Carrying asymptomatic gallstones is not associated with changes in intestinal microbiota composition and diversity but cholecystectomy with significant dysbiosis

Gallstone disease affects up to twenty percent of the population in western countries and is a significant contributor to morbidity and health care expenditure. Intestinal microbiota have variously been implicated as either contributing to gallstone formation or to be affected by cholecystectomy. We conducted a large-scale investigation on 404 gallstone carriers, 580 individuals post-cholecystectomy and 984 healthy controls with similar distributions of age, sex, body mass index, smoking habits, and food-frequency-score. All 1968 subjects were recruited from the population-based Study-of-Health-in-Pomerania (SHIP), which includes transabdominal gallbladder ultrasound. Fecal microbiota profiles were determined by 16S rRNA gene sequencing. No significant differences in microbiota composition were detected between gallstone carriers and controls. Individuals post-cholecystectomy exhibited reduced microbiota diversity, a decrease in the potentially beneficial genus Faecalibacterium and an increase in the opportunistic pathogen Escherichia/Shigella. The absence of an association between the gut microbiota and the presence of gallbladder stones suggests that there is no intestinal microbial risk profile increasing the likelihood of gallstone formation. Cholecystectomy, on the other hand, is associated with distinct microbiota changes that have previously been implicated in unfavorable health effects and may not only contribute to gastrointestinal infection but also to the increased colon cancer risk of cholecystectomized patients.


Results
We investigated the association of gallstone disease or cholecystectomy with intestinal microbiota profiles determined by 16S rRNA gene sequencing, comparing 580 individuals post-cholecystectomy (PCE , Table 1a) and 404 gallstone carriers (GC, Table 1b) to 580 and 404 control individuals without gallstone disease, respectively. All selected controls were matched to achieve similar distributions of the putative confounders age, sex, BMI, smoking, and diet (food frequency score, FFS) which are known to influence the gut microbiota composition [13][14][15] . None of the investigated participants received antibiotics at the time of sample collection.
Beta diversity analysis of individuals post-cholecystectomy and gallstone carriers as compared to controls. Beta diversity indices describe the variation between different samples, thereby estimating their similarity or dissimilarity, respectively. We calculated different beta diversity metrics, namely Jensen-Shannon divergence (JSD), Bray-Curtis dissimilarity (BC), Jaccard distance (JD), and Hellinger distance (HD). To investigate whether PCE or GC cases were associated with a shift in beta diversity compared to their respective control cohorts, we analyzed the correlation between these metrics and 'PCE' or 'GC' status. Figure 1a shows that PCE cases were associated with gut microbiota shifts mostly along principal coordinate (PCo) 3 and/or PCo4 of the different beta diversity indices. Figure 1b displays the shift of the centroids of PCE cases compared to controls. Further plots showing the individual sample distribution of PCE cases and controls score along PCo3 and PCo4 are given in Fig. S1. In contrast, GC cases did not exhibit any significant correlation with beta diversity as indicated in Fig. 1 and Fig. S2. These results were confirmed by permutational analysis of variance which indicated significant differences between the gut microbiota of PCE cases and controls based on JSD (p = 0.008), BC (p = 0.003), JD (p = 0.006), and HD (p = 0.004). GC cases, again, exhibited no significant differences compared to their controls in this analysis (Table S1). www.nature.com/scientificreports/ Taxon alterations at genus level comparing individuals post-cholecystectomy and gallstone carriers to controls. The analyses of beta diversity suggested specific alterations in the gut microbiome of PCE cases. To identify these taxa, we compared continuous taxon abundance values between PCE cases and controls of all genera that were present in at least ten percent of all samples identifying numerous taxon associations (Table S2). Figure 2a shows all taxa with significant abundance changes in PCE cases including a decrease in Faecalibacterium (− 14.0%, q = 0.004) and Haemophilus (− 22.7%, q = 0.027) as well as an increase of Clostridium XIVa (29.5%, q = 0.021), Escherichia/Shigella (36.7%, q = 0.004), Flavonifractor (19.1%, q = 0.027), and Mogibacterium (60.7%, q = 0.021) when compared to controls. The results of the beta diversity analysis implicated no large gut microbiota alterations in GC cases. To investigate, whether singular taxa express differences in their abundance between GC cases and controls, we performed a comparative analysis of continuous taxon abundance data as described above for PCE. After correction for multiple testing, no significant differences for any taxa were found (Table S3), confirming the negative global association results of the beta diversity analysis.
Analysis of taxon presence-absence profiles of individuals post-cholecystectomy and gallstone carriers as compared to controls. Furthermore, we analyzed whether PCE cases exhibited changes in the mere presence or absence of specific genera ( Fig. 2b and Table S4). This analysis focused again on all taxa that were present in at least ten percent of all samples. We found a positive association of PCE cases with the genera Escherichia/Shigella (q = 0.012) and Mogibacterium (q = 0.039). A negative association was identified with Haemophilus (q = 0.027). Again, if the same analysis was performed for GC cases, no significant differences were found compared to controls (Table S5).
Comparison of predicted metagenomic profiles of individuals post-cholecystectomy and gallstone carriers as compared to controls. Metagenomic microbial profiles were predicted 16 to evaluate functional differences of the gut microbiota. When comparing PCE cases and controls, we found a significant change in abundance of 59 predicted microbial pathways (Table S6), which belonged to the superclasses 'Deg-  (Table S7).
Alpha diversity analysis of individuals post-cholecystectomy and gallstone carriers as compared to controls. Alpha diversity metrics aim to describe the heterogeneity of microbial communities, the so-called 'within sample' diversity. To investigate the impact of gallstone disease on microbial diversity we calculated the alpha diversity indices 'Shannon diversity index' (H) and the 'Simpson diversity number' (N2). In PCE cases, median (1st-3rd quartile) scores for H and N2 were 4.4 (4.1-4.6), and 38.4 (26.2-53.0). Which were all lower than in controls with H 4.5 (4.2-4.7; p < 0.001) and N2 42.7 (29.7-56.3; p = 0.001). Comparing alpha diversity scores between GC cases and controls, however, did not exhibit any significant difference for the two alpha diversity metrics (Fig. 3).

Direct gut microbiota comparison between individuals post-cholecystectomy and gallstone carriers.
Our analyses revealed numerous alterations in the gut microbiota of PCE cases when compared to controls without gallstone disease. We then investigated whether these specific gut microbiota alterations were also detectable when comparing PCE to GC cases. To this end, we selected a subsample of 404 PCE cases achieving a similar distribution of the putatively confounding factors age, sex, BMI, smoking and FFS as in the group of 404 GC cases (Table S8). When comparing these phenotypically matched PCE with GC cases, we could confirm lower levels of Faecalibacterium (p = 0.042) and higher levels of Clostridium XIVa (p < 0.001), Escherichia/Shigella (p < 0.001), and Flavonifractor (p = 0.002) in PCE cases (Fig. 4a). No significant differences were found in case of Haemophilus and Mogibacterium. When evaluating presence-absence data, we could confirm an increased proportion of samples with Escherichia/Shigella (p = 0.001) in PCE samples when compared to GC (Fig. 4b). Again, no significant differences were found for Haemophilus and Mogibacterium.
The median (1st-3rd quartile) alpha diversity scores H and N2 in this subsample of PCE cases were 4.4

Discussion
Fecal microbiota profiles determined by 16S rRNA gene sequencing of individuals post-cholecystectomy (PCE), gallstone carriers (GC) and non-gallstone-disease controls were analyzed to characterize the gut microbiome changes associated with gallstone disease. We phenotypically matched the control cohorts to account for the known or putative confounding factors age 13 , sex, BMI, smoking, diet 14 , and sequencing batch and achieved a comparable distribution of parameters. In subjects with cholecystectomy we found numerous taxon abundance www.nature.com/scientificreports/ changes including an increase in abundance and presence of the genus Escherichia/Shigella. This confirms the observation of a smaller study which also identified an increased proportion of Escherichia coli in cholecystectomized individuals 8 . The Gram-negative facultative pathogenic bacterium Escherichia coli represents the most common isolate from either blood or bile in acute cholecystitis and/or cholangitis 17,18 . The detected overrepresentation of Escherichia/Shigella in the gut microbiome of PCE subjects may therefore increase the probability of developing such infections. Moreover, certain strains of Escherichia coli are known to induce DNA damage and increase gene mutation frequency. Increased levels of this taxon may therefore contribute to the known increase in risk for developing colorectal cancer in cholecystectomized individuals 8,10 . The predicted genetic microbial potential for LPS biosynthesis was also increased in PCE cases when compared to controls. LPS is a known promoting factor of tumorigenesis [19][20][21] and LPS levels have been described to be increased in individuals suffering from colorectal cancer 22 . Therefore, increased LPS biosynthesis by Gram-negative bacteria after cholecystectomy may also promote the risk for colorectal cancer in these individuals. Another characteristic of the gut microbiota composition after cholecystectomy was a decrease of Faecalibacterium. This bacterium is an important producer of short-chain fatty acids, the main energy source of colonocytes 23 . It also exhibits anti-inflammatory features, partially through the production of anti-inflammatory proteins 24 . A reduction in Faecalibacterium has previously  www.nature.com/scientificreports/ been reported in inflammatory bowel disease 25 . Its distinct reduction in cholecystectomized subjects may therefore represent a pro-inflammatory intestinal state. In contrast, another SCFA producer, Clostridium XIVa 26,27 showed an increase in abundance. The magnitude of this increase was, however, much lower compared to the reduction of Faecalibacterium and may therefore not compensate for the depletion of the latter. Analysis of the microbial alpha diversity scores revealed reduced levels after cholecystectomy in comparison to controls. In general, reduced alpha diversity scores have been associated with deteriorated health states such as obesity 28 , frailty 29 , or Crohn's disease 30 . In recurrent Clostridioides difficile associated diarrhea a reduced microbial diversity is believed to be the key feature for the persistence of the disease 31 . In the context of cholecystectomy, the reduced microbial diversity may result in an increased risk of microbiome perturbations and abdominal infections.
Based on a rodent model a connection between the development of gallstones and the gut microbiome has previously been proposed 11 . However, when we compared gallstone carriers with matched controls we found no significant difference in the gut microbiota composition-in contrast to the prominent changes found after cholecystectomy. The majority of gut microbiota changes identified after cholecystectomy in comparison to nongallstone disease controls could be replicated when we compared post-cholecystectomy subjects with gallstone carriers. This indicates that interindividual variation in the intestinal microbiome is not likely to be a contributing factor to the development of gallstones in humans. However, this does not rule out that variation in the biliary microbiome may contribute to gallstone formation 32 . On the other hand, cholecystectomy is directly and prominently associated with intestinal microbiota dysbiosis.
The question remains why cholecystectomy induces such prominent changes in gut microbiota composition and diversity. One possible explanation would be a change in the dynamics of bile flow towards the small intestine. The altered availability of bile in the gut (continuous versus pulsatile) could favor species with different requirements regarding bile components, or bile-dependent break-down products of fat, for their metabolism. Alternatively, the altered intestinal motility after cholecystectomy 33 could change the biophysical properties and fluid content in the colon, leading to more favorable conditions for certain taxa. Both explanations would be in line with the observation that the microbiome of the same patient changed in composition from the status before to that after cholecystectomy 7 . However, in that study no change in alpha diversity was detected after cholecystectomy, which is in contrast to our finding of a significantly reduced microbial diversity in PCE cases. An explanation for this difference could be small sample size of that study which included only 17 patients.
An alternative explanation for our findings would be that some of the, most likely adverse, changes, we identified in the gut microbiota of PCE subjects were already present before their respective cholecystectomy. In that case, the reduced gut microbial diversity as well as the increased abundance of Escherichia/Shigella could have contributed to turning an asymptomatic gallstone carrier into a symptomatic patient with infectious complications (such as cholecystitis or cholangitis), which ultimately prompted their cholecystectomy. A similar phenomenon has been reported in patients with non-Hodgkin lymphoma receiving chemotherapy, in whom a reduced microbial diversity prior to chemotherapy was associated with an increased rate of bloodstream infections during subsequent therapy 34 . Transmission of intestinal bacteria to the biliary tract may occur by ascending from the duodenum, via bloodstream or lymphatic vessels. Otherwise, although we excluded individuals under antibiotic treatment at the time of sample collection from the analysis, it is conceivable that a potential antibiotic treatment during the time of cholecystectomy may have caused lasting gut microbiota alterations in some of the PCE cases. Furthermore, the indication for cholecystectomy itself, which has not been recorded in this population-based study, may have affected the gut microbiota signatures.
The strength of this study includes its very large sample size and the thorough identification of individuals with gallstone disease or a status post-cholecystectomy by abdominal ultrasound. A potential drawback is the limitation that 16S rRNA gene sequencing does not allow us to identify the facultative pathogens on the species level.
This is currently the largest study investigating changes in fecal microbiota composition in subjects carrying asymptomatic gallstones or having undergone cholecystectomy for gallstone disease. While gallstone carriers showed no alterations in their gut microbiota compared to matched controls, we found individuals post-cholecystectomy to be characterized by a reduction in total microbial diversity and a depletion of the potentially beneficial genus Faecalibacterium, whereas the facultative pathogen Escherichia/Shigella was increased in abundance. While these significant changes were most likely caused by the altered flow of bile after cholecystectomy, we cannot rule out that they preexisted in affected subjects and contributed to the condition that ultimately required cholecystectomy. On the other hand, we found no evidence for the assumption that interindividual variation in the intestinal microbiota composition contributes to the risk of developing gallstones in the first place and, at the same time, carrying (asymptomatic) gallstones does not appear to induce changes in the gut microbiome. To what extent facultative pathogens with greater abundance after cholecystectomy contribute to post-cholecystectomy morbidity, specifically the increased colon cancer risk of cholecystectomized patients, can only be answered in large-scale long-term investigations. Whether or not the unfavorable microbiota composition after cholecystectomy can be restored to its original diversity by nutritional or microbiota-transfer techniques will have to be answered in randomized, controlled interventions. What we know at this point is that cholecystectomy comes at a cost, and with direct and unfavorable consequences for the intestinal microbiome. The results of the present study need to be considered in the discussion about a potential over-or under-use of cholecystectomy. They do not support the concept of performing cholecystectomy in asymptomatic gallstone carriers as this may result in microbiome perturbations with putatively adverse health effects for the human host. www.nature.com/scientificreports/ Methods Study participants. SHIP is a longitudinal population-based cohort study which aims to assess the incidence and prevalence of common risk factors and diseases 35 . It consists of the two independent cohorts SHIP (participants (n) = 4308, initial recruitment from 1997-2001 and recalled as SHIP-2 from 2008-2012) and SHIP-TREND (n = 4420, initial recruitment 2008-2012). Fecal samples for 16S rRNA gene sequencing were collected from SHIP-2 and SHIP-TREND participants. Abdominal ultrasound investigations were performed to evaluate the presence of gallstones or to confirm a status post-cholecystectomy. For the present work, a total of 6744 eligible datasets (SHIP-2, n = 2330; SHIP-TREND n = 4414) with diagnostic gallbladder ultrasound data were available, of which 148 participants were excluded due to indeterminate ultrasound gallbladder status. A further 159 individuals were excluded because of missing data regarding smoking, nutrition, or body mass index (BMI), which would have precluded phenotypic matching of cohorts. Another 89 datasets were removed due to the intake of antibiotics by the participants at the time of sample collection. The remaining datasets of 6348 individuals contained 687 subjects post-cholecystectomy and 491 cases with sonographically confirmed gallbladder stones. Of these, intestinal microbiota 16S rRNA gene sequencing data were informative for 580 post-cholecystectomy and 404 gallstone carrier subjects, which were included in the analysis. These cases were supplemented by an equal number of controls without gallstone disease with similar distributions of age, sex, BMI, smoking, diet, and sequencing batch. All participants provided written informed consent and the study was approved by the ethics committee of the University Medicine Greifswald (BB 39/08). All methods were carried out in accordance with the relevant guidelines and regulations (Declaration of Helsinki).
16S rRNA gene sequencing. 16S rRNA gene sequencing of fecal samples was performed as described previously in detail 36 37 (v.1.10) was used for amplicon-data processing which enables a single-nucleotide resolution of amplicons (amplicon sequence variants, ASVs). Data processing followed the authors' recommended procedure for large datasets (https:// benjj neb. github. io/ dada2/ bigda ta. html), adapted to the targeted V1-V2 amplicon. Briefly, on both reads, five bases were truncated from the 5′ end of the sequence. Forward and reverse reads were truncated to a length of 200 and 150 bp, respectively. A shorter resulting read length after truncation was possible if the sequence quality dropped below five. Read-pairs were discarded if they contained ambiguous bases, expected errors higher than two and when originating from PhiX spike-in. Error profiles were inferred using 1 million reads of the respective sequencing run, followed by dereplication, error correction and merging of forward and reverse reads. ASV abundance of tables of all samples were combined and chimeric amplicon sequences were identified and removed using the removeBimeraDenovo() function in consensus mode. Taxonomic annotation was assigned using a Bayesian classifier and the Ribosomal Database Project (RDP) training set version 16. Table S9 displays the read distribution after sequencing and several of the quality control steps. All data were rarefied to 10,000 reads per sample before computation of alpha or beta diversity or any between group comparisons at genus level. Prediction of metagenomic microbial functions was performed with the PICRUSt2 package 16 using the standardized workflow as described at https:// github. com/ picru st/ picru st2/ wiki/ Workfl ow with the ASVs from the DADA2 pipeline as input.
Abdominal ultrasound. Abdominal ultrasound examinations were conducted by trained personnel. For assessment of the gallbladder status a high resolution device (Vivid i, GE Healthcare) was used in B mode. Possible outcomes were "no gallstones", "gallstones present", "post-cholecystectomy", or "indeterminate".
Phenotypic data. BMI was calculated by dividing the body weight in kilogram by the square of the body height. For assessment of nutritional habits data from fifteen food categories were evaluated: meat (without sausage), sausage, fish, boiled potatoes, pasta, rice, raw vegetables, boiled vegetables, fruits, whole grain or black or crisp bread, oats and cornflakes, eggs, cake or cookies, sweets, and savory snacks. Frequency of food intake for each respective group was categorized as follows: "1" = daily or almost daily, "2" = several times per week, "3" = once a week, "4" = several times per month, "5" = once a month or less, "6" = never or almost never. Based on this data, a food frequency score (FFS) was calculated similar as described elsewhere 38,39 . In brief, the dietary patterns of the study subjects in each selected food group were classified according to the recommendations of the German Society of Nutrition to 0 (unfavorable), 1 (normal) or 2 (recommended) points (Table S10).
Data and statistical analysis. For  www.nature.com/scientificreports/ was determined as described elsewhere (http:// enter otype. embl. de/ enter otypes. html). The indices 'Bray-Curtis dissimilarity' (BC), 'Jaccard distance' (JD), and 'Hellinger distance' (HD) were computed using the R package 'vegan' (function 'vegdist' for BC and JD, function ' decostand' for HD). The 'vegan' function 'adonis' was used to perform permutational analysis of variance (1000 permutations). Principal coordinate analysis (PCoA) was conducted based on the different beta diversity indices using the 'vegan' function ' cmdscale' . For association of the traits PCE or GC with the first five PCos, point biserial correlations were computed ('stats' function ' cor. test'). The two-sided Mann-Whitney test ('stats' function 'wilcox.test') was applied for assessment of statistical significance in case of continuous phenotype, genus or predicted metagenomics data, whereas the two-sided Fisher's exact test ('stats' function 'fisher.test') was used for binary phenotype or genus data. When comparing genera or predicted metagenomic microbial pathways between PCE or GC cases and matched controls we only investigated those with a presence of at least ten percent in all samples. In case of genus or predicted metagenomics comparisons between PCE or GC cases and matched controls, p-values were adjusted for multiple testing by the method of Benjamini and Hochberg and thereafter called q-values. The same procedure was applied to p-values resulting from point biserial correlations between PCos and the traits PCE or GC. p-or q-values < 0.05 were considered significant.

Data availability
All microbiome and phenotype data were obtained from the Study-of-Health-in-Pomerania (SHIP/SHIP-TREND) data management unit and can be applied for online through a data access application form (https:// www. fvcm. med. uni-greif swald. de/ dd_ servi ce/ data_ use_ intro. php). www.nature.com/scientificreports/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.