-
ticipants in the present study were middle-aged adults (
≥
40 years) (
Supplementary Table S1
).
Therefore, Propionibacterium was expected to be more abundant in males than in females.
The present study has several strengths. First, it included comprehensive patient
characterization data, allowing for detailed analyses of different variables. Second, it
included detailed information on patient, injury, and treatment characteristics according to
each subgroup. Finally, no previous study has investigated the effects of various factors
affecting scar formation on the scar microbial communities in patients with burns.
The present study also has some limitations, such as the small sample size of the cross-
sectional design and subgroup analysis, rendering it difficult to conclude causality. Another
limitation of our study arises from the recruitment of patients with scars of a relatively low
thickness. Therefore, a second replication cohort with a much larger number of samples,
more refined groupings, and sampling points is required. Furthermore, a more in-depth
investigation is warranted through comparative studies involving patients exhibiting
significant variations in specific biomechanical characteristics, including thickness. This
Int. J. Mol. Sci. 2023,24, 15991 9 of 12
would allow us to better understand how the skin microbiome influences biomechanical
scar characteristics. Nevertheless, the present study can facilitate the remediation of the
scar microbial community in burn scar management.
4. Materials and Methods
4.1. Study Participants and Sample Collection
This study involved an analysis of 16S rRNA amplicon sequencing data obtained
from the burn scars of patients who underwent rehabilitation therapy for burns at the
Department of Rehabilitation Medicine between September 2021 and July 2022. The study
design and protocol were approved by the Institutional Review Board of Hangang Sacred
Heart Hospital (HG2020-007) and registered in the Clinical Research Information Service
registry (KCT0005228). Written informed consent for participation and publication was
obtained from all participants, and all methods were performed in accordance with relevant
guidelines and regulations.
Ninety-eight patients undergoing rehabilitation therapy in the Department of Re-
habilitation Medicine, who had received wound dressing and surgical treatment in the
Department of Burn Surgery at the burn center, were enrolled in this study. Of the 98 pa-
tients, 81 with burn scars surrounded by normal skin were selected. Then, 37 patients
were excluded based on the following exclusion criteria, and 44 patients were ultimately
swabbed for skin samples. The exclusion criteria were (1) the presence of infected scars
(accompanied by pus) (n= 6), (2) the presence of electrical burns (n= 5), (3) the presence
of metabolic diseases (diabetes, hypertension, and others) (n= 15), (4) the presence of
dermatitis or psoriasis (n= 2), and (5) a history of oral and topical antibiotic treatment
within 4 weeks before the study (n= 9). Of the 44 patients, 4 were excluded from the quality
control for extracted metagenomic DNA, and a total of 80 samples (40 from the central
areas of the scars and 40 from the margin of the scars) from the final 40 patients were used
for amplicon sequencing.
All patients used the Korean Medical Device moisturizers for burn patients twice
or three times a day as previously recommended scar management following complete
re-epithelialization of the burn wound. All patients avoided bathing in the morning
before swabbing. The skin samples analyzed in this study were collected from the central
and margin areas of burn scars using saline-soaked sterile cotton swabs, and they were
stored at
−
80
◦
C until DNA extraction. The biomechanical properties of the scars were
investigated using previously described equipment [
40
]. Blood samples were collected
after fasting for 8 h. The following characteristics were evaluated as possible microbiome-
influencing factors: age, burn degree, skin graft, sex, TBSA, ESR, CRP level, the length of
hospitalization in the Department of Burn Surgery, the length of stay in the ICU, admission
or non-admission to the ICU, time after injury, scar type, itch NRS, scar thickness, melanin,
erythema, TEWL, and skin hydration (Table S1).
4.2. Total DNA Isolation, 16S Library Preparation, Sequencing, and Analysis
Total DNA was extracted from each swab head using a DNeasy PowerSoil Pro Kit
(Qiagen, Hilden, Germany). The V4–V5 hypervariable region of the 16S rRNA gene was
amplified using polymerase chain reaction (PCR) with the following primers (5
0
–3
0
): 515F-
CGCTCTTCCGATCTGTGNCAGCMGCCGCGGTRA; 907R-GTGCTCTTCCGATCCGYCW
ATTYHTTTRAGTTT). Illumina sequence adapters were ligated to PCR products using a
Nextera
®
XT index kit (Illumina, San Diego, CA, USA), according to the manufacturer’s
protocol. The PCR products were purified using an AMPure XP bead purification kit
(Beckman Coulter, Brea, CA, USA), and the concentrations of the purified PCR products
were standardized. An Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA,
USA) was used to determine the exact concentrations required for sequencing. All samples
underwent sequencing on an Illumina MiSeq platform (Illumina) at the KNU NGS Core
Facility, Kyungpook National University, Daegu, South Korea, using a MiSeq Reagent Kit
v3 (300 bp paired-end reads; Illumina).
Int. J. Mol. Sci. 2023,24, 15991 10 of 12
4.3. Statistical Analyses
For a microbiome analysis, QIIME2 [
41
] was used. Primer and adapter sequences were
removed using the q2-cutadapt plugin [
42
]. The q2-quality-filter plugin was used to control
sequence quality, and the q2-deblur plugin was used for denoising [
43
,
44
]. Taxonomies
were assigned to representative sequences in the Greengenes database (version 13.8) using
a q2-feature-classifier [45]. Rooted and unrooted phylogenetic trees were generated using
Mafft, mask, and FastTree protocols [
46
,
47
]. The generated phylogenetic tree was used for a
diversity analysis. The smallest sample had 10,076 features, which were subjected to
α
- and
β
-diversity analyses. Alpha diversity was assessed by calculating Shannon’s and Faith’s
phylogenetic diversity indices. The variability of each variable was measured using Mann–
Whitney test (sex, skin graft, and ICU admission) or Kruskal–Wallis test (age, burn degree,
TBSA, ESR, CRP, the length of hospitalization, time after burn injury, and TEWL). Variables,
such as age, burn degree, TBSA, ESR, CRP level, the length of hospitalization, time after
burn injury, and TEWL, were grouped into quartiles. Analyses were performed using
Prism 8 software (GraphPad Software, San Diego, CA, USA). Statistical significance was
set at p< 0.05. Beta diversity was analyzed using CCA based on Bray–Curtis dissimilarity.
LEfSe was used to select the genera of burn scars associated with categorical variables [
48
].
A Pearson correlation analysis was performed using the “Hmisc” R package to assess the
correlations of continuous variables (age, TBSA, ESR, CRP, the length of hospitalization,
time after burn injury, and TEWL) with the burn scar microbiome [
49
]. Figures were created
in R using “ggplot2” and “ggpubr” [
50
,
51
]. Sequencing data are publicly available in the
NCBI Sequence Read Archive (accession number: PRJNA973215).
5. Conclusions
In this study, we determined the effects of various factors influencing scar formation
on the scar microbial community in patients with burns using a 16s rRNA analysis and next-
generation sequencing technology. This cross-sectional systems biology study found that
TBSA and ICU admission were major factors associated with alpha diversity changes and
variations in microbial community composition. Sex, skin graft, and inflammation levels
(ESR and CRP levels) were also explanatory variables associated with microbes. To date, no
study has evaluated the effects of factors influencing scar formation on the scar microbiome
composition with sufficient data on patient, injury, and treatment characteristics. This
study revealed the importance of different factors affecting burn injuries in relation to the
skin microbiome.
Supplementary Materials:
The following supporting information can be downloaded at https:
//www.mdpi.com/article/10.3390/ijms242115991/s1.
Author Contributions:
Conception: Y.J. and Y.S.C. Formal analysis and writing—original draft: Y.J.
Investigation: H.S.C. Data curation: E.K.L. Writing—review and editing: S.Y.J., C.H.S. and Y.S.C.
Supervision: Y.S.C. All authors have read and agreed to the published version of the manuscript.
Funding: This research was supported by the Hallym University Research Fund, 2022 (HURF-2022-40).
Institutional Review Board Statement:
The study design and protocol were approved by the Insti-
tutional Review Board of Hangang Sacred Heart Hospital (HG2020-007) and registered in the Clinical
Research Information Service registry (KCT0005228). Written informed consent was obtained from
all the patients.
Informed Consent Statement:
Informed consent was obtained from all participants involved in the
study.
Data Availability Statement:
The datasets generated and/or analyzed during the current study are
available on the NCBI Sequence Read Archive database (accession number: PRJNA973215).
Acknowledgments:
We gratefully acknowledge support from the Hallym University Research Fund,
2022 (HURF-2022-40).
Conflicts of Interest: All authors declare that they have no competing interest.
Int. J. Mol. Sci. 2023,24, 15991 11 of 12
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