Association between salivary microbiota and renal function in renal transplant patients during the perioperative period

Abstract

Introduction
Renal transplantation is an effective treatment for the end stage renal disease (ESRD). However, how salivary microbiota changes during perioperative period of renal transplant recipients (RTRs) has not been elucidated.

Methods
Five healthy controls and 11 RTRs who had good recovery were enrolled. Saliva samples were collected before surgery and at 1, 3, 7, and 14 days after surgery. 16S rRNA gene sequencing was performed.

Results
There was no significant difference in the composition of salivary microbiota between ESRD patients and healthy controls. The salivary microbiota of RTRs showed higher operational taxonomic units (OTUs) amount and greater alpha and beta diversity than those of ESRD patients and healthy controls, but gradually stabilized over time. At the phylum level, the relative abundance of Actinobacteria, Tenericutes and Spirochaetes was about ten times different from ESRD patients or healthy controls for RTRs overall in time. The relative abundance of Bacteroidetes, Fusobacteria, Patescibacteria, Leptotrichiaceae and Streptococcaceae was correlated with serum creatinine (Scr) after renal transplantation.

Discussion
In short, salivary microbiota community altered in the perioperative period of renal transplantation and certain species of salivary microbiota had the potential to be a biomarker of postoperative recovery.

Full text

TYPE Original Research
PUBLISHED 29 March 2023
DOI 10.3389/fmicb.2023.1122101
OPEN ACCESS
EDITED BY
Weiqi He,
Soochow University, China
REVIEWED BY
Jozsef Soki,
University of Szeged, Hungary
Zheng Sun,
Harvard Medical School, United States
*CORRESPONDENCE
Yingzi Ming
mingyz_china@csu.edu.cn
†These authors have contributed equally to this
work and share first authorship
SPECIALTY SECTION
This article was submitted to
Microorganisms in Vertebrate Digestive
Systems,
a section of the journal
Frontiers in Microbiology
RECEIVED 12 December 2022
ACCEPTED 07 March 2023
PUBLISHED 29 March 2023
CITATION
Xiang X, Peng B, Liu K, Wang T, Ding P, Li H,
Zhu Y and Ming Y (2023) Association between
salivary microbiota and renal function in renal
transplant patients during the perioperative
period. Front. Microbiol. 14:1122101.
doi: 10.3389/fmicb.2023.1122101
COPYRIGHT
©2023 Xiang, Peng, Liu, Wang, Ding, Li, Zhu
and Ming. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The use,
distribution or reproduction in other forums is
permitted, provided the original author(s) and
the copyright owner(s) are credited and that
the original publication in this journal is cited, in
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No use, distribution or reproduction is
permitted which does not comply with these
terms.
Association between salivary
microbiota and renal function in
renal transplant patients during
the perioperative period
Xuyu Xiang1,2†, Bo Peng1,2† , Kai Liu1,2, Tianyin Wang1,2 , Peng Ding1,2,
Hao Li1,2, Yi Zhu1,2 and Yingzi Ming1,2*
1The Transplantation Center of the Third Xiangya Hospital, Central South University, Changsha, China,
2Engineering and Technology Research Center for Transplantation Medicine of National Health
Commission, Changsha, China
Introduction: Renal transplantation is an eective treatment for the end
stage renal disease (ESRD). However, how salivary microbiota changes during
perioperative period of renal transplant recipients (RTRs) has not been elucidated.
Methods: Five healthy controls and 11 RTRs who had good recovery were
enrolled. Saliva samples were collected before surgery and at 1, 3, 7, and 14 days
after surgery. 16S rRNA gene sequencing was performed.
Results: There was no significant dierence in the composition of salivary
microbiota between ESRD patients and healthy controls. The salivary microbiota
of RTRs showed higher operational taxonomic units (OTUs) amount and
greater alpha and beta diversity than those of ESRD patients and healthy
controls, but gradually stabilized over time. At the phylum level, the relative
abundance of Actinobacteria, Tenericutes and Spirochaetes was about ten
times dierent from ESRD patients or healthy controls for RTRs overall in
time. The relative abundance of Bacteroidetes, Fusobacteria, Patescibacteria,
Leptotrichiaceae and Streptococcaceae was correlated with serum creatinine (Scr)
after renal transplantation.
Discussion: In short, salivary microbiota community altered in the perioperative
period of renal transplantation and certain species of salivary microbiota had the
potential to be a biomarker of postoperative recovery.
KEYWORDS
salivary microbiota, renal function, renal transplantation, perioperative period, 16S rRNA
1. Introduction
End-stage renal disease (ESRD) represents a serious public health problem fueled
by aging populations and a pandemic of chronic non-communicable diseases, which is
characterized by high mortality and economic burden. Renal transplantation is one of the
effective treatments, with the hope of recovery for patients to normal life. However, there
is still a lack of highly sensitive and specific biomarkers with minimal invasion and cost to
assess recovery or rejection during the perioperative period.
The oral cavity consists of teeth, gingival groove, tongue, soft and hard palates, buccal
mucosa, and tonsils. All the above areas are inhabited by microbiota and soaked in saliva
all the time. Each salivary gland is highly permeable and surrounded by capillaries, a feature
that allows for a freer exchange of substances between the salivary glands and blood (Wilson
et al., 2014). Therefore, the salivary microbiota has the potential to be a bridge between oral
(Belstrøm et al., 2018a) and systemic conditions.
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Xiang et al. 10.3389/fmicb.2023.1122101
Indeed, the relationship between chronic kidney disease (CKD)
and gut microbiota has been widely investigated, both regarding
changes in the floras of patients with CKD (Crespo-Salgado et al.,
2016;Meijers et al., 2019;Ren et al., 2020) and regarding the
mechanisms of gut microbiota in the development of CKD (Wang
X. et al., 2020;Zhu et al., 2021;Wang et al., 2023). Saliva, one of
the largest sources of gut microbiota, may play an important role
in kidney disease that salivary microbiota ectopically colonizing
the gut may be closely associated with the development of kidney
disease and renal function. At the same time, several studies
have discussed changes in salivary flora in patients with CKD
(Hu et al., 2018;Duan et al., 2020;Guo et al., 2022;Liu et al.,
2022). The overall composition of the salivary microbiota in
CKD patients is significantly different from that of the healthy
population, although the variation in individual flora or individual
indicators is not entirely consistent across studies. Hence, the
possibility of salivary microbiota functioning at CKD in situ cannot
be ruled out. In summary, salivary microbiota has the potential as
a diagnostic and therapeutic target for ESRDs or renal transplant
recipients (RTRs).
Based on previous studies, we speculate that salivary
microbiota in patients after renal transplantation will be
significantly different from the preoperative flora and this
change may be associated with renal function. Although the
alteration of salivary floras in patients with ESRD has been
studied, how salivary microbiota dynamic changes during the
perioperative period of RTRs and the association between
salivary microbiota and postoperative recovery have not been
elucidated. Therefore, our study is the first to examine the
variations of salivary microbiota during the perioperative
period of renal transplantation and the relationship between
salivary microbiota and renal function. We aimed to find some
special floras associated with the return of renal function as
clinical biomarkers.
2. Materials and methods
2.1. Subjects and sample collection
From 1 October 2022 to 18 October 2022, a total of 11
consecutive ESRD patients received renal transplantation
in our center and were enrolled. Saliva samples were
collected before surgery and at 1, 3, 7, and 14 days after
surgery. Saliva samples from five healthy people were also
collected as healthy controls. None of the above subjects
had oral antibiotics, cortisol, smoking, or drinking history
within 6 months.
Before collection, patients fasted for half an hour and
rinsed their mouths. Patients spit the saliva into a sterile tube
until it reaches 2 ml. Saliva was stored at −80◦C immediately
after collection.
The study protocol was approved (22207) by the Ethics
Committee of the Third Xiangya Hospital of Central South
University (Changsha, China). Written informed consent was
obtained from all study participants. Experiments were carried out
in accordance with the ethical guidelines set by the Declaration of
Helsinki 1964 and its later amendments.
2.2. Sequencing
2.2.1. Sampling and DNA extraction
Total genome DNA from samples was extracted using
the CTAB/SDS method. DNA concentration and purity were
monitored on 1% agarose gel. According to the concentration,
DNA was diluted to 1 ng/µl using sterile water.
2.2.2. Amplicon generation
16S rRNA genes were amplified using the specific
primer 341F (CCTAYGG-GRBGCASCAG) and 806R
(GGACTACNNGGGTATCTAAT) with the barcode. All PCR
reactions were carried out in 30 µl of reactions with 15 µl of
Phusion R
High-Fidelity PCR Master Mix (New England Biolabs);
0.2 µM of forward and reverse primers, and about 10 ng of
template DNA. Thermal cycling consisted of initial denaturation
at 98◦C for 1 min, followed by 30 cycles of denaturation at 98◦C
for 10 s, annealing at 50◦C for 30 s and elongation at 72◦C for 60 s.
And final extension at 72◦C for 5 min.
2.2.3. PCR products quantification and
qualification
The same volume of 1X loading buffer (containing SYB green)
with the PCR products and operate electrophoresis was mixed on
a 2% agarose gel for detection. Samples with a bright main strip
between 400 and 450 bp were chosen for further experiments.
2.2.4. PCR products mixing and purification
PCR products were mixed in equidensity ratios. Then, the
mixture of PCR products was purified with AxyPrep DNA Gel
Extraction Kit (AXYGEN).
2.2.5. Library preparation and sequencing
Sequencing libraries were generated using NEBNext R
UltraTM
DNA Library Prep Kit for Illumina (NEB, USA) following the
manufacturer’s recommendations, and index codes were added.
The library quality was assessed on the Qubit@2.0 Fluorometer
(Thermo Scientific) and Agilent Bioanalyzer 2100 system. Finally,
the library was sequenced on an Illumina NovaSeq 6000 platform,
and 250 bp paired-end reads were generated. Sequences are
deposited under SRA PRJNA904953.
2.3. Data analysis
2.3.1. OTU cluster and species annotation
Paired-end reads from the original DNA fragments were
merged using FLASH. Sequences analysis was performed by the
UPARSE software package using the UPARSE-OTU and UPARSE-
OTUref algorithms. In-house Perl scripts were used to analyze
alpha (within samples) and beta (among samples) diversity.
Sequences with ≥97% similarity were assigned to the same OTU.
We picked a representative sequence for each OTUs and used
the RDP classifier to annotate taxonomic information for each
representative sequence based on Silva 132 database.
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Xiang et al. 10.3389/fmicb.2023.1122101
2.3.2. Phylogenetic distance and community
distribution
Graphical representation of the relative abundance of bacterial
diversity from phylum to species can be visualized using the
Krona chart. Cluster analysis was preceded by principal component
analysis (PCA), which was applied to reduce the dimension of
the original variables using the QIIME software package. We
used weighted UniFrac distance for principal coordinate analysis
(PCoA) and Unweighted Pair Group Method with Arithmetic
mean for the abbreviation (UPGMA) Clustering.
2.4. Statistical analysis
Linear discriminant analysis Effect Size (LEfSe) was used for
the quantitative analysis of biomarkers within different groups.
To identify differences in microbial communities between the two
groups, ANOSIM and ADONIS were performed based on the
Bray–Curtis dissimilarity distance matrices. A Wilcoxon rank-sum
test and unpaired t-test were performed to evaluate differences
between the two groups in alpha diversity, principal coordinates,
and community difference analysis. Pearson correlation analysis
was used to assess the correlation between microbiota and
creatinine. A p-value of <0.05 was required for the results to be
considered statistically significant.
3. Results
3.1. Study population
The clinical information of RTRs (age 44.8 ±12.2 years;
63.6% males) is shown in Table 1. The mean body weight index
(BMI) was 20.5 ±3.8 kg/m² for RTRs. All RTRs received
antihuman thymocyte globulin (ATG) for induction, the same
triple immunosuppressive therapy, FK506, mycophenolate mofetil
(MMF) plus steroids, and meropenem as a primary antibiotic. The
saliva samples were collected before surgery (ESRD, n=11) and
at 1 day (RTR1, n=9), 3 days (RTR3, n=11), 7 days (RTR7, n
=11), and 14 days (RTR14, n=8) after surgery. Generally, the
specimens were divided into two groups, namely the ESRD group
and the RTR group.
Healthy controls (HCs, n=5) ranged in age from 30 to 56 years
and consisted of three men and two women. The mean BMI was
24.0 ±3.1 kg/m², and the mean serum creatinine (Scr) was 71.3
±16.7 umol/L for HCs. We collected saliva samples (n=9) from
each of them two times, 7 days apart, to form the HC group. They
all reported no history of chronic diseases or medication.
3.2. Impact of renal transplantation on
salivary microbiota in individuals
First, according to the rarefaction curve, Shannon curve, and
rank-abundance curve (Supplementary Figure 1), we found that
the number of reads of most samples is reasonable. The curves
tended to be flat, which indicated that the number of reads was
relatively large enough to reflect species richness.
The Venn graph demonstrated the shared and unique OTUs
between the three groups (Figure 1A). Overall, the RTR group
has more OTUs. However, there was no significant difference
in the number of OTUs in a single sample between the three
groups. Figures 1B,Cshow the species composition of each group
and individual samples at the phylum level. The ESRD and HC
groups had relatively similar species composition, whereas the RTR
group was quite different, especially in the relative abundance of
Actinobacteria, Tenericutes, and Spirochaetes.
Alpha diversity, including Shannon, Simpson, and so on,
provided a measurement of species diversity within a sample. The
larger the Shannon index, the greater the diversity. The ESRD group
was close to the HC group in alpha diversity. Group RTR always had
larger intra-group differences (Figure 1D, RTR vs. ESRD: p<0.05).
Beta diversity was used to study the intrinsic composition
of the microbial structure. The closer the samples were to each
other, the more similar the species’ composition was. The PCoA
was analyzed based on weighted UniFrac distance. According
to the PCoA, the microbial composition of the RTR group was
significantly different from those of the ESRD (Figure 1E) and
HC (Supplementary Figure 2) groups, which was proved by the
ADONIS analysis (RTR vs. ESRD: p=0.001, RTR vs. HC: p=
0.001). On the contrary, the beta diversity of the ESRD group was
not significantly different from that of the HC group (Figure 1F).
LEfSe analysis was used to describe differential species between
different groups. When the LDA value was >2, the species was
a statistically significant biomarker between groups. The results
showed that when compared with the ESRD (Figure 2A) and HC
(Figure 2B) groups, the relative abundances of Burkholderiaceae,
Lautropia, and Actinobacteria of the RTR group were significantly
increased, and Neisseriaceae and Neisseria were significantly
decreased. The composition of OTU sequences was further
transformed into KEGG orthodontics to analyze the differences
in predicted function. The pathways related to membrane
transport, carbohydrate metabolism, and signal transduction were
significantly enriched in the RTR group (Figures 2C,D).
3.3. The dynamic change in salivary
microbiota during the early stage
post-renal transplantation
The unique community occupied most OTUs of the RTR1,
RTR3, and RTR7 and differed between every two adjacent time
points (Figure 3A, RTR1 vs. RTR14: p<0.05). Over time,
the OTUs of RTRs gradually decreased and the shared OTUs
with ESRD or HC groups increased, and RTR1 or RTR7 was
significantly different from ESRD or HC (Figure 3B,p<0.05).
Figures 3C,Dshow that the Ace index and intra-group differences
of the RTR group descended and approached ESRD and HC
groups over time (RTR1 vs. ESRD: p<0.05, RTR1 vs. RTR14:
p<0.05). Other alpha diversity indexes also showed the same
changes in the salivary microbiota of RTRs at different time points
(Supplementary Figure 3). At the phylum level, the microbial
composition of the RTR group was constantly changing (Figure 3E)
but always differed from ESRD or HC groups (Figure 3F). From
PCoA, the intrinsic microbial composition of RTR1 and RTR14
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Xiang et al. 10.3389/fmicb.2023.1122101
TABLE 1 Characteristics of renal transplant patients and control subjects.
Patient Gender Age
(years)
Height
(m)
Weight
(kg)
Body
mass
index
(kg/m²)
Induction
therapy Immunosuppressive
therapy
Antibiotics Dialysis
type Dialysis
duration
(months)
Cause
of
end
stage
renal
disease
Scr
before
KT
(umol
/L)
eGFR
before
KT
(mL/min
/1.73
m²)
Scr 7
days
after
KT
(umol
/L)
Scr 14
days
after
KT
(umol
/L)
1 Female 45 1.58 51.5 20.63 ATG FK506 +MMF +Steroids Meropenem +
Cefminox
HD 12 IgA
Nephropathy
897 4.14 233 97
2 Male 53 1.62 67 25.53 ATG FK506 +MMF +Steroids Meropenem +
Tikaolin
HD 8 Unknown 1,058 4.26 300
3 Male 54 1.65 55.5 20.39 ATG FK506 +MMF +Steroids Meropenem +
Carbofengin +
Cefminol
HD 47 Unknown 753 6.37 204 136
4 Male 27 1.78 57.4 18.12 ATG FK506 +MMF +Steroids Meropenem +
Colistin sulfate +
Carbofengin+
Cephalosporin
HD 13 Unknown 601 10.12 238 103
5 Male 64 1.5 65 28.89 ATG FK506 +MMF +Steroids Meropenem +
Peracillin
HD 6 Unknown 623 7.47 135 114
6 Female 51 1.67 48.7 17.46 ATG FK506 +MMF +Steroids Meropenem PD 60 Unknown 1,016 3.42 87 95
7 Male 31 1.76 66.6 21.50 ATG FK506 +MMF +Steroids Meropenem+
Peracillin
PD 19 Unknown 1,104 4.72 132 104
8 Female 32 1.63 47 17.70 ATG FK506 +MMF +Steroids Meropenem PD 13 IgA
Nephropathy
974 4.11 73 78
9 Male 51 1.71 48.1 16.45 ATG FK506 +MMF +Steroids Meropenem +
Tikaolin +
Peracillin
HD 3 Unknown 1,231 3.59 109 132
10 Female 53 1.45 44 20.93 ATG FK506 +MMF +Steroids Meropenem +
Cefminox
HD 12 Unknown 999 3.44 65 87
11 Male 32 1.75 55 17.96 ATG FK506 +MMF +Steroids Meropenem HD 29 Unknown 1,556 3.09 124 98
Control Gender Age
(years)
Height
(m)
Weight
(kg)
Body
mass
index
(kg/m²)
Scr
(umol/L) eGFR
(mL/min/1.73 m²)
1 Female 50 1.57 48.5 19.68 73 212.54
2 Female 41 1.52 63 27.27
3 Male 56 1.5 49.3 21.91 49.3 313.91
4 Male 30 1.65 69 25.34 90 98.39
5 Male 32 1.87 90 25.74 73 316.2
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Xiang et al. 10.3389/fmicb.2023.1122101
FIGURE 1
Composition characteristics of salivary microbiota in RTR, ESRD, and HC groups: (A) Venn graph for the OTUs of RTR, ESRD, and HC groups; (B)
Salivary species composition of RTR, ESRD, and HC groups in the phylum level; (C) Salivary species composition of each individual in the phylum
level; (D) Shannon index of RTR, ESRD, and HC groups; (E) PCoA graph of RTR and ESRD groups; (F) PCoA graph of ESRD and HC groups.
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Xiang et al. 10.3389/fmicb.2023.1122101
FIGURE 2
Dierential species and KEGG analysis for RTR, ESRD, and HC
groups: (A) LEfSe analysis of the salivary microbiota composition
between RTR and ESRD groups; (B) LEfSe analysis of the salivary
microbiota composition between RTR and HC groups; (C) LEfSe
analysis of the predicted function between RTR and ESRD groups;
(D) LEfSe analysis of the predicted function between RTR and HC
groups.
groups significantly differed (p=0.001) from ESRD or HC
groups (Figure 3H and Supplementary Figure 4) and the intrinsic
microbial composition significantly changed between 3 and 7 days
after surgery (Figure 3G and Supplementary Figure 4).
3.4. Certain species of salivary microbiota
were associated with the recovery of renal
function
The correlation between the predominant species of salivary
microbiota in RTRs and ESRD and the corresponding Scr
on the day of saliva collection was analyzed (Table 2 and
Figure 4). In the RTRs group, Bacteroidetes, Fusobacteria,
Patescibacteria, and Leptotrichiaceae were positively
correlated with Scr, whereas Streptococcaceae was negatively
correlated with Scr after renal transplantation. However,
these floras were not significantly associated with Scr in the
ESRD group.
4. Discussion
Salivary microbiota is more stable than gut microbiota, and
factors that alter gut microbiota may not significantly alter salivary
microbiota (David et al., 2014;Tuganbaev et al., 2022). ESRD
patients have changes in the composition of their gut microbiota
compared with healthy people (Rysz et al., 2021;Shivani et al.,
2022). However, as shown in this research, the salivary microbiota
of ESRD patients was similar to that of HCs in terms of the number
of individual species, the relative abundance of dominant flora,
alpha diversity, and beta diversity, and did not alter significantly
due to chronic renal impairment, different long-term treatments,
or accompanying changes in life habits. In contrast, the salivary
microbiota of RTRs showed huge differences compared with ESRD
and HC groups. RTRs contained nearly 10 times as many species
of unique salivary microbiota. From both alpha diversity and
beta diversity, the RTR group showed higher richness and intra-
group differences than ESRD or HC groups. At the phylum
level, the relative abundance of Actinobacteria, Tenericutes, and
Spirochaetes was about 10 times higher than that of ESRD or
HC groups. Actinobacteria is a ubiquitous gram-positive phylum,
which has attracted much attention as a rich source of bioactive
substances and a complex evolution and diversification process
(Miao and Davies, 2010;Barka et al., 2016). As an oral bacterium,
Actinobacteria may play a role in the etiology of diabetes (Long
et al., 2017;Matsha et al., 2020). The Tenericutes were composed of
bacteria that lack a peptidoglycan cell wall. The most well-studied
branch of this phylum was Mollicutes, including Mycoplasma.
To date, most studies had focused on pathogenic strains of the
Mycoplasma order (Wang Y. et al., 2020). As reported, Mycoplasma
was associated with oral leukoplakia (Mizuki et al., 2015,2017),
mucositis (Morand and Hatami, 2018), and Fanconi anemia-
associated oral carcinoma (Henrich et al., 2014). Spirochaetes were
important pathogenic bacteria in the clinic, but they were not
well-understood. Some of these caused Lyme disease, leptospirosis,
syphilis, and other human diseases. Moreover, Spirochaetes were
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FIGURE 3
Composition characteristics of salivary microbiota at dierent time points and states: (A) the Venn graph for OTUs of the RTR group at dierent time
points; (B) the Venn graph for OTUs of the RTR1, RTR14, ESRD, and HC; (C) the Ace index of the RTR group at dierent time points; (D) the Ace index
of the RTR1, RTR14, ESRD, and HC; (E) salivary species composition of RTR group at dierent time points at the phylum level; (F) salivary species
composition of RTR1, RTR14, ESRD, and HC at the phylum level; (G) PCoA graph of RTR3 and RTR7; (H) PCoA graph of RTR14 and ESRD.
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TABLE 2 Pearson correlation between the salivary microbiota and Scr
after renal transplantation.
RTR ESRD
Pearson r P Pearson r P
p__Bacteroidetes 0.3329 0.0384 0.4095 0.2111
p__Fusobacteria 0.3436 0.0322 −0.3558 0.2829
p__Patescibacteria 0.704 <0.0001 0.2271 0.5019
c__Bacteroidia 0.3321 0.0389 0.4095 0.2111
c__Fusobacteriia 0.3436 0.0322 −0.3558 0.2829
o__Fusobacteriales 0.3436 0.0322 −0.3558 0.2829
f__Streptococcaceae −0.3166 0.0496 −0.0437 0.8984
f__Leptotrichiaceae 0.3409 0.0337 0.4137 0.2059
g__Streptococcus −0.3166 0.0496 −0.0437 0.8984
g__Leptotrichia 0.3411 0.0336 0.4475 0.1675
closely related to periodontal disease and gingivitis (Reed et al.,
2018;Yousefi et al., 2020;Zeng et al., 2021), and, in turn,
periodontal disease impacted the risk of systemic diseases such
as diabetes (Deng et al., 2018). Taken together, these changes
occurring in the salivary microbiota of RTRs appeared to be
associated with the new onset diabetes, periodontal disease, and
gingivitis after renal transplant.
During the early stage (<14 days) after surgery, the salivary
microbiota of RTRs was not static. From the Venn graph, we could
see that the total number of species decreased over time and most
of the salivary microbiota were species unique at each time point.
At the phylum level, the relative abundance of Actinobacteria,
Cyanobacteria, Epsilonbacteraeota, Tenericutes, and Spirochaetes
changed incrementally with time. Among them, Actinobacteria,
Tenericutes, and Spirochaetes in RTRs had changed the most. From
the point of alpha diversity, including kinds of evaluation indexes
such as Ace, Chao1, Shannon, and Simpson, the composition of
the salivary microbiota generally moved toward less richness and
less variation within groups for each index. As shown in the PCoA
figure, RTR1, RTR3, RTR7, and RTR14 groups all had different
flora structures. However, the microbiota structure had significant
differences only between RTR3 and RTR7 but not RTR1 and RTR3
or RTR7 and RTR14 which may be limited by the insufficient
sample size. Moreover, the Chao1 and Ace, two alpha diversity
evaluation indexes, described the distribution of bacteria with low
abundances and decreased as the number of OTUs decreased
during the perioperative period. Hence, we speculated that these
low-abundance floras, which occurred in huge changes at different
time points after renal transplant, occupy the majority of OTUs.
As discussed, RTR14 was closer to HC in terms of OTU number
and alpha diversity of salivary microbiota than RTR1. Hence,
we considered that changes in the composition of the salivary
microbiota of RTRs were a process of stabilization during the early
stage after renal transplant.
All renal transplant patients received ATG preoperatively, a
drug that inhibited thymocyte activity and helped patients fight
off rejection. ATG was also often used to treat severe aplastic
anemia and altered patients’ salivary microbiota but did not lead
to a clear change in diversity over time (Ames et al., 2019).
Similarly, RTRs were also all treated with FK506 and MMF which
were related to oral cancer (Li et al., 2021) or oral ulcer (Asare
and Gatzke, 2020), and these oral diseases were also related to
salivary floras (Lin et al., 2021;Bai et al., 2022). Not only salivary
microbiota but these immunosuppressants had been linked to the
altered intestinal microbiota. Intaking a moderate dose of FK506
maintained immunosuppression, induced normal graft function
of the liver, maintained gut barrier integrity, and low plasma
endotoxin levels. In addition, it also led to increased species
richness and rare species abundance which was consistent with
our findings (Jiang et al., 2018). FK506 treatment significantly
improved the relative abundance of Bacteroides (Zhang et al.,
2018). In our study, the relative abundance of Bacteroides decreased
than increased. As an ongoing drug, the effect of FK506 on the
elevating relative abundance of Bacteroides may not manifest until
the latter part of the early stage after kidney transplantation.
MMF enhanced colonic integrity and decreased sympathetic drive
in the gut which was associated with the improvement of gut
dysbiosis, including the increased abundance of Proteobacteria and
Bacteroidetes and decreased abundance of Firmicutes (Robles-Vera
et al., 2021) such as the results of our study that the abundance
of Firmicutes continuously reduced. MMF increased the alpha
diversity of gut microbiota embodied in the first postoperative
day of our research (Llorenç et al., 2022). Based on the induction
therapy and immunosuppressive therapy, RTRs had immune
dysfunction such as acquired immunodeficiency disease (AIDS)
patients, according to which we speculated that salivary microbiota
changes were similar to those in AIDS patients. In the research of
Perez Rosero et al., the significant reduction in the frequency of oral
neutrophils in the oral cavity of AIDS individuals was positively
related to their CD4+T cell count and observed OTUs indexes
raised in AIDS individuals as alpha diversity of salivary microbiota
(Perez Rosero et al., 2021). Interestingly, Alpha diversity altered as
the disease progresses (Guo et al., 2021). Compared with healthy
people, AIDS patients exhibited a lower abundance of salivary
Fusobacteria resembles our study (Yang et al., 2020). For AIDS
patients, antiretroviral therapy was an effective treatment. After
the treatment, the patient’s immune function would be restored to
some extent, which was due to the changes in immune function
during the perioperative period in RTRs as the gradual recovery
of T cell abundance occurred (Bouteloup et al., 2017). These two
statuses were all accompanied by decreased salivary alpha diversity
(Imahashi et al., 2021). Although RTRs were essentially on constant
antibiotics, previous studies had demonstrated that antibiotic
use appears to have little effect on salivary flora composition
(Tuganbaev et al., 2022). Coincidentally, some external factors
which may affect salivary microbiota for patients, such as diet
habits (Marsh et al., 2016), drinking water (Sinha et al., 2021),
oral hygiene (Belstrøm et al., 2018b), and living environment
(David et al., 2014), changed between these time points of saliva
collection. In conclusion, we speculated that the factors mentioned
earlier functioned together and led to the alternation of salivary
microbiota in RTRs like increasing and then gradually decreasing
the number of OTUs and alpha diversity index and changing the
Frontiers in Microbiology 08 frontiersin.org

Xiang et al. 10.3389/fmicb.2023.1122101
FIGURE 4
Correlation between Bacteroidetes, Fusobacteria, Patescibacteria, and Scr: (A) the scatter diagram of relative abundance of Bacteroidetes and Scr
concentration; (B) the scatter diagram of relative abundance of Fusobacteria and Scr concentration; (C) the scatter diagram of relative abundance of
Patescibacteria and Scr concentration.
composition of species and relative abundance of dominant flora
with various trends.
Finally, we analyzed the relationship between the dominant
flora in saliva and Scr. We found that Bacteroidetes, Fusobacteria,
Patescibacteria, and Leptotrichiaceae were positively correlated
with Scr, and Streptococcaceae was negatively correlated with Scr
after renal transplant. Therefore, these strains could be biomarkers
of postoperative recovery of RTRs.
Although the presence of the floras Bacteroidetes, Fusobacteria,
Patescibacteria, Leptotrichiaceae, and Streptococcaceae in saliva
and their potential correlation with renal function have barely
been researched, several studies have elucidated the relation of
some of them in the gut and renal dysfunction. Studies have
shown an increase in the relative abundance of gut Bacteroidetes
in patients with stage 4–5 chronic kidney disease or patients
with ESRD receiving hemodialysis (Crespo-Salgado et al., 2016;
Wu et al., 2021). Although urinary stones are unlikely to
cause kidney damage, urolithiasis patients had significantly lower
microbial abundance and higher proportions of Bacteroidetes
(Zhou et al., 2020). In a study by Li et al., uremic clearance
granules enhanced renal function and decreased levels of Scr, blood
urea nitrogen, inflammatory responses, and NF-κB and MAPK
expressions in renal tissues of ESRD rats. At the same time, the
relative abundances of gut Bacteroidetes descended in response to
uremic clearance granules (Li et al., 2022). As a prescription of
traditional Chinese medicine for treating chronic kidney disease,
the Shenyan Kangfu tablet alleviated renal dysfunction, glomerular
and tubular damage, and renal inflammation and reduced the
relative abundances of gut Bacteroidetes in the mouse with diabetic
kidney disease (Chen et al., 2021). Accompanied by the fecal
microbiota transplant, a significant increase of gut Bacteroidetes
had the closest correlation with worse response to high salt of salt-
sensitive rats, evidenced by increased albuminuria, systolic arterial
pressure, and renal T-cell infiltration (Abais-Battad et al., 2021).
Frontiers in Microbiology 09 frontiersin.org

Xiang et al. 10.3389/fmicb.2023.1122101
By contrast, SGL5213 and Daphnetin, two proven renoprotectants,
saved kidney function in mice or rats with renal injury and elevated
the relative abundances of gut Bacteroidetes (Ho et al., 2021;Zhou
et al., 2022).
The relative abundance of Fusobacteria in patients with
immunoglobulin A nephropathy or membranous nephropathy
exhibited significant elevation when compared with healthy
controls (Hu et al., 2020;Zhang et al., 2020;Sugurmar et al.,
2021). The microbiota structure showed the same change in type 2
diabetes mellitus, chronic kidney disease, and renal uric acid stone
patients (Salguero et al., 2019;Cao et al., 2022). Deltamethrin, as a
widely used pyrethroid insecticide, had brought serious problems
to the healthy breeding of aquatic animals. A high concentration
of deltamethrin damaged the intestine and trunk kidney of goldfish
or channel catfish in the early stage with a significant increase or
decrease in the abundance of Fusobacteria (Zhou et al., 2021;Yang
et al., 2022).
In summary, gastrointestinal Bacteroidetes and Fusobacteria in
humans and mice were positively correlated with renal dysfunction
which was consistent with our results. Hence, we speculated that
these two floras and even more flora may have some connection
with renal dysfunction. However, whether the changes in the
digestive tract environment brought by renal dysfunction favored
their colonization of the digestive tract or their colonization
of the digestive tract promoted renal dysfunction remained to
be proven.
5. Conclusion
This study has illustrated differences in salivary microbiota
communities among RTRs, ESRD patients, and HCs, examining
changes in the salivary microbiota community during the short
period after renal transplantation. We speculated that changes
in the salivary microbiota were a process of stabilization during
the early stage after renal transplant, and certain species of
salivary microbiota had the potential to be a biomarker of
postoperative recovery. Our study first discussed the salivary
microbiota variations associated with renal transplantation and the
relationship between salivary microbiota and renal function.
Data availability statement
The data presented in the study are deposited in the NCBI
repository, accession number PRJNA904953, https://www.ncbi.
nlm.nih.gov/bioproject/PRJNA904953.
Ethics statement
The study protocol was approved (22207) by the Ethics
Committee of the Third Xiangya Hospital of Central South
University (Changsha, China). Written informed consent was
obtained from all study participants. Experiments were carried out
in accordance with the ethical guidelines set by the Declaration of
Helsinki 1964 and its later amendments. The patients/participants
provided their written informed consent to participate in this study.
Author contributions
XX, BP, YZ, and YM conceived, designed, and directed the
manuscript. XX, BP, and KL wrote and revised the manuscript. XX,
PD, and HL participated in the performance of the research. XX,
TW, and BP analyzed data. All authors contributed to the article
and approved the submitted version.
Funding
This study was supported by the National Natural Science
Foundation of China (Grant Number: 81771722).
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found
online at: https://www.frontiersin.org/articles/10.3389/fmicb.2023.
1122101/full#supplementary-material
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