Research into the host-associated microbiome is at an inflection point. Decades of research have revealed that host-associated microbiomes are intimately linked to host health and touch all aspects of host physiology (Gomaa, 2020; Cox et al., 2022). The first phase of microbiome research was focused on descriptive studies that characterized differences in the microbiome by host species, body site, and disease phenotypes (Turnbaugh et al., 2007; Lloyd-Price et al., 2017; Wang and Jia, 2016). Clinical case:control metagenome-wide association studies that found differences in microbiome composition between healthy and disease cohorts gave rise to the ambiguous term ‘dysbiosis’ and much of the literature is focused on ‘balancing’ the gut microbiota (Tang and Hazen, 2024). In the second, and current, phase of research, studies have moved towards gaining more mechanistic insight into specific microbe-host interactions that influence host physiology and disease (Tang and Hazen, 2024; de Vos and de Vos, 2012; de Vos et al., 2022; Zimmermann et al., 2021). However, it is increasingly being recognized that, given the wide variability in microbiome composition, host genetics, and lifestyles, understanding mechanisms and causal relationships between gut bacteria and host physiological responses is not enough for the development of effective bacteriotherapies that target the gut microbiota (Vujkovic-Cvijin et al., 2020; Walter et al., 2020). In fact, many probiotic clinical trials, with known mechanistic links between the probiotics and host physiology, have exhibited wide variability in results (Barkhidarian et al., 2021; Rogers and Mousa, 2012; Ceccherini et al., 2022; Batagello et al., 2018). It is currently unclear what sources of variability may drive the response to targeted bacteriotherapies. Some variables that have been proposed include heterogeneity in microbiome composition, alcohol consumption, and bowel movement quality (Vujkovic-Cvijin et al., 2020).

Complex system theory is one means to potentially constrain hypotheses and bridge the gap between mechanistic studies and effective bacteriotherapies (Foster et al., 2008; Layeghifard et al., 2017). Studies of the host-associated microbiome often casually acknowledge that these systems are complex and, while, there has been progress made in developing mathematical approaches to complex modeling of host-microbiome relationships (Foster et al., 2008; Layeghifard et al., 2017; Ridenhour et al., 2017; Kumar et al., 2019), few studies have bridged the gap between experimental biology and complex system theory.

Mammals and their gut microbiome, are often considered as a holobiont (Richardson, 2017), defined as a discrete unit that exhibits collective action and evolves as a unit. Using evolutionary theory approaches, it is hypothesized that the form and function of the microbial part of the holobiont is driven primarily by host-microbe influences, but which require microbe-microbe interactions to help manage the enormous burden of microbiome constraint, which is termed an ‘ecosystem on a leash’ (Foster et al., 2017). Under this hypothesis, microbe-host interactions, in which host-associated microbes evolve to produce metabolic by-products for the sole benefit of the host, are necessarily rare. Essential features of complex systems include functional redundancy (Foster et al., 2017; Palla et al., 2005) and cooperation through chains of direct interactions (Foster et al., 2017; Palla et al., 2005; Lambiotte et al., 2019), distinct functional nodes that process and transfer resources (Palla et al., 2005; Lambiotte et al., 2019), fractality (Palla et al., 2005; Lambiotte et al., 2019), and adaptability through specific homeostatic feedback mechanisms to maintain relatively consistent internal conditions given a dynamic external environment (Foster et al., 2017; Cohen et al., 2022). While considerable progress has been made in understanding the nature of complex systems, there is a lack of consensus on basic terminology and broadly applicable analytical or experimental models (Torres et al., 2021). This limitation is due in part because complex systems necessarily transect many divergent entities in nature and is thus studied in diverse scientific disciplines (Torres et al., 2021). Thus, while some essential features of complexity have been elucidated within specific real-world systems, such as the conversion of polysaccharides to butyrate by multiple species in the gut (Roediger, 1980), or feedback loops in blood pressure regulation (Javorka et al., 2017), experimental evidence of multiple features of complexity within a single system is sparse, especially in microbe-host systems.

To overcome the limitations of applying complex systems theory to the microbe-host holobiont, new models and experimental frameworks are needed that balance complexity with tractability. Antinutrients, molecules produced in plants to deter herbivory, disrupt homeostasis by targeting the function of the microbiome, host, or both (Kolodziejczyk et al., 2019), and provide an effective focal point to study complexity. Oxalate is an antinutrient present in many plant-based foods, which typically provides the majority of oxalate in circulation (Crivelli et al., 2020), but is also produced as a terminal metabolite in the liver (Holmes et al., 2001). While some host genetic mutations increase endogenous production of oxalate (Cochat and Rumsby, 2013), mammals do not produce enzymes capable of degrading oxalate (Hodgkinson, 1977). However, multiple oxalate-degrading bacteria exist in the gut, which degrades oxalate through one of a handful of simple metabolic pathways involving one or two genes (Allison et al., 1985; Miller and Dearing, 2013; Liu et al., 2021a), which isolates this function to the gut microbiota. As such, oxalate degradation as a function, exhibits a moderate amount of complexity compared to other gut microbiota functions, such as the production of trimethylamine N-oxide (TMAO), that requires host input (Tang et al., 2015). Beyond the moderate complexity, it is known that oxalate-degrading bacteria are susceptible to antibiotics and that antibiotic use decreases oxalate degradation (Nazzal et al., 2021; Miller et al., 2019b; Kharlamb et al., 2011; Lange et al., 2012; Mittal et al., 2005). Elevated levels of oxalate induce oxidative stress, activate the inflammasome, and disrupt epithelial barrier function through tight junction proteins (Mulay et al., 2013; Peerapen and Thongboonkerd, 2011; Peerapen and Thongboonkerd, 2021). This molecule has been linked to diseases including kidney stones and chronic kidney disease (Holmes et al., 2016; Nazzal et al., 2016; Waikar et al., 2019), breast cancer (Castellaro et al., 2015), and cardiometabolic disorders such as atherosclerosis (Liu et al., 2021b), obesity (Efe et al., 2019), and diabetes (Efe et al., 2019). Oxalobacter formigenes, which uses oxalate as a sole carbon and energy source (Allison et al., 1985), is a well-studied oxalate-degrading species of bacteria. The negative, at times lethal, effects of oxalate, along with the identification and mechanisms of oxalate-degrading bacteria in the gut, have been worked out for several decades (Allison et al., 1985; James and Butcher, 1972; Anantharam et al., 1989; Baetz and Allison, 1989; Baetz and Allison, 1990), pre-dating the current microbiome era.

Despite this knowledge, clinical intervention trials involving Oxalobacter formigenes, which is perhaps the most effective oxalate-degrading species known, have successfully resulted in a significant reduction in urine oxalate levels in only 43% of studies (Batagello et al., 2018; Ariceta et al., 2023). Oxalate-degrading lactic acid bacteria have been successful in 37.5% of studies (Batagello et al., 2018). Both treatments led to a wide variability in patient responses. Given these data and the well worked out mechanisms of oxalate metabolism, it is clear that having an understanding of the mechanistic links between the gut microbiota and host physiology, alone, is demonstrably not sufficient to develop effective bacteriotherapies. Therefore, even though the history of oxalate-microbe-host interactions is much greater than most other microbe-host systems, oxalate degradation represents an accurate reflection of the challenges faced by microbiome research and is a prime candidate for complex system modelling to understand the critical variables that determine responsiveness to bacteriotherapies.

The objectives of the current study were to evaluate oxalate-microbe-host interactions, within the framework of complex systems. We used multi-omic approaches utilizing multiple independent in vivo and in vitro models to understand the critical variables that influence the gut microbiota’s maintenance of oxalate homeostasis and its impact on the host. We targeted several potential oxalate-microbe-host interfaces that include the gut microbiota itself, which is one of the first lines of defense against antinutrients (Dearing and Weinstein, 2022; Kohl et al., 2014b), intestinal epithelium, which is an important barrier between microbe and host (Foster et al., 2017), the liver, which is important for the biotransformation of dietary or microbial metabolites (Grant, 1991), along with the kidneys and vasculature, where calcium oxalate can potentially form calcified deposits or induce inflammation (Liu et al., 2021b; Robertson and Peacock, 1980; Nishizawa et al., 2023; Fishbein et al., 2008). Collectively, data indicate that multiple oxalate-microbe-host interfaces are influenced significantly by gut microbiota composition and that harboring diverse microorganisms capable of degrading oxalate can limit the impact of O. formigenes as a probiotic. Based on results from this study, we propose a phased approach for the development of bacteriotherapies whereby clinical case:control studies determine whether or not a clinical phenotype is associated with the microbiome and the microbial taxa/functions associated with specific phenotypes (Phase I).

Hypothesized taxa and/or microbial functions should be mechanistically resolved through in vitro and germ-free animal studies (Phase II). Finally, mechanistic insights should be applied to a complex systems theoretical framework to identify those variables that most impact the potential success of bacteriotherapies (Phase III). Such a phased approach would have broad implications for patient and probiotic selection in the development of targeted bacteriotherapies.

While research in the last two decades have made clear that the gut microbiota is intimately tied to all facets of host health, translating those insights into actionable biotherapies has been difficult due to the inherent complexity and heterogeneity present in the microbe-host system. The study of complex systems holds enormous potential to constrain hypotheses and offer insight into effective bacteriotherapy development (Lambiotte et al., 2019).

However, the field is limited by inconsistent terminologies, concepts, and tractable experimental frameworks (Torres et al., 2021.) The objective of the current study was to examine oxalate-microbe-host interactions within a complex systems framework. Collectively, the results of the study offer quantifiable and generalizable metrics of diet-microbe-host systems that can be used to guide more effective patient and probiotic selection criteria for clinical trials involving bacteriotherapies that target the gut microbiota. Specifically, with complex system modeling, we found through multiple in vivo and in vitro experimental systems, that exogenous oxalate exhibits systemic effects on multiple organs, including the gut microbiota, and oxalate homeostasis is specifically mitigated by diverse oxalate-degrading bacteria rather than a single bacterial species or factors such as host species, or microbe-microbe cooperation (Figure 7).

Figure 7

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Using our complex modeling approach, our findings establish that oxalate’s effects on both the host and the gut microbiota are primarily mediated by the metabolic redundancy of oxalate metabolism in the gut microbiota, rather than a single species or other factors. Specifically, experiments in Figure 1 demonstrated that host responses to oxalate were dependent on the composition of the gut microbiota. Expanding on this, experiments in Figure 2 revealed that oxalate’s impact on microbial community structure was similarly reliant on the whole microbiota, and direct multi-omic data integration confirmed that these microbial changes subsequently influenced host responses to oxalate. Once the gut microbiota composition was deemed to be critical, experiments in Figure 3 examined the specific oxalate impacts on the gut microbiota, using N. albigula and its native microbiota as the model for high oxalate tolerance. This analysis identified multiple genes associated with oxalate degradation, as well as acetogenic, methanogenic, and sulfate-reducing pathways. To assess the functional redundancy of these pathways, Figure 4 and Supplementary files 2-5 quantified the extent to which multiple microbial species contribute to these pathways. The high degree of redundancy observed suggested that oxalate degradation is maintained through diverse oxalate-degrading bacteria rather than a single keystone species. To explore the environmental factors influencing oxalate metabolism, we employed a culturomic approach to characterize microbial responses under controlled conditions and to evaluate oxalate degradation within our defined, hypothesized microbial communities (based on Figure 3 data and past oxalate diet trials). Finally, Figure 6 validated our metagenomic, metabolomic, and culturomic findings through targeted microbial transplants in mice, specifically demonstrating that the diversity of oxalate-degrading microbes were transferable and influenced host oxalate homeostasis more than a single species. The overall complex modeling approach was designed to systematically identify the most critical factors in the oxalate-microbiota-host relationship. Each experiment was informed by prior results, allowing for a stepwise refinement of hypotheses and experimental design. This integrative framework underscores the importance of metabolic redundancy in modulating oxalate metabolism and its downstream effects on host physiology.

Microbial oxalate metabolism, which has been researched for several decades, pre-dating the current microbiome era, is an ideal focal point to understand pertinent variables that impact probiotic success. Here, we found that heterogeneity in microbiome composition, independent of host genetics, significantly impacted multiple diet-microbe-host interfaces that included hepatic activity (Figures 1 and 2), gut microbiota metabolism (Figures 2 and 3), renal mineralization (Figure 6E and F; Figure 6—figure supplement 3E and F), intestinal inflammation (Figure 6H,I), and cardiac fibrosis (Figure 6—figure supplement 1H1). Importantly, we found that oxalate degradation was highly redundant among diverse species in the gut of N. albigula, which consumes a high oxalate diet in the wild (Figure 4). Consistent with past studies, administering O. formigenes led to a significant reduction in both stool and urine oxalate (Figure 6B and C). However, when co-administered with other oxalate-degrading microorganisms, there was no additive effect. Oxalate-based results were similar, but to a lower degree, in the metabolic cohort (Figure 6—figure supplement 3B and C). In taxonomic cohort studies, we did not see significant differences in the change in urine formate, based on the microbial transplant group (Figure 6D), in contrast to metabolic cohort studies (Figure 6—figure supplement 3D).

Collectively, data offer strong support for the hypothesis that metabolic redundancy among diverse taxa, is the primary driver of oxalate homeostasis, rather than metabolic cooperation in which the by-products of oxalate degradation are used in downstream pathways such as acetogenesis, methanogenesis, and sulfate reduction. However, data on the metabolic cooperation hypothesis were inconclusive and there are multiple known microbe and host sources of formate, which may confound results in these studies (Asanuma et al., 1999; Hughes et al., 2017; Pietzke et al., 2020; Ternes et al., 2022).

Modeling the microbe-host system through an evolutionary perspective, it has previously been suggested that while host control over the microbiome is the primary driver of the form and function of the gut microbiota, given the enormous biomass and diversity of the gut microbiota, microbe-microbe competition is also required to constrain the microbiota in mammalian microbe-host systems (Foster et al., 2017). In the current (Figures 2B and 5B) and previous studies (Allison et al., 1985; Miller and Dearing, 2013; Turroni et al., 2010; Turroni et al., 2007), we and others have found that oxalate can differentially exhibit positive or negative effects on microbial growth and metabolism dependent on the species and environment present. These data provide two alternative ecological pressures to degrade oxalate. The first is to use oxalate as a carbon and energy source for growth, as is the case with O. formigenes (Allison et al., 1985). The second is to degrade oxalate to remove it as a toxin, as is the case with some Lactobacilli and Bifidobacteria (Turroni et al., 2010; Turroni et al., 2007). The data showing oxalate degradation as a metabolically redundant function among multiple diverse microorganisms that maintain oxalate homeostasis (Figures 4 and 6, Figure 6—figure supplement 3), suggest that the amount of oxalate consumed or produced by the liver is too great for a single, slow-growing oxalate-degrading specialist can handle alone, in contrast to prevailing hypotheses (Daniel et al., 2021), and in support of the ecosystem on a leash hypothesis. Importantly, the near-universal presence of formate metabolism genes suggests that formate may be an even greater source of ecological pressure (Figure 3—figure supplement 2, Figure 3—figure supplement 5). In fact, multiple studies have shown that formate is toxic both to many bacterial species and to mammalian hosts, which supports this hypothesis (Hughes et al., 2017; Pietzke et al., 2020; Ternes et al., 2022; Kirkpatrick et al., 2001; Voskuhl et al., 2022). Collectively, data from the current and previous studies on the effect of oxalate exposure on the gut microbiota (Miller et al., 2017a; Miller et al., 2016b) support the hypothesis that the gut microbiota serves as an adaptive organ (Scott et al., 2013; Doré and Blottière, 2015; Britton and Young, 2014) in which specific, metabolically redundant microbes respond to and eliminate dietary components, for the benefit of themselves, but which can residually protect or harm host health depending on the dietary molecules and gut microbiota composition (Tang and Hazen, 2024; Scott et al., 2013; Doré and Blottière, 2015; Britton and Young, 2014).

Oxalate degradation, as a focal point in diet-microbe-host interactions, is a special case in which the effects of oxalate on host health and the mechanisms of microbial oxalate degradation have largely been worked out for decades (Allison et al., 1985; James and Butcher, 1972; Anantharam et al., 1989; Baetz and Allison, 1989; Baetz and Allison, 1990). Furthermore, oxalate degradation exhibits moderate complexity, both in that it is isolated to the gut microbiota and is performed through a handful of simple metabolic pathways (Cochat and Rumsby, 2013; Hodgkinson, 1977; Allison et al., 1985; Miller and Dearing, 2013; Liu et al., 2021a). Despite this knowledge, clinical trials designed to reduce systemic oxalate using either O. formigenes or other oxalate-degrading bacteria have exhibited a wide variety of patient and trial responses (Batagello et al., 2018). However, knowledge about other diet-microbe-host links are largely in their infancy. Many of the other known features exhibit much greater complexity than oxalate degradation. For instance, while the production of short-chain fatty acids are widely viewed as beneficial for both the gut microbiota and host health (Deleu et al., 2021; Kelly et al., 2015; Portincasa et al., 2022; Shimizu et al., 2019; Tian et al., 2018; Vieira et al., 2017), many short-chain fatty acids are known and are produced by a wide variety of species (Fusco et al., 2023; Mann et al., 2024). Other important diet-microbe-host links, such as the production of secondary bile acids or TMAO (Tang and Hazen, 2024; Tang et al., 2015; Chiang, 2009; Collins et al., 2023; Gérard, 2013), also involve host hepatic activity, increasing the complexity further. Given the complexity of diet-microbe-host interactions and the fact that understanding the mechanisms through which the gut microbiota modifies the diet to influence host health is demonstrably insufficient for the development of effective bacteriotherapies (Barkhidarian et al., 2021; Rogers and Mousa, 2012; Ceccherini et al., 2022; Batagello et al., 2018), we propose a three-phase, pre-clinical experimental workflow for the development of targeted bacteriotherapies. In the first phase of research, case:control metagenome-wide studies and research into other factors that modify the microbiome, such as prior antibiotic exposure, can identify disease phenotypes that are influenced by the microbiome, along with the microbial taxa and genes responsible for this association. In the second phase of research, hypothesized taxa and/or metabolic functions can be explored through in vitro and germ-free animal studies, using appropriate models to determine the mechanistic links that drive disease phenotypes through diet-microbe-host interactions. Finally, in phase three, these mechanistic links can be applied to a complex system theoretical framework, as done in the current study, to identify those variables most pertinent to successfully influence specific phenotypes through targeted changes to the gut microbiota. Such a phased research structure will provide for much more effective probiotic and patient selection criteria prior to clinical trials.

There were some important limitations to the current study. First, while we did conduct one study with N. albigula, most animal studies here sought to eliminate host genetics from the equation, as a means of simplification. Host genetics are another layer of complexity that were not examined here and may have an impact on oxalate homeostasis beyond the gut microbiota. Second, in animal studies with the refined communities of microorganisms, the taxonomic and metabolic cohorts studies were conducted separately and some batch effects are apparent, such as with the change in creatinine values or urine output. Finally, it is apparent from our data and the literature that there are multiple host and microbial sources of formate. As such, data pertaining to formate utilization by our transplant communities are inconclusive.

In conclusion, using a complex systems theoretical framework, we examined the oxalate-microbe-host interactions of multiple oxalate-microbe-host interfaces and found multiple microbiome-dependent effects of oxalate. The negative effects of oxalate were mitigated by metabolically redundant oxalate-degrading bacteria, more so than by metabolic cooperation or a single oxalate-degrading species. Critically, we found that while O. formigenes can lower urine oxalate when placed on a background of a poor oxalate-degrading community, as current theory predicts, this effect is lost when co-administered with other oxalate-degrading bacteria. Collectively, data help to resolve why the gut microbiota of the white-throated woodrat, N. albigula, effectively responds to and degrades even very high dietary oxalate levels (Miller et al., 2017a; Miller et al., 2017b; Miller et al., 2016a) and offer a clear pathway for more effective patient and probiotic selection for future clinical trials to reduce urine oxalate. More importantly, the conceptual and experimental framework developed in this study, based on complex systems theory, paves the way for a phased approach to microbiome research in which clinical microbiome insights (Phase I) drive mechanistic insights between the gut microbiota and host physiology (Phase II), that can be applied to a complex system model to constrain hypotheses and identify the most pertinent variables that drive microbe-host interactions that influence host physiology and health (Phase III). Such an approach will allow for much more efficacious probiotics and patient selection criteria in clinical intervention trials.

Sequence reads from the animal study are available at the Sequence Read Archive under Accession numbers for gut microbiota genomes: PRJNA833303, Shotgun metagenomics: PRJNA839366, Liver transcriptomics: PRJNA1018952, Gut microbiota data: 16S PRJNA1018559. Source data and code is provided on GitHub (copy archived at Miller, 2025).

1. 1. Cleveland Clinic

   (2022) NCBI BioProject

   ID PRJNA833303. Gut microbiota of wild rodents (Neotoma albigula) on a no or high oxalate diet.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA833303/
2. 1. Cleveland Clinic

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3. 1. Cleveland Clinic

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   https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA1018559

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Author details

1. Sromona D Mukherjee

   Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic, Cleveland, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1958-0220

2. Carlos Batagello

   Division of Urology, Hospital das Clínicas, University of Sao Paulo Medical School, Sao Paulo, Brazil

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Ava Adler

   Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, United States

   Contribution

   Data curation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Jose Agudelo

   Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic, Cleveland, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Anna Zampini

   Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Writing – review and editing

   Competing interests

   No competing interests declared

6. Mangesh Suryavanshi

   Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic, Cleveland, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

7. Andrew Nguyen

   M Health Fairview Southdale Hospital, Edina, United States

   Contribution

   Conceptualization, Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. Terry Orr

   Department of Biology, New Mexico State University, Las Cruces, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Denise Dearing

   School of Biological Sciences, University of Utah, Salt Lake City, United States

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

10. Manoj Monga

    Department of Urology, University of California San Diego, San Diego, United States

    Contribution

    Conceptualization, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

11. Aaron W Miller

    1. Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic, Cleveland, United States
    2. Department of Urology, Glickman Urological and Kidney Institute, Cleveland Clinic, Cleveland, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

    For correspondence

    millera25@ccf.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-8342-1449

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