An ecological framework to understand the efficacy of fecal microbiota transplantation

Factors of faecal microbiota transplantation applied to cancer management

The homeostasis of the microbiota is essential for human health. In particular, the gut microbiota plays a critical role in the regulation of the immune system. Thus, faecal microbiota transplantation (FMT), a technology that has rapidly developed in the last decade, has specifically been utilised for the treatment of intestinal inflammation and has recently been found to be able to treat tumours in combination with immunotherapy. FMT has become a breakthrough in enhancing the response rate to immunotherapy in cancer patients by altering the composition of the patient's gut microbiota. This review discusses the mechanisms of faecal microorganism effects on tumour development, drug treatment efficacy, and adverse effects and describes the recent clinical research trials on FMT. Moreover, the factors influencing the efficacy and safety of FMT are described. We summarise the possibilities of faecal transplantation in the treatment of tumours and its complications and propose directions to explore the development of FMT.

Probiotics and the microbiota-gut-brain axis in neurodegeneration: Beneficial effects and mechanistic insights

Neurodegenerative diseases (NDs) are a group of heterogeneous disorders with a high socioeconomic burden. Although pharmacotherapy is currently the principal therapeutic approach for the management of NDs, mounting evidence supports the notion that the protracted application of available drugs would abate their dopaminergic outcomes in the long run. The therapeutic application of microbiome-based modalities has received escalating attention in biomedical works. In-depth investigations of the bidirectional communication between the microbiome in the gut and the brain offer a multitude of targets for the treatment of NDs or maximizing the patient's quality of life. Probiotic administration is a well-known microbial-oriented approach to modulate the gut microbiota and potentially influence the process of neurodegeneration. Of note, there is a strong need for further investigation to map out the mechanistic prospects for the gut-brain axis and the clinical efficacy of probiotics. In this review, we discuss the importance of microbiome modulation and hemostasis via probiotics, prebiotics, postbiotics and synbiotics in ameliorating pathological neurodegenerative events. Also, we meticulously describe the underlying mechanism of action of probiotics and their metabolites on the gut-brain axis in different NDs. We suppose that the present work will provide a functional direction for the use of probiotic-based modalities in promoting current practical treatments for the management of neurodegenerative-related diseases.

Convergence of Nanotechnology and Bacteriotherapy for Biomedical Applications

Bacteria have distinctive properties that make them ideal for biomedical applications. They can self-propel, sense their surroundings, and be externally detected. Using bacteria as medical therapeutic agents or delivery platforms opens new possibilities for advanced diagnosis and therapies. Nano-drug delivery platforms have numerous advantages over traditional ones, such as high loading capacity, controlled drug release, and adaptable functionalities. Combining bacteria and nanotechnologies to create therapeutic agents or delivery platforms has gained increasing attention in recent years and shows promise for improved diagnosis and treatment of diseases. In this review, design principles of integrating nanoparticles with bacteria, bacteria-derived nano-sized vesicles, and their applications and future in advanced diagnosis and therapeutics are summarized.

Data-driven prediction of colonization outcomes for complex microbial communities

Microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse mechanisms governing microbial dynamics. Here, we propose a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validate this approach using synthetic data, finding that machine learning models can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conduct colonization experiments for commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approaches can predict the colonization outcomes in experiments. Furthermore, we find that while most resident species are predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., Enterococcus faecalis inhibits the invasion of E. faecium invasion. The presented results suggest that the data-driven approaches are powerful tools to inform the ecology and management of microbial communities.

Polyphenols synergistic drugs to ameliorate non-alcoholic fatty liver disease via signal pathway and gut microbiota: A review

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver disease with an increasing incidence worldwide. Single drug therapy may have toxic side effects and disrupt gut microbiota balance. Polyphenols are widely used in disease intervention due to their distinctive nutritional properties and medicinal value, which a potential gut microbiota modulator. However, there is a lack of comprehensive review to explore the efficacy and mechanism of combined therapy with drugs and polyphenols for NAFLD.

AIM OF REVIEW

Based on this, this review firstly discusses the link between NAFLD and gut microbiota, and outlines the effects of polyphenols and drugs on gut microbiota. Secondly, it examined recent advances in the treatment and intervention of NAFLD with drugs and polyphenols and the therapeutic effect of the combination of the two. Finally, we highlight the underlying mechanisms of polyphenol combined drug therapy in NAFLD. This is mainly in terms of signaling pathways (NF-κB, AMPK, Nrf2, JAK/STAT, PPAR, SREBP-1c, PI3K/Akt and TLR) and gut microbiota. Furthermore, some emerging mechanisms such as microRNA potential biomarker therapies may provide therapeutic avenues for NAFLD.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Drawing inspiration from combination drug strategies, the use of active substances in combination with drugs for NAFLD intervention holds transformative and prospective potential, both improve NAFLD and restore gut microbiota balance while reducing the required drug dosage. This review systematically discusses the bidirectional interactions between gut microbiota and NAFLD, and summarizes the potential mechanisms of polyphenol synergistic drugs in the treatment of NAFLD by modulating signaling pathways and gut microbiota. Future researches should develop multi-omics technology to identify patients who benefit from polyphenols combination drugs and devising individualized treatment plans to enhance its therapeutic effect.

Engraftment of essential functions through multiple fecal microbiota transplants in chronic antibiotic-resistant pouchitis-a case study using metatranscriptomics

BACKGROUND

Ileal pouch-anal anastomosis (IPAA) is the standard of care after total proctocolectomy for ulcerative colitis (UC). Around 50% of patients will experience pouchitis, an idiopathic inflammatory condition. Antibiotics are the backbone of treatment of pouchitis; however, antibiotic-resistant pouchitis develops in 5-10% of those patients. It has been shown that fecal microbiota transplantation (FMT) is an effective treatment for UC, but results for FMT antibiotic-resistant pouchitis are inconsistent.

METHODS

To uncover which metabolic activities were transferred to the recipients during FMT and helped the remission, we performed a longitudinal case study of the gut metatranscriptomes from three patients and their donors. The patients were treated by two to three FMTs, and stool samples were analyzed for up to 140 days.

RESULTS

Reduced expression in pouchitis patients compared to healthy donors was observed for genes involved in biosynthesis of amino acids, cofactors, and B vitamins. An independent metatranscriptome dataset of UC patients showed a similar result. Other functions including biosynthesis of butyrate, metabolism of bile acids, and tryptophan were also much lower expressed in pouchitis. After FMT, these activities transiently increased, and the overall metatranscriptome profiles closely mirrored those of the respective donors with notable fluctuations during the subsequent weeks. The levels of the clinical marker fecal calprotectin were concordant with the metatranscriptome data. Faecalibacterium prausnitzii represented the most active species contributing to butyrate synthesis via the acetyl-CoA pathway. Remission occurred after the last FMT in all patients and was characterized by a microbiota activity profile distinct from donors in two of the patients.

CONCLUSIONS

Our study demonstrates the clear but short-lived activity engraftment of donor microbiota, particularly the butyrate biosynthesis after each FMT. The data suggest that FMT triggers shifts in the activity of patient microbiota towards health which need to be repeated to reach critical thresholds. As a case study, these insights warrant cautious interpretation, and validation in larger cohorts is necessary for generalized applications. In the long run, probiotics with high taxonomic diversity consisting of well characterized strains could replace FMT to avoid the costly screening of donors and the risk of transferring unwanted genetic material. Video Abstract.

The Next Generation Fecal Microbiota Transplantation: To Transplant Bacteria or Virome

Fecal microbiota transplantation (FMT) has emerged as a promising therapeutic approach for dysbiosis-related diseases. However, the clinical practice of crude fecal transplants presents limitations in terms of acceptability and reproductivity. Consequently, two alternative solutions to FMT are developed: transplanting bacteria communities or virome. Advanced methods for transplanting bacteria mainly include washed microbiota transplantation and bacteria spores treatment. Transplanting the virome is also explored, with the development of fecal virome transplantation, which involves filtering the virome from feces. These approaches provide more palatable options for patients and healthcare providers while minimizing research heterogeneity. In general, the evolution of the next generation of FMT in global trends is fecal microbiota components transplantation which mainly focuses on transplanting bacteria or virome.

A computational method to dissect colonization resistance of the gut microbiota against pathogens

The mammalian gut microbiome protects the host through colonization resistance (CR) against the incursion of exogenous and often harmful microorganisms, but identifying the exact microbes responsible for the gut microbiota-mediated CR against a particular pathogen remains a challenge. To address this limitation, we developed a computational method: generalized microbe-phenotype triangulation (GMPT). We first systematically validated GMPT using a classical population dynamics model in community ecology and demonstrated its superiority over baseline methods. We then tested GMPT on simulated data generated from the ecological network inferred from a real community (GnotoComplex microflora) and real microbiome data on two mouse studies on Clostridioides difficile infection. We demonstrated GMPT's ability to streamline the discovery of microbes that are potentially responsible for microbiota-mediated CR against pathogens. GMPT holds promise to advance our understanding of CR mechanisms and facilitate the rational design of microbiome-based therapies for preventing and treating enteric infections.

Bakdrive: identifying a minimum set of bacterial species driving interactions across multiple microbial communities

MOTIVATION

Interactions among microbes within microbial communities have been shown to play crucial roles in human health. In spite of recent progress, low-level knowledge of bacteria driving microbial interactions within microbiomes remains unknown, limiting our ability to fully decipher and control microbial communities.

RESULTS

We present a novel approach for identifying species driving interactions within microbiomes. Bakdrive infers ecological networks of given metagenomic sequencing samples and identifies minimum sets of driver species (MDS) using control theory. Bakdrive has three key innovations in this space: (i) it leverages inherent information from metagenomic sequencing samples to identify driver species, (ii) it explicitly takes host-specific variation into consideration, and (iii) it does not require a known ecological network. In extensive simulated data, we demonstrate identifying driver species identified from healthy donor samples and introducing them to the disease samples, we can restore the gut microbiome in recurrent Clostridioides difficile (rCDI) infection patients to a healthy state. We also applied Bakdrive to two real datasets, rCDI and Crohn's disease patients, uncovering driver species consistent with previous work. Bakdrive represents a novel approach for capturing microbial interactions.

AVAILABILITY AND IMPLEMENTATION

Bakdrive is open-source and available at: https://gitlab.com/treangenlab/bakdrive.

Intestinal microbiota regulates colonic inflammation in fluorosis mice by TLR/NF-κB pathway through short-chain fatty acids

Intestinal inflammation and microbial dysbiosis are found simultaneously in patients with fluorosis. However, whether the inflammation derived from fluoride exposure only or intestinal microbial disorders has not been clarified. In this study, 100 mg/L NaF exposure for 90 days significantly elevated the expressions of inflammatory factors (TNF-α, IL-1β, IL-6, IFN-γ, TGF-β, and IL-10), and the levels of TLR4, TRAF6, Myd88, IKKβ, and NF-κB P65 in mouse colon, while the above factors were reduced in pseudo germ-free mice with fluorosis, hinting that disordered microbiota might play a more direct role in the development of colonic inflammation than fluoride. Fecal microbiota transplantation (FMT) lowered the levels of inflammatory factors and inactivated the TLR/NF-κB pathway in fluoride-exposed mice. In addition, supplementing short-chain fatty acids (SCFAs) exhibited the identical effects to the model of FMT. In summary, intestinal microbiota may alleviate the colonic inflammatory of mice with fluorosis by regulating TLR/NF-κB pathway through SCFAs.

Key determinants of success in fecal microbiota transplantation: From microbiome to clinic

Fecal microbiota transplantation (FMT) has achieved satisfactory results in preventing the recurrence of Clostridioides difficile infection, but these positive outcomes have only been partially replicated in other diseases. Several factors influence FMT success, including those related to donors and recipients (including diversity and specific composition of the gut microbiome, immune system, and host genetics) as well as to working protocols (fecal amount and number of infusions, route of delivery, and adjuvant treatments). Moreover, initial evidence suggests that the clinical success of FMT may be related to the degree of donor microbial engraftment. The application of cutting-edge technologies for microbiome assessment, along with changes in the current vision of fecal transplants, are expected to improve FMT protocols and outcomes. Here, we review the key determinants of FMT success and insights and strategies that will enable a close integration of lab-based and clinical approaches for increasing FMT success.

Probiotics: insights and new opportunities for Clostridioides difficile intervention

Clostridioides difficile infection (CDI) is a life-threatening disease caused by the Gram-positive, opportunistic intestinal pathogen C. difficile. Despite the availability of antimicrobial drugs to treat CDI, such as vancomycin, metronidazole, and fidaxomicin, recurrence of infection remains a significant clinical challenge. The use of live commensal microorganisms, or probiotics, is one of the most investigated non-antibiotic therapeutic options to balance gastrointestinal (GI) microbiota and subsequently tackle dysbiosis. In this review, we will discuss major commensal probiotic strains that have the potential to prevent and/or treat CDI and its recurrence, reassess the efficacy of probiotics supplementation as a CDI intervention, delve into lessons learned from probiotic modulation of the immune system, explore avenues like genome-scale metabolic network reconstructions, genome sequencing, and multi-omics to identify novel strains and understand their functionality, and discuss the current regulatory framework, challenges, and future directions.

Data-driven prediction of colonization outcomes for complex microbial communities

Complex microbial interactions can lead to different colonization outcomes of exogenous species, be they pathogenic or beneficial in nature. Predicting the colonization of exogenous species in complex communities remains a fundamental challenge in microbial ecology, mainly due to our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. Here, we proposed a data-driven approach independent of any dynamics model to predict colonization outcomes of exogenous species from the baseline compositions of microbial communities. We systematically validated this approach using synthetic data, finding that machine learning models (including Random Forest and neural ODE) can predict not only the binary colonization outcome but also the post-invasion steady-state abundance of the invading species. Then we conducted colonization experiments for two commensal gut bacteria species Enterococcus faecium and Akkermansia muciniphila in hundreds of human stool-derived in vitro microbial communities, confirming that the data-driven approach can successfully predict the colonization outcomes. Furthermore, we found that while most resident species were predicted to have a weak negative impact on the colonization of exogenous species, strongly interacting species could significantly alter the colonization outcomes, e.g., the presence of Enterococcus faecalis inhibits the invasion of E. faecium . The presented results suggest that the data-driven approach is a powerful tool to inform the ecology and management of complex microbial communities.

Controlling the human microbiome

We coexist with a vast number of microbes that live in and on our bodies. Those microbes and their genes are collectively known as the human microbiome, which plays important roles in human physiology and diseases. We have acquired extensive knowledge of the organismal compositions and metabolic functions of the human microbiome. However, the ultimate proof of our understanding of the human microbiome is reflected in our ability to manipulate it for health benefits. To facilitate the rational design of microbiome-based therapies, there are many fundamental questions to be addressed at the systems level. Indeed, we need a deep understanding of the ecological dynamics associated with such a complex ecosystem before we rationally design control strategies. In light of this, this review discusses progress from various fields, e.g., community ecology, network science, and control theory, that are helping us make progress toward the ultimate goal of controlling the human microbiome.

Antimicrobial Resistance and Recent Alternatives to Antibiotics for the Control of Bacterial Pathogens with an Emphasis on Foodborne Pathogens

Antimicrobial resistance (AMR) is one of the most important global public health problems. The imprudent use of antibiotics in humans and animals has resulted in the emergence of antibiotic-resistant bacteria. The dissemination of these strains and their resistant determinants could endanger antibiotic efficacy. Therefore, there is an urgent need to identify and develop novel strategies to combat antibiotic resistance. This review provides insights into the evolution and the mechanisms of AMR. Additionally, it discusses alternative approaches that might be used to control AMR, including probiotics, prebiotics, antimicrobial peptides, small molecules, organic acids, essential oils, bacteriophage, fecal transplants, and nanoparticles.

Clostridioides difficile bile salt hydrolase activity has substrate specificity and affects biofilm formation

The Clostridioides difficile pathogen is responsible for nosocomial infections. Germination is an essential step for the establishment of C. difficile infection (CDI) because toxins that are secreted by vegetative cells are responsible for the symptoms of CDI. Germination can be stimulated by the combinatorial actions of certain amino acids and either conjugated or deconjugated cholic acid-derived bile salts. During synthesis in the liver, cholic acid- and chenodeoxycholic acid-class bile salts are conjugated with either taurine or glycine at the C24 carboxyl. During GI transit, these conjugated bile salts are deconjugated by microbes that express bile salt hydrolases (BSHs). Here, we surprisingly find that several C. difficile strains have BSH activity. We observed this activity in both C. difficile vegetative cells and in spores and that the observed BSH activity was specific to taurine-derived bile salts. Additionally, we find that this BSH activity can produce cholate for metabolic conversion to deoxycholate by C. scindens. The C. scindens-produced deoxycholate signals to C. difficile to initiate biofilm formation. Our results show that C. difficile BSH activity has the potential to influence the interactions between microbes, and this could extend to the GI setting.

Microbial Modulation in Inflammatory Bowel Diseases

Gut dysbiosis is one of prominent features in inflammatory bowel diseases (IBDs) which are of an unknown etiology. Although the cause-and-effect relationship between IBD and gut dysbiosis remains to be elucidated, one area of research has focused on the management of IBD by modulating and correcting gut dysbiosis. The use of antibiotics, probiotics either with or without prebiotics, and fecal microbiota transplantation from healthy donors are representative methods for modulating the intestinal microbiota ecosystem. The gut microbiota is not a simple assembly of bacteria, fungi, and viruses, but a complex organ-like community system composed of numerous kinds of microorganisms. Thus, studies on specific changes in the gut microbiota depending on which treatment option is applied are very limited. Here, we review previous studies on microbial modulation as a therapeutic option for IBD and its significance in the pathogenesis of IBD.

Fecal Microbiota Transplantation Could Improve Chronic Diarrhea in Cynomolgus Monkey by Alleviating Inflammation and Modulating Gut Microbiota

Chronic diarrhea is associated with enteric dysbiosis and provokes the overuse of antibiotics. Fecal microbiota transplantation (FMT) is a promising therapy, but it shows discrepant clinical efficacy. Bacterial colonization in recipients has been studied, although little is known about the role of gut fungi and Archaea after FMT. In this study, we evaluated the efficacy of human-derived FMT on spontaneous chronic diarrhea cynomolgus monkeys and revealed the effector mechanisms. We demonstrated that FMT can mitigate the appearance of diarrheal symptoms and inhibit the increase in interleukin-6, interleukin-8, interleukin-1β, and interferon-γ and the decrease in interleukin-10 in serum. We confirmed that FMT restored the disturbance of gut bacteria by reducing the relative abundances of potential pathogens, including Cloacibacillus porcorum, Desulfovibrio desulfuricans, Erysipelotrichaceae bacterium 5_2_54FAA, and Erysipelotrichaceae bacterium 21_3, and increasing the levels of Lactobacillus fermentum and Lactobacillus ruminis CAG_367 in diarrheal monkeys. The metabolic pathways of healthy and FMT monkeys' gut bacteria were enriched in amino acid metabolism, carbohydrate metabolism, and lipid metabolism, while the metabolic pathways of pre-FMT monkeys' gut bacteria were enriched in antibiotic production. Moreover, a higher Ascomycota/Basidiomycota ratio, higher Aspergillus levels, and lower Trichosporon asahii abundance were present in intestinal fungi after FMT. Although the abundance of the Archaea Methanosphaera stastmanae did not change significantly, it was inversely correlated with the anti-inflammatory factor IL-4 after FMT. These results support the further development and application of FMT for chronic diarrhea.

Machine learning for data integration in human gut microbiome

Recent studies have demonstrated that gut microbiota plays critical roles in various human diseases. High-throughput technology has been widely applied to characterize the microbial ecosystems, which led to an explosion of different types of molecular profiling data, such as metagenomics, metatranscriptomics and metabolomics. For analysis of such data, machine learning algorithms have shown to be useful for identifying key molecular signatures, discovering potential patient stratifications, and particularly for generating models that can accurately predict phenotypes. In this review, we first discuss how dysbiosis of the intestinal microbiota is linked to human disease development and how potential modulation strategies of the gut microbial ecosystem can be used for disease treatment. In addition, we introduce categories and workflows of different machine learning approaches, and how they can be used to perform integrative analysis of multi-omics data. Finally, we review advances of machine learning in gut microbiome applications and discuss related challenges. Based on this we conclude that machine learning is very well suited for analysis of gut microbiome and that these approaches can be useful for development of gut microbe-targeted therapies, which ultimately can help in achieving personalized and precision medicine.

The biodiversity dose-response curve translates theory and practice from ecological restoration into research and clinical priorities for fecal microbiota transplantation

Discoveries of the beneficial effects of gut microbiota have led to efforts to cultivate healthy gut flora to treat disease. The field of ecological restoration specializes on reestablishment of desired species in disturbed ecosystems, which suggests that it may be applicable to microbe restoration in the gut. Common language can lower barriers to interdisciplinary insights. Here I introduce the concept of a "biodiversity dose-response curve" to translate ideas from ecological restoration into research and clinical priorities for fecal microbiota transplantation (FMT). The curve is based on a relationship between ecosystem structure, measured as species diversity found in both nature and gut ecosystems, and ecosystem function, which are the measurable parameters that contribute to ecosystem and human health. I explain why the biodiversity dose-response curve may follow the ecological model of a "rivet-redundancy" relationship, in which the overlap of multiple organisms' functional contributions to a system mask the impact of initial losses of diversity, but, at a certain level of loss, function declines sharply. (Imagine an airplane that flies with a few rivets missing, until it loses enough to fail.) The biodiversity dose-response curve indicates that seemingly healthy individuals may be suboptimal donors; it highlights the importance of recipient diet in FMT success; and it introduces the concept of "passive restoration" into the field of gut medicine. These insights, which may help to explain low success rates of FMT in the treatment of non-Clostridium dificile conditions, are less apparent in the absence of interdisciplinary integration.

Drivers and determinants of strain dynamics following fecal microbiota transplantation

Fecal microbiota transplantation (FMT) is a therapeutic intervention for inflammatory diseases of the gastrointestinal tract, but its clinical mode of action and subsequent microbiome dynamics remain poorly understood. Here we analyzed metagenomes from 316 FMTs, sampled pre and post intervention, for the treatment of ten different disease indications. We quantified strain-level dynamics of 1,089 microbial species, complemented by 47,548 newly constructed metagenome-assembled genomes. Donor strain colonization and recipient strain resilience were mostly independent of clinical outcomes, but accurately predictable using LASSO-regularized regression models that accounted for host, microbiome and procedural variables. Recipient factors and donor–recipient complementarity, encompassing entire microbial communities to individual strains, were the main determinants of strain population dynamics, providing insights into the underlying processes that shape the post-FMT gut microbiome. Applying an ecology-based framework to our findings indicated parameters that may inform the development of more effective, targeted microbiome therapies in the future, and suggested how patient stratification can be used to enhance donor microbiota colonization or the displacement of recipient microbes in clinical practice.

State-Space-Based Framework for Predicting Microbial Interaction Variability in Wastewater Treatment Plants

Substantial attempts have been made to control microbial communities for environmental integrity, biosystem performance, and human health. However, it is difficult to manipulate microbial communities in practice due to the varying and nonlinear nature of interspecific interaction networks. Here, we develop a manifold-based framework to investigate the patterns of microbial interaction variability in wastewater treatment plants using manifold geometric properties and design a simple control strategy to manipulate the microbes in nonlinear communities. We validate our framework using the readily available and nonsequential microbiome profiles of wastewater treatment plants. Our results show that some microbes in the activated sludge and anammox communities display deterministic rival or cooperative relationships and constitute a stable subnetwork within the whole nonlinear community network. We further use a simulation to demonstrate that these microbes can be used to drive a microbe in a target direction regardless of the community dynamics. Overall, our framework can provide a time-efficient solution to select effective control inputs for reliable manipulation in varying microbial networks, opening up new possibilities across a range of biological fields, including wastewater treatment plants.

Contribution of gut microbiota toward renal function in sepsis

Sepsis most often involves the kidney and is one of the most common causes of acute kidney injury. The prevalence of septic acute kidney injury has increased significantly in recent years. The gut microbiota plays an important role in sepsis. It interacts with the kidney in a complex and multifactorial process, which is not fully understood. Sepsis may lead to gut microbiota alteration, orchestrate gut mucosal injury, and cause gut barrier failure, which further alters the host immunological and metabolic homeostasis. The pattern of gut microbiota alteration also varies with sepsis progression. Changes in intestinal microecology have double-edged effects on renal function, which also affects intestinal homeostasis. This review aimed to clarify the interaction between gut microbiota and renal function during the onset and progression of sepsis. The mechanism of gut–kidney crosstalk may provide potential insights for the development of novel therapeutic strategies for sepsis.

Identification of clinical and ecological determinants of strain engraftment after fecal microbiota transplantation using metagenomics

Fecal microbiota transplantation (FMT) is a promising therapeutic approach for microbiota-associated pathologies, but our understanding of the post-FMT microbiome assembly process and its ecological and clinical determinants is incomplete. Here we perform a comprehensive fecal metagenome analysis of 14 FMT trials, involving five pathologies and >250 individuals, and determine the origins of strains in patients after FMT. Independently of the underlying clinical condition, conspecific coexistence of donor and recipient strains after FMT is uncommon and donor strain engraftment is strongly positively correlated with pre-FMT recipient microbiota dysbiosis. Donor strain engraftment was enhanced through antibiotic pretreatment and bowel lavage and dependent on donor and recipient ɑ-diversity; strains from relatively abundant species were more likely and from predicted oral, oxygen-tolerant, and gram-positive species less likely to engraft. We introduce a general mechanistic framework for post-FMT microbiome assembly in alignment with ecological theory, which can guide development of optimized, more targeted, and personalized FMT therapies.

Rejuvenating the human gut microbiome

Industrial advances have caused significant loss of diversity in our gut microbiome, potentially increasing our susceptibility to many diseases. Recently, rewilding the human gut microbiome - that is, bringing it back to an ancestral or preindustrial state (e.g., by transplanting stool material from donors in nonindustrial societies) - has been hotly debated from medical, ethical, and evolutionary perspectives. Here we propose an alternative solution: rejuvenating the human gut microbiome by stool banking and autologous fecal microbiota transplantation, that is, collecting the hosts' stool samples at a younger age when they are at optimal health, and cryopreserving the samples in a stool bank for the hosts' own future use. In this article we discuss the motivation, applications, feasibility, and challenges of this solution.

Ecological dynamics of the gut microbiome in response to dietary fiber

Dietary fibers are generally thought to benefit intestinal health. Their impacts on the composition and metabolic function of the gut microbiome, however, vary greatly across individuals. Previous research showed that each individual's response to fibers depends on their baseline gut microbiome, but the ecology driving microbiota remodeling during fiber intake remained unclear. Here, we studied the long-term dynamics of the gut microbiome and short-chain fatty acids (SCFAs) in isogenic mice with distinct microbiota baselines fed with the fermentable fiber inulin and resistant starch compared to the non-fermentable fiber cellulose. We found that inulin produced a generally rapid response followed by gradual stabilization to new equilibria, and those dynamics were baseline-dependent. We parameterized an ecology model from the time-series data, which revealed a group of bacteria whose growth significantly increased in response to inulin and whose baseline abundance and interspecies competition explained the baseline dependence of microbiome density and community composition dynamics. Fecal levels of SCFAs, such as propionate, were associated with the abundance of inulin responders, yet inter-individual variation of gut microbiome impeded the prediction of SCFAs by machine learning models. We showed that our methods and major findings were generalizable to dietary resistant starch. Finally, we analyzed time-series data of synthetic and natural human gut microbiome in response to dietary fiber and validated the inferred interspecies interactions in vitro. This study emphasizes the importance of ecological modeling to understand microbiome responses to dietary changes and the need for personalized interventions.

Fecal microbiota transplantation: a review on current formulations in Clostridioides difficile infection and future outlooks

INTRODUCTION

The role of the gut microbiota in health and the pathogenesis of several diseases has been highlighted in recent years. Even though the precise mechanisms involving the microbiome in these ailments are still unclear, microbiota-modulating therapies have been developed. Fecal microbiota transplantation (FMT) has shown significant results against Clostridioides difficile infection (CDI), and its potential has been investigated for other diseases. Unfortunately, the technical aspects of the treatment make it difficult to implement. Pharmaceutical technology approaches to encapsulate microorganisms could play an important role in providing this treatment and render the treatment modalities easier to handle.

AREAS COVERED

After an overview of CDI, this narrative review aims to discuss the current formulations for FMT and specifically addresses the technical aspects of the treatment. This review also distinguishes itself by focusing on the hurdles and emphasizing the possible improvements using pharmaceutical technologies.

EXPERT OPINION

FMT is an efficient treatment for recurrent CDI. However, its standardization is overlooked. The approach of industrial and hospital preparations of FMT are different, but both show promise in their respective methodologies. Novel FMT formulations could enable further research on dysbiotic diseases in the future.

Recurrent neural networks enable design of multifunctional synthetic human gut microbiome dynamics

Predicting the dynamics and functions of microbiomes constructed from the bottom-up is a key challenge in exploiting them to our benefit. Current models based on ecological theory fail to capture complex community behaviors due to higher order interactions, do not scale well with increasing complexity and in considering multiple functions. We develop and apply a long short-term memory (LSTM) framework to advance our understanding of community assembly and health-relevant metabolite production using a synthetic human gut community. A mainstay of recurrent neural networks, the LSTM learns a high dimensional data-driven non-linear dynamical system model. We show that the LSTM model can outperform the widely used generalized Lotka-Volterra model based on ecological theory. We build methods to decipher microbe-microbe and microbe-metabolite interactions from an otherwise black-box model. These methods highlight that Actinobacteria, Firmicutes and Proteobacteria are significant drivers of metabolite production whereas Bacteroides shape community dynamics. We use the LSTM model to navigate a large multidimensional functional landscape to design communities with unique health-relevant metabolite profiles and temporal behaviors. In sum, the accuracy of the LSTM model can be exploited for experimental planning and to guide the design of synthetic microbiomes with target dynamic functions.

Microbiome-based interventions to modulate gut ecology and the immune system

The gut microbiome lies at the intersection between the environment and the host, with the ability to modify host responses to disease-relevant exposures and stimuli. This is evident in how enteric microbes interact with the immune system, e.g., supporting immune maturation in early life, affecting drug efficacy via modulation of immune responses, or influencing development of immune cell populations and their mediators. Many factors modulate gut ecosystem dynamics during daily life and we are just beginning to realise the therapeutic and prophylactic potential of microbiome-based interventions. These approaches vary in application, goal, and mechanisms of action. Some modify the entire community, such as nutritional approaches or faecal microbiota transplantation, while others, such as phage therapy, probiotics, and prebiotics, target specific taxa or strains. In this review, we assessed the experimental evidence for microbiome-based interventions, with a particular focus on their clinical relevance, ecological effects, and modulation of the immune system.

Stochastic block models reveal a robust nested pattern in healthy human gut microbiomes

A key question in human gut microbiome research is what are the robust structural patterns underlying its taxonomic composition. Herein, we use whole metagenomic datasets from healthy human guts to show that such robust patterns do exist, albeit not in the conventional enterotype sense. We first introduce the concept of mixed-membership enterotypes using a network inference approach based on stochastic block models. We find that gut microbiomes across a group of people (hosts) display a nested structure, which has been observed in a number of ecological systems. This finding led us to designate distinct ecological roles to both microbes and hosts: generalists and specialists. Specifically, generalist hosts have microbiomes with most microbial species, while specialist hosts only have generalist microbes. Moreover, specialist microbes are only present in generalist hosts. From the nested structure of microbial taxonomies, we show that these ecological roles of microbes are generally conserved across datasets. Our results show that the taxonomic composition of healthy human gut microbiomes is associated with robustly structured combinations of generalist and specialist species.

Predicting microbiome compositions from species assemblages through deep learning

Microbes can form complex communities that perform critical functions in maintaining the integrity of their environment or their hosts' well-being. Rationally managing these microbial communities requires improving our ability to predict how different species assemblages affect the final species composition of the community. However, making such a prediction remains challenging because of our limited knowledge of the diverse physical, biochemical, and ecological processes governing microbial dynamics. To overcome this challenge, we present a deep learning framework that automatically learns the map between species assemblages and community compositions from training data only, without knowing any of the above processes. First, we systematically validate our framework using synthetic data generated by classical population dynamics models. Then, we apply our framework to data from in vitro and in vivo microbial communities, including ocean and soil microbiota, Drosophila melanogaster gut microbiota, and human gut and oral microbiota. We find that our framework learns to perform accurate out-of-sample predictions of complex community compositions from a small number of training samples. Our results demonstrate how deep learning can enable us to understand better and potentially manage complex microbial communities.

Development of a rational framework for the therapeutic efficacy of fecal microbiota transplantation for calf diarrhea treatment

BACKGROUND

Establishing fecal microbiota transplantation (FMT) to prevent multifactorial diarrhea in calves is challenging because of the differences in farm management practices, the lack of optimal donors, and recipient selection. In this study, the underlying factors of successful and unsuccessful FMT treatment cases are elucidated, and the potential markers for predicting successful FMT are identified using fecal metagenomics via 16S rRNA gene sequencing, fecal metabolomics via capillary electrophoresis time-of-flight mass spectrometry, and machine learning approaches.

RESULTS

Specifically, 20 FMT treatment cases, in which feces from healthy donors were intrarectally transferred into recipient diarrheal calves, were conducted with a success rate of 70%. Selenomonas was identified as a microorganism genus that showed significant donor-recipient compatibility in successful FMT treatments. A strong positive correlation between the microbiome and metabolome data, which is a prerequisite factor for FMT success, was confirmed by Procrustes analysis in successful FMT (r = 0.7439, P = 0.0001). Additionally, weighted gene correlation network analysis confirmed the positively or negatively correlated pairs of bacterial taxa (family Veillonellaceae) and metabolomic features (i.e., amino acids and short-chain fatty acids) responsible for FMT success. Further analysis aimed at establishing criteria for donor selection identified the genus Sporobacter as a potential biomarker in successful donor selection. Low levels of metabolites, such as glycerol 3-phosphate, dihydroxyacetone phosphate, and isoamylamine, in the donor or recipients prior to FMT, are predicted to facilitate FMT.

CONCLUSIONS

Overall, we provide the first substantial evidence of the factors related to FMT success or failure; these findings could improve the design of future microbial therapeutics for treating diarrhea in calves. Video abstract.

Establishment and resilience of transplanted gut microbiota in aged mice

The maintenance of healthy and resilient gut microbiota is critical for the life quality and healthspan of the elderly. Fecal microbiota transplantation (FMT) has been increasingly used to restore healthy gut microbiota. We systemically studied the establishment and resilience of transplanted microbiota after autologous versus heterologous FMT in aged recipients. Gut microbiota of aged mice (20 months old) failed to restore their original diversity and composition over 8 weeks via spontaneous recovery after antibiotics treatment; in contrast, FMT using either autologous or heterologous (2 months old from a different vendor) donors facilitated the recovery successfully, established donor-like microbiota states, and affected host gene expression profile. Furthermore, the transplanted microbiota established by heterologous FMT is not resilient during chemical-induced colonic inflammation, in contrast to that of autologous FMT. Our findings highlighted the need to monitor the long-term stability of transplanted gut microbiota and to perform multiple FMT when necessary.

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Aged mice microbiota restores slowly after antibiotics treatment

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Both autologous and heterologous FMT facilitate microbiota restoration in aged mice

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FMT affects long-term homeostasis of gut metagenome and colon gene expression

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Established microbiota after heterologous FMT is not resilient against colitis

The potential of microbiome replacement therapies for Clostridium difficile infection

PURPOSE OF REVIEW

There is a paradox when treating Clostridium difficile infection (CDI); treatment antibiotics reduce C. difficile colonization but cause further microbiota disruption and can lead to recurrent disease. The success of faecal microbiota transplants (FMT) in treating CDI has become a new research area in microbiome restorative therapies but are they a viable long-term treatment option?

RECENT FINDINGS

C. difficile displays metabolic flexibility to use different nutritional sources during CDI. Using microbiome therapies for the efficient restoration of bile homeostasis and to reduce the bioavailability of preferential nutrients will target the germination ability of C. difficile spores and the growth rate of vegetative cells. Several biotechnology companies have developed microbiome therapeutics for treating CDI, which are undergoing clinical trials.

SUMMARY

There is confidence in using restorative microbiome therapies for treating CDI after the demonstrated efficacy of FMT, where several biotechnology companies are aiming to supply what would be a 'first in class' treatment option. Efficient removal of C. difficile from the different intestinal biogeographies should be considered in future microbiome therapies. With the gut microbiota implicated in different diseases, more work is needed to assess the long-term consequences of microbiome therapies.

The interplay of gut microbiota between donors and recipients determines the efficacy of fecal microbiota transplantation

Fecal microbiota transplantation (FMT) is a promising treatment for microbiota dysbiosis associated diseases, such as Clostridioides difficile infection (CDI) and inflammatory bowel disease (IBD). The engraftment of donor bacteria is essential for the effectiveness of FMT, which to some extent depends on the matching of donors and recipients. However, how different types of donor-derived bacteria affect FMT efficacy has not been fully dissected. We recruited two longitudinal IBD cohorts of 103 FMT recipients and further analyzed 1,280 microbiota datasets from 14 public CDI and IBD studies to uncover the effect of donor-derived microbiota in recipients. We found that two enterotypes, RCPT/E and RCPT/B (dominated by Enterobacteriaceae and Bacteroides, respectively), consistently exist in both CDI and IBD patients. Based on a time-course-based multi-cohort analysis of FMT fecal samples, we observed the interplay between recipient and donor-derived microbiota during FMT, in which the FMT outcome was significantly associated with the enterotype and microbiota distance between donor and recipient after FMT. We proposed a new measurement, the ratio of colonizers to residents after FMT (C2R), to quantify the engraftment of donor-derived bacteria in the recipients, and then constructed an enterotype-based statistical model for donor-recipient matching, which was validated by both cross-validation and an additional IBD FMT cohort (n = 42). We believe that with the accumulation of FMT multi-omics datasets, machine learning-based methods will be helpful for rational donor selection for improving efficacy and precision FMT practices.

Gut associated metabolites and their roles in Clostridioides difficile pathogenesis

The nosocomial pathogen Clostridioides difficile is a burden to the healthcare system. Gut microbiome disruption, most commonly by broad-spectrum antibiotic treatment, is well established to generate a state that is susceptible to CDI. A variety of metabolites produced by the host and/or gut microbiota have been shown to interact with C. difficile. Certain bile acids promote/inhibit germination while other cholesterol-derived compounds and amino acids used in the Stickland metabolic pathway affect growth and CDI colonization. Short chain fatty acids maintain intestinal barrier integrity and a myriad of other metabolic compounds are used as nutritional sources or used by C. difficile to inhibit or outcompete other bacteria in the gut. As the move toward non-antibiotic CDI treatment takes place, a deeper understanding of interactions between C. difficile and the host's gut microbiome and metabolites becomes more relevant.

Potential Role of Akkermansia muciniphila in Parkinson's Disease and Other Neurological/Autoimmune Diseases

The composition of the gut microbiota, including Akkermansia muciniphila (A. muciniphila), is altered in many neurological diseases and may be involved in the pathophysiological processes of Parkinson's disease (PD). A. muciniphila, a mucin-degrading bacterium, is a potential next-generation microbe that has anti-inflammatory properties and is responsible for keeping the body healthy. As the role of A. muciniphila in PD has become increasingly apparent, we discuss the potential link between A. muciniphila and various neurological diseases (including PD) in the current review.

BEEM-Static: Accurate inference of ecological interactions from cross-sectional microbiome data

The structure and function of diverse microbial communities is underpinned by ecological interactions that remain uncharacterized. With rapid adoption of next-generation sequencing for studying microbiomes, data-driven inference of microbial interactions based on abundance correlations is widely used, but with the drawback that ecological interpretations may not be possible. Leveraging cross-sectional microbiome datasets for unravelling ecological structure in a scalable manner thus remains an open problem. We present an expectation-maximization algorithm (BEEM-Static) that can be applied to cross-sectional datasets to infer interaction networks based on an ecological model (generalized Lotka-Volterra). The method exhibits robustness to violations in model assumptions by using statistical filters to identify and remove corresponding samples. Benchmarking against 10 state-of-the-art correlation based methods showed that BEEM-Static can infer presence and directionality of ecological interactions even with relative abundance data (AUC-ROC>0.85), a task that other methods struggle with (AUC-ROC<0.63). In addition, BEEM-Static can tolerate a high fraction of samples (up to 40%) being not at steady state or coming from an alternate model. Applying BEEM-Static to a large public dataset of human gut microbiomes (n = 4,617) identified multiple stable equilibria that better reflect ecological enterotypes with distinct carrying capacities and interactions for key species.

Characterizing the ecological interactions among microbial members is an important step towards understanding the structure and function of diverse microbial communities. Widely used correlation based approaches for inferring interactions from cross-sectional microbiome sequencing data are not able to predict the directionality of interactions, and their results may not be interpretable. We developed an expectation-maximization algorithm (BEEM-Static) that can infer directed interaction networks from cross-sectional data based on an ecological model. Our benchmarking results showed that BEEM-Static inferred presence and directionality of interactions accurately, while correlation based methods had performance slightly better than random guesses. In addition, BEEM-Static was robust to various types of noises using statistical filters to identify and remove data points violating its assumptions. Applying BEEM-Static to a large public dataset of human gut microbiomes, we were able to identify multiple stable equilibria with distinct ecological properties.

Exploiting interbacterial antagonism for microbiome engineering

Interbacterial antagonism can significantly impact microbiome assembly and stability and can potentially be exploited to modulate microbes and microbial communities in diverse environments, ranging from natural habitats to industrial bioreactors. Here we highlight key mechanisms of interspecies antagonism that rely on direct cell-to-cell contact or diffusion of secreted biomolecules, and discuss recent advances to provide altered function and specificities for microbiome engineering. We further outline the use of ecological design principles based on antagonistic interactions for bottom-up assembly of synthetic microbial communities. Manipulating microbial communities through these negative interactions will be critical for understanding complex microbiome processes and properties and developing new applications of microbiome engineering.

An efficient and scalable top-down method for predicting structures of microbial communities

Modern applications involving multispecies microbial communities rely on the ability to predict structures of such communities in defined environments. The structures depend on pairwise and high-order interactions between species. To unravel these interactions, classical bottom-up approaches examine all possible species subcommunities. Such approaches are not scalable as the number of subcommunities grows exponentially with the number of species, n. Here we present a top-down method wherein the number of subcommunities to be examined grows linearly with n, drastically reducing experimental effort. The method uses steady-state data from leave-one-out subcommunities and mathematical modeling to infer effective pairwise interactions and predict community structures. The accuracy of the method increases with n, making it suitable for large communities. We established the method in silico and validated it against a five-species community from literature and an eight-species community cultured in vitro. Our method offers an efficient and scalable tool for predicting microbial community structures.

Inflammation-Associated Microbiota Composition Across Domestic Animals

Domestic animals represent important resources for understanding shared mechanisms underlying complex natural diseases that arise due to both genetic and environmental factors. Intestinal inflammation, particularly inflammatory bowel disease (IBD), is a significant health challenge in humans and domestic animals. While the etiology of IBD is multifactorial, imbalance of symbiotic gut microbiota has been hypothesized to play a central role in disease pathophysiology. Advances in genomic sequencing and analytical pipelines have enabled researchers to decipher the composition of the intestinal microbiota during health and in the context of naturally occurring diseases. This review compiles microbiome genomic data across domestic species and highlights a common occurrence of gut microbiome dysbiosis during idiopathic intestinal inflammation in multiple species, including dogs, cats, horses, cows, and pigs. Current microbiome data obtained from animals with intestinal inflammation are mostly limited to taxonomical analyses in association with broad clinical phenotype. In general, a pathogen or pathosymbiont were not detected. Rather, functional potential of the altered microbiota has been suggested to be one of the key etiologic factors. Among the domestic species studied, canine analyses are currently the most advanced with incorporation of functional profiling of microbiota. Canine IBD parallels features of the disease in humans, thus canines represent a strong natural model for human IBD. While deeper analyses of metagenomic data, coupled with host molecular analyses are needed, comparative studies across domestic species can reveal shared microbial alterations and regulatory mechanisms that will improve our understanding of intestinal inflammation in both animals and humans.

SARS-CoV-2-Induced Gut Microbiome Dysbiosis: Implications for Colorectal Cancer

The emergence of a novel coronavirus, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), in December 2019 led to a worldwide pandemic with over 170 million confirmed infections and over 3.5 million deaths (as of May 2021). Early studies have shown higher mortality rates from SARS-CoV-2 infection in cancer patients than individuals without cancer. Herein, we review the evidence that the gut microbiota plays a crucial role in health and has been linked to the development of colorectal cancer (CRC). Investigations have shown that SARS-CoV-2 infection causes changes to the gut microbiota, including an overall decline in microbial diversity, enrichment of opportunistic pathogens such as Fusobacterium nucleatum bacteremia, and depletion of beneficial commensals, such as the butyrate-producing bacteria. Further, these changes lead to increased colonic inflammation, which leads to gut barrier disruption, expression of genes governing CRC tumorigenesis, and tumor immunosuppression, thus further exacerbating CRC progression. Additionally, a long-lasting impact of SARS-CoV-2 on gut dysbiosis might result in a greater possibility of new CRC diagnosis or aggravating the condition in those already afflicted. Herein, we review the evidence relating to the current understanding of how infection with SARS-CoV-2 impacts the gut microbiota and the effects this will have on CRC carcinogenesis and progression.

New technologies for developing phage-based tools to manipulate the human microbiome

Gut bacteria play an essential role in the human body by regulating multiple functions, producing essential metabolites, protecting against pathogen invasion, and much more. Conversely, changes in their community structure are linked to several gastrointestinal (GI) and non-GI conditions. Fortunately, these bacteria are amenable to external perturbations, but we need specific tools for their safe manipulation as nonspecific changes can cause unpredicted long-term consequences. Here, we mainly discuss recent advances in cultivation-independent technologies and argue their relevance to different key steps, that is, identifying the modulation targets and developing phage-based tools to precisely modulate gut bacteria and restore a sustainable microbiome in humans. We finally suggest multiple modulating strategies for different dysbiosis-associated diseases.

Establishment of an In Vitro System of the Human Intestinal Microbiota: Effect of Cultivation Conditions and Influence of Three Donor Stool Samples

Fecal microbiota transplantation (FMT) is an alternative method for the treatment of gastrointestinal diseases with a high recovery rate. Disadvantages are ethical concerns, high donor requirements and the low storability of stool samples. The cultivation of an in vitro microbiota in a continuous bioreactor was established as an alternative to FMT to overcome these problems. In this study, the influence of the system parameters and donor stool characteristics was investigated. Each continuous colonic fermentation system was inoculated with feces from three different donors until a stable state was established. The influence of the fermentation conditions on the system's behavior regarding cell count, metabolic activity, short-chain fatty acid profile and microbiota composition as well as richness and diversity was assessed. Cultivation conditions were found to affect the microbial system: the number of cells and the production of short-chain fatty acids increased. The abundance of Actinobacteria and Firmicutes decreased, Bacteroidetes increased, while Proteobacteria and Verrucomicrobia remained largely unaffected. Diversity in the in vitro system decreased, but richness was unaffected. The cultivation of stool from different donors revealed that the performance of the created in vitro system was similar and comparable, but unique characteristics of the composition of the original stool remained.

DisBalance: a platform to automatically build balance-based disease prediction models and discover microbial biomarkers from microbiome data

How best to utilize the microbial taxonomic abundances in regard to the prediction and explanation of human diseases remains appealing and challenging, and the relative nature of microbiome data necessitates a proper feature selection method to resolve the compositional problem. In this study, we developed an all-in-one platform to address a series of issues in microbiome-based human disease prediction and taxonomic biomarkers discovery. We prioritize the interpretation, runtime and classification accuracy of the distal discriminative balances analysis (DBA-distal) method in selecting a set of distal discriminative balances, and develop DisBalance, a comprehensive platform, to integrate and streamline the workflows of disease model building, disease risk prediction and disease-related biomarker discovery for microbiome-based binary classifications. DisBalance allows the de novo model-building and disease risk prediction in a very fast and convenient way. To facilitate the model-driven and knowledge-driven discoveries, DisBalance dedicates multiple strategies for the mining of microbial biomarkers. The independent validation of the models constructed by the DisBalance pipeline is performed on seven microbiome datasets from the original article of DBA-distal. The implementation of the DisBalance platform is demonstrated by a complete analysis of a shotgun metagenomic dataset of Ulcerative Colitis (UC). As a free and open-source, DisBlance can be accessed at http://lab.malab.cn/soft/DisBalance. The source code and demo data for Disbalance are available at https://github.com/yangfenglong/DisBalance.

Decreased Ecological Resistance of the Gut Microbiota in Response to Clindamycin Challenge in Mice Colonized with the Fungus Candida albicans

The mammalian gut microbiota is a complex community of microorganisms which typically exhibits remarkable stability. As the gut microbiota has been shown to affect many aspects of host health, the molecular keys to developing and maintaining a "healthy" gut microbiota are highly sought after. Yet, the qualities that define a microbiota as healthy remain elusive. We used the ability to resist change in response to antibiotic disruption, a quality we refer to as ecological resistance, as a metric for the health of the bacterial microbiota. Using a mouse model, we found that colonization with the commensal fungus Candida albicans decreased the ecological resistance of the bacterial microbiota in response to the antibiotic clindamycin such that increased microbiota disruption was observed in C. albicans-colonized mice compared to that in uncolonized mice. C. albicans colonization resulted in decreased alpha diversity and small changes in abundance of bacterial genera prior to clindamycin challenge. Strikingly, co-occurrence network analysis demonstrated that C. albicans colonization resulted in sweeping changes to the co-occurrence network structure, including decreased modularity and centrality and increased density. Thus, C. albicans colonization resulted in changes to the bacterial microbiota community and reduced its ecological resistance.IMPORTANCECandida albicans is the most common fungal member of the human gut microbiota, yet its ability to interact with and affect the bacterial gut microbiota is largely uncharacterized. Previous reports showed limited changes in microbiota composition as defined by bacterial species abundance as a consequence of C. albicans colonization. We also observed only a few bacterial genera that were significantly altered in abundance in C. albicans-colonized mice; however, C. albicans colonization significantly changed the structure of the bacterial microbiota co-occurrence network. Additionally, C. albicans colonization changed the response of the bacterial microbiota ecosystem to a clinically relevant perturbation, challenge with the antibiotic clindamycin.