Comparing two DNA sequencing methods for gut bacteria in colorectal cancer, advanced gut lesions, and healthy people

The research cohort was recruited among individuals who participated in the COLSCREEN study [13]. Participants were men and women aged 50–69, invited to the ongoing population-based CRC screening program conducted during 2016–2020 by the Catalan Institute of Oncology in L’Hospitalet del Llobregat, Barcelona, Spain. Some clinically detected CRC cases were also included to enrich this group, because cancer incidence is a rare event in screening programs. One week prior to the colonoscopy preparation, participants were instructed to store a fecal sample at home at a temperature of -20 °C. On the day of the colonoscopy, participants delivered the stored sample which was preserved at -80 °C. The samples from all participants, including those from clinically detected CRC cases, were collected using the same standardized protocol, stored under identical conditions, and sequenced together in the same batch. Individuals were categorized based on the criteria commonly employed in CRC screening programs for risk assessment [30]. For this study, a subset of 156 cases belonging to the following three categories was selected: no-lesions/controls (n = 51), HRL (n = 54), and CRC cases (n = 51). Each stool sample was processed and sequenced with both shotgun and 16S techniques. The research protocol was approved by the ethics committee of Bellvitge University Hospital under the reference PR084/16.

The fecal DNA was extracted using the NucleoSpin Soil Kit (Macherey-Nagel, Duren, Germany) following the manufacturer’s instructions for shotgun analysis [13] and through Dneasy PowerLyzer Powersoil kit (Qiagen, ref. QIA12855↗) for 16S [31]. The details for shotgun whole genome metagenomic sequencing and 16S rRNA amplicon sequencing are detailed in Obón-Santacana et al., 2022 [13] and Khannous-Lleiffe et al., 2022 [32] respectively.

For 16S, we applied our in-house bioinformatic pipeline to increase the proportion of amplicon sequence variants classified to the species level, in contrast to the conventional strategy [23]. The 16S data was preprocessed as previously described [32]. In brief, the 16S rRNA gene hypervariable V3-V4 region amplicon data were processed and analyzed using DADA2 v1.22.0 [33]. Low-quality reads were filtered and trimmed based on the observed quality profiles using the filterAndTrim function, truncating forward and reverse reads below 290 and 230, respectively, and considering a value of 2 as the maximum expected error. Furthermore, the first 10 nucleotides of each read were removed. We combined identical sequencing reads into unique sequences, made a sample inference from the matrix of estimated learning errors, and merged paired reads. For the sample inference step, the argument of the pool was defined as True. Chimeras and contaminants are often rare but spread across samples, making them more effectively identified when the samples are pooled (pool = T). Chimeric sequences were removed by using the removeBimeraDenovo function and taxonomy was assigned utilizing the SILVA 16S rRNA database (v138.1). Subsequently, to increase the percentage of Amplicon Sequence Variants (ASV) classified up to species level, from the ASV sequences obtained through DADA2, an additional taxonomic classification was performed using custom BLASTN database constructed from the SILVA database version stated above and performing an extra taxonomical classification based on k-mers (Kraken2 and Bracken2) using the NCBI RefSeq Targeted Loci Project database. Only 2.2% of ASVs could not be found in the reference database and were excluded from further analyses. For approximately one-fifth of the lineages we found more than one candidate species. These ASVs are named after all potential candidates, separated by “/” and alphabetically arranged (e.g., Blautia obeum/wexlerae).

Concerning shotgun, raw data in fastq format were processed to filter out human sequence reads (using human genome GRCh38) through Bowtie2 (v2.3.4) with options –very-sensitive-local and -k 1. A fastq file was then generated from reads that did not align (carrying SAM flag 12) using Samtools (v1.8). Then, once human sequences were removed, quality procedures and filters were applied. In detail, sequences were deduplicated using clumpify from the BBTools suite (v38.26), followed by a quality trimming (PHRED > 20) on both ends, sequencing adapters removal, and exclusion of read pairs where one read had a length lower than 75 bases using BBDuk (BBTools suite). In addition, with fastqcr v0.1.2 R package, FASTQC (v0.11.7), and Multi-QC (v1.12), a detailed analysis and snapshot of the quality control process was tackled. After the cleansing process, the reads were classified using Kraken2 (v2.1.2), with a filtering threshold of 0.1, followed by Bayesian re-assignment at the species level using Bracken2 (v2.2), with the read length parameter set at 150. The database used for the taxonomy assignment corresponded to prokaryotic data from the UHGG database v2.0.1 [34]. We selected this database that is specific for human gut microbiome to reduce false assignments that we experienced in previous attempts with the default NCBI RefSeq database that Kraken2 uses. We excluded fungi, viruses, and protozoa and focused on bacteria and archaea for the shotgun and 16S comparison. Only 11.8% (± 2.04%) of reads per sample could not be mapped to the UHGG database and were excluded from the subsequent analyses.

Finally, we ensured an adequate matching of taxonomical lineages between both techniques because the DADA2 pipeline for 16S used SILVA v138.1 + NCBI RefSeq Target Loci, while for shotgun we had selected UHGG v2.0.1. A thorough search allowed identifying discrepancies, mostly due to outdated taxa names, which were corrected and homogenized according to the NCBI taxonomy (16th March 2023). We used R packages (myTAI v0.9.3 and taxonomizr v0.10.2) for this task.

The shotgun count matrix was normalized by genome length. Besides the original, species-level data, we also performed comparisons of the count matrices aggregated at genus and family taxonomic ranks. Some of the analyses (taxonomic overlap between shotgun and 16S, alpha diversity) were performed using the unfiltered abundance dataset, but the rest were performed after filtering all the species that were not present in at least 5% of the samples with 0.1% abundance or higher. To compare shotgun vs. 16S regarding sparsity, we computed the percentage of zeros by sample before and after filtering. In general, we favored a compositional treatment of the abundance matrices, as recommended for microbiome data [35]. Since metagenomic data are sparse, i.e., the taxa count matrix contains many zeros, zero-value replacement was performed using the square root Bayesian-Multiplicative method implemented in the R package zCompositions (v1.4.0–1). For the beta-diversity analysis, PERMANOVA, and ML models, we used the centered log ratio (clr)-transformation of this filtered and zero-replaced abundance matrix.

During this step, we noticed that, in 16S data, some candidate species appeared in more than one ASV (e.g., Eubacterium callanderi/limosum, Eubacterium limosum/maltosivorans). To avoid ambiguity in the microbial signature, we aggregated these taxa (and then applied the aforementioned data filtering, zero-replacement and clr-transformation) before training the 16S ML models.

Statistical analyses were performed using R v4.2.2. The global overlap between shotgun and 16S within a given taxonomy rank (i.e. taxa detected by both methods) was visualized with Venn diagrams. We excluded from this comparison the unnamed fraction of taxa from both methods that was only identified by accession codes in the reference database (for instance, in shotgun, those species that start with MGYG). Then, we studied the correlation (Spearman) of the average abundance of taxa shared by shotgun and 16S. To this effect, we replaced the zero values and computed the closed geometric means (centre of compositional variable [36]) of the species, genus and families datasets separately for 16S and shotgun. For this analysis, we considered only taxa that were present in both sequencing methods. Then, we computed the geometric mean of the relative abundance of the taxa found by both sequencing method across samples and we ordered them in decreasing order to select the top 50 most prevalent taxa for shotgun and 16S. These were represented in heatmaps (heatmap.2 function of v3.1.3 of package gplots). In addition to these analyses over “averaged” data, as the 156 stool samples were sequenced through both 16S and shotgun methods, we compared the taxonomic overlap and abundance profiles per sample. Moreover, taxon abundance agreement was assessed by the unweighted Cohen’s Kappa coefficient [37] after defining new binary variables for each taxon: 0 for those samples with less or equal counts than the median of that taxon and 1 to those with counts greater than the median. Furthermore, for all 156 samples, the top 15 most abundant species, genera and families detected by each of the two sequencing methods were represented in bar plots (comp_barplot function of v0.10.8 of MicroViz package). The order of the samples was the same in both sequencing methods, according to the Bray-Curtis dissimilarity distance [38] of 16S samples. To better identify the common shotgun-16S taxa, the same color legend is used in shotgun and 16 S barplot. We highlighted with “*” the species, genera and families with a prevalence > 90% (i.e. the core microbiota [39]).

Alpha diversity of the samples was computed from the raw data tables using two measures: Shannon (v2.6-4 package vegan) and Chao 1 Diversity Indexes (v0.4.0 package fossil). In order to allow a proper Shannon Index comparison [40], we adjusted the differences regarding the number of reads across the samples rarefying to the minimum number of reads in each method: the shotgun data at the value of 2,813,748 and 16S at 6,619. The Rarefaction Efficiency Indexes [41] were 0.94 and 0.92 respectively. As Chao 1 takes into account the unobserved species, it was calculated directly from raw abundance data [42]. Alpha diversity measures were represented with boxplots to facilitate the comparison of their distributions between the two sequencing methods. Additionally, scatterplots were used to visually illustrate the association between alpha diversity values obtained from shotgun and 16S sequencing for each sample. The strength of the association is measured by Spearman’s correlation coefficient [43] and represented by a regression line. Furthermore, the alpha diversity comparison between sequencing methods was statistically tested with the Wilcoxon Rank Sum Test.

We selected the Aitchison distance as beta-diversity measure due to its compatibility with compositional data analysis (Gloor et al., 2017), computed as the Euclidean distance over the clr-transformed data. Then, a principal coordinates analysis (PcoA) of this distance matrix was used to visualize the relationships between the 156 samples. Additionally, we conducted an Analysis of Similarities (ANOSIM) using the vegan v2.6-4 R library to quantify the similarities, and a PERMANOVA using the same library to test for differences (adjusted p-value < 0.05) among the three diagnostic groups (controls, HRL, and CRC). A post-hoc analysis of PERMANOVA was performed through the function pairwise.perm.manova (RVAideMemoire R package) and the multiple test correction of Benjamini and Hochberg was applied. Next, we computed the correlation between the 16S and shotguns’ PcoA projections with Procrustes r (library vegan) and Co-inertia RV coefficient (library ade4 v1.7-22) and used a permutation test for significance. To visually represent the relation between the 6 PCoAs, we projected the matrix K of pairwise RV coefficients onto a principal component analysis (PCA). First, we computed the singular value decomposition of K, which equals to K=𝐔𝚲²𝐔^T, as K is a squared, symmetric, and positive semidefinite matrix. Here, 𝚲² is the diagonal matrix that contains the singular values of K, and U is an orthogonal matrix that contains the eigenvectors in the columns. Then, the PCA projections were obtained as 𝐔𝚲.

We used the clr-transformed abundance data to train ML algorithms predicting the diagnosis label: controls/HRL/CRC. We opted for linear SVM (kernlab package v0.9-31) and the nonlinear RF (randomForest package v4.7-1.1) methods. Our main objective was to compare if the shotgun and 16S data were able to fit similar classification models. To do so, we studied four items:

The training/test splitting was done via stratified random sampling. The test set for all models consisted of the same 30 individuals (N controls = 10, N HRL = 10, N CRC = 10), while the remaining 126 individuals (N controls = 41, N HRL = 44, N CRC = 41) were used for training. Hyperparameter optimization was done via 5 x cross-validation in the training set. For SVM, we optimized the cost using candidate values {0.1,1,10,100}. For RF, we performed a grid search over the number of trees (500, 1000, or 2000) and minimum node size (2 or 3). As SVM performs only binary classification, we used a one-versus-one strategy (i.e. we trained three models: Control vs. HRL, Control vs. CRC and HRL vs. CRC) to give a multi-class prediction. The whole setup was performed in parallel for the species, genus and family datasets.

The median (range) sequencing reads of the 16S samples was 26,438 (ranging from 6,619 to 80,095) and 5,698,549 reads (from 2,813,748 to 397,298,642) for shotgun. Shotgun sequencing detected 4,512 different species belonging to 1,049 different genera and 214 families. Instead, 16S detected 525 species belonging to 239 genera and 80 families. We verified that rarefaction curves reached a plateau in terms of identified species, genera, and families (Additional Figure S1). Next, we computed the overlap between the taxa detected by 16S and shotgun. As expected, we found a great overlap in higher taxonomic ranks, where the 16S taxa is a subset of the shotgun taxa (Fig. 1). This changes in the family, genus, and species ranks. Overall, there were 15 phyla, 23 classes, 37 orders, 68 families, 179 genera, and 272 species that appear in both 16S and shotgun datasets (Fig. 1; Table 1). 6 phyla, 10 classes, 34 orders, 93 families, 434 genera and 666 species were present in shotgun samples but not in the 16S ones. Conversely, 5 families, 51 genera, and 203 species were present only in 16S sequenced samples but not in the shotgun ones. When analyzing sample by sample, the percentage of shared taxa was consistently lower than that observed in the overall comparison across all taxonomy levels (Table 1). In all scenarios, the lower the taxonomic rank, the lower the shotgun-16S agreement.

We found a positive correlation between the 16S and shotgun average counts of shared taxa within the Species, Genus, and Family taxonomic ranks (Fig. 2: Spearman correlation coefficient ranged from 0.50 to 0.53, p-values < 2e-16). We observed that the abundant species in 16S data also exhibited a high abundance in shotgun; however, the opposite was not always true: high-abundance shotgun species may appear in 16S in low frequencies. There were also some prevalent taxa according to one of the sequencing methodologies that were completely absent according to the other. Here we highlight some abundant taxa (> 64 counts per sample) in 16S that did not appear in the shotgun dataset: at the species level (Fig. 2a) Coprococcus comes, Roseburia inulinivorans, Neglectibacter timonensis, Ruminococcoides bili, Erysipelotrichaceae UCG-003 bacterium, Phocaeicola vulgatus, Massiliprevotella massiliensis, Alistipes inops and Succinivibrio dextrinosolvens; at the genus level (Fig. 2b) Neglectibacter, Ruminococcoides, Erysipelotrichaceae UCG-003, Massiliprevotella, Mesorhizobium, Labrys, Escherichia-Shigella and at the family level (Fig. 2c) Erysipelatoclostridiaceae, Xanthobacteraceae. When comparing abundance sample by sample, both the Spearman coefficient for counts and Cohen’s Kappa coefficient for binary variables revealed a substantial agreement between shotgun and 16S (Table 1). Specifically, the Spearman coefficient ranged from approximately 0.80 to 0.99 at higher taxonomy levels and from 0.38 to 0.72 at species level. Meanwhile, Cohen’s Kappa coefficient ranged from approximately 0.20–0.30 to 1 at higher taxonomy levels and from 0.07 to 0.62 at species level. To compute these coefficients, we exclusively used the 16S species that were unambiguous (i.e. without slashes).

Regarding the relative abundance of the shared 16S and shotgun taxa, we did not find major visual differences in most of the top 50 most prevalent species (Additional Figure S2a), genera (Additional Figure S2b) and families (Additional Figure S2c). Nevertheless, a few taxa showed differences between the sequencing methods. Some of them were Prevotella copri, Eubacterium coprostanoligenes, Blautia massiliensis, Methanobrevibacter smithii and Holdemanella biformis at the species level; Collinsella, Streptococcus, Alistipes, Ruminococcus, Bacteroides, and Blautia at genus level and Bacteroidaceae, Methanobacteriaceae, Streptococcaceae, and Erysipelotrichaceae at the family level, among others.

Finally, we compared the relative abundance of the top 15 most prevalent species between the two sequencing methods across the 156 samples (Fig. 3a and b). It can be observed that 16S samples were dominated by fewer microbial species than shotgun samples. The predominant species in both shotgun and 16S was Faecalibacterium preausnitzii. Other shared species in top 15 were: Ruminococcus bromii, Akkermansia muciniphila, Gemmiger formicilis, Bacteroides uniformis and Blautia wexlerae. Moreover, the top 3 genera were the same in both methods: Blautia, Ruminococcus and Faecalibacterium (Additional Figure S3a; Additional Figure S3b), though in a different order. The other genera in the top 15, like Phocaeicola, Bacteroides, Bifidobacterium, were shared by the two methods. Finally, Lachnospiraceae, Oscillospiraceae and Bacteroidaceae were in the top four families both in 16S and shotgun (Additional Figure S3c; Additional Figure S3d). The other shared families in the top 15 most prevalent were Bifidobacteriaceae, Prevotellaceae, Eubacteriales Order and Enterobacteriaceae.

Both Shannon and Chao 1 diversity indices showed statistically significant differences between the two sequencing methods at the species, genus and family level (Fig. 4; Additional Table S1). Samples sequenced using shotgun had a statistically significant higher diversity than the 16S ones, especially in richness, estimated by Chao1. However, for the Shannon index, differences were smaller as we ascended from the species to the family level. When we computed the Spearman correlation between shotgun and 16S’ alpha-diversities, we found a positive correlation for both diversity indexes at the three taxonomy ranks (Fig. 5). Spearman correlation index ranged from 0.16 to 0.42, statistically different from 0 in all cases (p-values < 1e-4 for Shannon index and < 0.02 for Chao1). Specifically for the Shannon index, there was a slight increase in correlation values with the higher the taxonomic rank. We identified the outliers in the regression lines in Fig. 5, which can be attributed to technical issues. The two 16S CRC samples with lower alpha-diversity than their shotgun counterparts had very few reads. In fact, according to the rarefaction curves, they were the two 16S samples with the less observed species. The outlier HRL shotgun sample had 50% of Escherichia coli resulting in a notably low diversity (in its paired sample in 16S, Escherichia coli/Shigella sonnei account for 25% of the total abundance). This particular sample had the minimum coverage and species richness in the shotgun analysis.

The 16S count matrix was much sparser than shotgun’s (Wilcoxon Rank Sum Tests p-value < 2.2e-16), in the three studied ranks (family, genus, and species) before and after filtering rare taxa (Fig. 6). When comparing diagnosis, we could observe that in the 16S data controls, HRL and CRC samples presented similar sparsity levels (Additional Table S2). In the shotgun species and genera datasets, HRL were slightly sparser than the other two groups. However, these differences were no longer significant after Bonferroni correction (α = 0.05/12).

From this point forwards we exclusively utilized the filtered datasets. After filtering, shotgun retained 466 species, 213 genera and 67 families, while 16S retained 191 species, 105 genera, and 44 families. Figure 7 shows the Aitchison distance PCoAs, representing the shotgun and 16S data for the three taxonomic ranks studied. There was a great overlap among the three diagnosis groups. In the shotgun PCoAs, HRL appeared as an intermediate group between controls and CRC cases. Instead, in the 16S PCoAs, HRL samples clustered aside from both controls and cases; this is especially striking in the Family clr-PCoA. Nevertheless, these were minor differences observed visually in the graph. It is important to note that the PCoA plot depicts only the first two principal components, and thus does not capture all the variability in the data. Additionally, we found extremely similar R statistic values when comparing the similarity of samples within groups respect to between groups for each method. At the species level, the ANOSIM R values were 0.08 for 16S and 0.07 for shotgun. At the genus level, the R values were 0.08 for 16S and 0.09 for shotgun. At the family level, the R values were 0.10 for both 16S and shotgun. Values larger than zero indicate differences among groups, which were confirmed by a PERMANOVA and its posthoc analysis with adjusted p-value < 0.05, irrespective of the sequencing technique and taxonomic rank (Additional Table S3).

Subsequently, Co-Inertia Analysis and Procrustes analysis were used to assess and compare the correlation between the Shotgun and 16S PCoAs. In all cases the permutation test gave p-values = 0.001, statistically different from 0. The co-inertia RV coefficient had the following values: Species = 0.52, Genus = 0.43 and Family = 0.35. Procrustes r had the following values: Species = 0.77, Genus = 0.64 and Family = 0.44. That is, both analyses of the PCoA projections showed a decreasing similarity pattern: the higher the rank, the lower the similarity. Finally, the relations between the 6 datasets (shotgun vs. 16S across 3 taxonomic ranks) were visually summarized using a PCA (Additional Figure S4). It is clearly evident that the primary difference among datasets is the sequencing technique (first PC: shotgun vs. 16S) followed by the aggregation of taxa from species to genus to family (second PC).

Finally, an assessment was conducted to determine the similarity between prediction models trained either on shotgun or 16S abundance data. Table 2, shows the accuracies for both training (i.e. the fitting) and test sets of the RF and SVM models (shotgun and 16S across 3 taxonomic ranks). Test accuracies were clearly lower than those of the training sets’, indicating a certain degree of overfitting, a common drawback when dealing with small training sets. 16S appeared to be more affected by overfitting than shotgun. Overall, SVM gave better results in shotgun and RF in 16S data, although it was difficult to make a conclusive statement as the accuracy 95% CIs were very wide due to the small size of the test. In fact, only two models achieved accuracies above the random model threshold and thus could be presumed to have some degree of predictive power: SVM shotgun Species = 56.7 (95%CI: 38.9, 74.4), and SVM shotgun Family = 53.3% (95%CI: 35.5, 71.2).

Next, the agreement between shotgun and 16S models built from data of the same taxonomic rank was assessed. Cohen’s kappa and its 95% CI was also computed, and both results are presented in Table 3. Overall, the agreement was lower in the RF models, which can be attributed to the random nature of the RF algorithm. The fitted models presented a fair-moderate agreement (∼ 0.37) over the training data. However, agreement in the test was much lower, which is consistent with the overfitting we observed in Table 2. Likewise, wide 95%CIs were observed and the only Cohen’s kappa not including zero belonged to the SVM models over species data: 0.33 (95%CI: 0.05, 0.60).

The SVM classification models are binary by nature, and so the SVM model presented earlier was an ensemble of the three two-group models (control vs. HRL, control vs. CRC and HRL vs. CRC). In Additional Table, the agreement results are presented separately for these three classifiers. It can be observed that the aforementioned high Cohen’s Kappa was driven by a substantial concordance between shotgun and 16S control vs. CRC models: Cohen’s kappa = 0.47 (0.14, 0.80). Instead, in higher taxonomic ranks, control vs. HRL and HRL vs. CRC are the most concordant. Following our exploration of the SVM models, Additional Tableshows the concordance between models with regard to their Support Vectors (SV), i.e. the training patients each model considers relevant to discriminate between the classes. In the three taxonomic ranks, the control vs. CRC models were the more akin with regard to their SV, followed by the HRL vs. CRC models. S4 S5

Finally, we studied the similarity between “microbial signatures” (the taxa considered most important by a model to perform the prediction). The complete list of shotgun and 16S taxa, ranked by importance, were compared with the Kendall rank correlation coefficient (Table 4) The strength of Kendall correlation between shotgun and 16S was fair-moderate (around 0.22–0.31) and did not show great differences across taxonomic rank or ML method (RF, SVM). The top-50 species for the shotgun and 16S SVM and RF models are presented in Fig. 8, while the equivalent plot for Genus and Family models can be viewed in Additional Figures S5 and S6. In the Species models, we can find several taxa that have been previously related to the ACS (Parvimonas micra, Bacteroides fragilis, Streptococcus thermophilus or Fusobacterium nucleatum), and their lineages were also present in the genus and family signatures. Some of these CRC-related species (e.g., Parvimonas micra) were common to shotgun and 16S models. For clarity, Fig. 8 highlights all taxa shared by the shotgun and 16S, while Table 4 details the percentage of agreement between signatures. In RF models, there was at least 22% of coincidence between the top-50 taxa, while in SVM there was at least 18% (as a fraction of the taxa is unnamed in 16S and/or shotgun databases, real agreement may be greater). If we compare these species’ signatures counting the genera they share, both RF and SVM showed an agreement of 36%. Thus, we can consider that the “true” agreement is bounded by these two limits. Similar agreements are by the genus and family signatures. Additional Figure S7 provides the signature of the SVM binary models. Again, the highest coincidence was found in the control vs. CRC model, with agreement ranging from 22 to 28%.

A primary limitation of our study is the relatively modest sample size. Though the sample size probably is enough for our main aim, the comparison between 16S and shotgun diversity, and composition, it is insufficient for the comparison of CRC and HRL prediction models. A larger sample size would have favored narrower CIs and prevent ML overfitting. Another important challenge was the fraction of ambiguous (in 16S) or unnamed species (especially prevalent in shotgun), which we were forced to exclude to enable a taxonomic comparison between shotgun and 16S. In fact, we have proven with different examples that part of the differences between both sequencing methods are related to differences between the reference databases. Therefore, the issue of “closed annotation” of the taxonomical databases imposes a strong limitation when comparing shotgun with 16S. Recently, a new version of the Greengenes database appeared (Greengenes2) [53] that provides a single reference database for both sequencing technologies, which could aid in this up-to-date limitation of having independent databases. Currently, the Greengenes2 is available in Qiime2 pipeline for 16S and in the Woltka for shotgun. Surely this update will be available in the near future in other commonly used taxonomical classifiers (for example, DADA2 for 16S and Kraken2 for shotgun, which currently offers the older Greengenes database). On the other hand, despite involving the same participants, the 16S and shotgun data were obtained at different times. As explained in the Methodology, different DNA extraction kits were used, and sequencing was conducted by two different companies. This was required by the service contracting policies that govern our institution.

Each participant provided written informed consent at recruitment. The University Hospital of Bellvitge ethics committee approved the protocol of the study (PR084/16). The study was performed in accordance with the Declaration of Helsinki and the Spanish legislation regarding personal data privacy.

Not applicable.

The authors declare no competing interests.

16S rRNA raw data available in the European Nucleotide Archive (ENA) under the project PRJEB71787. Shotgun raw data available in the European Genome-Phenome Archive (EGA) under the study ID EGAS00001007025. 16S rRNA and shotgun abundance tables, metadata and their R script codes used for the present work are available in the Zenodo repository: https://zenodo.org/records/12545235↗.