Investigating the Anna Karenina principle of the breast microbiome

Abstract

The relationship between the microbiome and disease has long been a central focus of research in human microbiome. Inspired by Leo Tolstoy’s dictum, the Anna Karenina Principle (AKP) offers a framework for understanding the complex dynamics of microbial communities in response to perturbations, suggesting that dysbiotic individuals exhibit greater variability/heterogeneity in their microbiome compared to healthy counterparts. While some studies have proved the alignment of microbiome responses to disease with the AKP effect, it remains uncertain whether the human breast microbiome responds similarly to breast disease. This study used beta-diversity and similarity in Hill numbers, along with shared species analysis (SSA), to explore this issue. We observed that during mastitis, changes in both the taxa richness and composition in the breast milk microbiome align with the AKP effect, while alterations in abundant taxa exhibit an anti-AKP effect. The response of breast tissue microbiome to breast cancer differs from that of milk microbiome to mastitis. Breast cancer induce anti-AKP effects in taxa richness, and non-AKP effects in common taxa and taxa composition. Overall, our findings identified different responses to breast diseases across taxa abundance in the breast microbiome. Mastitis primarily involves increasing the heterogeneity of rare taxa in the breast milk microbiome, while breast cancer associates with decreased dispersion of rare taxa in the tissue microbiome.

Introduction

The “Anna Karenina principle”, originating from Leo Tolstoy’s literary masterpiece “Anna Karenina”, postulates that “all happy families resemble one another; each unhappy family is unhappy in its own way.” This principle is also applicable to describe a pattern observed within animal microbiomes in respond to stressors, including disease, external microbial invasion, and host internal environment disorder [1]. Specifically, perturbed animal microbiomes may exhibit increased heterogeneity compared to the healthy state, indicating distinctive alterations in microbial composition among individuals. This phenomenon, termed the “AKP effect”, suggests that alterations within dysbiotic microbiomes may be more stochastic and diverse. Several studies have provided evidence to support the presence of AKP effects in animal microbiome [2, 3]. According to AKP effect, it can be hypothesized that human microbiome related diseases (MADs) may correlate with microbiome instability, resulting in the rise of heterogeneity or stochasticity. For example, AKP effects are present in the oral microbiome with periodontitis [4], as well as in the gut microbiome influenced by immune-related diseases such as Type I diabetes, ulcerative colitis (UC), and HIV [1, 5, 6]. Changes in the microbiome of the Bacteroides 2 (Bact2) enterotype, commonly associated with dysbiosis, follow the AKP, as compared to non-dysbiotic enterotypes [7]. Ma (2020) utilized beta diversity in Hill numbers and stochastic analysis to test this hypothesis with 27 human MAD cases. The finding revealed that approximately 50% of cases exhibited AKP effects, 25% showed anti-AKP effects, and the other had no significant AKP or anti-AKP effects [8]. However, the AKP effect in the breast microbiome, especially breast tissue microbiome, is still unclear.

Traditionally, the breast was considered to be sterile. Nevertheless, increasing evidence suggests the presence of microbiome within the mammary gland, forming a unique ecosystem [9, 10]. Some microorganisms in the breast may originate from the host’s skin and the infant’s oral cavity, while another portion may enter from the host’s intestinal tract through the “gut-mammary axis” pathway [11,12,13,14]. The composition of the breast microbiome is likely influenced by various factors, including the physiological status, hormone levels, genetic factors, and lifestyle of the host. For instance, hormonal fluctuations during lactation and pregnancy may impact the dynamic changes in the mammary microbiome. The breast microbiome plays a crucial role in the health and disease of the mammary gland, with some microorganisms participating in immune regulation, nutrient metabolism, and antimicrobial functions [15].

The microbiome of breast tissue and breast milk constitutes essential components of the mammary microbiome. The microbes in breast milk serve as the primary seeds for the newborn’s gut microbiome, contributing to the establishment of the intestinal microbiome of infant. These milk microbes play crucial roles in the development of the infant’s immune system, defense against pathogenic infections, and promotion of digestive metabolism. Mastitis, commonly occurring during lactation, affect approximately 2-20% of women [16]. Mastitis may be associated with dysbiosis in the breast milk microbiome [17]. Healthy breast milk microbiota typically comprises abundant Lactobacillus and Bifidobacterium [9]. During mastitis episodes, there is an increase in pathogenic bacteria such as Escherichia coli, Klebsiella, and Staphylococcus aureus [18,19,20,21], with S. aureus and Streptococcus often serving as guide treatment for mastitis [22]. The breast microbiome is proposed to play a role in maintaining healthy breast tissue, potentially by stimulating resident immune cells and influencing metabolic activity [23]. The predominant phyla in breast tissue microbiome are Proteobacteria and Firmicutes [24, 25]. Some reports showed differences in bacterial abundance, diversity, and specific genera between breast tumor tissue and adjacent normal tissue, such as Methylobacterium and Sphingomonas, potentially associated with cancer development [22,23,24,25,26,27,28].

This study aimed to investigate the AKP of breast microbiome, specifically examining the AKP effect of mastitis on the breast milk microbiome and the AKP effect of breast cancer on the breast tissue microbiome. We used the beta-diversity and four similarity indices in Hill numbers, along with shared species analysis (SSA), to detect AKP and anti-AKP effects. SSA, conceived as a specialized form of beta-diversity, assesses heterogeneity in the composition of microbial communities between samples. According to AKP definition, the microbiome composition of dysbiotic individuals varies more than that of healthy individuals, implying that the microbiome of dysbiotic individuals is more heterogeneous and less similar than that of healthy individual. When comparing the microbiome of disease samples with that of healthy samples, if the beta-diversity (similarity indices) of the former is significantly higher (lower) than that of the latter, it indicates that the disease can induce the AKP effect. Conversely, if the beta-diversity (similarity indices) of the former is significantly lower (higher) than that of the latter, it indicates that the disease induce anti-AKP effect. If there is no significant difference between the two, there is no AKP effect.

Materials and methods

A brief description of the breast microbiome datasets

This study included two datasets, one for breast milk microbiome and the other for breast tissue microbiome (Table 1). The breast milk microbiome dataset consisted of 126 samples, including 44 samples from healthy controls, 44 samples from mothers during mastitis symptoms, and 38 samples from mothers after mastitis symptoms cessation. The DNA was extracted from breast milk, and V3-V4 regions of 16 S rRNA were sequenced using Illumina MiSeq platform [17]. The breast tissue microbiome dataset consists of two groups: normal tissues (47 samples) and tumor tissues (47 samples). The normal and tumor tissue samples were simultaneously obtained from the same female patients with primary invasive breast cancer during breast cancer surgery [29]. V1-V3 regions of 16 S rRNA were sequenced using Illumina MiSeq platform. More detailed information on these datasets is provided in Boix-Amorós et al. (2020) and Kim et al. (2021) [17, 29]. Sequencing data and assemblies are publicly available at NCBI’s GenBank and SRA databases under BioProject PRJEB34421 and PRJEB37724. 16 S rRNA sequencing data were classified to the species-level and genus-level using Kraken 2 v2.1.2 with default parameters (--confidence 0) against the reference sequence databases [30,31,32]. After taxonomic classification, the species abundance was estimated using Bracken v2.6 [33, 34].

Table 1 Brief information on the microbiome datasets of breast milk and breast tissue samples

Beta-diversity in hill numbers

In present study, microbiome diversity was quantified using Hill numbers, which provides a more general framework for measuring biodiversity [35, 36]. Within this framework, gamma diversity (^qD_γ) can be decomposed multiplicatively into independent alpha (^qD_α) and beta (^qD_β) diversity.

Assume that each group or treatment is a meta-community consisting of N sample microbial communities (microbiomes) and S taxa. Let y_ij is the abundance of the ith taxa in jth community, i = 1, 2, …, S, j = 1, 2, …, N. The alpha diversity (^qD_α) measure the within-community diversity and is defined for q ≠ 1 as:

$${}^q{D_{\alpha \:}} = {\left( {\sum\limits_{i = 1}^S {{p_i}^q} } \right)^{1/(1 - q)}}$$

(1)

where p_i is the relative abundance of the ith taxa, and q is the order number of diversity. The formula when q = 1is:

$${}^1{D_{\alpha \:}} = \mathop {{\text{lim}}}\limits_{q \to \:1} {}^q{D_{\alpha \:}} = exp\left( { - \sum\limits_{i = 1}^S {{p_i}{\text{log}}\left( {{p_i}} \right)} } \right)$$

(2)

The parameter q determines the sensitivity of the measure to the taxa abundances. Hill numbers at different q orders correspond to special ecological diversity indices. For example, ⁰D is equal to taxa richness, ¹D represents the exponential of the Shannon index, ²D represents the reciprocal of the Simpson index. The diversity in Hill numbers is adjusted for sensitivity to taxa abundance through the diversity order q. The larger the diversity order q, the more sensitive ^qD is to taxa with high abundance. When q = 0, species abundance is not considered, making the measure equivalent to taxa richness. At q = 1, all taxa are weighted equally by their frequency, reflecting the diversity of common taxa. At q = 2–3, greater weight is given to abundant taxa, thus reflecting the diversity of dominant taxa.

The gamma diversity (^qD_γ) measures the total diversity of the meta-community and is defined as:

$${}^q{D_{\gamma \:}} = {\left( {\sum\limits_{i = 1}^S {{{\left( {{{\mathop p\limits^ - }_i}} \right)}^q}} } \right)^{1/(1 - q)}}$$

(3)

in which $\:{\mathop p\limits^ - _i} = \left( {\sum\nolimits_{j = 1}^N {{y_{ij}}} } \right)/\left( {\sum\nolimits_{i = 1}^S {\sum\nolimits_{j = 1}^N {{y_{ij}}} } } \right)$.

The beta diversity measures the between-community diversity, which is the ratio of gamma to alpha:

$$\:{}_{\:}{}^{q}{D}_{\beta\:}={}_{\:}{}^{q}{D}_{\gamma\:}/{}_{\:}{}^{q}{D}_{\alpha\:}$$

(4)

Similarity indices from beta-diversity

We also estimated four similarity indices between communities within each treatment (meta-community), including C_q, U_q, S_q and V_q. These similarity indices can be obtained by the transformations of beta-diversity in Hill numbers (Eq. 4), and illuminate different aspects of similarity.

The similarity C_q quantifies the effective average proportion of shared taxa (overlap) per community, which takes the following form:

$${C_q} = \frac{{{{(1/\,{}^q{D_{\beta \:}})}^{q - 1}} - {{(1/N)}^{q - 1}}}}{{1 - {{(1 - N)}^{q - 1}}}}$$

(5)

The similarity U_q quantifies the effective average proportion of shared taxa (overlap) in the meta-community, which is defined as:

$$\:{U_q} = \frac{{{{(1{/^q}\,{D_{\beta \:}})}^{1 - q}} - {{(1/N)}^{1 - q}}}}{{1 - {{(1/N)}^{1 - q}}}}$$

(6)

The similarity S_q is homogeneity measure, which quantifies the proportion of meta-community diversity contained in the average community. That is :

$${}\:\:\:{S_q} = \frac{{1{/^q}\,{D_{\beta \:}} - 1/N\:}}{{1 - 1/N\:}}$$

(7)

The similarity V_q measures the relative taxa turnover rate per community, i.e.,

$$\:{V_q} = \frac{{N{ - ^q}\,{D_{\beta \:}}\:}}{{N - 1\:}}$$

(8)

Detecting AKP effects with beta-diversity and similarity indices in hill numbers

Beta diversity reflects the heterogeneity in community composition, while the similarity indices are the opposite. AKP effect of the animal microbiome is manifested by greater differences in the microbiome composition of dysbiotic individuals compared to healthy individuals, that is, the microbiome of the dysbiotic individuals is more heterogeneous than that of the healthy individuals, or less similar than that of the healthy individuals [1, 8]. In this study, the beta-diversity and four similarity indices in Hill numbers were used as metrics to determine AKP effect. We used Wilcoxon rank sum test to examine differences in beta-diversity or similarity indices between two treatments. A p-value of < 0.05 indicated significant difference in measures between two treatments.

Shared species analysis (SSA)

The decrease in the number of shared taxa between samples can also reveal rising heterogeneity in microbiome composition. Let SS be the set whose members are the number of taxa shared between each pair-wise samples in the treatment. The non-parametric Wilcoxon test was used to examine whether there was a significant difference in the values of the SS set between the two treatments. If the SS values of the dysbiotic treatment is significantly lower (higher) than that of the healthy treatment (p < 0.05 from Wilcoxon test), then the test reveals AKP (anti-AKP) effects; otherwise, dysbiosis has no effect on the microbiome.

Results

In this study, we investigated the association between mastitis and AKP effect on breast milk microbiome, as well as the association of breast cancer and AKP effect on breast tissue microbiome. The mastitis dataset comprised breast milk microbiomes from three groups: healthy controls (referred to as the healthy treatment), mothers during mastitis symptoms (referred to as the mastitis treatment), and mothers after mastitis symptoms had ceased (referred to as the recovered treatment). In the comparison between the mastitis treatment and the recovered treatment, the former represented the diseased state, while the latter served as the healthy control. The breast cancer dataset included microbiomes from normal tissues (referred to as NT, the healthy control) and tumor tissues (referred to as TT, the diseased treatment). All analyses were performed at both the species and genus levels.

Detecting AKP effects with beta-diversity in hill numbers

To assess AKP effects using beta-diversity in Hill numbers, we initially computed pairwise beta-diversity within each treatment for all samples. Subsequently, we examined the differences in beta-diversity values between each pair of treatments within each dataset. The mean and standard error of beta-diversity for each treatment are detailed in supplementary Table S1.

The association between mastitis and AKP effects in the breast milk microbiome is depicted in Fig. 1A (species level) and Supplementary Figure S1A (genus level). The results were consistent across both the species and genus levels. The beta-diversity of the mastitis treatment was significantly higher than that of the healthy treatment only at the diversity order q = 0, but was significantly lower at other diversity orders (q = 1–3). This suggests that mastitis exhibits AKP effects on taxa richness in the breast milk microbiome, while demonstrating anti-AKP effects on common or dominant taxa. Across all diversity orders (q = 0–3), the beta-diversity of the recovered treatment was significantly lower than that of the mastitis treatment, indicating a robust AKP effect. Furthermore, when compared to the healthy controls, the recovered treatment displayed significantly lower beta-diversity across all diversity orders (q = 0–3). The results of the difference test between each pair of treatments in the mastitis dataset were statistically significant, with a p-value of the Wilcoxon test < 0.001 (see Supplementary Table S2).

Fig. 1

AKP effects in breast milk and breast tissue microbiomes at the species level, detected using beta-diversity in Hill numbers. The boxplots illustrate the beta-diversity (at the diversity order q = 0–3) for each treatment of breast milk and breast tissue microbiome datasets. Treatments were compared using the Wilcoxon rank sum test, with significance denoted by asterisks or pound signs (*p < 0.05, **p < 0.001 for AKP effect; #p < 0.05, ##p < 0.001 for anti-AKP effect). Figure (A) represents the association between mastitis and AKP effect of breast milk microbiome, and (B) represents the association between breast cancer and AKP effect of breast tissue microbiome

The effects of breast cancer on the variability of breast tissue are illustrated in Fig. 1B (species level) and Supplementary Figure S1B (genus level). When comparing the species diversity of TT and NT, we observed that at diversity order q = 0, the former was significantly lower than the latter (p-value of the Wilcoxon test < 0.001), at q = 1, there was no significant difference between the two, while at q = 2–3, the former was significantly higher than the latter (p-values of the Wilcoxon test < 0.05; Supplementary Table S3). At the genus level, TT was significantly lower than NT at q = 0 (p < 0.001), with no significant differences at q = 1–3 (Supplementary Table S3). These results indicate that breast cancer induces strong anti-AKP effects in both species and genus richness, with no discernible effects on common species or genus. Breast cancer also triggers AKP effects in dominant (highly abundant) species, but no significant effects in dominant genera.

Detecting AKP effects with similarity indices in hill numbers

We employed four similarity indices in Hill numbers to assess AKP effect, yielding consistent test results from these four indices. Supplementary Table S4 presents the mean and standard error of similarities for each treatment. The response of the breast milk microbiome to mastitis is displayed in Fig. 2 (species level) and Supplementary Figure S2 (genus level), as well as the response of the breast tissue microbiome to breast cancer is shown in Fig. 3 (species level) and Supplementary Figure S3 (genus level). The difference test results for similarity indices are opposite to those for beta-diversity, indicating that AKP (or anti-AKP) patterns observed based on similarity indices align with those identified through beta-diversity. The detailed results of the difference test are expounded below.

Fig. 2

AKP effects in breast milk microbiome at the species level, detected using four similarity indices in Hill numbers. The boxplots display the similarity (at the diversity order q = 0–3) for each treatment. Treatments were compared using the Wilcoxon rank sum test, and significance is marked with asterisk or pound sign (*p < 0.05, **p < 0.001 for AKP effect; #p < 0.05, ##p < 0.001 for anti-AKP effect). Figure (A) represents the result of similarity C, (B) represents the result of similarity S, (C) represents the result of similarity U, and (D) represents the result of similarity V

Fig. 3

AKP effects in breast tissue microbiome at the species level, detected using four similarity indices in Hill numbers. The boxplots display the similarity (at the diversity order q = 0–3) for each treatment. Treatments were compared using the Wilcoxon rank sum test, and significance is marked with asterisk or pound sign (*p < 0.05, **p < 0.001 for AKP effect; #p < 0.05, ##p < 0.001 for anti-AKP effect). Figure (A) represents the result of similarity C, (B) represents the result of similarity S, (C) represents the result of similarity U, and (D) represents the result of similarity V

At diversity order q = 0, the similarities of mastitis treatment were significantly lower than those of the healthy and recovered treatment. At q = 1–3, the similarities of the mastitis treatment were significantly lower than those of the recovered treatment but higher than those of the healthy treatment. Across all diversity orders q = 0–3, the similarities of healthy treatment were significantly lower than that of recovered treatment. The difference test results between each pair of treatments in the mastitis dataset were significant, with a p-value of the Wilcoxon test < 0.001 (Supplementary Table S5). In comparison to NT, the intra-group similarities of TT were significantly higher at q = 0 (p-value of Wilcoxon test < 0.001), showed no significant difference at q = 1, and were significantly lower at q = 2–3 (p-values of the Wilcoxon test < 0.05; see Supplementary Table S6).

Detecting AKP effects with shared species analysis (SSA)

We used SSA to detect AKP effect at both species and genus levels (Fig. 4 & Supplementary Figure S4). Supplementary Table S7 lists the mean and standard error of the shared species numbers for each treatment. The number of shared species between samples in the mastitis treatment was significantly lower than that in the healthy and recovered treatment (p-values of the Wilcoxon test < 0.001). This suggests that changes in the composition of the breast milk microbiome during mastitis followed the AKP. The healthy treatment exhibited more shared species than the recovered treatment (p-value of the Wilcoxon test < 0.001). No significant difference was observed in the number of shared species between NT and TT, indicating that breast cancer showed a non-AKP effect on the species composition of the breast tissue microbiome (p-value of the Wilcoxon test > 0.05). The results at the genus level fully align with those at the species level.

Fig. 4

AKP effects in breast tissue microbiome at the species level, detected using shared species analysis. The bar graph displays the mean shared species numbers for each treatment. Treatments were compared using the Wilcoxon test, and significance is marked with asterisk or pound sign (*p < 0.05, **p < 0.001 for AKP effect; #p < 0.05, ##p < 0.001 for anti-AKP effect)

Discussion and conclusions

This study explored the AKP effect in the breast microbiome using beta-diversity, similarity, and SSA. Beta-diversity and similarity metrics in Hill numbers represent two sides of the same coin, offering consistent conclusions. SSA, incorporating taxonomy identity information, provides additional insights beyond diversity and similarity measures. Our analyses were conducted at both the species and genus levels, yielding similar trends and conclusions.

We observed distinct effects (AKP or anti-AKP) of mastitis on taxa with different abundance in the breast milk microbiome when compared to healthy controls. Specifically, mastitis induced an AKP effect on taxa richness (q = 0) and an anti-AKP effect on taxa with higher abundance (q > 0). This indicates differential impacts of mastitis on rare and abundant taxa in the breast milk microbiome, whereby it increases inter-individual variation (heterogeneity) in rare taxa while decreasing it in abundant ones. Moreover, the increase in variation of rare taxa may exceed the decrease in variation of abundant ones, resulting in an AKP effect on total taxa. The AKP effect observed in total taxa could be also associated with increased heterogeneity in microbiome composition found by SSA (Fig. 4). In summary, under mastitis, the breast milk microbiome exhibit increased dispersion in taxa richness and composition.

The anti-AKP pattern induced by mastitis in common or dominant taxa might be attributed to their extremely low bio-diversity of them in the breast milk. The decrease in alpha-diversity during mastitis, as observed in previous research [17], is confirmed using Hill numbers in this study (Tables S9). In healthy controls, the average of species richness is 55 (q = 0), the diversity of common species was 11.9 (q = 1), and the diversity of dominant species was less than 6.7 (q > 1). During mastitis, the diversity is further reduced significantly, with almost 30% fewer species richness and almost 50% fewer common and dominant species (Tables S10). In communities with very low species diversity, and the few species present have similar abundances, leading to higher similarity among these communities. This may elucidate why there is higher (lower) similarity (heterogeneity) among abundant microbiome of mastitis treatment. Compared to intestinal and skin microbiomes, the diversity of breast milk microbiome is relatively low, especially the abundant taxa. Limited diversity, along with fewer abundant taxa, may make the breast milk microbiome ecosystem relatively fragile and reduce the ability of system to buffer damage weak. During mastitis, the already low diversity is further reduced, potentially impairing the microbiome’s ability to maintain normal functions, which could hinder recovery or exacerbate disease symptoms.

After the cessation of mastitis symptoms, a significant decrease in inter-individual heterogeneity was observed in both rare and abundant taxa in the breast milk microbiome. If considered the recovered treatment as another healthy state, the AKP effect is present across all diversity orders (q = 0–3) and species compositions. However, compared to healthy controls, the recovered treatment exhibited higher similarity in microbial diversity but lower similarity in composition. In addition, the alpha diversity of common and dominant species was significantly reduced in the recovered treatment compared to the healthy controls. These results indicate that following the cessation of symptoms, the microbiome did not immediately revert to its pre-illness state.

Distinct effects (AKP, anti-AKP or non-AKP) of breast cancer on taxa with different abundance were also found in the breast tissue microbiome. Breast cancer induces an anti-AKP effect on total species of tissue microbiome (q = 0), non-AKP effect on common species (q = 1), and AKP effect on dominant species (q = 2–3). In other words, the decrease in beta-diversity of rare taxa exceeded the increase in heterogeneity of very abundant ones, resulting in an anti-AKP effect at the total level. Additionally, the effect of breast cancer on total genera was consistent with the anti-AKP, and not on abundant genera. It shows that rare taxa in the breast tissue microbiome are sensitive to breast cancer, whereas high-abundance genera within the community exhibit a greater resilience to cancer-associated disturbances. These differences between species- and genus-level analyses highlight the limitations of focusing solely on a single taxonomic level, as it may obscure potential microbial characteristics. Investigating the microbiome across multiple taxonomic levels provides deeper insights into the impacts of disease on microbial communities.

However, we did not observe any disturbance of the tumor on the composition of the tissue microbiome through SSA analysis (Fig. 4). Additionally, when comparing the alpha diversity between tumor tissue and normal tissue, we found no significant difference between them (Tables S9, S10). This suggests that beta-diversity is more sensitive than alpha diversity in identifying tissue microbiome dynamics associated with breast cancer.

In summary, through testing the AKP hypothesis, we identified distinct effects of breast diseases on rare and abundant taxa within the breast microbiome. Mastitis primarily involves increasing the inter-individual variation of rare taxa in the breast milk microbiome, while breast cancer primarily involves decreasing the inter-individual variation of rare taxa in the tissue microbiome. Moreover, AKP effect in mastitis is also influenced by taxa composition of breast milk microbiome. Overall, AKP offers an insight into observing the microbiome dynamics associated with breast diseases, and may serve as a marker or indicator of disease states, as alterations in microbial diversity and composition could reflect underlying pathophysiological processes.

The primary limitation of this study is the lack of additional high-quality, large-sample datasets to further support or validate our findings. The available breast microbiome datasets related to mastitis and breast cancer are generally limited in sample size, and many of them lack appropriate healthy controls for comparison. Another limitation is that our work focused specifically on bacterial communities in the breast microbiome, without addressing viral, fungal, or archaeal communities, which may also play significant roles in breast health and disease. Additionally, we employed 16 S-rRNA sequencing data rather than whole-genome sequencing (WGS), the latter of which could provide higher species-level resolution, as well as insights into gene abundance and functional annotations. Consequently, the taxonomy confidence scores for both species- and genus-level classifications were relatively low, ranging from 0.002 to 0.130 at the species level and from 0.002 to 0.251 at the genus level. Future studies expanding on these findings by incorporating larger datasets, exploring additional microbial communities, and leveraging WGS data for functional analysis, are warranted.