Differential prevalence of pathobionts and host gene polymorphisms in chronic inflammatory intestinal diseases: Crohn's disease and intestinal tuberculosis.

BACKGROUND AND OBJECTIVES: Crohn's disease (CD) and Intestinal tuberculosis (ITB) are chronic inflammatory ulcero-constrictive intestinal diseases with similar phenotype. Although both are disease models of chronic inflammation and their clinical presentations, imaging, histological and endoscopic findings are very similar, yet their etiologies are diverse. Hence, we aimed to look at differences in the prevalence of pathobionts like adherent-invasive Escherichia coli (AIEC), Listeria monocytogenes, Campylobacter jejuni and Yersinia enterocolitica in CD and ITB as well as their associations with host-associated genetic polymorphisms in genes majorly involved in pathways of microbial handling and immune responses.
METHODS: The study cohort included 142 subjects (69 patients with CD, 32 with ITB and 41 controls). RT- PCR amplification was used to detect the presence of AIEC, L. monocytogenes, C. jejuni, and Y. enterocolitica DNA in colonic mucosal biopsies. Additionally, we tested three SNPs in IRGM (rs13361189, rs10065172, and rs4958847), one SNP in ATG16L1 (rs2241880) and one SNP in TNFRSF1A (rs4149570) by real-time PCR with SYBR green from peripheral blood samples in this cohort.
RESULTS: In patients with CD, AIEC was most frequently present (16/ 69, 23.19%) followed by L. monocytogenes (14/69, 20.29%), C. jejuni (9/69, 13.04%), and Y. enterocolitica (7/69, 10.14%). Among them, L. monocytogenes and Y. enterocolitica were significantly associated with CD (p = 0.02). In addition, we identified all the three SNPs in IRGM (rs13361189, rs10065172, and rs4958847), one SNP in ATG16L1 (rs2241880) and TNFRSF1A (rs4149570) with a significant difference in frequency in patients with CD compared with ITB and controls (p<0.05).
CONCLUSION: Higher prevalence of host gene polymorphisms, as well as the presence of pathobionts, was seen in the colonic mucosa of patients with CD as compared to ITB, although both are disease models of chronic inflammation.

Introduction

Crohn’s Disease (CD) is a chronic, relapsing, transmural, inflammatory disorder of gastrointestinal tract, which results from the prolonged, uncontrolled immune response to pathogenic or commensal microflora, in genetically susceptible individuals. Though the etiology remains elusive, CD results from the complex interplay of host genetics and alterations in lifestyle or host environment [1]. These host-associated changes potentiate alterations in sensing and handling of gut commensals, which along with changes in structure and function of the gut microbial community, perpetuates the vicious cycle of gut dysbiosis and inflammation [2, 3].

Inflammatory bowel disease (IBD)-associated gut dysbiosis is characterized by a reduction in number and activity of protective gut commensals involved in the production of short-chain fatty acids, secondary bile acids and indole-based aryl-hydrocarbon receptor ligands [4-6]. The onset of toxic pro-inflammatory gut environment supports an overgrowth of enteropathogens and functionally altered and potentially pathogenic commensal flora called the ‘pathobionts’. Several of these pathogenic bacteria associated with CD, include adherent-invasive Escherichia coli (AIEC), Listeria monocytogenes, Campylobacter jejuni, Mycobacterium avium subspecies paratuberculosis (MAP) and Yersinia enterocolitica [7-11]. Even though an upsurge of numbers and pathogenic activities of these bacteria have been documented in CD, whether their expansion is one of the causes of the disease or is just an effect of changing gut environment remains undetermined.

Apart from the involvement of entero-pathogens and pathobionts, host genetics has also been highlighted to have a determinative impact in shaping disease etiology in CD. Genome-wide association studies (GWAS) have identified 240 genetic loci and single nucleotide gene polymorphisms (SNPs) which are associated with the risk of developing CD [12-14]. These studies have implicated various pathways involved in microbial handling and sensing and maintenance of epithelial barrier integrity, to be compromised in CD, further strengthening the role of microbial members as ‘perpetuators’ of gut inflammation. From the CD GWAS and meta-analyses, autophagy has been underlined as a key pathway implicated in CD etiology [15]. Autophagy is involved in antigen presentation, clearance of invading pathogens and secretion of antimicrobial peptides from the Paneth cells in the gut [16, 17]. SNPs in autophagy genes such as autophagy-related gene 16 like 1 (ATG16L1) and immunity-related GTPase family M (IRGM) have been associated with CD [15, 18–20]. Apart from the autophagy-mediated cellular innate immunity functions, GWAS studies have also highlighted other immune-associated functions accounting for the genetic susceptibility to CD. Tumour necrosis factor-alpha (TNFα), one of the proinflammatory cytokines, is established as a mediator of the inflammation in CD. TNFα receptor superfamily 1A (TNFRSF1A) encodes tumor necrosis factor receptor 1 (TNF-R1) and mutations in the gene can cause autoinflammatory disorders. Polymorphisms in TNFRSF1A have been studied for susceptibility, phenotypes and pharmacogenetics of CD. The TNFRSF1A- 609, G>T (rs4149570) has been shown to be associated with increased risk of CD [21-23].

The IBD burden is on the rise in tuberculosis endemic countries including India [1, 24]. Intestinal tuberculosis, a bacterial infection, presents itself as a chronic granulomatous disorder with phenotypic similarities with Crohn’s Disease. This mimicry of clinical, radiological, endoscopic and histological manifestations, makes the differential disease diagnosis an enormous challenge [24]. Chronic inflammation is pathognomonic of both ITB and CD. Whether this chronic inflammation is a sole accountable factor for the ensued pathobiont bloom and gut dysbiosis or is attributable to disease-specific etiological events, is worth exploring.

Our recent study had shown significantly increased prevalence of MAP (23.2%, p = 0.03) in biopsy samples from patients with CD as compare to non-IBD controls [11]. The prevalence of key entero-pathogens and pathobionts namely adherent-invasive Escherichia coli (AIEC), L. monocytogenes, C. jejuni, and Y. enterocolitica in intestinal biopsy tissues of patients with CD, patients with ITB and non-IBD controls were not tested earlier. This study investigated the prevalence of these pathobionts and their association with single nucleotide genetic polymorphisms (SNPs) of IRGM, ATG16L1 and TNFRSF1A gene responsible for microbial sensing and handling in the CD and ITB patients and non-IBD control. The objective of this study is to compare the prevalence of bacterial entero-pathogens and SNPs in two diverse models of chronic immunoinflammatory granulomatous disease of the intestine: CD and ITB. The comparisons of these parameters to a bacterial infection of similar phenotypic presentation shall further consolidate the prevalence and potential role of these bacterial members and genetic polymorphisms in IBD.

Materials and methods

Study subjects

A total of 142 study subjects, including 101 patients with ulcero-constrictive disease of the ileocolonic region and 41 patients with suspected haemorrhoidal bleed undergoing sigmoidoscopy served as controls, were recruited from the All India Institute of Medical Sciences, New Delhi, India. Among the 101 patients with ulcero-constrictive disease, 69 cases were diagnosed as CD (65.2%, Male), and 32 cases were diagnosed as ITB (62.5%, Male). Consecutive treatment naïve adult (age >18 yrs.) patients who have not received any immunomodulator or antitubercular therapy were included in this study. Patients previously treated with steroids; diagnosed with other autoimmune diseases, history of malignant tumour or complications were excluded. Diagnosis of CD and ITB were determined according to established guidelines based on standard clinical, radiological, endoscopic and histological criteria [24, 25]. Disease location and severity of CD were scored according to the Montreal classification [26]. The patients with ileocolonic transverse ulcers and/or strictures were diagnosed ITB with demonstration of caseating granulomas or acid fast bacilli or a positive culture on mucosal biopsies. The patients with presentation suggestive of ITB and concomitant active pulmonary tuberculosis were also included. The patients with diagnostic dilemma of ITB vs CD were given antitubercular therapy (ATT) trial for obtaining sustained response (clinical and mucosal healing). The patients achieved sustained clinical response at 6 months post-ATT were categorized as ITB and those do not respond to ATT but showed response to steroids or immunomodulators were categorized as CD. This study was approved by the institute ethics committee, All India Institute of Medical Sciences, New Delhi, India (Approval no. IEC/NP-165/2010). Written, informed consent was obtained from all patients and control subjects prior to study inclusion. All the samples were collected during the period 2011–2013. Study protocols were based on the ethical principles for medical research involving human subjects as per the Declaration of Helsinki.

Genomic DNA isolation from intestinal biopsies for detection of pathogenic bacteria

Intestinal biopsies collected for detection of pathogenic bacteria were immediately snap frozen in liquid nitrogen and stored at—80°C for isolation of genomic DNA later. Genomic DNA was isolated from intestinal biopsies (~15 mg) by commercial DNA extraction kit (DNeasy Blood & Tissue Kit, Qiagen, USA) using manufacturer’s protocol. Detection of pathogenic bacteria (AIEC, L. monocytogenes, C. jejuni and Y. enterocolitica) were carried out by real-time PCR (Mx3005p, Agilent Technologies, USA) of genomic DNA isolated from intestinal biopsies using pathogen specific detection primer. These detection primers were designed for FimH, iap, 16S-23S ITS, and ail gene of bacteria (S1 Table). Briefly, real-time PCRs were carried out in 20-μl reaction volume containing 5 μl of DNA (~ 400 ng), 10 μl of Maxima SYBR Green qPCR Master Mix (Thermo Fisher Scientific), 1 μl of forward and 1 μl of reverse primers (20 pmol each), and 3 μL of nuclease-free water. Thermocycling conditions were initial denaturation at 95°C for 5 minutes, followed by 35 cycles of denaturation (94°C for 30 sec), annealing (mentioned temperature at S1 Table for 30 sec) and extension (72°C for 30 s). Specific amplification was confirmed by plotting a dissociation curve through melting at the temperature range of 60°C to 95°C. The real-time PCR products for desired amplicon size were further confirmed by agarose gel (2%) electrophoresis (Fig 1).

Fig 1

Agarose gel electrophoresis of the RT-PCR products.

a) PCRs for AIEC. Lane M: Marker 100 bp; Lanes 1–4: AIEC positive samples; b) PCRs for L. monocytogenes. Lane M: Marker 200 bp; Lane 1: Positive control; Lanes 2–4: Positive samples; c) PCRs for C. jejuni. Lane M: Marker 100 bp; lane 1: Positive control; Lanes 2–4: Positive samples; d) PCRs for Y. enterocolitica. Lane M: Marker 100 bp; lane 1, 2: Positive samples.

SNP genotyping of DNA isolated from peripheral blood

Genomic DNA was isolated from peripheral blood using the QIAmp DNA Blood kit (Qiagen, USA). The concentration and purity of DNA were determined by measuring absorbance at 260 nm and 280 nm. SNP genotyping was carried out for IRGM gene (rs13361189, rs10065172, and rs4958847), ATG16L1 gene (rs2241880), and TNFRSF1A gene (rs4149570) by real-time PCR using SYBR green-based chemistry. Allele-specific primers were designed and synthesized by introducing mismatches at the 3’ terminal position of all forward primers (S2 Table). Briefly, SNP genotyping reactions were carried out in 20 μl volume containing 10 μl of Maxima SYBR Green qPCR Master Mix (Thermo Fisher Scientific), 1 μl (20 pmol) each forward and reverse primers and 3 μL of nuclease-free water. Allele-specific RT-PCR thermal cycling conditions were as follows: 95°C for 5 min, followed by 40 cycles of denaturation (95°C, 30 sec), annealing for 30 sec at respective annealing temperature for each primer set and extension (72°C for 30 sec), followed by a melting curve analysis at 65°C to 95°C.

Statistical analysis

Kruskal–Wallis and Mann-Whitney U tests were used to compare the prevalence of bacteria in 3 groups. Genotype and allele distributions among patients with CD and ITB versus healthy controls were compared using the χ2 test or Fisher test as appropriate. Odds ratios (ORs) with a confidence interval (CI) of 95% were assessed to measure the strength of association. A chi-square test was used to analyze the deviation from Hardy-Weinberg equilibrium (HWE). Chi-square test P-value <0.05 was considered as an association. Association between the IRGM, ATG16L1, TNFRSF1A genotypes and bacterial positivity were assessed using unconditional logistic regression analysis. A P-value of less than 0.05 was considered statistically significant. Data were analyzed using STATA 14 software. Association of pathobiont prevalence and CD-associated SNP with the clinical variables were performed by Goodman and Kruskals (GK) Tau test using the GK tau data frame function of the Goodman Kruskal R package. This test determines the fraction of variability in one categorical variable that can be explained by the other categorical variable.

Results

Prevalence of L. monocytogenes, Y. enterocolitica, C. jejuni and AIEC in patients with CD, ITB and controls

Clinical characteristics of the study subjects are presented in Table 1. The presence of bacterial DNA in the mucosal biopsy samples of CD (n = 69), ITB (n = 32) and control (n = 41) subjects were detected by real-time PCR using pathogen-specific detection primers. Percentage of cases found positive for L. monocytogenes in CD, ITB and controls were 20.29% (14/69), 3.13% (1/32) and 7.32 (3/41) respectively. The prevalence of L.monocytogenes in patients with CD was statistically significant as compared to the ITB and control groups (p = 0.026) (Table 2). The prevalence of Y. enterocolitica in colonic biopsies of patients with CD was 10.14% (7/69), which was also significantly higher than ITB and controls (p = 0.02). We have also detected adherent invasive Escherichia coli (AIEC) and C.jejuni in CD, ITB and control subjects but the prevalence of these two pathogen were not found significant among groups (Table 2).

Table 1

Characteristics of the study population.

Variables	CD(n = 69)	ITB(n = 32)	Controls(n = 41)	P value
Gender: (M/F)	45/24	20/12	29/12	0.7
Age at diagnosis	37.55	38.34	32.02	0.07
Mean duration of disease(months)	63.0	17.4		0.001
Behaviour of disease (Montreal Classification)
Non stricturing (B1)	41 (59.4%)	13 (40.6%)		0.1
Stricturing (B2)	27 (39.1%)	19 (59.4%)
Penetrating (B3)	1 (1.5%)	0
Perianal disease (P)	0	0
Location of disease
Ileal (L1)	22 (31.9%)	9 (28.1%)		0.9
Colonic (L2)	19 (27.5%)	10 (31.3%)
Ileocolonic (L3)	22 (31.9%)	12 (37.5%)
Isolated upper digestive (L4)	2 (2.9%)	0
L1+L4	1 (1.5%)	1 (3.1%)
L2+L4	2 (2.9%)	0
L3+L4	1 (1.5%)	0
Site of Biopsy
Rectum	1 (1.5%)	0	2 (4.9%)	0.001
Rectosigmoid	1 (1.5%)	1 (3.1%)	2 (4.9%)
Sigmoid	3 (4.4%)	0	35 (85.4%)
Descending Colon	6 (8.7%)	1 (3.1%)	0
Transverse Colon	6 (8.7%)	3 (9.4%)	0
Ascending Colon	9 (13.0%)	6 (18.8%)	0
Caecum	4 (5.8%)	4 (12.5%)	0
Ileocaecal	13 (18.8%)	10 (31.3%)	0
Terminal Ileum	26 (37.7%)	7 (21.9%)	2 (4.9%)

Table 2

Prevalence of bacteria isolated from biopsies of patients with CD, ITB and controls.

Name of the bacteria	CD (%)n = 69	ITB (%)n = 32	Controls (%)n = 41	p-Value
Adherent-invasive E. coli (AIEC)	16 (23.2)	5 (15.6)	9 (21.9)	0.679
Listeria monocytogenes	14 (20.3)	1 (3.1)	3 (7.3)	0.026
Campylobacter jejuni	9 (13.0)	3 (9.4)	2 (4.9)	0.379
Yersinia enterocolitica	7 (10.1)	0	0	0.02

Genotype and allele frequency distributions of SNPs in IRGM, ATG16L1 and TNFRSF1A gene among patients with CD and ITB

Genotyping by allele-specific real-time PCR for three SNPs in IRGM (rs13361189, rs10065172, and rs4958847), one SNP in ATG16L1 (rs2241880) and one SNP in TNFRSF1A (rs4149570) were carried out in total 142 subjects which included controls (n = 41) and patients as cases (CD, n = 69 and ITB, n = 32). Frequency of minor alleles and genotypes for each SNP in both cases (CD and ITB) and control populations are mentioned in Table 3. Expected genotypes were derived from observed genotypes frequency in case and controls by population genetic approaches. The test of deviations from Hardy-Weinberg equilibrium (HWE) in both case (CD, ITB) and control populations were evaluated by chi-square test using observed and expected genotype frequencies for each SNP. The test of deviation from HWE among control populations was not found significant (Table 3) which indicated that SNP loci were not influenced by evolutionary forces (mutation, genetic drift and migration) and suitable for genetic association studies. Among cases, HWE p-value was found significant for CD patients but not for ITB cases (Table 3).

Table 3

Frequency and distribution of SNP allele and genotypes in cases (CD, ITB) and controls.

Gene/SNP	Genotype, Minor allele	CD (%)n = 69	HWEP- value	ITB (%)n = 32	HWEP-value	Controls (%)n = 41	HWEP value
IRGM	CC	10 (14.49)	0.024	8 (25)	0.307	17 (41.46)	0.057
rs13361189	CT	21 (30.43)		13 (40.6)		14 (34.15)
(C/T)	TT	38 (55.07)		11 (34.4)		10 (24.39)
	C	0.30		0.45		0.59
IRGM	CC	14 (20.29)	0.0002	12 (37.5)	0.930	14 (34.15)	0.594
rs10065172	CT	17 (24.64)		15 (46.8)		19 (46.34)
(C/T)	TT	38 (55.07)		5 (15.6)		8 (19.51)
	T	0.67		0.39		0.44
IRGM	AA	29 (42)	0.001	8 (25)	0.491	6 (14.63)	0.677
rs4958847	AG	21 (30.43)		14 (43.75)		21 (51.22)
(A/G)	GG	19 (27.54)		10 (31.25)		14 (34.15)
	A	0.57		0.47		0.40
ATG16L1	CC	34 (49.28)	0.0002	5 (15.65)	0.930	7 (17)	0.815
rs2241880	CT	18 (26)		15 (46.88)		19 (46.34)
(C/T)	TT	17 (24.64)		12(37.5)		15 (36.59)
	C	0.62		0.39		0.40
TNFRSF1A	GG	20 (28.99)	0.0001	14 (43.75)	0.721	11 (26.83)	0.049
rs4149570	GT	17 (24.64)		15 (46.88)		26 (63.41)
(G/T)	TT	32 (46.38)		3 (9.38)		4 (9.76)
	T	0.59		0.33		0.41

SNP = Single nucleotide polymorphism; CD = Crohn’s disease; ITB = Intestinal tuberculosis; HWE = Hardy-Weinberg equilibrium

Genetic association of IRGM, ATG16L1 and TNFRSF1A gene SNPs with CD and ITB patients as compared to healthy controls

Genetic association of IRGM, ATG16L and TNFRSF1A gene SNPs were evaluated by estimating odds ratio (OR) of genotypes and alleles for case (CD and ITB) and healthy control populations. Dominant allele and genotypes for each SNP were assumed as reference or protective whereas, minor allele/genotypes were assumed as the risk for developing the disease as compared to healthy control. In our study population, the genetic association of above-mentioned gene SNPs was not observed for ITB cases as OR was not found significant (Table 4). The OR for risk allele/ genotypes was found significant (Table 4) for CD cases indicating the genetic association of IRGM, ATG16L and TNFRSF1A gene polymorphisms with the disease. The strength of association with significant OR >1 for each SNP indicated a risk for developing the disease, which was observed for the minor allele (T) and genotype (TT) of IRGM rs10065172; minor allele (A) and genotype (AA) of IRGM rs4958847 (A/G); minor allele (C) and genotype (CC) of ATG16L1 rs2241880 (C/T) and minor allele (T), genotype (TT and GT) of TNFRSF1A rs4149570 (G/T) (Table 4). The allele C was observed as the minor allele for IRGM SNP rs13361189 (C/T) in Indian CD patients. The OR <1 indicated protective for disease as shown for minor allele C indicating T as risk allele and TT as risk genotype (Table 4).

Table 4

Association of risk allele and genotype of single nucleotide polymorphisms with CD and ITB cases as compared to healthy controls.

Gene/SNP	Genotype	CDOR (95% CI)	P-valueHWE	ITBOR (95% CI)	P-value
IRGM rs13361189 (C/T)	TT	Ref		Ref
	CT	0.39 [0.15–1.04]	0.057	0.84 [0.27–2.64]	0.771
	CC	0.15 [0.05–0.44]	0.0002	0.42 [0.12–1.42]	0.162
	CC+CT	0.26 [0.11–0.62]	0.0017	0.61 [0.22–1.70]	0.349
	C	0.29 [0.16–0.53]	0.00003	0.58 [0.30–1.13]	0.112
IRGM rs10065172 (C/T)	CC	Ref		Ref
	CT	0.89 [0.33–2.40]	0.825	0.921 [0.33–2.57]	0.875
	TT	4.75 [1.64–13.75]	0.0029	0.72 [0.18–2.83]	0.647
	CT+TT	2.03 [0.85–4.87]	0.1067	0.86 [0.33–2.26]	0.766
	T	2.77 [1.57–4.87]	0.0003	0.86 [0.44–1.67]	0.659
IRGM rs4958847 (A/G)	GG	Ref		Ref
	AG	0.73 [0.29–1.84]	0.513	0.93 [0.32–2.68]	0.898
	AA	3.56 [1.16–10.89]	0.022	1.86 [0.49–7.08]	0.356
	AG+AA	1.36 [0.59–3.14]	0.464	1.14 [0.42–3.06]	0.793
	A	1.98 [1.14–3.46]	0.014	1.31 [0.67–2.53]	0.422
ATG16L1 rs2241880 (C/T)	TT	Ref		Ref
	CT	0.83 [0.32–2.15]	0.710	0.98 [0.35–2.72]	0.930
	CC	4.28 [1.47–12.48]	0.005	0.89 [0.22–3.53]	0.871
	CT+CC	1.76 [0.76–4.08]	0.182	0.96 [0.36–2.50]	0.935
	C	2.45 [1.40–4.29]	0.001	0.95 [0.48–1.85]	0.884
TNFRSF1A rs4149570 (G/T)	GG	Ref		Ref
	GT	0.36 [0.13–0.93]	0.033	0.45 [0.16–1.24]	0.123
	TT	4.40 [1.23–15.72]	0.017	0.58 [0.10–3.20]	0.537
	GT+TT	0.89 [0.37–2.13]	0.807	0.47 [0.17–1.25]	0.130
	T	2.00 [1.15–3.49]	0.013	0.68 [0.34–1.36]	0.284

SNP = Single nucleotide polymorphism; CD = Crohn’s diseae; ITB = Intestinal tuberculosis; HWE = Hardy-Weinberg equilibrium

Association of IRGM, ATG16L1 and TNFRSF1A gene SNPs with bacterial infection susceptibility in CD patients

Increased prevalence of L. monocytogenes and Y. enterocolitica infection was observed in CD patients compared to ITB patients and control (Table 2). Association of IRGM, ATG16L1 and TNFRSF1A SNP with increased prevalence of these bacteria in CD patients was tested by chi-square test at 2 degrees of freedom for genotype and one degree of freedom for alleles. None of the SNPs in IRGM, ATG16L1 and TNFRSF1A gene was found to be significantly associated with susceptibility to bacterial infection (Table 5).

Table 5

Association of SNPs genotype with bacterial infection or persistence in CD patients.

		AIEC		L. monocytogenes		C. Jejuni		Y. enterocolitica
	Genotype	Yes	No	Yes	No	Yes	No	Yes	No
IRGM rs13361189 (C/T)	CC	1	9	1	9	0	10	0	10
	CT	7	14	5	16	2	19	2	19
	TT	8	30	2	36	7	31	5	33
	χ2, df	2.287, 2		1.809, 2		2.698, 2		1.516, 2
	p value	0.318		0.404		0.259		0.468
IRGM rs10065172 (C/T)	CC	3	11	4	10	2	12	1	13
	CT	3	14	2	15	2	15	3	14
	TT	10	28	8	30	5	33	3	35
	χ2, df	0.526, 2		1.371, 2		0.044, 2		1.399, 2
	p value	0.768		0.503		0.978		0.496
IRGM rs4958847 (A/G)	AA	8	21	6	23	4	25	3	26
	AG	4	17	4	17	1	20	1	20
	GG	4	15	4	15	4	15	3	16
	χ2, df	0.565, 2		0.029, 2		2.359, 2		1.333, 2
	p value	0.753		0.985		0.307		0.513
ATG16L1 rs2241880 (C/T)	CC	8	26	5	29	6	28	3	31
	CT	5	13	7	11	2	16	2	16
	TT	3	14	2	15	1	16	2	15
	χ2, df	0.508, 2,		5.269, 2		1.463, 2		0.132, 2
	p value	0.775		0.0717		0.481		0.935
TNFRSF1A rs4149570 (G/T)	CC	4	16	5	15	2	18	1	19
	CT	4	13	4	13	3	14	2	15
	TT	8	24	5	27	4	28	4	28
	χ2, df	0.174, 2		0.815, 2		0.489, 2		0.824, 2
	p value	0.916		0.665		0.783		0.662

χ2, df = Chi-square, degrees of freedom

Association of prevalent pathobionts with the clinical variables and group analysis

Goodman and Kruskal’s Tau (GK τ) association measure between the pathobiont prevalence (noted as ‘Positive’ and ‘Negative’ for each pathobiont type) and the clinical parameters of patients with CD (age at onset of symptoms, age at which disease was diagnosed, location and behaviour of the disease) were plotted in Fig 2A. Association is indicated by the degenerate ellipse which is reflected as a straight line (GK τ = 1) for strong association and as full circle (GK τ = 0) for no association. The association measure between the pathobiont prevalence and clinical features failed to indicate any significant associations between the variables, except for a weak association between behaviour of disease (as per Montreal classification) and prevalence of C.jejuni (GK τ = 0.1) in patients with CD (Fig 2A). The GK τ association measure between the risk SNP genotype (noted as ‘presence’ and ‘absence’ for risk allele for each gene type) and the clinical features of the CD patients (age of disease onset, location and behaviour of the disease were plotted in Fig 2B. The location of disease showed weak association with the risk associated genotype in IRGM rs13361189 (GK τ = 0.17) and ATG16L1 rs2241880 (GK τ = 0.13), and a similar association was evident for the ‘age at the onset of symptoms’ with IRGM rs13361189 (GK τ = 0.11). No significant association was observed between risk loci and other clinical parameters. The pathobionts prevalence overlapping was observed by plotting the Venn diagram (Fig 2C) by using the ‘venn’ function of plots R package. Highest overlap was observed for the prevalence of AIEC and L. monocytogenes in patients with CD. This overlap of the two pathobionts was also evident from the Goodman and Kruskal’s Tau value of 0.33, highlighting the co-occurrence of AIEC and L.monocytogenes in patients with CD (Fig 2A).

Fig 2

Measurement of association and co-occurrence of the prevalence of pathobionts and risk alleles with the clinical features in patients with CD.

A) Association measurement between and amongst pathobiont prevalence (noted as ‘Positive’ and ‘Negative’ for each pathobiont type) and the clinical features of the patients with CD using Goodman and Kruskal’s Tau association; B) Association measurement between the risk allele prevalence (noted as ‘presence’ and ‘absence’ for risk allele for each gene type) and amongst and the clinical features of the patients with CD using Goodman and Kruskal’s Tau association; C) Venn Diagram depicting the co-occurrence of pathobionts in patients with CD.

Discussion

Chronic gastrointestinal inflammation and disease-associated alterations in gut microenvironment act as stressors to drive disease-associated gut dysbiosis. Expansion of gut pathobionts and dwindling structure and function of beneficial members have been previously linked with CD [27, 28]. However, there is very less report about comparative assessment for the prevalence of various pathobionts in patients with CD and ITB. With an aim to explore the impact of chronic gastrointestinal inflammation in propelling a disease-associated bloom of major gut pathobionts, we have investigated the prevalence of Yersinia enterocolitica, Listeria monocytogenes, Campylobacter jejuni, and adherent-invasive Escherichia coli in two (CD and ITB) chronic intestinal inflammatory disease models of diagnostic dilemma. Simultaneously, the study also examined the prevalence of major risk SNP genotype/allele in the genes implicated for microbial sensing and handling such as IRGM (rs10065172, rs13361189 and rs4958847), ATG16L1 (rs2241880), and TNFRSF1A (rs4149570).

The prevalence of bacterial pathobiont was more in CD patients than ITB and control subjects. The control and ITB groups were found to have significant low infection incidences of L. monocytogenes and Y. enterocolitica as compared to CD patients whereas there was no significant difference in the incidences of the AIEC and C. jejuni among the groups. These observations are consistent with the study findings by Kang et al and Kallinowski et al respectively [4, 29]. Generally, the interconnection and exacerbations between L. monocytogenes and CD endure to be an imperceptible dilemma in which more investigation is needed [9]. Although statistically insignificant, our results also reported enhanced prevalence of C. jejuni in patients with CD as compared to the ITB and control group. However, the prevalence of AIEC in patients with CD was similar to controls and, higher than ITB without any statistical insignificance. In a recent meta-analysis of 12 studies, the pooled prevalence of AIEC among patients with CD was 29% which is similar to our study, and in controls was 9%, which is much lower than our study [30]. This difference could be explained by the difference in patient population (only 1 study in this meta-analysis was from Asia (South Korea), where the prevalence of AIEC in controls was 22.2%, similar to our study), and analytical techniques, as in contrast to our study (which used PCR) these studies utilized adhesion and invasion assays. The two bacteria (C.jejuni and AIEC) have been reported to be prevalent in patients with CD and their pathogenic mechanisms have been implicated in the disease pathophysiology [10, 31]. Even though the sizes of the case and the control cohorts aid us to manifest the pathobiont expansion between the groups, a larger sample size would have helped to gain better statistical insights into our observations.

Despite the shared chronic inflammatory milieu in CD and ITB, the higher prevalence of these bacteria in CD stressed the explanatory power of CD-specific etiology in shaping the gut bacteria. CD is accompanied by an aberrant T-cell response, with an expansion of inflammatory Th1 and Th17 cell population and diminished gut T-regulatory cells, which in turn results in elevated oxygen radicals and nitric oxide. This remoulding of the gut environment might drive the expansion of inflammophilic gut pathobionts [32-35].

Human genetic association studies have enforced genetic contribution in the onset and progression of IBD. Genome-wide association studies (GWAS) have linked specific genetic polymorphisms with increased susceptibility to the development of IBD. The underlined genes and their products are important components of cellular pathways involved in, microbial sensing and handling, innate and adaptive immunity and maintenance of gut mucosal integrity.

Autophagy is involved in immune-specific functions including regulation of antigen presentation, secretion, inflammasome formation and mitophagy [36, 37]. Genetic polymorphisms in key autophagy genes like, ATG16L1 and IRGM have been linked to enhanced IBD susceptibility [15, 38]. The products of these genes actively regulate intracellular bacterial clearance in innate immune cells and may affect the structure of gut microbiome. IRGM rs13361189 minor allele carriers have been reported to have reduced expression of IRGM in whole blood and terminal ileum, along with altered expression of other genes associated with autophagy and inflammatory responses. Baskaran et al. have reported higher prevalence of risk SNPs genotype in the IBD patients of India. These SNPs also influence the disease pathophysiology by shaping the cellular microRNA milieu. IRGM rs10065172 has been reported to alter the binding site of miR-196 and downregulates the IRGM protective variant to dysregulate autophagy and intracellular bacterial handling process. Studies have shown that the CD-associated Thr300Ala mutation in ATG16L1 hampers the process of xenophagy, mediating an impaired efficiency of autophagy-mediated clearance of the intracellular enteric pathogen Salmonella typhimurium. Pierre et al. have shown the decreased efficiency of autophagy-mediated clearance of pathogenic adherent-invasive Escherichia coli (AIEC) in CD patients expressing the ATG16L1 variant [39]. In accordance with previous studies [15, 38, 40, 41], our results reveal a significantly enhanced prevalence of the risk alleles of IRGM (rs13361189, rs4958847 and rs10065172) and ATG16L1 (rs2241880) genes.

Tumor Necrosis Factor-alpha (TNF alpha) is an innate immune cytokine which mediates inflammation. TNF alpha acts as a ligand to the cell surface and membrane-bound receptors referred to as the TNF Receptor Superfamily 1A (TNFRSF1A) and plays a role in cell survival, apoptosis and inflammation. In our study, the TNFRSF1A polymorphism (rs4149570) was found to be significantly associated with CD occurrence, thereby aligning with other studies, which highlight association of TNFRSF1A(rs4149570) genotype with increased risk of CD [23].

We could not demonstrate any association between the CD associated genetic polymorphisms and the prevalence of various pathobionts. Polymorphisms in NOD2 gene have been associated with AIEC colonization in patients with IBD [42, 43], however, we did not test for NOD2 in our cohort as previous studies have not found any association between NOD2 polymorphisms and Indian CD patients [44, 45].

The co-occurrence analysis of the pathobionts using the Goodman and Kruskal’s Tau test, showed significant association between the prevalence of AIEC and Listeria monocytogenes in patient with CD. AIEC adhesion and invasion of intestinal epithelium in CD modulates the autophagy, which has been highlighted to enhance the survival of intracellular pathogens. One such mechanism by which AIEC dampens the autophagy process is through impairing SUMOylation process, which in turn controls intracellular survival of pathogens like Listeria monocytogenes in the gut epithelium [46]. Upon analysis of associations between the clinical parameters of patients with CD and prevalence of pathobionts and risk-associated genotypes, weak correlations were noted between the behaviour of disease (as per Montreal classification) and the prevalence of C.jejuni, and between the location of the disease and prevalence of risk variants of genes IRGM rs13361189 and ATG16L1 rs2241880. These associations are noteworthy and further investigations can yield interesting mechanistic insights.

Although these observations are encouraging, it is important to note the limitations. Firstly, the results must be validated with a larger cohort, keeping in mind that subjects represent wider ethnic and geographic backgrounds. Secondly, the genetic polymorphisms considered in this study are only restricted to genes which have already been implicated in CD. There might be other genes involved in microbial handling, which could promote the growth of pathobionts or decrease beneficial gut microbes when mutated. Therefore more such genetic loci must be taken into account.

Conclusions

The present study highlights the differential prevalence of major gut pathobionts and genetic risk alleles in patients with CD and ITB. The study reveals that despite similar intestinal manifestations and chronic inflammation in CD and ITB, the complex disease-specific gut microenvironment is what determines the pathobiont inhabitation in the gut. The present study also discusses the prevalence of specific SNPs in genes implicated in IBD, in a North Indian cohort.

The raw uncropped agarose gel, obtained post electrophoresis of the qPCR products.

(A) Amplicons corresponding to AIEC (Lanes 1–9). Lane M loaded with 100 bp DNA ladder. (B) Amplicons corresponding to L.monocytogenes (Lanes 2–6), Bands in lane 1 and lane M correspond to amplicon of positive bacterial culture and 200 bp ladder resp. (C) Amplicons corresponding to C.jejuni (Lanes 2–6), Bands in lane 1 and lane M correspond to amplicon of positive bacterial culture and 100 bp ladder resp. (D) Amplicons corresponding to Y.enterocolitica (Lanes 1 and 2), Bands in lane 5 and lane M correspond to amplicon of positive bacterial culture and 200 bp ladder resp.

(PDF)

Click here for additional data file.

Primers used for detection of selected bacteria in the present study.

a F and R indicate forward and reverse primers, respectively.

(DOC)

Click here for additional data file.

Allele-specific primers used for genotyping.

a F and R indicate forward and reverse primers respectively. b The polymorphic base is in bold; underlined letters indicate mismatched bases.

(DOCX)

Click here for additional data file.

7 Jun 2021

PONE-D-21-12204

Differential prevalence of pathobionts and host gene polymorphisms in chronic inflammatory intestinal diseases: Crohn’s Disease and Intestinal Tuberculosis

PLOS ONE

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Reviewer #1: Overlapping clinical characteristics in Crohn’s Disease (CD) and intestinal tuberculosis (ITB) make the differential diagnosis a daunting task for clinicians, especially in case of patients from TB endemic countries like India. Differentiation between these two deadly diseases cannot be done with a standalone evaluation and requires a comprehensive approach which includes the sum of clinical, endoscopic, radiological, microbiological laboratory and culture studies for accurate diagnosis. Most of these approaches bear one or other limitations of low sensitivity and specificity. This dilemma results in misdiagnosis and delay in treatment contributing to increased morbidity and mortality. In this direction, study of both genetic and environmental risk factors may prove to be instrumental in deciphering newer biological markers for efficient diagnosis of CD and ITB. Dysbiosis in human gut microbiome and genetic susceptibility has been now associated with various diseases including CD. Thus, differential prevalence of pathobionts and host gene polymorphisms can serve as good biomarker to differentiate between CD and ITB.

The research article entitled “Differential prevalence of pathobionts and host gene polymorphisms in chronic inflammatory intestinal diseases: Crohn’s Disease and Intestinal Tuberculosis” (PONE-D-21-12204) by Khan et.al highlights the prevalence of pathobionts like adherent-invasive Escherichia coli (AIEC), Listeria monocytogenes, Campylobacter jejuni and Yersinia enterocolitica in Crohn’s Disease (CD) and Intestinal Tuberculosis (ITB). Consistent with previous studies, this study reveal a significantly enhanced prevalence of the risk alleles of IRGM (rs13361189, rs4958847 and rs10065172) and ATG16L1 (rs2241880) TNFRSF1A polymorphism (rs4149570) genes with CD.

The manuscript is well written and suitable statistical tools and analysis have been applied to determine the significance of the data. Although, the study is limited with lack of statistically significant data, possibly due to the small sample size as also admitted by authors in the manuscript, the findings from this study are very important especially the prevalence of specific SNPs in genes implicated in IBD, in a North Indian cohort. This study takes the field one step closer to a non-invasive and affordable diagnostic procedure for IBD and may prove to be useful for the follow up studies with a bigger sample size in Indian population. Such investigative studies should be pursued with further experiments involving the colonization of wild type, germ-free, and genetically modified mice with an individual bacterial species or with a combination of bacteria, in order to identify the exact causal bacterial strain or core microbiome and clarify the fate of the gut microbiota in IBD.

The manuscript may be considered for publication after addressing the following comments:

Comments:

1. In the introduction, authors mentioned Mycobacterium avium subspecies paratuberculosis (MAP) as one of the pathogen associated with CD. Also there is moderately high seroprevalence (23.4%) of MAP in human population of north India (SV Singh et.al., J Biol Sci 2014) so, adding MAP prevalence data in this study would have been useful and enhanced our understanding towards the association of MAP with CD in Indian population. Any specific reasons that authors didn’t included it in the study?

2. Refer lines 120-122 “Diagnosis of CD and ITB ………. based on standard clinical, radiological, endoscopic and histological criteria” Authors have provided data only from Behaviour of disease (Montreal Classification), location of disease and site of biopsy (refer Table 1). The other clinical, radiological histological, microbiological data is missing. Considering the overlapping clinical characteristics and knowing that the exclusive features between CD and ITB are caseation necrosis on biopsy, positive smear for acid-fast bacillus (AFB) and/or AFB culture, and necrotic lymph node on cross-sectional imaging in ITB (Kedia et.al.,2019), including supportive data from histology of biopsy samples (if possible) and clinical symptoms ( representative images of endoscopy of selected patients among all the groups) will be helpful in validation of the accurate diagnosis of the patients in this study. Alternatively, multivariable double logistic regression analysis of endoscopic and clinical features should have been performed. (Pls refer to research article by Li et.al., 2011, DOI 10.1007/s10620-010-1231-4).

3. Since sample storage conditions can affect the quantification of the target microbial markers especially fast growing E.coli, the storage conditions of the clinical samples (Biopsies) before the isolation of the nucleic acid should be described in the methods.

4. Refer lines 183-184 (C. jejuni prevalence) & 186-188 (AIEC prevalence). Since the Prevalence data for these two strains is not statistically significant, affirmative statements should be avoided. Sentences can be rewritten for better clarity and avoiding any misunderstanding.

5. Refer to lines 258-260, Pls provide suitable references of the previously published studies from the literature, if any, where the comparative assessment of the prevalence of various pathobionts, between CD and ITB was done. A comparison between their methods & findings with that in the present study will be useful.

6. Refer lines 269-270, The statement “The control and ITB groups were found to have low infection incidences” holds true only for L. monocytogenes and Y. enterocolitica as only these were significantly less prevalent in the control and ITB groups as compared to CD patients, whereas there was no significant difference in the incidence of the AIEC and C. jejuni between the groups. So sentence need to be modified accordingly.

7. Recent case study of an Asian female patient (Korean) with Crohn's Disease reported her case to be initially misdiagnosed as Intestinal Tuberculosis due to active pulmonary tuberculosis (Park et.al., Korean J Gastroenterol, 2021). Given that India is TB endemic country, it may be worthy to include the active pulmonary tuberculosis in the clinical history of the patient to avoid such misdiagnosis specifically in studies involving comparison of CD Vs. ITB. Also, since the baseline features of gut microbiota after or during anti-TB treatment among ITB patients may differ, it may be worth to mention the ATT treatment in recent past of subjects included in this study. The inclusion and exclusion criteria (especially history of tuberculosis, previous/existing anti-tubercular drug therapy at the time of specimen collection) for various groups (CD, ITB and Controls) under this study have not been defined and should be included in the manuscript.

8. As this study doesn’t demonstrate any association between the CD associated genetic polymorphisms and the prevalence of various pathobionts, its implications in pathogenesis of CD and ITB should have been discussed in correlation with clinical symptoms of patients under different groups in this study. This would have provided more insights to understand the role of these marker genes in etiology of CD and ITB.

Reviewer #2: The authors in this study aimed to look at differences in the prevalence of pathobionts like adherent-invasive Escherichia coli (AIEC), Listeria monocytogenes, Campylobacter jejuni and Yersinia enterocolitica in CD and ITB as well as their associations with host-associated genetic polymorphisms in genes majorly involved in pathways of microbial handling and immune responses. The study looks interesting; however, the authors should address the following concerns. The authors should also perform a general proof reading for typographical errors in the manuscript.

My comments are as below

1. The authors in this study aimed to look at the differences in the prevalence of various pathobionts as well as their associations with host-associated genetic polymorphisms in genes majorly involved in pathways of microbial handling and immune responses. Why were these 4 organisms chosen? It would have been better to perform a metagenome analysis to understand the profile of microorganisms in confirmed patients in a North Indian population.

2. Was the sample collection in the study prospective? It will be useful to include the details of the guidelines used for the classification of CD and ITB.

3. Genomic DNA was isolated from the intestinal biopsies. Kindly provide the details of the kit used. Why did the authors check the qPCR products on agarose gel electrophoresis? Usually a melt curve analysis is sufficient to prove specific amplification. What was the advantage of using qPCR? Was there any discrepancy between agarose gel profile and qPCR results?

Also, were the primers designed in the study or used from published literature? I am unable to see the point of final extension in real time PCR experiments, also the sizes of amplicons are too high for use in a qPCR assay. How did the authors confirm specificity of the assay? Were all samples performed with all assays simultaneously? What is the positive control?

4. Fig. 1-No amplicon sizes are marked here. This figure should not be a part of the main manuscript.

5. How did the allele specific PCR work in qPCR format? Was absence of amplification taken as presence of the SNP or was it done by melt curve analysis? Can the authors elaborate on the same?

6. The prevalence data for the organisms, are they overlapping? I think a grouped analysis should also be done.

7. The study reports interesting findings, but the authors should discuss about the link they expected to find between the presence of these specific pathobionts and genetic polymorphisms. The effect of these polymorphisms on the population should also be discussed. It will be interesting to understand the various polymorphisms associated with CD and ITB patients in this study. This will be evident in a grouped analysis i.e. in the 69 CD patients what all polymorphisms were significantly associated with CD, and not with ITB and controls.

The differential prevalence of major gut pathobionts and genetic risk alleles in patients with CD and ITB can be explored as a screening tool.

Minor

1. Line 82- remove ‘the’ before autophagy

2. TNF a is ‘TNFα’

3. Line 108, it is ITB not TB

4. Line 180 pathogen specific detection ‘primers’

5. Table 2 and 3, remove the term healthy control/HC. Replace with 'controls'

6. All tables, please provide the abbreviations in the footnote

7. Please provide reference for Montreal classification

8. Line 278, it should be statistical significance

9. All microorganisms’ names in italics in Fig. Legend

**********

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: Review for Plos One Article by Khan et.al._PONE-D-21-12204.pdf

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22 Jul 2021

Ref: [PONE-D-21-12204] - [EMID: c79ffcd3e85c6421]

Response to the Editor’s comments

1) Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf andhttps://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2) In your Methods section, please provide additional information about the participant recruitment method and the demographic details of your participants. Please ensure you have provided sufficient details to replicate the analyses such as: a) the recruitment date range (month and year), b) a description of any inclusion/exclusion criteria that were applied to participant recruitment, c) a description of how participants were recruited.

Ans: Inclusion and exclusion criteria, methodology, demographic details have been mentioned in the text (Ref line-128-133, 136-143).

Among the 101 patients with ulcero-constrictive disease, 69 cases were diagnosed as CD (65.2%, Male), and 32 cases were diagnosed as ITB (62.5%, Male). Consecutive treatment naïve adult (age >18 yrs) patients who have not received any immunomodulator or antitubercular therapy were included in this study. Patients previously treated with steroids; diagnosed with other autoimmune diseases, history of malignant tumor or complications were excluded.

3) Please ensure you have discussed the scientific rationale for the selection of the specific SNPs analysed in your genotyping analysis.

Ans: The rationale behind selection of the specific SNPs has been expanded in the “Discussion” section of the manuscript (Line No 301-310).

“However, there is very less report about comparative assessment for the prevalence of various pathobionts in patients with CD and ITB. With an aim to explore the impact of chronic gastrointestinal inflammation in propelling a disease-associated bloom of major gut pathobionts, we have investigated the prevalence of Yersinia enterocolitica, Listeria monocytogenes, Campylobacter jejuni, and adherent-invasive Escherichia coli in two (CD and ITB) chronic intestinal inflammatory disease models of diagnostic dilemma. Simultaneously, the study also examined the prevalence of major risk SNP genotype/allele in the genes implicated for microbial sensing and handling such as IRGM (rs10065172, rs13361189 and rs4958847), ATG16L1 (rs2241880), and TNFRSF1A (rs4149570)”.

4) Please provide a sample size and power calculation in the Methods, or discuss the reasons for not performing one before study initiation.

Ans: The present study is an extension to our earlier paper published by our group in 2016 (Khan IA, Pilli S, A S, Rampal R, Chauhan SK, Tiwari V, Mouli VP, Kedia S, Nayak B, Das P, Makharia GK, Ahuja V. Prevalence and Association of Mycobacterium avium subspecies paratuberculosis with Disease Course in Patients with Ulcero-Constrictive Ileocolonic Disease. PLoS One. 2016 Mar 28; 11(3):e0152063. doi: 10.1371/journal.pone.0152063. PMID: 27019109; PMCID: PMC4809507). Our earlier study was analysed for the prevalence of Mycobacterium avian subspecies paratuberculosis (MAP) in patients with Crohn’s disease and intestinal tuberculosis. In this present study, we have utilizedsame samples for the prevalence of other four pathobionts and genetic association with risk SNP genotypes/ alleles of the genes implicated for microbial sensing and handling. Constraints for sample size and power calculation occur due to the exploratory nature of this study to carry out with the available samples.

5) PLOS ONE now requires that authors provide the original uncropped and unadjusted images underlying all blot or gel results reported in a submission’s figures or Supporting Information files. This policy and the journal’s other requirements for blot/gel reporting and figure preparation are described in detail at https://journals.plos.org/plosone/s/figures#loc-blot-and-gel-reporting-requirements and https://journals.plos.org/plosone/s/figures#loc-preparing-figures-from-image-files. When you submit your revised manuscript, please ensure that your figures adhere fully to these guidelines and provide the original underlying images for all blot or gel data reported in your submission. See the following link for instructions on providing the original image data: https://journals.plos.org/plosone/s/figures#loc-original-images-for-blots-and-gels.

In your cover letter, please note whether your blot/gel image data are in Supporting Information or posted at a public data repository, provide the repository URL if relevant, and provide specific details as to which raw blot/gel images, if any, are not available. Email us at plosone@plos.org if you have any questions.

Ans: Provided

6) Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Ans: Captions for Supporting Information is included at the end of the manuscript.

Response to Reviewers’ Comments:

Reviewer #1: Overlapping clinical characteristics in Crohn’s Disease (CD) and intestinal tuberculosis (ITB) make the differential diagnosis a daunting task for clinicians, especially in case of patients from TB endemic countries like India. Differentiation between these two deadly diseases cannot be done with a standalone evaluation and requires a comprehensive approach which includes the sum of clinical, endoscopic, radiological, microbiological laboratory and culture studies for accurate diagnosis. Most of these approaches bear one or other limitations of low sensitivity and specificity. This dilemma results in misdiagnosis and delay in treatment contributing to increased morbidity and mortality. In this direction, study of both genetic and environmental risk factors may prove to be instrumental in deciphering newer biological markers for efficient diagnosis of CD and ITB. Dysbiosis in human gut microbiome and genetic susceptibility has been now associated with various diseases including CD. Thus, differential prevalence of pathobionts and host gene polymorphisms can serve as good biomarker to differentiate between CD and ITB.

The research article entitled “Differential prevalence of pathobionts and host gene polymorphisms in chronic inflammatory intestinal diseases: Crohn’s Disease and Intestinal Tuberculosis” (PONE-D-21-12204) by Khan et.al highlights the prevalence of pathobionts like adherent-invasive Escherichia coli (AIEC), Listeria monocytogenes, Campylobacter jejuni and Yersinia enterocolitica in Crohn’s Disease (CD) and Intestinal Tuberculosis (ITB). Consistent with previous studies, this study reveal a significantly enhanced prevalence of the risk alleles of IRGM (rs13361189, rs4958847 and rs10065172) and ATG16L1 (rs2241880) TNFRSF1A polymorphism (rs4149570) genes with CD.

The manuscript is well written and suitable statistical tools and analysis have been applied to determine the significance of the data. Although, the study is limited with lack of statistically significant data, possibly due to the small sample size as also admitted by authors in the manuscript, the findings from this study are very important especially the prevalence of specific SNPs in genes implicated in IBD, in a North Indian cohort. This study takes the field one step closer to a non-invasive and affordable diagnostic procedure for IBD and may prove to be useful for the follow up studies with a bigger sample size in Indian population. Such investigative studies should be pursued with further experiments involving the colonization of wild type, germ-free, and genetically modified mice with an individual bacterial species or with a combination of bacteria, in order to identify the exact causal bacterial strain or core microbiome and clarify the fate of the gut microbiota in IBD.

The manuscript may be considered for publication after addressing the following comments:

Reviewer 1

Comment1: In the introduction, authors mentioned Mycobacterium avium subspecies paratuberculosis (MAP) as one of the pathogen associated with CD. Also there is moderately high seroprevalence (23.4%) of MAP in human population of north India (SV Singh et.al., J Biol Sci 2014) so, adding MAP prevalence data in this study would have been useful and enhanced our understanding towards the association of MAP with CD in Indian population. Any specific reasons that authors didn’t included it in the study?

Response 1: We have included prevalence of MAP as reported by us in the introduction section. The section was modified as follows (Line 108-115)

Our recent study had shown significantly increased prevalence of MAP (23.2%, p=0.03) in biopsy samples from patients with CD as compare to non-IBD controls (Khan PLoS One. 2016). The prevalence of key enteropathogens and pathobionts namely adherent-invasive Escherichia coli (AIEC), L. monocytogenes, C. jejuni, and Y. enterocolitica in intestinal biopsy tissues of patients with CD, non-IBD controls and patients with ITB were not tested earlier. This study investigated the prevalence of these pathobionts and their association with single nucleotide genetic polymorphisms (SNPs) of IRGM, ATG16L1 and TNFRSF1A gene in CD and ITB patients as compare to non-IBD control.

Comment 2: Refer lines 120-122 “Diagnosis of CD and ITB ………. based on standard clinical, radiological, endoscopic and histological criteria” Authors have provided data only from Behaviour of disease (Montreal Classification), location of disease and site of biopsy (refer Table 1). The other clinical, radiological histological, microbiological data is missing. Considering the overlapping clinical characteristics and knowing that the exclusive features between CD and ITB are caseation necrosis on biopsy, positive smear for acid-fast bacillus (AFB) and/or AFB culture, and necrotic lymph node on cross-sectional imaging in ITB (Kedia et.al.,2019), including supportive data from histology of biopsy samples (if possible) and clinical symptoms ( representative images of endoscopy of selected patients among all the groups) will be helpful in validation of the accurate diagnosis of the patients in this study. Alternatively, multivariable double logistic regression analysis of endoscopic and clinical features should have been performed. (Pls refer to research article by Li et.al., 2011, DOI 10.1007/s10620-010-1231-4).

Response 2: We have considered other clinical, radiological histological, microbiological parameters for diagnosis of ITB and CD which are mentioned in the methodology section. However, intestinal biopsies were collected at baseline for diagnosis of CD/ITB from naïve cases who had not received any immunomodulator therapy or antitubercular therapy. Briefly, diagnostic criteria is mentioned in the methodology section as follows and the same has been mentioned in the text as follows (line-136- 143)

“The patients with ileocolonic transverse ulcers and/or strictures were diagnosed ITB with demonstration of caseating granulomas or acid fast bacilli or a positive culture on mucosal biopsies.The patients with presentation suggestive of ITB and concomitant active pulmonary tuberculosis was also included. The patients with diagnostic dilemma of ITB vs CD were given antitubercular therapy (ATT) trial for obtaining sustained response (clinical and mucosal healing). The patients achieved sustained clinical response at 6 months post-ATT were categorized as ITB and those do not respond to ATT but showed response to steroids or immunomodulators were categorized as CD.”.

Comment 3: Since sample storage conditions can affect the quantification of the target microbial markers especially fast growing E.coli, the storage conditions of the clinical samples (Biopsies) before the isolation of the nucleic acid should be described in the methods.

Response 3: We have included in the text Line 148-153. The text reads as follows:

Intestinal biopsies collected for detection of pathogenic bacteria were immediately snap frozen in liquid nitrogen and stored at - 80°C for isolation of genomic DNA later. Genomic DNA was isolated from intestinal biopsies (~15 mg) by commercial DNA extraction kit (DNeasy Blood & Tissue Kit, Qiagen, USA) using manufacturer’s protocol.

Comment 4: Referlines 183-184 (C. jejuni prevalence) & 186-188 (AIEC prevalence). Since the Prevalence data for these two strains is not statistically significant, affirmative statements should be avoided. Sentences can be rewritten for better clarity and avoiding any misunderstanding.

Response 4: As suggested, we have modified the sentence as follows (Line 207-209)

We have also detected adherent invasive Escherichia coli (AIEC) and C.jejuni in CD, ITB and control subjects but the prevalence of these two pathogen were not found significant among groups (Table 2).

Comment 5: Refer to lines 258-260, Pls provide suitable references of the previously published studies from the literature, if any, where the comparative assessment of the prevalence of various pathobionts, between CD and ITB was done. A comparison between their methods & findings with that in the present study will be useful.

Response 5: Corrections have been made in the “Discussions” section of the manuscript.

Line-301-302

Comment 6: Refer lines 269-270, The statement “The control and ITB groups were found to have low infection incidences” holds true only for L. monocytogenes and Y. enterocolitica as only these were significantly less prevalent in the control and ITB groups as compared to CD patients, whereas there was no significant difference in the incidence of the AIEC and C. jejuni between the groups. So sentence need to be modified accordingly.

Response 6: As suggested we have changed the sentence in the Discussion section. Line-311-314.

The prevalence of bacterial pathobiont was more in CD patients than ITB and control subjects. The control and ITB groups were found to have significant low infection incidences of L. monocytogenes and Y. enterocolitica as compared to CD patients whereas there was no significant difference in the incidences of the AIEC and C. jejuni among the groups.

Comment 7: Recent case study of an Asian female patient (Korean) with Crohn's Disease reported her case to be initially misdiagnosed as Intestinal Tuberculosis due to active pulmonary tuberculosis (Park et.al., Korean J Gastroenterol, 2021). Given that India is TB endemic country, it may be worthy to include the active pulmonary tuberculosis in the clinical history of the patient to avoid such misdiagnosis specifically in studies involving comparison of CD Vs. ITB. Also, since the baseline features of gut microbiota after or during anti-TB treatment among ITB patients may differ, it may be worth to mention the ATT treatment in recent past of subjects included in this study. The inclusion and exclusion criteria (especially history of tuberculosis, previous/existing anti-tubercular drug therapy at the time of specimen collection) for various groups (CD, ITB and Controls) under this study have not been defined and should be included in the manuscript.

Response 7: Details have been added to the relevant paragraph in the Methods section.

Ref line-136-143

The patients with ileocolonic transverse ulcers and/or strictures were diagnosed ITB with demonstration of caseating granulomas or acid fast bacilli or a positive culture on mucosal biopsies.The patients with presentation suggestive of intestinal TB and concomitant active pulmonary tuberculosis were also included. The patients with diagnostic dilemma of ITB vs CD were given antitubercular therapy (ATT) trial for sustained response (clinical and mucosal healing) to ATT. The patients with sustained clinical response at 6 months post-ATT were categorized as intestinal TB and those do not respond to ATT but shows response to steroids or immunomodulators were categorized as Crohn’s disease patients.

Comment 8: As this study doesn’t demonstrate any association between the CD associated genetic polymorphisms and the prevalence of various pathobionts, its implications in pathogenesis of CD and ITB should have been discussed in correlation with clinical symptoms of patients under different groups in this study. This would have provided more insights to understand the role of these marker genes in etiology of CD and ITB.

Response 8: As suggested we reanalyze the data for association of CD-associated genetic polymorphism and the pathobiont prevalence with the clinical symptoms of patients using Goodman and Kruskals Tau test. The methodology is added in the line 190-193.The association data is mentioned in the result section along with Figure 2 a, b.

Association of pathobiont prevalence and CD-associated SNP with the clinical variables were performed by Goodman and Kruskals Tau test using the GKtaudataframe function of the GoodmanKruskal R package. This test determines the fraction of variability in one categorical variable that can be explained by the other categorical variable.

Figure 2a shows the results of the Goodman and Kruskal’s Tau association measure between the pathobiont prevalence (noted as ‘Positive’ and ‘Negative’ for each pathobiont type) and the clinical features of the patients with CD (age of disease onset, location and behaviour of the disease). The association measure between the pathobiont prevalence and clinical features indicate that location of disease explains variability in L.monocytogenes and AIEC prevalence in patients with CD (GK τ = 0.33).

Figure 2b shows the results of the Goodman and Kruskal’s Tau association measure between the risk allele prevalence (noted as ‘presence’ and ‘absence’ for risk allele for each gene type) and the clinical features of the patients with CD (age at disease diagnosis, age at onset of symptoms, location and behaviour of the disease). The location of disease showed weak association with the risk associated genotype in IRGM rs13361189 (GK �=0.17) and ATG16L1 rs2241880 (GK �=0.13), and a similar association was evident for the ‘age at the onset of symptoms’ with IRGM rs13361189 (GK �=0.11). No significant association was observed between risk loci and other clinical parameters.

Reviewer 2

Reviewer #2: The authors in this study aimed to look at differences in the prevalence of pathobionts like adherent-invasive Escherichia coli (AIEC), Listeria monocytogenes, Campylobacter jejuni and Yersinia enterocolitica in CD and ITB as well as their associations with host-associated genetic polymorphisms in genes majorly involved in pathways of microbial handling and immune responses. The study looks interesting; however, the authors should address the following concerns. The authors should also perform a general proof reading for typographical errors in the manuscript.

Comment1: The authors in this study aimed to look at the differences in the prevalence of various pathobionts as well as their associations with host-associated genetic polymorphisms in genes majorly involved in pathways of microbial handling and immune responses. Why were these 4 organisms chosen? It would have been better to perform a metagenome analysis to understand the profile of microorganisms in confirmed patients in a North Indian population.

Ans 1: The pathobionts analyzed in the study were selected on the basis of their previously reported positive association with the occurrence of Crohn’s Disease. The vicious cycle of gut pathobiont bloom and persistent inflammation has been highlighted to be an important aspect of inflammatory bowel disease. The metagenome analysis for understanding the community-level bacterial composition will be an important addition to the study and shall be attempted in the next phase. However, with the present aim to investigate the role of complex disease-specific gut micro-environment in supporting the pathobiont inhabitation in the gut, 16S-based, marker gene analysis shall undermine the prevalence of the pathobionts of interest, and would have added to the costs of sequencing and analysis.

Comment 2: Was the sample collection in the study prospective? It will be useful to include the details of the guidelines used for the classification of CD and ITB.

Ans 2: The samples analyzed in the present study were collected prospectively and the detail guidelines were included in the methodology section.

Comment 3: Genomic DNA was isolated from the intestinal biopsies. Kindly provide the details of the kit used.

Ans 3: Kit detail has been added. Line- 151-152. (DNeasy Blood & Tissue Kit, Qiagen, USA).

Comment 4: Why did the authors check the qPCR products on agarose gel electrophoresis? Usually a melt curve analysis is sufficient to prove specific amplification.

Ans 4: During real time PCR, melt curve analysis step was included to visualize nonspecific amplification. At end reaction, we have rechecked RT-PCR product for amplicon of desired size and agarose gel photographs were taken(Fig.1).

Comment 5: What was the advantage of using qPCR?

Ans 5: We have done qualitative real time PCR using SYBR green chemistry. We have chosen this method over conventional PCR for detection low copy number of target DNA. Allele Specific Real time PCR assay is well established rapid protocol for SNP genotyping of large number of samples at same time.

Comment 6: Was there any discrepancy between agarose gel profile and qPCR results?

Ans 6: The amplified RT-PCR product corresponded with the predicted amplicon size. However concentration of amplicon or band visualization in the agarose gel was inversely proportional to the Ct value of the real time PCR.

Comment 7: Also, were the primers designed in the study or used from published literature?

Ans 7: The allele-specific primers used in this study were designed using the Primer 3 software and bacterial primers used from published literature (reference added in the Supplementary Table 1).

Comment 8: I am unable to see the point of final extension in real time PCR experiments; also the sizes of amplicons are too high for use in a qPCR assay.

Ans 8: There was no final extension step in the real time PCR. So corrections have been made in the text (methodology section). In Taqman based qPCR assay, amplicon size is usually 100bp or small amplicon size. In our study, we have used SYBR Green chemistry based qualitative real time PCR where amplicon sizes varied from 110-457 bp. This size of amplicon can be amplified in RT-PCR without affecting Ct value.

Comment 9: How did the authors confirm specificity of the assay?

Ans 9: The pathogen specific primer specificity was initially checked by conventional PCR using bacterial culture. In real time PCR, melt curve analysis was done and further reaction end products were checked in agarose gel for desired amplicon size.

Comment 10: Were all samples performed with all assays simultaneously?

Ans 10: All genomic DNA isolated from intestinal biopsies was tested simultaneously using pathogen specific detection primer set. Allele specific SNP genotyping PCR were performed in two separate tube (biallelic) using template genomic DNA isolated from peripheral blood.

Comment 11: What is the positive control?

Ans 11: Positive controls for bacteria obtained from lab culture identified previously.

Comment 12: Fig. 1-No amplicon sizes are marked here. This figure should not be a part of the main manuscript.

Ans 12: Now amplicon sizes are marked in gel picture. We want to keep this figure in the manuscript because it represents pathogen detection and the amplicon of desired sizes when amplified by pathogen specific detection primer set.

Comment 13: How did the allele specific PCR work in qPCR format? Was absence of amplification taken as presence of the SNP or was it done by melt curve analysis? Can the authors elaborate on the same?

Ans 13: For allele specific PCR, we have designed one common reverse primer and two (wild/mutant type) forward primers with one nucleotide mismatch at the end following polymorphic site. Two tubes PCR method using wild or mutant type primer set were used for genotyping. There will be no amplification when there is mismatch allele. The polymorphisms were confirmed either as the presence or absence of amplification with or without Ct value.

Comment 14: The prevalence data for the organisms are they overlapping? I think a grouped analysis should also be done.

Ans 14: The grouped analysis was performed for the prevalent pathobionts.

Figure 2c shows the Venn diagram describing the overlap in the prevalence of the pathobionts. Highest overlap can be observed in the prevalence of AIEC and L.monocytogenes in patients with CD. Figure 2a Goodman and Kruskal’s Tau association measure across the prevalence of various pathobionts indicate the similar co-occurence relationship between the AIEC and L.monocytogenes prevalence.

Comments 15: The study reports interesting findings, but the authors should discuss about the link they expected to find between the presence of these specific pathobionts and genetic polymorphisms. The effect of these polymorphisms on the population should also be discussed. It will be interesting to understand the various polymorphisms associated with CD and ITB patients in this study. This will be evident in a grouped analysis i.e. in the 69 CD patients what all polymorphisms were significantly associated with CD, and not with ITB and controls.

Response 15: First of all, we studied association of genetic polymorphism with CD or ITB. We did not find any association of these polymorphisms with ITB but association was observed with CD. Then we have studied whether these polymorphisms impaired clearance or favor growth of pathobionts in the risk allele/genotype. Association risk genotypes with bacterial persistence in CD patients were studied. Due to small sample size we did not find any significant association. However, association of clinical variable with CD-associated genetic polymorphism and prevalent pathobiont were observed in Goodman and Kruskals Tau test and grouped analysis. Large cohort study will give deep insight to it. Small number size is the limitation of our study.

Comment 16: The differential prevalence of major gut pathobionts and genetic risk alleles in patients with CD and ITB can be explored as a screening tool.

Ans: Yes, we will plan to explore it as screening tool. Gut microbiome study is ongoing in our laboratory and broader knowledge will help us to design screening tool. There is genetic predisposition for CD and these SNPs can be explored as screening tool for risk assessment.

The following Corrections have been made in the revised version of the manuscript.

Minor

1. Line 82- remove ‘the’ before autophagy

Ans: Corrected

2. TNF a is ‘TNFα’

Ans: Corrected

3. Line 108, it is ITB not TB

Ans: Corrected

4. Line 180 pathogen specific detection ‘primers’

Ans: Corrected

5. Table 2 and 3, remove the term healthy control/HC. Replace with 'controls'

Ans: Corrected

6. All tables, please provide the abbreviations in the footnote

Ans: Corrected

7. Please provide reference for Montreal classification

Ans: Corrected

8. Line 278, it should be statistical significance

Ans: Corrected

9. All microorganisms’ names in italics in Fig. Legend

Ans: Corrected

Submitted filename: Response to reviewers.doc

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2 Aug 2021

Differential prevalence of pathobionts and host gene polymorphisms in chronic inflammatory intestinal diseases: Crohn’s Disease and Intestinal Tuberculosis

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9 Aug 2021

PONE-D-21-12204R1

Differential prevalence of pathobionts and host gene polymorphisms in chronic inflammatory intestinal diseases: Crohn’s Disease and Intestinal Tuberculosis

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