Strongyloides stercoralis infection induces gut dysbiosis in chronic kidney disease patients.

BACKGROUND: Strongyloides stercoralis infection typically causes severe symptoms in immunocompromised patients. This infection can also alter the gut microbiota and is often found in areas where chronic kidney disease (CKD) is common. However, the relationship between S. stercoralis and the gut microbiome in chronic kidney disease (CKD) is not understood fully. Recent studies have shown that gut dysbiosis plays an important role in the progression of CKD. Hence, this study aims to investigate the association of S. stercoralis infection and gut microbiome in CKD patients. METHODOLOGY/PRINCIPAL
FINDINGS: Among 838 volunteers from Khon Kaen Province, northeastern Thailand, 40 subjects with CKD were enrolled and divided into two groups (S. stercoralis-infected and -uninfected) matched for age, sex and biochemical parameters. Next-generation technology was used to amplify and sequence the V3-V4 region of the 16S rRNA gene to provide a profile of the gut microbiota. Results revealed that members of the S. stercoralis-infected group had lower gut microbial diversity than was seen in the uninfected group. Interestingly, there was significantly greater representation of some pathogenic bacteria in the S. stercoralis-infected CKD group, including Escherichia-Shigella (P = 0.013), Rothia (P = 0.013) and Aggregatibacter (P = 0.03). There was also a trend towards increased Actinomyces, Streptococcus and Haemophilus (P > 0.05) in this group. On the other hand, the S. stercoralis-infected CKD group had significantly lower representation of SCFA-producing bacteria such as Anaerostipes (P = 0.01), Coprococcus_1 (0.043) and a non-significant decrease of Akkermansia, Eubacterium rectale and Eubacterium hallii (P > 0.05) relative to the uninfected group. Interesting, the genera Escherichia-Shigella and Anaerostipes exhibited opposing trends, which were significantly related to sex, age, infection status and CKD stages. The genus Escherichia-Shigella was significantly more abundant in CKD patients over the age of 65 years and infected with S. stercoralis. A correlation analysis showed inverse moderate correlation between the abundance of the genus of Escherichia-Shigella and the level of estimated glomerular filtration rate (eGFR).
CONCLUSIONS/SIGNIFICANCE: Conclusion, the results suggest that S. stercoralis infection induced gut dysbiosis in the CKD patients, which might be involved in CKD progression.

Introduction

An imbalance within the microbiota in the gastrointestinal tract, termed gut dysbiosis, contributes to the development and progression of many diseases including chronic kidney disease (CKD) [1]. Many studies have shown a significant difference in the abundance of bacterial populations in the gastrointestinal tract (GI) between CKD and control individuals. Substantially lower proportions of Bifidobacterium, Lactobacillaceae, Bacteroidaceae and Prevotellaceae were seen in CKD patients, including those undergoing hemodialysis, while the proportions of Enterobacteriaceae, especially Enterobacter, Klebsiella and Escherichia, were notably higher [2-5]. The production of uremic toxins (indoxyl sulphate (IS), trimethylamine-N-oxide (TMAO)), which results from nutrient processing by gut microbiota, and the reduction of fiber-derived short-chain fatty acids, are linked with CKD progression [6-8]. Recent studies have found various factors involved in microbial dysbiosis and CKD, such as the use of antibiotics [9], decreased consumption of dietary fiber [10], and oral iron intake [11]. However, many etiological factors associated with CKD remain obscure [12], particularly those due to infection with intestinal parasites.

The ability of GI parasitic infection to change the gut microbiota and host-microbiota interactions has been clearly identified. Infection with intestinal parasites either induced gut dysbiosis or provided protection against dysbiosis and inflammatory disease [13]. Strongyloides stercoralis is one of the most medically important parasites in northeastern Thailand, where the prevalence of CKD is also high [14]. Typically, S. stercoralis infection causes only mild GI symptoms. However, when immunity is suppressed by, for example, CKD or HIV infection, the parasite can rapidly multiply leading to hyperinfection and disseminated strongyloidiasis, which is a life-threatening condition [15-18].

Recent studies have demonstrated that S. stercoralis induces an increase in bacterial diversity and changes faecal microbiota [19,20]. By using metagenomic analysis, microbial alpha diversity was found to increase and beta diversity decrease, in the faecal microbial profiles of S. stercoralis-infected individuals compared to uninfected. Faecal metabolite analysis detected marked increases in the abundance of selected amino acids and decrease in short-chain fatty acids in S. stercoralis infection, relative to uninfected controls [20]. Taken together, we therefore hypothesize that S. stercoralis infection changes the gut microbiome, contributing to progression of CKD.

To test this hypothesis, metagenomic analysis was done in patients with CKD to investigate the changes in the gut microbiota that can be attributed to S. stercoralis infection. The result from this study might be useful for identifying strategies to limit development and progression of chronic kidney disease.

Methods

Ethics statement

The human ethical review committee of Khon Kaen University (HE631200) approved the protocol of study. Informed consent was obtained from all participants under the CKD project and was verbal or written [14].

Study population

The study was conducted between January 2017 and May 2018 at Donchang sub-district, Khon Kaen Province, northeastern Thailand as a part of the Chronic Kidney Disease Northeastern Thailand (CKDNET) project. Included in the study were individuals (>35 years of age) with chronic kidney disease. Their diagnosis, done by a nephrologist [14], included clinically proven impaired kidney structure or renal function, as detected using ultrasonography, and a finding of reduced eGFR. The staging (stages 1 to 5) based on the estimated glomerular filtration rate (eGFR) [21,22] was estimated for each individual. In patients with eGFR > 60 ml/min/1.73 m2, kidney damage was confirmed based on urine albumin-to-creatinine ratio (UACR), hematuria and abnormal renal ultrasound. Stool examination was performed on CKD patients using the modified formalin ethyl acetate concentration technique (FECT) and modified agar plate culture (mAPC) as previously reported [23].

Exclusion criteria were as follows: use of antibiotics or probiotics, diabetes, autoimmune disease, urinary tract infection and infection with intestinal parasites other than S. stercoralis. Twenty CKD patients with S. stercoralis infection (Ss+) met these criteria and were included. The control group consisted of 20 CKD patients free of S. stercoralis infection (Ss-) who otherwise matched the characteristics (including sex, age and biochemical factors) of the Ss+ group (Table 1). These datasets were obtained from the medical records of CKDNET and from a recent study [23]. Absence of S. stercoralis infection in members of the control group was also confirmed using PCR tests.

Table 1

Characteristics of chronic kidney disease patients.

Parameters	Normal range	Ss- (n = 20)	Ss+ (n = 20)	P value
Sex	Male	12	12
	Female	8	8
Age years		64.60±11.3	64.85±13.4	0.95a
BMI kg/m2	18-25	23.6±3.8	22.14±4.1	0.26a
MCV fL	79.0-94.8	80.53±8.9	83.45±9.7	0.33a
MCH pg	25.6-32.2	26.25±3.7	27.48±3.7	0.30a
MCHC g/dL	32.2-36.5	32.49±1.2	32.84±0.9	0.29a
eGFR ml/min/1.73 m2	>=90	73.3±20.3	71.3±25.4	0.78a
Neutrophil (NE)%	50-70	52.00±7.0	48.50±13.6	0.3a
Lymphocyte (LY)%	20-40	31.99±6.6	33.42±10.0	0.60a
Monocyte (MO)%	2-8	7.56±1.8	7.27±1.9	0.63a
Eosinophil (EO)%	1-3	7.60±4.5	9.98±8.7	0.62b
Basophils (BA) %	0-1	0.78±0.4	0.84±0.5	0.84b
Glucose mg/dL	70-100	89.3±11.2	90.45±13.0	0.90b
LDL Cholesterol mg/dL	10-150	121.0±34.4	114.75±28.4	0.94b
Hemoglobin g/dL	13.0-16.7	12.18±2.4	12.44±1.8	0.70a
Hematocrit %	34-51	37.8±5.7	37.7±4.9	0.95a
Uric acid mg/dL	2.7-7.0	6.14±1.6	5.85±1.6	0.57a
Urine creatinine mg/dL	25-400	128.89±63.2	114.82±61.7	0.43b
Microalbumin mg/dL	0.2-1.9	6.90±18.3	5.55±6.2	0.29b
UACR mg/g	<30	50.02±114.8	140.43±229.2	0.096b
Hemoglobin A1c %	4.6-6.5	5.50±0.6	5.45±0.5	0.75a
Bpsys mmhg	<140	125.94±17.7	129.56±16.5	0.54a
Bpdia mmhg	<90	81.38±7.87	76.33±9.43	0.46a

Data are presented as mean ± standard deviation of the mean. Independent t-tests (a) and Mann-Whitney U tests (b) were used to calculate P values.

Abbreviations: BMI, body mass index; Bpsys mmhg, Blood pressure systolic (mmhg); Bpdia mmhg, Blood pressure diastolic (mmhg); MCV, Mean corpuscular volume; MCH, Mean Corpuscular Hemoglobin; MCHC, mean corpuscular hemoglobin concentration; U, urine; UACR, urine albumin-to-creatinine ratio.

Sample collection, DNA extraction

Faecal and serum samples (n = 40) were collected from the CKD Northeast Thailand project and kept at -20°C until analyzed. DNA was extracted from faecal samples using the QIAamp Kit (Qiagen, Germany). A Nanodrop 2000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) was used to measure DNA concentration and 1.5% agarose gel electrophoresis was used to check the DNA quality.

16S rRNA gene sequencing and analysis

To confirm that V3-V4 regions (about 450-500 bp) of the prokaryotic 16S rRNA gene could be amplified, PCR was used to amplify DNA from each faecal sample. Specific V3-V4 primers were used, V3-forward: 5’-CCTACGGGNGGCWGCAG and V4-reverse: 5’-TACNVGGGTATCTAATCC [24]. Each reaction (20.1 μL) contained 2 μL of 10x buffer MgCl2, 0.4 μL of 50mM MgCl2, 0.6 μL of 10mM dNTP, 1 μL of 5 μM of each primer, 0.1μL Platinum Taq DNA polymerase and distilled water. The amplification profile was initial denaturation at 94°C for 5 min, at 94°C for 40 sec, then 35 cycles of 52.8°C for 30 sec, 72°C for 2 min, followed by a final extension at 72°C for 10 min. PCR product was electrophoresed in a 1.5% agarose gel to confirm the presence of a band of the expected size.

A sequencing library was generated for each sample using NEBNext Ultra DNA Library Prep Kit for Illumina (Thermo Scientific) following the manufacturer’s recommendations. 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 platform and 250 bp paired-end reads were generated. Processing and quality control of these reads used the following steps: 1) Data split (based on their unique barcode) and truncation by cutting off the barcode and primer sequences; 2) Sequence assembly (paired-end reads were merged using FLASH [25] to generate raw tags; 3) Data filtration (quality filtering on the raw tags was performed to obtain high-quality clean tags [26] according to the QIIME(V1.7.0, http://qiime.org/index.html) quality-control process [27]; 4) Chimera removal (the tags were compared with a reference database [28] using the UCHIME algorithm [29] to detect chimera sequences, which were then removed [30].

Sequences sharing ≥97% similarity were assigned to the same operational taxonomic unit (OTU) by using Uparse v7.0.1001) [31] and species annotated by reference to the GreenGene Database [32] based on RDP 3 classifier [33] algorithm. The sequence alignment was conducted using the MUSCLE software (Version 3.8.31) [34]. Information on abundance of each OTU was normalized relative to the sample with the fewest sequences. Subsequent analyses of alpha diversity were performed based on this normalized data. Alpha diversity indicates species diversity for a sample using six indices (observed-species, Chao1, Shannon, Simpson, ACE and Good’s coverage). All these indices were calculated using QIIME (Version 1.7.0) and displayed with the help of R software (Version 2.15.3). To show beta diversity, weighted and unweighted UniFrac metrics were used to evaluate differences of samples in species complexity by using QIIME software (Version 1.7.0). Principal Coordinate Analysis (PCoA) was performed to visualize complex and multidimensional data. PCoA analysis was done using the WGCNA package, stat packages and ggplot2 package in R software (Version 2.15.3). Metastats was used to detect taxa with significant intra-group variation. The potential biomarkers were detected by using LEfSe (linear discriminant analysis (LDA) Effect Size) [35]. Raw data are available from Mendeley Data (DOI: 10.17632/hvbvrtc34x.1).

Polymerase Chain Reaction (PCR)

Conventional PCR was used to confirm the absence of S. stercoralis infection in members of the control group (n = 20). Primers were designed specifically to amplify a 125-bp fragment from a S. stercoralis dispersed repetitive sequence, GenBank: AY08262 [36]. The forward primer was SSC-F 5′ CTCAGCTCCAGTAAAGCAACAG 3′ and reverse primer was SSC-R 5′ AGCTGAATCTGGAGAGTG AAGA 3′. PCR amplification was performed in a 12.5 μL volume with Dream Taq PCR Master-mix (Thermo Fisher Scientific, Vilnius, Lithuania), 1 μL of each primer, 5 μL of 9–155 ng/μL DNA, and PCR-grade water. The amplification profile was initial denaturation at 95°C for 10 min, followed by 35 cycles of 95°C for 1 min, 60°C for 1 min 30 s and 72°C for 1 min; then a final extension at 72°C for 10 min. To confirm amplification and amplicon size, the PCR products were resolved on a 2% agarose gel stained with ethidium bromide.

Statistical analysis

Statistical analyses, including ANOVA, independent t-test, Welch’s t-test, Kruskal-Wallis test, Mann-Whitney U tests and Pearson’s correlation coefficient, were conducted using IBM SPSS Statistics version 20 (IBM, Armonk, New York). Statistically significant features were further examined with post-hoc tests (Tukey-Kramer) to determine which groups of profiles differed from each other. Non-normal distribution data of two genera (Anaerostipes and Escherichia-Shigella) in association with CKD were log-transformed into normal distributions. Statistical significance and 95% confidence intervals (95%CI) were calculated and considered as P < 0.05.

Results

Study population characteristics

Demographic, socioeconomic and clinical characteristics of CKD patients with and without S. stercoralis infection were matched (Table 1). No significant differences in these characteristics were found between the Ss+ and Ss- groups.

Characterization of bacterial diversity and community structure

In total, 1551 OTUs were identified based on the 97%-similarity rule, with an average of 477 OTUs per sample. Sequences were classified into 16 bacterial phyla, 26 classes, 45 orders, 72 families and 258 genera and 189 species (including unidentified species). The species accumulation curves showed a saturation phase (Fig 1). This indicates that the sample size was sufficient to capture the overall microbiota structure.

Fig 1

Species accumulation curve.

X-axis: Number of samples, Y-axis: number of OTUs. Following an initial sharp rise in the number of OTUs as number of samples increases, there is a levelling of the plot. The narrow spread of the boxplots as the total number of samples is approached indicates that the number of samples was adequate to capture most of the microbial diversity present.

The unweighted UniFrac distances, reflecting beta diversity, were significantly greater in the Ss- group than the Ss+ group (P = 0.00019). In terms of alpha diversity overall, there were no significant differences in estimated OTU richness, Chao1, the ACE metric, the Shannon diversity index and Good’s coverage, (P > 0.05) (Table 2). In contrast, the alpha diversity in males in the Ss- group was significantly higher than in males in the Ss+ group (Shannon diversity index, P = 0.015; Simpson diversity index, P = 0.058) (Fig 2B and 2C).

Table 2

Alpha diversity of the gut microbiota in Ss- and Ss+ groups, calculated according to several indices.

Group	No. of Reads	No. of OTUs	Good’s (%)	ACE	Chao 1	PD whole tree	Shannon	Simpson
Ss-	73173.3	483.7	0.99765	515.713	517.886	32.7308	5.25775	0.9182
Ss+	74770.1	470.15	0.9976	507.982	504.266	32.5759	4.82045	0.88145
P		0.45a	0.706b	0.78a	0.76b	0.92a	0.08b	0.218b

Independent t-tests (a) and Mann-Whitney U tests (b) were used to calculate P values.

Abbreviations: OTU: Operational taxonomic units; ACE: Abundance-based coverage estimator; PD: Phylogenetic diversity

Fig 2

Comparison of alpha diversity indexes and beta diversity in CKD patients with and without S. stercoralis infection.

(A) Shannon index (B) Shannon index in males (C) Simpson index in males. (D) Boxplot based on unweighted UniFrac distance. (E) Principal coordinate analysis (PCoA).

The principal coordinate analysis (PCoA) was used to illustrate the beta diversity based on the unweighted UniFrac distances (Fig 2D). PCoA analysis revealed that the gut microbiota of Ss+ subjects deviated from the Ss- group (Fig 2E).

The LDA score showed a significant difference in abundance of certain taxa between the two groups. The candidate biomarker for the Ss- category was order Bradymonadales and for CKD with S. stercoralis infection (Ss+) was species E. coli, genus Escherichia-Shigella as well as the genus Dialister, family Veillonellaceae and order Selenomonadales (Fig 3).

Fig 3

Histogram of cladogram and linear discriminant analysis (LDA) score.

The histogram of the LDA scores presents taxa (potential biomarkers) whose abundance differed significantly among groups (Ss+ vs. Ss-) order Bradymonadales in Ss- (green color). Species E. coli belongs to the genus Escherichia-Shigella; genus Dialister belongs to the order Selenomonadales, class Negativicutes and family Veillonellaceae in Ss+ (red color). The cladogram shows specific taxa relevant to Ss+ and Ss- in the red and green nodes. The highest taxonomic level is towards the center of the diagram. The diameter of each circle represents the relative abundance of the taxon.

Differences in bacterial abundance between the Ss+ and Ss- groups

Proportions of sequence reads were compared between groups at the phylum and genus levels using Metastats. At the phylum level, there were no significant differences (Fig 4). For example, relative abundances of some principal taxa were: Firmicutes (Ss- 64.14% vs. Ss+ 59.33%; P = 0.39), Proteobacteria (Ss- 15.01% vs. Ss+ 18.83%; P = 0.50), Bacteroidetes (Ss- 12.61% vs. Ss+ 12.68%; P = 0.98), Actinobacteria (Ss- 3.46% vs. Ss+ 4.34%; P = 0.67) and Fusobacteria (Ss- 1.88% vs. Ss+ 3.81%; P = 0.52). However, at the genus level, 42 taxa were differentially relative abundance (Table 3).

Fig 4

The gut microbiota composition.

(A) and (B), Control group compared with S. stercoralis-infection group (Ss+) at the phylum and genus levels, respectively.

Table 3

Taxa in the gut microbiome differing significantly between CKD patients with and without S. stercoralis.

Phylum	Family	Genus	Change in abundance
			Ss+	Ss-
Actinobacteria	Coriobacteriaceae	Atopobium	↑
		Coriobacteriaceae_UCG-003	↑
		Gordonibacter		↑
		unidentified_Coriobacteriaceae		↑
	Corynebacteriaceae	Corynebacterium	↑
	Micrococcaceae	Rothia	↑
Bacteroidetes	Porphyromonadaceae	Petrimonas	↑
		Proteiniphilum		↑
	Prevotellaceae	Paraprevotella		↑
		Prevotella_1	↑
Cyanobacteria	unidentified_Gastranaerophilales	unidentified_Gastranaerophilales		↑
Firmicutes	Carnobacteriaceae	Lacticigenium	↑
	Leuconostocaceae	Leuconostoc		↑
	Christensenellaceae	Christensenella		↑
	Eubacteriaceae	Anaerofustis		↑
	Family_XI	Gallicola	↑
		Peptoniphilus	↑
		Tissierella	↑
	Lachnospiraceae	[Eubacterium]_xylanophilum_group		↑
		Anaerosporobacter		↑
		Anaerostipes		↑
		Coprococcus_1		↑
		Lachnospiraceae_UCG-010	↑
		Tyzzerella_3		↑
	Peptostreptococcaceae	Paeniclostridium		↑
	Ruminococcaceae	Pseudoflavonifractor		↑
		Ruminiclostridium_1	↑
		Ruminococcaceae_UCG-011	↑
	Erysipelotrichaceae	Erysipelotrichaceae_UCG-003		↑
		Erysipelotrichaceae_UCG-004		↑
		unidentified_Erysipelotrichaceae		↑
	Veillonellaceae	Dialister	↑
Fusobacteria	Leptotrichiaceae	Leptotrichia	↑
Proteobacteria	Neisseriaceae	Eikenella	↑
	Rhodocyclaceae	Dechlorobacter	↑
	Desulfobulbaceae	Desulfobulbus	↑
	Cardiobacteriaceae	Cardiobacterium	↑
	Enterobacteriaceae	Cronobacter		↑
	Enterobacteriaceae	Escherichia-Shigella	↑
	Pasteurellaceae	Actinobacillus	↑
	Pasteurellaceae	Aggregatibacter	↑
	Xanthomonadaceae	Arenimonas	↑

To study the similarity among different samples, clustering analysis was applied. The unweighted pair group method with arithmetic mean (UPGMA), a type of hierarchical clustering method widely used in ecology. This showed that Ss- vs. Ss+ samples tended to cluster separately (Fig 5).

Fig 5

Clustering using the unweighted pair group method with arithmetic mean (UPGMA).

UPGMA cluster tree based on unweighted UniFrac distances between CKD patients with or without S. stercoralis infection. The red branches represent individuals with S. stercoralis infection (Ss+) and the dark blue branches indicate uninfected (Ss-) individuals.

The trends of some bacteria in S. stercoralis infection

Fig 6 shows the comparisons of abundance of some bacteria between Ss- and Ss+ groups at the genus level. Pathogenic taxa were more abundant in the Ss+ group and included genera such as: Escherichia-Shigella (3.36% vs. 13.33%; P < 0.05), Streptococcus (0.97% vs. 2.18%; P > 0.05), Haemophilus (0.46% vs 0.71%; P > 0.05), Rothia (0.024% vs. 0.11%; P < 0.05), Actinomyces (0.038% vs. 0.067%; P > 0.05) and Aggregatibacter (0.0013% vs. 0.025%; P < 0.05). Reduction of some SCFA-producing bacteria in the Ss+ group was observed, including Eubacterium rectale_group (4.51% vs. 3.78%; P > 0.05), Eubacterium hallii_group (1.24% vs. 0.94%; P > 0.05), Anaerostipes (0.54% vs. 0.074%; P < 0.05), Coprococcus_1 (0.11% vs. 0.057%; P < 0.05) and Akkermansia (0.081% vs. 0.043%; P > 0.05).

Fig 6

Comparisons of abundance (numbers of sequence reads) of some bacteria between Ss- and Ss+ group.

Pathogenic bacteria: Escherichia-Shigella, Streptococcus, Haemophilus, Rothia, Actinomyces, Aggregatibacter. SCFA-producing bacteria: Eubacterium rectale_group, Eubacterium hallii_group, Anaerostipes, Coprococcus_1, Akkermansia.

Anaerostipes and Escherichia-Shigella exhibit opposing trends in abundance and correlate with sex, age and CKD stage

Fig 7 shows the opposing trends in abundance in the genera Anaerostipes and Escherichia-Shigella. The proportion of reads of Anaerostipes was lower in those aged over 65 years (Fig 7A), in females (7B), and with increasingly advanced CKD stage (7C) and in those infected with S. stercoralis (Fig 7A, 7B and 7C). The opposite was observed in the case of Escherichia-Shigella: higher proportions of sequence reads of this genus were seen in elderly (>65 years) individuals (Fig 7D), in females (Fig 7E) and in those infected with S. stercoralis (Fig 7D, 7E and 7F). More reads of Escherichia-Shigella were seen with increasingly advanced CKD stage (Fig 7F).

Fig 7

Opposing trends in abundance of two genera, Anaerostipes and Escherichia-Shigella.

(A) and (D) Different trends related to age; (B) and (E) sex; (C) and (F) CKD stages. * P < 0.05, **P < 0.01, ***P < 0.001. Analysis of the difference among groups of sex, age and CKD stages based on one-way ANOVA test.

Discussion

In this study, we first characterized the gut microbiota of CKD patients with and without S. stercoralis using high-throughput sequencing of the V3–V4 region of the 16S rRNA gene. The results showed that S. stercoralis infection altered gut microbiota composition in CKD patients, leading to lower microbial diversity. This study also suggested that microbial candidate biomarkers for CKD concurrent with S. stercoralis infection include Escherichia coli (genus Escherichia-Shigella, phylum Proteobacteria) and the genus Dialister (family Veilslonellaceae, order Selenomonadales, class Negativicutes, phylum Firmicutes).

Various parameters including gender, age and other factors have been reported to affect the gut microbiota [37]. With this in mind, we matched Ss+ and Ss- subjects for these parameters to reduce confounding factors. This allowed us to identify changes in the gut microbiome due to infection with S. stercoralis in CKD patients. Our results revealed that the alpha-diversity indices (Chao1, the ACE metric, the Shannon diversity index, Good’s coverage) did not significantly differ between the two groups. However, the Shannon diversity index in males (n = 12) in Ss+ group was significantly lower than in the Ss- group. In addition, the beta diversity, based on the unweighted UniFrac distances, was significantly lower in the Ss+ group, suggesting a decrease in ecological diversity in CKD concurrent with S. stercoralis infection. These slight differences spanned all taxonomic levels of the microbiota. At the phylum level, abundance of Firmicutes was reduced while abundance of Proteobacteria and Fusobacteria increased in Ss+ subjects. At the family level, there was an increase of Clostridiaceae, Streptococcaceae, Desulfovibrionaceae and Enterobacteriaceae in the Ss+ group. A previous study demonstrated that these families were associated with trimethylamine (TMA) production [38]. The high abundances of families Enterobacteriaceae, Clostridiaceae and Veillonellaceae in Ss+ subjects are in agreement with a previous study [39]. These bacteria are associated with increasing fecal pH [39] to a level where most opportunistic bacterial pathogens prefer to grow [40]. The change of microbiota composition that we observed may influence the environment in the gut, suggesting that S. stercoralis infection may influence the microbiota and modulate the pH of the gut environment.

Forty-two genera showed contrasting abundances between the two groups. Interestingly, there were significant increases of pathogenic bacteria including Escherichia-Shigella, Rothia and Aggregatibacter and some increase of Actinomyces, Streptococcus and Haemophilus in CKD patients infected with S. stercoralis compared to uninfected controls. In contrast, significant reduction of some SCFA-producing bacteria, such as Anaerostipes and Coprococcus_1 and some decrease of Akkermansia, Eubacterium rectale_group and Eubacterium hallii_group were noted in the Ss+ group. Specifically, abundance of the genus Escherichia-Shigella is known to be positively correlated with uremic toxins such as trimethylamine-N-oxide and indoxyl sulfate [41,42]. Our results demonstrated a significant inverse correlation of Escherichia-Shigella with the estimated glomerular filtration rate (eGFR r = -0.37, P = 0.018). eGFR is one criterion for diagnosis and staging of CKD. Thus, high abundance of Escherichia-Shigella was correlated with low eGFR value and higher CKD stage. In addition, Enterobacteriaceae and E. coli are markedly more abundant in individuals with impaired kidney function as demonstrated previously [43], highlighting that there is an association between the genus Escherichia-Shigella and CKD with concurrent S. stercoralis infection.

Interestingly, the genus Anaerostipes was less abundant in CKD patients with concurrent S. stercoralis infection than in those without. Members of this genus are Gram-variable, obligate anaerobes which produce acetate, butyrate and lactate from glucose fermentation [44]. Our findings were partially consistent with those of a previous study, which found that Anaerostipes had low relative abundance in CKD in an animal model and noted that this genus was negatively correlated with amount of intestinal urea. Nephrectomized mice with low levels of Anaerostipes exhibited negative effects on kidney parameters (BUN and creatinine) [45]. However, we found no correlation here between Anaerostipes and kidney parameters. This may be due to the limited sample size. Recent research indicated that elevated levels of Anaerostipes led to increased eGFR and improvement in renal function [46]. Furthermore, the relative abundance of the genus Anaerostipes was markedly reduced in nonsurvivors with end-stage kidney disease (ESKD) [47]. Specifically, we found that one species, Anaerostipes hadrus, an important microbe in maintaining intestinal metabolic balance [48], was significantly reduced in CKD with concurrent S. stercoralis infection.

A previous study using a rat model revealed that increased levels of Rothia were positively associated with creatinine levels in acute kidney injury and with severity of kidney damage [49]. Rothia spp. are Gram-positive cocco-bacilli that cause a wide range of serious infections, especially in immunocompromised hosts. Rothia is often identified in blood cultures from patients with bacteremia [50]. In this study, we found a positive correlation between Rothia and the genus Streptococcus (r = 0.47, P = 0.002). A similar relationship was observed in a recent study, which found that the log-ratio between the presence of the genera Rothia and Streptococcus was the best predictor of creatinine level [49].

The main SCFA-producing bacteria in humans [51] including Faecalibacterium prausnitzii (butyrate-producing bacteria in the phylum Firmicutes), Eubacterium rectale and E. hallii (family Lachnospiraceae) and Anaerostipes spp. (sugar/lactate-utilizing bacteria producing butyrate from lactate and acetate) were all less abundant in S. stercoralis infection. A previous study observed a decrease in the level of serum SCFAs in CKD patients and an inverse correlation between butyrate level and renal function [52]. Our study suggested that SCFA-producing bacteria are depleted in CKD with concurrent S. stercoralis infection, which may affect CKD progression. However, further studies are needed to confirm this association.

Our study has strengths and limitations. An important strength of this study is that we used groups that were pair-matched for sex, age and biochemical factors. However, we did not obtain data for concentrations of uremic toxins (TMAO and IS) or for amounts of SCFAs. Moreover, we did not record the clinical manifestation of S. stercoralis infection in CKD patients so we were not able to show the association between some pathogenic bacteria and S. stercoralis infection in CKD. In addition, the sample size in this study was small due to the limited number of individuals in the population with CKD and infection with S. stercoralis only. The small sample sizes affect the estimation of microbiome alpha diversity and statistical power in analyses.

Conclusions

This study suggests that S. stercoralis infection reduces the diversity of the gut microbiota in CKD patients. An increased abundance of harmful bacteria and reduction of some SCFA-producing bacteria in S. stercoralis infection was found. In addition, the abundance of members of the genus Escherichia-Shigella was significantly and inversely correlated with eGFR levels. Significant elevation of members of this genus in CKD patients with S. stercoralis infection may indicate potential diagnostic markers for CKD in S. stercoralis-endemic areas. Thus, we suggest that these changes in the composition of the gut microbiome in S. stercoralis infection may result in disruption of the gut barrier structure and absorption of harmful products that can contribute to toxicity, inflammation and malnutrition, contributing to CKD progression. Future metabolomics studies are required to unravel the relationship between CKD and S. stercoralis infection.

28 Jun 2022

Dear Dr Pinlaor,

Thank you very much for submitting your manuscript "Strongyloides stercoralis infection induces gut dysbiosis in chronic kidney disease patients" for consideration at PLOS Neglected Tropical Diseases. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

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Alessandra Morassutti, PhD

Associate Editor

PLOS Neglected Tropical Diseases

Abhay Satoskar

Deputy Editor

PLOS Neglected Tropical Diseases

***********************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: A clear testable hypothesis "that S. stercoralis infection changes gut microbiome, contributing to progression of chronic kidney disease" is stated. The study design and population are clearly described. This study compared the bacterial composition of the faecal microbiome of infection-driven chronic kidney disease CKD patients who were infected with Strongyloides stercoralis Ss+ with that of those who were uninfected with S. stercoralis Ss-. The groups were pair-matched for sex, age and biochemical factors.

PCR testing was undertaken to ensure that the “uninfected” group was free from S. stercoralis infection.

The microbial composition of the faeces of each patient was characterised by the V3-V4 region of the 16SrRNA. A sequencing library was generated for each sample. These were subjected to data control processes ensuring the quality of the data.

The sample size was limited by the availablity of suitable participants. Nevertheless, given that the Ss+ patients were matched with Ss- patients, meaningful results were obtained.

Suitable statistical analysis was used to support the conclusions.

The manuscript did not indicate whether informed consent had been obtained from the participants.

--------------------

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: The analysis presented matched the analysis plan. The results were clearly presented and the figures of sufficient quality for clarity.

The data showed a relationship between S. stercoralis infection and altered microbial composition in CKD patients.

258 genera from 16 phyla were present in the total group. Overall, alpha diversity was similar in the two groups, but in males, alpha diversity was significantly greater in the Ss- group. Beta diversity was also significantly greater in the Ss- group.

At the phylum level, there was no significant difference in abundance between the Ss+ and Ss- groups, but at the genus level 42 taxa differed in relative abundance. Pathogenic genera Escherichia-Shigella, Steptococcus, Haemophilus, Rothia, Actinomycetes, Aggregatibacter were significantly increased in the Ss+ group. Short chain fatty acids- SCFA-producing bacteria Eubacterium rectale_group, Eubacterium hallii_group, Anaerostipes, Coprococcus and Akkermansia were significantly decreased in the Ss+ group.

The abundance Anaerostipes was significantly lower in those aged over 65 years, in females, in increasingly advanced CKD stage, and in those infected with S. stercoralis. The abundance of Escherichia-Shigella was significantly higher in those aged over 65 years, in females, in increasingly advanced CKD stage and in those infected with S. stercoralis.

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Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: The conclusions are supported by the data presented and the limitations are clearly described.

The authors conclude that the changes in the composition of the microbiome in S. stercoralis infections may result in disruption of the gut barrier that can contribute to toxicity, inflammation and malnutrition and progression of CKD, and its public health importance in areas endemic for S. stercoralis. They also indicate what kind of studies are needed to further elucidate this relationship between S. stercoralis infection and CKD.

--------------------

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: Minor issues

Line 31: In the abstract, “infection-driven” chronic kidney disease is mentioned, but this is the only time “infection-driven” is mentioned in the manuscript. Would you please clarify whether this paper is about infection-driven CKD or CKD of any aetiology. If genuinely “infection-driven” CKD, please clarify in the author summary and the manuscript eg in line 116.

Line 125: renal infection with other intestinal parasites. Should this be “renal infection, infection with other parasites.”?

In Table 1, eosinophilia is elevated in both the Ss+ and Ss- groups, which suggests that there were other helminth infections in the Ss- group. Would you please explain this anomaly?

Line 208: Is the number of species correct here? The number should be more than or equal to the number of genera.

Line 211-212: State in the text which group had the greater beta diversity [the Ss- group, according to Fig 2].

Minor grammatical, spelling or punctuation changes:

Line 72: change “significant” to “significantly”

Line 116: change “age) of” to “age) with”

Line 220: change “significantly” to “significance”

Line 306: change “Anearostipes” to “Anaerostipes”

Line 583 (Caption for Fig 3): change “Oder” to “Order”

Line 618 (Caption for Fig 7): insert “;” after “age”

Recommendation: Minor Revision

--------------------

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: To my knowledge, this study is the first to show in CKD patients that S. stercoralis infection is associated with decreased diversity of the faecal microbiota, increased abundance of pathogenic microbial genera and decreased abundance of SCFA-producing genera. These are very important findings.

Although the study involved a small number of participants, the use of matched pairs ensured that the S. stercoralis-infected group was similar to the uninfected group in characteristics other than S. stercoralis infection status. PCR testing was used to ensure as much as possible that the uninfected group was truly S. stercoralis-free. The authors were careful to ensure that there was sufficient DNA in the extracted samples to carry out gene sequencing.

Although the manuscript states that the study protocol was approved by the human ethical review committee of Khon Kaen University (HE631200), the manuscript does not state whether informed consent was obtained from the participants. Details about informed consent should be included in the manuscript.

--------------------

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Reviewer #1: Yes: Jennifer Shield

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References

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article's retracted status in the References list and also include a citation and full reference for the retraction notice.

25 Jul 2022

Submitted filename: Response to Reviewers PNTD-D-22-00290R1.docx

Click here for additional data file.

9 Aug 2022

Dear Somchai Pinlaor, We are pleased to inform you that your manuscript 'Strongyloides stercoralis infection induces gut dysbiosis in chronic kidney disease patients' has been provisionally accepted for publication in PLOS Neglected Tropical Diseases.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Alessandra Morassutti, PhD

Academic Editor

PLOS Neglected Tropical Diseases

Abhay Satoskar

Section Editor

PLOS Neglected Tropical Diseases

***********************************************************

Reviewer's Responses to Questions

Key Review Criteria Required for Acceptance?

As you describe the new analyses required for acceptance, please consider the following:

Methods

-Are the objectives of the study clearly articulated with a clear testable hypothesis stated?

-Is the study design appropriate to address the stated objectives?

-Is the population clearly described and appropriate for the hypothesis being tested?

-Is the sample size sufficient to ensure adequate power to address the hypothesis being tested?

-Were correct statistical analysis used to support conclusions?

-Are there concerns about ethical or regulatory requirements being met?

Reviewer #1: This is a re-review of the manuscript with minor modifications.

The study meets the requirements listed.

**********

Results

-Does the analysis presented match the analysis plan?

-Are the results clearly and completely presented?

-Are the figures (Tables, Images) of sufficient quality for clarity?

Reviewer #1: The analysis and the presentation of results are clearly and completely presented, the figures are appropriate and clear.

**********

Conclusions

-Are the conclusions supported by the data presented?

-Are the limitations of analysis clearly described?

-Do the authors discuss how these data can be helpful to advance our understanding of the topic under study?

-Is public health relevance addressed?

Reviewer #1: The conclusions are supported by the data presented, the limitations are clearly described, the authors discuss the relevance of the results to our understanding of CKD, Strongyloides infection and impact on the microbiota. The public health relevance is addressed.

**********

Editorial and Data Presentation Modifications?

Use this section for editorial suggestions as well as relatively minor modifications of existing data that would enhance clarity. If the only modifications needed are minor and/or editorial, you may wish to recommend “Minor Revision” or “Accept”.

Reviewer #1: Accept

**********

Summary and General Comments

Use this section to provide overall comments, discuss strengths/weaknesses of the study, novelty, significance, general execution and scholarship. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. If requesting major revision, please articulate the new experiments that are needed.

Reviewer #1: To my knowledge, this study is the first to show in CKD patients that S. stercoralis infection is associated with decreased diversity of the faecal microbiota, increased abundance of pathogenic microbial genera and decreased abundance of SCFA-producing genera. These are very important findings.

Although the study involved a small number of participants, the use of matched pairs ensured that the S. stercoralis-infected group was similar to the uninfected group in characteristics other than S. stercoralis infection status. PCR testing was used to ensure as much as possible that the uninfected group was truly S. stercoralis-free. The authors were careful to ensure that there was sufficient DNA in the extracted samples to carry out gene sequencing.

This is an important study, and I look forward to its publication.

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Jennifer Shield

31 Aug 2022

Dear Professor Pinlaor,

We are delighted to inform you that your manuscript, "Strongyloides stercoralis infection induces gut dysbiosis in chronic kidney disease patients," has been formally accepted for publication in PLOS Neglected Tropical Diseases.

We have now passed your article onto the PLOS Production Department who will complete the rest of the publication process. All authors will receive a confirmation email upon publication.

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Thank you again for supporting open-access publishing; we are looking forward to publishing your work in PLOS Neglected Tropical Diseases.

Best regards,

Shaden Kamhawi

co-Editor-in-Chief

PLOS Neglected Tropical Diseases

Paul Brindley

co-Editor-in-Chief

PLOS Neglected Tropical Diseases